International Rural Development Edited by Béatrice Knerr 4 David Eche Land Degradation, Small-Scale Farms’ Development, and Migratory Flows The case of Tapachula / Chiapas ISBN 978-3-86219-478-0 This research evaluates the impacts of land degradation on rural development and migration, using a comparative-analysis platform and quantitative and qualitative approaches, based on data from empirical investigations in six rural communities of Tapachula, Chiapas. The results show that deforestation, heavy rains and extreme weather events are the main determinants of land degradation, and that land degradation, smallholder farms’ income and outmigration are highly correlated. In addition, they portray a new migration dynamic, from rural areas in the highlands directly to urban centers in the US, and demonstrate that the poverty marginalization context contributes substantially to global migration flows. Despite the harsh labour conditions and the poor economic basis in the area, temporary Guatemalan workers rapidly replace the out-migrated local labour force on coffee plantations and small farms, giving evidence of their life at the fringe of the globalized economy. David Eche obtained an Agriculture Engineer degree at the Pontifical University of Ecuador-Ibarra. He pursued a MSc. pro- gramme in International Organic Agriculture at the University of Kassel in Germany. With his research work, he won the prize for the best master thesis of the faculty. Subsequently, he joined a double PhD program offered by the Universidad Autonóma de Zacatecas, and the University of Kassel, wherein he graduated with academic distinctions. His main research interests focus on climate change, environmental degradation, migration, organic agriculture and rural development. 4 La n d D eg ra d at io n , S m al l- Sc al e Fa rm s’ D ev el o p m en t, a n d M ig ra to ry F lo w s D av id E ch e International Rural Development Vol. 4 Editor: Prof. Dr. Béatrice Knerr David M. Eche Land Degradation, Small-Scale Farms’ Development, and Migratory Flows The case of Tapachula / Chiapas kassel university press This work has been accepted by the faculty of Organic Agricultural Sciences of the University of Kassel as a thesis for acquiring the academic degree Doktor der Agrarwissenschaften (Dr. agr.). First Supervisor: Prof. Dr. Béatrice Knerr Second Supervisor: Prof. Dr. Raúl Delgado-Wise Day of disputation: 22nd January 2013 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.dnb.de Zugl.: Kassel, Univ., Diss. 2013 ISBN print: 978-3-86219-478-0 ISBN online: 978-3-86219-479-7 URN: http://nbn-resolving.de/urn:nbn:de:0002-34799 2013, kassel university press GmbH, Kassel www.uni-kassel.de/upress Cover illustration: © Béatrice Knerr, Göttingen Printing Shop: docupoint GmbH, Barleben Printed in Germany Preface  The  complex  relationships  between  land  degradation,  small‐scale  farms’  development,  labour  migration  and  rural  development,  evolving  under  the  pressure  of  increasingly  globalizing markets, are high on  the political and scientific agendas, on national as well as  international  level.  However,  obtaining  concrete  empirical  results  which  can  frame  and  comprehensively  explain  the  relation  between  land  degradation,  rural  development  and  migration phenomena  is difficult, as well as designing a methodology  that combines social  and natural sciences tools meets challenges. Against that background, the research by David  Eche  which  is  presented  in  this  volume  is  a  substantial  contribution  to  broadening  the  knowledge  base  in  the  area  and  to  lay  the  foundations  for  the  construction  of  linkages  between  the  research  topics. Comprising a broad  literature  review and an examination of  relevant  theories,  and  concentrating  in  its empirical part on  the  case of Tapachula  in  the  Southern Mexican state of Chiapas where global phenomena are reflected within a narrow  local space.  The results of this study hence are relevant far beyond this regional case. It also  reviews  theoretical  debates  about  the  relationship  between  development  and migration,  and in an excellent way contrasts two major schools of thought. The volume gives the reader  who  is  less  familiar with  the environmental degradation –  rural development – migration  nexus an excellent  introduction  into the topic, while at the same time provides a wealth of  new  insights  to  those who have already  in‐depth knowledge but are  still  faced with open  questions.   A major achievement  is the provision of valuable primary data collected by the  author  in an extensive survey  in rural regions of Chiapas, Mexico. The empirical foundation  of  the volume based on  the author’s profound knowledge of  the  region and his  first‐hand  experiences provide a detailed description of the region of Chiapas, including the situation of  land degradation, small‐scale farms development and migratory movements, thus breaking  these global phenomena down to their basis.   As an additional aspect the author highlights the social conditions under which the migrants  live and work, and on the development of smallholders’ livelihood security. Using the decent  work  frame  stated  by  the  ILO,  and  considering  declining  policy  support  of  smallholders’  livelihood  in  an  increasingly  globalizing  and  competitive economic world he derives  some  requests to policymakers putting human aspects in the centre.  Altogether, the results obtained in the study possess important international relevance and  provide valuable  food  for  thought and  follow‐up  research. At  the same  time,  the  research  exhibits  high  standards  of  scientific  quality.  With  its  specific  emphases,  this  book  is  a  valuable contribution to the  International Rural Development Series, which brings together  solid founded research with empirical focus and policy relevance.    Prof. Béatrice Knerr  University of Kassel  vii Acknowledgements To my parents and brothers, who did not see me much during the last years This research work would never have been completed without the support and collaboration of many people. It is my very pleasant duty to thank Prof. Dr. Beatrice Knerr for directing me and supporting me during many years. She always seemed to believe in my abilities as student and researcher in and out of the university campus, thus it was vital in motivating me to finish on time the work I started. Second, with his incredible patience and thoroughness Dr. Raul Delgado-Wise provided me much needed constructive criticism from the very beginning, and guidance to finish the program in Estudios Del Desarrollo. Third, I’m very thankful to Dr. Darcy Tetreault for his patience and enormous support during the last steps of this thesis. Forth, I would like to express my great thanks to the Consejo Nacional de Ciencia y Tecnología CONACyT for funding my studies in the University of Kassel in Germany, and in the Universidad Autonóma de Zacatecas in México. I also would like to appreciate my gratitude to the International Center for Development and Decent Work ICDD for partially financing the fieldwork research. My deepest gratitude to Greta Papke for her kindness, love, patience and support during the harsh times of this process. Finally yet importantly, all my gratitude to the ejidatarios, local authorities, coffee farmers, migrants, and everybody who opened doors for me to achieve successfully this research project in Tapachula. The printing of this book was financed by the Secretaria Nacional de Educación Superior, Ciencia, Tecnología e Innovación del Ecuador (SENESCYT). viii Abstract Using qualitative and quantitative approaches, this study first analyses the effects of farmland degradation on small-scale farming households’ income. A Multiple Regression Analysis (MRA) of the data shows that there is a significant difference between farms with and without degradation problems in relation to income, where income of farming households affected by farmland degradation reduces $97.3 MXN, ($7.5 USD) ha/month. Land degradation originates from unsustainable land use practices, deforestation, heavy rainfalls, hurricanes and lack of governmental technical and financial assistance to protect the soil and improve farm productivity. It also has become a constraint for rural development and one of the main determinants for outmigration in the region. By means of cross-tabulation analysis and Chi-square test was possible to discover a relation between farmland degradation and sending migrant households, showing that small-scale farms with degraded land have more family members migrating than farms without such problems. Second, regarding the government’s lack of attention to small-scale farmers, the results of a factorial ANOVA test reveal that economic aid and technical assistance provided by the government do not help small-scale farms to improve farm productivity or household income. They also demonstrate that there is no difference of income between governmental support (technical and economical) receiving and non-receiving households. Third, the outmigration directionality and distances found in the research regions are rural- urban to the city of Tapachula, internal-short migration to the neighbouring state of Quintana Roo toward the Rivera Maya, internal-traditional migration to the centre states of Mexico City and Guadalajara, internal-long migration to the northern state of Baja California toward Tijuana city and rural-international, a new outflow from rural communities in the highlands towards North Carolina, Virginia, Florida and California in the US. In addition, the implemented economic neoliberal globalization model in the frame of NAFTA, which is affecting the already precarious economic and environmental conditions of rural communities, is eventually pushing villagers to out-migrate. Outmigration undermines the labour market structure, creating a labour space due to loss of local labourers, which are replaced by temporary cross-border workers from Guatemala to Tapachula. The research shows that almost half of the small-scale farms are hiring external labour force from Guatemala (30%) and neighbouring ejidos (18%). Concerning the employment of Guatemalan workers, farmers stated that the main reasons are the lack of local workers due to ix outmigration, and Guatemalan workers provide cheaper and better labour force than local workers. Finally, Guatemalan temporary migration to the coffee plantations in Tapachula is not a new phenomenon, they consider that the employment opportunities, food, better wages, and living and health facilities (in some cases) provided by the coffee farms are better than in Guatemala. Therefore, Guatemalan workers stated to have good labour and living conditions. Although, this research demonstrates that their current working and living conditions are not in accordance with the decent work frame stated by the ILO. This research sets the foundations for linking dialectically land degradation, with the reduction of rural capabilities for development, and the increase of outmigration. Key words Land degradation, rural development, migration, decent work, small scale farmers, Guatemalan temporary migrants, Tapachula, Chiapas x Table of contents: Abstract ................................................................................................................................... viii List of tables .............................................................................................................................. xv List of figures ......................................................................................................................... xvii List of charts .......................................................................................................................... xvii List of maps........................................................................................................................... xviii List of text boxes ................................................................................................................... xviii Glossary of Acronyms: ......................................................................................................... xviii Symbols and abbreviations ...................................................................................................... xxi 1. INTRODUCTION ....................................................................................................................... 1 1.1. Posing the problem ......................................................................................................... 1 1.2. Overview of the research ................................................................................................ 4 1.3. Structure .......................................................................................................................... 5 2. KEY CONCEPTS ....................................................................................................................... 6 2.1. Migration......................................................................................................................... 6 2.2. The land degradation phenomenon ............................................................................... 10 2.3. Development ................................................................................................................. 12 2.4. Human development ..................................................................................................... 13 2.5. Rural development ........................................................................................................ 14 2.6. Decent work .................................................................................................................. 15 2.7. Neoliberal globalization ................................................................................................ 16 3. THEORETICAL FRAMEWORK AND RESEARCH HYPOTHESES ........................... 19 3.1. Historical analysis of migration-development .......................................................... 19 3.2. Migration theories ..................................................................................................... 23 3.2.1. Neoclassical economics: macro theory ..................................................................... 24 3.2.2. Neoclassical economics: micro theory...................................................................... 25 3.2.3. The New Economics of Labour Migration NELM: household decision making ..... 27 3.2.4. Historical structural perspectives .............................................................................. 28 xi 3.3. Research hypotheses ..................................................................................................... 31 3.4. State of research ............................................................................................................ 33 3.4.1. Land degradation and rural development ................................................................. 33 3.4.2. Migration................................................................................................................... 35 4. DEBATE ON MIGRATION, RURAL DEVELOPMENT, AND LAND DEGRADATION ............................................................................................................................... 39 4.1. The multi-faceted dimensions of migration .................................................................. 39 4.2. Climate change and migration: framing the issue......................................................... 41 4.3. Environmental-related migration .................................................................................. 42 4.4. Rural development features .......................................................................................... 43 4.5. Global status of land degradation ................................................................................. 46 4.5.1. Dynamic causes of land degradation ........................................................................ 48 5. TAPACHULA: LAND DEGRADATION, SMALL-SCALE FARMS’ DEVELOPMENT AND MIGRATORY FLOWS......................................................................... 57 5.1. Problem statement ......................................................................................................... 57 5.2. General characteristics of the case-study area .............................................................. 59 5.3. Methodological approach.............................................................................................. 62 5.3.1. Site setting ................................................................................................................. 62 5.3.2. Selection criteria for ejidos ....................................................................................... 62 5.3.3. Description of selected ejidos ................................................................................... 63 5.3.4. Selection criteria for farms........................................................................................ 66 5.3.5. Sampling size ............................................................................................................ 68 5.3.6. Sources of data/information ...................................................................................... 70 5.3.7. Fieldwork research .................................................................................................... 72 5.3.8. Fieldwork research timeline ...................................................................................... 72 5.3.9. Participatory research methods ................................................................................. 73 5.4. Consent and permission ................................................................................................ 75 5.5. Limitations and problems: ............................................................................................ 75 xii 6. CHIAPAS: LAND DEGRADATION AND RURAL DEVELOPMENT IN THE CONTEXT OF NEOLIBERAL GLOBALIZATION ................................................................... 78 6.1. The natural environment ............................................................................................... 78 6.2. Land degradation .......................................................................................................... 80 6.2.1. Extent ............................................................................................................................ 80 6.2.2. Human-induced land degradation ............................................................................. 81 6.2.3. Natural phenomena and disasters .............................................................................. 86 6.3. Development characteristics of the poorest of the poor ................................................ 88 6.4. An overview of neoliberal globalization in Mexico ..................................................... 91 6.4.1. Development strategies ................................................................................................. 91 6.4.2. Repercussions for the environment and agriculture .................................................. 93 7. CHIAPAS: RESHAPING OF MIGRATORY FLOWS ....................................................... 97 7.1. Features of the southern Mexican-Guatemalan border ................................................. 97 7.2. Migration flows of Chiapanecos and the emergence of new destinations .................... 98 7.3. Rural Urban migration ................................................................................................ 102 7.4. Internal migration........................................................................................................ 104 7.4.1. Short migration pattern “South to South” ............................................................... 105 7.4.2. Traditional migration pattern “South to Centre” .................................................... 106 7.4.3. Long migration pattern “South to North” ............................................................... 106 7.5. Environmentally-forced migration.............................................................................. 107 7.6. The emergence of an international migration trend .................................................... 112 7.7. Remittances ................................................................................................................. 116 7.8. Immigration................................................................................................................. 117 7.8.1. Regional labour immigration .................................................................................. 117 7.8.2. International immigration ....................................................................................... 119 7.8.3. Agricultural temporary cross-border workers ......................................................... 120 7.8.4. Daily cross-border merchant migrants .................................................................... 123 7.8.5. Domestic service workers ....................................................................................... 125 xiii 7.8.6. Transit migration: increase of numbers and risks ................................................... 125 7.9. Migrants’ labour conditions and human rights ........................................................... 131 8. ANALYSIS AND RESULTS OF FIELDWORK RESEARCH ...................................... 133 8.1. Data set construction ................................................................................................... 133 8.2. Descriptive statistics of small-scale farming household’s survey .............................. 135 8.2.1. Small-scale farming household’s characteristics .................................................... 135 8.2.2. Farm characteristics ................................................................................................ 136 8.2.3. Farmland topography .............................................................................................. 138 8.2.4. Soil type and soil quality......................................................................................... 139 8.2.5. Land degradation .................................................................................................... 140 8.2.6. Small-scale farms’ agricultural characteristics ....................................................... 142 8.2.7. Agricultural subsidies ............................................................................................. 147 8.2.8. Production of Crops and Price changes .................................................................. 149 8.2.9. Livestock production and price changes ................................................................. 151 8.2.10. Small-scale farm development ................................................................................ 152 8.2.11. Physical assets ......................................................................................................... 153 8.2.12. Rural development constraints ................................................................................ 155 8.2.13. Farm productivity determinants and constraints since 2005................................... 155 8.2.14. Pollution problems in the ejido or small-scale farm ............................................... 158 8.2.15. Labour force ............................................................................................................ 159 8.2.16. Outmigration ........................................................................................................... 161 8.2.17. Determinants of outmigration ................................................................................. 165 8.2.18. Migrant remittances ................................................................................................ 165 8.2.19. Local alternatives views for development .............................................................. 166 8.3. Description statistics of Central American migrant workers survey .......................... 168 8.3.1. Socio-demographic characteristics ......................................................................... 168 8.3.2. Household characteristics ....................................................................................... 170 8.3.3. Migration characteristics ......................................................................................... 170 8.3.4. Migrant workers time in Mexico ............................................................................ 172 xiv 8.3.5. Transit-migration .................................................................................................... 172 8.3.6. Remittances ............................................................................................................. 174 8.4. Statistical inference ..................................................................................................... 174 8.4.1. Multiple linear regression analysis ......................................................................... 174 8.4.2. Analysis of Variance (ANOVA) ............................................................................. 178 8.4.3. Factorial ANOVA ................................................................................................... 179 8.4.4. Crosstabulation analysis and Chi-square test .......................................................... 179 8.5. Hypotheses testing and results .................................................................................... 180 8.5.1. First hypothesis ....................................................................................................... 180 8.5.2. Second hypothesis ................................................................................................... 187 8.5.3. Third hypothesis...................................................................................................... 191 8.5.4. Fourth hypothesis .................................................................................................... 193 8.5.5. Fifth hypothesis ....................................................................................................... 196 9. CONCLUSIONS .................................................................................................................... 201 9.1. Findings....................................................................................................................... 205 ZUSAMMENFASSUNG ............................................................................................................... 207 LITERATURE CITED:.................................................................................................................. 213 ANNEXES ....................................................................................................................................... 240 APPENDIXES: ................................................................................................................................ 258 Appendix 1: First hypothesis, SPSS results for Multiple Regression Analysis ...................... 258 Appendix 2: Second hypothesis, SPSS results for multifactorial ANOVA............................ 263 Appendix 3: Third hypothesis, SPSS results for crosstab and chi-square .............................. 268 Appendix 4: Fourth hypothesis, SPSS results for crosstab and chi-square ............................ 270 Appendix 5: Fith hypothesis, SPSS results for crosstab and chi-square................................. 272 xv List of tables Table number page Table 3.1: Opposing views on migration and development 21 Table 3.2: Main migration theories: 19th century and second half of the 20th century 23 Table 3.3: Summary of hypotheses, indicators, data sources and statistical methods 31 Table 5.1: Profile of fieldwork research ejidos 63 Table 5.2: Characteristics of research coffee farms 66 Table 5.3: Households and ejidatarios in the research areas 68 Table 5.4: Ejidos’ sample size 69 Table 5.5: Number of Central American migrants surveyed by farm 70 Table 5.6: Main characteristics and structure of the questionnaires 72 Table 5.7: Fieldwork research chronogram by field site 73 Table 6.1: Table 6.1: Land degradation in Chiapas by principal process in 2010 (’000 ha) 80 Table 6.2: Chiapas land use and cover changes 1975-2005 (ha) 83 Table 6.3: Population growth and land use changes in Chiapas 85 Table 6.4: Chiapas’ demographic and poverty indicators 2010 90 Table 7.1: Population change of main urban centres and high marginalized municipalities of Chiapas 104 Table 7.2: Migrants from Chiapas to other Mexican states 2000-2005 107 Table 7.3: Cost of damages caused by hurricane Stan in million USD 108 Table 7.4: Damages caused by hurricane Stan October in Chiapas 2005 109 Table 7.5: Population change in main municipalities affected by hurricane Stan in Chiapas, 2000-2005 110 Table 7.6: Foreign born population living in Chiapas, 2010 118 Table 7.7: Socio-demographic composition of transit migrants 130 Table 8.1: Data collected using top-down and bottom-up methods 134 Table 8.2: Household’s demographic characteristics 136 Table 8.3: Farm Size 137 Table 8.4: Chiapas: area of farms by size (ha) 137 Table 8.5 Topography of the study area 138 Table 8.6: Farms’ soil type and soil quality 140 Table 8.7 Farms’ land degradation 141 Table 8.8: Causes of land degradation in the study areas 142 Table 8.9: Agriculture current crops and subsidies 146 xvi Table 8.10: Amount of subsidies in MXN/ ha/year 148 Table 8.11 Current crop production t/ha 149 Table 8.12: Crop Production changes and determinants 150 Table 8.13: Changes of average prices in comparison with 2005 of main crops in MXN/kg 150 Table 8.14: Livestock units and price changes since 2005 151 Table 8.15: Household heads’ economic assets 153 Table 8.16: Household heads’ education level 153 Table 8.17: Households’ physical, natural resources and living conditions 154 Table 8.18: Current economic-situations in relation to 2005 155 Table 8.19: Factors causing a decrease on farm productivity since 2005 and further farmers’ responses 156 Table 8.20: Farm production constraints 158 Table 8.21: Pollution problems 159 Table 8.22: Labour force sources, activities and origins 160 Table 8.23: Household heads’ social assets 161 Table 8.24: Out-migrants’ main destinations 162 Table 8.25: Household members’ reasons to migrate 165 Table 8.26: Remittances and uses 166 Table 8.27: Frequency and amount of remittances in MXN$ 166 Table 8.28: Guatemalan migrant workers’ socio-demographic characteristics 169 Table 8.29: Household characteristics 170 Table 8.30: Migration characteristics 171 Table 8.31: Cross tabulation between living time of temporary and resident migrants 172 Table 8.32: Transit-migration 172 Table 8.33: Guatemalan migrant workers’ remittances 174 Table 8.34: MRA summary 183 Table 8.35: Analysis of variance 184 Table 8.36: Summary of the table of coefficients from the MRA 186 Table 8.37: ANOVA of income between study regions 186 Table 8.38: Factors and equations 188 Table 8.39: Levene's Test for homoscedasticity 189 Table 8.40: Tests of between-subjects effects 190 Table 8.41: Usefulness of government technical/economical assistance 191 xvii Table 8.42: Crosstabulation between sending migrant households with degraded areas by region 192 Table 8.43: Chi-square test results for the third hypothesis 192 Table 8.44: Crosstabulation between hiring labour and sending migrant households 195 Table 8.45: Chi-square test results for the fourth hypothesis 196 Table 8.46: Chi-square test results for the fifth hypothesis 197 Table 8.47: Economic activities and labour conditions in Guatemala and Mexico 198 Table 8.48: Comparison of wages and working hours between Guatemala and Mexico 199 Table 8.49: Guatemalan migrant workers living conditions 200 List of figures Figure 3.1: Mechanisms leading equilibrium 24 Figure 4.1: Specific underlying and proximate land degradation drivers and their impacts on land users, rural development and migration 54 Figure 7.1: Destination of Guatemalan immigrants by occupation and trajectory 121 Figure 8.1: Main subsistence and commercial activities in the research areas according to altitude and distance 146 List of charts Chart 6.1: Area affected annually by fires in Chiapas from 1990-2008 88 Chart 7.1: Comparison of international migrants from Chiapas with major migrant’s sending states between 1993 and 2006, in percentages. 115 Chart 7.2: Immigrants in Chiapas from other Mexican states 2010 118 Chart 7.3: Labour activities of Guatemalan workers once they worked in Mexico 2007-2010 122 Chart 7.4: Influx of temporary cross-border migrants holding a FMTF document to work in Chiapas 123 Chart 7.5: Undocumented Central American transit migrants 1995-2010 (thousands) 129 Chart 8.1: Land obtaining method 138 Chart 8.2: Type of crops 143 Chart 8.3: Natural factors striking the farms since 2005 157 Chart 8.4: Age groups of Guatemalan migrant workers 169 Chart 8.5: Means plot of monthly income in the three research regions (MXN) 187 Chart 8.6: Technical assistance and government economic aid by regions 190 Chart 8.7: Means plot of monthly income in MXN of out-migrants’ households and the number of out-migrants’ households with land degradation problems in the three research regions. 193 Chart 8.8: Small-scale farmers labour force employment and labour force origins 194 xviii Chart 8.9: Reasons for hiring Guatemalan workers. 195 Chart 8.10: Crosstabulation between labour condition and its determinants 197 List of maps Map 5.1: Economic Regions of Chiapas 60 Map 5.2: Location of the research site 67 Map 7.1: Migration from the Los Altos toward the Soconusco and Lacandona forest regions 101 Map 7.2: Distribution of Chiapaneco migrants in border cities in their attempt to cross the border in percentages 114 Map 8.1: Principal regional migration flows from the study regions 164 Map 8.2: Principal international migration flows from the study regions 164 List of text boxes Box 8.1: Soil degradation 142 Box 8.2: Agricultural subsidies 148 Box 8.3: Coffee prices and production constraints 158 Box 8.4: There are not enough workers 160 Box 8.5: Environmental migration 162 Box 8.6: Migrants’ regional destinations 163 Box 8.7: Migrants’ international destinations 163 Box 8.8: Seeking new development alternatives 167 Box 8.9: Origin countries and risks for transit-migrants in Mexico 173 Glossary of Acronyms: AMEC Asociación Mexicana de Exportadores de Café BANRURAL Banco Nacional de Crédito Rural (National Rural Credit Bank) BAU Business As Usual BECA Border Environmental Cooperation Agreement BLUE Best Linear Unbiased Estimates CEIEG Comité Estatal de Información Estadística y Geográfica CENAPRED: Centro Nacional de Prevención de Desastres (National Center for Disaster Prevention) CONABIO Comisión Nacional para el Conocimiento y Uso de la Biodiversidad CONAFOR Comisión Nacional Forestal (National Forestry Commission) CONASUPO Compañia Nacional de Subsistencia Popular (National Company of Popular Subsistenc) xix EACH-FOR Environmental Change and Forced Migration Scenarios ECLAC Economic Commission for Latin America and the Caibbean ED Economic Development EDP Environmentally Displaced People EMIF-GUAMEX Encuesta Sobre Migración En La Frontera Guatemala-México (Migration survey in the Border of Guatemala- Mexico) EMIF-Norte Encuesta Sobre Migración en la Frontera Norte de México (Migration survey in the North Border of Mexico) EMIF-SUR: Encuesta Sobre Migración en la Frontera Sur de México (Migration survey in the South Border of Mexico) ENSO El Niño/La Niña-Southern Oscillation ESI Export Substitution Industrialization EZLN: Ejercito Zapatista de Liberación Nacional (Zapatista Army of National Liberalization) FAO Food and Agriculture Organization FMTF Forma Migratoria para Trabajadores Fronterizos (Migration Form for Cross-border Workers) FMTL Forma Migratoria para Trabajadores Locales (Migration Form for Local Workers) FMVA Forma Migratoria Para Visitantes Agrícolas (Migration Form for Temporary Agricultural Visitors) GFMD Global forum on Migration and Development GHG Green House Gases GIMMS Global Inventory Modelling and Mapping Studies GLASOD Global Assessment of Soil Degradation HH Household HH-h Household heads IFAD International Fund for Agriculture and Development, IDP Internally Displaced Person IDMC Internal Displacement Monitoring Center ILO International Labour Organization, INAFED Instituto Nacional Para el Federalismo y Desarrollo Municipal (National Institute for Federalism and Municipal Development) INEGI Instituto Nacional de Estadística y Geografía (National Institute of Statistics and Geography) INM Instituto Nacional de Migración (Migration National Institute) INMECAFE Instituto Mexicano del Café (Mexican Coffee Institute) xx IMF International Monetary Fund IOM International Organization for Migration IPCC Intergovernmental Panel on Climate Change IRPAT Instituto de Reconversión Productiva de Agricultura (Tropical Productive Reconversion Institute of Tropical Agriculture) ISI Import Substitution Industrialization LADA Land Degradation Assessment in Drylands LD Land Degradation LUC Land Use Change LMMC Like-Minded Megadiversity Countries MDG’s Millennium Development Goals MNC Multinational Corporations MRA Multiple Regression Analysis NAAEC North American Agreement on Environmental Cooperation NAALC North American Agreement on Labour Cooperation NAFTA North American Free Trade Agreement NDVI Normalised Difference Vegetation Index NELM New economics of labour migration NLD Natural Land Degradation NWO New World Order RNVC Removal of Natural vegetation Cover PACCC Programa de Acción Ante el Cambio Climático del Estado de Chiapas (Programme of Action on Climate Change of the State of Chiapas) PEOT Programa Estatal de Ordenamiento Territorial (State Program of Land Management) PPP Plan Puebla Panama SAP Structural Adjustment Policies SEMARNAT Secretaria del Medio Ambiente y Recursos Naturales (Ministry of Environment and Natural Resources of Mexico) SENASICA Servicio Nacional de Sanidad, Inocuidad y Calidad Agroalimentaria (National Health Service, Food Safety and Food Quality) SIACON Sistema de Información Agroalimentaria de Consulta (Agri-Food Information System Consultation) SIMDE Sistema Sobre Migración y Desarrollo (System of Migration and Development) xxi SNIEG Sistema Nacional de Información Estadística y Geográfica (National Statistics and Geographical Information) SNIM Sistema de Nacional de Información Municipal (National System of Municipal Information) SSE Squares of the Estimated Errors UAZ Universidad Autónoma de Zacatecas (Autonomous University of Zacatecas) ULU Unsustainable Land Use UNCED United Nations Conference on Environment and Development UNFCCC United Nations Framework on Combating Climate Change UNCCD United Nations Convention to Combat Desertification UNCCD UNEP United Nations Environment Programme UN-OHCHR United Nations- Office of the High Commissioner for Human Rights WB World Bank WCED World Commission on Environment and Development WTO World Trade Organization Symbols and abbreviations - Before the number means negative value α Alpha (significance level) β Beta (coefficient) RD: Rural Development LD: Land Degradation Cc: Climate Change M: Migration D: Development Currency conversion $1 USD= $13MX (2012) 1 1. Introduction 1.1. Posing the problem Since the late 1990s, societies have witnessed the migratory phenomenon induced and provoked by environmental degradation, environmental hazards, lack of water and other natural and anthropogenic disasters (Afifi & Warmer, 2008) on the one hand. On the other, environmental degradation is a consequence of complex interactions between societal and environmental processes (Berry, Olson & Campbell, 2003). The scientists who have examined this issue (Myers, 2002; Stern, 2006; Brown, 2008; Global Humanitarian forum, 2009; Migration and Global Climate Change, 2011) argue that this problem fed by the severity and frequency of extreme weather events, will displace several million environmental migrants to flee from the most vulnerable areas to safer places and to look for better conditions, either within the country or abroad. The UN has considered Land Degradation (LD) as one of the most threatening global environmental challenges. One that exacerbates poverty (Fabrige, Renaud, & Bogardi, 2006; IPCC, 2012), affects the economic growth of rural areas and of entire nations (Campbell et al., 2003), insofar as it is an agricultural productivity constraint and a co-determinant for migration in many places (Raleigh & Urdal, 2006). Climatic variations and human activities in arid, semiarid and dry sub-humid areas are causing the formation, expansion and intensification of LD (UN Millennium Ecosystems Assessment: Desertification Synthesis, 2005; UNU, 2007). The UNU (2007) states that by 2050 around 50 million people will be at risk of displacement due to increasing of LD. Additionally, the removal of natural vegetation, unsustainable agricultural practices, overgrazing and urban expansion accelerate the pace of LD in marginal lands. As the land becomes more unsuitable for farming and grazing, people living in these marginal-degraded farmlands will migrate (Campbell et al., 2003) as soon as they find an alternative. The LD process has detrimental consequences for farmland productivity and leads to increasing costs of production of agricultural crops; resulting in a declining income of farmers that can trigger migration. Where environmental-induced migration does take place it will be mainly internal rather than international, it can be a positive and effective strategy for escaping from poverty (Castles & Rajah, 2010). 2 Subsequently, the abandonment of degraded land will affect people’s livelihoods and rural development in several ways. The LD of farmland in the poor and highly marginalized state of Chiapas has intensified since the implementation of neoliberal policies in 1983 (EACH- FOR Mexico Case Study Report, 2009), with the increasing severity of catastrophic events, clear-cutting of rainforest and inadequate land uses (Richter, 2000). Due to its geographic location, Chiapas is susceptible to hurricanes, tropical storms and other natural hazards that come periodically from the Pacific Ocean, the Gulf of Mexico, as well as the Caribbean Sea. These natural causations are leading to inundations and landslides that contribute to extend the process of soil erosion, LD, ecologic and economic damage, and human casualties, where the affected populations can no longer gain a secure livelihood on their farmlands ((EACH- FOR Mexico Case Study Report, 2009; Castles, 2002). In this context, migration is the means to arrive to less difficult living conditions. Furthermore, the geopolitical location of Chiapas contributes to its role as a receiving and transit state for Central American migrants. In this regard it is essential to understand that before the signing of NAFTA in 1994, the southern Mexican border of Chiapas had a close economic-commercial relationship with Central American countries, entailing the free movement of labourers and other migrants (Ordóñez, 1996). Villafuerte & Garcia (2006) argue that the migration phenomenon in Chiapas increased strongly after the 1988 rural crisis1, followed by the coffee crisis in the late 1980’s, the increase of tropical storms and hurricanes, and the uprising of the National Liberation Movement by the Zapatista Army of National Liberation (Ejército Zapatista de Liberación Nacional, EZLN) in 1994. Chiapas is a region with different migratory flows. There is regional migration within the boundaries of the state and to the neighbouring states, international migration to the United States, to search better living conditions and attracted by the wide economic differences between both countries, whereby the existing migratory networks in the USA facilitate the Mexican out-migration. Notwithstanding, the international migration of Chiapanecos2 1 Since the 1970’s the economic sector of Mexico started to deteriorate by the vulnerable macroeconomics policies to external conditions, led to a financial crisis in the early 1980’s which slowly recovered in the 1990’s (Whitt, 1996). “Deteriorating economic conditions in Mexico in the 1980s forced many rural Mexicans to adopt international labor migration as a household subsistence strategy. Even with the relative improvement of the Mexican economy since the 1989, most of those driven into the migratory stream by economic crisis of the 1980s are continuing to migrate” (Cornelius & Martin, 1993:488) 2 The Chiapaneco term is applied to all the Chiapas’ inhabitants without differencing ethnic groups and social classes. 3 towards the United States compared with the long Mexican migration history, it is a new trend that originated at the 1990s. Chiapas is the major entrance gate for international migration (Casillas, 2011). There is transit migration of migrants from a wide number of countries; most of them are from Central America on their journey to the north of the continent. Central America transmigration has become a problem for Mexico since the US new migratory policies (Casillas, 2008). Finally yet importantly, there is international labour migration of Central American migrants, mainly from Guatemala who arrive to Chiapas to work temporarily or seasonally in the agricultural sector. The migration authorities document just a part of this migratory flow, temporary workers obtain a permit allowing them to stay and work in Mexico. These documented migrants accompanied by the increasing number of un- documented migrants getting into Chiapas, in many cases have become victims of human rights violation (Artola, 2006; Anguiano, 2008a; Angeles, 2008; Casillas, 2011). Therefore, a hypothesis has been included in this research to gauge the labour conditions for Central American migrants taking part in agricultural activities by using the Decent Work approach, based on working and living conditions, as well as “opportunities to obtain decent and productive work in conditions of freedom, equity, security and human dignity” (ILO, 2010: 8). The impacts of LD and rural development on the multi-causal phenomenon of migration as well as the migratory flows have not been sufficiently studied yet. This research addresses the dynamic relations between LD and migration flows in the state of Chiapas. The links between LD and migration can be identified by examining natural conditions, the impacts of the various natural extreme events that have affected the region, land uses, rural development, the socio-demographic characteristics of Chiapas, as well as neoliberal restructuring of policies, which are key determinants for internal and international migration. Overall, this research seeks to discover the dynamics of migratory flows caused by the current LD processes, the neoliberal globalization policies and their links in the development of small-scale farms in the municipality of Tapachula in the state of Chiapas. 4 1.2. Overview of the research The research project presented analyses the dynamics of land degradation, development of small-scale farms and the different migration patterns in the municipality of Tapachula, Chiapas. It focuses on the development and environmental conditions faced by several communities located in different altitudes and geographic regions, from the coastland up to the highlands. According to the former conditions, it studies the effects of land degradation on small-scale farms, and farmers. It attempts to explain the key determinants for land degradation and how this phenomenon affects and shapes the small-scale farms’ development conditions, in an effort to determine the extent in which it contributes to making the decision to migrate. In addition, it gauges the technical and economic support provided by the government to the small-scale farmers in order to cope with farm constraints caused by land degradation. It examines the migratory flows of Chiapaneco small-scale farmers and the labour conditions of temporary Guatemalan migration employed in the coffee farms. This research used two different surveys and interviews with key informants to gather data for assessing the dynamic changes in the agricultural land uses, environmental changes, natural disasters, transformations in socio-economic living conditions, and the current in and out- migration patterns. 5 1.3. Structure This research work is organized into nine chapters. Following the introduction to the problem, and overview of the research, chapter 2 gathers, explains the concepts underlying the pillar topics under study, based on a critical review of the relevant literature. Chapter 3 provides a sound theoretical framework of the topics under study; it portrays the research questions and hypothesis, as well as the state of the art for land degradation, rural development and migration. Chapter 4 puts into debate migration, rural development and land degradation in order to find the links and interactions between them. Chapter 5 describes the methodological approach, fieldwork research design, research sites, methods and techniques used for collecting and analyzing the data, as well as some pitfalls encountered along the way. Chapter 6 provides information about the natural features of Chiapas; it analyzes closely land degradation and rural development problems, as well as their determinants and consequences. Chapter 7 firstly describes the Mexican-Guatemalan border and details the migration background for the state of Chiapas; secondly, it examines the flows and directionality of migration and its determinants. Chapter 8 shows the fieldwork surveys’ results by descriptive and inference statistics. Chapter 9 details the key general conclusions based on the study’s statistical and dialectical outcome. 6 2. Key Concepts This chapter conceptualises the key pillars of this research: migration, land degradation and development. It also describes the concepts of decent work and neoliberal globalization, which also are of major importance for this study. 2.1. Migration In front of the image of the multi-causal migration phenomenon, the migration decision is justified: as an economic necessity for finding the means for a better livelihood, security, moving forward and thriving (Knerr, 2004). Usually, migration does not represent just a personal yearning and aspiration, but a duty imposed by the harsh economic features and responsibility of supporting the household (Anguiano, 2009). Massey (2005: 3) depicts that, “the shift from a peasant or command economy to a market systems entails radical transformation of social structures at all levels; a revolutionary ship that displaces people from traditional ways of life and creates a mobile population on the look out for alternative ways of making a living”. Additionally, the globalized capitalist system, through local and international agreements, has increased the gap between developed and developing economies (Delgado-Wise, 2006). In this regard, migration from developing towards developed regions gains momentum. After the 1990s, the number of migrants has increased as a response to the economic and political changes brought about by neoliberal reforms and socio-political instability (Durand & Massey, 2003). Yet, there is no generally agreed definition for migration and migrants (IOM, 2012b). Castles (2000: 270) points out that the definitions of migration “are the result of state policies, introduced in response to political and economic goals and public attitudes”. Other definitions conceptualize the migration phenomenon as the fact of “crossing the boundary of a political or administrative unit for a certain minimum period of 6 to 12 months” (ibidem). Moreover, migration fuels a series of related activities that affect social, political and cultural life, as well as economies on both the local and regional levels (Delgado-Wise & Márquez, 2007). Moreover, migration gives rise to the formation of “migrant networks” defined as “sets of interpersonal ties that connect migrants, former migrant and non-migrants in origin and destination areas through bonds of kinship, friendship and shared community origin” (Massey et al., 1993: 448). 7 The concepts of the different migration dynamics embedded in international and internal spheres are the following: Immigration:” a process by which non-nationals move into a country for the purpose of settlement” (IOM, 2012b: key migration terms). Internal migration: “refers to a move from one area to another within a country either rural to urban or urban to urban” Castles (2000: 269). International migration: “means crossing the frontiers which separate one (...) state from another” (ibidem). Emigration: “the act of departing or exiting from one State with a view to settling in another” (IOM, 2012b: key migration terms). Forced Migration:“a migratory movement in which an element of coercion exists, including threats to life and livelihood, whether arising from natural or man-made causes (e.g. movements of refugees and internally displaced persons IDP, as well as people displaced by natural or environmental disasters, chemical or nuclear disasters, famine, or development projects)” (IOM, 2012: key migration terms). It is “caused by the prevailing social conditions in developing countries, and originated from mechanisms of unequal development under neoliberal capitalist system [...], where large poor and excluded population segments lacking basic materials and subjective necessities to guarantee their survival or nurturing the expectations of a decent life are forced to migrate, and relocate within their own country or abroad” (Marquez & Delgado-Wise, 2012: 21). Forced migration as the counterview of voluntary migration does not apply to all migrants; it has been limited to refugees, displaced people, and asylum seekers. However, massive migrations flows due to economic, insecurity, and environmental determinants are being included in this scheme. Labour migration: “is the movement of persons from one State to another, or within their own country of residence, for the purpose of employment” (IOM, 2012b: key migration terms). 8 Temporary and circular migration: refers to the recruitment of migrant labour periods of six to nine months before returning to their countries of origin at the end of the contact. Depending on performance and the demand for such labour in the country of destination, the worker may repeat the cycle one or more times” (IOM, 2009: 41) Return migration: the process of going back to the country of origin A from another place B (Knerr, 1992), after finishing the working life to be again part of the home country (Castles, 2000: 271). Transit migration: the process of moving by overland and maritime transportation through buffer zones located on the fringe of the destination countries (Wallace et al., 1996; de Haas, 2008) Interrupted migration: it occurs when people who did not achieve the aim of crossing the border to reach certain destination country are deported or voluntarily decide to stop the journey and return (author’s own definition). Migration has a long history that goes back to before nations were fully established, where the term emigrant refers to an individual that leaves one place to move to another for any reason, and the term immigrant to a person that comes from other localities (Castles, 2000). In the case of Mexico, the National Institute for Migration (INM) recognizes three different migratory statuses. Non-immigrant status is granted to foreigners holding a permit that allows them to stay temporarily as a tourist, visitor, student, refugee, transit-migrant, local visitor, or correspondent. Immigrant refers to all foreigners legally dwelling in Mexican territory; and the status of permanent resident (“inmigrado”) is given to a foreigner who acquires the rights of a permanent resident, after living a required and mandatory number of years in the country (INM, 2011). The main types of migrants recognized in this research are the following: Migrant workers: “refers to a person who is engaged or has been engaged in a remunerated activity in a state of which he or she is not a national” (UN Convention, 1990) Temporary labour migrants: “skilled, semi-skilled or untrained workers who remain in the destination country for definite periods as determined in a work contract with an individual worker or a service contract concluded with an enterprise. Also called contract migrant workers” (IOM, 2012b: key migration terms) 9 Permanent labour migration: is the resentment of a worker in a place outside his own country in perpetuity (ibidem). Irregular or undocumented migrants: “people who enter or sneak into a country usually in search of employment without the necessary documents and permits” (Castles, 2000: 270). Refugees: “are people with a permanent or temporary status which are unable or unwilling to return because of persecution fear for religion, nationality, political opinion and activity, race, and membership in a particular social group” (ibidem). Family reunification: “is migration to join people who have already entered an immigration country under one of the above categories” (ibidem). Return migrants: “are people who return to their home countries after a period in another country” (ibidem). Transit migrants: are migrants who stay in certain country for some period of time while seeking to migrate permanently to another country (IOM, 2003). High skilled migrants:”are people with tertiary education, typically adults who have completed a formal two-year college education or more” (IOM, 2008a: 52) Low skilled migrants: “the notion of low or semi-skilled employment is more nebulous and more difficult to define in any meaningful way. Put simply, as such workers fall outside the definition of skilled and highly skilled, this often means that in the immigration context they do not qualify under ordinary work permit schemes or pints systems regulating admission to a country for the purpose of employment” (idem: 78) Environmental migrants: “are those people who have been forced to leave their traditional habitat, temporarily or permanently, because of a marked environmental disruption that jeopardized their existence and/or seriously affect the quality of their life” (El-Hinnawi, 1985: 4). 10 2.2. The land degradation phenomenon The soil is an important element of the ecosystem in which biodynamic processes are constantly occurring. It is also a natural storage for water, air, nutrients and microorganisms, all of this interlinked on different temporal and spatial bases. These properties are useful for holding a wide variety of productive activities connected to the socio-economic development of certain region (Maass et al., 2007). The rupture of these biodynamic processes caused by natural phenomena and human activities, originated by the rift of relations between nature and society and not on ad hoc basis, has largely affected the quality of land. Since natural resources are used up at a faster pace than nature’s capacity to produce them (Rozanov et al., 1990; Foster, 1999). These are determined by the modality and development of the productive forces (Foladori & Pierri, 2005; Foladori & Melazzi, 2009), which damage fragile ecosystems, especially concerning soil. Hence, the biophysical and socio-economic aspects represent the main factors affecting land vulnerability and productivity, resulting in decrease of land carrying capacity, underdevelopment and migration (Knerr, 2000). Several definitions of land degradation have been given since the first studies of this phenomenon: (1) the term “desertification” was introduced by the French scientist Aubreville in his report “Climats, forêts et désertification de l´Afrique tropicale” published in 1949 in allusion to spreading of deserts or desert-like conditions (sand movement and land encroachment). The UN at the UNCED 1992 redefined this definition putting forward that desertification is the same as land degradation (Hellden, 2003). (2) The UN Millennium Ecosystem Assessment: Desertification Synthesis (2005: 1) defines land degradation as “the reduction or loss of the biological or economic productivity of the soil” because of the combination of various factors, including climatic variations and human-induced activities acting upon the land (Conacher. & Conacher., 1995) or a combination of processes such as soil erosion, deterioration of soil properties, and long-term loss of natural vegetation (Giordano & Marini 2008) from which land cannot recover unaided (Bai, Dent, Olsson, & Schaepman, 2008). (3) In the same regard, Consultancy Report prepared by the United Nations Convention to Combat Desertification UNCCD states that manifestations of land degradation can be seen particularly in cover vegetation changes. Based on this viewpoint land degradation is a “decrease in plant cover, density and productivity, reduction in soil quality, accelerated soil erosion and physiognomic changes in landscapes, compositions, and configurations” UNCCD (2010: 4); it essentially describes circumstances of reduced 11 biological productivity of the land (Reynolds & Stafford, 2002; UNEP, 1994). (4) The Land Degradation Assessment in Drylands (LADA) defines LD as a reduction in the capacity of land to perform ecosystem functions and services that support society and development, in which interactive processes occur that involve multiple factors, such as climate and human activities (UNEP, 1994). The concept of ‘degradation’ implies adverse consequences for people and ecological systems. ‘Land degradation’ is considered to be caused or, at least, exacerbated by human actions, and is thereby is distinguished from ‘natural hazards.’ The global introduction and spread of the problem have been closely linked to the introduction, spread and growth of human populations and our increasing demands on the natural resource base. Inappropriate land-use practices, which in turn are influenced by social, economic, political and technological factors, are the primary cause (Conacher, 2009: 91). Additionally, current LD processes are forcing communities to leave their farmlands behind, adding more migrants to the rural-urban, regional and international migration patterns. In this way, the number of unplanned, unmanaged, forced migrations will be enlarged with further land degradation (Castles, 2002; Castles & Rajah 2010; IOM, 2008b; Piguet, 2008; Nkonya, 2011). A survey carried out by the Global Assessment of Soil Degradation (GLASOD) describes the degrees of degradation in terms of reductions in land productivity. Light degradation somewhat reduces agricultural suitability. Moderate greatly reduces agricultural productivity. Strong largely destroys biotic functions; non-reclaimable at farm level. Extreme fully destroys biotic functions; non-reclaimable (FAO, 1991) 12 2.3. Development The first attempts to construct a concept for the term “development” emerged between 1950’s and 1960’s in order to describe the trends of progress of that epoch; where economic growth was the horizon of a society to meet the basic needs required for livelihood security and lasting human welfare and well-being (Veltmeyer, 2010). Ever since, the concept of development has been on the cutting edge of the most important political debates and progressive ideas. Hence, development can have many meanings according to the scientific field it is applied to. In order to elaborate on the concept of development, the following considerations need to be kept in mind. (1) Sachs (2009: xi) observes that the term development has been used for “everything from pulling up skyscrapers to putting in latrines, from drilling for oil to drilling for water” as such it “is a concept of monumental emptiness [...] easily used as a projection screen for contradictory perspectives”. (2)According to Veltmeyer, (2010) development denotes a set of improvements in social conditions along with the structural changes needed to achieve such improvements. (3) O’Malley (2009: 19) points out that: “Development can be viewed as both the results of actions consciously taken in pursuit of a defined goal (strategic dimension of development) and the explanation portraying the dynamics of the structure of the social system (structural dimension of development). [...] Both dimensions should be taken into account in determining development outcomes. [...] Development should be viewed as not only interdisciplinary but also multidimensional and it should casts its glance not only on economic development but also on relevant and concomitant political, social, cultural, geographical, environmental, and of course historical dynamics as well”. (4) Subsequently, the idea of sustainability was imbedded; that is, development ought to be sustainable in terms of environment and livelihoods, capable to capacitate and empower the poor, thus that they can be the agents of their own development (Cohen & Uphoff, 1977). This is to be done by “realizing very fundamental human values and about finding the means to extend the fruits of these values to the greatest majority of the world’s population” (Cypher & Dietz, 2004: 10). (5) The concept of development in the context of social liberal reforms seeks to allow societies to progress in an equitable manner (social, economic, gender) towards meeting the basic needs of the population and reducing or alleviating poverty in the process (Veltmeyer, 2010). 13 In this light, in September 2000 the UN Millennium Summit representatives from the scientific community, the world’s leading development institutions, and world leaders adopted the Millennium Development Goals (MDGs) for 2015, thus setting targets for reducing poverty, hunger, diseases, illiteracy, environmental degradation and discrimination against women. The eight MDGs aim to end poverty and hunger, bring about universal education, gender equality, child health and maternal health, environmental sustainability and global partnership, and to combat AIDS/HIV (UN, 2010). Inside of the broad context of development, this research primarily focuses on human development and rural development. 2.4. Human development Amartya Sen’s (1989: 47) work on capabilities and functioning provided a solid foundation and sound knowledge for a development paradigm shift. His approach defines human development (HD) as the process of “enlarging a people’s functioning and capabilities to function, enhancing human capabilities and freedoms, enabling to live long and healthy life, have access to knowledge and a decent standard of living, and participate in the life of their community and decisions affecting their lives”. Seen from this point, HD is a tool to remove people’s life obstacles that hinder individual development such as: lack of education (i.e. illiteracy), lack of human freedoms (e.g. civil and political), and lack of access to resources (e.g. water and land). HD puts people at the centre of development as the active actors shaping the development process, equity, and sustainability (UNDP, 2009; 2010). In fact, the idea of equity is meant to “create an egalitarian access to societies productive resources and a more socially [fairly] distribution of government resources for education, health and other social conditions as a model of human development” (Streeten, 1984: 978). Mahbub ul Haq, together with the United Nations Development Programme (UNDP) published the first Human Development Report (HDR) in 1990, in which the new comparative measures of live expectancy, education and standards of living for all countries were included, denoting that national development should not be measured simply by national income or monetary per capita income (UNDP, 1990; Martinussen, 1996). In this way the Human Development Index (HDI) was developed to classify countries according to a differentiated scale of “very high, high medium and low human development” 14 HD is considered by Mahbub ul Haq and other economist (e.g. Amartya Sen and Paul Streeten) as the essence of development in which “national economic growth and increased incomes are the preconditions for improvements in standards of living but not the only elements” (Martinussen, 1996). HD is about creating an environment in which people can develop their full potential and lead productive, creative lives in accord with their needs and interests (UNDP, 2011), practicing ideas of progress, equality and freedom, in order to expand the choices available for each individual and encourage them to take advantage of their opportunities to ensure greater social inclusion (Veltmeyer, 2010). In addition, HD is about fighting poverty and living with dignity, the goal is “human freedom” (UNDP, 2011). 2.5. Rural development “For 70% of the world’s poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations” (World Bank, 2011: 1). For more than half- century until the 1990’s, development goals have been dominated by planned development, comprising the national pursuit of economic growth by economic expansion (O’Connor, Renting, Gorman & Kinsella, 2006), either as an end in itself by attempting to ‘catch up’ with the wealthier nations, or as a means to create the wealth needed for quality of life (Gabriel, 1991: 8). However, since the 1990’s and in some developing countries an alternative new approach has evolved, which sees development more in terms of better social standards of life and human improvement, rather than just economic growth. The UN World Conference on Agrarian Reform and Rural Development broached the notion of rural development for the first time, which claimed that: “Rural development based on growth with equity will require full integration of villagers, including equitable access to land, water and other natural resources, inputs and services, and equal opportunities to develop and employ their skills” (UN, 1979: 179). Cristóbal Kay (2009) points out that in part rural development has not been achieved due to the division between development thinkers and approaches. There are two clear development positions. (1) The agrarianists who believe that agriculture is the key for development, they tend to neglect industry’s development and argue that agriculture can perform in the process of industrialization. (2) The industrializers who argue that it is only by industrialising that 15 development can be achieved, neglecting agriculture’s development and holding that industry can perform in the process of agricultural development. In a deep analysis of the relationship between agriculture and industry as well as between rural and urban sectors in the process of development, he states that: “a development strategy which creates and enhances the synergies between agriculture and industry and goes beyond the rural-urban divide offers the best possibilities for generating a process of rural development able to eradicate rural poverty” (Kay, 2009: 104). 2.6. Decent work The concept of ‘decent work’ first appeared during the International Labour Conference in 1999. The ILO sees decent work as a mean for achieving equitable, inclusive and sustainable development, and it considers decent work as a key factor to sustainable poverty reduction (ILO, 2012). It provides “the opportunities for women and men to obtain decent and productive work in conditions of freedom, equity, security and human dignity” (Anker et al., 2002: vi), “in which rights are protected and adequate remuneration and social coverage are provided” (Barrientos, 2007: 1). The ILO points out that: “Decent work sums up the aspirations of people in their working lives. It involves opportunities for work that is productive and delivers a fair income, security in the workplace and social protection for families, better prospects for personal development and social integration, freedom for people to express their concerns, organize and participate in the decisions that affect their lives and equality of opportunity and treatment for all women and men” (ILO, 2011). Anker et al., (2002: 3) in a deeper conceptualization state that, “the word decent connotes this, referring to the need for works to have acceptable or adequate work and working conditions”. They also pointed out that “decent work is especially concerned about the poorest and most vulnerable” people. This definition of decent work includes six dimensions that are relevant for all people in the workforce. (1) “Opportunities for work refer to the need for all persons who want to work to be able to find work. (2) Work in conditions of freedom underscores the fact that work should be freely chosen and not forced on individuals and that certain forms of work are not acceptable in the 21st century” (idem: 2). (3) Productive work is essentially for workers to have acceptable livelihoods for themselves and their families. (4) Equity in work represents workers’ need to have fair and equitable treatment and opportunity in work. (5) Security at work is mindful of the need to help 16 safeguard health, pensions and livelihoods, to provide adequate financial and other protection in the event of health problems and other contingencies. (6) Dignity at work requires that workers must be treated with respect at work, and be given the chance to voice concerns and participate in decision-making about working conditions (Anker et al., 2002). The first two dimensions are concerned with the availability of work and the acceptability of the type of work involved; the other four dimensions denote the extent to which work is considered decent (Anker et al., 2002; Bescond, Chataignier & Mehran, 2003). Based on the work of Anker et al., (2002), Bescond et al., (2003: 180) elaborated seven indicators for decent work deficiencies: “low hourly pay, excessive hours of work for economic or voluntary reasons, national unemployment, children not at school (as a proxy of child labour), youth unemployment, male-female gap in labour force participation, and old age without pension”. All the concepts presented until this section form the pillars for the study of development as one of the topics addressed in this research. They are part of the formulation of a set of questions to construct the questionnaires for two surveys concerning small-scale farms and Central American migrantion in the municipality of Tapachula. It is also relevant to mention that the indicators mentioned above are among the most useful to measure development; however, they are not the only indicators used to answer the main research questions and to approve or reject the research hypotheses. 2.7. Neoliberal globalization Since the beginning of the globalization project in the 1970s, under the influence of large multinational corporations, international development bodies and the most influential governments, the global economy has undergone a deep transformation (Petras & Veltmeyer, 2000), and the disparities amongst developed and developing have increased. Neoliberalism can be understood as the latest institutional modality of capitalism, which promotes profit- making and forms a framework for capital accumulation (Bernstein, 2012). Dumeil & Levy (2004: 1-2) state that neoliberalism as “the expression of the desire of a class of capitalists owners and the institutions in which their power is concentrated, which we call finance, to restore the class’s revenues and power”. 17 “Neoliberalism is both a body of economic theory and a policy stance. Neoliberal theory claims that a largely unregulated capitalist system a “free market economy” not only embodies the ideal of free individual choice but also achieves optimum economic performance with respect to efficiency, economic growth, technical progress, and distributional justice. The state is assigned a very limited economic role: defining property rights, enforcing contracts, and regulating the money supply. State intervention to correct market failure is viewed with suspicion, on the ground that such intervention is likely to create more problems” (Kotz, 2002, 1). Neoliberal economists also argue that international free trade is beneficial for all countries. For the developing world economy, it supposedly increases employment opportunities for the local population, investment, better infrastructure and greater technological development. According to neoclassical economics, when restrictions on the circulation of goods and money are removed, capital moves to areas where its marginal productivity is highest, and it is in these areas where its marginal performance and productivity is greater. “The policy recommendations of neoliberalism are concerned mainly with dismantling what remains of the regulationist welfare state. These recommendations include deregulations of business; privatization of public activities and assets; elimination of, or cutbacks in, social welfare programs and reduction of taxes on business and the investing class. In the international sphere, neoliberalism calls for free movement of goods, services, capital, and money (but no people) across national boundaries” (Kotz, 2002, 1). Delgado-Wise, Marquez and Puentes (2010: 4) resume and describe the main features of neoliberal globalization in the following terms: (1) the internationalization of capital which seeks the reinsertion of developing countries into the global economy in order to ensure an abundant supply of natural resources and cheap labour. (2) Financialization which suggests an expanding role for finance in economic activity through neoliberal speculative strategies, which on the one hand offers short-term high profit margins of “invest funds, sovereign funds, pension funds and social savings”. On the other hand, it can produce widespread financial fraud, economic depression and speculative excess. (3) Environmental degradation is the result of the present growth trends of population, pollution, industrialization, food production, urbanization, and resource overconsumption extracted for the benefit of large multinational corporations to fuel development in the developed regions. (4) The restructuring of innovation systems has been developed to increase the profits of large multinational corporations, through the improvement of biotechnology, nanotechnology, information technology and communications. It has also opened the doors to the flow of southern scientists who produce vast numbers of patents for the developed countries. (5) 18 Labour precariousness as a consequence of the neoliberal model itself, it has become the propeller for capitalism, and it has “led to the growing transnalization, differentiation, and precariousness of labour markets (...) which allows large corporations to benefit from cheap and flexible workforce sources, particularly from the global South”. (6) Outmigration, firstly it is an outcome of the mentioned features of neoliberalism, secondly it originates due the lack of employment opportunities, vulnerability and exploitation of the labour reserve army in the migrant sending countries. Moreover, the dominant neoliberal economic vision for development based on integration into global markets has polarized societies, creating high levels of poverty in an unmanageable manner and concentrating the wealth in privileged economic sectors. Hunter (2004: 567) states that “the world is an open system in the sense that country mobility up the income/wealth hierarchy”, and that the “North-South, developed-developing, rich country- poor country divide is being eroded as globalization proceeds”. On the other hand, the growth of industrial activities and capitalist agriculture have altered the structural functioning of rural areas and deteriorated the natural resources, therefore affecting the development of small economies that survive from a traditional agriculture that has been replaced by modern methods. The abandonment of land and farmers’ migration to urban centres has been an effect of the neoliberal globalism policies that impoverished peasants and farmers, by destroying their means of production (e.g. natural resources). Subsequently, the rural exodus originated by declining farm numbers and a drop in employment opportunities was seen as one inevitable outcome of neoliberal globalization. 19 3. Theoretical framework and research hypotheses This chapter presents a historical analysis of the relationship migration-development and reviews the theoretical debates about migration and development. It gauges the different scientific positions in search of an approach that combines the natural and social viewpoints, in order to comprehend the complexity of the linkages between migration-development. It summarizes the different theories of migration that explain the roots and dynamism of the migration phenomenon. 3.1. Historical analysis of migration-development History tells us that migration has occurred for several centuries and that it is a process with a certain degree of continuity (Delgado-Wise, Márquez & Puentes, 2010). Migration has increased since the political formations of states, and the establishment of boundaries has triggered wars and colonization campaigns shaping the migration dynamics and volumes. Castles (2000), in a historical analysis of the dynamics of migration, holds that European emigration with colonialist purposes during the fifteenth century onwards was replaced by African slaves, indentured workers and servants steaming from colonized countries. On the other side of the Atlantic in colonial North America, the labour force was scarce due to the vast and easy colonization of land. This encouraged bringing migrants from Western Europe (30 million from 1861 to 1920) to a region going through an era of rapid industrialization (idem). Hence, to compensate the outflow of migrants, Western Europe’s industrialized nations relied on migration from neighbouring countries: “Irish in Great Britain, Poles in Germany and Italians in France” (Noiriel, 1988: 308). There are many other cases where immigrant recruitment has been a primordial need, either for replacement for the outmigration of the local labour force, or for replacing the work force that has been called by governments to defend their nations in wartime (Castles, 2000). In order to achieve development there are positive and detrimental consequences for the communities involved in the process. Developmental factors influencing migration decisions are also likely to change the development outcomes in both migrant receiving and sending countries (Taylor, 1999). Delgado-Wise & Márquez (2009: 87) underline that, “development underwent a historical change after World War II, and the interests of hegemonic nations took precedence. During the 1950s and in the context of the Cold War the concept of modernization was employed on behalf of an imperialist project”. The development shift also 20 changed the dynamics of migration. From 1945 to 1973, more countries were affected by migration; it was a period, where the demand for labour migration from developing countries towards industrialized nations (i.e. the countries in Western Europe, Oceania and North America) mushroomed (Castles, 2000). The same process of transformation and restructuring of the world’s economy from the 1970s onwards, known as new world order (NWO) or neoliberal globalization, promoted and supported by the world’s richest governments, multinational corporations (MNC), and international bodies such as the World Bank (WB), International Monetary Fund (IMF) and the World Trade Organization (WTO), changed the debates on migration and development (Petras & Veltmeyer, 2000). In this regard de Haas (2010) states that during the 1950s and 1960s the debate was dominated by mainstream development economists with a Keynesian bent, swinging to a neo-Marxist alternative approach in the 1970s and 1980s, and returning to a mainstream economist view in the 1990s and 2000s, this time with a neoliberal vision. Since the early 1990’s there has been much debate among scholars from different scientific fields, think tanks and schools of thought regarding the relation migration-development. These debates leave behind the viewpoint that sees development and migration as two different phenomena that need to be treated in separated portfolios. However, the migration- development nexus has given rise to two distinct strands of literature. (1)The dominant view, which suggests that migration can contribute positively to economic, social, human and technological development under the prism that migrants’ remittances can boost and foster development in the migrant’s sending countries (Borjas, 1989; Durand & Massey, 2003; Lewis, 1954; Harris & Todaro 1970; Massey et al, 1993; Taylor, 1999; Todaro, 1996). And (2) the alternative view, which holds that migration increases spatial disparities in development levels between developed and developing countries, as well as within migrant sending communities (Castles, 2000; Delgado-Wise, Márquez & Rodriguez 2009; Delgado- Wise &Márquez, 2007; de Haas, 2010a). The opposing views on migration and development are summarized in table 3.1. The following paragraphs address the differences between the two groups. 21 Table 3.1: Opposing views on migration and development Dominant Alternative Functionalist Structuralist Neo-classical Neo-Marxist Modernization Disintegration Net North-South transfer Net South-North transfer Brain gain Brain drain More equality More inequality Remittance investment Consumption Development Dependency Less migration More migration Source: adapted from de Haas (2010a) Nowadays, the “dominant” migration-development approach that sees migrants as development agents and remittances as a main pillar to lever development in the receiving countries, is characterized by the following assumptions: (1) remittances are a tool for development, (2) remittances lead to financial democratization, and (3) remittances make the economic empowerment of the poor strata possible (Delgado-Wise & Márquez, 2009). In other words, migration is perceived as a form of optimal transfer of human capital (Castles, 2000) from developing to developed countries, it generates a balanced growth; it is a prerequisite for economic growth, and a component of the entire development process (Todaro & Maruszko, 1987). In addition, it considers migration as the development tool that “enables people to increase the returns on their skills and human capital, which is to their own benefit as well as to the benefit of the economies as a whole” (de Haas, 2012: 13). Subsequently, opponents of this view argue that migration goes with “brain drain”, this occurs due to the low labour productivity and high unemployment and underemployment present in the developing migrant sending regions (Delgado-Wise, Márquez and Puentes, 2010). Remittances are an important piece in the development puzzle; they contribute massively to the national account and could help to fund development investment in migrant-sending countries (Castles, 2000). Remittances also support social stability, in that they mitigate poverty and marginalization while offering an escape valve from constraints of local, regional, and national labour markets. Moreover, the dominant view considers returning migrants to be important agents for development. Aside from their economic contribution through remittances, they also contribute with ideas, knowledge, and entrepreneurial attitudes that speed up the development process in their home countries. Overall, the dominant view 22 argues that migration and the sending of remittances contribute to both developing and developed countries’ development. In contrast, the “alternative” approach sees the dominant view as a feeble scheme, because it overlooks the root causes of underdevelopment in the migrant-sending countries (Delgado- Wise, 2006; Delgado-Wise & Márquez, 2009). The alternative approach sees migration as a consequence of a larger process of capitalist expansion, carried out in the context of an unequal distribution of economic and political power among developed and developing regions (de Haas, 2012). From this view, it is precisely, “the underdevelopment conditions and particularly the neoliberal restructuring process that deepen underdevelopment and increase North-South ─developed-developing─ asymmetries, which hasten labour expulsion and nourish the new migration’s dynamics” (Delgado-Wise, Márquez & Rodriguez, 2009: 29). Other scholars hold that migration is aggravating problems in developing regions and that it is a “flight from misery” (de Haas, 2010a: 233). Along these lines, migration hampers development in sending countries due to the outflow of young people and of qualified workers; developed regions reap the benefits, while sending countries have to bear the price of child rearing and many years of education (Baldwin, 1970; Castles, 2000). In turn, “migration undermines regional and local economies,(...) increasing dependence on developed countries (of which remittances are but one manifestation), stimulating further out- migration” (de Haas, 2010: 234), fostering uneven development (wealth polarization), precarization of labour markets as well as increasing the dispartities between developed and developing economies (Delgado-Wise & Márquez, 2009). This alternative approach considers remittances to be a temporary, external and unreliable source of income, usually invested in conspicuous consumption rather than productive projects (de Haas, 2012). These two approaches to analyzing the development-migration nexus tend to be one-sided. The need for a dualistic approach that takes into account the positive and negative effects of migration on the different social and economic development dimensions, as well as the effects of development on human mobility has manifested itself in a paradigm shift, which considers development-migration as a more heterogeneous and pluralistic relationship. This heterogeneity consists of a large number of aspects and a complete multidimensional perspective should comprise “economic, political, social, environmental, cultural, racial, ethnic, gender, geographical, demographic and [historical] factors” (Delgado Wise & Márquez, 2009: 93). Hence, in order to gain a deeper understanding of the relationship between migration and development, it should be seen as reciprocal (de Haas, 2010). In the 23 words of Delgado-Wise and Márquez (2009: 94), “neither migration nor development are independent variables, they are, after all, inscribed within the broader historical context of neoliberal globalization”. 3.2. Migration theories Although migration is considered to be mostly driven by economic factors, “this alone cannot explain the actual, patterned and geographically clustered morphology of migration” (de Haas, 2010: 1589). There are also other forces that also trigger migration, including political, sociological, cultural (Castles, 2003), demographic and environmental ones. These factors shape in time and space the different migratory dynamics from rural-urban, urban-urban (within a country and between countries) and from short-term to permanent relocation. Out of the large empirical and theoretical literature explaining the causes of migration (table 3.2) there are some theories that have been widely used to formulate an explanation about the determinants and consequences of human migration. Table 3.2: Main migration theories: 19th century and second half of the 20th century Dimension Micro level Meso level Macro level Migratory movement: beginning and causes Neo-classical economics: micro (Borjas) Behavioural models Social systems (Hoffmann- Novotny) New economics of labour migration (Stark) Neo-classical economics: Macro (Todaro) Dual labour market (Piore) Marxist theory (Castles and Kosack) Mobility transition (Zelinsky) Durability of the movement/ maintenance of the movements Social network (Massey) Institutional theory Migration functions Cumulative causation (Massey) Integration of immigrants Migration systems (Zlotnik) Social capital (Massey) World system theory (Wallerstein) Source: Adapted from Blanco (2000) and Hagen-Zanker (2008) The theoretical field of migration is characterized by a pronounced opposition between the standard neoclassical orthodoxy (neoclassical economies) and the historical-structural heterodoxy (neo-Marxist political economy). 24 To explain the dynamics of migration, this part of the chapter considers the most representative positions on migration within the two main theoretical perspectives dominating this field. The neoclassic approach combined with a neoclassical model focuses on differences in wages and employment conditions (macro), and it envisages the migration movement as an individual decision for income maximisation (micro). An approach known as the New Economics of Labour Migration (NELM) emerged from the neoclassical perspective, taking into account macro and micro considerations, including remittances and the household’s participation in the migration decision process. In contrast, historical- structural perspectives (heterodoxy) stem from an array of structuralist, dependency, and neo- Marxist theories. 3.2.1. Neoclassical economics: macro theory The dominant approach applies neoclassical economic theory to international migration to explain labour migration in the process of economic development (Harris & Todaro, 1970; Todaro, 1976). Massey et al., (1993: 433) state that “according to this theory and its extension, international migration like its internal counterpart, is caused by geographic differences in the supply and demand of labour”, mostly between the rural traditional agricultural sector and the urban modern manufacturing sector. This theory has the following assumptions. (1) The difference of wages amongst developing and developed countries attract individuals from the low wage countries to migrate to the high wage countries (Todaro & Maruszko, 1987; Borjas, 1989). (2) Workers migrate from small labour to large labour markets. (3) Migration will stop when the wage differential between developing and developed countries becomes even, (both net labour migration and net capital flows will be equal to zero when a new equilibrium is achieved). (4) Labour markets in comparison with other kinds of markets “are the primary mechanism by which international flows of labour force are induced”, and (5) international migration flows are controlled by governments through the regulation of labour markets in sending or receiving countries (Massey et al., 1993: 434). As a consequence of this distinct human mobility, the developing countries face a decrease in the supply of labour, a rise in wages, and a flow of investment capital from developed countries leading to equilibrium (figure 3.1) (Massey et al., 1993). The capital flow is comprised of mainly labour-intensive industrial capital and it is accompanied by high- skilled labour migration from developing to developed regions. Figure 3.1: Mechanisms leading equilibrium Todaro (1969) and Harris & Todaro (1970) explained the relationships between rural migration based on the dual-economy models of Lewis (1954), in which migration occurs as a result of differences in the supply and deman The modern sector absorbs the labour surplus in the traditional agricultural sector and it grows trough capital accumulation (Lewis, 1954; Ranis & Fei, 1961). Harris & Todaro (1970) augmented these models wit developing countries. The Harris and Todaro model considers migration as the mechanism that helps enlarge the migrants expected incomes by moving to a different labour market. They concluded that, due to city, migration is not completely risk long as there is a positive income differential. However, before making the decision to migrate associated with migration (Durand & Massey, 2003). individual choice can explain this 3.2.2. Neoclassical economics: micro theory This theory conceptualizes migration as a form of investment i individuals assess the positive net return in monetary terms through a cost (Todaro, 1969). It also suggests that: low-wage country large endowment of labour supply Labour Capital 25 Source: adapted from Massey d of labour between the rural and urban sector. h significant empirical urban unemployment data from the difficulties of finding a job right away upon arrival in the -free. Nevertheless, rural-urban migration will occur as , individuals gauge the costs and benefits The microeconomic model of . n human capital, where - high-wage country low endowment of labour supply Labour Capital Flows Migration Capital et al., (1993) -urban benefit calculation 26 “People choose to move to where they can be most productive, given their skills; but before they can capture the higher wages associated with greater labour productivity they must undertake certain investments, which include the material costs of travelling, the cost of maintenance while moving and looking for work, the effort involved in learning a new language and culture, the difficulty experienced in adapting to a new labour market, and the physiological costs of cutting old ties and forging new ones” (Massey et al., 1993: 434). The decision to migrate is taken when the benefits of migrating in terms of expected net returns exceed the costs (Borjas, 1989). Massey et al., (1993) frames the decision making process in the equation below, in which if the result is positive for the elected migration destination the individual migrates; in contrast, if the result is negative, the process of migrating does not occur. 0 =       −     − 0 (1) Where: ER (0) = expected net return to migration calculated before departure at time 0 P1 (t) = probability of avoiding deportation from the destination country* P2 (t) = probability of employment at the destination country Yd (t) = earnings if employed at the destination country P3 (t) = probability of employment at the origin country Y0 (t) = earnings if employed at the origin country NR= non-monetary returns r= the discount factor C (0) = the sum total of the costs of movement (including psychological costs) * 1 for legal migrants and <1 for undocumented migrants This model predicts that the expected net returns (ER) depend on the expected benefit and migration costs. Migration costs (C) include travel, opportunity, and psychological cost variables on the one hand. On the other, the expected benefits are comprised of the discounted earnings and non-monetary returns (NR). Furthermore, the age of the migrants is a key variable; the model assumes that the expected net returns are discounted over the remaining lifetime of the individual and that the young and educated migrants are the first to migrate. Notwithstanding their strengths, the macro and micro approaches of neoclassic economic have been criticised. The critics fall upon the macro-approach prediction that equilibrium will take place when production factors and wages are balanced out between low-wage and high-wage countries. This prediction has not been found in empirical data. In fact, some empirical researches have shown that the wage differences have enlarged. 27 Likewise, the micro approach’s assumes that migrants are the only players in the decision making process; it does not take into account the structural factors of migration. In general, this model is merely a theory of migrant agency without incorporating a full theoretical explanation of all migration process. 3.2.3. The New Economics of Labour Migration NELM: household decision making For some scholars this theory represents an extension of the neoclassical economics model (Arango, 2004), but for others the “NELM theory rejects neoclassical models, which largely ignore constraints and were evaluated as too rigid to deal with the diverse realities of the migration and development interactions” (de Haas, 2010a: 242). In any case, NELM constitutes a fundamentally new theory where agency and structure viewpoints and mechanisms of the neoclassical model are jointed (Hagen-Zanker, 2008) to yield a greater variety of outcomes (de Haas, 2010a). Seen from this light, NELM holds that the family or households is the more appropriate unit than the individual decision making concerning migration. It considers migration as a household strategy in developing countries, in which household members jointly decide to maximize expected income, minimize risks (Stark, 1991), and loosen constraints associated with a variety of market failures, apart from those in the labour market (Stark & Bloom, 1985), by sending a family member abroad for the wellbeing of the family as a whole. Household members choose to work in international markets where wages are better off than in the local region (Massey et al., 1993). Stark (1991) points out that the household strategy of sending one of its members to other regions is aimed at increasing the household’s economic status in comparison with other households in the region (relative deprivation/income). Moreover, by taking into consideration the possibility of facing calamities, migration becomes an instrument for risk diversification where the household will rely on remittances for support (Massey et al., 1993). Additionally, in the neoclassical migration theory, remittances are not taken into account; whereas, in the NELM remittances are one of the key motives for migrating (de Haas, 2010a). Hence, through international remittances, migration can be a household strategy to overcome market constraints, reduce risk and investing in productive activities, thereby diversifying income sources (Stark, 1980). The development of risks and risk-management differs amongst developing and developed economies. On the one hand, the risks to a household’s income in the developed countries are 28 reduced by the implementation of governmental programs, through private insurance markets and credit markets enabling households to finance new projects. On the other hand, there is a lack or inaccessibility of mechanisms for managing risks in the developing countries, where loans or credits programs are not available or represent a higher cost, and market failures exert strong pressures for international movement (Massey et al., 1993). All things considered, although the NELM transcends some of the limitations of the neoclassical perspective as Arango suggests, its theoretical foundations are rooted in the same theoretical paradigm 3.2.4. Historical structural perspectives This approach has been the traditional alternative to the neoclassical economics approach of migration; it is typically presented as a rather loose set of theoretical propositions that focus primarily on the structural demand for migrant labour in the capitalist regions (Piore, 1979; Castles & Kosach, 1973; Sassen, 1988). In addition, it applies Marxist and neo-Marxist development insights to the topic of migration (Hagen-Zanker, 2008). Wood (1982: 301) argues, “this approach is more difficult to summarize” because its theoretical roots can be found in a “variety of models” or theories. However, there are a few common characteristics embedded in the various theoretical accounts, including the following. (1) Its rejection of equilibrium (macro theory) as a structuring principle. (2) Its methodology tends to be inductive-dialectical not hypothetical-deductive, as in the case of neoclassical. (3) “both internal and international migration are regarded as part and parcel of a broader processes of structural change [development] rather than as a discreet element of social reality that can be subjected to separate investigation” (idem: 301-302). The world systems theory (Wallenstein, 1974) argues that international migration is a consequence of the world economy’s penetration into the developing regions, whereby international labour migration follows the direction of goods and capital created by the globalization of the market economy. International migration is more accentuated in regions with a history of colonization due to the existence of cultural, linguistical, social, economical, communicational and logistical linkages. Myrdal (1957) contributes with the “cumulative causation” theory, which holds that after initial migration, migration tends to become more common due to the joining of more people from the community to the migration outflow, and the aid provided to new migrants from the already settled migrant networks. These factors turn migration into a self-sustaining 29 phenomenon and cause the migration flow to increase progressively over time (Massey, 1990). In sum, the more migration there is, the more migration may occur in the future. However migration cannot go on forever, migration will be halted when the available migration networks in the destination countries are replete, and when the labour force in the sending regions becomes scarce, forming societies of elderly people and children with low migration potential. Furthermore, this theory holds that differential economic growth among developed and developing economies foster out-migration from the developing region towards fast growing developed regions. Such differences are exacerbated by the deepening of spatial welfare inequalities, brought about by the expansion of globalized capitalist development (de Haas, 2010a). Myrdal (1957) stated that, in order to stop the widening of disparities between developed and developing regions, a strong state policy is required. He also argued that the negative effects are not exceeded by the apparently positive effects, in the form of an increased demand for agricultural products and raw materials from developing regions. Migration and the expansion of migration networks make migration more likely and accessible for other local households. In the long term, it might also change the tastes and motivations of the local culture in the migrant sending areas. In addition, due to selectivity3 of migration, the economy in migrant origin regions tends to stagnate. In other words, skilled labour leaves, resulting in the depletion of human capital (Massey, 1993). This approach distinguishes both the characteristic processes of migrant-destination areas and what take place in the migrant sending areas. Regarding the migrant destination countries, it holds that capitalist development in the developed-industrialized countries requires a constant inflow of workers, which creates a structural incentive for immigration to occur (Castles & Kosack, 1973; Delgado-Wise, 2006). Hence, migration is the product of the existing inequalities between developed and developing countries, and a way for developed countries to develop underdevelopment in the developing countries (Massey et al., 1993). Castles (2000), indicates that migration is a key indicator of the globalization phenomenon that has enhanced transnational networks, modern information, and communication technology. Also, he argues that migration is both a result and a cause of development. This implies a dialect relationship between migration and development. The character and nature of migration processes are closely related to the particular dynamic of the capitalist system and its inherent uneven development processes (Delgado-Wise, 2006). International migration originated by 3 Migration tends to become less selective over time as the costs and risks fall because labour formation (Massey et al., 1993) 30 the restructuring of labour market under neoliberal globalization (Portes & Walton, 1981) has unleashed destabilization and altered the development in the global south (Delgado-Wise & Marquez, 2007; Narvaez, 2007), creating a population displacement from developing to developed areas and also within developing countries, from the poorest countries to richer and emerging economies in the south. Furthermore, the increasing return from migration (remittances) changes the local income distribution and the distribution of land, through the purchase of new land (Massey, 1990). In addition, remittances foster the use of capital- intensive methods in rural agricultural activities, which in turn lowers the demand for local labour (Massey, 1990; de Haas, 2010). To sum up, the neoclassical economic sees a positive relationship between migration and development, as well as considers migration as a lever for developments. The NELM approach built with neoclassical roots claims that development can be achieved through the migrant’s contribution to social, economical and political transformation in migrants sending countries. However, there is plenty of evidence showing that migration and remittances are neither a panacea to overcome structural development constrains, nor a pathway for poverty alleviation. Furthermore, this theoretical approach fails to explain the determinants of migration under the ongoing neoliberal globalization conditions, and to what extent it triggers human mobility and alters the migration dynamics. It does not take into consideration the real costs and impacts of migration on the socio-economic structure in the migrant sending countries. It also discards the risks that migration represents for migrants. The historical structural approach provides a more comprehensive theoretical framework, capable of overcoming the limitations of the neoclassical perspective. 31 3.3. Research hypotheses Based on literature review, primary and secondary data, as well as descriptive and inferential statistical analyses, this research aims to provide answers the research hypotheses: H1: The land degradation phenomenon in Tapachula deteriorates the production conditions in small-scale farms, decreasing the household’s income. H2: The government’s abandonment of Tapachula’s agricultural sector erodes the small-scale farms’ production conditions, diminishing the household’s income. H3: Land degradation is a central factor for generating, intensifying and diversifying migration flows from the rural areas of Tapachula. H4: Central American workers close the labour gaps derived from the rural out-migration from Tapachula and the demand for labour in the region. H5: The Central American labour force in the agricultural sector of Tapachula is subject to labour standards, which are beneath ILO’s decent work agenda. The table below depicts the main indicators, as well as the statistics analyzes used for each of the hypotheses that will help obtain a solid foundation to approve or reject them. Table 3.3: Summary of hypotheses, indicators, data sources and statistical methods Hn Indicators Data source Method of data analysis H1 • Farming households’ demographic characteristics: age, gender, education, and main economic activity • Household heads’ income • Farm characteristics: size, topography, and altitude • Soil type and soil quality • Swampy and degraded areas • Land degradation causes: heavy rains and deforestation • Type of cultivated crops • Farm pollution problems: agrochemicals, sewage water/trash from neighbours, and industrial wastes Farm level survey Descriptive statistics Statistical inference: multiple linear regression analysis MRA and ANOVA H2 • Agricultural subsidies: amount and frequency • Impacts of NAFTA on households: fall of coffee prices, fall of prices of agricultural and meat products, coffee monopoly, increase of staple food prices, and Mexican labour migration to the USA • Farm production constraints: lack of technical assistance Farm level survey Descriptive statistics Statistical inference: factorial 32 and lack of loans/credits/aid from the government ANOVA H3 • Households’ economic situation in relation to 2005 • Crop Production changes from 2005 to 2010 and determinants • Household heads’ income • Households’ physical assets • Households’ natural resources • Households’ living conditions • Farm productivity determinants and constraints since 2005: eroded/degraded soils, droughts/lack of water, floods, pests and diseases, lack of water, poor soils, crops/livestock low prices, lack of economic resources to improve production • Household heads’ social assets: households with out- migrants and number of out-migrants toward urban, internal and international destinations • Outmigration determinants: low agriculture productivity, low wages, unemployment, existence of migration networks, and in search of better economic opportunities • Remittances: amount and frequencies Farm level survey Descriptive statistics Statistical inference: crosstabulation and Chi-square test H4 • Labour force sources: all the household members, all the adult male of the household, Mexican workers, foreign worker • Labour force origin regions: neighbouring ejidos and Guatemala • Reasons for hiring foreign labour force: cheaper labour force, outmigration of local workers, and better workers than local workers Farm level survey Descriptive statistics Statistical inference: crosstabulation and Chi-square test H5 • Guatemalan migrant workers’ socio-demographic characteristics: sex, civil status, education and place of birth • Guatemalan migrant workers’ household characteristics: number of household members older than 18 years of age, number of household members younger than 18 years of age, and household size mean • Causes of migration: unemployment and poverty • Purpose of migration: work temporary and residing • Migration support: family members, contractor • Opportunities in Mexico: better salaries and more employment opportunities • Migratory document and types • Time living in Mexico since last visit • Transit migration • Remittances: amounts and frequencies • Working and living conditions Central American migrants survey Descriptive statistics Statistical inference: crosstabulation and Chi-square test 33 3.4. State of research In order to develop an in-depth understanding of land degradation, rural development and migration studies, this part of the chapter aims to gather the most important literature and scholars from several disciplines that help to set solid foundations for this research. It analyses and discusses influential scholarly themes that are important in this study, such as land degradation processes in the southern Mexican State and their effects on the small-scale farms’ development. It describes the dynamics of the migration flows in Tapachula-Chiapas, the working conditions of Central American in-migrants in the agricultural sector of Tapachula, as well as the consequences of neoliberal policies in the frame of NAFTA for the agricultural and environmental sector of Chiapas. 3.4.1. Land degradation and rural development In 1994, a study carried out by Leighton-Schwartz and Notini concluded that farmland degradation is a determinant of migration in Mexico, and that further land degradation will increase the numbers of migrants, migration flows, and directionality toward internal and international destinations. Later, a case study carried out by the Environmental Change and Forced Migration Scenarios (EACH-FOR) published in 2009, concluded that “environmental degradation in Mexico is mainly interlinked with the degrading quality of soil and insufficient availability of water”, which is partially due to a change of traditional agriculture for an export-led agriculture model, thus failing to promote development and sustainability. Subsequently, Wise and Gallagher (2002), in their book “NAFTA: a cautionary tale”, state that environmental degradation increased since the implementation of neoliberal policies. They concluded, that in the period between 1985 and 1999, farmland erosion increased by 89% in the rural areas of Mexico. Villafuerte (2009) and Villafuerte & Garcia (2008) depict that the impacts of neoliberal globalization led to the government abandonment of the rural areas, which has affected the socio-economic and environmental sectors of Chiapas. Another researcher, Saldaña-Zorrila (2008), argues that most Mexican farmers live in poverty and are vulnerable to an increasing number of natural and socio-economic hazards. He remarks on how extreme environmental events are causing severe damages to agriculture, especially to subsistence farmers. He also explains that these natural hazards are among the factors for agriculture to produce only 4% of Mexico’s GDP despite employing around one-quarter of the national labour force. Despite the neoliberal implications in the state of Chiapas, these recurrent natural disasters and extreme weather changes impacted on the production of main 34 cultivated crops (e.g. coffee, maize and banana) and affected the farmers’ income. According to his results, the most affected are the small-scale farmers, who hold farm properties in a mean size of 5.2 hectares used for subsistence farming. In a study conducted about the biophysical and socioeconomic diagnostic of the river basin Cohatan in the Soconusco region of Chiapas, Grajales, De la Piedra and Lopez (2008) arrived at the conclusion that the heavy rains are derived from tropical cyclones, hurricanes and storms provoking severe damages to ecosystems, population, infrastructure and basic services. They also argue that the soil of Chiapas is naturally prone to degradation due to all environmental features of the region. In terms of soil degradation, Ruiz’s (2010) research found that from the 15 economic regions of Chiapas, the most affected regions by water erosion are the coast, Soconusco and the sierra of Chiapas; their rough topography, shallow soils, and land use changes have resulted in erosion levels of 200 to 415 ton/ha/a. In addition, this phenomenon is reducing around 40 to 60% the soil’s water holding capacity, affecting upon the performance of the river basins, and increasing the vulnerability to droughts in some areas and floods in the lower lands. Overall, land degradation has reduced the biological and economical productivity capacity of the Chiapaneco agricultural sector and weighted on the rural development by reducing the income of farmers. Campbell & Berry (2003) point out that an indirect factor playing an important role in both, the economic development of Mexico and the environmental sector, is the application of policy changes associated with Structural Adjustment Programs (SAP’s) and NAFTA, which potentially exacerbate and accelerate environmental destruction and land degradation. The environmental Change and Forced Migration Scenarios (EACH-FOR, 2009), shows that since the implementation of free trade policies as part of NAFTA agreement, soil erosion increased by 89% from 1985 to 1999. Moreover, in the context of land degradation they state that: “The rural economy of Chiapas, strongly dependant on agricultural production, is threatened by the frequent passage of hurricanes and other tropical storms, whose impacts are aggravated by legal and illegal logging of forests, leading to a higher risk of flooding and landslides. The path of hurricanes Mitch (1988) and Stan (2005) had disastrous consequences for the local population, above all in remote mountain areas and in shanty-towns of the urban centres” (EACH-FOR, 2009: 60) In detail, the authors analyze in a Mexican case study the impact of environmental change on migration in two selected regions: Western Tlaxcala (about 70 km east of Mexico City) and the Sierra and Soconusco regions in Chiapas (Southeast-Mexico), near the border with 35 Guatemala. They concluded that since 1990’s, both areas have become major sending regions for international migrants. Altogether, land degradation, intensification of natural hazards, changing rainfall patterns, the already existing migration networks, and the negative impacts of NAFTA (see chapter 7) are the major factors that trigger and speed up migration processes from Chiapas. 3.4.2. Migration Since the 1990’s, the southern Mexican-Guatemalan border received more attention from both and international governments, NGO’s, academics and researchers due to the trans- border dynamics. The migration dynamics in the Mexico-Guatemala border should not be overlooked because of the existence of historical cross-cultural and labour mobility. Villafuerte (2007; 2008) states that since the 1980’s and 1990’s, agricultural crisis, extreme natural events, and social problems have triggered a wave of migrants to other areas. He particularly studied the effects of globalization through the implementation of international agreements, which affected the Mexican and Central American relationships. He argues that NAFTA, and Plan Puebla Panama (PPP) failed to respond to the socio-economic development of the region. Instead, Chiapas transformed into a place of destination, transit and origin for migrants. Along with rural and urban migrants, the migration phenomenon in the region has become more complex, converting Chiapas in a laboratory to study the different migration flows. Furthermore, the international migration of Chiapanecos to the United States is a new phenomenon. Several prominent scholars carried out significant studies about migration from Chiapas to either internal or international destinations. Garcia and Villafuerte (2004; 2006; 2008) collected significant information from a socio-economic and political perspective to understand the increase of migrants from Chiapas to the USA. Their findings depict that the rural crisis in 1980s, the fall of international coffee prices in 1989, the strike of extreme weather events, and as of 1994 the uprising of the Zapatista Army of National Liberation (EZLN) have generated a continuous outflow of migrants from Chiapas. Additionally, existing and formation of new migration networks eventually foster the outmigration of Chiapanecos. Garcia and Villafuerte, Jáuregui and Ávila (2007) also examine the main determinants of the outflow of Chiapanecos to the USA. They agree that the agricultural crisis, the fluctuation of international coffee prices, the NAFTA, the rise of the EZLN, the struggles for land, and the intensification of natural disasters provoked people to migrate. 36 Various authors have produced a number of studies that examine migration within the state of Chiapas. Angulo (2008) and Viquiera (2008) studied the historical, political, social and economic circumstances that produced the mobility of Chiapanecos within Chiapas. They particularly studied the historical migration of the indigenous populations from the “Los Altos” region towards the coffee plantations in the “Soconusco” and to the “Lacandona” region (rural-rural). They state that the Mexican government’s initiatives to convert farmland to pastureland and the increase of Guatemalan workers to the agricultural regions led to a migration of indigenous populations to cities (rural-urban). Their studies provide results about rural migration, development and dynamic transformations of migration in Chiapas. Cruz-Burguete’s (2007 & 2008) works contribute to the analysis of indigenous population displacements from the last decades due to agrarian conflicts, political and economical crisis that led to the rise of the EZLN. Guatemalan temporary workers coming from poor and marginalized regions to the coffee sector of Chiapas, depend on this sector to earn their livelihoods. Many of these labourers have been working in the agricultural sector of Guatemala under precarious living and working conditions (Casillas, 2011). Most of the studies carried out in the area focused on the traditional Guatemalan mobility to the region of Tapachula; and did not take the replacement of local labour force into account. Hence, the new migration dynamics of Chiapanecos toward internal and international destinations related to the increase of temporary cross-border workers from Guatemala creates a new topic that is studied and debated in this research. Castillo (2003) and Casillas (2008; 2011) explore the basic features of the Guatemalan migration, their routes and destination places. They agree about the impacts of free trade agreements on traditional local relations between Mexico and Central America, as well as about repercussions on international and regional levels. They argue that despite the efforts employed by international governments to control migration coming from the south and other regions of the world, such as an enhancement of control and migration policies; the porosity and corruption of the Mexican-Guatemalan border facilitate migration, especially Central Americans that transit through Mexico to reach the USA. Castillo (2003) states that: “Mexico’s southern border continues to show the great social, economic, and political backwardness. In this context, the region appears to reaffirm the features of continuity with neighbouring Guatemalan and Belizean border areas, which constitute, especially in the case of Guatemala, a zone of inequalities and shortages”. 37 From a macro empirical approach, Anguiano’s (2008a; 2008b; 2009) studies use information from official sources (Consejo Nacional de Población CONAPO (National Council of Population), the Instituto Nacional de Migración INM (National Migration Institute), the Instituto Nacional de Estadística y Geografía INEGI (National Institute of Statistics and Geography)), and surveys conducted by the Colegio de la Frontera Norte. Her main findings state that Chiapas’ exploitation of natural resources failed to propel socio-economic development as well as to create jobs. These circumstances would place Chiapas as an important migrant sending region to the USA and to other Mexican states. The oil industry in the neighbouring states of Campeche and Tabasco, the increase of touristic activities (touristic boom) in the Riviera Maya (Quintana Roo state), as well as, the growth and expansion of commercial and service sectors in the urban centres of Cancun, Chetumal, Campeche and Villahermosa have attracted migrants from Chiapas. Moreover, the Mexican states of Baja California, Oaxaca, Veracruz and Mexico are among the traditional destination areas for Chiapanecos. According to the II Population and Housing Census 2005, between 2000 and 2005 around 90 thousand Chiapanecos left the state toward internal and international destinations. In the international context, the State of California is the main destination for migrants coming from Chiapas. She also shows that Chiapas is a territory for temporary and daily cross-border workers from Guatemala, and since the mid-1990’s it has become the gate for transit-migrants coming from Central America. Human rights organizations, NGO’s and governments have voiced their concerns about the treatment of international migrants from a civil and political dimension, neglecting social, cultural and economic rights. Nevertheless, the Mexican migration literature produced, which focuses specifically on international migrant rights, hardly covers the rights and working conditions of internal and cross-border migrants. The ILO has encouraged studying workers and migrant workers rights and furthermore to formulate policies that provide decent work conditions for migrant workers. Many scholars have studied the historical agricultural temporary cross-border migration to the coffee plantations of Chiapas, but only a few have closely approached their rights and labour conditions. Angeles (2004; 2008; 2009) explores the Central American migration flows to the Soconusco region from a historical and demographical perspective. Based on data from the “Encuensta Sobre Migración en la Frontera Guatemala-Mexico” EMIF-GUAMEX (Migration Survey in the Border of Guatemala-Mexico) he studied the socio-demographic characteristics, the migration dynamics, labour experience and conditions of Guatemalan temporary workers in 38 Chiapas. He found out that 93.5% of Guatemalan workers in Mexico are employed in activities related to agriculture, livestock, farming and silviculture, and concludes that these labourers work under precarious conditions. Taking into account salaries, labour contracts and employment benefits, the employment conditions of Guatemalan temporary workers in Mexico do not assure the conditions of decent work. Other authors like Villafuerte and Garcia (2004) explored the labour conditions of indigenous workers in the Soconusco coffee region. They show that salaries in the northern states of Mexico range between 700 to 1,000 pesos (US$54 to US$77) per week, and that indigenous workers from Chiapas receive salaries or wages as low as 400 to 600 pesos (US$31 to $US46) per week. This difference in salaries constitutes a motivation for locals to migrate to northern states but for Guatemalan temporary workers who take over the jobs left, the salaries are slightly better than those in their home country. The poor labour conditions offered by the coffee plantation are not attractive to Mexican workers, but Guatemalan workers are coming from regions where these are similar or even more precarious. All these studies have made significant empirical contributions to migration studies and have established the basis for more in-depth research of the main factors that transformed Southern Mexico into a hot spot of migration. A great deal of environmental degradation and migration literature (Piguet, 2008; Kniveton, et al., 2009; Hummel et al., 2012; Migration and Global Environmental Change, 2011; IOM, 2012a) assesses the roots of these movements, and tries to forecast the number of environmental migrants in the coming years. However, research about land degradation as a driver for outmigration and its impacts on rural development under the specific conditions of Chiapas has not been conducted yet. This research contributes to the scientific knowledge by considering the migratory movements from Chiapas to neighbouring states and to the US due to the current land degradation and liberalization of international trade, as well as the significance of Central American in- migrant workers’ role as replacement labour force and their working conditions. 39 4. Debate on migration, rural development, and land degradation This chapter analyzes rural development, the migration phenomenon with its trends, causes, and consequences. Subsequently, it explores the plurality of factors that cause land degradation (LD), as well as the current grades and dimensions of this environmental problem. Additionally, environmental deterioration in terms of land degradation has been shortly included in the agenda of international development-migration debates. This chapter also articulates both in a discussion about the development-migration nexus. In addition, it presents and analyzes up-to-date reliable scientific information for a better understanding of the existing relations between rural development, migration and land degradation. In this way, it provides some insight into the linkages that can take place at more than one scale and can cross scales (i.e. local, regional and global). 4.1. The multi-faceted dimensions of migration Migration may stem from disparity in levels of income, employment and social well-being between different regions (Castles 2000), and as long as differences exist between regions, regardless of the distance, people in search for “better school, social services, higher income opportunities, cultural amenities, new modes of living, technological innovation and links to the world will move” (UNDP, 2009: 18). The IOM (2011) state that the number of international migrants in the world has increased from 111 million in 1985, to 190 million in 2005, and reaching 215.8 million in 2010. Moreover, the UNDP (2009) estimates that there are about 740 million internal migrants. International and internal migrants add up to around one billion people, in other words migrants make up about one seventh of the world’s population (IOM, 2011). International migrants constitute nearly 3% of the world’s population. There are about 74 million people (34% of migrants) moving between developing countries (south-to-south migration), 97.5 million (45%) from developing to developed economies (south to north migration), 6.5 million (3%) from developed to developing regions (north to south migration), and 37.8 million people (18%) migrated between developed countries (north-to-north migration) (World Bank, 2011a). In 2010, the countries with the largest emigrant populations were Russian Federation (12.1 millions) Mexico (10.1), India (0.9), Bangladesh (0.6), China (0.5), and Ukraine (0.5) (IOM, 2010). Net migration to developed regions is projected to remain at about 2.4 million per year, with Asia and Latin America (including the Caribbean) as the major sources of migrants, providing 1.3 and 1.1 million migrants per year respectively (UN, 2007). Mendola (2006) 40 argues that despite the efforts to estimate more accurately the number of migrants, aggregated data of international migration fail to capture the vast scale of migration that also takes place within countries as temporary and seasonal labour migration (e.g. 200 million temporary and seasonal migrants in India, and about 120 million internal migrants within China). Global migration seems to have the tendency to increase due to the establishment of social networks in the destination areas “based on colonisation, political influence, trade investment or cultural ties” that “provide shelter, work, assistance with bureaucratic procedures and support in personal difficulties” (Castles, 2000: 272). Hence, social networks offer a safer and more manageable way to migrate for individuals and their families. Delgado-Wise and Mañán (2005) state that migration occurs due to the economy’s low structural capacity to generate and expand employment, leading to losses of valuable assets by the sending countries’ economies as well as for the migrant’s households, in the sense that skilled people and young workers are part of the migration outflow. Additionally, when an adequate number of migrants at the destination place is able to build social structures to sustain migration (Castles & Miller, 2009); when “there is an effect of remittances on income distribution in sending communities” (Massey, 1990: 6); and when the established migration in the place of destination creates (irregular) employment related to the growth of informal economies, migration becomes a self-perpetuating phenomenon. Therefore, “the formation of an established migrant community at one destination will increase the likelihood of subsequent migration to the same place” (de Haas, 2010: 1590), this in turn “induces changes in social and economic structures, which make additional migration likely”, creating a snowball effect. (Massey, 1990: 6) Remittances contribute to increased investment in human and physical capital of recipient households (Özden & Schiff, 2007). They can lead to improved agricultural production and help overcome credit constraints (Durand, Kandel, Parredo & Massey, 1996), as long as they are used to finance entrepreneurial ventures. In contrast, sending remittances to migrant’s country of origin “generates a progressive socio-economic dependency to remittances, production disruption and stagnation, deepening social inequalities, family disintegration” (Wiest, 1979: 87) engagement in conspicuous consumption, and inflation of land prices (Stuart & Kearney, 1981). Migration “undermines the economies of migrants’ sending communities by depriving them of human and material resources and increasing their dependence on the outside world” (de Haas, 2010a: 8). Worldwide, remittance flows are estimated to have exceeded $440 billion USD. The recorded amount of remittances received 41 by developing countries from international migrants is estimated to be $325 billion USD in 2010. Although, remittance flows to developing countries fell 5.5% during the 2009 global financial crisis, they registered a quick recovery in 2010. The top remittance recipient countries in 2010 were India ($55 billion USD), China ($51 billion USD), Mexico ($22.6 billion USD), the Philippines ($21.3 billion USD), and France ($15.9 billion USD). In contrast, the top remittance sender countries were the United States ($48.3 billion USD), Saudi Arabia ($26 billion USD), Switzerland ($19.6 billion USD), and Russia Federation ($18.6 billion USD) in 2009 (World Bank, 2011a). 4.2. Climate change and migration: framing the issue In the early 21st century, the relation between climate change and migration represents a challenge for governments, society and aid organizations. The different approaches to analyze the relationship between climate change and migration have brought to the fore disciplinary, methodological, and political disagreements between environmentalist and migration scholars. Castles (2011) points out that the climate change-migration nexus needs to be tackled from an interdisciplinary approach, wherein different scientific groups put efforts together to merge climate change into migration studies and vice-versa. Doing so will yield substantial knowledge regarding the complexity of the phenomenon, the human responses and finally to promote action. Additionally, the current neoliberal policies, has not only increased poverty and migration; it has also affected the earth’s natural resources (Foster, 2009). The overconsumption of natural resources to fuel development and meet the needs of a growing population has become a threat for all living organisms on the planet. This problem is manifest in what has become known as “global climate change”. Since the middle of the 20th century greenhouse gases (GHG) responsible of generating climate change have increased drastically (IPCC, 2007). The IPCC (2012: 6) states that: “a changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of extreme weather and climate events, and can result in unprecedented extreme weathers. In particular, all these effects can affect livelihood options and resources and the capacity of societies and communities to prepare for and respond to future disasters”. Stern (2006: 111) pin points that the migrant outflows will increase from regions with less resources to mitigate or to adapt to environmental changes; where “greater resource scarcity, desertification, risks of droughts and floods, and rising sea levels could drive many millions of people to migrate”. Even though, there are no precise future forecasts of the numbers of 42 climate change-induced migrants, there are projections suggesting that by 2050 the number of people forced to migrate ─regionally or internationally, permanently or temporarily ─ will range from 25 million to one billion (IOM, 2012a). Under this scenario, the poorer segments of the population will be most severely affected (Campbell & Berry, 2003; Hummel et al., 2012; IOM, 2009). However, being at risk does not necessarily mean that affected communities or people will migrate. It will depend on the dimension of the risks and the availability of resources to migrate. Once they get to the point where the households’ capabilities sets are significantly reduced, migration would occur (Piguet, 2008). In many rural areas, climate change has affected agricultural production, increasing the pressure to migrate (EACH-FOR, 2009), which in turn results in irreversible losses of human and economic resources. Here, migration occurs because livelihoods are no longer maintained and it is a traditional coping mechanism. Climate change is more likely to induce internal migration and more likely to affect southern countries than industrialized regions. In addition, international human displacement will occur but on a lesser scale (Brown, 2008). At the early 21st century, the scientific evidence regarding global warming and its impacts ─especially on living conditions and livelihoods, and the responses from the affected communities─ is more reliable due to the accumulation of empirical research (Castles, 2011). Along these lines, there has been a step forward in understanding the complex nexus between migration and climate change. This is due to multidisciplinary studies that recognize that “the natural factors are not the sole cause of migration in themselves, and that the economic, social and political situation of the zone under threat can, depending on the case, increase or decrease the flow of migrants” (Piguet, 2008: 3). 4.3. Environmental-related migration The scientific community, governments and policy makers are frequently debating the relations between environment and migration. Furthermore, the increasing number of environmentally displaced people and damage to the environment put scientists to study these phenomena together and seek for links (Hummel et al., 2012). “Yet, knowledge in this field still remains vague and fragmented: the significance of environment and climate within the broader social, political and economic context of migration is currently a subject of fierce debate” (idem: 8). 43 Environmental factors in the regions of origin play an important role in the migratory dynamisms. Thus, when the ecosystems can nolonger provide services at adequate levels, livelihoods and basic human needs become compromised (Adeel et al., 2006). Thereafter, the compromised populations are forced to leave their residences, in search of places where they can diversify their source of income and restore their livelihoods. Kniveton et al., (2009: 51) state that “migration may represent a planned adaption to climate [and environmental] change or uncertainty, or may be a last resort – a decision made in distress when other options have failed”. Since the early 21st century, the increased frequency and the intensification of impacts of natural hazards (e.g. floods, droughts, hurricanes), driven by climate change and environmental degradation of natural resources ─both mostly human-induced─ are transforming and deteriorating the ecosystems, leading to the loss of land, food insecurity, and ultimately contributing to forced migration. “Forced migration” ─as a result of environmental degradation and natural-resource depletion, which in turn is caused by human activities─ is brought about mainly by rapid deterioration of natural resources, and population growth. Furthermore, the forces of globalization, changes in traditional agriculture brought about by modernization, and the shifting of land use in order to satisfy the world’s food demand, contribute largely to enhance migration (UNU, 2007) and to land degradation. All of this decreases land productivity and reduces agricultural production, which in turn affects the prices of agricultural goods and inputs (Berry et al., 2003). In the Mexican context, the EACH-FOR (2009: 3) concluded, “further degradation of Mexican farmland would increase internal and international migration”. When the family’s needs cannot be met by exploiting the land, it is forced to change its livelihood strategies in response to LD. In addition, migration increases in times of crisis (i.e. economic, social, political and environmental) and it is more likely to cause the loss of young human capital in rural areas. 4.4. Rural development features Rural development is a significant part of a broader economic and social development, which is not surprising since the rural population constitutes 49% of the total world population (World Bank, 2011). Thus, rural communities are important for development as a whole. Roughly, 70% of the people living in rural areas are highly dependent on agriculture, grazing and hunting for subsistence. Agriculture is still the most important sector for the world’s poorest countries and a key area for achieving sustainable development and poverty 44 reduction. It employed 34.5% of the world’s workforce in 2005. For low- and middle-income economies, it accounted for 11% of the GDP in 2008 (World Bank, 2010). Rural areas in developing countries suffer from the seasonal or annual fluctuations of food prices, as a function of the interplay between global and domestic factors (UN, 2008b). The variation between supply and demand factors caused by droughts, floods, oil prices, biofuel production, growing demand, and speculation played an important role in the increase of food prices. For example, in 2008 maize and wheat prices increased twofold, rice prices tripled. Overall, between 2006 and 2008 international prices of agricultural products doubled (Heady, 2010). For farmers who depend on agrochemicals, the rise in the cost of inputs was similar to that of food prices. The impact of food prices’ “surge reverberated globally, though the worst hit were low-income, and food-deficit countries with meagre stocks. In total, about 100 million rural and urban people were pushed into the ranks of the world’s hungry” (IFAD, 2011: 14). Additionally, low farmland productivity and environmental risks in poor agricultural areas have created waves of human migration to urban and international destinations, often implying the abandonment of land. Bernstein (2012) point outs that the population depending on agriculture is decreasing in some sub-regions of low and middle- income countries while industrialization growths. In regards to the rural exodus, the Global Forum on Migration and Development (2010) held in Mexico addressed the changing distribution of population between the urban and rural sectors. It reported that the world has become more urban, with 51% of the world population living in cities. At the same time, it projects a continuation of this trend, with as much as 70% of the population living in cities by 2050. Between 1995 and 2005 urban populations worldwide increased by between 20% and 60% (IFAD, 2011). Thus, it is clear that urbanization proceeds rapidly. Poverty, however, remains largely a rural problem. In 2005 approximately one billion people or around 70% of the population in rural areas were living in extreme poverty (defined as those living on less than $1.25 USD/day) (UN, 2010). The abandonment of agricultural land, called “de-agrarisation” (Van derGeest & FAO, 2010) is due to the rural exodus towards urban centres. Land is abandoned either because it is not sufficient productive or due to the onset of natural calamities, whether this is sudden or slow. From a different angle, the implementation of modernization and neoliberal policies has led to negative ecological consequences, creating disparities between farmers and nature (O’Connor et al., 2006). 45 Moreover, neoliberal globalization has negatively affected the working class, the middle class, and peasants (Otero, 2006). These groups have responded by organizing themselves into social movements to reclaim for a better and sustainable development. These grassroots includes peasants’ movements, the so-called “peasant wars of the twenty century”, which emerged in response to the pervasive and uneven changes brought about by the expansion of the capitalist world economy. “Such peasant’s movements mobilized around issues of land, of rent and tax, of pauperization, and of extreme oppression and social injustice, often in conditions of generalized social upheaval and war” (Bernstein, 2012: 135-136). In the current context of neoliberal globalization, in the developing countries new types of agrarian movements have emerged incorporating small and medium-scale farmers. Their claims are to “opposed to the corporatization of agriculture that has been globally synchronized to the detriment of farming populations everywhere”, and “to revalorize rural cultural-ecology as a global good by mobilising a global agrarian resistance, an agrarian counter-movement that strives to preserve or reclaim the peasant way” (McMichael, 2006: 472, 473, 480 cited by Bernstein, 2012). One of the best-known international agrarian movements is the via campesina, founded in 1993. “The main goal of the movement is to realize food sovereignty and stop the destructive neoliberal process. It is based on the conviction that small farmers, including peasant fisher-folk, pastoralists and indigenous people, who make up almost half the world's people, are capable of producing food for their communities and feeding the world in a sustainable and healthy way” (La via campesina, 2012). Despite the abandonment of rural areas, declining farm size, environmental degradation, land expropriation, rapid population growth, poverty, and a drop in employment opportunities, agriculture is still at the centre of the development agenda for meeting the Millennium Development Goals (MDG’s) of reducing poverty and hunger by 2015. Although growth in the agricultural sector will not be enough to eliminate poverty, it is as a powerful tool that will yield significant results. Furthermore, poverty reduction requires a productive revolution in small-holder farming “by designing policies and decision processes most suited to each country’s economic and social conditions, by mobilizing political support, and by improving the governance of agriculture” (World Bank, 2008b: 73) particularly in the agriculture-based countries. 46 4.5. Global status of land degradation Historically, society has benefited from the conversion of natural ecosystems to human- dominated ecosystems, through the exploitation of renewable natural resources to achieve economical development (UN Millennium Assessment: Biodiversity Synthesis, 2005). Hence, natural assets play an important role in improving the economic productive conditions for society, which depends on them as the basic for economic processes (Constantino, 2007). As fertile land and other key natural resources become scarcer, land tenure and the management of these resources become more complex (Kay, 1998). Mainly, land degradation has been brought about by changes in the world economic system and the creation of globally integrated systems of production (Foster, 2009). These factors have entailed new power alliances, which have increased the pressure to exploit land and to appropriate it. They have also induced a shift in agricultural activities, from subsistence production to market-oriented production, exacerbating soil erosion and other forms of environmental destruction (Dreher, Gaston, & Martens, 2008). Moreover, we have seen an intensification of land appropriation and accumulation due to (1) the increasing demand of industrial and commercial crops to produce agro-fuels and export food (Borras & Franco, 2010); (2) decreasing agricultural productivity as a result of ongoing land deterioration (Berry et al., 2003); and (3) shrinking agricultural area by the expansion of urban centres (Wessels et al., 2004). These factors have led to another round of enclosures, that is, the privatization of community land (Borras & Franco, 2010), which reduces the viability of livelihood systems in many areas (Berry et al., 2003), undermines human security, (Barnett & Adger, 2007), and increases social vulnerability4. “The degradation of land is a major threat to life on earth, including for humans” (UNCCD, 2011: 2). The latest report published by the UNFCCC (2011) states that LD adversely affects farmland productivity, and reduces economic income related to activities such as ecotourism. Also, it represents a risk to the health of humans and livestock and entire ecosystems; it impacts on the ecosystems in the sense of replacement of ecologically-valuable species that protect the environments to less valuable ones. Furthermore, LD triggers socio-economic 4 Social vulnerability is the exposure of groups of people to stress as a result of the impacts of the environmental change and land degradation, increasing land vulnerability is mutually linked with social vulnerability (Adger, 2000) 47 imbalances, social unrest, migration, enhances the impoverishment of communities, and acts as a major obstacle to farmers’ sustainable development (UNCCD, 2008). Indeed, it affects the whole of society and the environment, and there are no signs that LD trends are abating on a global scale (Millennium Ecosystem Assessment, 2005). The geographical extent of LD needs to be considered in order to gain a comprehensive understanding of its causes and consequences. LD is widely recognized as a “global problem” associated with desertification in arid, semiarid and dry sub-humid zones, commonly called the ‘drylands’ (Gisladottir & Stocking, 2005), which cover some 47% of the globe’s surface and home two billion people. Arable land loss is estimated to proceed at 30 to 35 times the historical rate and 12 million hectares of land are transformed into new man-made deserts each year, with a potential loss of 20 million tons of grain (UNCCD, 2011). The Millennium Ecosystem Assessment, Desertification Synthesis (2005), points out that dryland regions and populations, at least 90% of which are situated in developing economies are amongst the most susceptible to LD. LD is present in more than hundred countries that cover an area of 33% of the earth’s land surface and affects 2.6 billion people (FAO, 2000; Adams & Eswaran, 2000). Moreover, LD “is closely related with poverty, with 42% of the very poor living in degraded areas, compared with 32% of the moderately poor and 15% of the non- poor” (Nachtergaele et al., 2010: 25). The Global Assessment of Human-Induced Soil Degradation (GLASOD)5 indicates that 15% of the Earth’s total land area is degraded (13% light and moderate, 2% severe and very severe). The highest proportions were reported for Europe (25%), Asia (18%) and Africa (16%), while the lowest occur in North America (5%). Other forms of degradation are very unequally distributed; nutrient depletion affects 4% of the global land mass, salinity occurs on less than 4%; chemical contamination accounts for about 1%; and physical problems with the soil occur on 4%. Eswaran, Reich, and Beinroth (1998: 24) state, “the productive capacity of the land is stressed by mismanagement, generally by resource poor farmers”, as well as high-input agricultural systems. Tension zones “arise due to excessive use of agrochemicals, uncontrolled use of irrigation, and monoclonal plantations with minimal genetic diversity” (ibidem). These factors frequently lead to reductions in yields, with negative socio-economic impacts that are probably most severe in countries where agriculture is the main source of income. They also threaten political stability, sustainability and the quality of life. 5 The project represented “the basis of the most recent UN studies of global land degradation and desertification” (Thomas & Middleton, 1994). It is the only usable source of global data on the status of land degradation. Despite its known limitations, it remains an impressive evaluation (Nachtergaele et al, 2010) 48 The Global Land Degradation Assessment (GLADA) ─another global assessment of land degradation using long-term, remotely sensed normalized difference vegetation index (NDVI) data from the Global Inventory Modelling and Mapping Studies (GIMMS) from 1981 to 2003─ shows that on the global level degraded land covers approximately 35 million km2 (23%) and it affects nearly 1.6 billion people (23.8%) (Bai et al., 2008). On the aggregate and global scale, land degradation significantly affects food security and food sovereignty, especially for poorest nations. There are several LD processes, each of which interlocks with many others. The main are vegetation degradation in rangelands, water and wind erosion, salinization of irrigated land in certain semiarid lands, and soil compaction (Dregne, 2002). LD also includes many urban and industrial problems such as pollution, mine tailings, smog and waste dumping (Stocking & Murnaghan, 2001). 4.5.1. Dynamic causes of land degradation Land degradation implies adverse and sometimes complex causes due to interactions between indirect and direct drivers; it could be long-term (non-reversible) and short-term (reversible). LD is caused or at least exacerbated by “human actions”, and is thereby distinguished from “natural hazards” (Conacher, 2009). The main contemporary human activities in line with business-as-usual overwhelm and break the adaptive and resilience capabilities of land, as well as the whole environment (UNEP, 2007), and reach beyond the site where land degradation has occurred. Sometimes causes are local and relatively simple; sometimes they are natural, complex and global (Barrow, 1991). At the same time, ascertaining the level of human responsibility is a complex task because of the complexity of interactions between nature and human society, where cause-effect and process-response are ambiguous (Johnson & Lewis, 1995) and variables play different roles as; some cause LD directly, others merely enable the action of the former (UN-POPIN, 1995). Natural degradation has transformed the planet’s land through the ages. The considerations about natural LD are based on the idea that any area, with any environmental characteristic, can undergo degradation for being openly exposed to natural climatic conditions. In addition, natural hazards (e.g. evolution, volcanoes, wildfires, etc.) also affect soil, biodiversity, individuals, and communities in several ways (EACH-FOR, 2009). Human causation of LD On the early 21st century, scientific evidence of the human causes and future paths of LD are increasing. Although LD is a physical process, its underlying causes are also firmly rooted in 49 the socio-economic, political and cultural environment, involving people not only as causative factors but also as victims (Blaikie & Brookfield, 1987). Land degradation results from “drivers of change such as population growth, increased consumption, and inequity generate excessive demands on land productivity” (UNCCD, 2011: 2). The Global Assessment of Human-Induced Land Degradation (GLASOD) considers LD to be a “human- induced phenomenon” that lowers the current and/or future capacity of the soil to support human life (FAO, 2000). Human activities have led to a rapid increase in the planet’s temperatures, through greenhouse gas (GHG) emissions (Wenner, 1997; Hudson, 1985; Mainguet, 1991; Lal, 1994; Morgan, 1995; Steiner, 1996; World Bank, 2010). Such human- initiated degradation has and will exert a strong effect on the poorest communities (Millennium Ecosystem Assessment, 2005; UNEP, 2007; IPCC, 2012). Even though vulnerable and poor communities have played a minor role in the evolution of environmental degradation (Foster, 2009), they are victims of the externalization of environmental/land degradation, caused by the developed and transition countries in their quest to achieve economic growth (Castles & Rajah, 2010). The effects of LD on the developing countries are greater than on the developed countries; LD strongly affects and even threatens the livelihoods of these developing economies (Conacher, 2009). Nevertheless, it is important to cautiously assess which human activities harm the environment and which improve it. Recently, the World Bank (2010) assessed the impact that human-induced environmental degradation has on developing countries, finding that these countries suffer around 75-80% of the cost of such damage. What is more, environmental degradation reduces their GDP by 4 to 5%, especially in African and Asian nations. There is a wide range of human activities that can trigger or exacerbate LD. The anthropogenic drivers outlined below are considered by scholars, governments and international organizations as the major threats to sustainable development, millennium development goals, and the environment overall. Within this perspective of human-induced LD, this section takes into account three particular dimensions: unsustainable land use (ULU) and land use change (LUC), livestock production, and deforestation. Unsustainable Land Use (ULU) and Land Use Change (LUC) Land is a vital resource for humankind but this resource is easily over-exploited, its sustainability depends on how intensively the land is exploited and the land conservation methods applied to it. Indeed, 11% of the world’s land is already used for agriculture, it 50 provides a livelihood for 75% of the poor in developing countries (UNEP, 2011), and in many places little room exists for agricultural expansion due to land or water shortages (UNEP, 2007). Moreover, inappropriate land management techniques are determinants of LD (Gisladottir, & Stocking, 2005). These unsustainable and inappropriate land-uses are ubiquitous; they threaten livelihoods and economies on the local, national and international levels. Soil is a non-renewable resource; it is the most important production factor in farm enterprises. The transformation of terrestrial biomass into agriculture, has historically been the major strategy for developed countries to increase economic revenues, which in turn allowed them to invest in industrialization, poverty alleviation (Millennium Ecosystem Assessment, 2005), development and human well-being (UNEP, 2007). On the other hand, land transformation or LUC, which results from the complex interaction of social, ecological, economic and geophysical processes (Bai et al., 2008), has exerted an enormous pressure on the environment in the form of LD. It has also contributed to exacerbate poverty in vulnerable-poor human communities. The LUC’s direct impact on the productive potential of the land is often identified as a major driver of LD in tropical areas (Gisladottir, & Stocking, 2005). In fact, tropical areas are undergoing a rapid transformation through deforestation and the expansion of farming into forested areas to cultivate cash crops (Altieri & Toledo, 2011). Cash crops are increasingly demand to assure the provision of raw materials, food, and agro- fuels by the increasing production and consumption patterns mostly of the rich and the middle classes. In general “the demands of a burgeoning population, economic development and global markets have been met by unprecedented LUCs”, (UNEP, 2007: 82) and environmental problems. The so-called “Green Revolution”, which started at the mid twentieth century, resulted in substantially higher yields, through the implementation of new agricultural technologies and the modernization of management techniques. These technologies included modern irrigation projects, pesticides, synthetic fertilizers and improved crop varieties. They have helped to expand the agricultural frontier, by bringing non-arable land under cultivation, especially in arid areas. Irrigated lands increased from 4.4 to 7.3 million hectares between 1987 and 2002 (FAO, 2005). Over time, the implementation of these techniques caused their own environmental problems. For example, Steiner (1996: 12) reminds us that “improper use of fertilizer can result in acidic soil, mechanical tillage can aggravate run-off and erosion, and irrigation farming often produces saline soil”, which accelerates the LD processes. At the same time, monocropping has led to increasing use of pesticides and fertilizers, with 51 environmental consequences such as leaching and fertilizer runoff (Dregne, 2002). In this manner, local ecosystems are modified and human communities are affected. Subsequently, biotechnology including genetic modification has been a key factor in the development of agriculture, due to its potential to increase agricultural production and contribute to human health advances (UNDP, 2005). Notwithstanding, it remains subject to much controversy, for three main reasons: its potential detrimental effects on human health; its contribution to the loss of genetic diversity and other forms of environmental degradation; and its social effects, considering that it is mostly controlled by large transnational corporations which operate in accordance with the profit maximizing imperative. The environmental damage caused by agricultural activities is not limited to land degradation; there are also problems of water pollution, cultural eutrophication6, air pollution by GHG emissions, loss of biodiversity alteration, and habitat transformation. The LUC has contributed to increasing the amount of GHG’s, especially CO2 stemming from net deforestation, and CH4 from rice cultivation (IPCC, 2001). In this regard, many GHG emissions are attributable to agriculture: 23% of the global CO2emissions, 74% of CH4, and 70% of anthropogenic nitrous oxide (N2O).Most of these gases come from ULU and LUC as well as nitrogen fertilizers (UNEP, 2007; IPCC, 2001). Water is another natural asset that has been vital for the expansion of the agricultural world, and ULU has led to a reduction of drinking water, ground water, conflicts around natural resources, and loss of ecosystem services and biodiversity (Altieri & Toledo, 2011) Livestock production Another distinctive LUC is the transformation of farmland and/or forestland to grassland for livestock production purposes. Although pasture farming is a productive way of using land to conserve resources, the overgrazing of this area destroys the protective ground cover, exposing it to water and wind erosion (Steiner, 1996). It leads to soil compaction due to the weight of the animals and the mechanical forces caused by cattle when walking on the soil (Steffens, Kölbl, Totsche & Kögel-Knabner, 2008). Worldwide, livestock production is expected to increase over time in order to fulfil the enormous animal protein demand for human consumption and in some regions due to the implementation of subsidies for beef production. These have encouraged more ULU and LUCs. The overstocking of livestock 6 Cultural eutrophication is the human-caused enrichment of water with nutrients, usually phosphorus. Most of the eutrophication occurring today is anthropogenic. Natural eutrophication also takes place, but it is insignificant by comparison. 52 affects the quality of the soil and adds to the exceeding of land’s carrying capacity (Olsson, 1993), frequently culminating in an irreversible destruction of the land and herbaceous vegetation cover (Alados et al., 2004). Deforestation and Removal of Natural Vegetation Cover7 (RNVC) Forest systems provide a wide variety of highly valuable ecological, economic and social services (FAO, 2000a). Hence, forest remains an important natural resource, providing for diverse livelihood options and benefitting both urban and rural populations. For example, logging can be industrial and export-oriented or just to provide firewood for the local population. Deforestation and RNVC (e.g. secondary forest) are faster in developing countries, driven by industrial export oriented activities geared towards meeting the demands of developed countries. Clear cutting, deforestation and RNVC adversely affect agriculture and other economic activities, as well as the health of humans and ecosystems. Both affect the soil insofar as they reduce its water-holding capacity, thereby inducing LD (Gisladottir & Stocking, 2005). Therefore, forest cover and natural vegetation in general play an important role in maintaining the soil’s fertility; they reduce wind and water erosion and they help to maintain the nutrient’s cycle in the soil (UNEP, 2011). The four major direct deforestation drivers (commercial agriculture, subsistence farming, wood extraction, and urbanization) together with the underlying drivers (e.g. structural changes in land tenure or in agricultural or forest policies), diminish the forest area at an average rate of 12.9 million hectares per year in the period 2000-2005 (UNFCCC, 2007). The FAO (2005) calculated a worldwide rate of deforestation of 20,000 hectares per day. The CO2 emitted into the atmosphere due to deforestation in 2008 alone was 1.5 billion tons of CO2, mostly to conversion of forested areas into farmland or grassland (Brown, 2009). Deforestation and the RNVC hence have become a global challenge. The substitution of forest and natural vegetation cover by crops or pastures have detrimental consequences for the physical, chemical and biological properties of the soil (Krishnaswamy & Richter, 2002). In addition, these phenomena drive climate change and contribute to soil infertility and biodiversity losses, especially in fragile ecosystems, where adversely affects the livelihoods of people living in rural communities. 7 In this approach the term of Natural Vegetation Cover is given to the observed natural bio-physical cover on the earth's surface, without considering primary forests 53 A statement entitled “Warning to Humanity”, written by the Union of Concerned Scientist, 1992, signed by 1860 scientific leaders in 70 countries, including 104 Nobel Laureates. “The earth is finite. Its ability to absorb wastes and destructive effluents is finite. Its ability to provide food and energy is finite. Its ability to provide for growing numbers of people is finite. And we are fast approaching the earth’s limits. Current economic practices which damage the environment, in both developed and developing nations, cannot be continued without the risk that vital global systems will be damaged beyond repair”. Natural causation of LD LD is taking place in all ecological zones. In fact, natural land degradation (NLD) has long affected the planet. The proximate causes of NLD are biophysical and have to do with climate, geography, and natural calamities8. They contribute simultaneously to the severity of LD, as well as, human induced-climate change has altered the potential and severity of natural events. In this regard, the UNEP (2007: 302) states in the fourth Global Environmental Outlook Report that: “Exposure to natural hazards [calamities] has increased as a result of climate change (...); risks are also increasing as a result of the continuing concentration of people in highly-exposed areas. Over the past 20 years, natural disasters have claimed more than 200 million people annually. More than 90 per cent of people exposed to disaster live in the developing world and more than half of disasters deaths occur in countries with a low human development index”. Two-thirds of all natural disasters are hydro-meteorological events (EM DAT, 2011; Munich Re, 2004; Munich Re, 2006). Therefore, floods are a destructive factor for both human communities and the environment, with an increase in the frequency of floods observed since 1950. Further rising of the precipitation levels represent a greater potential damage that is fortified by the removal of natural vegetation cover (RNVC). Temperature determines the speed of chemical and biological processes in charge of breaking down minerals in the soil (Johnson & Lewis, 1995). Drought is a natural occurring phenomenon that can accelerate LD. Stern (2006) predicts that droughts will increase with the rise of global temperatures caused by climate change. He also points out that in 2005, 10% of the world’s land surface experienced severe drought. If the global temperature 8 “Natural calamities are not limited to extreme climatic events but also comprise calamities conditioned by physiographic such as volcano eruptions, earthquakes and tsunamis” (FAO, 2007). 54 increases by3-4 °C in the future, the global area affected by droughts will increase by 40%. “Drought may arise because vegetation has been damaged [or removed], once the land surface changes, drought may become more common or persistence” (Barrow, 1991: 131). Drought in particular leads to high economic loss and human suffering in countries that heavily depend on rain-fed agriculture (Nkonya et al., 2011). Droughts are in certain areas are the result of unsustainable water management, including over-consumption. This has dried up numerous natural springs, lakes and rivers to dry up. Since the 1990s the evidence of significant increases in the intensity and frequency of extreme natural events e.g. tsunamis, hurricanes, tropical storms, and earthquakes have shown that their impacts range from “hunger and susceptibility to disease, to loss of income and livelihoods, affecting the survival and human well-being of both present and future generations” (UNEP, 2007: 215). Salinization is another phenomenon that occurs naturally on low-lying sites in semi-arid to arid regions (FAO, 2000). Likewise, wildfires reduce the capacity of natural forest to buffer the impact of heavy rainfall and hurricanes (UNEP, 2007). Indeed, all natural disturbances and events, regardless of their magnitude and type will end up affecting land areas, degrading the soil. They also will affect the human well-being. This includes events such as heat waves and crop failures. In addition, “when human interactions are superimposed over the physical interactions, the environmental responses to change become more complex” (Johnson & Lewis, 1995: 75), and natural disasters might speed up. Figure 4.1 summarizes the indirect and direct drivers of land degradation on farmland. 55 Figure 4.1: Specific underlying and proximate land degradation drivers and their impacts on land users, rural development and migration. Source: Adapted from Millennium Ecosystem Assessment-Ecosystems and Human Well-Being: Synthesis Synthesis The debate on the migration-development nexus has two sides. On the one hand, there are those who argue that migration plays a positive role and that remittances act as a lever for economic development; on the other, there are authors that hold that migration is a response to underdevelopment conditions with negative effects on the developing countries. However, there are scholars that have formulated a more nuanced approach that considers migration and development as a reciprocal relationship. The real structural factors affecting development, especially rural development, stem from the long domination of the current capitalist model. It concentrates economical and political Human Well-Being, Poverty and Migration ∴ Basic material for a good life ∴ Health ∴ Social relations ∴ Security (e.g. natural asset’s conflict) ∴ Food security ∴ Livelihood security ∴ Natural assets LAND USERS Direct driversof LD (Proximate) ∴ Unsustainable land Use (ULU) ∴ Land Use Change (LUC) ∴ Commercial agriculture (e.g. external inputs: fertilizer, pesticides) ∴ Deforestation ∴ Livestock production ∴ Harvest and resource consumption ∴ Industry (e.g. pollution, mine tailing, dumping waste) ∴ Urbanization ∴ Climate change ∴ Natural, physical and biological Indirect Drivers of LD (Underlying) ∴ Demographic ∴ Economic (e.g. globalization, trade market, and policy framework) ∴ Sociopolitical(e.g. governance, institutional and legal framework) ∴ Science and technology ∴ Cultural (e.g. consumption) Land Degradation ∴ Land productivity declines ∴ Loss of biological diversity ∴ Soil Salinization ∴ Landscape alteration ∴ Nutrient depletion ∴ Erosion (e.g. water, wind, etc.) ∴ Soil compaction GLOBAL REGIONAL LOCAL Short-term Long-term 56 power for few and produces unemployment, social inequalities, natural-resource depletion, human insecurity for the rest of the population, as well as it increases disparities between developed and developing countries. Consequently, many rural communities have had to face hunger, poverty, natural-resource degradation, and insufficient support, all of which place them in a vulnerable and risky situation. In the context of unequal development, populations from developing regions search for a way to diversify their incomes and to escape from poverty by migrating to better-off regions, either internally or internationally. In the international debates on migration-development, the topic of climate change has been left out of the agenda. However, there is some discussion of ongoing environmental degradation, recognizing that environmental degradation exerts downward pressure on the world’s economy and causes social problems, such as the displacement of human populations. Nowadays, environmental degradation factors are been thoroughly study to accurately determine their effects on the socio-economical aspects of individuals, and their responses. Clearly, the land degradation phenomenon has become a threat and an obstacle to farmers and rural development. LD either anthropogenic or natural-originated is caused by a combination of direct and indirect factors that change over time and vary by location. The causes of LD which lower the current and/or future capacity of the soil to support human life can be local and relatively simple or global and relatively complex. For rural communities living in vulnerable environments with low carrying capacity, low agriculture productivity, and harsh economic conditions, LD occurs faster and exacerbates socio-economic and environmental problems. In a broader context, LD can destabilize societies, exacerbate poverty and endanger food security. It will likely spur on migration by adding land-related migrants to the whole. Land degradation and migration is a vicious circle that reinforces itself through time and there is no sign that this trend is abating on the global level. Therefore, sustainable management of natural assets ─soil in particular─ is necessary to achieve long- term development, because healthy soils and environment are fundamentals for a country’s economic, social and environmental security. The scientific community acknowledges the crippling effects of LD on ecosystems and life but the threats and far-reaching impacts are diminished by economic visions and goals. In short, migration and land degradation can shape a nation’s economy, society and environment, making more difficult to achieve sustainable development. Therefore, there is an urgent need for collaborative thinking and proactive participation by organized civil society, NGO´s, industries and governments in order to tackle these problems. 57 5. Tapachula: land degradation, small-scale farms’ development and migratory flows 5.1. Problem statement Land degradation (LD) threatens the livelihood security of communities that depend on the use of land as a source of food production and income. When soil fertility is lost and farmland productivity decreases, it can lead to a mass out-migration of farmers leaving poor lands in search for better lives. Developing countries are at the greatest risk of suffering from the adverse effects of LD. In Mexico LD represents a problem for the rural economy, it worsens the already precarious situation of rural communities and it is a backdrop to migration, both rural-urban and to the US (Berry et al., 2003). In the case of Mexico, there is between 700,000 and 900,000 Mexican migrants leaving their lands each year, and the number of vulnerable villages is rising. In this regard, Campbell and Berry (2003) point out that these emigrants are driven out of their communities by the declining ecological conditions, in a country where 60% of the land is classified as severely degraded. Soil erosion is also present in Chiapas as well. The heavy rains that occur in the region easily wash away the topsoil, barely protected by crops or trees. Moreover, the loss of soil fertility reduces the capacity of the land to sustain rural populations, contributing to the tendency of outward migration. The state of Chiapas is the most important state for Mexico’s southern border in terms of migration. In this light, the southern border has not been treated the same as the northern border, because it has received less attention from the government, making it into a “forgotten border” and Chiapas, a forgotten state (Castillo, 2003). Nevertheless, the migratory dynamics along this border are in some ways more complex than the northern border. Due to its geographical location, Chiapas presents a complex territory for human mobility, associated with economic, cultural and historical interactions (Anguiano, 2009). Inside the long migration history of Mexico, Chiapas has become a new migrant-sending state with several types of migration movements. On the one hand, strong influencing factors, like economic differences with other states within Mexico and with other countries, have pushed people to out-migrate. Since the 1990’s, Chiapas has sent migrants to other Mexican states and to the United States (regional and international migration). On the other hand, Chiapas has become a migration corridor for Central Americans in their search of better opportunities in United States and Canada. 58 Additionally, Chiapas attracts international seasonal migrants from Guatemala to fill labour gap (e.g. coffee plantations) in the Soconusco region, including Tapachula (Anguiano, 2008a; 2008b). Over the last decade, the number of Chiapaneco emigrants has increased significantly, accompanied by a growing diversity of migrants’ origins countries and an increase in travel-distances, associated with complex changes that have affected the economy of the state. These changes stem from commercial agreements such as the North American Free Trade Agreement NAFTA, and the Mesoamerican Integration and Development Project, also known as the Puebla-Panama Plan. Another factor is the uprising of the Ejercito Zapatista de Liberación Nacional (Zapatista Army of National liberalization EZLN) (Programa Ecológico de Ordenamiento Territorial de Chiapas PEOT, 2005). Other key determinants for the out-migration of Chiapanecos are the agricultural crisis initiated in the 1980’s, the falling coffee prices, and natural disasters (e.g. hurricane Mitch in 1998, Stan and Wilma both in 2005). These factors have altered the socio-economic conditions of the region, contributing to an increase in the number of poor people (Villafuerte, 2008). In addition, the different forms of LD, which is affecting three-quarters of Mexico’s territory, are to some extent contributing to higher levels of poverty and greater numbers of out-migrants (Campbell & Berry, 2003). The existing migration networks in the United States which have developed over tim facilitate the migratory movements of people from Chiapas, who see migration as strategy for acquiring additional family income. Chiapaneco migrants have moved to several destination places, chosen according to distances and purposes. Firstly, the states of Tabasco, Veracruz, Oaxaca, Quintana Roo (Rivera Maya), are part of the short distance migratory pattern. In addition, the state of Mexico, especially Mexico City, has historically formed part of the central traditional migratory circuit. Finally, the northeast Mexican border (Baja California) and the US are the destinations included in the long-distance migratory pattern (Anguiano, 2008b). The Migration Survey in the North Border of Mexico 2005 (Encuesta Sobre Migración en la Frontera Notre), carried out by the National Institute of Migration (Instituto Nacional de Migración INM), points out that 90,000 Chiapaneco left their homes to move to the United States or to other Mexican states in search of work in the period 2000-2005. 59 5.2. General characteristics of the case-study area The state of Chiapas is one of the poorest states in Mexico, with the lowest GDP per capita $27,698 MXN ($2,130 USD) in 2004 and with the lowest Human Development Index (HDI): 0.72 compared to a national HDI of 0.8 in 2006 (UNDP, 2007). Tapachula is one of its 118 municipalities. Its population, infrastructure and economic situation make the city of Tapachula the second most important one in Chiapas, after the capital, Tuxtla Gutierrez. It is located in the southern part of the state occupying an area of 303 km2 (1.2 % of the Chiapaneco territory) between 14°37-15°14’ (North latitude) and 92°09’-92°28’ (West longitude). Its altitude ranges from 0 to 2,700 m.a.s.l.. It is located next to the municipality of Motonzintla to the North and shares a border with Guatemala. The municipalities of Tuxtla Chico, Cacaoatán, Frontera Hidalgo, and Suchiate are a to the Eastern border, and the Pacific Ocean lies to the South. The municipalities of Tuxantán, Huhuetán and Mazatán are located to the West. According to its special geographical location, Tapachula acts as a commercial, political, and migratory centre point for Central American countries. Central American workers have arrive Tapachula to work temporarily in informal markets, services, and the agricultural sector mainly on the coffee plantations (Ayuntamiento Municipal de Tapachula, 2007). Chiapas is compraised by 15 socio-economic regions, Tapachula is part of the Soconusco region (Map 5.1) at the Mexican-Guatemalan border and has 493 ejidos (INEGI-INAFED, 2005). According to the 2010 census, carried out by the National Population Council (CONAPO), Tapachula has a population of 319,517 people, 52% of them are women (167,629). Tapachula represents 6.7% of the Chiapas’ population (SIMDE-UAZ, 2011), with 71.1% living in the urban areas. A specific demographic characteristic of Tapachula is the predominance of youth: 63% of population is below 30 years old, and the population average is 22 years (Ayuntamiento Municipal de Tapachula, 2007). In addition, 1.6% of its inhabitants speaks an indigenous language (SIMDE-UAZ, 2011). There are several dominant soil units, such as Luvisol 39.73%, Phaeozem 33.61%, Acrisol 10.42%, Cambisol 7.6%, Fluvisol 3.46%, Solonchak 2.41%, and others 2% (INEGI, 2009). Map 5.1: Economic regions of Chiapas Source: Comité Estatal de Información Estadística y Geográfica Tapachula has different climates, which differences. A humid tropical climate with abundant rains in summer dominates in 40.24% of the municipality’s territory, sub The average annual temperature is (Ayuntamiento Municipal de Tapachu created favourable conditions for crops such as maize, sorghum and soy (i of the municipality’s territory, grasslands 15.70%, forests 7.88%, mangroves 3.20% urban areas 5.76% (INEGI, 2009). economic activities in Tapachula, and it 27,774 hectares were producing coffee with a value over $215.2 million pesos ($16.5 million USD). Coffee has been cultivated for several decades; it had its boom in the 1940s and 1950 of World War II (Bartra, 1999). Bartra (1999) points out 60 vary according to topographic and altitudinal -humid tropical (30.82%), and humid temperate (17.20%) 26.2 °C and the average annual precipitation is la, 2007). These differences in climate the cultivation of bananas, coffee, cacao, mango and basic dem). Hence, agricultural land accounts for 67.13% Agriculture, livestock, forestry and fishing are the is the biggest coffee producer in Chiapas (SIACON, 2010); they produced 56,666 s because of a price hike that the coffee producers CEIEG, 2011 . 2,503 mm. and soil have and main . In 2010, MT of coffee in the context faced a 61 “mega crisis” between 1989 and 1994, with a dramatic fall of coffee prices, partly due to the saturation of the coffee market. Due to its geographical location, a big share of the state’s public, private and social assets are located in the municipality of Tapachula, making it a privileged place for development. Nonetheless, the implementation of the neoliberal economic model in the 1980’s and the ratification of the NAFTA between Mexico-Canada- USA in 1994 have had negative regional effects, especially for small-scale farmers. Income inequality has increased in Tapachula, widening the gaps between social classes and social opportunities (Villafuerte, 2007). These tendencies have worsened the living conditions, an increased the unemployment rates, and foster outmigration. Along these lines, 72% of the economically active population receive an income of less than $1,600 MXN ($123 USD) in 2007 (Ayuntamiento Municipal de Tapachula, 2007), and in 2000 the average monthly income was $4,634 MXN ($356 USD) (SIMDE-UAZ, 2011). The uneven income distribution has affected largely the quality of life, limiting access to education, housing, health, recreation, basic services, and better nutrition in the small villages and communities located on the outskirts of the city (Vasquez et al., 2009). To a lower extent, due to rising levels of poverty and precarious living conditions, the population has suffered acute social degradation, leading to social problems such as alcoholism, prostitution, drug consumption, and other illicit activities (Ayuntamiento Municipal de Tapachula, 2007). Over the past decade, unlawful activities have mushroomed as well as trafficking of drugs and migrants. Part of the problem is the lack of control mechanisms and municipal administration. The municipal government, which is partly responsible for promoting social wellbeing, has also failed to protect the natural environment. There is little to no environmental control of industry, agriculture and other sources of pollution, all of which are gradually degrading rivers, farmland, mangroves and marshland. Along the same lines, there is a lack of control and a dearth of preventative measures regarding overexploitation of natural resources, including forests. Deforestation provokes a rapid loss of soil from the middle and high zones, increases the load of sediments in rivers and streams, and represents a threat to human settlements, since it can lead to the occurrence of landslides and floods, for example. For this reason, fragile ecosystems (flora and fauna) are at risk of slowly dying off. The recuperation of affected species can take decades, if it can be restored after all. Agricultural crises, poverty, unemployment, and the intensification of natural events (hurricanes, tropical storms, floods, landslides and droughts) are among the main factors that enhance the migration outflow from Tapachula (Villafuerte, 2008), where the annual net 62 migration rate is -1.3% (INEGI, 2011). At the same time, Tapachula is a destination place for migrants who have chosen this city as an entering point to Mexico, in order to find work in Chiapas’ agricultural sector or in other Mexican states. In 2010, the foreign population residing in Tapachula has reached 11,000 people, 7,359 of which are Guatemalans, 1,164 Hondurans, 1,125 Salvadorians, 714 Nicaraguans, and 540 of other nationalities (SIMDE- UAZ, 2011). In addition, Tapachula serves as an entrance gate for Central and South American transit migrants on their journey to the United States (Villafuerte, 2008). 5.3. Methodological approach 5.3.1. Site setting Based on the main research questions and objectives of the study, the field research conducted in the municipality of Tapachula in the state of Chiapas presents two levels: (1) On the farm level, in the six communities or ejidos9 Rio Florido, La Independencia, Veintiséis de Octubre, El Edén, El Naranjo, and Pavencul. In these communities, the research methodology described below was applied to evaluate current levels of farmland degradation, small-scale farms’ development, and migratory flows. (2) On the individual level, it was focussing on migrants coming from Central America and working on the coffee farms/fincas Finca Brasil, Finca Laguna, Finca Santa Teresa, Finca Santa Anita, Finca Génova, Finca Irlanda, and Finca Hamburgo. This research was conducted in order to analyze living and working conditions, demographic profiles, and migration motives. 5.3.2. Selection criteria for ejidos The selected study area is located in the south of Chiapas, near the Mexican-Guatemalan border. The research area altitude ranges from 0 to 2300 m presenting an array of climatic and geographic characteristics that have influenced agricultural activity, and socio-economic development. The number of inhabitants and geographic location (altitude) are the key selection criteria. Firstly, Tapachula is divided into three sub-areas with an altitudinal difference of 550 m, from the Pacific Ocean up to the highlands. Secondly, the number of ejido inhabitants is a primordial consideration for choosing the study areas. The ejidos with a 9 An Ejido is a rural property of collective use, managed by either all the members of a community or separately by individual farmers. It was institutionalized in the 1930’s as an outcome of the Mexican Revolution (1910- 1920). The aim of forming ejidos was to prevent the “re-emergence” of large private rural states. 63 population range between 400 and 1000 were selected from 493 ejidos legally established in Tapachula (INEGI-INAFED, 2005). Combining both criteria resulted in the final selection of six ejidos in three different regions (Table 5.1). Table 5.1: Profile of fieldwork research ejidos Ejido Zone Inhabitants Altitude m.a.s.l Rio Florido Low land 725 50 La Independencia Low land 443 540 El Edén Middle-land 851 580 Veintiséis de Octubre Middle-land 880 800 El Naranjo High land 705 1240 Pavencul High land 848 1940 Source: author’ elaboration 5.3.3. Description of selected ejidos Ejido Rio Florido Ejido Rio Florido is the closest ejido to Tapachula in comparison with the other selected ejidos. It is located 13 km southeast of Tapachula, between 14°51’13” North latitude and 92°20’28” West longitude in the direction to Puerto Madero harbour. It is located at an altitude of 50, which creates a characteristic sub-humid tropical climate. The predominant soil type of the area is Phaeozem. There are 148 households living in this area, of which 133 are male-headed and 15 female-headed. From a population of 725 inhabitants, 481 do not have access to the Mexican public health care system (INEGI-INAFED, 2005). Due to its closeness to Tapachula, access to social services and agricultural programs are better than in other ejidos. It also receives more attention from local authorities. The ejido Rio Florido is located in the low zone with plenty of fertile flatland near the Pacific coast. Its land and weather are perfect for growing crops such as mango, cacao, sesame, sugar cane, soy and other tropical fruits. Furthermore, its plains are used as pastures to breed cattle and for constructing facilities for poultry production (PEOT, 2005). However, during the rainy season, from July until December, hurricanes, tropical storms, and cyclones turn a great part of the flatland into flooded landscapes, which in turn affects the normal growing and development of crops and livestock. Additionally, heavy rains wash large amounts of garbage produced and dropped in the streets of the city spreading it out among crops and streets, which clog the few streams used to discharge excess water. The heavy rains brought by the strike of hurricane Stan in 2005 have damaged infrastructure, access roads and crops. The 64 lack of maintenance of roads has created potholes of different sizes; these affect the transportation of people and products towards the urban centre. (Programa de Acción Ante el Cambio Climático Del Estado de Chiapas PACCC, 2010). Ejido La Independencia Ejido La Independencia lies at an altitude of 540 m. It has a very green landscape made up especially by coffee plantations, tropical fruits and secondary forest. It is located 30 km north of Tapachula between 15°03’27” North latitude and 92°17’05” West longitude with a wet tropical climate and a Luvisol soil type. The number of inhabitants living in this ejido is 443, distributed among 109 households, of which 96 are male-headed and 13 are female-headed. From the whole population, around 330 persons have no access to public health care (INEGI- INAFED, 2005). This ejido is part of the area influenced by the Tacaná biosphere reserve, and it participates in the community forestry program launched by the National Forestry Commission (Comisión Nacional Forestal CONAFOR) to encourage and promote productive activities, forest conservation and restoration, as well as the implementation of sustainable forest management. Among the physical-climatic characteristics of this area, the most relevant are moderate slopes and wet tropical weather. Thus, it has an abundance of secondary forests, in which shade-grown coffee of the Robusta variety is cultivated. Moreover, this area is part of the natural water catchment of Tapachula. Ejido El Edén Located between 15°03’32” North latitude and 92°18’00” West longitude at 580 m.a.s.l., ejido El Edén is very close to its neighbouring ejido, La Independencia, with which it shares the same topographic, climatic environmental and physical characteristics. Two different types form its soil: Acrisol and Luvisol. Its population is twice that of La Independencia with 851 inhabitants, distributed among 181 households, of which 149 are male-headed and 32 are female-headed. The population without access to governmental medical services is 608 people, almost twice that of its neighbour ejido. In spite of being an area allocated essentially to coffee production, new tropical exotic fruits introduced into the area as a response to the low coffee prices that prevailed until early 2000’s are increasing. Through programs geared towards the conversion of agriculture to new and more profitable crops, implemented by the Instituto de Reconversión Productiva de Agricultura Tropical (Productive Reconversion Institute of Tropical Agriculture IRPAT), mangosteen (Garciniamangostana L.) and other crops have adapted successfully to regional conditions. 65 Ejido Veintiséis de Octubre Ejido Veintiséis de Octubre situated at an altitude of 800 m in the central area of Tapachula. It is located between 15°05’04” North latitude and 92°15’06” West longitude, about 60 km northwards of Tapachula city. It has a wet tropic climate and its characteristic soil is Luvisol. The predominant crops are coffee (Robusta and Arabica varieties) and tropical fruits, employing 168 people out of a total labour force of 223. There are 159 households in the area, of which 136 are male-headed and 23 are female-headed. Of the total population of 880 inhabitants, 829 do not have access to health care. Surrounded by coffee plantations, this area possesses a dense secondary forest with high ecological value of its flora and fauna. Ejido El Naranjo It is located in a mountainous area at the foot of the San Pedro hill, at the edge of the border between Tapachula and the municipality of Motozintla. Its high elevation (1240 m.a.s.l) makes it difficult to access by public transportation. The ejido is located between 15°11’57” North latitude and 92°19’25” West longitude. Its climatic characteristics exhibit wet tropics in the winter and sub-humid temperate conditions in summer. The soil types in the area are Acrisol and Luvisol. There are 705 inhabitants in the ejido, living in 141 households, of which 120 are male-headed and 21 are female-headed. Ejido Pavencul Ejido Pavencul is one of the communities with a high indigenous population (26%). It is located at an altitude of 1940 m.a.s.l. between 15°12’03” North latitude and 92°10’47” West longitude, near the foot of the Tacaná volcano, along the border with Guatemala, within the boundaries of the Tacaná’s ecological reserve. It is comprised of 10 neighbourhoods: Pavencul Centre, Bijahual, Carrizal, Vega Molina, El Pinal, Buena Vista, Las Flores Nuevo Milenio, La cueva, Frontera Veinte de Octubre and Vega Malacate. In 2005, it had 116 male- headed and 9 female-headed households. The total population is 848 inhabitants, where 99% of the population does not have access to public health care (INEGI-INAFED, 2005). The soil of Pavencul is mainly Cambisol, the climate is humid and temperate, and the landscape is mountainous, with rugged topography, shaped by waterfalls and streams that over time formed gullies, ravines, and narrow gorges. The area is also highly vulnerable to seismic events. The high degree of deforestation on the hillsides has damaged the land’s natural cover and prompted land degradation. In this way, agricultural production, socio-economic 66 development and livelihoods are severely affected. Yet, the traditional system of agriculture, which revolves around the milpa10, is still the main livelihood for several households. Milpa, in some cases barely fulfils its objective, whilst in other cases it cannot sustain the household in terms of food production neither generates enough monetary income through the selling of agricultural products due to its low productivity. Because of the high altitude of the ejido, the production of high land coffee (Coffea arabica L.) can only take place in its lowest parts. Coffee production is very low, it is mainly affected by the quick changes of temperatures, heavy rains, poor soils, landslides, strong winds, and mismanagement. According to personal interviews, the annual production is between 300-500kg/ha, which is far below the national average of 2.6 tons/ha (SIACON, 2010). 5.3.4. Selection criteria for farms This methodological approach is elaborated accordingly the literature review in the field of Central American migratory movements, labour force migration, personal visits to farms, and interviews with farm owners and workers. In this way, pilot results suggested that the biggest share of Central American migrant workers are in the coffee farms. There is a high demand of foreign labour during the coffee harvesting season. Hence, the research focuses on coffee farms with an area between 50 and 300 ha, expecting to find a greater number of Central American migrants on large farms. Table 5.2: Characteristics of research coffee farms Coffee Farm Altitude (m.a.s.l) Area (ha) Seasonal workers (#) Permanent workers (#) Total S+P workers (#) Finca Brasil 420 50 60 10 70 Finca Laguna 500 100 90 15 105 Finca Santa Teresa 670 150 120 25 145 Finca Santa Anita 880 250 250 40 290 Finca Génova 880 250 280 40 320 Finca Irlanda 1100 250 280 60 340 Finca Hamburgo 1200 300 300 60 360 Total 1630 Source: author’s elaboration 10 A crop association of different cultivars of maize in association with beans and pumpkin crops mostly. Although, there are combination with other crops such as tomatoes, chilies, medicinal plants and vegetables; the main function of the milpa is to provide food to the household. 67 Map 5.2: Location of research sites Source: INEGI. Adapted from continuo Nacional Del Conjunto de Datos Gráficos de la Carta de Uso del Suelo y Vegetación, 1:250 000, 68 5.3.5. Sampling size At the Farm Level the Sistema Nacional de Información Municipal SNIM (National System of Municipal Information) software provided the total number of ejidatario and non-ejidatario households. The next step was to visit the ejidos to get to know the number of registered ejidatario households living in the ejido, as well as the characteristics of their farms and agriculture. The other households in the ejidos without “ejidatario title” do not have the right to take part in the decisions for the development of the ejido. These dwellers bear the name of “avecindados”, which means new residents or neighbours who have bought the land from another ejidatario or inherited it from their parents without the corresponding ejidatario rights. Table 5.3: Households and ejidatarios in the research areas Ejido Land Households Ejidatarios Rio Florido Lowland 148 56 Independencia 109 34 El Edén Middle-land 181 55 Veintiséis de Octubre 159 48 El Naranjo Highland 141 86 Pavencul 125 186 Total - 863 465 Source: author’s elaboration According to the geographical location and altitude the classification of the ejidos resulted in having two ejidos in the lowland (LL), two in the middle-land (ML), and two in the highland (HL). Table 5.4 describes the sample size for the survey with 10% error  =   +   (2) Where: n= Sample size N= Population size e= Error 69 Table 5.4: Ejidos’ sample size Source: author’s elaboration This resulted in the following number of households and questionnaires for each geographic zone: lowland 47, middle- land 51, highland73. It is worth mentioning that the number of questionnaires, which according to this calculation should be filled in by households in the highland ejidos was not achieved in practice; only 51 questionnaires were completed, the reasons are described in the limitations and problems section of this chapter (p.77). At the individual-immigrant level In what follows, the approach used to collect information on the coffee farms differs from the method used at the farm level. The number of Central American migrants, working either legally or undocumented on the coffee farms of Tapachula was difficult to assess. In this case, the methodological approach was to find as many migrants as possible on the previously selected coffee farms located in the central land of Tapachula. During the pilot survey, information regarding the number of seasonal and permanent workers in the coffee farms was briefly obtained, either from the foreman, manager or human resources supervisor in each coffee farm. In this way, it was possible to obtain an average number of seasonal and permanent Central American workers per farm; yet, these numbers are sketchy. Moreover, the coffee harvesting season determines directly the number of seasonal workers on the coffee farms. The coffee harvesting season varies according to the altitude where the coffee is grown. There are two species of shade-grown coffee grown in the region: Arabica (Coffea arabica sp.) and Robusta (Coffea canephora; syn. Coffea robusta). The Arabica or mountain coffee habitat is found in the central-high lands of Tapachula; it is usually cultivated between 1200 and 1500 m altitude whereat Robusta is produced in the lowest and medium part of the central. Robusta coffee beans ripen earlier in the year than the Arabica ones. The harvesting period for both coffee species in the region starts in September and finishes at the end of (e)2 Ejidos Ejidatarios 0.0025 0.0049 0.0100 Lowland 90.00 73.47 62.46 47.37 Middle-land 103.00 81.91 68.45 50.74 Highland 272.00 161.90 116.60 73.12 Total 171 70 February. It was of paramount importance for the research project to consider the harvesting period in the region, in order to: (a) understand the international labour force mobility; (b) to determine the period with a high inflow of Central American immigrant workers to the coffee farms of Tapachula; and (c) to maximize the potential number of possible respondents. Table 5.5 displays the number of Central American workers surveyed on the selected coffee farms. Table 5.5: Number of Central American migrants surveyed by farm Coffee farm Frequency Percent Brasil 4 5.0 Génova 10 12.5 Hamburgo 10 12.5 Irlanda 22 27.5 Laguna 16 20.0 Santa Anita 4 5.0 Santa Teresa 14 17.5 Total 80 100.0 Source: author’s elaboration 5.3.6. Sources of data/information In order to meet the research objectives, data was gathered using both top-down and bottom- up methods. The qualitative and quantitative methodologies are based on questionnaires, interviews, and geographic information systems, and secondary statistical databases, which provided more accurate information regarding different key aspects of the various subtopics, including demography, agriculture, migration, economic development, natural resource uses, migrant’s labour conditions, climatic and geographic characteristics. Secondary source information was obtained inter alia from the National Council of Population (CONAPO), the State Council of Population (COESPO), the National Institute of Statistics and Geography (INEGI), the National Institute of Migration (INM), the Ministry of Environment and Natural Resources (SEMARNAT), the Secretary of Agriculture, Ranching, Rural Development, Fisheries, and Food Supply (SAGARPA), the National Institute for Federalism and Municipal Development (INAFED), the Food and Agriculture Organization (FAO), the United Nations (UN), and the World Bank. Supplementary information was acquired from Mexican media outlets, such as newspapers, magazines, radio, television, and internet in order to gain insights into present-day public opinion regarding migration, development and land degradation in Tapachula. 71 The fieldwork conducted in two different stages used two different structured surveys. The collected data was entered into a statistical program to run descriptive and multivariate statistical analyses that can provide empirical and contextual information that can possibly be used for making a empirical and policy contribution. The research project phases were the following. At the farm level survey, the structured survey carried out in six ejidos in the municipality of Tapachula: Rio Florido, La Independencia, El Edén, Veintiséis de Octubre, El Naranjo, and Pavencul used a socio-economic, production and migration-oriented close-ended questionnaire. Additional information gathered through the implementation of participatory research methods that facilitated the establishment of a knowledge interface between the actors, contributed to a deeper understanding of the topics under research (annex 1). Central American migrants’ survey, it was applied on seven coffee farms: Finca Brasil, Finca Laguna, Finca Santa Teresa, Finca Santa Anita, Finca Génova, Finca Irlanda, and Finca Hamburgo using questionnaires to gathere information from immigrants that are currently working in the agricultural area of Tapachula. Personal interviews with Central American migrant workers held in the coffee plantations provided additional information (annex 2). The surveys were accompanied by short personal interviews with the household heads and main authorities of the ejidos. The aim was to reinforce the major questions and to get information regarding changes in the use of farmland and environmental degradation, with a special emphasis in land degradation. These interviews were a valuable source of narratives from former migrants and non-migrants, of information about uses of remittances, and, at the same time, to gain cross-sectional and retrospective information regarding land degradation, migration, and development. Table 5.6 summarises the characteristics and structure of the questionnaires used in the surveys. 72 Table 5.6: Main characteristics and structure of the questionnaires Farm level survey Central American Immigrants survey Household composition Socio-demographic Characteristics Characteristics and uses of the farmland Motivations to migrate Farmland production Changes (5 years) Mistreatment on the way Prices of Agricultural products (5 years) Paths and destination points Livestock activities (5 years) Aid to migrate and social networks Use of forestry products Labour aspects and conditions Further use of the farmland Labour activities an income Use of labour force Living and health conditions Migration and remittances Satisfaction and vulnerability Technical and governmental assistance Living and health conditions Source: author’s elaboration 5.3.7. Fieldwork research The fieldwork research was conducted over the course of eight months, from September 2010 until April 2011 (Table 5.7). The weather conditions were appropriate to carry out the surveys and to visit the different ejidos because the rainy season was leaving and the beging and the sub-humid season started. At that time, the coffee farms were preparing for the upcoming harvesting season, which attracts thousands of Guatemalan and other Central Americans migrant workers to Tapachula. It had three different phases. The first phase implemented in the low-zone and part of the middle-zone from September to November 2010, consisted in surveying the household heads and interviewing key informants in Tapachula and ejidos. Due to the arrival of the coffee-harvesting season, the second phase consisted to survey the Central American migrants working on the selected coffee plantations from December 2010 to February 2011. Once the coffee-harvesting season was about to end and a big share of these workers either moved to other areas or were on their way back home, the third phase started with the rest of the middle-land and highland regions, which ended in April 2011. 5.3.8. Fieldwork research timeline In general, the fieldwork schedule was elaborated to achieve the objectives quickly and accurately, taking into consideration climatic conditions, coffee harvesting season, and the willingness of the local population to participate. The fieldwork chronogram (Table 5.7) was 73 developed mainly on an ad hoc basis, due to the approaching of a couple communities at the time. While conducting research, certain external factors appeared that hindered the planned visits to the ejidos, e.g. transport was not ready or broke down, sudden heavy rains occurred, community leaders or ejidatarios did not attend the meetings, etc. Table 5.7: Fieldwork research chronogram by field site 2010 2011 Field site: ejidos Sep Oct Nov Dec Jan Feb Mar Apr LL Rio Florido X Independencia X ML El Edén X Veintiséis de Octubre X HL El Naranjo X Pavencul X X Field site: coffee farms Brasil X Génova X Hamburgo X Irlanda X Laguna X Santa Anita X Santa Teresa X 5.3.9. Participatory research methods To gather additional data regarding the social meanings and ordinary activities of the household heads, the research used an ethnographic qualitative method, as well as structured and semi-structured interviews to several key informants inside and outside of the studied populations. The selection criteria for key interviewees in the ejidos were mainly based on the level of authority, hence the “comisariado ejidal”11 (commissioner of communal lands) were primarily selected. He/she is an authority of the ejido who represents the ejido, is responsible for implementing acts and resolutions adopted by the General Assemblies held in Tapachula, and has to be aware of the main socio-demographic and environmental problems of the ejido. Other key informants, like former commissioners of communal lands, persons 11 To become a commissioner of communal land, ejidatarios select among themselves two candidates who are in full possession of land rights, have worked in the ejido for at least six months preceding the date of elections, and have not been sentenced for an intentional crime that warrants a custodial sentence. Following an election, the elected commissioner of communal land will last in this position for a term of three years, with the opportunity to be reelected only once after the termination of his first term of office. 74 involved in developing communal projects and other ejido members without state participation provided useful information to the research. Focus groups and ejido workshops Focus group discussions are a cost-effective qualitative research tool, in which specific topics are discussed in detail, probing into people’s feelings, opinions, views and perceptions. This tool was very useful during the fieldwork research. In-group settings, most of the questions came from the survey’s questionnaire, additional questions that were not included in this set of questions were added to dig deep in the main research themes. Fruitful information was obtained after discussions and interviews with ejido authorities and voluntarily participating ejidatarios, since the interviewees often were more apt to answer questions in-group settings than in one-to-one interviews. It provided additional information to the research that could be triangulated to assess and measure certain variables during the data analysis. During the workshops, it was feasible to select potential candidates for the survey. The major topics introduced in the workshops were in relation to land degradation, migration, and rural development. For these and other topics of importance to the ejido a problem tree analysis helped to identify and guide the discussion. With the identification of the main ejido’ constraints, the participants discussed local views on short and long-term adaptive strategies to face or tackle the pitfalls; likewise new alternatives that came up during the workshop were debated. Transect walks and visual analysis of the area Transect walks were useful to observe conditions, people, problems, opportunities, agricultural crops, and landscapes of each community. They provided a better overview of the research area and helped identifying issues that might merit further exploration. By looking carefully at the layout of each ejido’s diverse territory, this spatial data-gathering tool helped describe the communities’ landscape, soil type, land use, crops, vegetation, etc., as well as their differences. Additionally, information about the ejido from knowledgeable local guides provided the opportunity to gain personal information about the guides and their families, and caught a glimpse of the communities. The visual analysis also contributed to picture the ejido’s level of development, house construction, vehicles and other material objects. It also gave the opportunity to identify households that might have one of its members living abroad. 75 5.4. Consent and permission During the visits to the ejidos, in the meetings and in some interwies, a letter describing the aim of the research, research approval and support by the academic institutions involved had to be developed. In addition, this document was required to gain access to the list of current commissioners by the “Oficina de Agentes Municipales de Tapachula” (Tapachula’s Municipal Commissioner of Communal Lands Office). After providing to its coordinator an explanation of the essence of the research and objectives, he kindly provided the names, phone numbers, and addresses of each of the commissioners of communal lands for the selected research ejidos. Moreover, he and his team of co-workers visited several ejidos and were keen to be involved in the research. 5.5. Limitations and problems: In order to start the data-collection process and surveys with smallholders, it was necessary to approach the municipal delegates of the rural zones to obtain their approval. The president of the municipality of Tapachula, who represents a certain Mexican political party, selects these authorities who are replaced when his term in office finishes. In this regard, the different political attitudes and affiliations of some ejidatarios were sometimes a pitfall. During the households’ visits, some ejidatarios indicated that they disagreed with the current authorities and therefore were not willing to participate in the research. Moreover, the arrival of a new municipal president, who took office in December 30th 2011, brought changes of personnel, including new municipal delegates. This brought implications for the whole research, creating limitations while visiting the ejidos, since some ejidatarios wanted to see the letter of approval for conducting research by the new authorities. Indeed, approvals were difficult to obtain at that time because the political transition and the settling in of the new authorities took about eight weeks. Nonetheless, the visits to the ejidos continued as stated in the chronogram of activities. The calculated sampling size for the highlands was 73 households but only 51 questionnaires were obtained. The major impediments found were in the Ejido Pavencul, where after been granted access and consent from the indigenous affairs department and the municipal’s presidency; the commissioner and the board of communal land of Pavencul were not working together because of different political affiliations. Additionally, Pavencul is an ejido with an indigenous population and it is one of the biggest ejidos of Tapachula. For decades, politicians have come to this highland ejido to collect votes by promising schools, better 76 roads, clinics, development projects and prosperity. It turned out that the residents were unsatisfied with the ineffective government assistance and broken promises. As a result, Pavencul’s ejidatarios and authorities want to turn Pavencul into a separate municipality. Due to its geographical location, Pavencul is difficult to access; it is a poor ejido with high levels of deforestation, heavy rains and a susceptibility to landslides. Its inhabitants are tired of political words instead of actions, and due to this, they were reluctant to take part in the survey. In order to reach the goal of including 73 households in the survey for this area, the research relied on the aid and company of the new local commissioner of communal land and other local authorities. After some meetings with the whole community, the ejidatarios still did not arrive at a consensus as to whether or not to participate in the research. Thus, the commissioner of communal land chose ejidatarios who were interested in the research. These participants explained that they were willing to collaborate with the research, because they considered it as a mean for shedding light on the existing socio-economic and environmental problems of this ejido. In another scenario, the appearance of thieves on the roads to the ejidos during the coffee- harvesting season have caused local transportation shuttles, (combies), to reduce the frequency and hours of their service. It also led to a general distrust among ejidatarios towards foreign visitors. In the combi the drivers and local residents, authorities and enumerators who were travelling felt the fear. One weekend, while travelling in a combi to Ejido El Naranjo, several people wearing masks forcedly stopped the combi by throwing a rock onto the combi’s windshield. According to local residents, thieves use to stalk combies and other trucks on their way back to the ejido. These unfortunate events occur during the coffee harvesting time, assuming that ejidatarios, whose main income is based on the coffee production, are bringing money home after having sold their product in Tapachula. Thus, people are aware of the risks of travelling at this time of the year along these routes. Therefore, many ejidatarios rather save the money in the bank or buy agrochemicals, food, clothing and medicine instead of carrying cash with them. Other ejidatarios just bring sufficient money home for the coming few days. Furthermore, some limitations and problems appeared in the course of the survey among the Central American workers. The questionnaire for this purpose had to be reviewed and approved by the Tapachula-Soconusco’s coffee producers association. The outcome was that the association would allow the visits to the coffee farms to conduct surveys and interviews, 77 when some of the questions about the migrant’s employment and living conditions were change or removed from the questionnaire. As these questions were an important part of the research, therefore their removal was not feasible and new arrangements for another way to approach the Central American workers on the coffee plantations was designed. After locating the coffee farms to conduct the surveys, in some of them, the alternative research technique was to ask for employment as an undocumented migrant from Guatemala. This in turn, provided to the research the opportunity to get a close look at the workers’ living, and labour conditions, as well as to live and experience their situation. Notwithstanding, it was a tough job to fill in the questionnaires, pick coffee cherries in this mountainous region, and at the same time avoid being discovered or laid off for due to the low performance. Generally, a coffee worker collects around 50-60 kilograms of coffee cherries per day, but for investing more time in finding migrant workers, I collected 10 kilograms. Subsequently, a dozen workers responded to the questionnaires and few interviewed. In other coffee farms, the owners or supervisors were helpful and provided information and access to their facilities and workers. 78 6. Chiapas: land degradation and rural development in the context of neoliberal globalization Chiapas features an abundance of natural resources; it has the richest biota of Mexico and Central America. However, its landscape has been dramatically altered over the transition from self-sufficiency towards market-driven use of natural resources. For understanding problems of degradation that have affected the whole region economically and sociologically, it is important to know the physical characteristics of the Chiapaneco territory. Aside from being naturally prone to land degradation, Chiapas also faces environmental deterioration as a consequence of human activities. In this light, its central problems include land degradation driven essentially by extensive agricultural production, deforestation (Alscher, 2008), gradual degradation of ecosystems, overgrazing, urbanization, and other forms of human development (CONAFOR, 2009). Before describing the land degradation and development issues in the Chiapaneco territory, general information about Chiapas’ natural environment is detailed in order to identify the factors that influence overall land degradation and development. 6.1. The natural environment The Mexican Environment and Natural Resources Secretariat (SEMARNAT, 2011) states that the vast mosaic of environmental conditions and microclimates in Mexico is due to its complex topography and the diversity of climates existing in the country’s territory. Six types of ecological zones are distinguished: (1) tropical warm-wet, (2) tropical hot and humid, (3) warm humid, (4) warm sub-humid, (5) arid and semi-arid, and (6) flood zone or sea-land transition (Toledo & Ordóñez, 1993). These different terrestrial habitats place Mexico among the group of 17 Like Minded Megadiverse Countries12 (LMMCs) (Mittermeier, Gil & Mittermeier, 1997). In this regard, the state of Chiapas is characterized by a diversity of terrestrial habitats that exhibit variations in humidity, temperature, vegetation and soil conditions. Chiapas is located in the far southeast of Mexico and forms part of the Mesoamerica Maya Zone. It is adjacent to the states of Tabasco, Campeche and Quintana Roo and it borders on the East with Guatemala and Belize (Programa Estatal de Ordenamiento Territorial Chiapas PEOT, 2005). Chiapas is one of the richest Mexican states in terms of natural resources; but also one of the poorest in terms of living standards. Chiapas is the eighth largest state in Mexico, with a territorial area of 74,415 km2, covering 3.8 % of 12 The Like Minded Megadiverse Countries Group, which contains more than 70% of all biodiversity, and 45% of the world’s population. 79 the Mexican surface (INEGI, 2011). It has enormous water resources, producing 56% of the country’s hydroelectricity. Other resources, such as oil, uranium and aluminium make the subsoil valuable (Cruz, 2007). Flora and fauna are more diverse than in most other Mexican states, with 1,208 species of vertebrates (101 amphibious, 218 reptiles, 198 mammals, and 691 birds) (PEOT, 2005) and 4,548 vegetable species13 (693 pteridophytes, 3,833 angiosperms and 22 gymnosperms) (Flores-Villela & Gerez, 1994; CONABIO, 2008). The PEOT (2005), committed to exploring different geographic regions in detail, reported that the topography of Chiapas is made up of mountainous areas by (69%), hills (17%), plains (12%) and valleys (2%). Steep hillsides characterize much of the state’s farmland: 67.5% of Chiapas’ area has a slope degree of 0-18%, 20.4% is between 18-36%, 9% is between 36- 58% and 2.8% is very steep with a slope degree of 58-100% (PEOT, 2005). Out of the 26 soil types found in Mexico, 15 are present in Chiapas. According to the FAO soil classification system, the main soil types found in Chiapas are Lithosol, Rendzina and Acrisol, together accounting for 53% of the state’s surface area. Lithosol (20%) is extremely shallow; the topsoil is only about 10 cm deep. It is found on steep slopes and in mountainous regions and is therefore very susceptible to erosion. Rendzina (17%) is a dark, greyish-brown, humus-rich soil, clayey, shallow and stony soil typically found in tropical and temperate forests with shrubs and calcareous grassland (Garrison, 2008). It produces low yields under agricultural cultivation and the risk of erosion is extremely high, especially on steep slopes (PEOT, 2005). Acrisol (16.2%) is associated with humid, tropical climates and often supports forested areas (Garrison, 2008). It presents accumulations of red, yellow or light yellow clays on the subsurface, usually exhibiting an acidic or very acidic character. The soil’s low productivity poses limitations for agriculture and livestock. On the other hand, it can be a favourable base for forestry (PEOT, 2005). Altitudinal differences, ranging from 0 to 4,100 m.a.s.l. create a climatic variety in Chiapas. It divides the region into two climatic groups: hot humid and humid temperate. The main different ecological zones and their occurrence are wet tropics (31,700 km2), sub-humid tropical zone (13,990 km2), humid temperate zone (7,600 km2), and sub-humid temperate zone (18,745 km2). The presence of several types of climate gives rise to different types of vegetation. The temporality of precipitation divides the year into a rainy season and a dry season. The average rainfall ranges from 2,100 to 3,250 mm per year, concentrated mainly in 13 Chiapas occupies 3rd place nationally with regards to the number of vertebrates species and 2nd place in vegetable species 80 the period between May and November (INEGI, 2011). The natural vegetation also shows a wide variety including temperate forests (dominated by coniferous forest), tropical forests (evergreen and deciduous forest), secondary vegetation of rainforest, scrubs, pastures and grasslands, mangroves, savannah and tule vegetation (PEOT, 2005). The combination of steep slopes, soil types and other natural factors make Chiapas a fragile territory prone to environmental degradation. 6.2. Land degradation 6.2.1. Extent The mountainous system of Chiapas integrated into the “Sierra Madre”, a region made up of igneous rocks, with a high degree of steepness that makes the region highly sensitive to land degradation. Soil depth, decreasing biota diversity, water holding capacity and filtration have negative implications for farming in Chiapas (Barrow, 1991). Different types of biophysical processes are influencing the soil’s properties. For example, chemical degradation affects 2.4 million hectares (Table 6.1) or 32 % of Chiapas’ area. Land degradation has important implications for livelihoods and food security all over the world, particularly to the poor people living in the rural areas, whose livelihoods depend on agriculture and livestock production. Out of Chiapas’ 15 economic regions, the most affected by water erosion are the coast, Soconusco and the sierra of Chiapas; their rough topography, shallow soils, and inadequate land use have resulted in erosion levels of 200 to 415 ton/ha/a (Ruiz, 2010). Table 6.1: Land degradation in Chiapas by principal process in 2010 (’000 ha) Erosion type Light Moderate Strong Extreme Total ha % ha % ha % ha % ha % Water 226.8 3.1 139.1 1.87 1.1 0.1 0.0 0.0 367.1 4.9 Physical 689.1 9.3 102.9 1.38 28.6 0.4 149.7 2.0 970.6 13.0 Chemical 1,123 15.1 1,131 15.21 141.4 1.9 3.2 0.04 2,399 32.3 Source: SEMARNAT, 2011 Overall, the biodiversity and the soils of Chiapas are being deteriorated by economic development, in this case the implementation of the export-led model in Mexico, which is based on the exploitation of natural resources, deforestation, intensification of farming activities, and land use changes (EACH-FOR, 2009; Ruiz, 2010). In addition, wildfires and natural events such as hurricanes, tropical storms and heavy rains have damaged the soil throughout the region. 81 6.2.2. Human-induced land degradation Deforestation The international community and local governments consider deforestation to be a problem of large dimensions with serious implications for biodiversity, due to the removal of trees, shrubs, bushes, and other vegetation, which are the shelter for an uncountable number of animal species. In Mexico, the main causes for deforestation are agricultural activities, illegal deforestation and wildfires. Estimates for deforestation are sketchy and controversial with a certain degree of disparity because of the use of different criteria and methods of assessment. With this in mind, the rate of deforestation in Mexico in the period 1988-2005 has been estimated to be between 316,000 and 800,000 ha/year. However, these estimations depend on the region, the demography, and the availability of resources (Velazquez et al., 2002). According to FAO (2011) Mexico’s forested area is composed of around 65 million ha or 33% of the country’s territory. Of this, 34.3 million ha (53%) are classified as primary forest with high biodiversity. During the last twenty years (1990-2010), Mexico lost an average of 275,000 ha (0.4%) of forests per year, and almost 5.5 million ha or 7.8% of its forest covers were cut down, nearly the size of the state of Sinaloa. The annual average wood production between 1983 and 2003 was 7.6 million m3. The main harvested wood species in 2003 were pine, accounting for 5.5 million m3 (74.8%), oak representing 0.8 million m3 (10.9%), and other wood species, adding up to 0.8 million m3 (10.7%) (SEMARNAT, 2005). The fertile land of Chiapas and its entire biodiversity are increasingly exposed to degradation because of both legal and illegal logging (Bilsborrow, 2002). Especially in the “Sierra Madre of Chiapas” and the Lacandona rain forest 14 (Selva Lacandona), logging is regarded as a business with strong economical potential. Taking the Lacandona rain forest as an example to depict the degree of deforestation in an area with high ecological value demonstrate de severity of the problem. Its forested area was about 1.5 million ha when the Spaniard explorers found it in the early 16th century. By the end of the 20th century, this area had declined to 500,000 ha. Shockingly, most of this deforestation occurred between 1990 and 2012. This evergreen area has provided wood (fuel and construction material) to local inhabitants, timber for international export, and soil for agriculture and cattle grazing purposes. All of these land use changes (LUC) led to the fragmentation and colonization of 14 The Montes Azules Biosphere Reserve, one of the 15 economic regions of Chiapas has an area of 957,240 ha, representing 13% of the state’s territory, and contains the “largest unfragmented tract of forest and the largest remaining tract of tropical rain forest in Mexico” (PEOT, 2005). 82 primary forest areas by squatters, settlers, ranchers and farmers. Other developmental projects such as road construction, hydroelectric and oil projects have decreased the forest areas considerably. In an era of high-energy prices and demand, the oil deposits in the Lacandona forest are already targeted for extraction (PEOT, 2005). In Chiapas, the reported deforestation rate from 1995 to 2000 was 45-60,000 ha p.a., while the amount of recovered forest area was only 22,500 ha p.a., to which reforestation contributed 20,000 ha, forest management 2,000 ha, and through the implementation of new forest plantations 500 ha (INEGI, 2010). Largely, deforestation leads to losses of biodiversity, soil degradation, and environmental degradation. To a lesser extent, it affects the socio-economic conditions of poor people who use the forests to obtain firewood for cooking, and as an additional source of income. In these ways, deforestation has resulted in human migration, prompting people to move out from entirely cut down areas and to travel long distances high into the hills in order to gather wood. According to the Population and Housing Census (2010), 50.3% of the Chiapaneco households use wood or wood-based charcoal for cooking purposes. However, there are marked differences between municipalities, mostly related to the specific rural living conditions and the availability of this resource in the environment. While some have a high percentage of more that 99%, others show only around 2%. Urbanization and population Population growth and urbanization in Chiapas have contributed to land degradation and to reducing the utilization rate of potential arable land because of uncontrolled deforestation (Richter, 2000). Between 1970s, the population size of Chiapas has tripled, while from 1985 to 2007 the forested areas have shrunk by more than a third (see below, Table 6.2 and Table 6.3). The population growth has led to the colonization of forest-covered areas for the settlement of new villages; and has transformed the forestland into grasslands or agricultural land. Forests are also cut down to obtain construction materials for housing and to open new roads. Altogether, human activities in forestlands have affected the forest ecological. Another underlying cause of land degradation is the high incidence of poverty in rural Chiapas. The local farmers’ lack of economic resources leads to inappropriate land uses, including inefficient agricultural practices. Extensive agriculture production The different topographic and climatic conditions of Chiapas are perfect for the production of a variety of industrial and traditional crops (i.e. corn, beans, avocados, coffee, sugar cane, 83 cotton, cacao, palm oil, sesame, and others.), tropical fruits (e.g. papaya, banana, mango, rambután), flowers and other products. Development models geared towards opening up of international market via WTO regulations, industrialization, modernization and a market- driven economy have led to the implementation of export-led agriculture strategies, which tends to displace traditional agriculture (Campbell & Berry, 2003; Alscher, 2008; Ruiz, 2010). In this process, agricultural machinery replaces human labour; the use of synthetic fertilizers, pesticides and genetic modified seeds enhance agricultural production; where commercial crops replace traditional ones; and large forest areas are cut down (O'Brien, 1995; Macias-Samano, 2004; Grajales et al., 2008). The export-oriented agriculture has frequently failed to encourage and maintain sustainable forestry management and has aggravated environmental problems. It has led to the expansion of the agricultural frontier, i.e. the conversion of forest into grasslands for cattle production, resulting in deforestation and soil degradation. The increasing demand for agricultural land affects natural ecosystems, reduces the area and quality of natural spaces, and causes losses of biodiversity (Ochoa & Gonzalez, 2000). The advance of the agricultural frontier also has considerably decreased the composition of different types of forest areas. Dry forest is the most affected one and has been completely destroyed (Table 6.2). Table 6.2: Chiapas land use and cover changes 1975-2005 (ha) Land Use 1975 2000 2005(a) Irrigated agriculture 61,895 108,167.2 116,127 Rain-fed agriculture 742,117 1,172,171.2 1,218,144 Pastureland 1,184,082 1,682,164.2 1,874,976 Deciduous forest 214,560 94,034.20 89,329 Coniferous forest 833,697 652,382.20 305,733 Cloud forest 405,287 401,813.80 265,062 Rainforest 1,405,581 1,231,102.30 706,600 Dry forest 38,510 0 0 Secondary vegetation 2,043,728 1,549,767.3 2,453,220 Urban zones 0 29,788.7 35,273 Other/unproductive* 555,747 563,620.50 422,530 Total 7487,646 7486,474 7,486,994 *Includes water bodies, natural grasslands and scrublands Source: compiled by the author from PEOT, 2005; (a) INEGI, 2010a 84 In the long agricultural history of Chiapas, the traditional system revolves around the milpa system, which is still the system most widely employed by small-scale farmers. However, it faces severe limitations, for various reasons, in particular high population density, intensification of land use, reduction of soil fertility, an asymmetrical relationship between the indigenous and mestizo populations (Pérez-Grovas, 1998), and its increasing replacement by industrial crops. In certain regions of Chiapas, agro-forestry has led to the production of shade-grown coffee, which largely reduces run-off and erosion of the soil, thereby preventing landslides and floods (Richter, 2000). Chiapas is the main coffee producer among Mexico’s twelve coffee producing states15. On land with steep slopes, coffee production helps to prevent soil degradation and supports biodiversity as compared to other cash crops produced in Chiapas, coffee causes less erosion, and even increases the soils’ water holding capacity. It also serves as an important water-catchment for surrounding areas, regulating precipitation and mitigating the effects of natural disasters such as hurricanes, landslides, droughts and floods (Saldaña-Zorilla, 2008). Land degradation in areas under coffee production is less than in those used for other crops. A hectare of coffee loses an average of 0.8 tons of topsoil per year, while a hectare of corn may lose around 20 t. Moreover, coffee farming on slopes can promote the formation of topsoil at an average rate of nine tons per year. (Pérez-Grovas, 1998). A phenomenon observed elsewhere in Mexico is the shifting of coffee farming to cattle ranching, because coffee farming has become more risky due to the fluctuation and decline trend of international coffee prices and the intensification of climatic adverse events. Ranching threatens forest and natural grasslands in tropical environments (Pagiola et al., 2004). Tropical cattle farming in Chiapas causes a reduction in tree coverage, increases green house gas emissions, and leads to soil compaction, loss of soil fertility and reduction of biodiversity. The exact magnitude of these problems depends on the farming system. The Frailesca region of Chiapas known for its cattle ranches with large areas of pasture. In this region, due to improper grazing management, combined with increasing rates of animal stocking, usually there is not sufficient time for natural regeneration of pastures, which increases land degradation (Schroth et al., 2009). Hence, overgrazing degrades and reduces the plant coverage and changes the composition of the plant community, mainly through weed invasion and soil erosion (Pérez-Grovas, 1998). The growth of ranching activities has 15 Other coffee producer states are: Tabasco, Oaxaca, Veracruz, Guerrero, Puebla, Hidalgo, Querétaro, Colima, Jalisco, Nayarit and San Luis de Potosí 85 degraded 47% of the forested areas, because this conventional cattle-ranching system clear trees by logging and by fire (Monjaraz, 2002). In the Soconusco region in the mid-2000s, 10 to 20% of the land was moderately degraded and 5% strongly degraded (i.e. unsuitable for agriculture), due to agriculture and cattle farming activities (PEOT, 2005). Population growth promotes the intensification of farming and ranching activities. Under such conditions, farmers tend to reduce or avoid fallow periods in order to increase the production of crops to meet the increasing local food demand. The cleared forestland quickly loses productivity and after a few years, it is converted into pastureland for cattle (Collier, 1975; Richter, 2000; Campbell & Berry, 2003). In this way, a vicious circle is established that deteriorates the natural resources in a relentless way. In addition, pressure on forests is directly exerted by reduced agricultural productivity and rising timber prices. These factors have fostered a shift from traditional crop production systems to silvopasture-agroforestry systems. However, a lack of technical know-how and assistance about establishing and managing forestry plantations have led to the formation of small-scale plantations, scattered throughout the state without a meaningful economic and ecologic contributions (Macias-Samano, 2004). Forest degradation and land degradation impair the development of farms, thus enhancing out-migration in many areas. In order to increase food production and improve the economic and environmental conditions of rural communities, which strongly depend on renewable resources for livelihood security, soil restoration and conservation, is urgent, especially in Chiapas’ mountainous regions. Table 6.3: Population growth and land use changes in Chiapas Population 1970 1990 2010 Mexico (a) 48,377 81,140 112,336 Chiapas (b) 1,569,053 3,210,496 4,796,580 Pop. Density (people per sq.km) Mexico (a) 26 44 57.3 Chiapas (b) 21.24 43.45 65.32 Chiapas’ land use changes 1985 2007 Land use ha ha Agriculture 1,100,483 2,252.972 Cattle ranching 2,456,360 1,426.563 Forest 817,356 256,438 Source: Compiled from the 1970, 1990 and 2010 Demographic Yearbook; UN Statistics Divisions (2012); b INEGI: Aspectos demográficos sociales 1970, 1990, 2010;INEGI anuario estadistíco de Chiapas; 1985, 2005 86 The Comisión Nacional Forestal (National Forestry Commission) (CONAFOR 2009) states that anthropogenic activities, (i.e. crops and grasslands) severely affected to nearly 50% of Mexico’s vegetation cover. Similarly, the SEMARNAT (2011) points out that practically almost all of the country’s ecosystems have suffered deep transformations, altering hydrological processes, soils, and biodiversity. Despite the severe changes in vegetation, Mexico still conserves 70% of its primary forest areas, 56% of its rainforests, 77% of its scrublands and 55% of its pasturelands. 6.2.3. Natural phenomena and disasters Historically, extreme weather events have had a strong influence on Chiapas’ natural and human environment (Grajales, De la Piedra & Lopez, 2008). However, according to climatic models produced in the early 21st century, these events will become extremer, longer and more complex (Stern, 2006; IPCC, 2007). In the last decade, a large number of natural disasters have affected Chiapas, including hurricanes, tropical storms, floods and droughts, causing severe damage to agriculture with negative impacts especially on small-scale farmers (Saldaña-Zorrila, 2008), the overall economy, as well as the society in general. It has also caused ecological transformations in the region. Severe climate Chiapas is on the pathway of hurricanes and tropical storms coming from the Pacific, and it is located at the border of tectonic plates. As such, its geographical position makes it susceptible to natural disasters (CENAPRED, 2001). Its topographic characteristics also contribute to this susceptibility. Steep slopes facilitate to land degradation in the highlands and along with the torrential tropical rainfalls, the soil particles are washed away. With the formation of secondary soils from material brought from the highlands, the lowland area becomes more productive, with a high content of organic matter in the soil. Yet, while, the runoff of silts and clays from the highlands has created fine soils in the lowlands, the thin soils might quickly deplete the accumulated extra nutrients (Nkonya, 2011). The higher frequency of tropical storms since the early 2000’s is accompanied by a rising occurrence of severe-extreme climatic events. The clear-cutting of rainforest and inappropriate land uses enhanced the impacts and soil erosion (Richter, 2000). In this way, floods and landslides, with high economic and human losses, have reshaped Chiapas’ landscape. 87 Particularly, the intensification of such natural events has undermined the agricultural sector. For example, after hurricane Stan had wreaked havoc in Chiapas in 2005, destroying extensive coffee farms and other farm areas through landslides, the Chiapaneco coffee sector faced a decline in productivity as well as in the cultivated area. In 2005, coffee production had reached 637,000 tons, by 2006, it had dropped to 588,000 tons, and it kept falling until 2008 went to a low of 512.100 tons (a reduction by 20% since 2005). Since then it has slowly recovered, reaching 546.700 tons in 2010 (SIACON, 2010). Hence, this data gives an impression of the extent to which extreme natural events have caused massive destruction to the environment. In extreme cases, they also have caused human casualties and triggered population mobility (CENAPRED, 2006; Grajales et al., 2008) Forest fires Between 1990 and 2008, Mexico lost an average of 31,216 ha of forest area per year due to fires. The danger for fires, either natural or human-induced, increases during the dry season. Fires are among the major causes of deforestation and land degradation, especially in the Frailesca, Istmo-Costa, Sierra Fronteriza and Central region of Chiapas, due to the scant precipitation and high temperatures. The vulnerability of these areas to fires increases, first, by the seasonal arrival of a climatic event known as El Niño Southern Oscillation (ENSO), and second because people light fires. As such, fires represent a major threat to forests and the biodiversity therein. Among the natural events that strike Chiapas, the most severe climatic event caused by ENSO occurred in 1998, it provoked rising temperatures and a declining precipitation. Under these conditions, a large number of fires scorched 198,808 ha of forests (chart 6.1). While between 1990 and 2008, the average area annually affected by fires was only 31,216 ha. Grasslands were the most damaged form of vegetation with 19,203 ha (61.5%), followed by high and medium secondary vegetation with 3,326 ha (10.6%), and secondary pine forest with 2,553 ha (8.2%) (Programa de Acción Ante el Cambio Climático Del Estado de Chiapas PACCC, 2010). Most fires in the region are of anthropogenic origin. Farmers, who burn crop residues, weeds and secondary vegetation in order to prepare the land for the crop season, start them. Chiapas is one of the Mexican states most affected by fires (PEOT, 2005). Chart 6.1: Annual affected area by fires in Chiapas from 1990 6.3. Development characteristics The richness of Chiapas’ biodivers (Ruiz, 2010). With 27.2%, the Lacandona forest of Chiapas is the most resource diverse area of Mexico and the Mesoamerica region, of one of Mexico’s poorest indigenous populations. indigenous; this place Chiapas as the second among languages spoken in the state are Tzeltal (Tseltal), Tzotzil (Tsotsil), Chol (Ch´ol), Zoque Tojolaba and Mam (INEGI, 2011). Cultural factors, along with production and environmental conditions have produced a vast number of small, loosely integrated human settlements, which are grouped in small micro poverty and marginalization, because demographic dispersion communities to receiving marginalization in Chiapas has its origin in the 2005), characterized by social exclusion overall production structure. In this regard, Chiapas’ marginalization index 16 The aim of the marginalization index is to measure the social outcast status, expressing several aspects of social development; using several variables associated with social exclusion, level of education, literacy and health insurance coverage. It allows iden and is divided as very low, low, medium, high and very high. 10 22 14 0 50 100 150 200 19 90 19 91 19 92 in th o u sa n ds ha 88 -2008 Source: PACCC, (2010) of the poorest of the poor ity and culture is contrasted by the poverty but at the same time is 27.2 % of the Chiapaneco population is Mexico’s states. The main indigenous -regions scattered throughout the state. This reduces the likelihood of adequate social support. The structural phenomenon of state’s historical development and difficulties to promote technical progress of its tifying the deprivation suffered by the population in a region or state, 12 13 22 18 48 198 5 30 35 25 70 10 23 10 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 Years of its people -rich and bio- the living space , translates into pattern (PEOT, 16 is very high, at 12 10 20 06 20 07 20 08 89 2.33. Out of the 118 municipalities in Chiapas, 48 have a very high marginalization degree, affecting 29.8% of the population. Another 39 municipalities have a high marginalization index wherein the additional 25.3% of the population is included (CONAPO, 2010). Basically, the areas with very high marginalization correspond to the ethnic territories of Tzeltales and Tzoltiles, located in the Lacandona forest region (PEOT, 2005). In general, 70% of the population is living in poverty and 60.4% of them live in rural areas. Because of the economic development has occurred in such a way as to result in regional inequality. Thus, the Centre and the Soconusco regions account for 60% of the GDP, leaving 40% to the other 13 regions (idem). Chiapas’ participation in the national GDP in 2010 was 1.9%, while, the state’s GDP per capita reached $34.700 MXN ($2,269 USD). These economic characteristics put Chiapas in 17th and 32nd place respectively in relation to the other states. Fundamentally, its development has been based on the primary sector (agriculture, forestry and fishing), and the secondary sector (extraction of oil, gas and generation of electricity) which have generated 35.3% of its GDP (INEGI, 2011). According to the 2010 Population and Housing Census, Chiapas has 4.8 million inhabitants, with a comparatively young population structure, of which 34 % is younger than 15 years (Table 6.4) being the state with the youngest population in Mexico. This demographic feature is due to high fertility rates in the past, as high as 4.4 %. Although it has in the early 21st century, it is still high enough to place Chiapas the second highest fertility rate state in the national context. CONAPO (2010) estimates that the fertility rate is still particularly high in municipalities with a high share of indigenous populations and in rural areas, where women on average have five to seven children. On the other hand, the mortality rate has decreased by 2010 the country’s mortality rate was 4.3 per thousand inhabitants. However, Chiapas has the second highest rate of Mexico with 18.8 deaths per thousand individuals. It becomes even higher in poor rural and indigenous communities, where the lack of health workers, medicines and infrastructure aggravate the problem (idem). The state’s ethnic composition, high marginalization index, and high incidence of poverty make the population particularly vulnerable to natural and economic shocks. Chiapas’ health sector, neglected by the authorities, operates with a low impact on the population. Furthermore, the unequal access to health services deteriorates the poor health and living conditions that are determined by socio-economic, cultural, environmental and infrastructural factors. The PEOT (2005) points out that these problems, along with the dispersion of the population and gender/ethnic discrimination, influence the epidemiological 90 pattern, which is characterized by endemic and infectious diseases such as malaria and dengue, and chronic degenerative diseases such as malnutrition. These diseases are quite common in the region, 43.1% of the population do not have access to public health services, while the national average is only 33.2% (INEGI, 2011). Altogether, the health conditions in Chiapas are the most precarious in the country; the lack of adequate infrastructure for tap water, electricity and drainage widen the problems. At the national level Chiapas ranks 29th regarding the provision of these services. There are also problems in the area of education, due to the lack of human resources and the low quality of the education system. Chiapas is ranked 32nd in the national schooling level, with an average duration of schooling of 6.7 years against the national average of 8.6 (INEGI, 2011). Along these lines, there are red lights in several communities and municipalities. For example in Majastic, a community in the municipality of Sabanilla with 70% of ethnic population, the average number of school years is less than one year (PEOT, 2005). Even though illiteracy rates have declined since the 1990’s, Chiapas still holds the first place nationally. In 2010, almost 18% of the population was illiterate (INEGI, 2011). Table 6.4: Chiapas’ demographic and poverty indicators 2010 Demographic and poverty indicators National Chiapas Rank* Pop. in settlements greater than 2500 inhabitants (%) 76.8 48.7 32 Pop. younger than 15 years (%) 28.9 34.3 1 Pop. with indigenous language (%) 6.7 27.2 3 Pop. illiterate (%) 6.9 17.8 1 Pop. without access to health care (%) 33.2 43.1 28 Fertility rate 2.0 2.2 2 Birth rate 17.5 20.4 1 Mortality rate 5.0 4.3 26 Child mortality 13.7 18.8 2 Average level of schooling (years) 8.6 6.7 32 Illiteracy (pop. 15 years +) (%) 6.9 17.8 1 Unemployment rate 5.6 2.4 32 Average household size 3.9 4.4 1 Households with tap water 88.2 73.5 29 Households with electricity 97.8 95.9 29 Migration rate -1.3 30 * Between 32 states Source: compiled by the author using data from INEGI, 2011 91 Health and education services are fundamental to improve life quality; they are of paramount importance in the formation of human resources and human development. Using the Human Development Index (HDI) to reveal the welfare inequalities in relation to other states, we can observe that Chiapas had a HDI of 0.73 (medium) in 2005, which was below the national average of 0.82. Its Gender-related Development Index (GDI) was 0.71 and the Gender Empowerment Measure (GEM) 0.48, both indexes were lower than the national average as well. Due to its low HDI, GDI and GEM indexes Chiapas ranks 32nd in the national context (UNDP, 2005). Chiapas is a state of extremes, where abundant natural resources, pronounced extreme poverty, slow economic growth, marginalization, poor health and low levels of education coexist. It is the place where most of the poorest of the poor people of Mexico reside. Additionally to the history of repression and exploitation, the worsening of economic, social and environmental conditions promoted by NAFTA, have triggered events leading to indigenous armed struggle and to the formation of the Zapatista Army of National Liberation EZLN (Villafuerte & Garcia, 2004 & 2006), which rose up in open rebellion in 1994 (Rosset & Cunningham, 1994; Harvey, 1998; and Richter, 2000). 6.4. An overview of neoliberal globalization in Mexico 6.4.1. Development strategies Prior the ratification of the North American Free Trade Agreement (NAFTA) by the governments of Mexico, Canada and the United States in 1994, Mexico’s socioeconomic development was based on the “Import-Substituting Industrialization” (ISI) model. It was pursued from the 1930’s until the 1980’s, with three sub-periods: (1) light industrialization during the 1930’s and the 1940’s. (2) Heavy industrialization in the 1950’s and the 1960’s, and (3) an industrialization crisis stemming from the economic and politic turmoil of the 1970’s and 1980’s, which led to the replacement of the ISI strategy by a new economic model (De la Garza, 2006). The export-substitution industrialization (ESI) model developed free trade multilateral agreement such as NAFTA. In 1983, at the beginning of Miguel de la Madrid’s presidential term, Mexico’s economy started the transition from ISI to neoliberalism, thus becoming one of the most opened economies in the world. Founded on the privatization of state enterprises, reorganization of public finances and market liberalization (Villafuerte & Garcia, 2006; Lopez, 2007), the structural change reached its milestone on January 1, 1994, with the signing of the NAFTA in the last governing year of 92 Carlos Salinas de Gortari. The proponents and advocates of NAFTA argued that it would be beneficial for both nations’ economic and environmental interest. The US markets would be opened to Mexican products, allowing Mexico to be more competitive due to its lower environmental standards and wages, on the other hand, the Mexican economy would benefit through foreign direct investment (FDI) (DeBellevue et al., 1994). According to DeBellevue et al., (1994: 55): “Any nation that regulates for environmental preservation will impose costs on its corporations, which they will then pass on to consumers in the form of higher prices. So under a system of free trade, environmentally regulating nations who are forced to ‘internalized externalities’ are less efficient and subsequently less competitive. This will jeopardize a regulating nation’s domestic economy due to its inability to compete with similar products from nations who do not require costs internalization.” Additionally, NAFTA promised to bring about significant changes to the Mexican economy through the elimination of trade barriers, either immediately or over time, depending on the commodity (Abler & Pick, 1993; McKeown, 1994; DeBellevue et al., 1994). By liberalizing trade, corporations could compete in a broader free market to sell their products where competitive success and profits always come first, under the assumption that the whole world would prosper (DeBellevue et al., 1994). In contrast, the Trade and Development Report observed “total liberalization of financial and capital markets without microeconomic stability and tight prudential regulations in place, (...) in few cases, financial liberalization has ended in a crash, (...) crash is not the outcome of a free-trade agreement, the establishment of an unsustainable economic base could result in a less developed Mexican economy” (UNCTAD, 1991:18). Delgado-Wise (2006) argues that since the ratification of NAFTA Mexico’s economy has undergone significant changes through the restructuration of work process in both Mexico and the USA, strongly affecting the labour force and increasing the number of migrants towards the USA. Additionally, “since the NAFTA came into effect the social and economic division lines within Mexico (North-South; urban-rural; rich-poor; mestizo/majority- indigenous/minority, etc.) became even more pronounced” (EACH-FOR, 2009). Furthermore, NAFTA’s apparently positive impact on the macroeconomic level has been accompanied by negative impacts on the microeconomic level, severely affecting many Mexican farmers (Mace & Bélanger, 2004; EACH-FOR, 2009) through the removal of agricultural subsidies and production and commercialization support (Villafuerte& Garcia, 93 2006). Moreover, the “free market” ideology enhances labour exploitation and environmental degradation (Delgado-Wise & Márquez, 2010). 6.4.2. Repercussions for the environment and agriculture NAFTA became the first free trade agreement to include supplementary agreements on environment (North American Agreement on Environmental Cooperation NAAEC), and labour (North American Agreement on Labour Cooperation NAALC) (Altmann, 2002). Nevertheless, NAFTA and its parallel environmental accord remain the source of intense debate; documented in hundreds of studies (DeBellevue et al., 1994, De la Garza, 2006; Villafuerte & Garcia, 2006; Lopez, 2007, Delgado-Wise 2006) which have produced an array of contradictory statements about the possible impacts and outcomes of the NAFTA process. The bottom line is that NAFTA is responsible for weakening the United States’ environmental standards and especially for putting Mexico at severe environmental risk. NAFTA’s policies influence areas of domestic concern such as food security, agriculture, and natural conservation, through environmental policy changes (Vaughan, 2004). DeBellevue et al., (1994: 57) in their report “The North American Free Trade Agreement: an ecological-economic synthesis for the United States and Mexico” explain that environmental degradation acts as a potential constraint for economic development, because the loss of natural assets. Free trade agreements can motivate nations with lower industrial and economic development, to use “environmental deregulation, resource exploitation and labour exploitation”, in order to compete with more developed nations and to catch up with their higher growth. Thus: “Mexican firms will be forced to become suppliers of raw materials to more efficient firms in the United States, depleting the Mexican natural resources and decreasing the level of profits that could be gained from the value-added of more processed products. The consequences that might stem from this type of economic system include irresponsible depletion of natural resources and irreparable environmental damage” (DeBellevue et al., 1994: 57). Wise and Gallagher (2002) found that since transition from ISI to neoliberalism, between 1983 until 1999, the real spending on the environment in Mexico declined by 45%. Additionally, soil erosion increased by 89%, municipal solid waste by 180% and air pollution by 97%. Indeed, these environmental damages add up to a cost equal to 10% of the GDP, greater than the 2.6% gained by economic growth from trade. These data contradict to the 94 NAFTA’s dogma of having an upward harmonization of environmental standards and performance through economic integration. The expansion of manufacturing exports (maquiladora-industry) and related activities did not significantly promote economic growth within Mexico (Delgado-Wise & Márquez, 2007a); instead, the industrial pollutants have been leaking into land and water due to the lack of treatment. From the 8 million tons of hazardous waste produced by the nearly three thousand maquiladoras, only 12% receive adequate treatment. Another 30% are returned to the country of origin for further treatment, and the remaining 58% are not undergoing any type of waste treatment (Wise & Gallagher, 2002) but instead are illegally dumped onto public or private land in most of the cases. NAFTA did nothing to strengthen Mexico’s environmental protection programs; quite the opposite, between 1994 to 1999 the total spending on environmental protection decreased against the pre-NAFTA period 1983 to 1993 (INEGI, 2000). In 2006, the estimated cost of environmental damage was 8.8% of the GDP ($71.3 million USD), while the amount spent on environmental protection by the Mexican government was only 0.6% of GDP ($4.6 million USD). In order to keep environmental degradation in check, the government would need to invest 14.4 times more than it has been spending in 2008 (INEGI, 2008). Market-led economic growth has increased the inequality between Mexico’s northern and southern regions. While the large northern urban centres have achieved economic growth fostered by the maquiladora industry, the southern states have had to face a different reality, as they were not able to compete with the subsidized U.S. agricultural products of the US agro-business sector (EACH-FOR, 2009). As a result agricultural prices in Mexico dropped, thus threatening farmers’ livelihoods. Notwithstanding, agriculture exports have been the central pillar of Chiapas’ economic development for more than a century (Santacruz de Leon & Perez, 2009). It bottomed out when some subsidies and other support programs for agricultural production and commercialization where reduced or removed as part of the neoliberal process. Furthermore, changes in agrarian polices as a consequence of NAFTA stopped the land redistribution, provoking land seizures and the rise of the EZLN (Villafuerte & Garcia, 2006). NAFTA induced a restructuring of productive agricultural areas, as evidenced by increasing corn imports from the US, which compelled many small-scale corn farmers out of the market (Nadal, 2000). In 2009, around 7.7 million ha from the 21.8 million ha of total cultivated area 95 were designated to corn (SIACON, 2010). In Chiapas, corn production represents the main source of income for 125,500 of the state’s 364,400 farms (34.4%) (INEGI, 2007). However, since the withdrawal of subsidies, farmers have experienced a restructuring process which has fostered a progressive decrease in corn production (Villafuerte & Garcia, 2006), the number of corn producers, and the area under corn. In 1992 Chiapas produced 10.3% of the national corn production, in 2004 had declined to 8.9 % (INEGI, 2007). There are other reasons, associated with neoliberal reforms that further explain the fall of the corn sector: the dismantling of regulatory institutions and development institutions such as the Compañia Nacional de Subsistencias Populares (National Company of Popular Subsistence CONASUPO) and the Banco Nacional de Crédito Rural (National Rural Credit Bank BANRURAL). In 2005, the price of one ton of corn from the U.S., arriving to the port of Veracruz was $113.5 USD, while that for the corn produced in Chiapas was $140 USD (Lopez, 2007). Moreover, this uneven competition led to rising unemployment, because it destroyed more jobs than were created in the export sector. This has prompted the abandonment of agricultural activities, as former corn producers migrated to urban areas. The flooding of the Mexican market with cheap U.S. corn, facilitated by free trade in agricultural products, has had far-reaching destructive implications for large numbers of smallholder farms, which have not been compensated by advantages in other subsectors of Mexico’s agriculture namely commercial agriculture (Ruiz, 2010). Indeed, NAFTA provides net benefits for the U.S. agricultural sector alone, whereat in Mexico, it has exacerbated poverty and spurred migration from rural areas towards cities, especially along the country’s northern border where employment positions are offered by the maquiladoras. NAFTA has triggered unemployment, enhanced the precarization of labour force and urged people to migrate to regional or international destinations (Delgado-Wise, 2006). Chiapas’s small-scale coffee producers have also been affected by neoliberal globalization due to the removal of subsidies to coffee. Some were not able to sustain their livelihoods and hence, forcing them to either reorient their farms towards subsistence agriculture (Gonzalez, 2010) or shift towards shade-grown coffee production to maintain their households (Richter, 2000). NAFTA has also contributed to reduce the share of agriculture in the GDP, leading to a greater impoverishment of rural areas (Villafuerte & Garcia, 2006). 96 Wise and Gallagher (2002) summarize the destructive impacts and implications of trade under NAFTA on agriculture, society and environment in both Mexico and the USA: “The surge in U.S. exports has put pressure on poor corn farmers in Mexico. This has caused not only increased poverty and out-migration, but also threatens the rich stock of plant biodiversity cultivated by Mexico's traditional farmers and relied on as a public good by the world's crop breeders. [..] The rise in U.S. corn production has provided a stimulus to some of the most environmentally destructive agricultural practices in the United States. Corn is very chemical-intensive, both in terms of fertilizers and pesticides. Recent expansions of corn production have taken place in some of the drier states, necessitating irrigation at unsustainable levels. It has also encouraged the recent rise in the cultivation of genetically modified corn, as the product is particularly designed to resist pests that are more prevalent in dry conditions. In effect, the U.S. is serving as a ‘pollution haven’ for corn, with more environmentally destructive U.S. practices supplanting more sustainable practices in Mexico.” 97 7. Chiapas: reshaping of migratory flows 7.1. Features of the southern Mexican-Guatemalan border The border between Mexico and Guatemala has exhibited a remarkably dynamic relationship in terms of culture, economy, and labour between the region’s inhabitants, and indigenous groups (Choles, Mam and Chuj) (Castillo, 2003). Historically, this region has received little attention from the government and it has been kept away from the central Mexican economic, political, social and cultural dynamics. Interactions across the border are complex and have a long history, with human mobility that even goes back to before the demarcation of a geopolitical border in the 16th century (Chavez, 2009). Nonetheless, since the beginning of mass migration from Central America, triggered by the armed conflicts in the 1980s and by economic disparities, the forgotten Mexican-Guatemalan border has attracted significant attention from the governments of Mexico, various Central American states and the United States (Angeles, 2004). The border measures 871 km, running along the states of Chiapas, Campeche, Tabasco and Quintana Roo, in Mexico, and the Guatemalan states of San Marcos, Huehuetenango, El Petén, and El Quiche. Chiapas shares a 654 km borderline with Guatemala. It is an important gate for legal and illegal migrants, mostly coming from Central America, and to a lesser extent from South America. Since, migrants from other regions such as the Middle East and Asia have joined the traditional migration flows to Mexico, either as a final destination or as a transit-corridor towards the US. Changes to migration policies after the September 11th attacks have changed the dynamics of migration in terms of directionality, temporality, selectivity, costs, risks and composition. In this context, border security and migration policies implemented between 2001 to 2003 by Mexico, Guatemala and Belize (South Border Plan), redirected the routes used by international and transit migrants passing across the southern border (Artola, 2006). Furthermore, corruption and institutional porosity at the border crossings have facilitated the population dynamics (Casillas, 2011), and intensified human right violations. The migration dynamics along the Mexican-Guatemalan border have converted Chiapas into a place where different human displacements can be studied (Villafuerte, 2008; Villafuerte & Garcia, 2008; Anguiano, 2008b). The evolution of the Chiapaneco labour market opened job opportunities for natives, internal, and international migrants. The most remarkable population movement in the 20th century was the Guatemalan cross-border mobility to the Soconusco region. Temporary Guatemalan immigration increased because of the vast 98 agricultural-labour opportunities created by coffee plantations. Although this sector suffered a crisis since 1980, it continued to demand additional temporary workers (Villafuerte & Garcia, 2008). Lately, more attention has been paid to migration in Chiapas, especially since out-migration to other Mexican states and to the United States has increased. The new “migration explosion” involves 115 of the 118 municipalities in Chiapas (Anguiano, 2008b). Compared to other states, Chiapas joined the international Mexican migration flows to the US in a slower and later process than other states. 7.2. Migration flows of Chiapanecos and the emergence of new destinations Chiapas has undergone deep transformations in terms of migration17. The Chiapaneco population have participated in the formation of regional and international migratory patterns that intensified in the last decades. Causes for the intensification include the coffee crisis, the struggle for land, the indigenous crisis, the North American Free Trade Agreement (NAFTA), the Zapatista uprising, and natural disasters. This chapter describes the different migration dynamics that occur in Chiapas and the different migratory dynamics that are still present on its border with Guatemala. 7.2.1. Intra migration (rural-rural) The mobility of indigenous populations within the Mesoamerican region, extending from Mexico City to Costa Rica, existed before the Spanish colonization of the Americas. After the addition of Chiapas to Mexican territory in 1824, the indigenous displacements in the state were limited to coffee regions (Soconusco and Sierra Madre), sugar regions (central valley), maize regions (Frailesca), and forestry regions (Selva Lacandona) (Cruz, 2007; Angeles, 2004; Anguiano 2008a). Indigenous migration from Los Altos region towards the coffee plantations in the Soconusco region during the coffee boom (1880-1930). Firstly, Germans, North Americans, Spaniards, French, Swiss, Italians and Chinese18 arrived to the Soconusco region, because of migration policies promoted by Porfirio Díaz19. These 17 Juan Pedro Viquiera (2008) depicts clearly the migratory mobility in the region of Chiapas since the beginning of the XV century until the end of the XX century in his article “Indios y Ladinos, Arraigados y Migrantes en Chiapas: Un Esbozo de Historia Demográfica de Larga Duración”. 18 Chinese migrants did not become owners of agricultural properties in the region; they instead offered their labour force in the construction of the railroad. 19 Foreign investment was the base for the development of Mexico during the Porfiriato. It allowed foreign entrepreneurs and investors to have access to natural resources that could not be exploited by Mexicans due to 99 international immigrants became the owners of large tracts of land, which later were transformed into coffee farms (Santacruz & Perez, 2009). In this 50-year period, the development of coffee plantations changed Chiapas’ migration flows and demographic characteristics of Chiapas (Viquiera, 2008). Secondly, thousands of indigenous and peasant migrants from Los Altos region, particularly Tzeltales and Tzotziles, as well as Guatemalan indigenous people were pulled by the vast employment offered by the coffee plantations. Coffee plantations emerged after the strengthening of the “finca system” with German, North American and Spanish capital investments in order to export coffee (Angulo, 2008). The coffee boom led to the formation of new human settlements in the highlands and low laying fertile lands of Soconusco (near coffee areas), to provide a constant labour force and to cover the highest demand for coffee workers during harvesting season. Whilst the number of coffee plantations increased and the development of other agricultural crops continued (i.e. sugar cane, tobacco, banana and horticulture) (Ordóñez, 1996), the demand for labour increased as well. At the end of the 19th century, when Mexico started exporting coffee, about 20,000 indigenous workers were brought to Soconusco by labour recruiters (enganchadores) and then taken to the coffee farms on foot from Los Altos region. Thousands of them ended up being bound labourers with debts that they could not repay to the enganchadores; even though their work was invariable greater than the sum of money borrowed. Later, in 1936, with the formation of the Coffee Plantations Workers Union (Sindicato de Trabajadores de las Fincas Cafetaleras), the enganchador system was eliminated, minimum wages/salaries were established and regulations to protected the worker’s rights were enforced (Angulo, 2008). Indigenous migration towards the Jungle region of Chiapas (Lacandona forest) from the 1930’s to1950’s. The region known as Los Altos not provided indigenous migrants to the Soconusco’s coffee plantations only, but also to the Lacandona forest in order to colonize new territories. The opening of roads to connect the Lacandona forest with the main surrounding cities (Ocosingo, Altamirano, Las Margaritas, and Comitán) by timber companies, oil companies and the Mexican government propelled regional migration (Angulo, 2008). The labour force of the indigenous people from Los Altos was required for the forestry industry. In this way, it was internal civil wars. During his dictatorship, industry and export agriculture of tropical products grew significantly (Internal Displacement Monitoring centre, 2009). 100 reduced from 68,000 inhabitants in 1880 to 58,000 in 1930. Due to the hard working conditions in the forestry sector, thousands of them sought employment on coffee plantations, ranches and corn farms (Viquiera, 2008). In the first phase of land distribution from the 1920’s to 1940’s, remote rural areas saw an increase in the number of migrants that arrived with a dream of owning a piece of land (Viquiera, 2008). Later in the 1950’s the Mexican government, under pressure by the rural populations’ demand for land and for the extension of the agricultural frontier in more realistic terms, developed agrarian policies to promote the colonization of the Mexican tropics. Chiapas and other southern states have large tropical areas, to which landless indigenous populations were directed through agrarian reform. “Chiapas followed the national example of land redistribution, combining the break-up of latifundia with the opening or ‘colonization’ of national lands, particularly at the expanses of the Lacandona Jungle in the eastern part of the state. The hired hands on the coffee fincas brought coffee beans back to their pockets, planted them at home, in the Highlands and northern parts of the state (when the altitude was not to high), and carried the coffee with them when they migrated to the lowland-mountain rainforests” (Pérez-Grovas, Cervantes & Burnstein, 2001: 2) Moreover, hydraulic power became a valuable source of energy, thus the Mexican government built several hydroelectric plants in Chiapas, including the Angostura and Chiocasén in the territory of Tzotziles and Tzeltales. Many indigenous inhabitants, whose lands were expropriated by the government, moved to the Lacandona forest (Angulo, 2008). Consequently, due to the construction of several large hydroelectric dams in Chiapas, young workers20 from the rural highlands of Chiapas migrated temporary as low-wage construction labour force, abandoning their traditional agricultural activities (Ochoa & Gonzalez, 2000). From 1930 to 2000, the average population growth in the Lacandona forest escalated from 1.43 to 2.90%. The population of this area represented 5.70% of the entire population of Chiapas in 1930; this share increased to 7.38% in 2000. Contrarily, the indigenous migrant sending region of Los Altos went through a depopulation phase. The region also underwent a labour transition, from providing labour force to other regions of Chiapas to work in their own farmlands in order to secure the indigenous workers livelihoods. Therefore, foreign 20“Thanks to their labour inexperience, strength and vitality, young people make up the largest share of the labour population working under undecent and precarious labour conditions for coping with the needs of the majority of their families “(Cruz & Cruz, 2009:133). labourers from Guatemala provided additional labour force beginning of the 1990’s when the marginalized rural Lacandona forest began to organize politically and economically in order to survive. Without the migration from Los Altos toward the Lacandona forest, the formation of the EZLN would not have been possible. The migration flows that occurred at the end of the 19 beginning of the 20th century were the most important changes faced by Chiapas in its long history. Map 7.1: Migration from the Los Altos towards the Soconusco and Lacandona forest regions Source: adapted by the author from economic regions of Chiapas, CEIEG, 20 101 (Angulo, 2008) -indigenous people living in the . It was in the th and the 12 102 7.3. Rural Urban migration The fast population growth experienced by the Soconusco and the Lacandona forest regions, together with the addition of temporary Guatemalan indigenous labour migrants and Guatemalan refugees from the internal civil war (1960-1996), exerted pressure on the labour market for the local indigenous workers from Los Altos and the Sierra Madre21 (Angulo, 2008). To make matters worse, in the 1970’s thousands of indigenous workers were laid off. In this decade many coffee, tobacco, banana, and maize plantations turned to cattle production, a development fostered by the subsidies provided by the federal Mexican government for reactivating the livestock industry. This system did not bring socio-economic development to the southern Mexican states; on the contrary, it increased inequalities among Mexican regions (Lopez, 2007). These economic measures created a crisis for the indigenous populations (Viquiera, 2008). By the 1980’s the entire rural population faced a severe agricultural crisis that affected corn and coffee crops, as well as cattle farming, because of national policies that dismantled governmental support for the agricultural sector and led to a fall in agricultural prices (Villafuerte, 2007). After the insertion of Chiapas ─the biggest coffee producer nationally─ in the neoliberal economic globalization by the reinforcement of export-oriented agriculture, the annual Chiapaneco coffee income (1994 to 2004) oscillated between $ 100-350 million USD. This abrupt fluctuation was brought about by the deregulation of the international markets, the dismantling of national governmental institutions that supported the national agricultural production such as Instituto Mexicano del Café (the Mexican Coffee Institute INMECAFE). The consequences for the Mexican coffee sector (and therefore mainly Chiapas) was the loss of presence in the global market: from a share of 5% in 1980 it dropped to 3% in 2004 (Perez-Grovas et al., 2001; Lopez, 2007). “The coffee areas that in the past lived certain economic growth nowadays have joined the population exodus. The new migrants do not come only from the arid mixteca or from the mountain region of Guerrero, today they come from the once proud Soconusco coffee centre” (Bartra, 2006: 46) Hence, to cope with the harsh economic environment of that time, the migration outflow changed, from rural-rural to rural-urban/industrialized centres. This new human mobility was driven by the need to find temporary employment in nearby cities, such as San Cristobal de 21 The Sierra Madre is a territory of transit migration for indigenous workers from Los Altos de Chiapas to the Soconusco region. It is also a space for the reproduction of indigenous labour, feeding the labour demand created by the coffee plantations located in the fertile lands of the Soconusco region (Villafuerte & Garcia, 2004). 103 las Casas (Angulo, 2008). Although, San Cristobal de las Casas’ population decreased from the 1930’s to the 1940’s, due to out-migration to rural areas, motivated by the land redistribution, its population began to grow again in the 1970’s (Viquiera, 2008). In fact, three quarters of the 45 villages of San Cristobal de las Casas were established after 1974, and the rest of the region’s human settlements appeared after the Zapatista uprising in 1994 (Angulo, 2008). According to Cruz (2007), armed conflict was the main cause of displacement of Chiapaneco indigenous people, in particular, the clash between the EZLN and the Mexican Army in 1994; the counterinsurgency operations carried out by the army that killed many indigenous in 1995; and the killing of indigenous people by paramilitary groups in 1997. In recent decades, the cities of Tuxtla Gutierrez, Tapachula, and Comitán de Domínguez with medium and low degree of marginalization have become new urban-centre- destinations for migrants. Other places with high and very high degrees of marginalization have experienced a very slow and even negative population growth, because out-migration has become a key strategy for rural families responding to negative economic changes (Table 7.1). Rural migrants were incorporated into urban labour markets in the informal sector, domestic service, the elaboration of handicrafts, and construction in the 1970’s. By the 1990’s, with a better level of organization, they started to take part in public transportation, local markets, and tourism. Religious persecution was another notable factor that drove migrants out of indigenous communities in Los Altos ─the Chamulas─ to migrate to urban centres (i.e. San Cristobal de las Casas) (Angulo, 2008). The already multi-causal internal migration has other factors that contribute to its complexity, including intra-communal conflicts, human rights violations, political clashes, and the current drug war. Indeed, these factors have forced many to flee22 in order to protect their families. Because of all of these factors, rural-urban migration continues. Rural migrants settle on the outskirts of cities, creating a form of ‘rural- urbanization’. There is little doubt that rural-urban migration leads to the aging and diminishment of rural communities and in some cases it contributes to the feminization of agriculture. 22 This type of migrant is known under the term of internally displaced Persons (IDPs). The Mexican Human Right Diagnosis of 2003 concluded that “IDPs are among the most unprotected groups because they generally lack means of subsistence and legal mechanisms to claim and enjoy their rights. Given their invisibility, they face a high risk of abuse, exploitation and threats to their life” (UN-OHCHR, 2003: 170) 104 Table 7.1: Population change of main urban centres and highly marginalized municipalities of Chiapas Total population Municipalities 1970 1990 2010 Chiapas state 1,569,053 3,203,428 4,741,518 San Cristóbal de las Casas 32,833 88,906 181,666 Tuxtla Gutiérrez 70,999 294,906 547,527 Tapachula 108,056 221,865 320,034 Comitan de Domínguez 39,006 78,617 139,720 Ocosingo 34,356 120,808 182,160 Reforma 6,763 30,650 40,537 Negative and slow population growth of municipalities with very a high degree of marginalization Francisco León 6,171 3,903 7,000 Chapultenango 5,654 5,549 7,332 Mazapa de Madero 5,355 7,488 7,790 Sunuapa 1,630 1,739 2,217 Source: compiled by the author from Population and Living Census (INEGI) 1970; 1990; and 2010 7.4. Internal migration In the past, the directionality of migration in Chiapas was from Los Altos region to the Soconusco region and the Lacandona forest. Over the past few decades, Chiapas has experienced a change in its migration dynamics, from rural-urban to urban-urban, resulting in an increase in the number of people living in industrial and large cities (Cruz, 2008). The 2010 Population and Living Census states that 3.16% of Chiapas’ population participates in intrastate migration, with diverse patterns. From a different angle, the following significant factors have forced Chiapanecos to look for better opportunities elsewhere in Mexico (a) Chiapas ranks among the least developed states in Mexico, with a large indigenous population living below the poverty line; (b) the positive development of other Mexican states in the last decades; (c) the arrival of Guatemalan immigrants, fleeing from poverty and conflict in Guatemala, has produced a malleable and desperate local labour force; (d) political and social unrest; and (e) the agricultural crisis, manifest in a decrease in productivity and less employment opportunities. All of these factors have shaped migration patterns from and in Chiapas, which can be classified according to a tripartite scheme: short, traditional and long-distance patterns (Villafuerte & Garcia, 2004; Anguiano, 2008a). 105 7.4.1. Short migration pattern “South to South” The petrochemical industries ─oil extraction and refinery plants─ located in the Gulf of Mexico in the states of Campeche, Veracruz and Tabasco have been a labour force magnet for workers from Chiapas and Central America since their emergence in the 1970’s (Anguiano, 2008a). Furthermore, these operations had consequences on family structure and land-use in the rural areas of Chiapas (Ochoa & Gonzalez, 2000). Particularly, the indigenous Maya, Tzoque, Chol, and Tzeltal Chiapanecos began migrating to the surrounding states for the first time in the mid-1970’s to work in the oil industry. The increased extraction of oil prompted a rapid growth of cities and created non-oil industry jobs associated with construction and janitorial services in the cities. Ever since, these jobs have been set aside for non-state residents, such as Chiapanecos and Central Americans (Angulo, 2008). In the 1980’s the mushrooming of touristic activities in the state of Quintana Roo led to a “touristic boom”, especially in the ritzy area of the Rivera Maya, where labour force demand increased rapidly. The growth of commercial, construction, and service sectors in the urban centres of Cancun, Chetumal, Campeche, and Villahermosa attracted workers from the neighbouring state of Chiapas and Guatemala (Anguiano, 2008a; 2009). The northern municipalities of San Cristobal de las Casas, Simojovel, Chilón, and Ocosingo expelled a significant number of migrants towards Quintana Roo (Villafuerte & Garcia, 2006). These municipalities are the most marginalized regions of Chiapas and home to Tseltal, Tsotsil, Tojolabal, Chol, and Zoque indigenous communities. From 2000 to 2005, 88% of the nearly 13,000 Chiapaneco migrants (Table 7.2) that arrived to Quintana Roo were concentrated in the cities of Cancun and Playa del Carmen (Anguiano, 2008b). Migration from Chiapas to the Rivera Maya occurs irregularly, only a few migrants have settled permanently. The temporary migrants, mostly young single males, leave their families back home for a short-term of three to six months, and sometimes their mobility to the Rivera Maya occurs more than once a year (Villafuerte & Garcia, 2006). The same irregularity did not allow migration networks to get established in the Rivera Maya, which makes the individual migrant, more vulnerable to exploitation by those who control the labour markets in the area. Contractors known as “piratas”, who offer a place to live, a steady job in the construction sector, and higher income compared to farming businesses, ejidos, or cities, hire Chiapaneco indigenous migrants. However, they have realized that not all of the promises offered are true. Chiapaneco indigenous migrants working in tourism and the construction 106 industry are subject to very similar precarious working and living conditions as the undocumented Guatemalan workers ─Central American migrants─ (Angulo, 2008). 7.4.2. Traditional migration pattern “South to Centre” Traditionally, the migration outflow of Chiapaneco migrants to the State of Mexico and to Mexico City is find better education and living opportunities. For instance, migrants from different communities of the municipality of Union Juarez often relocated to the middle-class neighbourhood of Ciudad Azteca in the municipality of Maztepec de Morelos in Mexico State (Santacruz and Perez, 2009). 7.4.3. Long migration pattern “South to North” Nowadays, the young Chiapaneco labour migrants are undertaking longer journeys to find employment in the maquiladora industry and agriculture fields. This distant labour markets are in Tijuana and Mexicali in the state of Baja California (Villafuerte & Garcia, 2004), as well as Nogales and Agua Prieta in the state of Sonora, (Santacruz, Perez & Palacios, 2007; Anguiano, 2008b). Between 2000 and 2005, nearly 16,000 Chiapaneco migrants comprised the third biggest group of migrants that arrived to Baja California. Tijuana was the main host city for 8 out 10 Chiapaneco migrants, who chose this city for settling (Anguiano, 2008b). Migration to Mexico’s northernmost state of Baja California is mostly undertaken with the intention of crossing the Mexican-USA border. Job losses in Tijuana’s maquiladora sector23, the fortification of U.S. immigration security along the border, raids and deportations in the US, and cultural-environmental differences are leading Chiapanecos to migrate to the neighbouring states of Baja California. 23 The incessant drug war, kidnappings, increasing violence and economic downturn cost Tijuana tens of thousands of jobs and shuttered hundreds of businesses. According to Angeles-Villarreal et al. (2012), Tijuana’s employment decreased from 174,105 in July 2007 to 136,957 in December 2009, a loss of 37,148 jobs or 21 %t of total jobs. In general the maquiladora industry employment decreased 14 % from 2007 to 2009, it lost 268,647 jobs. 107 Table 7.2: Migrants from Chiapas to other Mexican states 2000-2005 Directionality State % # of migrants Short Quintana Roo 13.9 12,510 Tabasco 6.3 5,670 Veracruz 5.4 4,860 Oaxaca 4.5 4,050 Traditional Mexico state 6.8 6,120 Mexico City 5.7 5,130 Long Baja California 17.5 15,750 Sub-Total 60.1 54,090 Other states 39.9 35,910 Total 100 90,000 Source: compiled by the author from INEGI, II Population and Living Census final results 2005 7.5. Environmentally-forced migration Other external factors, both natural and human-induced, have triggered migration in Chiapas (Villafuerte & Garcia, 2006). Due to its geographical location between the Pacific Ocean and the Gulf of Mexico, where tropical storms, cyclones, and hurricanes strike continuously, environment and population have suffered from severe weather events, which are intensifying with time. In this regard, large human displacements occurred after hurricane Diana 1990, Cesar-Douglas 1996, Pauline 1997, Mitch 1998, and the double-strike of Stan and Wilma in 2005. Sudden onset natural calamities and slow onset hazards determine a new cycle for the migration movement in the southern Chiapaneco state and Central American region. In October 1998, hurricane Mitch hit the Atlantic coast of the southern states of Yucatan, Campeche and Quintana Roo, subsequently crossing through Chiapas. Hurricane Stan hit Chiapas in October 2005; its destructive effects were stronger than Hurricane Mitch. Thousands of hectares of crops were affected in the coastal area by floods and in the highlands by landslides and mudslides. The coffee sector, represented by the Asociación Mexicana de Exportadores de Café (Mexican Association of Coffee Exporters –AMEC), reported that Stan resulted in the loss of 46,800 tons of coffee with a value of $1.8 million USD; this loss corresponded to 50% of the annual coffee production. From the total 228,000 hectares of coffee, between 150,000 to 170,000 hectares were affected, mostly in the municipalities of Tapachula, Huixtla, Jaltenango and Motozintla. Furthermore, the municipality of Suchiate, the biggest producer of Bananas in Chiapas with 5,000 hectares, was the most severely affected. Additionally, there was a negative impact on the production of corn; all these areas amounted to 75,000 to 80,000 tons (Villafuerte & 108 Garcia, 2006). In general, it was estimated that around 208,000 hectares of crops were damaged and more than 100,000 farmers were affected by hurricane Stan (CENAPRED- ECLAC, 2006). It bears repeating, in this connection, that Chiapas is a very vulnerable state. The state’s topographical, climatological and sociological characteristics, as well as the lack of urban planning, the overall population growth, and hazardous natural events have put the population in this vulnerable situation. It is estimated that hurricane Stan caused more than US$1.4 billion worth of damage in Chiapas (Table 7.3), including the destruction of agriculture (crops, livestock, forestry and aquaculture), landscapes (landslides, mudslides and floods),infrastructure (houses, tourism, communications, transport, health etc.) in 41 municipalities. Deforestation and soil degradation in hillside regions made it more susceptible to damage (EACH-FOR, 2009). Although casualty statistics are difficult to determine, the government of Mexico calculates a death toll of 98 in the southern Mexican states. The heavy rains produced by hurricane Stan over parts of southeast Mexico and Central America led to the flooding of rivers, dams, reservoirs and irrigation channels. Pasch and Roberts (2006) estimate that there were 2000 fatalities in Mexico and Central America; because Guatemala was particularly hard hit, most of the casualties occurred in this country. Table 7.3: Cost of damages caused by hurricane Stan in million USD States Casualties Direct damage Indirect damage Total Puebla 3 76.90 10.5 87.4 Oaxaca 5 133.00 34.4 167.4 Hidalgo 4 74.20 7.4 78 Veracruz 0 193.70 47.7 241.4 Chiapas 86 836.90 594.7 1431.6 Total 98 1314.70 694.7 2005.8 Source: CENAPRED-ECLAC, 2006 The Soconusco, Istmo-Costa, Frailesca and Lacandona forest regions that previously were recipients of migrants today have become senders of internal and international migrants in high numbers. These areas lag behind in industrialization and infrastructure. Here, the strike of hurricane Stan caused significant damage. Around 100,000 people, living in approximately 33,000 houses, were affected (Table 7.4). After hurricane Stan it was necessary to relocate 4,962 dwellings, (22.3%) in rural areas, because the ground and the area where these houses were located became highly vulnerable and risky. The number of houses (5,416) in the urban 109 centres for relocation was similar to those in rural areas, but the percentage was higher, at 52.7%. These data demonstrate that there were thousands of people in a homeless situation, who were depending on governmental or international aid. A share of this environmentally displaced population (EDP) likely joined the internal or international migration flows. Table 7.4: Damages caused by hurricane Stan October in Chiapas 2005 Description Numbers Municipalities in emergency 41 Affected population 99,571 Evacuated population 50,469 Harboured population 63,425 Temporary refugees 300 Victims 86 Affected rural dwellings 22,241 Affected urban dwellings 10,273 Most affected zones: Soconusco (Tapachula), Costa, Sierra, Frailesca and Selva Lacandona Source: compiled by the author from CENAPRED 2006 & CENPRED-ECLAC, 2006 Some municipalities declared a state of emergency, especially in areas with many poor rural indigenous people and with a high or very high degree of marginalization. This suggests that the socio-economic damages in these areas were greater than in urban areas. In this regard, the population of dozens of communities decreased between 2000 and 2005. This was the case for the municipality of Motozintla in the Soconusco region, heavily affected by hurricanes Mitch and Stan. The losses of population in other communities, as well as a shift in the distribution of male and female inhabitants, also suggest that the hurricanes triggered out-migration. There are exceptions, however, and a few municipalities hit by hurricane Stan, such as Angel Albino Corzo and Chiapas de Corzo, registered an increase in population (Table 7.5). Likewise, some urban areas experienced significant population growth due to the arrival of environmentally displaced people (EDP); here they came in useful because of the labour demand for cleaning and restoration, after the natural disasters. Further immigration regarded to environmental degradation ─climate change─ is expected to flow to Chiapas from other vulnerable states in Mexico in the coming years, a considerable increase of Central American migrants (PACCC, 2010), as well as from Chiapas to internal and international better-off areas. T ab le 7 .5 : P op ul at io n ch an ge in m ai n m un ic ip al it ie s af fe ct ed b y hu rr ic an e S ta n in C hi ap as , 2 00 0- 20 05 20 00 20 05 P op . C ha ng e 00 -0 5 M un ic ip al it ie s T ot al M al e Fe m al e T ot al M al e Fe m al e T ot al M al e Fe m al e A ca co ya gu a 14 ,1 89 7, 27 6 6, 91 3 14 ,6 53 7, 29 5 7, 35 8 3. 27 0. 26 6. 44 A ca pe ta hu a 25 ,1 54 12 ,7 21 12 ,4 33 24 ,1 65 11 ,9 93 12 ,1 72 -3 .9 3 -5 .7 2 -2 .1 0 A m at en an go d e la f ro nt er a 26 ,0 94 13 ,1 16 12 ,9 78 25 ,3 46 12 ,1 86 13 ,1 60 -2 .8 7 -7 .0 9 1. 40 A ng el A lb in o co rz o 21 ,8 48 11 ,1 36 10 ,7 12 28 ,8 83 14 ,5 68 14 ,3 15 32 .2 0 30 .8 2 33 .6 4 A rr ia ga 37 ,9 89 18 ,7 60 19 ,2 29 38 ,5 72 18 ,8 15 19 ,7 57 1. 53 0. 29 2. 75 B ej uc al d e oc am po 6, 67 3 3, 37 7 3, 29 6 6, 61 2 3, 30 9 3, 30 3 -0 .9 1 -2 .0 1 0. 21 B el la v is ta 18 ,2 05 9, 10 2 9, 10 3 17 ,5 53 8, 59 9 8, 95 4 -3 .5 8 -5 .5 3 -1 .6 4 C ac ah oa tá n 39 ,0 33 19 ,4 15 19 ,6 18 40 ,9 75 19 ,9 69 21 ,0 06 4. 98 2. 85 7. 08 C hi ap a de c or zo 60 ,6 20 30 ,1 96 30 ,4 24 73 ,5 52 36 ,5 53 36 ,9 99 21 .3 3 21 .0 5 21 .6 1 C hi co m us el o 24 ,9 94 12 ,5 83 12 ,4 11 28 ,2 60 13 ,9 70 14 ,2 90 13 .0 7 11 .0 2 15 .1 4 C in ta la pa 64 ,0 13 31 ,9 20 32 ,0 93 73 ,6 68 37 ,4 34 36 ,2 34 15 .0 8 17 .2 7 12 .9 0 L a co nc or di a 39 ,7 70 20 ,4 31 19 ,3 39 40 ,1 89 20 ,1 74 20 ,0 15 1. 05 -1 .2 6 3. 50 E sc ui nt la 28 ,0 64 13 ,9 29 14 ,1 35 27 ,3 64 13 ,2 44 14 ,1 20 -2 .4 9 -4 .9 2 -0 .1 1 Fr on te ra c om al ap a 52 ,1 68 25 ,5 04 26 ,6 64 57 ,5 80 27 ,3 49 30 ,2 31 10 .3 7 7. 23 13 .3 8 L a gr an de za 5, 96 9 3, 00 5 2, 96 4 6, 72 3 3, 35 3 3, 37 0 12 .6 3 11 .5 8 13 .7 0 H ue hu et án 31 ,4 64 15 ,5 97 15 ,8 67 30 ,4 50 14 ,7 20 15 ,7 30 -3 .2 2 -5 .6 2 -0 .8 6 H ui xt la 48 ,4 76 23 ,8 96 24 ,5 80 47 ,9 53 23 ,1 01 24 ,8 52 -1 .0 8 -3 .3 3 1. 11 M ap as te pe c 39 ,0 55 19 ,7 15 19 ,3 40 37 ,9 45 18 ,6 23 19 ,3 22 -2 .8 4 -5 .5 4 -0 .0 9 M az ap a de m ad er o 7, 18 0 3, 57 7 3, 60 3 6, 84 5 3, 39 5 3, 45 0 -4 .6 7 -5 .0 9 -4 .2 5 M a z at án 24 ,0 79 12 ,1 59 11 ,9 20 24 ,0 17 11 ,9 94 12 ,0 23 -0 .2 6 -1 .3 6 0. 86 M et ap a 4, 79 4 2, 37 3 2, 42 1 4, 80 6 2, 35 7 2, 44 9 0. 25 -0 .6 7 1. 16 M ot oz in tla 59 ,8 75 30 ,1 21 29 ,7 54 58 ,1 15 28 ,6 81 29 ,4 34 -2 .9 4 -4 .7 8 -1 .0 8 P ij ij ia pa n 46 ,9 49 23 ,5 28 23 ,4 21 46 ,4 39 22 ,9 15 23 ,5 24 -1 .0 9 -2 .6 1 0. 44 P or ve ni r 11 ,6 41 5, 85 4 5, 78 7 12 ,8 31 6, 44 4 6, 38 7 10 .2 2 10 .0 8 10 .3 7 S il te pe c 32 ,4 57 16 ,6 46 15 ,8 11 35 ,8 71 18 ,1 13 17 ,7 58 10 .5 2 8. 81 12 .3 1 S uc hi at e 30 ,2 51 14 ,9 70 15 ,2 81 32 ,9 76 16 ,1 05 16 ,8 71 9. 01 7. 58 10 .4 1 T ap ac hu la 27 1, 67 4 13 1, 65 3 14 0, 02 1 28 2, 42 0 13 5, 87 3 14 6, 54 7 3. 96 3. 21 4. 66 T on al a 78 ,4 38 39 ,0 28 39 ,4 10 78 ,5 16 38 ,5 94 39 ,9 22 0. 10 -1 .1 1 1. 30 T ux tl a ch ic o 33 ,4 67 16 ,6 26 16 ,8 41 34 ,1 01 16 ,6 76 17 ,4 25 1. 89 0. 30 3. 47 T uz an ta n 23 ,1 80 11 ,6 37 11 ,5 43 24 ,4 17 11 ,9 58 12 ,4 59 5. 34 2. 76 7. 94 U ni ón J ua re z 13 ,9 34 6, 96 3 6, 97 1 13 ,4 59 6, 53 2 6, 92 7 -3 .4 1 -6 .1 9 -0 .6 3 V il la c om al ti tl an 26 ,7 06 13 ,5 13 13 ,1 93 26 ,4 14 13 ,0 05 13 ,4 09 -1 .0 9 -3 .7 6 1. 64 V il la c or zo 68 ,6 85 34 ,6 56 34 ,0 29 67 ,8 14 33 ,4 60 34 ,3 54 -1 .2 7 -3 .4 5 0. 96 V il la fl or es 85 ,9 57 42 ,9 23 43 ,0 34 93 ,0 23 45 ,7 79 47 ,2 44 8. 22 6. 65 9. 78 M ai n ur ba n m un ic ip al it ie s th at e xp er ie nc ed p op ul at io n gr ow th ; M un ic ip al it ie s w it h lo w a nd n eg at iv e po pu la ti on g ro w th So ur ce : c om pi le d by th e au th or f ro m th e P op ul at io n an d L iv in g ce ns us , I N E G I: 2 00 0 - 20 05 112 Overall, sudden natural disasters and gradual/cumulative deterioration in the productivity or habitability of a place encourage people to migrate. Natural disasters reproduce poverty in these areas because the Mexican government lacks the capacity to provide assistance rapidly. Many of the victims, then, become internally displaced people (IDP) (IOM, 2012a). If a natural disaster occurs close to an international border, then people will cross the border seeking refuge. Subsequently, the rural communities, which lost crops and the balance of the natural system in their communities or farms, could face an increase in human, plant, and animal diseases, due to the reduction of food and the growth of pests. Communities that have lost natural resources, due to either natural or anthropogenic causes, tend to establish monocultures; this compounds the degradation problem and leads to the outflow of migrants (Blanco, 2000; Castles & Rajah, 2010). Furthermore, the natural fragility of the Chiapaneco territory, combined with other factors, make natural disasters a trigger for emigration. 7.6. The emergence of an international migration trend For many decades, the state of Chiapas has had some of the lowest human, social and economic indicators in the country. It has also had a low number of out-migrants in comparison with the other Mexican states. Historically, then, the involvement of Chiapas in the international migration scene has been nearly null. The data from the “bracero programme” (1942-1962) shows that the participation of Chiapanecos in this working program was 0.02% (Jauregui & Avila, 2007). Durand and Massey (2003) state that before the bracero programme another recruitment system known as “enganche24” attracted only internal migrants to Chiapas, and the low participation of Chiapanecos did not trigger international migration, unlike the population of other Mexican states. They also point out that extreme poverty is not associated with international migration due to the high monetary costs that international migration represents. In the Case of Chiapas, farmers’ children, young farmers that have given up farming, people with economic means whose small businesses have gone bankrupt, and a small sector of professionals comprise this group (Villafuerte and Garcia, 2006). Furthermore, long distances and lack of infrastructure for communication and transport are among the main factors that limited the incorporation of Chiapanecos in international mobility. Nowadays, Chiapas shows a more intense general mobility than before, including a migration dynamic to the U.S. Thus, international migration from Chiapas 24 “The enganche recruitment system was a private business run by recruitment houses that used an extreme model of exploitation leaving the recruitment, transfer, salary, internal control of the camps and workloads in private hands. The consequences of this system were unfair contracts, perpetual debt, life and work miserable conditions, child labour, private police and recruitment houses” (Duran, 1993). 113 is relatively new, and little is known about the socio-demographic characteristics and the number of migrants from this state. Therefore, this important new dynamic of mobility to international places has to be studied more deeply and backed up with empirical research. Villafuerte and Garcia (2006) argue that the rural crisis of the late 1980s, particularly with regards to the fall of international coffee prices in 1989, the strike of hurricanes (Mitch 1998 and Stan 2005) and tropical storms, as well as the Zapatista uprising, combined to trigger a continuous flow of migrants from Chiapas to the U.S.. Subsequently, the expansion of some U.S. sectors such as construction, restaurants, hotels, and agriculture, which require temporary low-skilled labour, spurred on international migration from the southern states. In 2010, the share of the total Chiapaneco population born outside of Chiapas was 3.6%. Hence, Chiapas is the state with the lowest percentage of its population born outside of the state, putting it in 32ndplace in the national context (INEGI, 2011). However, migration to the U.S. has caused commotion within the poorest sectors of the Chiapaneco populations, including indigenous communities from the Lacandona forest, and the Sierra, Los Altos, and Frontera regions. These regions lag behind in socio-economic development compared to other parts of Chiapas, especially after the strike of hurricane Mitch in 1998. The same regions have become the leading senders of migrants to the U.S. within the state of Chiapas (Viquiera, 2008). Although substantial migration flows from Chiapas to the U.S. is a new phenomenon, it should be noted that the same flow was first recorded during the coffee crisis in 1980s. Another thing to keep in mind is that migration from Chiapas has unfolded in the context of rapid population growth, especially with regards to the working age population (15 to 59 years); it grew from 633,000 in 1970 to 1.6 million in 1990, reaching 2.75 million in 2010 (INEGI, 1970, 1990, 2010). Countless Chiapanecos are migrating for the first time (Jauregui & Avila, 2007). Anguiano (2008b) points out that the new international migration pattern makes Chiapanecos vulnerable, because of their inexperience in crossing the Mexican-U.S. border and establishing in the U.S. They are the migrants that suffer the most accidents, deaths, deportations and extortions. In this regard, 19,143 Chiapanecos were deported to Mexico in 2010 alone. This number is higher than in states with long history of migration, such as Zacatecas (10,790) (INM, 2010). On the other hand, the heightened security along the Mexican-U.S. border changed migration dynamics and entering places. Now, instead of entering through Tijuana, Tecate and Mexicali, most Chiapaneco migrants go through Nogales, Agua Prieta and Altar (Map 7.2) (Villafuerte and Garcia, 2006). In addition, illegal 114 migrants tend to choose places with less security control to cross the border, such as Sesabe in the rural deserts of the Sonora-Arizona border. Security in this area may be lower but the danger and risks are higher, many migrants died in the journey through the desert. From 2005 to 2006 the state of Chiapas received 38 bodies of migrants, of which 40% were found in Arizona (Jauregui & Avila, 2007). Map 7.2: Distribution of Chiapaneco migrants in border cities in their attempt to cross the border in percentages Source: Jauregui & Avila, (2007) Despite these problems, the Chiapanecos’ capacity to find labour markets in the US was outstanding. They now have a presence in the states of Florida, Texas, California, Virginia, New York, North Carolina, South Carolina, and even in the northern state of Vermont. Surprisingly, Chiapanecos are living in nearly all US states. It is also worth noting the improvement in the migrants’ organizational capacity and the establishment of social migration networks in the U.S. during the last few years (Villafuerte & Garcia, 2006). While some migrants from Chiapas hire “coyotes” ─that is, people who they do not know─ to help them cross the border, most hire “facilitators” who are acquaintances from the same ejido or municipality with previous experience crossing the border and the dessert (Jáuregui & Ávila, 2007). Villafuerte and Garcia (2006) note that migrants from certain areas in Chiapas conglomerate in certain US municipalities, allowing for the identification of Chiapaneco migration areas. For instance, groups of migrants from Tapachula are residing in San Diego, California, while 115 migrants from Huixtla and Frontera Comalapa are living in Miami, Florida. The latter group are working with migrants from Oaxaca, Guerrero and Hidalgo in the production of fruits and vegetables in Immokalee, Florida. Although there are migrants with other nationalities working in the same region, the Mexican workers have the lowest salaries (Jáuregui & Ávila, 2007). With time, more and more Mexican municipalities25 are joining the international migration process, and many “tijuanero26” travel agencies exist on the outskirts of the cities of Ocosingo, Comitan, and Tapachula, just to mention a few. These agencies offer bus and plane trips to cities along the Mexican-U.S. border, such as Tijuana and Altar, as well as jobs in a maquiladora (Villafuerte & Garcia, 2006). Since 1995, the Chiapaneco population living abroad has increased 400%, growing from 4,948 to 21,604 individuals by 2010 (INEGI, 2010). Other sources estimate that 30,000 Chiapanecos migrate to the U.S. each year, some 65% of whom are indigenous people from Los Altos, Centro, Sierra Madre, Istmo-Costa and Soconusco regions (INEGI, 2005a). The Migration Survey of Northern Mexico (EMIF-Norte, 201227) provides data regarding the migration of Chiapanecos to the Mexican-U.S. border or to the U.S. The international migration from Chiapas and Veracruz, as new migration sending states to the U.S., was compared with that of traditional states with large international migration flows, such as Guanajuato, Michoacán, Jalisco and Zacatecas. The results show that from 1993 to 2006 the emigration volume from the states of Michoacán, Zacatecas and Jalisco decreased. The emigration volume from the state of Guanajuato displays vicissitudes and it has been recovering his position during the past two years. In contrast, the states of Chiapas and Veracruz display a 12-fold and 6-fold increase, respectively (see Chart 7.1). Therefore, Chiapas experienced a transition in terms of international migration intensity, from a very low to a low-medium level (Santacruz & Perez, 2009). 25 In the Sierra and Frontera regions: Amatenango de la Frontera, Siltepec, El Porvenir, Bejucalde Ocampo, Motozintla, Mazapa de Madero, Bella Vista, Chisomuselo, Frontera Comalá, and Margaritas. In the Soconusco and Coast regions: Suchiate, Escuintla, Mapastepec and Tonalá. In the Los Altos region: San Juan Chamula, Zinacantán, Pantelhó, San Andrés, and Larraínzar (Villafuerte and Garcia, 2006:24). 26 These are travel agencies with direct shuttles to Tijuana city 27 The EMIF only includes people over 12 years old, not born in the U.S. or residents of the border cities either in Mexico or the U.S., who arrive to the border without a returning date. Movement is due to reasons such as work, study, change of residency, visit to family and relatives, or in transit to the north (EMIF-Norte, 2012). 116 Chart 7.1: Comparison of international migrants from Chiapas with major migrant’s sending states between 1993 and 2006, in percentages. Source: adapted from Anguino, 2008a 7.7. Remittances The number of migrants sending remittances from the U.S. exceeded 26 thousand in 1996; by 2002 the number jumped to almost 38 thousand, which meant an increase of 44.5%. In 2003 the amount of remittances reached $360 million USD, three times the foreign currency earnings derived from exporting coffee and equal to 3.6% of Chiapas’ GDP (Villafuerte & Garcia, 2006; Cruz & Cruz 2009). In 2005, Chiapas received remittances worth $ 655.3 million USD, representing 4.5% of Chiapas’ GDP (Cruz & Cruz, 2009). According to the Bank of Mexico (2012), the amount of remittances sent to Chiapas in 2011 was about $600 million USD. From 2007 to 2010, the remittances to Chiapas and to all other states have decreased significantly. The main receiving municipalities of Chiapas regarding remittances are Benemérito de las Américas, Comitan, Frontera Comalapa and Suchiate. Altogether they obtained 36.4% of the total remittances (Villafuerte & Garcia, 2006). During the 1995-2005 decade, Chiapas ascended nationally from 27th to 11th place in terms of remittances, surpassing states with high migratory intensity such as Zacatecas, Morelos and San Luis Potosi (Santacruz & Perez, 2005). 0 2 4 6 8 10 12 14 93 -'9 4 94 -'9 5 96 -'9 7 98 -'9 9 99 -'0 0 00 -'0 1 01 -'0 2 02 -'0 3 03 -'0 4 04 -'0 5 05 -'0 6 % Chiapas Guanajuato Jalisco Veracruz Michoacan Zacatecas 117 7.8. Immigration 7.8.1. Regional labour immigration Migrants and settlers arrived to Chiapas in significant numbers during the agrarian land reform in Mexico (1910-1990). Most of them came from neighbouring and central states (i.e. Veracruz, Tabasco, Oaxaca, Michoacán, Mexico State, Tlaxcala and Hidalgo) (Cruz & Robledo, 2001). They were attracted to Chiapas by the availability of fertile lands and natural resources. Furthermore, in the 1970s, the construction of roads, the concentration of economic activities (Jauregui and Avila, 2007), oil exploitation, the construction of hydro- electrical plants, intensive farming, tourism, and urbanization led to massive population displacements in the southern Mexican states (Guillén, 2003; Anguiano, 2008a). Labour migration flows occurred before Chiapas became part of Mexico. Close labour and economic relations between the bordering Mexican and Guatemalan states, historically led to the movement of workers from one side to another, temporarily employed mainly in the primary sector (Castillo, 2003) or for trading merchandise. Coffee plantations also shaped the dynamics of migration in the south, especially in the late 19th century and the early 20th century. Workers from the Mexican-Guatemalan region and from other parts of Mexico were drawn by the demand for labour on coffee plantations (Tovar, 2000; Villafuerte & Garcia, 2004; Anguiano, 2008a). In addition, oil exploitation and the construction of hydro-electrical plants in Chiapas (including Malpaso, La Angostura, Peñitas and Chicoasén, just to name a few) forced Tzotziles and Tzelzales living in flooded-affected areas to emigrate and abandon their lands. These developments, created a temporary demand for low-skilled labour ─Mexican and Guatemalan─ to work on construction projects, as well as highly-skilled Mexican workers to work in the oil and hydrology sectors (Del Rey, 1998; Guillén, 2003; Anguiano, 2008b). This influx of workers from several parts of Chiapas and other Mexican states, and also from Guatemala and China, led to a six-fold increase in the population of the Soconusco region, from 17,000 inhabitants in 1880 to 97,000 inhabitants in 1930 (Viquiera, 2008). Chinese migrants that were unable to cross over to the US ended up employed in the construction of Mexican railways. According to the Population and Housing Census 2005, between 2000 and 2005 about 24,000 people from other Mexican states were living in Chiapas. Most internal migrants come from the states of Tabasco (28,199), Mexico City (24,600), Oaxaca (19,995), Veracruz (21,048), Puebla (6,890) and Mexico State (5,656) (INEGI, 2010). These five states account for 76% of the internal migrant population living in Chiapas (Chart 7.2). Chart 7.2: Immigrants in Chiapas from other Mexican states 2010 Source: Compiled by the author from 2010 Population and Housing Census, (INEGI, 2010) The immigration of foreigners to Chiapas was quantified officially for the first time in (First Population Census) and it has been continuous outlined above, certain policies encouraged foreigners to migrate to hand, political crisis and armed conflicts provoked human displaceme Table 7.6: Foreign-born population living in Chiapas, 2010 0 3 0 2 1 0 2 25 0 5 10 15 20 25 30 A gu as ca li en te s B aj a C al if or ni a B aj a C al if or ni a Su r C am pe ch e C oa hu il a C ol im a C hi hu ah ua D is tr it o Fe de ra l Th o u sa n ds m ig ra n ts Country Guatemala Honduras United States El Salvador Nicaragua China Venezuela Argentina Other country Total 118 ly evaluated since then ( Chiapas. On nts (Ortiz, 2009). Source: Population and Housing Census, 2010 1 1 4 1 3 6 3 1 1 1 20 7 1 2 1 1 1 D is tr it o Fe de ra l D ur an go G ua na ju at o G ue rr er o H id al go Ja li sc o M éx ic o M ic ho ac án M or el os N ay ar it N ue vo L eó n O ax ac a Pu eb la Q ue ré ta ro Q ui nt an a R oo Sa n L ui s Po to sí Si na lo a So no ra Population % 20,998 66.2 3,449 10.9 3202 10.1 1984 6.3 954 3.0 222 0.7 134 0.4 105 0.3 656 2.1 31,704 100.0 1985 Table 7.6). As the other 28 2 1 21 2 0 So no ra T ab as co T am au li pa s T la xc al a V er ac ru z Y uc at án Z ac at ec as 119 7.8.2. International immigration Over the last decades, the Mexican-Guatemalan border has witnessed an increase in migration numbers (Anguiano, 2009). Mexico has become one of the countries where illegal international migrants are vulnerable and exposed to human rights violations, sexual exploitation, and slavery (Grayson, 2002). Lately, they are used by drug cartels as drug carriers or “mules”. Villafuerte (2007) classifies immigrants to Chiapas ─which mainly come from Guatemala and to a lesser extent from other Central American countries─ in four types of human flows: (a) temporary cross-border workers (agriculture, commerce, and domestic services; (b) local temporary visitors,(c) transit migrants moving through Mexico to reach the U.S. and (d) refugees28. These migration dynamics reflect the migrants’ level of mobility and the distances they travel to get to their destinations (figure 7.1). This subchapter focuses on the first and third flows, because of their relevance to the topic under study. Figure 7.1: Destination of Guatemalan immigrants by occupation and trajectory Source: author’s elaboration The dynamics of human displacement in the region along the Mexican-Guatemalan border are complex. The migratory statistics gathered by the National Migration Institute (INM) reveal some of its characteristics (Angeles, 2004). The migration survey on the southern border of Mexico EMIF-SUR (2011) indicates that the majority of Guatemalans that work in 28 Central American diaspora that escaped the violence from their countries’ civil wars during the late 1970’s and 1980’s, who still remain in Mexican territory United States Other Mexican states Chiapas: Soconusco Local immigration Regional immigration Transit-migration Temporary cross-border workers (agriculture, merchant Sex/bar, construction, service sector Not properly engaged to any sector (random activities) Short D is ta nc e Medium Large M ob il it y Low Medium High 120 Mexico are in the agricultural sector. In 2007, 40% of agricultural workers in Chiapas were from Guatemala; by 2010, this percentage increased to 78%. While the participation of Guatemalan workers in the manufacture sector is steady, the other sectors have experienced a significant decrease (Chart 7.3). Chart 7.3: Labour activities of Guatemalan workers once they worked in Mexico 2007-2010 Source: compiled by the author from EMIF-SUR 2007-2010 7.8.3. Agricultural temporary cross-border workers Temporary workers are coming from Guatemalan, Honduras, and El Salvador to participate in the Chiapaneco labour market. While Guatemalan workers have been working in Mexico for decades; the participation of Hondurans and Salvadorians is more recent. Given the difficulties in reaching the U.S., some of these labour migrants have started to stay in Mexico in order to minimize risks (Cortez, Caceres & Venegas., 2005). Since the late 19th century, many Guatemalans, especially those from the Guatemalan highlands close to the Mexican border, considered Chiapas ─particularly the Soconusco region─ to be an area that provides diverse seasonal job-opportunities on agricultural plantations. The first recorded movement of Guatemalan migrant and guest workers dates back to 1876, when the coffee boom attracted them. Guatemalans arrived in high numbers and settled in the lowlands and foothills of the Soconusco region, providing a solution for the increasing demands of labour, not just for coffee production but also for the production of other plantation crops such as bananas and sugar cane. In the first stage of immigration, from 1880 to 1930, the population growth in this area jumped from 17,000 to 97,000 (Viqueira, 2008). Apparently, the demand for labour 0 10 20 30 40 50 60 70 80 90 2007 2008 2009 2010 % o f G u a te m a la n w o rk er s Agriculture Manufacture Costruction Commerce Services 121 in Soconusco’s coffee plantations was only the tip of the iceberg; in 1953, the Mexican government, under pressure by coffee farmers and landowners, signed an agreement with the government of Guatemala to employ Guatemalan workers in the region temporarily (Angulo, 2008). In the 20th century, Guatemalan migrants continued flowing to Chiapas because of the recruiting of cheap labour by agricultural plantations (Angeles, 2004; Villafuerte, 2007; Casillas, 2008). The implementation of migratory regulations, as well as the implementation of systems for controlling and patrolling the border made migration more difficult. One of the Mexican governments’ requirements for granting a work visa was the verification of individual personal wealth of at least $10,000 MXN. By the 1970’s the Mexican labour market was employing around 180,000 Guatemalans in comparison to 97,000 in 1930. These numbers demonstrate that migration laws and controls had little impact on reducing or slowing down the undocumented entrance of Guatemalan workers (Viqueira, 2008). Due to a dramatic increase in immigration from Guatemala during the 1970’s, 80s and 90s, it was difficult to distinguish between political refugees and migrants searching for better economic conditions. Therefore, in 1997 the governments of Mexico and Guatemala signed a bilateral agreement to create a system that regulates the status of migrants and validates guest worker arrangements for one year. This was enforced by the expedition of the Forma Migratoria para Visitantes Agrícolas FMVA (Migration Form for Temporary Agricultural Visitors), applicable to immigrants entering Chiapas from the southern border. From 2008 on, the Instituto Mexicano de Migración INM (Mexican Institute of Migration) replaced the FMVA with the Forma Migratoria para Trabajadores Fronterizos FMTF (Migration Form for Cross-border Workers)29, applicable for Guatemalans and Belizeans, who claim to work temporarily across the border in the states of Chiapas, Tabasco, Campeche and Quintana Roo, according to the extended official agreement (INM, 2008a). With the issuing of the FMTF, the INM highlighted the existence of a cross-border labour market and the importance of Guatemalan immigrants for development along Mexico’s southern border, especially for the agriculture sector. In 2010, the INM issued almost 27,000 FMTF’s, of which 87% were attributed to agricultural workers, and 13% to workers that are making incursions into other economic sectors to fill 29 The FMTF is a document issued to Guatemalan and Belizean migrants over the age of 16, who hold a legitimate job offer from a Mexican employer to work in the southern Mexican states. It is valid for one year and in order to renew it, the immigrant has to obtain a new job offer, either by the same employer or a different one. The FMTF may also be issued to the worker’s companions to facilitate family unity (INM, 2011). 122 the labour gaps left behind by Mexican out-migration. The majority of FMTF’s were issued in the state of Chiapas (99.8%), mostly in the three main entering points: Talisman (58.5%), Ciudad Hidalgo (21.6%) and Ciudad Cuauhtémoc (19.8%). The workers who received a FMTF are predominantly male (84 %) and young (66% are between 16 and 44 years old). Furthermore, 76% of the Guatemalan workers are natives of the Guatemalan states close to the Mexican-Guatemalan border: San Marcos (38%), Huehuetenango (19%), Alta Verapaz (11%), Quetzaltenango (8%) and others (24%). The INM states that 90% of registered Guatemalan agricultural workers are male30; the majority of them are 15 to 48 years of age; 63.5% are working on coffee plantations, 15.9% are employed on banana plantations and 5.2% on sugarcane plantations. According to the INM (2011), the farming sector (coffee, banana, sugarcane, and cattle farms) located along the border between Mexico and Guatemala employs every year about 98% of the Guatemalan workers registered and documented by the same institution. However, from 2001 to 2008, there was a gradual decrease in the demand for temporary workers, largely due to the fall of international coffee prices. To a lesser extent, the strike of hurricane Stan in late 2005, as well as the global economic crisis in 2008 (Chart 7.4), affected the agricultural productivity and labour demand in the following years. The ability for absorbing the Guatemalan agricultural workers in the coffee and banana sectors was particularly diminished by the devastating effects caused by hurricane Stan. However, during the last two years the demand for temporary manpower has skyrocketed, in general due to the recovery and restoration of damaged agricultural areas and also because farmers have reorganized to produce new alternative crops such as rambután (Nephelium lappaceum L.), mangosteen (Garciniamangostana L.), and jatropha (Jatrophacurcas L.). 30 The low participation of women in the agricultural sector is due to the low salaries they receive. Their labour is mostly needed in post-harvesting agricultural activities that require more delicacy Chart 7.4: Influx of temporary cross Chiapas Source: compiled by the author from annual immigration statistics bulletins 1999 to 2011 of the INM. According to Villafuerte (2007), the long lasting international coffee crisis resulted in prices that were insufficient to cover production expenses, reducing the salaries and demand for temporary Guatemalan migrants. It is worth noting that the statistics recorded by the INM o temporary Guatemalan agricultural workers do not include a large percentage of migrant workers that cross the border illegally (Anguiano, 2009). Traditionally agricultural workers migrate in groups with different composition: mixed groups of males, mostl neighbours and friends (Cortez male migrant workers, are not included in the statistics (Angeles, 2009). Hence, the places of destination and origin of cross and traditional characteristics (INM, 2011). 7.8.4. Daily cross-border merchant migrants The long tradition of commerce Mexican-Guatemalan border forms part of the economic life The informal basis of the economic activities is particularly evident in the city of Tapachula, 31Many agricultural workers bring their children along to teach them coffee plantations, the place where generations of indigenous Guatemalan worker extra money during the harvest of coffee 65 69 42 0 20 40 60 80 100 120 140 19 99 20 00 20 01 Th o u sa n d m ig ra n ts 123 -border migrants holding a FMTF document to work in et al., 2005). Women and children31, who -border workers confirm that the migration flow has regional -exchange between people in the border towns along the of the region (Angeles, 2009). how to carry out agricultural activities s have worked, and to gain cherries. 39 45 42 46 40 28 24 46 84 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 cross-border workers n y relatives, often accompany on 131 20 10 20 11 124 located in the Soconusco region. Most of the merchants come from the Guatemalan states or departmentos32 of San Marcos and Huehuetenango (Castillo, 1994). The proximity between place of work and homeland makes it easier for Guatemalan merchants to cross the border on a daily basis. It is a part of the ordinary scene on the streets of Tapachula to see daily merchant migrants selling their products ─handicrafts, sweets and cigarettes─. Most of the merchants are men. According to Arriola (1995) only a few female merchants have established their businesses in stalls inside the Tapachula Market (tianguis), and a few more are outside the market occupying a small spot on the sidewalk or walking around carrying a basket on top of their heads. These are known as “canasteras”. The merchants in the region are officially allowed to stay in Mexico, as long as they hold a “local pass” issued by the Government of Guatemala, which allows them to stay for 72 hours, or a local visitor migratory form (FMVL)33 (INM, 2008b). Nonetheless, this document does not regulate their economic activities (Castillo, 1994). Hence, merchants face harassment and abuse from local authorities; they are asked for money to have the right to sell their products and in some cases their products are taken away (Grayson, 2002). Merchant migrants predominantly travel in families and with relatives, although there are a few travelling alone or with neighbours, for example (Castillo, 1994). To sum up, due to geographical proximity, and traditional, ethnical, and kinship links between the people residing in the towns along the border, merchant migrants and temporary agricultural migrants make up the longest existing migration flows from Guatemala to Chiapas, especially to the Soconusco region (Castillo & Casillas, 1987; Castillo, 2003). In the 1990’s other forms of migration developed in the border region to join the traditional temporary migration of Guatemalan workers to the coffee plantations. These include: (a) the recruitment of female workers, either for domestic services or in bars, (b) child workers in the streets, (c) and the diversification in the agriculture opened new spaces in the labour market of Chiapas (Angeles, 2004). 32 “The departamento is the second level of political-administrative unit in Guatemala, somewhat similar to the state in other countries, although lacking autonomy from the central government. It comprises municipios, the smallest units of local government” (Castillo, 2003:50). 33 The FMVL is a migratory document that facilitates the entry to the southern Mexican Border States for Guatemalan and Belizean migrants. The FMVL is available for Guatemalans from the departments of Quetzaltenango, San Marcos, Quiché, Petén, Retalhuleu and Alta Verapaz. With this document Guatemalan visitors can enter to the states of Chiapas, Tabasco and Campeche, which are in a 100 kilometer range of the Mexican-Guatemalan border (INM, 2011). 125 7.8.5. Domestic service workers Domestic-service workers are predominantly Guatemalans from rural or semi-rural areas in the bordering departments. The majority of these workers is female and comes from the department of San Marcos (Angeles, 2004). There is a close family relationship between agricultural workers and domestic service workers, both used to work in the same region (Angeles & Rojas, 2000). For example, while male agricultural workers are selling their labour on coffee plantations in Soconusco, their wives, daughters and relatives are working in the city of Tapachula, just few kilometres away. In this way, we can observe the ‘feminization’ of migration along the border. The EMIF GUA-MEX34 gathered data on the overland Guatemalan migration to Mexico in 2005, documenting that 21% (55,262) of the migrant population is female (Najera, 2009). Angeles and Rojas (2000) consider that the insertion of female migration into the Chiapaneco urban labour markets needs to be studied carefully, because Guatemalan domestic workers carry out fundamental activities inside the homes of middle-class and well-off families. Even though there is a tradition for domestic-service workers to migrate to Chiapas, there has been an increase in volume over the past two decades. Authorities have yet to create a specific program suitable for this kind of work and migratory movement (Najera, 2009). The time these workers spend in Mexico varies according to different personal circumstances (Angeles, 2009). Unlike to male agricultural workers from Guatemala, 95% of female migrants travel to Mexico without company. The majority (75%) of them are between the age of 20 to 39 years, and 6% is younger (15 to 19 years) (Najera, 2009). The success in finding employment in the domestic sector, as well as in staying in Mexico, depends on their personal experience, social capital and the social networks at their disposal (Anguiano, 2008a). 7.8.6. Transit migration: increase of numbers and risks Transit migration is a discussed phenomenon and has become a central element of the growing complexity and heterogeneity of the migration flows on the southern Mexican border. Transit migrants cross Mexico in order to reach the U.S. in an undocumented manner. The main origins of transit migrants are the Central American countries of Guatemala (50%), 34 In 2004 the Encuesta sobre Migración en la Frontera Guatemala-Mexico EMIF GUA-MEX changed its name to Encuesta sobre Migración en la Frontera Sur de México (Migration Survey on the South border of Mexico (EMIF SUR), 126 Honduras (30%), and El Salvador (14%) (INM, 2011). Because of Chiapas’ proximity to these countries, it functions as gate for transit migrants. Furthermore, since the beginning of the 21st century, Chiapas has become an entry door for transit-migrants from South America and the Caribbean, as well as for Trans-Atlantic migrants from other developing countries. Villafuerte (2007) points out that many Central Americans are still utilizing the networks and routes established by the refugees in 1980’s. He also observes that post-conflict scenarios, structural adjustment policies (SAPs), free trade agreements, the international coffee crisis, and natural disasters like hurricane Mitch have contributed to the influx of Central American migrants to Mexico. The majority of Central America migrants pass through the fertile plains of the Soconusco region in their journey to the US (Angeles, 2004). Migration routes have diversified during the years. The most common route used by Central American migrants is the south route in the Soconusco region through the Suchiate River, from Tecún Umán to Ciudad Hidalgo, and from Talismán in Guatemala to El Carmen in Mexico. Historically, merchants and temporary agricultural workers on their way to Tapachula have used this route (Cortez et al., 2005).Today it has become an entry point for undocumented migrants, who cross the river on rubber rafts, reinforced with planks, or by swimming. The main means of transport used by transit-migrants are trains and public transport systems; each one requires migrants to use different strategies. Rail transportation is the most common. It is nearly free but with more risks in the long run (Casillas, 2011). In late 2005, hurricane Stan hit the southeast of Chiapas; it destroyed 70 bridges and damaged about 280 km of railway between Chiapas and Mayab35. In this way, part of the main transit migration route, where “el tren de la muerte” (the death train) used to run, was disabled. This provoked a dispersion of transit migrants and a shift in migration routes; Tabasco and Campeche began to gain importance as alternatives (Najera, 2009). The transit-migrants who crossed through the coastal area of the Soconusco region had to evade all of the migration checkpoints placed along the highways, as well as the main routes that connect the entry points with the train station of Arriaga, where the Mexican train network begins (see annex 3). Many transit-migrants who choose this means of transportation are from Central America with few economic resources. Although undocumented migrants travel as stowaways, they have to pay private guards, the train staff, 35 In the south of Mexico the railway crosses the states of Chiapas, Oaxaca, Tabasco, Veracruz Campeche and Yucatan, connecting different urban centres like Merida, Campeche, Coatzacoalcos, Salina Cruz, Ixtepec, Tapachula and Ciudad Hidalgo. Even though this train is utilized for transporting goods and materials, Central American migrants use it to cross certain parts of Mexico (Anguiano, 2009). 127 and officials. As such, they are exposed to extortion. In addition to these economic costs, travel is very risky, sometimes resulting in severe injuries or even death (Villafuerte, 2007). This route, surrounded by the Sierra Madre on one side and by the Pacific Ocean on the other, continues to the Istmo-Costa region, where it crosses the border with Oaxaca. The majority of trans-migrants pass through Mexico City, where the migration route splits in several directions, according to the different final destination points in the U.S. (Casillas, 2008). Additionally, the southern route is used to ship many smuggled goods, drugs and weapons for the Mexican and U.S. black markets; this has turned the route and the border cities on its side of the river into even more risky and unsafe places (Grayson, 2002). In this manner, a more complex double system of movements with opposing effects has emerged. On the one hand, the cross-border mobility of people and legal goods contributes to regional development; on the other hand, illegal activities have negative effects on regional security and development. Accordingly, transit migration has been seen as a problem due to its association with illegal transnational activities. The central route Cuauhtémoc-La Mesilla is also a traditional route for Guatemalan agricultural workers on their way to coffee plantations; it is also the fastest route for migrants who are heading to Quintana Roo. Nowadays, has become an entry point for undocumented workers from Guatemala use it. T The Northeast route Frontera Corozal-Bethel, which is divided by the Usumacinta River, it is mostly used by Honduran migrants and it is the entryway for other migrants who are heading to the states of Tabasco and Yucatan (Cortez et al., 2005). Migration, drug trafficking and contraband has converted certain geographic areas along the Mexican-Guatemalan border into geopolitical black holes36, where the conditions for keeping order do not exist and where it is nearly impossible to govern. Illegal activities, with local and regional impact, are mushrooming in these areas (Naím, 2005). Actually, the transit-migration in Mexico is synonymous with illegal migration, because the majority of transit-migrants do not carry a document on their journey through Mexico (Casillas, 2008). Most transit-migrants originate from Central America, where it is almost impossible for poor people to acquire a US visa. 36 In order to qualify as a geopolitical black hole, it has to have the capacity to irradiate instability across the globe. A geopolitical black hole can be found on the borders between Mexico and the United States, where a war has taken place during the last years between rivalling traffickers, trying to control the border. Additionally, the government is involved, although one never knows which part of the government is on which side, and if there is even a government present (Naím, 2005). 128 Hence, they are forced to migrate in an illegal way and make use of migrant smugglers, known as “coyotes” (Fernandez, 2009). In the US and Mexico, the migration phenomenon has been subordinated to security issues (Casillas, 2008). The U.S. government increased its security along its border with Mexico after September 11th, 2001. Likewise, the government of Mexico implemented migration policies as part of the “Plan Sur” (South Plan), in order to strengthen control of its southern border and migrant flows from the Istmo de Tehuantepec region to the southern border. Due to enhanced security measures, trans-migrants are highly persecuted and restricted, which in turn makes the journey more dangerous. They have to choose less secure routes and this generally puts them in a highly vulnerable position (Villafuerte, 2007). Even though, using alternative routes with less security or migration checkpoints, transit-migrants face many hazard, since there are many obstacles to be overcome (Grayson, 2002). The illegal status of transit-migrants puts them at risk of being apprehended by Mexican migration authorities, and of being abused by coyotes, gangs, authorities and sometimes even local people (Fernandez, 2009). Migration police take advantage of undocumented transit- migrants, sometimes by accepting small bribes to allow them to cross, sometimes by stealing the little cash migrants carry with them and sometimes by abusing them physically and mentally (Grayson, 2002). Furthermore, the journey gets more tough and dangerous with the presence of transnational gangs like Mara Salvatrucha (MS-13) and Mara 18 (M-18). These two groups’ illicit activities include robberies, vehicle theft, homicides, human smuggling, as well as drug and weapon trafficking (Angeles, 2008). The gangs have a high presence in the Soconusco region. They are located along the migration routes used by trans-migrants. They exploit migrants by stealing from them and by abusing them physically and sexually (Villafuerte, 2007). Due to the increase in human rights violations committed against transit- migrants, NGOs and representatives of the Catholic church who want to somehow protect the rights of migrants and help them during their journey, have created “casas solidarias” (solidarity houses), which provide information, food, shelter, health services, cleaning and legal services. Unfortunately, migrant smugglers use these houses either to abandon their “pollos”37 (chicken) or to catch more migrants (Casillas, 2008). 37 In this case the term “pollo” refers to the undocumented transit-migrants who hire the services of a coyote and/or a pollero (chicken herder), who smuggles migrants to Mexico and to the U.S. Generally the coyote is in charge of planning and arrange the whole journey for the migrants, while polleros guide the migrants during Assessing the exact volume and nature of transit because there is no precise data regarding the number of migrants that enter Mexico via all its entry points located along the southern Mexican border (Angui it is possible to make estimates (Casillas, 2008) and identify trends, based on the information provided by the EMIF-SUR on migrants returned from Mexico to Central America. Rodriguez, Berumen, and Ramos (2011) suggest that th American transit migrants can be estimated indirectly by adding up the numbers from three sources of information: (1) migrants recorded by the INM of Mexico, (2) migrants apprehended by the US Border Patrol, and (3) those w (Chart 7.5). Chart 7.5: Undocumented Central American transit migrants 1995 Undocumented Central American transit migration grew steadily from 1995 to 2002 between 2002 and 2005, the volume doubled. Thus, implementation of the Plan Sur (2001) and the “orderly and secure repatriation” program launched by the INM failed to reduce the magnitude of the transit migration pheno (Grayson, 2002). However, after 2005 the panorama changed: the flow of migrants reduced by 70% during the period 2005 short distances or provide temporary shelt by the coyote and salaried for the service given to the migrants. For discussion see: Spener, D., (2008): El apartheid global, el coyotaje y el discurso de la migración clandestina: distinciones entre violencia personal, estructural y cultural. 129 -migration in Mexico is a difficult task, ano, 2008a). Notwithstanding, e number of undocumented Central ho successfully arrive to the U.S. -2010 (thousands) Source: adapted from Rodriguez ( ad hoc deportations after the -2010. The factors that can explain this change er and food. Anyone can become a pollero, once he/she is contacted Migración y Desarrollo, Nro. 10, pp. 127 et al., 2011) . Then, menon include the -156. 130 economic downturn (Rodriguez et al., 2011), more security control on the borders (Castillo, 2003; Villafuerte, 2007), and the increase in violence and insecurity in Mexico. Concerning this last factor, transit migrants have increasingly become victims of kidnapping, extortions and assassinations (Casillas, 2008). As such, those who follow reconsider and look for new and safer routes. Transit migration seems to have become more stable since 2009 (Rodriguez et al., 2011). The transit routes are not the only ones that have changed; the transit migrants’ socio- demographic and labour characteristics have changed with them. This can be observed thanks to sociological and anthropological studies. In addition, the data and records gathered by the INM have provided information regarding the characteristics of the migration inflows (Najera, 2009). According to the INM (2011), 96% of the foreigners apprehended in Mexican territory are from Central America. From the 66,764 apprehensions, 50% are Guatemalans, 30% Hondurans, 14% Salvadorians, and 2% Nicaraguans. The female and minors account for 13.1% and 6.2% respectively (Table 7.7). Table 7.7: Socio-demographic composition of transit migrants Age and Sex 2011 Percentage Total 66,764 100 Over 18 years old 62,595 93.8 Female 54,380 13.1 Male 8,212 86.9 Under 18 years old 4,172 6.2 Male 3,137 75.2 Female 1,035 24.8 Source: compiled by the author from INM, 2011 Guatemalan workers have joined the transit migration flow. This has led to a significant decrease in the number of temporary Guatemalan workers in the Mexican agricultural sector (Villafuerte & Garcia, 2008). In 2011, transit migrant were apprehended or housed in the states of Chiapas (30,068), Tabasco (6,304), Oaxaca (5,054) and Veracruz (4,033) (INM, 2011). A vast number of Central American transit migrants crossed the Mexican-U.S. border in the state of Tamaulipas towards Texas. To make the journey across Mexico, 43% of the Central American transit migrants hired a coyote, and 60% employed a coyote only for crossing the border between Mexico-U.S. (Rodriguez et al., 2011). 131 7.9. Migrants’ labour conditions and human rights In the context of neoliberal globalization, informal employment has mushroomed and working conditions have become more precarious, especially for undocumented migrant workers (Likic-Brboric & Schierup, 2011). Furthermore, there has been a re-composition of migration dynamics and an increase in the numbers of people migrating. The lack of opportunities in formal labour markets and the massive increase of informal activities under conditions of extreme exploitation have expelled and forced a part of the population to migrate. In addition, mobility restrictions implemented by governments to keep illegal migrants out of their territories, place these migrants in a situation of high vulnerability regarding social exclusion and exploitation (Delgado-Wise et al., 2010). “Nonetheless, many migrant workers do not enjoy decent work, but instead suffer from low wages, unsafe working environments, non-payment of wages, a virtual absence of social protection, denial of freedom of association and workers’ rights, discrimination and xenophobia”. (ILO, 2010) It has been demonstrated by many studies that agriculture, the primary sector of the economy, offers workers the lowest wages and the worst working conditions (Kay, 1998; Bartra, 2006; Angulo, 2008). In this sector, employers and intermediaries control the labour market in an informal and unilateral manner. In Chiapas, undocumented and documented immigrant workers are employed under unofficial contracts, where terms and conditions are reached verbally (Villafuerte & Garcia, 2004). The informality of these contracts acts as a double- edged sword. On the one hand, it allows migrant workers to have flexible employment in time and space; on the other, the lack of formal-employment documents exposes migrants to different forms of labour exploitation. It should be kept in mind that most of migrant workers come from poor regions (i.e. Guatemalan indigenous). In the case of temporary Guatemalan workers, Macip (2007) observes that the indigenous workers on coffee plantation only react to harsh treatments. From this view, the best labour discipline is deprivation and exploitation. The labour exploitation on coffee plantations is seen as a way to create surplus value by increasing the working time and/or reducing the wages and conditions for the social reproduction of the labour force (Marini, 1978). Hence, the type of work and payment during the harvest of the coffee cherries is by “destajo” (piecework), that is, the “pisqueros de cafe” (coffee harvesters) are paid by the number of kilograms harvested during a week. In order to increase the amount of coffee picked, some pisqueros come along with their wives and children. This type of work is apparently based on 132 the capacity of voluntary, individual and sometimes child exploitation. The agreed wage between coffee producers and workers generally tends to cover only the minimum needs of the labour force during the harvest season (Macip, 2007). For temporary cross-workers from rural areas, emigration constitutes a mean to earn a better income in order to tackle problems associated with poor local economic conditions. The vast majority is dependent on the primary sector, where wages are very low (Casillas, 2011). However, the low wages paid by the coffee plantations are still better than the ones earned in Guatemala. Typically, Guatemalan temporary workers under unofficial contracts have lower wages in comparison with legal workers. These workers are subject to working hours that far exceed the eight-hour maximum stipulated by Mexican law, to verbal and labour maltreatments, to minimal or no medical services, and to risks of violence and diseases. Cortez and co-authors (2005) estimate the wages for migrant workers in the agricultural sector of Chiapas in a range between 45 to 80 pesos per day ($3.5 to $6.1 USD, sometimes including food and shelter), and the wages in the domestic service sector 40 to 120 pesos ($3 to $9.2 USD). In addition, Chiapaneco migrant workers, especially those coming from indigenous communities aiming to the northern states of Sinaloa, Baja California, and Chihuahua, neither have been paid attention nor recognized and protected by the governments, NGO’s and human right watchers. Although the trip to the north requires a considerable investment, Chiapaneco migrants are attracted by the northern wages. Villafuerte and Garcia (2004) state that whilst salaries in the north range between 700 and 1,000 pesos ($54 and $77 USD) per week, indigenous workers from Chiapas accept salaries or wages of 400 and 600 pesos ($31 and $46 USD) per week. 133 8. Analysis and results of fieldwork research 8.1. Data set construction Using descriptive analyses of the Small-Scaled Farms Survey data (SSFs) (annex 1), this chapter describes the characteristics and uses of farmland, farm production and crop-price changes, livestock activities, use of forestry products, and use of farmland. It describes the main causes and consequences of land degradation in the area of study, including its impacts on the socio-economic development of farmers and farms. Additionally, denotes the household’s (HH) socio-demographic characteristics and composition, as well as the HH member’s out-migration patterns. Furthermore, this chapter provides descriptive analyses of data from the Central American Survey at the individual level, which includes data concerning the socio-demographic profile of respondents, labour activities, labour conditions, migration patterns and migration experiences. A summary of the methods used for data collection and of the number of participants is presented in table 8.1. In preparation for analysis, the data from the SSFs and from the farm level dataset (n=149) is split in three sets according to geographical location. Lowland LL (n=47) comprised by the Rio Florido and Independencia ejidos; middle land ML (n= 51) comprised by the Edén and Veintiséis de Octubre ejidos; and high land HL (n=49) comprised by Naranjo and Pavencul ejidos. 134 Table 8.1: Data collected using top-down and bottom-up methods Method Participants/Samples Step 1: Establishing context Pilot survey 15 Interviews with key informants 2 Literature review Step 2: Data collection Farm household level questionnaires 170 Central American migrants questionnaire 80 Semi-structured interviews with case study households 12 (two per ejido) Semi-structured interviews with case study immigrants 6 Semi-structured interviews with farmers 6 (mainly coffee farmers) Focus groups 6 (one per ejido) Transect walks 6 Interviews with key informants 12 (NGO’s SAGARPA, SEDESOL, PROCAMPO, and local authorities) Temperature and rainfall data Weather stations close to study area Land use change mapping from time- series aerial photographs (If available) 1 per ejido, spanning 5 years Land cover type (e.g. forest) 6 (one per ejido using transect walks, visual assessment) Soil analysis 6 (one per ejido using transect walks data and local historical knowledge and indicators) Literature review Step 3: Information treatment Data analysis Descriptive and inferential statistics Disseminating results Book, articles and hand outs 135 8.2. Descriptive statistics of small-scale farming household’s survey 8.2.1. Small-scale farming household’s characteristics The first step in the analysis of the data was to determine the demographic profile of the households (HHs) and household heads (HH-h) in the three zones under study, which are depicted briefly in table 8.2. The household size represents the major difference between the HH of the three research regions. The HHs in the HL region has the highest number of household members, with a mean size of 6.9 members, in comparison with 4.6 and 4.3 in the ML and LL regions respectively. The two latter regions’ household sizes are relatively close to the state’s mean household size (4.4), while the same figure on the national level is 3.9 (INEGI, 2010). Another important demographic characteristic related to the size of households is the number of young members. In this regard, the HL has 185 (46.5%) inhabitants who have not reach adulthood yet. This percentage is close to the 55.5% provided by the INAFED (2011) for this region. To a large extent this result is related to the levels of poverty levels and higher fertility rates in Chiapas: 3.07, compared with the national fertility rate of 2.39 (INEGI, 2010). The number of female-headed households is relative low in the LL and HL zones. In the ML zone the number of female heads is greater, being the case in 14 households (27%). Additional findings during the fieldwork guided me to determine that, in the ML fertile land is used for the production of shade-grown coffee, which requires less labour for maintenance, and it demands much labour for the harvesting of coffee cherries. Thus, the production of shade-grown coffee in the ML allows male household heads to migrate temporary to other states, or to perform other economic activities in the area until the coffee harvest season arrives, and in the mean time their wives take over the farm activities. Furthermore, the data show that the average age of household heads is 57.8, 59.7, and 54.5 years in the LL, ML and HL respectively. Subsequently, the main civil status of households in the research areas is free union; the LL has 36 (73%), the ML 27 (53%) and the HL 17 (33%) household heads under this characteristics. Agriculture is the key economic activity in the three zones. The HL has the larger number of household heads 48 (94%) involved in this activity; a slightly lower participation is evident in the LL with 44 (93%), with 42 (82%) households in the ML. 136 Table 8.2: Household’s demographic characteristics Household’s demographic characteristics Zone LL ML HL Number of HHs 47 51 51 Total population (incl. out-migrants) 253 286 400 Total population living in the HHs 204 236 354 HH # of adults +18 205 215 215 HH # of young -18 48 71 185 Male headed HHs 42 37 45 Female headed HHs 5 14 6 Household size (mean) 4.3 4.6 6.9 HH-h civil status Married 4 15 25 Free union 36 27 17 Single 3 1 3 Other 4 8 6 HH h age average (years) 57.8 59.7 54.5 HH h main eco-activity Farmer 44 42 48 Housewife 3 9 3 Source: author’s survey 8.2.2. Farm characteristics The three regions of study have different topographic and climatic characteristics, giving rise to a variety of soil and vegetation types. Table 8.3 depicts the main characteristics of the farms in the three regions. In this regard, the mean farm size for the HL, ML and LL is 7.86, 3.00, and 6.95 hectares respectively. Moreover, there is a big difference in the minimum and maximum sizes, in the same order, the HL farm sizes range from 1 up to 20 hectares; in the ML the scene is different, the biggest farm is 7 hectares; and the LL, a flat coastal plain, has a wide range of farm sizes, from 0.5 up to 50 hectares. A characteristic of the ML is the presence of large coffee plantations, with a history that goes back to the 19th century, when German and Italian migrants arrived to the Soconusco region to buy large tracts of land for coffee production (Villafuerte & Garcia, 2004; 2006; Tarrío & Concheiro, 2006; Viqueira, 2008). This development has limited the growth of small-scale farms. The PEOT (2005) states that, 46% of Chiapas’ territory is in the hands of private landowners, known as “latifundios”, and 47% is owned by ejidatarios. As such, there are many small-scale farms, or “minifundios”, many of which are oriented towards subsistence 137 production, and most of which range in size between 5 to 20 hectares (Table 8.4). Farm size is related to climatic and topographic conditions, and to the presence irrigation systems or lack thereof. Table 8.3: Farm size Farm size Zone LL ML HL Total area (ha) 326.65 153.40 401.00 Farm size mean (ha) 6.95 3.00 7.86 Minimum Size 0.50 1.00 1.00 Maximum Size 50.00 7.00 20.00 St. deviation 8.11 1.45 5.93 Source: author’s survey Table 8.4: Chiapas: area of farms by size (ha) Farm size (ha) Chiapas Smallholder “minifundio” Small farms Medium farm Big farms (up to 5 ha) (5 to 20 ha) (20 to 100 ha) ha (+ 100 ha) 265,687.38 1’548,587.70 510,574.31 969,916.19 Source: Programa de Ordenamiento Territoral de Chiapas PEOT, 2005 For Chiapaneco farmers, land is the most essential asset for social and economic reproduction, and it has been a central demand for indigenous and peasant movements. The Mexican government responded to the continuous indigenous and peasants fights for the land after the 1910 revolution by carrying out land reform (Tarrío & Concheiro, 2006). In the area of study, the most common method for obtaining land was by inheritance. land was inherited from parents38 for 45, 55, and 46% of respondents in the LL, ML and HL respectively (Chart 8.1). People who purchase the ejido land do not automatically become a member of the ejido, they are called “avencidados”, that is, neighbours without the right to participate in community-level decision making or to run as a candidate for a position of authority in the ejido. If they want to become a member of the ejido, need the approval of the community and the commissioner of communal land. In order to be accepted, new landowners have to show a commitment to the ejido and participate in several activities within a certain time span. 38The ejido land use attributes allow to parents to pass the ejidatario rights onto their children, thus keeping the ejidatario title and rights for the next generations. Chart 8.1: Land obtaining method 8.2.3. Farmland topography The state of Chiapas has diverse geographic characteristics, vegetation and climatic conditions, as described in Chapter hills, 12% plains, and 2% valleys. The LL locat levelled topography with 28 farms (59.6%), in comparison with the gully topography of the ML, with 16 farms (31.3%) and another 13 (25.5%) in areas that combine flat areas and gullies. In contrast to both steep slopes (see Table 8.5). The HL is made up mostly of igneous rocks, which combined with the type of soil (Acrisol) and either natural or human-induced land degradation. Table 8.5 Topography of the study area Soil topography Flat Flat/Gully Flat/Undulating Gully Slope Undulating Other 23 45 0 10 20 30 40 50 60 70 LL % o f h o u se ho ld s Government 138 Source: 7. Hence, 69% of Chiapas’s land is mountainous, 17% ed on the coastal plains of Chiapas has a zones, the HL 38 farms (74.5%) are in areas with a high level of deforestation; make this region prone to Zone LL ML HL 28 4 1 5 6 2 0 3 3 4 16 3 5 10 38 5 9 4 - 3 - Source: autho 10 10 55 61 32 35 29 ML HL Inheritance Purchase author’s survey -dominant Total 33 13 6 23 53 18 3 r’s survey 139 8.2.4. Soil type and soil quality The predominant description of the soil type given by farmers in the three regions is “dark soil”. This response was given by 42 respondents (89 %) in the LL, 31 (61%) in the ML and 36 (71%) in the HL (Table 8.6). Although dark soils are known for being rich in organic matter, nutrients, and microorganisms, the farmers in these three regions only rated this type of soil as fair or acceptable. As mentioned in Chapter 6, in the section “description of the selected ejidos”, two different types of soil are found in the LL: Phaeozem and Luvisol (FAO soil classification system 1988). Phaeozem features include a very dark horizon, and high organic matter content. It is very arable, ideal for growing wheat and soybeans, or for serving as pasture for cattle. It tends to be very deep, with a high availability of oxygen, high water containing capacity, good rooting possibilities, generally resistance to erosion and often associated with Chernozens. Luvisols also known for having a high level of nutrients, high water containing capacity, high rooting possibilities and nutrients content, often associated with Cambisols. It is the tropical soil most used by small-scale farmers because it is easy to cultivate. On the other hand, it is susceptible to water erosion and generally has moderate resilience to degradation (FAO, 1991; Eswaran et al., 1998; Garrison, 2008). Due to the presence of the above-mentioned types of soil, 24 (51%) of respondents from small-scale farming households in the LL said that the quality of their farmland was fair, and 16 (34%) said they had good quality soil. In the ML, the main soil type is Luvisol, where 27 (53%) of respondents from small-scale farming households stated, that the soil quality is fair and 14 (27%) described it as good. The soil types found in the HL are Acrisol and Cambisol. Acrisol is found in tropical climates and forested areas with undulating topography, it possesses low levels of plant nutrients, it has an excess of aluminium, and it is generally one of the most infertile soils in the tropics. It erodes easily and it has very low resilience to degradation. Such factors hinder agricultural productivity in the HL. The Cambisol, by contrast, has a young parent material of yellowish-brown colour with high stone content, and a shallow rooting zone in mountainous areas. Depending of the different parent materials, Cambisols can be rich or poor in their nutrient content. In the humid tropics they are poor in nutrients and are best kept under forest (Stocking & Murnaghan, 2001). According to these soil features, 31 farmers (61%) in the HL ranked their soils as fair; 11 farmers (22%) stated that their soil quality was poor. 140 Table 8.6: Farm’s soil type and soil quality Soil type Zone LL ML HL Dark 42 31 36 Greyish 2 5 6 Red 3 11 3 Yellow 0 4 6 Soil quality Very good 7 9 4 Good 16 14 5 Fair 24 27 31 Bad 0 1 11 Source: author’s survey 8.2.5. Land degradation The state of Chiapas has more than 70% of its territory covered with forests and vegetation; it ranks second in the Mexican context regarding forestland area and timber volume. Nonetheless, this valuable natural asset is becoming more and more deteriorated (PEOT, 2005). Steep slopes, and the large number of communities spread out through the region, have hindered rational forest exploitation. Otherwise, agroforestry could be used as an alternative to reduce the degradation of natural resources and preserve environmental services (De Jong, 2000; Kötz, 2003). The low level of forest management, especially concerning reforestation, has caused several zones to lose their natural mix of forest species, replacing them with pine monocultures for commercial purposes. Fine timber is more often extracted, leading to ecosystems and land degradation. Indeed, 32% of the state’s territory presents moderate levels of degradation, the remaining 68% shows high and very high levels of degradation (PEOT, 2005). Soil erosion as a direct consequence of land use change, especially due to the loss of tree and vegetation cover, and poor agricultural practices. In Chiapas, a great number of farms in the area of study have degraded or eroded tracts of land. The HL is the most-affected area. Here, 47 farms (92%) have degradation problems, making up a total area of 74 ha. The number of small-scale farms with degradation problems decreases as the altitude reduces. There are 21 (41%) and 13 (28%) farms with degraded areas in the ML and LL respectively. 141 From a different angle, Tapachula has a high annual precipitation that ranges between 1,000 to 5,000 mm, with an annual average of 2,502 mm (Ayuntamiento Municipal de Tapachula, 2007).Here, the degradation of water catchment areas are generating flooding problems in the urban centre, as well as in the low part of the surrounding ejidos, and this affects agricultural productivity. Table 8.7 shows the number of farms that flood periodically. There are 19 farmers (40 %) in the LL, 13 farmers (25%) in the ML, and 28 farmers (55%) in the HL who responded that parts of their farms are negatively affected by the consequences of the rainy season. Table 8.7: Farmland degradation Land degradation Zone LL ML HL Swampy/flooded (1/0)a 19.00 13.00 28.00 Area (ha) 90.31 9.66 35.41 Degraded/eroded (1/0)b 13.00 21.00 47.00 Area (ha) 23.38 11.47 73.94 (a) 1= presents flooded areas; 0= otherwise. (b) 1=presents degraded areas; 0= otherwise Source: author’s survey The wet season causes flooding in several farms, wrecking havoc throughout the region. Tapachula and Chiapas are constantly strike by hurricanes, typhoons and extreme weather events that have a remarkable impact on the landscape and on farmland in particular. The HL, naturally prone to land degradation, seems to be the most affected by heavy rains, given that these impinged on 37 farms (79%) out of the 47 with degradation problems (Table 8.8). During the visits to the HL region, I distinguished three different types of erosion caused by water. (1) Sheet erosion, this is not severe in zones with deep arable land, but in the HL the soil depth is less than half a meter, thus water easily detaches earth material from the surface and runs down causing other types of erosion. Additionally, steep slopes and high levels of deforestation characteristics of its landscape make it more susceptible to land degradation and erosion. (2) Rill erosion is caused by the concentration of water in narrow channels due to the high-intensity rainfall in the area; and (3) gully erosion, which has carved out the hill sides of the HL. Indeed, I observed how the accumulation of large quantities of water has overflowed from small creeks, detaching rocks and earth, thereby forming steep-sided trenches. 142 Table 8.8: Causes of land degradation in the study areas Degradation causes Zone LL ML HL Heavy Rains 12 14 37 Deforestation 1 7 5 Other - - 5 Source: author’s survey Source: author’s interview 8.2.6. Small-scale farms’ agricultural characteristics The municipality of Tapachula has an area of 93,615 ha; it represents 1.2% of the Chiapaneco territory. 17.3% of the land in Tapachula is devoted to agriculture, 21.3% is grassland, being agriculture and livestock farming the predominant economic activities; 26.3% consists of forest, 31.3% is covered by jungle and 3.8% is used for other activities. The staple crops are maize, sorghum, and soybeans; the main industrial crops intended for export are coffee, cacao, mango, papaya and bananas (Ayuntamiento Municipal de Tapachula, 2007). The different climatic gradients, soils, temperatures, and vegetative covers among the three zones are favourable for the production of a variety of crops, where annual crops are mostly cultivated in the LL and ML. The HL, for its part, presents a combination of annual and perennial crops adapted to the uneven topographic and climatic features of the area. These crops allow small-scale farmers to diversify their income and provide food for auto- consumption (chart 8.2). Box 8.1: Soil degradation The land here is still fine for growing pastures and breeding cattle, but it cannot retain all the water that runs down from Tapachula, me and my children have 20 cows in 15 ha, for three months in the rainy season (July to October), the water rises up to 1 meter in some areas of my farm, thus I’m forced to move my cows to dryer land or rent land from other neighbours. Besides, the huge amounts of water we receive down here, the drainage system is not enough and the few channels to discharge the water are clogged up with garbage, construction residues, and synthetic foam from Tapachula and the tuna factory (annexe 28). Mariano Ruiz 47, ejidatario of ejido Rio Florido in the LL region Chart 8.2: Type of crops The three regions under study present different climatic and cropping zones (figure LL, located from the Pacific coast with the city of Tapachula in the middle, ends at the foot of the mountainous, with a perfect climate for cultivating a subsistence and commercial crops. It occupies a much larger portion than the other two regions. The ML begins at roughly 600m and extends to 1,200m elevation create mild-humid conditions that are perfect for growing coffe plantations mostly owned by German families are located on the sides of the “ (coffee route), which has become and fresh cloud forests. Ecotourism has become an alternat void created by the coffee crisis and severe weather events. However, this beautiful region is somewhat inaccessibility, because much of the ruta del cafe is in a dreadful state. Further up the HL region it is located at 1,250m, to Tapachula city. Here, trees were cut down cooking and for generating additional income. certain areas and, during the rainy season, the region suffers from landslides. Nowadays, due to the above mention conditions, people in this region are mainly carrying out subsistence agricultural activities, which revolve around the milpa and small vegetables. 12 29 0 10 20 30 40 50 60 LL # o f r es po n de n ts 143 Source: author’s survey . Here, the differences in a tourist destination known for its mountainous landscape ive for a few coffee farms in order to fill the economic 60 km far from the coast and 25 for providing construction materials, fuel for Deforestation has speeded up soil erosi gardens for growing 0 16 51 86 0 27 ML HL Region Annual Perennial Both 8.1).The is a flat plain that range of e. The coffee ruta del cafe” , -30 km far from on in 144 Figure 8.1: Main subsistence and commercial activities in the research areas according to altitude and distance Source: author’s elaboration Chiapas is the most important coffee producer in Mexico, with 31% of the country’s total production; and Tapachula is the most important coffee-producing region in the state of Chiapas. Because there are few alternative employment opportunities, coffee continues to have an important economic and social function in Tapachula. In the LL, there are 22 (47%) small-scale farms producing coffee in an area of 89.5 ha from a total cultivated land of 282.2 ha (Table 8.9).The main type of shade-grown coffee produced in this area is Robusta (Coffea robusta sp.). All of the small-scale farms in the ML produce coffee, covering in an area of 143 ha. The shade-grown coffee produced in this region, which includes not just small-scale farms but also large plantations, is Arabica (Coffea arabica sp.) and Robusta (Coffea canephora sp.; syn. Coffea robusta sp). In the HL, there are 37 (73%) farms producing mostly Arabica coffee in 98 ha. From personal interviews with coffee farmers about the different varieties of Arabica coffee, Caturra, Bourbon and Typica are the most common, and to lesser extent Catimor, Mundo Novo and Catuaí. In terms of land conservation and degradation, it is well known that coffee causes less erosion to the soil in comparison with other crops grown in the region. Shade grown-coffee permits the growth of a semi-forested 0k 0km 15km 30km 45km 60km Tapachula 2500m 2000m 1500m 1000m 500m 0m Lowland Middle-land Highland Subsistence activities Commercial activities Small-scale cultivation of: soy, corn, sesame, tropical fruits and fishing Large-scale farming of: banana, mango, grassland, cattle, fishing and tourism Monocultural production of coffee and ecotourism Milpa, vegetables, firewood collection, and animal husbandry Small-scale cultivation of bananas, tropical fruits and medicinal plants Limited to cut down of fine wood or none Distance Elevation 145 area supporting greater diversity; it regulates precipitation, retains rainwater and mitigates the effects of natural disasters like hurricanes, floods, droughts and landslides. Tapachula is characterized for being an important coffee producer area, which produces different types of coffees in conventional, organic and free trade methods. The big coffee plantations located in the ML and HL regions are mostly producing coffee in a conventional other have turned to organic production, and just few have organic and fair trade certifications. There are coffee farms shifting from conventional to organic agriculture and from conventional or organic to fair trade and even to biodynamic coffee to export. In interviews with coffee producers, they indicated that producing organic coffee is not only environmental sustainable, it also represents an opportunity to gain better incomes by reducing agricultural inputs and better international coffee prices. As an example, the average price of conventional robusta coffee in 2011 was $13 MXN/Kg ($1 USD), and Arabica coffee was $30 MXN/Kg ($2.37 USD); the organic coffee prices were about $10 to 15 MXN/Kg higher. In contrast, a big number of small-scale coffee farmers are producing uncertified organic coffee. This occurs because their limited economic conditions, don’t allow them to buy agrochemicals and to obtain an organic certification, as well as they lack knowledge for producing organic coffee. Some of the small-uncertified organic coffee producers sell the coffee to big neighbouring coffee farms. This coffee is purchased as conventional coffee, which is later, exported as organic coffee through the organic certifications of the coffee plantations. Thus, the big coffee farms profit and enhance their incomes from small-scale producers. The poor road infrastructure and the lack of transportation for shipping coffee sacks to the market, make farmers dependent on retailers typically called “coyotes”, who purchase coffee on the farm at lower prices than the ones offered in Tapachula’s coffee market. Another commercialisation option is to take the coffee sacks to Tapachula’s coffee market, where dealers and retailers adjust the prices. Here, the uncertified organic coffee is sold as conventional coffee. In the research areas, the small-scale coffee producers have tried to organize in cooperatives to obtain the certification, to have a bigger volume of coffee to export, to avoid retailers, and to better off their incomes. Nevertheless, lacks of commitment, participation, and common effort have contributed to the failure of such as good initiatives. Furthermore, some small-coffee farmers receive chemical fertilizers as part of the Program of Direct Rural Support PROCAMPO (Programa de Apoyos Directos al Campo) managed by the Support Services for Agricultural Marketing ASERCA (Apoyos y Servicios a la 146 Comercialización Agropecuaria). Each one of these coffee farmers receives an amount of six sacks of fertilizer per year to help improve the coffee productivity. Maize (zea mays l.) is an annual crop and staple food in Mexico. The LL, ML and HL have designated their farmland to produce mostly coffee. Yet, a large number of small-scale farmers in the HL region, in this case 43 (84%), are still cultivating maize in an area of 67.85 ha, as part of their traditional milpa system, which is at the center of a subsistence economy that depends on diversified agriculture. Tropical fruits produced in the LL, such as Mango and Banana, together cover an area of 125 ha distributed between 12 small-scale farms (26%). Grassland or pastures for livestock production in the LL cover a large share of the land: 91 ha (32%), divided among eight small-scale farmers (17%). There are few forested areas ─that does not form part of an agri-silviculture system─ in the LL and none in the ML. By contrast, 28 small-scale farmers (55%) located in the HL still maintain forested areas on their farms, covering an area of 121.5 ha and represents 41% of the total HL area. 147 Table 8.9: Agriculture current crops and subsidies (1/0) 1= receive subsidy; 0=otherwise Source: author’s survey 8.2.7. Agricultural subsidies Chiapas’ agricultural sector is in crisis. It has suffered from inappropriate governmental aid (technical and financial). Furthermore, there are few subsidies to help improve the precarious situation of farmers in the area (Ayuntamiento Municipal de Tapachula, 2007). Agricultural subsidies in Mexico only represent a small fraction of those prevailing in developed countries (Rubio, 2006).Due to SAPs, agricultural subsidies in Mexico were greatly reduced during the 1980s and 90s.jeopardizing the productivity and viability of Mexican farmers (Bartra, 1999). Because of the government’s policy to eliminate supply-side subsidies in the agricultural sector, coffee farmers in the area of study not only had to face low coffee prices until recently, but also the reduction of support of technical, marketing, and financial programs in the agriculture sector. Coffee subsidies are granted by PROCAMPO to 16 (73%) of the 22 coffee producers in the LL; whereas in the ML, 27 (53%) coffee producers are receiving subsidies. In the HL the scenario is worse: there are only nine (24%) small-scale farms producing coffee with subsidies out the total 37 coffee farms. The scenario is similar in the case of Maize in the HL, where 24 (56%) of the 43 maize producing farms are subsidised. Other farmers (i.e. tropical fruits and soy), and ranchers are in the same situation as the coffee Low-Land Middle-land High-land Annual crops n=47 ha Sub. (1/0) n=51 ha Sub. (1/0) n=51 ha Sub. (1/0) Soy 7 24 6 - - - - - - Maize 7 12 5 - - - 43 67.85 24 Sesame 3 7 1 - - - - - - Vegetables - - - - - - 3 5.2 - Other 1 1.7 - - - - 2 1 - Perennial crops - - - Coffee 22 89.5 16 51 143 27 37 98.13 9 Mango 9 35.5 5 - - - - - - Banana 3 18 3 - - - - - - Other P. 4 3.5 1 - - - - - - Grassland 8 91 - - - - 1 1 - Forest - - - - - - - - - Forest < 7 years - - - 1 1 - - - - Forest > 7 years 6 7 - - - - 28 121.5 - Other - - - 1 1 - - - - Total cultivated area - 282.2 - - 143 - - 173.18 - Forest area - 7 - - 2 - - 121.5 - 148 and maize producers. Moreover, the subsidies are not evenly distributed among farmers or among zones. For instance, coffee farmers in the LL receive a mean of $1,981 MXN ($152 USD) ha/yr; while farmers in the ML and the HL receive $898 MXN ($69 USD) ha/yr and $1,304 MXN ($100 USD) ha/yr, respectively. Maize producers also receive different amounts of subsidies, the largest number of maize producers is in the HL, they receive an average of $1,158 MXN ($89 USD) ha/yr. Table 8.10: Amount of subsidies in MXN/ ha/year Zone Crop N Min Max Mean Std. Dev. LL Soy 6 800 1300 971.6 184.4 Maize 5 800 1100 962.0 108.2 Coffee 16 1500 3000 1981.2 488.8 Mango 5 960 1600 1100.0 279.6 Banana 3 960 1300 1086.6 185.8 ML Coffee 27 285 1500 897.9 258.0 HL Maize 24 600 1500 1157.6 223.3 Coffee 9 700 1500 1303.89 294.34 Source: author’s survey Source: author’s interview Box 8.2: Agricultural subsidies In spite of the lack of control to the subsidised farmers by the people of SAGARPA (PROCAMPO), the amount of coffee subsidies are not enough for those who receive the aid, because there are other ejidatarios that have sold or rented their farms still receiving subsidies. This is a big problem for all of us, we don’t receive the same amount of economic aid offered by the government and in other cases there is not help at all. Especially now with the heavy rains, I’m losing half of the harvest because the damage to the coffee plants during the blossoming was severe, the flowers fell down as well as the production. The government should invest more in the small-scale farmers otherwise the agriculture will disappear, it is not longer profitable. I myself want to sell my property and go to the north (USA) to support my children; now they are going to school. Jose Reyes 37, ejidatario of ejido el Eden in the ML region 149 8.2.8. Production of Crops and Price changes Table 8.11 shows the minimum, maximum, sum and mean values of the main crops produced in the three study zones. Taking the case of coffee as an example, the 22 LL farms produce a mean of 0.95 t/ha each, the production increases in the ML to 1.07 t/ha and is drastically reduced to 0.55 t/ha in the HL. Furthermore, there are remarkable differences in the mean of harvested maize between the LL (1.70 t/ha) and HL (0.48 t/ha). An explanation for such different productivity levels is that the regions have several topographic and climatic differences, different soil types and qualities. Moreover, degradation processes differ among the regions as is shown above in tables 8.6, 8.7, and 8.8. Table 8.11: Current crop production t/ha Zone Crop N Min Max Sum Mean Std. dev. LL Soy 7 1.0 2.5 9.9 1.4 0.6 Maize 7 1.0 3.0 11.9 1.7 0.7 Sesame 3 0.5 0.7 1.7 0.5 0.1 Coffee 22 0.4 2.0 20.8 0.9 0.3 Mango 9 0.7 5.0 25.2 2.8 1.5 Banana 3 7.0 15.0 32.0 10.6 4.0 Other perennial 4 0.4 3.0 4.4 1.1 1.2 ML Coffee 51 0.5 2.0 54.7 1.0 0.4 HL Maize 43 0.1 1.0 20.4 0.4 0.2 Vegetables 3 2.0 5.0 11.0 3.6 1.5 Coffee 37 0.1 1.0 20.4 0.5 0.3 Source: author’s survey Small-scale farmers stated that suffered a drop in crop productivity in comparison with the 2005 production. They indicated that one of the main reasons for this was the occurrence of natural disasters and farmland has become less productive (Table 8.12). 150 Table 8.12: Crop Production changes and determinants LL ML HL Production 2005 Soy Maize Coffee Mango Coffee Maize Coffee n= 7 n=7 n=22 n=9 n=51 n=43 n=37 Greater - 1 2 3 10 3 4 Lesser 6 5 13 4 34 33 31 Same 1 1 7 2 7 7 2 Why lesser? Less productive land 1 2 2 - 12 9 6 Old plants - - 3 - 8 3 4 Weeds - - 1 - - 1 - Pests - - 1 1 - 3 2 Natural disasters 5 3 6 2 13 16 18 Other - - - 1 1 1 1 (Sesame, banana, and other perennial crops are not included for having a small sample number) Source: author’s survey The prices of coffee and maize have increased since 2005. As mentioned above, they are the two most common crops in the three zones. The HL is the region that experienced the greatest increase in coffee prices $15 MXN ($1.15 USD) per kg. The two maize producing regions (LL and HL) saw an increase in price in the order of $0.93 MXN ($0.07 USD) and $1.19 MXN ($0.09 USD) per kg respectively. Table 8.13: Changes of average prices in comparison with 2005 of main crops in MXN/Kg Region Crops Price 2005 Current Price Difference % LL Soy 3.50 4.99 1.49 42.6 Maize 2.14 3.07 0.93 43.0 Sesame 13.67 12.33 -1.33 -10.0 Coffee 8.41 12.82 4.41 54.4 Mango 3.06 3.34 0.28 9.1 Banana 3.00 4.33 1.33 44.3 Other perennial 8.33 9.17 0.83 10.0 ML Coffee 7.09 13.75 6.67 94.0 HL Maize 2.31 3.50 1.19 51.5 Vegetables 6.67 10.00 3.33 50.0 Coffee 16.84 31.89 15.05 89.4 Source: author’s survey 151 8.2.9. Livestock production and price changes Because of its topographic characteristic, the LL is very suitable for cattle breeding. Eight of the small-scale farms in this region (17% of the total) have 165 heads of cattle (Table 8.14). Over the past five years, the number of cattle raised in this zone has increased considerably and so have cattle prices. In the ML, there are no cattle. The farmers here produce only coffee. With a mean farm size of three ha, there is no space for cattle. The limited physical space is used to produce and process coffee beans. Any sort of livestock could directly ruin the coffee beans, quality and quantity, as well as the coffee plants. In the HL, the farmers keep livestock as a way to enhance their income and to produce food for the household. Here, eight (16%) small-scale farms breed sheep and seven (14%) raise poultry. Even though the price for sheep has dropped over the past five years, farmers continue raise sheep ─albeit fewer than five years ago─ as a part of their subsistence strategy. Table 8.14: Livestock units and price changes since 2005 Zone Livestock Farms Units, Price MXN Min Max Sum Mean Std. Dev. LL Cattle N=8 Current units 4 70 165 20.63 20.91 Units 2005 0 30 97 12.13 11.27 Current price 2500 5000 31500 3937.50 903.86 Price 2005 3000 4000 27300 3412.50 496.96 HL Sheep N=8 Current units 3 22 63 7.88 6.62 Units 2005 2 35 101 12.63 12.44 Current price 0 800 2850 356.25 317.85 Price 2005 0 500 1970 246.25 217.05 Poultry N=7 Current units 3 30 76 10.86 8.84 Units 2005 5 30 100 14.29 9.21 Current price 120 180 950 135.71 22.99 Price 2005 80 100 660 94.29 9.76 In the ML poultry n=2 and in the HL cattle n=3 are not included for having a small sample number Source: author’s survey 152 8.2.10. Small-scale farm development Chiapas is a state where both “profit-oriented agribusiness and survival-oriented subsistence farming” are carried out (Richter, 2000). The fundamental foundations of development in the Chiapaneco state have been agricultural production for exportation for more than a century. Even though the national development strategy has shifted from “Import Substitution Industrialization (ISI)” to “Export Substitution Industrialization (ESI)” through a set of structural adjustment policies SAP, the agro-export sector has always been the economic pillar of the state (Santacruz & Perez, 2009). However, the current model has brought poverty and marginalization upon the small-scale farmers, peasants and indigenous populations (Villafuerte & Garcia, 2008); it has given priority to the production of fruits, vegetables and flowers for export (Santacruz & Perez, 2009). Villafuerte and Garcia (2008) argue that the impoverishment of peasant societies, especially indigenous peasants in Chiapas, is reflective of a deeper crisis in the development model based on the export-oriented agriculture and extensive cattle farming. Low farm productivity caused by natural disasters, social inequalities, and economic disequilibrium has triggered the out-migration of populations and the abandonment of the land. There are broad differences in income among the small-scale farming households included in this study. The ML, with all its farmers producing coffee, has a mean income of $2,206 MXN ($170 USD) per month (Table 8.15). In contrast, the HL, with a large share of its farmers producing coffee and a greater number producing maize, has the lowest mean income of the three zones, at $794 MXN ($61 USD) per month. In the LL, an area with a variety of export-oriented crops, farmers have a mean income of $1,760 MXN ($135 USD) per month. Low incomes do not just reflect low productivity; they are also a result of the absence of measures to strengthen the production of rural economies. Natural events also come into play insofar as they reduce productivity and damage infrastructure. As a way of increasing income, farmers in all three regions have chosen to undertake other economic activities such as day labourer, trader and dealer. 13 (28%), 11 (22%), and 16 (31%) farmers in the LL, ML and HL respectively carry out economic activities outside of farming. In addition, many households receive remittances. In this regard, both the ML and HL have 13 (25.4%) households receiving remittances; and the LL has two. 153 Table 8.15: Household heads’ economic assets Zone Economic asset LL n=47 ML n=51 HL n=51 HH head income mean (MXN/month) 1760 2206 794 Minimum 0 0 0 Maximum 6000 6000 2000 St. deviation 1065 1641 535 Secondary eco. activities (1/0)a 13 11 16 Receive remittances (1/0)b 2 13 13 (a) 1= performs a secondary eco activity; 0= otherwise. (b) 1= receives remittances, 0= otherwise Source: author’s survey Chiapas ranks last place in economic and social development, and it is also one of the poorest regions in terms of standard of living for the vast majority of its inhabitants. Table 8.16 describes the household head’s education level. The major difference encountered among the three zones is the number of illiterate household heads. There are 22 (43%) household heads in this circumstance in the HL, 10 (21%) and seven (14%) in the ML and LL zones respectively. Another aspect that catches the eye, is that a large share of household heads have attended elementary school, although some never finished, it is likely that almost all these household heads can write and read. Table 8.16: Household heads’ education level Education level Zone LL n=47 ML n=51 HL n=51 Illiterate 7 10 22 Elementary school 23 30 22 Middle school 13 9 4 Other 4 2 3 Source: author’s survey 8.2.11. Physical assets Table 8.17 presents data, firstly, regarding the physical assets owned by the households investigated. I consider home ownership to be an indicator of the variable “physical assets”. All the ML and HL zones households are living on their own properties, and 43 out of the 47 households in the LL own the physical space where they live. Secondly, natural assets in this region are an important source of resources, providing materials for construction or firewood for the household. The LL and ML have a small number of farms with forest, in contrast the HL region, where forest remains in 28 farms, covering an area of 121.5 ha. This mountainous 154 area is a natural water catchment that provides water to the ML and LL. Thirdly, the household’s construction material is a useful indicator to measure the economic development in each zone. Concrete is the main material used for building houses. The number of farmers using this wall material in the HL is lower than in the ML and LL; same scenario occurs with the use of concrete as material for floors. Concrete is a material not commonly used in the construction of roofs in rural areas, predominantly the roofs in the three zones are built with corrugated steel roof sheet. Access to basic services such as water and electricity can roughly estimate the living conditions of the households. Even though all the households have electricity, tap water is not available on several farms. There are 27 (53%) small-scale farming households with access to tap water in the ML and HL zones, and 21 (45%) in the LL. According to the semi-structured interviews applied to farmers and additional data gathered in the questionnaires, the lack of public water supply (piped water) is due to the poor infrastructure and low maintenance of the water system. Several water pipes collapse in the rainy season. Moreover the construction of wells in the LL region by farmers, and the water availability in the form of streams and rivers in the ML region, supply water to those who do not have access to tap water. A reduction in the quality and quantity of water for both domestic and agricultural purposes is another consequence of deforestation in the main water-catchment areas. Table 8.17: Household’s physical, natural resources and living conditions Physical and Natural resources Zone LL n=47 ML n=51 HL n=51 Home ownership (1/0)a 43 51 51 Farm size mean (ha) 6.9 3 7.9 H.H. with livestock (1/0)b 8 2 19 Forest (1/0) 6 2 28 Forest area (ha) 7 2 121.5 Living conditions Wall materials (1/0)c 30 28 13 Roof materials (1/0)d 40 50 47 Floor materials (1/0)e 40 46 20 Access to tap water (1/0)f 21 27 27 Electricity (1/0)g 47 51 51 (a) 1= own house; 0= otherwise. (b) 1= owns livestock, 0= otherwise. (c) 1= blocks; 0= otherwise. (d) 1= corrugate sheet; 0=otherwise. (e) 1= concrete; 0= otherwise. (f) 1= have access; 0= otherwise. (g) 1= have electricity; 0= otherwise Source: author’s survey 155 8.2.12. Rural development constraints The survey with small-scale farmers also provided information regarding the current economic situation in relation to the year 2005, in order to find the determinants that have jeopardized the farm and households development (Table 8.18). Many of the households surveyed stated that their economic situation has gotten worse. Along these lines, 34 (67%) small-scale farming households in the HL region have poor economic performance; and for 18 of these the main economic constraint is low crop productivity. The same case occurs for eight of 24 (51%) small-scale farming households in the LL. However, while low productivity is a constraint for farmers in the LL and HL, the main economic constraint for the nine small-scale farming households in the ML is perceived to be NAFTA. In fact, the economic conditions of this coffee region (ML), with the implementation of NAFTA, have gotten worse. These circumstances, combined with extreme natural events and the degradation of natural resources, have had detrimental impacts on the development of the study areas. Table 8.18: Current economic situation in relation to 2005 Economic situation and determinants Zone LL ML HL Same 18 13 8 Better 5 9 9 Worse 24 29 34 Determinants (worse) Low yields 8 4 18 Increase of pests and diseases 6 5 2 There are not jobs - 4 - NAFTA has worsened the economic situation 4 9 1 Everything is more expensive 2 3 7 Weather has worsened 1 2 5 Other 3 2 1 Source: author’s survey 8.2.13. Farm productivity determinants and constraints since 2005 Farm productivity is an important indicator that will be useful for further land degradation analyses. The three zones have a large number of farmers who stated that productivity decreased since 2005 (Table 8.19). In the LL region, for nine of 29 farms, soil erosion and land degradation represent the main constraint upon farm productivity; while the same constraint affects 20 out of 41 farms, and 18 out of 49 farms in the ML and HL respectively. 156 Floods and pests/diseases are also important factors influencing farm’s productivity. Ejido land is a fundamental natural asset for rural households. It has been kept by several generations, with parents bequeathing the land to children. Accordingly, the survey revealed that 16 (34%), 15 (29%), and 9 (18%) of small-scale farming households intend to bequeath their farm to the next generation. Moreover, 28 (60%) small-scale farming households in the LL, followed by 32 (63%) in the ML, and 40 (78%) in the HL, indicated their intention to continue to manage their farms in spite of low productivity. Table 8.19: Factors causing a decrease in farm productivity since 2005 and further farmers’ responses Source: author’s survey Additional data shows that environmental factors have contributed to reducing the farms’ productivity and fertility since 2005 (Chart 8.3). In particular, hurricanes have severely affected productivity during the last five years (i.e. hurricane Stan and Wilma in 2005). Hurricanes have altered coffee production in several regions and exacerbated environmental damage already caused by deforestation. This has been the case for 31 (61%) farms in the HL, 22 (43%) farms in the ML, and nine (19%) in the LL region. Other sudden natural events have also affected the economic development and enlarged the number of poor people in the region. In the LL, droughts, floods, pests and diseases have been the determinants for nine (19%), 13 (28%), and 12 (26%) farms respectively. In the ML region, the propagation of plant pests/diseases has affected productivity for 18 (35%) farms over the last five years. Determinants Zone LL ML HL Don't know 3 - - No 15 10 2 Yes 29 41 49 Yes why Eroded/degraded soils 9 20 18 Droughts/lack of water 6 4 8 Floods 6 7 18 Pests and diseases 7 9 5 Other 1 1 Responses Continue to manage it 28 32 40 Bequeath land to children 16 15 9 Other 3 4 2 Chart 8.3: Natural factors affecting farms since 2005 Villafuerte and Garcia (2006) point out that the government’s support small-scale farmers and of agricultural crops, provoking a progressive decreasing crop yield per unit area of land under cultivation. Table 8.20 clearly depicts that the lack of government aid in terms of loans and credits to propel small-scale agricultural production is affecting 20 (43%), 11 (22%) and 14 (27%) farms in the LL, ML, and HL respectively. income due to low yields, limit farmers’ investments in agriculture. In fact, there are 15 (29 %) farms in both the ML and the HL, and 11 (23%) in the LL, whose farm production is constrained by the lack of economic assets needed to improve farm production and agricultural techniques. 9 13 1 11 0 5 10 15 20 25 30 35 D ro ug ht s # o f r es po n de n ts 157 Source: author’s survey withdrawal of economic aid the reduction of generalized subsidies led to a re The lack of economic assets and reduced farm 0 12 9 2 21 0 18 22 8 1 3 5 31 0 Fl oo ds Fr os t Pe st s/ di se as es H ur ri ca ne s N on e LL ML HL to -composition 1 0 O th er 158 Table 8.20: Farm production constraints Constraints Zone LL ML HL Total Lack of water 7 - 10 17 Poor soils - 10 3 13 Eroded/degraded farmland 5 8 4 17 Crops/livestock low prices 2 4 5 11 Lack of economic resources to improve production 11 15 15 41 Lack of technical assistance 2 3 - 5 Lack of loans/credits/aid from the government 20 11 14 45 Source: author’s survey Source: author’s interview 8.2.14. Pollution problems in the ejido or small-scale farm The pollution of water, air, and soil affect the development of agriculture and social conditions in the three zones. Pollution contributes to health problems, lower life quality and decreasing crop production and soil fertility. Pollution is found in 34 (72%) farms in the LL, 27 (53%) in the ML, and12 (24%) in the HL (Table 8.21). Due to its proximity to the urban centre Tapachula, the LL is polluted by sewage water and garbage that is hauled by the rainwater and increases during the rainy season. The overuse of agrochemicals is the main air pollutant in the ML and HL, although data gathered in the survey regarding the use of Box 8.3: Coffee prices and production constrains I have 10 ha of coffee and maize, in this year the amount of coffee and maize harvested was small because of the heavy rains and frosts. It rained too much that coffee flowers fell down. Thankfully the coffee prices have slightly increased; I sold a sack of Arabica coffee (65kg) to $2,900 MXN ($223 USD). However, although the price is good but the damage cause to the coffee trees and production by the weather doesn’t allow us to have an income to provide all our needs, therefore I have to do other economic activities. In this year I harvested five coffee sacks per ha, few years back I used to harvest nine to ten sacks per ha. Nowadays, the agriculture is not longer profitable. Emiliano Perez 40, ejidatario of ejido el Naranjo in the HL region 159 agrochemicals indicates that the use is very low. In personal interviews, farmers from these zones stated that the National Plant Protection Program launched by SAGARPA through the SENASICA (National Health Service, Food Safety and Food Quality) to control and eradicate the “Mediterranean fruit fly” (certainties capitates) pollutes the air due to random aerial spraying of pesticides in the region. Table 8.21: Pollution problems Zone LL ML HL Total Pollution problems (1/0) 34 27 12 73 Rivers/wells 10 6 3 19 Air 9 13 8 30 Soil 15 8 1 24 Pollution source Overuse of agrochemicals 12 13 7 32 Sewage water/trash from neighbours 17 14 5 36 Industry 4 - - 4 Other 1 - - 1 1= yes; 0= otherwise Source: author’s survey 8.2.15. Labour force There is a greater demand for labour during the harvesting season in Tapachula. Household’s members cannot supply the current demand for labour on small-scale farms (Table 8.22). This is the case in the LL and ML, where 13 (28%) farms and 19 (37%) farms respectively have hired foreign labour. By contrast, 27 (53%) farms located in the HL are still depending entirely on household members as the sole source of labour. On the other hand, during the last agricultural year, 29 (62%) farms in the LL have made use of additional labour from neighbouring ejidos, as well as workers from Guatemala. They carry out planting and harvesting activities on perennial crops (i.e. coffee and mango) whereat 32 (63%) farms in the ML hired workers mainly from Guatemala to plant and harvest perennial crops. Only 11 farms (22%) in the HL hired external labour force from neighbouring ejidos to sow and harvest annual and perennial crops (i.e. coffee, maize and vegetables). The common factor for hiring Guatemalan labour in the three zones is due to the out-migration of local labour. 160 Table 8.22: Labour force sources, activities and origins Zone Labour force LL ML HL Total Main source All the household members 8 16 27 51 All the adult male of the household 15 12 18 45 Mexican worker 10 4 5 20 Foreign worker 14 19 1 33 Type of work Sow and harvest annual crops 2 - 5 7 Plant and harvest perennial crops 19 26 2 47 Weeding 6 5 4 15 Other (esp.) 2 1 - 3 Origen Neighbouring ejidos 12 6 9 27 Guatemala 17 26 2 45 Reasons for hiring foreign labour force Cheaper labour force 5 2 - 7 Migration of local workers 9 15 2 26 Better workers than local workers 3 9 - 12 1= yes; 0= otherwise Source: author’s survey #Source: author’s interview Box 8.4: There are not enough workers Almost all the chavos (youngsters) are in the USA, other went to the maquiladoras (sweatshops), other are working in Tapachula, the few remaining don’t want to work in the coffee farm. They are also planning to leave the ejido for better opportunities. Because of our proximity to big coffee farms and the Guatemalan border, many Guatemalans from San Marcos have come to ask for work. They work for 65 pesos ($5 USD) a day and we provide them food and shelter. We are content with them, they are good workers and reliable. They have experience in harvesting coffee, that is a big advantage known by the big coffee farms for many years, that’s why they have come to work in the coffee region for many years. Now more and more ejidatarios are hiring Guatemalan workers. Lázaro Ordoñez 75, ejidatario of ejido El Eden in the ML region 161 8.2.16. Outmigration Since the creation of the México-Guatemala border, the city of Tapachula has become a border town that many refer to as Little Tijuana, similar to Tijuana on the U.S.-México border (Grayson, 2002). The description of the household’s demographic and farm characteristics, crop productivity, available assets for development, and main environmental and socio-economic constraints, is necessary to understand the out- migration in the region. In this regard, the number of migrant sending households in the LL is 12 (26%) with 49 household members. Most of these out-migrants have moved to urban centres and other Mexican states. The 50 household members from 23 households (45%) in the ML region have migrated to international and regional destinations. Moreover, there are 22 (43%) migrant sending households in the HL that account for 46 migrants who have mostly chosen to migrate beyond Mexico’s boundaries, and to a lesser extent to other Mexican states. Table 8.23: Household heads’ social assets Zone Social assets LL ML HL Total HH with out-migrants 12 23 22 57 Out-migrants # 49 50 46 145 International 3 21 25 49 Regional 16 28 15 59 Urban 30 1 6 37 Source: author’s survey The main destinations of these migrants differ among zones. To begin with, in the LL, internal destinations of out-migrants include traditional urban centres, such as Tapachula (Table 8.24). Secondly, regional migration from the LL’s joins the long distance migratory pattern toward the Gold Coast of Baja California to Tijuana city, where migrants try to find employment in a manufacturing-sweatshop centre or attempt to cross the Mexican-US border. Regional migration from the ML region tends to be towards the states of Baja California (Tijuana), Jalisco (Guadalajara), Veracruz (Veracruz), and Mexico (Mexico City), which have been traditional destination sites for immigrants. Additional findings point out that regional migration from the HL occurs mainly toward Baja California (Tijuana). Thirdly, international migration from the ML tends to be toward the West American coast (California) and to the East American coast, mostly to the states of Florida, Georgia, Virginia, and North Carolina. The HL region’s international migration is dominated by an outflow toward North 162 Carolina. Summing up, the migration directionality from the LL goes toward urban centres; the regional migration is predominated by out-migrants from the ML; while the HL out- migration flow is characterized by its international mobility. Table 8.24: Out-migrants’ main destinations Zone LL ML HL Total # of out-migrants 49 50 46 145 Urban 30 1 6 37 Tapachula/CHIS 26 1 6 33 Other 4 - - 4 Regional 16 28 15 59 Cancun/ROO 4 1 3 8 Tijuana/BCN 6 7 9 22 Veracruz/VER - 5 - 5 Guadalajara/JAL - 6 2 8 Mexico DF/MEX - 4 1 5 Other 6 5 0 11 International 3 21 25 49 California/USA - 3 - 3 Florida/USA - 3 3 6 Georgia/USA - 3 - 3 North Carolina/USA - 3 17 20 Virginia/USA - 3 2 5 Other 3 6 3 12 Source: author’s survey Source: author’s interview Box 8.5: Environmental migration Yet, the last year we had coffee, now I ask myself whether we are going to have coffee in this year because it has been pouring on water during the coffee blossoming, bringing detrimental consequences to the production. Therefore, my “compañeros” ejido-mates have to go to work far away and leave the farms and families behind. I have a boy and I tell to my son, son let’s go to work to the north, our relatives in North Carolina can help us with the money to cross the border. We have no more opportunity, here there is no way out and things are tough. Reynaldo Gonzales 39, ejidatario of ejido Veintiséis de Octubre in the ML region 163 Source: author’s interview Source: author’s interview The maps below provide a visual representation of the volumes and destinations to regional and international areas from the different ejidos in the LL, ML and HL regions. Box 8.7: Migrants’ international destinations We are 84 Ejidatarios, from the last seven years ten ejidatarios have migrated and many of our children are already in the North (USA). I have three children in North Carolina and two siblings in Florida, they left the ejido fourth years ago but because of the crisis they want to return next year they say. Now it is difficult to save money, luckily they built a good house. The other ejidatario children in the North are also in North Carolina, but they come every four or five years, they stay for a year or until the money lasts and they go back again. Jesus Morales 75, ejidatario of ejido Pavencul in the HL region Box 8.6: Migrants’ regional destinations When I’m not busy taking care of my coffee trees, I do other activities to gain more money. My two sons and other young people have the custom to go to Sonora to work in the grape fields. Every year there are advertisements flying around in Tapachula to recruit workers. A bus come in January to take the workers to the border, they work there until August and return just when the coffee harvest starts. Other young boys take the way to Cancun in Quintana Roo to work as bricklayers. Not everybody returns for helping with the harvest though, some like to live in those states and others with money in their pockets rather try to cross the border with the USA. Simón Roblero 43, ejidatario of ejido Veintiséis de Octubre in the ML region 164 Map 8.1: Principal regional migration flows from the study regions Map 8.2: Principal international migration flows from the study regions Source: author’s own elaboration Tapachula Virginia North Carolina Florida California Veracruz Tapachula Tijuana Guadalajara Cancun Mexico City 165 8.2.17. Determinants of outmigration The survey collected information from 145 household members regarding migration destinations and the reasons for migrating. Table 8.25 presents the key determinants for migration in the three regions. The lack of employment in the rural areas is the main determinat to 65 (45.1%) household members to migrate; other 58 (40%) migrants abandoned their households because they considered the wages paid in the agricultural sector as low. These two groups account for 85% of the migrant population. Despite the lack of employment opportunities, small-scale and big-scale coffee farmers offer temporary employment during the harvesting season, which is rejected by local workers. According to the survey data, small-scale farmers pay a mean of $67.7 MXN ($5.20 USD) per day, while big coffee farms pay a mean of $72 MXN39 ($5.53 USD). Hence, local workers tend to reject this wages for being too low; they prefer to migrate towards urban centres. Table 8.25: Household members’ reasons to migrate Reasons Frequency Percent Low agriculture productivity 2 1.4 Low wages 58 40.0 Unemployment 65 45.0 Existence of network 8 5.4 Insecurity/violence 2 1.4 In search of better economic opportunities 10 6.8 Total 145 100.0 Source: author’s survey 8.2.18. Migrant remittances Remittances vary between regions and depend on migration patterns. Because the LL is close to Tapachula city, urban migrants do not remit money to their households. The picture is different for the ML households; there are 13 households receiving remittances, using them mainly for consumption (Table 8.26). Both ML and HL households receive remittances monthly in mean amounts of $1,371 MXN ($105 USD) and $1,340 MXN ($103 USD) respectively (Table 8.27). 39 This value was obtained from the mean wage paid by coffee farmers to Guatemalan workers, and divided by the number of working days in a month (24). See table 7.49 166 Table 8.26: Remittances and uses Zone ML HL HH receive remittances (1/0) 13 13 Uses Household consumption 11 10 Other 2 3 Source: author’s survey Table: 8.27: Frequency and amount of remittances in MXN$ Source: author’s survey 8.2.19. Local alternatives views for development In my visit to the ejido of Pavanecul in the HL region, I had the opportunity to have a closer look at the agricultural crops that are replacing those used in the traditional milpa system. Indeed, new crops are being introduced to the area as an alternative to the lower productivity of the traditional milpa poly-culture of maize, bean and pumpkin. Some households have decided to organize themselves in order to explore crops that are suited to the area and help increase their incomes. They have introduced vegetables: cauliflower (brassica oleracea var. botrytis), cabbage (brassica oleracea var. alba l.), wild cabbage (brasica silvestris l.), radish (raphanus sativus), carrots (daucus carota), potatoes (solanumt uberosum), and faba beans Zone Frequency/amount N Min Max Mean Std. Dev. ML Fortnightly 2 700 3000 1850.00 1626.35 Monthly 7 400 3000 1371.43 977.61 Quarterly 4 1000 2000 1375.00 478.71 HL Monthly 5 1000 2000 1340.00 421.90 Quarterly 4 1000 3000 1750.00 866.03 semi-annual 3 500 1500 1166.67 577.35 Yearly 1 2000 2000 2000.00 - 167 (vicia faba); flowers: easter lily (zantedeschia aethiopica) and cereals: wheat (triticum aestivum) and barley (hordeum vulgare). In addition, cattle production was part of the new alternatives and as mean to establish an integrated farming system, where cattle consumes the crop residues and cattle dung is used as an organic fertilizer to enhance the land’s fertility and crops productivity. The current economic, social and environmental conditions of the region have encouraged small-scale farmers to search for new alternatives. Source: author’s interview Box 8.8: Seeking new development alternatives Since ten years ago the government was supporting the agriculture but a few years ago this aid has been halted. The lack of social and agricultural assistance by the government to the ejido has stimulated us to organize and diversify our crops. Now, we are producing organically because we know the organic products have a better price in the markets, unfortunately our local market consumers and retailers do not distinguish between conventional and organic products. For the same reason, our organic products cost the same than conventional. We sell most of our products in Tapachula’s market and weekend tianguis (open air market held on certain market days in a town or city). We use cow manure to fertilize our crops and we have seen that producing in this sustainable way is more profitable because our vegetable, cereals and flowers production have increased. Nonetheless we also suffer from the lack of technical advice either by the government or other non-governmental institutions. We desperately need agricultural advice to cope with our needs and to foster our development. Furthermore, we don’t have the right equipment for threshing our cereals efficiently; we have adapted from old agricultural tools a device that is not very efficient therefore we lose part of our cereals during the harvesting. We are willing to cooperate with the implementation of an organic project with the participation of other ejidatarios and ejidos. Lorenzo Lopez, 47 ejidatario of ejido Pavencul in the HL region 168 8.3. Description statistics of Central American migrant workers survey The data gathered by this survey shows socio-demographic characteristics, household composition, motivations to migrate, mistreatment during the migration journey, paths and destination points, aid to migrate and social networks, labour aspects and conditions, labour activities and income, as well as living and health conditions of 80 respondents working on seven coffee plantations. The entire surveyed population nationality is Guatemalan, because the participation of other Central American workers in the coffee sector of Tapachula is very limited. 8.3.1. Socio-demographic characteristics The surveyed migrant population is relatively young; there are 31 Guatemalan migrant workers (39%) within the major age group 20 to 30 (Chart 8.4) and the mean age is 32.7 years (Table 8.28). The migrant population is predominantly male (66 respondents, representing 83%). 51 respondents (64%) have certain level of education (i.e. they can read and write). The long lasting and close relations between the bordering Mexican and Guatemalan states in terms of economy, trade, and labour began before the addition of Chiapas to Mexico (Castillo, 2003). This trend continues to this day, as this study demonstrates: 67 of the respondents (84%) come from the Guatemalan state of San Marcos, located at the South westernmost part of the border area between Mexico and Guatemala. The closeness of San Marcos to the study region allows the people who live there to bring their families along when they cross the border. Thus, 61 respondents (76%) live with one or more family members on the coffee plantations. Chart 8.4: Age groups of Guatemalan migrant workers Table 8.28: Guatemalan migrant work Characteristics Age Sex (1/0)a Civil status Free union Married Single Other Education (1/0) Place of birth Escuintla Huehuetenango Quetzaltenango San Marcos Suchitepequez Family members (1/0) (a) 1= male; 0= otherwise, (b) 1= with education; 0= illiterate, (c) 1= with family member; 0= otherwise 12 0 5 10 15 20 25 30 35 15 to 2 0 # o f r es po n de n ts 169 Source: author’s survey ers’ socio-demographic characteristics Min Max Mean Std. dev. 16 65 32.788 12.177 n=80 % 66 83 25 31 30 38 23 29 2 3 b 51 64 3 4 5 6 3 4 67 84 2 3 c 61 76 Source: author’s survey 31 14 15 7 1 21 to 3 0 31 to 4 0 41 to 5 0 51 to 6 0 61 to 7 0 Age groups 170 8.3.2. Household characteristics Guatemalan workers in the coffee plantations of Tapachula used to bring family members to help in agricultural activities such as picking of coffee cherries. A number of 197 household members accompanied Guatemalan workers, where 54% of them are underage, and 46% are over 18 years. However not all workers live with family members, they live with other friends, or co-workers. In some cases the community rooms where the workers live with their family have to be shared with other workers. By counting the number of Guatemalan workers living with family members, the mean of the household size 3.5 people (Table 8.29). Table 8.29: Household characteristics n % # of Guatemalan migrants 80 100 # of household members 197 100 HH # of adults +18 91 46 HH # of young -18 106 54 Household size mean 3.5 - # of non- household members 416 100 Live with non-household members (1/0) 53 66 (1/0) 1= yes; 0= otherwise Source: author’s survey 8.3.3. Migration characteristics Chiapas has become an important place for immigration and transit-migration for Central Americans, especially for Guatemalan workers (Anguiano, 2008). Guatemala presents acute historical problems of poverty and political conflicts (Casillas, 2008).This has led Guatemalan families to establish new livelihood strategies to cope with economic deterioration. There are 48 (60%) respondents who said that they migrated to escape from poverty, and 29 (36%) who left their home country due to unemployment conditions (Table 8.30). The shifting from traditional agriculture to export-oriented agriculture has attracted migrant workers in search of temporary employment, particularly to the Soconusco region. Along these lines, 57 (71%) migrants arrived to Tapachula to work temporarily mostly during the harvesting of coffee cherries. This temporary migration trend has a historical background; many migrants had worked on the coffee plantations previously. As such, these migrants already knew how to migrate and were aware of the opportunities, destinations and labour conditions in the region. 171 When the local and foreign labour force is scarce, the plantation owners approach Guatemalan workers on the border. In 28 (35%) cases, the respondents stated that a contractor who works either for one or several coffee farms hired them on the border. Regarding labour permits, 42 (53%) migrant workers holding a migratory work temporary in the region. In many cases, the coffee-farms help migrants to obtain the migratory form. An additional 38 (48%) migrants are employed without any type of labour permit or migratory form. They stated that they had been employed on the same farm few times before, thus they were able to get work for the acquaintance established with the foreman. Other migrants came from the highlands of Guatemala at the border with Mexico, where there is not any migration control; and some have taken advantage of the porosity of the Mexican- Guatemalan border, crossing the Suchiate River in the border towns of Talisman and Ciudad Hidalgo. Table 8.30: Migration characteristics Causes for migration n=80 % Unemployment 29 36 Poverty 48 60 Other 3 4 Purpose in Mexico Working temporary 57 71 Residing 23 29 Migration support No one 39 49 Contractor 28 35 Other 13 16 Opportunities in Mexico Better salaries 21 26 More employment 58 73 Other 1 1 Migratory document None 38 48 Yes as sum of: 42 53 FMTA (Agricultural worker) 22 52 FMTF (Frontier worker) 14 33 FM2 Immigrant 3 7 Other 3 7 Source: author’s survey 172 8.3.4. Migrant workers time in Mexico Both temporary and permanent migrations are measured according to the time the migrant spent in Mexico since his or her last visit. Table 8.31 depicts 38 temporary migrants living in Mexico in a period of one to three months. By contrast, six migrants have been living in Mexico for one to three years. Table 8.3:1 Cross tabulation between living time of temporary and resident migrants Purpose in Mexico Time in Mexico (months) Temporary migrants Residing in Mex. Total 1-3 38 - 38 4-6 13 - 13 7-12 7 2 9 13-36 - 5 5 37-72 - 5 5 73-120 - 4 4 121-360 - 6 6 Source: author’s survey 8.3.5. Transit-migration There is only a small sample size of migrants with previous migration experience to the United States. Likewise only, few are planning to cross Mexico in order to reach the Mexican-US border. Thus, it is better to describe the reasons why most workers do not migrate further north. Table 8.32 shows that, out of 70 (88%) migrants who are not planning to migrate to USA, 26 (33%) are restrained due the lack of money, while 20 (25%) consider the journey through Mexican territory too dangerous (see annex 3). Table 8.32: Transit-migration Transit migration n=80 % Previous migration to USA (1/0) 75 94 Plan to migrate to USA (1/0) 70 88 Not enough money 26 33 The journey to the border is dangerous 20 25 Not interested 11 14 You like living in Mexico 6 8 It is difficult to cross the border 5 6 Other 2 3 (1/0) 1=no; 0=otherwise, Source: author’s survey 173 Source: author’s interview A shelter for migrants in Tapachula, called “Red Casas del Migrante Scalabrini”40, provides the following statistics for the first six months of 2010 regarding the number of migrants who have sought support and stayed there. During this time, the shelter welcomed 2,657 migrants from Honduras (42%), El Salvador (35%) and Guatemala (23%). Only 10% of the migrants were female. Additionally, the information from this shelter suggested that despite the risks of the journey41, Guatemalan transit-migration has increased because agricultural workers are abandoning coffee plantations and joining the migration flow to the USA (Red Casas del Migrante Scalabrini, 2011). 40 This is a shelter that provides housing, food, spiritual support, counselling, medical care and defence and promotion of human rights to migrants, deportees and refugees. 41 In August 22nd as of 2010 a new shocked the media, 72 transit-migrant from Central and South America were massacred in the state of Tamaulipas. It was known as the “San Fernando massacre” where the “Zetas” drug cartel executed by firearms to 58 men and 14 women. Yet, the reasons remain unknown. Source: http://www.eluniversal.com.mx/estados/77602.html Box 8.9: Origin countries and risks for transit-migrants in Mexico The district attorney’s office for assisting the migrants opened its doors in Tapachula in 2010. Personnel from this office come daily to the Scalabrini shelter to take reports of abuse to Central American migrants who have had a problem with the employer (non-payment, breach of working hours, sexual harassment, etc). However, the flow of Central American transit-migrants has decreased because the news of the killings to transit-migrants in Tamaulipas [August 2010]. Many of them prefer to seek a temporary job in Tapachula or returned to their country of origin. Most of the transit-migrants would rather have legal documents for a better access to benefits and salaries than crossing Mexico. In general from the increasing insecurity on the routes used by transit-migrants along Mexico, there are fewer and fewer migrants arriving to the Scalabrini shelters. Transit-migrants begin their journey from Tecún Umán where there is another shelter they remain for three days in this place and then come to Tapachula. According to what part of the USA they are heading to, transit-migrants can reach Nuevo Laredo or Tijuana where other two Scalabrini shelters are supporting migrants who have crossed Mexico. Most transit-migrants are traveling alone from Honduras, El Salvador, Guatemala and Nicaragua. The main reasons for migration are the poverty that has developed in the country and insecurity caused by the Mara Salvatrucha. Sra. Irmi from Red Casas del MigranteScalabrini (Scalabrini’s migrant shelters network) 174 8.3.6. Remittances The number of migrants who send remittances to Guatemala is 26 (33%), wherein 16 (20%) migrants remit a mean amount of $871 MXN ($67 USD) per month. Table 8.33: Guatemalan migrant workers’ remittances Remittances (1/0) Amount MXN$ Frequency 26 min max mean Std.dev Fortnightly 6 300 1500 850 423 Monthly 16 400 1500 871 263 Quarterly 2 2000 4000 3000 1414 Semi-annual 2 450 3000 1725 1803 1= yes; 0=otherwise Source: author’s survey 8.4. Statistical inference 8.4.1. Multiple linear regression analysis Multiple regression analysis (MRA) is based on the same idea, as simple linear analysis (SLA) .The difference is that MRA uses several independent variables to predict the dependent variable. MRA was introduced by Pearson (1908) to make decisions concerning the value or significance of regression coefficients, and with the purpose of analyzing in detail the relationship between several independent or predictor variables (Xj) and a dependent or criterion variable (Yj). The MRA and its estimate uses ordinary least squares (OLS), which is doubtless the most widely used tool by scientist who want to make predictions about past, present or future events (Stockburger, 2001). It also provides an objective and systematic way to analyze data (Armstrong, 2012). MRA “is a technique that allows additional factors to enter the analysis separately thus that the effect of each can be estimated” (Sykes, 1992: 8), to combine the idea of correlation and linear regression and to quantify the impact of various simultaneous influences upon a single dependent variable (Taylor, 2009). Many of the regression techniques have long been central to the field of economic statistics known as “econometrics”. They have also become important for law, social sciences, medical sciences and other fields (Kleinbaum et al., 2008). Notwithstanding, Soyer and Hogarth (2011) showed that some of the world’s leading experts in econometrics can be misled by the standard statistics provided with regression analysis: t, p, F, R2, which can suggest illusions 175 of predictability. Friedman and Schwartz (1991) also pointed out that one should focus on test of outputs (forecasts) rather than statistically significant outputs; the more complex the regression model gets, the more sceptical one becomes. In contrast, there is ample evidence to suggest that regression analysis often provides useful forecasts (Allen & Fildes, 2001). Armstrong (2012: 5) coins that “regression-based prediction is most effective when dealing with a small number of variables, large amounts of reliable and valid data, where changes are expected to be large and predictable, and when using well-established causal relationships”. Where this is the case, decisions based on regressions are less likely to be subject to bias, they are consistent, and the basis for these decisions can be fully explained and they are generally useful (Taylor, 2009). The uses of MRA, when its underlying assumptions are satisfied, are the following: (1) description or model of reality; (2) testing of hypothesis; and (3) predictions about the future. This model describes a criterion variable Yj by predictor variables X1j,X2j,Xkj (kj>1) and is expressed by the equation as: YJ= β0+β1X1j + β2X2j + …+βkXkj + εj (3) Where: YJ criterion variable (outcome) Xj,k predictor variable β0 parameter or constant amount, it is the Y intercept β1 slope coefficient ε the error term reflecting other factors that influence the criterion variable The parameters β0, β1k and ε (error term) are unobservable, hence the task of MRA consists in producing an estimating the two parameters, using the information of the data set based upon some assumptions about the characteristics of ε.. Due to the nature of the parameters they can also be called (partial) regression coefficients because they allow for the effect of other variables. On the other hand, the effects of extraneous exogenous factors (including measurement error) affecting endogenous variables are depicted by the ε term in the model (Kleinbaum et al., 2008). MRA assumes that ε is in average zero; this assumption estimates that the lines (or plane) lie roughly in the midst of the data. Although many lines can be produced, MRA will select a plane for which the sum of the squares of the estimated errors is at a minimum (minimum SSE). The intercept of the plane with the YJ axis (where X1j and X2j are zero) provides the estimate of α, its slope in X1j implies the coefficient β1, and its slope in the X2j implies the coefficient β2. With n explanatory variables, MRA will estimate the 176 equation of a “hyper-plane” in n space (Sykes, 1992). The resulting output will tell how much of the variance of YJ was accounted for by the joint predictive power of knowing X1j, and X2j. This value is denoted by “R2” (known as the coefficient of determination), which is the square of the measure of the correlation (r) between the predicted and criterion variables. The value of R2 is always between 0 and 1, since the regression line is never worse than worthless (R2=0) and cannot be more than perfect (R2=1) (Brace et al., 2006). Both R and R2 values only provide a guide to the “goodness-of-fit” and do not indicate whether an association between the variables is statistically significant. The equation for R2 is the following. R = ""#""$ (4) Explained, it is the sum of squares (SSR) over the total sum of squares (SST), i.e. the ratio of the explained variability to the total variability. Furthermore, the outcome of the MRA will also explain if the model allows predicting YJ at a rate better than chance through the significance level of the overall “F” of the model (Taylor, 2009). The value of “significance F” denotes whether the variances between the means of populations are statistically significant, this depends on the results of the MRA and the confidence level chosen for the model. In a confidence level of 95% if “significance F” is <0.05, then the model is considerable significant and we reject the null hypothesis because there is a statistically significant association between the criterion variable YJ and the predictor variables X1j, X2j,… Xkj. Essential assumptions of MRA Osborne and Waters (2002: 1) state that “most statistical tests rely upon a certain assumptions about the variables used in the analysis. When these assumptions are not met [or violated] the results may not be trustworthy, resulting in a Type I error or Type II error, or over, or-under- estimation of significance or effect sizes” and under-report the strength of the relationships. However, MRA is “robust” in the sense that it will typically provide estimates that are reasonably unbiased and efficient, even when one or more of the assumptions are not completely met (Kleinbaum et al., 2008). The number of assumptions that should be considered varies from statistician to statistician because some needed conditions are treated as assumptions by some and not by others. There are four key assumptions to consider and check before conducting a MRA in order to have unbiased and efficient estimates (Ostrom, 1990). 177 • Normal distribution of variables: All regressions assume that variables have a normal distribution, due to the central limit theorem, which holds that the sum of a large number of random variables will end up approximately distributed. Therefore, the continuous variables need to have a normal distribution before entering the model. If they do not fulfil this assumption, they need to be corrected by applying tests that have assumptions of normality. The non-normal distributed variables can distort relations and significance tests, this difference of distribution can occur when the variables are highly “skewed42” or “kurtotic43” or the variables have substantial outliers (Osborne & Waters, 2002). When the data is non-normally distributed a non-linear transformation (square root, logarithm, arcsine, reciprocal, inverse) could fix the problem. This assumption can best be tested with scatter histograms and a fitted normal curve or a Q-Q-Plot, subsequently checked with a “goodness of fit test”, e.g. Kolmogorov-Smirnov test (Taylor, 2009). • Linearity: MRA assumes that relationships between criterion and predictor variables are linear in nature, it is important to check for outliers since MRA is sensitive to outlier effects. The linearity assumption can be tested with scatter plots. If relationships are non- linear, the solution may be to transform the data to make the relationships linear or to use an alternative statistical model that deals with non-linear relationships (Osborne & Waters, 2002). • Homoscedasticity: This assumption suggests that the criterion variable has an equal level of variability for each of the values of the predictor variables (Kleinbaum et al., 2008), in other words: “Homoscedasticity means that the variance of errors is the same across all levels of the predictor variables. When the variance of errors differs at different values of the predictor variables, heteroscedascity is indicated. This assumption can be checked by visual examination of a plot of the standardized residuals (errors) by the regression standardized predicted value” (Osborne & Waters, 2002: 4). Heteroscedasticity often occurs when there is a large difference among the sizes of the observations, and it also might occur when the errors increase as the value of a predictor 42 Skewness refers to the lean of a data distribution. A positive skew will have a longer tail to the right than to the left; a negative distribution is the other way around. A common rule of thumb is that this value should be between +2 to -2 43 Kurtosis measures the heaviness of the tail or how flat a distribution is. The values should be between -3 to +3 178 variable increases. Heteroscedasticity is often a by-product of other violations of assumptions. The violation of homoscedasticity assumption is tested by the Levene’s test. The homoscedasticity assumption is satisfied, when the Levene statistic value is greater than the level of significance. The main consequences of the violation of this assumptions result in biased parameter estimates and estimates are not longer the best linear unbiased estimates (BLUE). Depending on the nature of the heteroscedasticity, the significance test can be too high or too low (Field, 2005). • No or little multicollinearity: Multicollinearity exists when more than two predictor variables in the MRA might be strongly intercorrelated. According to Sykes (2002: 28), “multicollinearity problem does not result in biased coefficient estimates, but does increase the standard error of the estimates and thus reduces the degree of confidence that one can place in them, the difficulty arises when two predictor variables are closely correlated, creating a situation in which their effects are difficult to separate”. If multicollinearity is discovered it might be necessary to drop one of the two variables that are highly intercorrelated. Otherwise, one of the predictor variables will be assigned to all the variance associated with the criterion variable and the second predictor variable will be treated as if has none. It could be the case than none of the predictor variables will be treated as having an effect because all the explained variance is held constant by other predictor variables (Kleinbaum et al., 2008). In general, when variables fail to satisfy the assumptions, it may be possible to correct the deficiency by using a transformation either of the criterion or of the predictor variables in the MRA for a better performance. Such transformations are useful for improving the interpretability, to make the visualization easier and are usually applied to bring the data to meet the assumptions required. 8.4.2. Analysis of Variance (ANOVA) ANOVA is a powerful and common statistical procedure in the social sciences whose goal is to compare the differences between means, and to assess the plausibility of the hypothesis stating that the means of these normal distributions are indeed equal. ANOVA is usually the appropriate method when the groups of observations are created by a categorical independent variable and the dependent variable is a metric (interval) variable (Johnson & Bhattacharyya, 2010). ANOVA designs typically are referred by the number of factors and/or by the number 179 of levels within a factor. Hence, the main types of ANOVA are: (1) the one-way ANOVAs, used in comparisons involving one factor or independent variable, (2) two-way ANOVAs or factorial ANOVAs, used when there are two factors or independent variables, and (3) N- ANOVA with more than three factors (Fink, 1995). A two-by-three ANOVA is a two-way ANOVA with two levels of the first factor and three levels of the second factor, and so on. The ANOVA produces a p-value: whether the p-value is less than α (level of significance), the hypothesis of equality of means will be rejected. 8.4.3. Factorial ANOVA The factorial ANOVA is an extension of the one-way ANOVA. The difference is that factorial ANOVA has more than one independent variable and provides a factorial combination of those independent variables. In a factorial ANOVA, an independent variable is often referred to as a factor. Factorial ANOVA methods are very popular in social sciences because it has a few advantages over single variable methods. One of the features of this statistical method is that it can provide some unique relevant information about how variables interact or combine in the effect they have on the dependent variable (Johnson & Bhattacharyya, 2010). It is better to use factorial ANOVA rather than t-test because it is more accurate in calculating p-values (Stockburger, 2001). Assumptions The formal theory of analysis of variance requires the observations to satisfy certain assumptions: • It assumes that variances of the populations from different samples must be normal or approximately normal distributed. This assumption of homogeneity usually is tested by the Levene’s test. • The two groups must have the same sample size. • The observations must be independent. 8.4.4. Crosstabulation analysis and Chi-square test After examining the distribution of each of the variables, the next step is to look for relationships among two or more of the variables. A Crosstabulation analysis measures these relationships by displaying a distribution of cases by their values on two or more variables, known as contingency table analysis. It is one of the more commonly used analytical methods 180 in the social sciences (Friedman & Schwartz, 1991). Basically, a crosstabulation is a joint frequency distribution of cases based on two more categorical variables, where the joint frequency distribution can be analyzed with the Chi-square statistic (χ2) (Fink, 1995). The Chi-square test is commonly used to compare the expected frequency in each cell with the frequency that actually occurs (observed frequencies). The differences between observed and expected frequencies are combined to form the Chi-square statistics (Field, 2005). The Chi-square assumes that both variables are measured at the categorical level, and it will test if there is a relationship between these two discrete variables. The assumptions for the Chi- square include: • The sample was randomly drawn from the population • Values for the variable are mutually exclusive • Minimum expectation of five occurrences in each category (5 observations for each category) • Mutually exclusive row and column variable categories that include all observations 8.5. Hypotheses testing and results 8.5.1. First hypothesis In the data set, the variable “monthly income” was designed to measure the economic development of farmers in the research area. Several exogenous factors influence upon this criterion variable. The survey gathered predictor variables related to demographic characteristics of small-scale farming household heads, farm and environmental characteristics as well as additional income sources to determine their effects on the criterion variable income. • Demographic characteristics are associated with monthly income received by the small-scale farming household heads. o Gender: males and females are associated with monthly income. o Age: is associated with monthly income. o Main economic activity: farmer and housewife activities are associated with monthly income. 181 o Level of education: elementary, middle school and illiterate are associated with monthly income. • Farm characteristics: farm size is associated with monthly income. • Environment: the presence of degraded areas in the farm is associated with monthly income. • Other income sources: remittances received by small scale-farming households are associated with monthly income. When testing normality of the variables to be used in the model, I retained the variables with Skewness and Kurtois values inside of the accepted parameters. Moreover, continuous variables were tested using the Kolmogorov-Smirnov test. All the predictor variables were tested for multicollinearity through Pearson correlation coefficient (r), which is a measure of the strength of the association between predictor variables. The Pearson correlation coefficient in the correlations table showed that most of the predictor variables have little or no association (-0.3 to 0.3), this coefficient ranges between -1 and +1. The closer the correlation is to +/-1, the closer to a perfect linear relationship. Based on the Levene’s test, the variance of the criterion variable is homogeneous for each predictor variable. In sum, all of the diagnostics statistics and plots supported the conclusion that all the variables met all of the assumptions for multiple regression analysis. Other predictor variables such as: soil type, soil quality, soil topography and region, which were gathered in the questionnaires, were not included in the model because they explained little of the variability of the data. Since they violated one of several MRA assumptions in the tests as well, they were dropped from the regression model. To estimate the impacts of predictor variables of demographic, environmental and farm characteristics and other sources of income (remittances) on monthly income of small-scale farming household heads, I used a MRA with the help of a statistical package SPSS. 182 The theoretical equation for the model here is: sqr(Inc)=β0+β1(Gnd)+β2(EleD)+β3(Mid_schD)+β4(illiD)+β5(Age)+β6(MEA)+β7log(SAP)+β 8(DegA)+β9(Rem)+ε (5) Where: Sqr(Inc) square root of income Gnd gender Age age MEA main economic activity EleD dummy variable elementary school Mid_schD dummy variable middle-school illiD dummy variable illiterate Log(SAP) logarithm of size of agricultural property DegA degraded areas Rem remittances The criterion variable (Inc) did not follow the normal distribution assumption; therefore, it had to be fixed by a square root transformation. The predictor variable (SAP) did not meet with the assumptions required by the model, likewise it had to be fixed, but in this case by a logarithm transformation. Multiple regression models do not accept categorical variables with more than two levels; these variables require special attention and more treatment than continuous variables (e.g. interval, ratio scale) or dichotomous variables. Categorical variables cannot be entered into the regression equation just as they are. Instead, these variables need to be recoded into a series of variables, which can then be entered into the regression model. The method chosen for recoding the dummy variables was simple coding, which compares each level of a variable to the reference level. The number of dummy variables needed were= number of levels – 1. The null hypothesis is the following: H0: The land degradation phenomenon in Tapachula does not deteriorate the production conditions in small-scale farms, decreasing the household’s income. Null (H0): β1= β2=…= βk= ternate (Ha): at least one of β is not zero 183 MRA was conducted to test if the relationship between monthly income and various potential predictors. The results of the MRA indicated the variability of the nine predictor variables explained 39.8% of the variation in the criterion variable income (R2=0.398, F9,139= 10.219, p<0.05) (Table 8.34). The remaining 60.2% can be explained by unknown or inherent variability. R2 in MRA is the measure of the goodness-of-fit. Moreover, the R2 of the model could be increased with the inclusion of more predictor variables to the model. I also calculated the coefficient of variation for the regression from the following formula: %&' = ()) Ŷ = .++ ,.--. = 0.046 (6) Where: SEE: standard error of the estimate Ŷ= criterion variable std. deviation (it needs to be transformed from root square) As a rule of thumb the result has to be smaller than 10%, if it is greater means that there is a lot of variability in the model, and the prediction intervals will be very wide or large. The result of this multiple regression model is 4.6%, which means that the model is very useful for prediction purposes. Table 8.34: MRA summary Model Summary (b) Model R R Square Adjusted R Square Std. Error of the Estimate 1 .631(a) .398 .359 13.88363 a Predictors: (Constant), receive remittances, elementary, Farm size ha, Age, Gender, Degraded areas, Middle school, Main economic activity, Illiterate b Dependent Variable: Income sq root The ANOVA table below indicates that the model, as a whole, is statistically significant, F(9,139)=10.219, p<0.001. It fits to the data, because at least one of the predictor variables is not zero. Furthermore, the Fobs (10.219) is greater than the Fcrit value (2.005) from the table of F-statistics p=0.05, which suggests rejecting the null hypothesis, and it suffices to claim that 184 the model explains the effects of the predictor variables on monthly income obtained by small scale-farming household heads. Table 8.35: Analysis of variance ANOVA(b) Model Sum of Squares df Mean Square F Sig. 1 Regression 17728.603 9 1969.845 10.219 .000(a) Residual 26792.984 139 192.755 Total 44521.586 148 a Predictors: (Constant), receive remittances, elementary, Farm size ha, Age, Gender, Degraded areas, Middle school, Main economic activity, Illiterate b Dependent Variable: Income sq root In MRA, the unstandardized coefficient “B” for each predictor variable gives the size of the effect that a predictor variable is having on the criterion variable, in which the sign on the coefficient (+ or-) gives the direction of the effect. Additionally, it expresses how much the criterion variable is expected to increase when the predictor variables increases by one, holding all the other predictor variables constant. On the other hand, the standardized coefficient “Beta” values are obtained from the MRA when all predictor and criterion variables have been standardized before the model was run. By standardizing the variables, it is meant to put all the variables in the same scale, to compare the coefficients and to observe which one has more of an effect. The summary of the coefficients table (8.36) (for the whole coefficients table see appendix 1) depicts that decisions made to retain or reject the null hypothesis are based on α= 0.05 level of significance. Whether the p-value<0.05, I reject the null hypothesis and retain it otherwise. • The constant or intercept term (β0) for the line of best fit, when x=0 is 30.899 square root, where the inverse of square root is $954.748 MXN ($73 USD), and it is significantly different from 0 at the 0.05 alpha level. • The coefficients for gender (-6.223), is not significantly different from 0 because its p- value is 0.186. Nevertheless, it estimates that male small scale-farmers tend to have a greater income than female small-scale farming household heads. • Regarding the level of education, none of the coefficients for the dummy variables elementary, middle school and illiterate are significantly different from 0. 185 • The coefficient for age (0.305) is significantly different from 0 because its p-value is 0.02, which is smaller than 0.05. Thus for every year increase in age, income increases 0.302 ($0.09 MXN, $0.007 USD). The standardized regression coefficient (Beta) is 0.230; a one standard deviation change in the explanatory variable results in a 0.230 standard deviation change in the criterion variable income. • The slope or coefficient for main economic activity (-31.843) is statistically significant because its p-value is 0.000 at the usual 5% significance level. The main economic activity is coded 0/1 (0=housewife, 1=farmer). Therefore, holding all the other variables constant, the monthly income will be estimated -31.8432 ($1,013.97 MXN, $78 USD) lower for household heads in the housewife position. The standardized regression coefficient is -0.554, a one standard deviation change in the explanatory variable results in a 0.554 standard deviation change in the criterion variable income. • The coefficient for farm size (3.310) is significantly statistic because its p-value is 0.020, which is smaller than the 0.05 significance level. It is a positive value that expresses an increase of 3.3102 ($10.9 MXN, $0.83 USD) for the increasing of one hectare to the size of the agricultural property. • The coefficient for degraded areas (-9.865) is statistically significant, its p-value is 0.000 which is smaller than 0.05. Because it was coded 0/1 (0=yes, 1= no), this coefficient estimates that small-scale farming household heads with the presence of degraded areas in their farms, will have a lower monthly income of – 9.8652($97.3 MXN, $7.5 USD), holding the rest of the variables constant. • The last coefficient for remittances (0.451) with p-value 0.887 is not statistically significant at significance level α = 0.05 as p >0.05. 186 Table 8.36: Summary of the table of coefficients from the MRA Predictor Ba Betab t p Decision Constant 30.899 3.384 0.001 Reject H0 Gender^ -6.223 -.135 -1.328 0.186 Retain H0 Elementary -2.550 -.074 -.512 0.610 Retain H0 Middle-school .414 .009 .075 0.940 Retain H0 Illiterate -8.932 -.227 -1.671 0.097 Retain H0 Age (years) .305 .230 3.120 0.002 Reject H0 Main eco. activity^^ -31.843 -.554 -5.283 0.000 Reject H0 Farm size (ha) 3.310 .161 2.339 0.021 Reject H0 Degraded areas^^^ -9.865 -.284 -3.986 0.000 Reject H0 Remittances ^^^ .451 .010 .143 0.887 Retain H0 Notes: aunstandardized coefficient b standardized coefficient ^ coded as 1= female and 0= male ^^ coded as 1= housewife and 0= farmer ^^^ coded as 1= no and 0= yes From the MRA results depicted above, the decision to be taken based on the number of rejected variables p-value<0.05 was rejection of the null hypothesis and retention of the alternate hypothesis. Additionally, to determine the differences of income between regions, I used the analysis of variance (ANOVA). The table below (8.37) denotes that the main effects of regions were highly significant, F(2,146)= 19.25, p<0.001. Lowland (LL), medium-land (ML) and highland (HL) regions differed on the reported amounts of monthly income. Table 8.37: ANOVA of income between study regions ANOVA Monthly income Sum of Squares df Mean Square F Sig. Between Groups 53040746.732 2 26520373.366 19.250 .000 Within Groups 201138308.302 146 1377659.646 Total 254179055.034 148 However, ANOVA does not show the differences between regions. To present this additional analysis in a clearer manner, the means plot below shows the mean for LL is $1,761 MXN ($ 135 USD), for the ML is $2,206 MXN ($170 USD) and for the HL is $794 MXN ($61 USD) per month. 187 Chart 8.5: Means plot of monthly income in the three research regions (MXN). Source: author’s survey 8.5.2. Second hypothesis The structural change of the Mexican economy from import substitution industrialization (ISI) to export substitution industrialization (ESI) in the 1980’s has transformed the closed Mexican economy to one of the most opened economies in the world. This was possible by the privatization of state enterprises, reorganization of public finances and market liberalization (Villafuerte & Garcia, 2006; Lopez, 2007). The implementation of free trade multilateral agreements such as NAFTA in 1994 with an apparently positive impact on the macroeconomic level, also carried negative impacts on the microeconomic level, affecting some Mexican farmers severely (Mace & Bélanger, 2004; EACH-FOR, 2009) by the removal of agricultural subsidies, as well as production and commercialization support (Villafuerte & Garcia, 2006). In this light, the aim of the 2nd hypothesis is to determine whether the government’s abandonment of the agricultural areas in the research regions has a significant impact on small-scale farming household incomes. The data gathered through the surveys on this subject firstly refers the lack of technical assistance, and secondly the lack of economical assistance in terms of subsidies provided to small-scale farmers. The null hypothesis is the following: 1761 2206 794 0 500 1000 1500 2000 2500 LL ML HL M ea n o f m o n th ly in co m e Region 188 H0: The government’s abandonment of Tapachula’s agricultural sector does not erode the small-scale farms’ production conditions, diminishing the household’s income. This hypothesis is tested by a factorial ANOVA, which considers the predictor variables as factors. For each factor and the interactions between factors, a null hypothesis has to be tested, in order to reject it or to retain it otherwise (see appendix 2). The dependent variable income was transformed using square root transformation for keeping the variances normally or approximately normally distributed, in order to avoid the violation of assumption of homogeneity. Y= (square root) monthly income Factors: GTA: government technical assistance GEA: government economic aid Table 8.38: Factors and equations Factor Equation In words GTA Null (H0): α1=α2 GTA has no effect on income Alternate (HA): Not H0 GTA does have an effect on income GEA Null (H0): β1= β2 GEA has no effect on income Alternate (HA): Not H0 GEA does have an effect on income GTA x GEA interactions Null (H0): αβ1,1= αβ1,2= αβ2,1= αβ2,2 The effect of GTA does not depend on GEA Alternate (HA): Not H0 The effect of GTA does depend on GEA The predictor and criterion variables were tested for violation of the homoscedasticity assumption with the Levene’s test that measures if there is an equal variance. The result showed (Table 8.39) a p-value 0.295, since it is not significantly different, it denotes there is homogeneity across factors. 189 Table 8.39: Levene's Test for homoscedasticity Levene's Test of Equality of Error Variances(a) Dependent Variable: Income sq root F df1 df2 Sig. 1.246 3 145 .295 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+GTA+GEA+GTA * GEA The monthly income of small-scale farming household heads was analyzed by means of a factorial ANOVA test with two levels of governmental technical assistance (GTA) and two levels of governmental economic aid (GEA). All effects between groups did not have a significant effect, as well as the interaction effect, F(1, 145)= 1.582, p= n.s (Table 8.40). That is, in this case the low and uneven amount of subsidies and technical assistance received by farmers do not have any effect on farmers’ income. However, considering the overall adverse conditions ─land degradation, extreme weather events, poverty and marginalization, raise in the prices of staple food, increases in agricultural inputs costs, and the loss of family labour due to forced migration─ faced by small-scale farmers in Tapachula, the governmental economic and technical support become insufficient. Hence, the null hypothesis should be rejected. Furthermore, secondary literature depicts that small-scale farmers income reduces by the withdrawal of subsidies and technical support, the descriptive and inferential analysis show that the effect of both subsidies and technical support on income is null. Taking coffee subsidies as an example, LL coffee producers receive a mean of $1981 MXN ha/year ($152.3 USD), $897.9 MXN ha/year ($69 USD) is given to ML and $1303.8 MXN ha/year ($100.2 USD) to HL coffee producers (see Table 8.10). These amounts represent an additional monthly income of $165.3 MXN/ha ($12.7 USD) for farmers in the LL, $74.8 MXN/ha ($5.7 USD), and $108.4 MXN/ha ($ 8.3 USD) for farmers in the ML and HL regions respectively. These agricultural financial supports don’t play any role for investing or improving the agricultural sector and the income of farmers. Additionally, this argument is supported by the number of households receiving either technical or economic support, as well as the usefulness of both shown in chart 8.6 and table 8.41 Table 8.40: Tests of between-subjects effects Dependent Variable: Income sq root Source Type III Sum of Squares Corrected Model 1021.277 Intercept 30854.611 GTA GEA 550.195 GTA * GEA 474.524 Error 43500.309 Total 235743.000 Corrected Total 44521.586 a R Squared = .023 (Adjusted R Squared = .003) Moreover, descriptive data show that from the entire surveyed population there small-scale farming household heads receiving technical assistance from the government, the rest 113 (76%) responded otherwise. In regards to government economic aid, small-scale farming household heads receiving economic aid in terms of subsidies is (69%), compared to 25 (31%) who regional level, chart (8.6) shows that the HL region is the one where 45 farming household heads don’t receive technical assistance and from government. Chart 8.6: Technical assistance and government economic aid 16 14 0 5 10 15 20 25 30 35 40 45 50 Yes Tech. assistance # o f r es po n de n ts 190 df Mean Square F (a) 3 340.426 1.135 1 30854.611 102.848 80.482 1 80.482 .268 1 550.195 1.834 1 474.524 1.582 145 300.002 149 148 do not receive economic aid from the government. surveyed small 20 don’t have by regions Source: author’s survey 31 36 11 37 37 14 6 45 31 No Yes No Gov. aid Region LL ML Sig. Partial Eta Squared .337 .023 .000 .415 .605 .002 .178 .012 .211 .011 are 36 (24%) the number of 104 At the -scale economic aid 20 HL 191 The share of the population receiving either technical assistance and/or government economic aid also responded about the helpfulness of both types of assistance; 75 (72%) from a total of 104 small-scale farming household heads consider that the government technical and/or economical assistance was not very helpful. Table 8.41: Usefulness of government technical/economical assistance Frequency Percent Valid Percent Valid Much 17 11.4 16.3 not much 75 50.3 72.1 not useful 12 8.1 11.5 Total 104 69.8 100.0 Missing System 45 30.2 - Total 149 100.0 - Source: author’s survey 8.5.3. Third hypothesis Land degradation-related migration For testing this hypothesis, the data set was disaggregated according to small-scale farming household heads who responded that at least one of the household members has out-migrated. The new data set consisted of 57 small-scale farming household heads. In order to determine whether land degradation acts as a determinant for migration in the research regions, a crosstabulation analysis and chi-square test were carried out (appendix 3). The null hypothesis is the following: H0: Land degradation is not a central factor for generating, intensifying and diversifying migration flows from the rural areas of Tapachula. The crosstabulation table below (8.42) shows that 33 small-scale farming households (57.9%) with out-migrants responded to have degraded areas in their farms. The greatest number of sending migrant households (22) with farmland degradation problems is living in the highland region. 192 Table 8.42: Crosstabulation between sending migrant households with degraded areas by region Degraded areas * Region Crosstabulation Region Total LL ML HL Degraded areas No Count 9 15 0 24 % of Total 15.8 26.3 .0 42.1 Yes Count 3 8 22 33 % of Total 5.3 14.0 38.6 57.9 Total Count 12 23 22 57 % of Total 21.1 40.4 38.6 100.0 Source: author’s survey The dataset of 57 small scale-farming households with one or more of their members as out- migrants was analyzed using a Chi square test. The results of the Chi-square test X2(2)= 26.367, p=0.000, summarized in table 8.43, show that there is a high statistically significant association between sending migrant small-scale farming households and degradation problems in the research regions at p-value 0.01. According to the table of Chi-square when df=2 and p=0.01 gives a critical value of 9.210, this is smaller than the value of the Chi- square test 26.367. These circumstances lead to rejecting the null hypothesis. Table 8.43: Chi-square test results for the third hypothesis Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 26.367(a) 2 .000 Likelihood Ratio 34.376 2 .000 Linear-by-Linear Association 21.813 1 .000 N of Valid Cases 57 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.05. Source: author’s survey Additionally, the mean income per region of the 57 sending migrants’ households was added to the analysis to gauge the relationship between the sending migrants’ households’ income and the number of households with degradation problems in their farms. To illustrate in a better manner, chart 8.7 explains that the mean income of sending migrants’ households decreases as the number of sending migrants’ household with degradation problems increases. Indeed, this also supports the first hypothesis, confirming that households with 193 more degradation problems receive less income; hence, these households’ members tend to migrate as consequence. Chart 8.7: Means plot of monthly income (MXN) of out-migrants’ households and the number of out-migrants’ households with land degradation problems in the three research regions. Source: author’s survey 8.5.4. Fourth hypothesis Labour force replacement In 2010 the INM issued almost 27,000 “Formas Migratorias Para Trabajadores Fronterizos” (migration form for cross-border workers – FMTF), where 87% were issued to agricultural workers and the rest to workers that are making incursion into other economic sectors. Guatemalan labour force is employed to fill the labour gaps left by the Chiapaneco out- migration. With the issue of the FMTF, the INM highlighted the existence of a cross-border labour market and the importance of Guatemalan immigrants for the southern border’s development. This hypothesis was assessed by using descriptive statistics and data from personal interviews with small-scale farming household heads. For a deeper analysis of the data, I elaborated a crosstabulation between the number of small-scale farmers that are hiring local or regional labour to meet the demand on their small-scale farms, and the number of migrant sending small-scale farming households. Although, the additional analysis of data will have no effect on the hypothesis testing decision, its purpose is to provide a close-up look at the relation between outmigration and employment of Guatemalan labour at the household level (appendix 4). 1725 1602 807 3 8 22 0 5 10 15 20 25 0 200 400 600 800 1000 1200 1400 1600 1800 2000 LL ML HLM ea n o f m o n th ly in co m e M X N mean income out-migrants' HH with degradation problems # of H o u seh old s The proposed null hypothesis is the following: H0: Central American workers do not close the labo migration from Tapachula and the demand for labour in the region From 72 (48%) small-scale farming regions, the LL with 29 and the ML with 32 are the regions with scale farming households hiring labour. In contrast household labour force than on external there are 45 small-scale farming (29) and LL (32) regions were the higher employers of this international labour force. The HL region exhibits the lowest level of employing foreign labourers. Here, the main source of external labour comes from ne Chart 8.8: Small-scale farmers’ labour force employment and labour force origins *SSF= small-scale farmers Furthermore, in order to determine the reasons for hiring Guatemalan scale farming households declared the lack of local labour force Again, the local labour force replacement by Guatemalan workers is greater in the ML (9) and HL (15) regions. Another 12 small regions have hired Guatemalan workers because they are considered to be better workers than local workers. Finally, a small share, only 7 small Guatemalan workers because the Guatemal labour force (Chart 8.9). 29 12 17 0 5 10 15 20 25 30 35 40 45 LL # o f s m a ll- sc a le fa rm er s SSF* who employed labour force Neighbouring ejidos 194 ur gaps derived from the rural out . households who hired labour last year in the three study the highest share of small , the HL region depends more on the work force (see the bars in Chart 8 households that hired labour from Guatemala; where the ML ighbouring ejidos (see the lines in chart 8.8). Source: author’s survey labourers, 26 small because -scale farming households in the LL (3) and ML (9) -scale farming households, an labour force is considered 32 11 18 19 40 6 26 ML HL SSF* who did not employ labour force Guatemala - - .8). Additionally, - of out-migration. chose to hire cheaper than local 9 2 195 Chart 8.9: Reasons for hiring Guatemalan workers. Source: author’s survey For a deeper analysis of the data, a crosstabulation analysis was conducted to gauge the relationship among sending migrants small-scale farming households with the employment of labour, either local or Guatemalan. This statistical tool allowed me to identify relations between cross-tabulated categorical (nominal) variables. As we can see in the table below (8.44), there are 30 (20.1%) sending migrant small-scale farming households of the surveyed population, who have hired labour, either Guatemalan or local, over the last year. Table 8.44: Crosstabulation between hiring labour and sending migrant households Hired labour force last year Sending migrants SSF households No Yes Total No Count 50.0 27.0 77.0 % of total 33.6 18.1 51.7 Yes Count 42.0 30.0 72.0 % of total 28.2 20.1 48.3 Total Count 92.0 57.0 149.0 % of total 61.7 38.3 100.0 The resulting relations among variables are tested by the Chi-square test of statistical significance. The table below depicts the results of the Chi-square test χ2(1)= 0.687, p= 0.407. There is no statistically significant association between sending migrant small-scale farming households and the hiring of labour during last year at p-value 0.05. 5 2 0 9 15 2 3 9 0 2 4 6 8 10 12 14 16 LL ML HL # o f s m a ll- sc a le fa rm er s Cheaper labour force Migration of local workers Better workers than local workers 196 Table 8.45: Chi-square test results for the fourth hypothesis Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square .687(b) 1 .407 Continuity Correction(a) .435 1 .509 Likelihood Ratio .687 1 .407 Fisher's Exact Test Linear-by-Linear Association .682 1 .409 N of Valid Cases 149 a Computed only for a 2x2 table b 0 cells (.0%) have expected count less than 5. The minimum expected count is 27.54. From the above analysis, I conclude that there is a lack of local labour force, which has been caused by the outmigration phenomenon at the level region, therefore the null hypothesis is rejected. 8.5.5. Fifth hypothesis Guatemalan seasonal labour force and living conditions The following null hypothesis corresponds to the fifth hypothesis articulated in the introduction of this thesis: H0: The Central American labour force in the agricultural sector of Tapachula is not subject to labour standards, which are beneath ILO’s decent work agenda. This null hypothesis was analysed by a crosstabulation method and a chi-square test (appendix 5). On the one hand, the collected data from 80 Guatemalan workers included questions regarding their personal perceptions of the labour conditions on the farm where they were working. On the other hand, the survey collected data about the determinants of labour conditions. The crosstabulation analysis’ chart below (8.10) illustrates that 43 (54%) Guatemalan workers consider their current labour situation to be good, because they receive a fair44 income in comparison with those in Guatemala (see below wages and working hours). There are 32 (40%) Guatemalan workers who indicated that their labour conditions were fair, and out of these 32 workers 17 (21%) stated that the main reason that the conditions were only considered to be fair, was the low salaries they were receiving. 44 Fair income is considered when the amount of money paid to Guatemalan workers is similar or higher than the minimum daily income established by the Mexican government for the state of Chiapas. Chiapas is classified as a geographic zone of type B, in which agricultural day labourers earn $60.57 MXN ($4.65 US) per day (Commission Nacional Para los Salarios Minimos, 2012) Chart: 8.10: Crosstabulation between labour conditions and its determinants The Pearson Chi-square table below (8 between the variable labour conditions and the variable reasons, χ2(8)= 55.3, p<.001. The test between these two variables found that Guatemalan workers with good labour conditions were significantly more likely to receive a fair salary. Table 8.46: Chi-square test results Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases a 11 cells (73.3%) have expected count less The χ2 critical value in the table of value of 20.090 which is smaller than the statistical Pearson circumstances lead to rejecting the null hypothesis. Guatemalan agricultural workers in the coffee farms believe they have good working conditions; however, for a better understanding of the topic it is necessary to look at the labour conditions in Guatemala through a descriptive analysis of labour activities, wages, a working hours. In what follows, these data will be contrasted with the data collected through the application of surveys about migrant workers’ labour and living conditions in the Mexican coffee sector. 43 3 0 10 20 30 40 50 Good # o f r es po n de n ts 197 Source: author’s survey .46) indicates that there is a significant relationship determinants of labour conditions or for the fifth hypothesis Chi-Square Tests Value df Asymp. Sig. (2 sided) 55.376(a) 8 .000 39.310 8 .000 14.586 1 .000 80 than 5. The minimum expected count is .01. Chi-square statistics when df=8 and p=0.01 gives a critical Chi-square value 55.376. These 11 0 17 00 2 11 2 Fair Bad Labour conditions Fair salary Unfair salary No health insurance Other - nd 0 198 Guatemalan workers labour background Table 8.47 depicts in detail the previous economic activities carried out in Guatemala by the migrant workers who were surveyed while working on coffee farms in Chiapas. It reveals that 53 migrant workers (66%) have been economically active, of which 29 (36%) and 24 (30%) were employed in seasonal and permanent jobs respectively. 47 (59%) of the migrant workers had been performing farming activities. Concerning the labour conditions in Guatemala, 32 migrant workers (40%) responded that the labour conditions were fair; and for 15 (19%) they were bad. The main determinants for labour conditions were unfair wages for 38 (47.5%) migrant workers. In contrast with economic activities fulfilled in Mexico, the labour conditions for 6 migrant workers (7%) are good while for 15 (19%) were noted as bad. However, unfair or low wages are still the predominant variable for both countries, Mexico and Guatemala. Moreover, Guatemalan migrant workers are currently employed by coffee farms as day labourers without a permanent contract; therefore, 60 migrant workers (75%) maintain a seasonal job. Table 8.47: Economic activities and labour conditions in Guatemala and Mexico Guatemala Mexico Eco. Activity n=80 % n=80 % Employment (1/0) 53 66 80 100 Permanent 24 30 20 25 Seasonal 29 36 60 75 Type of employment Farming 47 59 - - Self-employed farmer 26 33 - - Day labourer 21 26 80 100- Other 6 7 - - Labour conditions Good 6 7 47 59 Fair 32 40 32 40 Bad 15 19 1 1 Reasons Fair wages 11 14 54 68 Unfair wages 38 47.5 20 25 Unpaid overtime 3 4 1 1 Without health insurance 1 1 3 4 Other 2 2 (1/0) 1= with employment; 0= otherwise Source: author’s survey 199 Wages and working hours In Guatemala, the wages and the number of working hours per week, in comparison with Mexico, are relatively low. In this regard, the labour force in Guatemala earns a mean wage of $599.5 MXN ($46 USD) in contrast to $1,729 MXN ($133 USD) per month, in Mexico. This in a more specific way means, a daily income of 30 MXN ($2.3 USD) in Guatemala, and 86.5 MXN (6.65 USD). The disparity is also considerable regarding the number of hours per week, in which the mean of working hours per week is 30.3hrs in Guatemala and 49hrs in Mexico (Table 8.48). Table 8.48: Comparison of wages and working hours between Guatemala and Mexico Guatemala Mexico Wages (MXN/month) n=80 % n=80 % 0= unemployed+ 33 41 - - 1-1000 22 28 9 11 1000-1999 23 29 48 60 2000-2999 2 2 18 23 3000-4000 - - 5 6 Min 0 - 600 - Max 4500 - 5000 - Mean 599.56 - 1729.13 - Std.dev. 706.63 - 724.92 - Working hrs/week n=53 66 n=80 100 30-40 26 32 10 13 41-50 15 19 51 64 51+ 12 15 19 23 Min 0 - 30 - Max 90 - 90 - Mean 30.34 - 49.03 - Std.dev. 23.67 - 8.51 - Source: author’s survey Living conditions Both documented and undocumented Guatemalan migrant workers are subject to mistreatments and labour abuses. While collecting data on several coffee plantations, it was clear for me that the living conditions offered were not adequate. They have built facilities to provide shelter and food to hundreds of Guatemalan workers that arrive every yeare. Hence, the available facilities during this season become crowded. As summarized in Table 8.49, 68 migrants (85%) sleep a single that is shared with many others, among them family and non- 200 family members. Because of the harsh labour, living and health conditions to which workers are subjected, accidents and diseases become common. Out of the entire migrant population A few coffe farms have built small medical facilities or farm clinics to provide medical aid at any time, to either the workers or his family members. However workers are not cover by a heath insurance, there are 15 workers (19%) with health insurance which is provided by the coffee farmers. Table 8.49: Guatemalan migrant workers living conditions Farmhouse wall materials n=80 % Blocks 48 60 Blocks and Timber 7 9 Timber 23 29 Other 2 2 Farmhouse roof materials Corrugated sheet 78 98 Other 2 2 Farmhouse floor materials Concrete 62 77 Bare 8 10 Timber 10 13 # of rooms 1 68 85 2 9 11 3 3 4 Drinking water source n=80 % Tap water 30 37 Well 6 8 Spring 28 35 River 16 20 Drinking water quality Good 55 69 Average 16 20 Bad 9 11 Latrine Yes 72 90 No 8 10 Health insurance Yes 15 19 No 65 81 Source: author’s survey 201 9. Conclusions This research demonstrates the importance of taking into account geographical location, natural climatic and topographic features, in order to build up a comparative-analysis platform for measuring and evaluating the impacts of land degradation on rural development and migration. The survey’s outcomes showed that the characteristics of households and farms were different in the three study regions. Land degradation limits the households’ capacity to carry out subsistence agriculture in the research area. This type of agriculture hardly fulfils the household consumption requirements, and it is no longer economically sustainable due to low yields, which have decreased since 2005. In addition, land degradation in all three regions of study (LL, ML, and HL) represents an obstacle for development, reducing the size of the farm’s productive area, soil fertility, and crop productivity. These constraints are indicators of the small-scale farming sustainability, or lack thereof. The economic costs of land degradation impinge directly on rural development and the quality of life of individuals and communities. Land degradation is due to deforestation, heavy seasonal rains, and the increase in frequency of sudden extreme weather events as result of climate change, which altogether provoke landslides and floods. Another farm constraint, according to that 51% of the small-scale farmers that responded to my survey, is the implication of NAFTA, which caused low government economic and technical support to farmers. These factors have motivated and forced many household members to look for new income alternatives far beyond the border of Chiapas and Mexico. Although, the Mexican government provides subsidies for agricultural crops, the number of households that receive these subsidies and their amount vary amongst households and regions. In addition, statistical results also showed that the abandonment of the government in terms of technical assistance and economic aid has had a marginal affect on the income of small-scale farmers. The argument is that the low and uneven amount of economic aid does not help the household’s economic situation, nor does it improve agricultural productivity through investment. There are less small-scale farming households receiving technical assistance than the ones who receive subsidies. A thorough analysis proved that land degradation affects the income of small-scale farming households in the three regions, where the HL is significantly the most affected. Additionally, outmigration is higher in small-scale farming households with bigger land degradation 202 problems. This indicates that land degradation is a driver of migration, among others. This is because reduced fertility leads to lower production and a decrease in household incomes. The migration dynamics are different for each region: in the LL region, due to its closeness to Tapachula, the main migration flows are directed to this urban centre (rural-urban). The coffee producing ML region sends temporary migrants to work in other agricultural regions and/or to the maquiladora industry in the northern Mexican state of Baja California, especially to Tijuana (rural-regional). The low-income HL region, with a high degree of human-related land degradation and high levels of marginalization, sends migrants to both regional and international destinations, the region’s different economic and socio- demographic characteristic have, including its indigenous origin, become an obstacle to finding employment in the urban area of Tapachula. More precisely, land degradation is intensifying, diversifying and adding new land-degradation-related migrants to the current migration flows. It is therefore probable that further land degradation will strongly jeopardize the region’s rural economies and force more human migration towards better-off areas, including broader destination targets. The international migration of Chiapanecos towards the U.S. is a new phenomenon, which has so far received relatively little academic attention. More research is needed in order to disentangle the different stages of the migratory process: arrival, settlement, return, and residence. However, this thesis has found that, to a large extent, international migrants going to the U.S. have recently established migration networks in the state of North Carolina, which facilitate human mobility from the HL region (rural-international). To a lesser extent, unemployment, low wages and low household incomes in the study regions are the drivers forcing migration. In terms of wages, the mean wage paid by small-scale farmers is $67.7 MXN ($5.20 USD), and the big coffee farms pay a mean of $72 MXN ($5.53 USD) per day. These wages are not high enough to satisfy the economic needs of local workers. Therefore, these workers are not willing to accept them and instead try to find employment in Tapachula city, Cancun, Guadalajara, Mexico city, and Tijuana. The migration of locals generates a local shortage of agricultural workers. This shortage opens opportunities for Guatemalan workers that are willing to work under the precarious labour conditions prevailing in Tapachula. Paradoxically, the upsurge of forced emigration is linked to the emergence of new immigration patterns that portray a peculiar geography of poverty and precarious working conditions. 203 The above trend has been fostered by a) the easiness for Guatemalan agricultural migrant workers to arrive to Tapachula from the border Guatemalan states, including the relative ease in obtaining legal work permissions, and b) the long historical and cultural tradition of temporary migration by Guatemalans agricultural workers towards the coffee region of Tapachula. It is worth mentioning that the incorporation of Guatemalan workers to the small- scale farms does not necessarily mean that its economy has improved and modernized. The average amount of remittances received by 25% of the households in both ML and HL, is $1,371 MXN ($105 USD) and $1,340 MXN ($103 USD) per month respectively. The low contribution of remittances to recipient households does not improve the households’ economic conditions, because households primarily invest remittances in consumption. In the research areas, out-migration as a product of the existing inequalities and high levels of marginalization is undermining the economy and deepening conditions of underdevelopment. In particular, the detrimental consequences of neoliberal globalization in Chiapas includes an increase in labour precariousness, the restructuration of migration dynamics outflow of Chiapaneco populations, less government support for small-scale agriculture and more environmental degradation. These results clearly contrast with the theoretical assumptions and predictions of neoclassical economics and they support the alternative approach to migration and development provided by the historical structural paradigm. Given the nature of migration to or through Mexico, documented and undocumented Guatemalan migrant workers alike have become a vulnerable population that demands special attention by the government, development institutions, and civil society. In terms of remittances, due to the temporary nature of Guatemalan labour in the region of study, only 33% remit money back to Guatemala, at an average of $871 MXN ($67 USD) per month. The rest of the workers take their savings in the return to Guatemala, regardless of whether they migrate alone or with family members. The working and living conditions to 54% of Guatemalan workers in the coffee plantations are good. They mostly judge “labour fairness” in terms of income, because the wages gained in Mexico are greater than the ones in Guatemala. Although, food and shelter are provided by coffee plantations, the quality, quantity and space are impecunious. From the criterion of decent work established by the ILO, based on the principles of freedom, equity, security and human dignity, these labour and decent conditions are far to be decent. Local workers, who rather find employment in the city or migrate, reject these same labour conditions. 204 The main conclusions are summarized as follows: • Land degradation, poverty and migration encompass a vicious circle. • Neoliberal policies are far from shortening the gaps between rich and poor regions. Instead, they foster unsustainable socio-economic and environmental conditions. • Migration is a complex multi-causal phenomenon, in which land degradation is an important but not an exclusive determinant. • This research study reinforces the beliefs of international migration is associated to poverty and marginalization, and shows with empirical data that migration from rural areas directly to urban centres in the US. • Given the deterioration of household income and the need to work abroad as a family survival strategy, migration cannot be conceived as a free option. It becomes a necessity and therefore a part of the population is forcibly displaced. • Under forced migration conditions, migrants are subjected to vulnerable and often discriminatory working conditions that are far behind the standards outlined in the ILO’s decent work agenda. • Remittances cannot be conceived as a pathway for development or a pathway out of poverty. • There is an interrelation among the different migration flows: international emigration, internal migration and immigration that is often off the radar screen of contemporary migration studies. This interrelation is derived from the redefinition of family reproduction strategies triggered by the impacts of neoliberal globalization and the effects of land degradation, which altogether worsen the working and living conditions of farming households, both in Tapachula and Guatemala. • In order to overcome the vicious circle of land degradation, poverty, underdevelopment and migration in Tapachula, a drastic change in rural and sustainable development strategies is needed. This demands the Mexican State to play a new and fundamental role in the economic and technical support of small-scale farmers, and the regulation of international trade and environmental standards. 205 9.1. Findings This section describes the key findings from the results of the quantitative and qualitative approaches, by each of the formulated hypotheses. H1 The land degradation phenomenon in Tapachula deteriorates the production conditions in small-scale farms, decreasing the household’s income. Retained Explanation Key findings The MRA results showed that the model is very useful to predict the effects of social and natural variables (land degradation) on the income criterion variable. Additionally, the ANOVA of the model was highly statistically significant The unstandardized coefficients (B) of the predictor variables from the MRA demonstrated that: • On the one hand, gender, level of education and remittances statistically do not have an effect on income. Notwithstanding, female farmers have lower income than male farmers; the illiterate population earn lower incomes than elementary and middle school education levels; • On the other hand, age, main economic activity, farm size and degraded areas have a significant effect on income. A thorough statistical analysis proved that land degradation reduces the household’s monthly income by $ 97.3 MXN ($7.5 USD) per each ha undergoing process of degradation of the farmland H2 The government’s abandonment of Tapachula’s agricultural sector erodes the small-scale farms’ production conditions, diminishing the household’s income. Rejected Explanation Key findings A factorial ANOVA found that neither the technical assistance nor the economic aid provided by the government have a positive or negative effect on small-scale farming household incomes. • According to the mean household income, the yearly amount of agricultural subsidies (economic aid) granted to farmers and the technical assistance they used to receive neither increases nor decreases the income. • Descriptive analyses showed that subsidies received by households are not distributed evenly and the low amounts are not helping improve the economic and productive situation of farms and households. H3 Land degradation is a central factor for generating, intensifying and diversifying migration flows from the rural areas of Tapachula. Retained Explanation Key findings Crosstabulation analysis and chi- square test results showed that there is a statistically significant correlation between sending migrant’s households and households with farmland degradation problems in the study regions. Farmland degradation affects the household’s income, therefore the labour force needed and the wages in the regions are low. This is a constraint for the local labour force, which is not content especially with the local wages • The data set of 57 sending migrant households showed that the HL region, which in fact is the most affected by land degradation, has the greatest number of households sending migrant households (22). • In general, from the sample size of 51 households for this region, 22 (43%) are sending migrant households, and all of these have degradation problems. • Out of 144 out-migrants, 65 (45%) and 58 (40%) have migrated due to unemployment and low wages. The mean wage paid by small-scale farmers is $67.7 MXN ($5.20 USD), while large coffee farms pay a mean of $72 MXN ($5.53 USD) per day. However, most of the jobs offered are seasonal. • The availability of transportation and job offers with 206 and the lack of employment opportunities, therefore they prefer to out-migrate better salaries in other states attract local workers. All the above-mentioned reasons are key determinants for the migration outflow from the region to urban, regional and international destinations. H4 Central American workers close the labour gaps derived from the rural out- migration from Tapachula and the demand for labour in the region. Retained Explanation Key findings Descriptive analysis showed that there are households hiring local and international labour. It also revealed the small-scale farmers’ main reasons for hiring international (Guatemalan) labour. • There is a total of 72 (48%) small-scale farmers hiring labour, 29 in the LL, 32 in the ML, and 11 in the HL regions. The HL region’s agricultural activities depend more on the household’s labour force. • 45 small-scale farmers hire Guatemalan labour: 17, 26 and 2 in the LL, ML and HL respectively. • The main reason to hire Guatemalan labour force for 26 small-scale farmers in the LL is due to the outmigration of local workers. On the regional level, Guatemalan labour has made up for the lack of local labour. Guatemalans are willing to work and receive the low wages discarded by local workers. H5 The Central American labour force in the agricultural sector of Tapachula is subject to labour standards, which are beneath ILO’s decent work agenda. Retained Explanation Key findings Crosstabulation and chi-square test results showed that there is a statistically significant correlation between the variables: labour conditions and determinants for labour conditions 54% of Guatemalan agricultural workers employed on Tapachula’s coffee farms responded to have good working conditions; another 40% considered them as fair. Due to better wages in Mexico than Guatemala, they consider income as the main indicator of decent work, ignoring the living, health and sanitation conditions on coffee plantations. The agricultural sector of Tapachula has slightly improved the Guatemalan immigrant labour working and living conditions in comparison with Guatemala, however the labour standards to which they are subjected are far beneath ILO’s decent work agenda. 207 Zusammenfassung Im Rahmen dieser Forschungsarbeit wurde die Dynamik von Bodendegradation, Entwicklung von kleinbäuerlichen Betrieben und verschiedenen Migrationsmustern im Bezirk Tapachula, Chiapas (Mexiko) untersucht. Dabei stehen die Entwicklungs- und Umweltbedingungen mehrerer Gemeinden in verschiedenen Höhenlagen und geographischen Regionen – von der Küste bis ins Hochland – im Mittelpunkt der Untersuchungen. Durch Umfragen und Literaturauswertung wurden Daten gesammelt, um die dynamischen Veränderungen in der landwirtschaftlichen Landnutzung, die Veränderungen der Umwelt, das Auftreten von Naturkatastrophen, die Transformationen der sozio-ökonomischen Lebensbedingungen und die aktuellen Abwanderungstendenzen einschätzen zu können. Ferner werden die vorliegenden Hauptursachen der Bodendegradation erklärt, und wie dieses Phänomen die Entwicklungsbedingungen der Kleinbauern beeinflusst, um das Ausmaß mit dem sie zur Migration beiträgt zu bestimmen. Aufgrund seiner geografischen und geopolitischen Position liegt Chiapas einerseits im Pfad von Hurrikanen, tropischen Stürmen und anderen Naturgefahren, die regelmäßig im Pazifischen Ozean, dem Golf von Mexiko und der Karibik entstehen. Zusammen mit den topografischen Bedingungen machen diese Naturgefahren Chiapas zu einem Gebiet, das naturgemäß anfällig für Umwelt- und Bodendegradation ist. Gleichzeitig bildet der Staat Chiapas 654 km der mexikanisch-guatemaltekischen Grenze. Damit ist Chiapas das Haupteingangstor Mexikos für Transit-Migranten auf ihrer Reise in die USA. Zusätzlich ist es – aus geschichtlichen, kulturellen und ethnischen Gründen – ein Aufenthaltsgebiet für Zeitarbeiter und Grenzarbeiter (legale und illegale), die für ihre Arbeit zum Teil täglich die Grenze nach Mexiko überqueren. Diese Arbeiter sind hauptsächlich auf Kaffeeplantagen oder in anderen landwirtschaftlichen Produktionszweigen beschäftigt. Chiapas ist ein Staat mit vielen Gegensätzen; er besitzt eine Fülle an Biodiversität und natürlichen Ressourcen, gleichzeitig ist er einer der ärmsten mexikanischen Bundesstaaten, hat einen hohen Anteil an indigener Bevölkerung und weist einen niedrigen Human Development Index und einen hohen Grad an Marginalisierung der Bevölkerung auf. Die indigenen Siedlungen sind in kleinen Gruppen über den ganzen Staat verstreut. Diese Zerstreuung der Bevölkerung reduziert die Möglichkeiten für eine angemessene soziale Unterstützung zur Sicherung der Lebensgrundlage zusätzlich. Darüber hinaus trägt sie zu zunehmender Armut und Marginalisierung bei. Alle diese Faktoren unterstreichen die 208 Anfälligkeit der Gesellschaft gegenüber jeder Art von zusätzlichen Einschränkungen von Wirtschaft und Umwelt. Die Naturereignisse der letzten Jahrzehnte führten zu schwerwiegenden Schäden in der Landwirtschaft, insbesondere für Subsistenzlandwirte. Die wiederkehrenden Naturkatastrophen hatten einen starken Einfluss auf die Produktion von landwirtschaftlichen Gütern. Hinzu kam ein Preisfall der Hauptfrüchte Kaffee, Mais und Bananen. Dadurch leben viele Kleinbauern in Armut und sind noch empfindlicher gegenüber negativen Veränderungen der natürlichen und sozioökonomischen Bedingungen. Zusätzlich verursachten Extremregenereignisse Schäden am Ökosystem, der Infrastruktur und der Grundversorgung. Die Auswirkungen sind schlimmer in den Gebieten mit hohen Entwaldungsraten, die auf einen gesteigerten Verbrauch von Holz durch Industrie, Export und Haushalte beruhen und verstärkte Bodendegradation mit sich bringen. Die Abholzung führt zu einem raschen Verlust von Boden aus den mittleren und hohen Höhenlagen und nachfolgend zu einer Anreicherung von Sedimenten in Bächen und Flüssen. Dadurch entsteht eine Gefahr für menschliche Siedlungen, da die Sedimentansammlungen zu Erdrutschen in den hohen Höhenlagen und zu Flutereignissen in den mittleren Höhenlagen und Tälern führen können. Das Phänomen der Bodendegradations, eingebettet in den globalen Kontext von Umweltzerstörung, hat sich zu einer Bedrohung für die Bauern und die ländliche Entwicklung in Chiapas entwickelt. Die Bodendegradation, entweder anthropogen oder natürlich entstanden, wird durch eine Kombination aus direkten und indirekten Faktoren verursacht, die zeitlich und örtlich variieren. In den armen ländlichen Gebieten von Chiapas mit ihren harten ökonomischen Bedingungen, tritt die Bodendegradation schneller auf und verstärkt Umwelt- und sozioökonomische Probleme. Generell kann Bodendegradation und Umweltzerstörung zu verstärkter Armut und einer gefährdeten Ernährungssicherheit und damit zu einer Destabilisierung der Gesellschaft führen. Dies erhöht auch die Ausprägung der aktuellen Migrationsströme und deren Dynamik. Es ist ein Teufelskreis, der sich mit der Zeit selbst verstärkt, und es gibt keine Anzeichen dafür, dass dieser Trend abebben wird. Politischen Veränderungen, verbunden mit Strukturanpassungsprogrammen (SAP) und dem Nordamerikanischen Freihandelsabkommen (NAFTA), das indirekt die Umweltzerstörungen und die Bodendegradation beschleunigt, haben zu einer Abwanderung von Chiapanecos zu nationalen und internationalen Zielen geführt. Chiapas hat sich in den letzten Jahrzehnten zu einem Herkunftsgebiet, einer 209 Transitstation und einem Ziel für Migranten entwickelt. Durch das gleichzeitige Vorhandensein von ländlichen, städtischen und regionalen Migrationsbewegungen ist das Gesamt-Phänomen der Migration in der Region vielschichtiger geworden. Daher kann Chiapas als Labor für die Untersuchung unterschiedlichster Migrationsflüsse angesehen werden. Hier ist die internationale Migration von Chiapanecos in die USA eine relativ neu auftretende Bewegung. Sie hat sich über die letzten Jahre verstärkt, da sich einerseits durch inzwischen etablierte Netzwerke von Chiapanecos in den USA die Durchführung von Ausreise und Arbeitssuche für nachfolgende Migranten vereinfacht hat Die Verbindung zwischen Bodendegradation, ländlicher Entwicklung und Migration kann durch die Identifizierung von natürlicher Gegebenheiten bzw. den Einfluss von verschiedenen Naturereignissen, der Landnutzung, der ländlichen Entwicklung und der sozio- demografischen Charakteristik von Chiapas hergestellt werden. Diese Arbeit strebt an, die Dynamik von Migrationsflüssen, verursacht durch den Bodendegradationsprozess, die neoliberale Globalisierungspolitik, sowie deren Verbindung zu der Entwicklung kleinbäuerlicher Existenz im Bezirk Tapachula im mexikanischen Bundesstaat Chiapas darzustellen. Die Feldforschung für diese Arbeit wurde in drei verschiedenen Regionen der Gemeinde Tapachula durchgeführt, im Flachland (LL), in einer mittleren Höhenlage (ML) und im Hochland (HL). Jede Region wurde durch zwei „Ejidos“ (Siedlungen/Gemeinden) repräsentiert, diese wurden abhängig von Höhenlage und Einwohnerzahl ausgewählt. Die Daten wurden mit Hilfe von zwei verschiedenen Fragebögen erhoben, einer für die Kleinbauern und ein zweiter für die zentralamerikanischen Arbeiter, die in der Kaffeeanbauregion beschäftigt waren. Die Daten wurden mit quantitativen und qualitativen Ansätzen analysiert.Diese Studie beschäftigte sich mit der Analyse der Auswirkungen von Bodendegradation der landwirtschaftlich genutzten Flächen auf das Einkommen der Kleinbauern. Eine Multiple Regressions Analyse (MRA) der Daten zeigt, dass es signifikante Unterschiede zwischen dem Einkommen von Kleinbauern mit und ohne Bodendegradationsprobleme gibt. Dabei war das Einkommen bei Kleinbauern mit dieser Problematik um $ 97,3 MXN ($7,5 USD) pro Hektar und Monat geringer als bei Kleinbauern ohne Bodendegradationsprobleme. Ein ANOVA-Test bestätigte die Signifikanz des dazu entwickelten Modells. Die Bodendegradation in dem untersuchten Gebiet entsteht durch nicht nachhaltige Landnutzung, 210 Abholzung, Starkregen und Hurrikane. Auch erfolgt wenig staatliche und wirtschaftliche Unterstützung um die landwirtschaftliche Produktivität zu verbessern. Die Bodendegradation ist sowohl ein Hemmnis für die ländliche Entwicklung durch die Reduzierung von Anbauflächen, Bodenfruchtbarkeit und Pflanzenerträgen, als auch ein immer stärker werdender Faktor für die Entscheidung für eine Auswanderung aus der Region. Eine ausführliche Analyse bestätigte, dass die Einkommen der Kleinbauern in allen drei untersuchten Regionen beeinflusst wird. Das Hochland ist am stärksten betroffen. Durch eine Kreuztabulierung und einen Chi-Square-Test konnte eine Beziehung zwischen Bodendegradation und den Haushalten, die Auswanderer stellen, festgestellt werden. Diese zeigte, dass in Kleinbauernfamilien mit derartigen Problemen mehr Familienmitglieder migrieren als in solchen ohne. Dies weist direkt auf die Rolle von Bodendegradation als einen entscheidenden Einflussfaktor - neben anderen - hin. Die Wirkungskette besteht aus einer reduzierten Bodenfruchtbarkeit, die zu einer geringeren Produktion führt, die dann direkt eine Senkung des Haushaltseinkommens hervorruft. Als weiteres Hemmnis wird von 51 % der befragten Kleinbauern die Einführung der NAFTA gesehen, da dies zu einer reduzierten technischen und wirtschaftlichen Unterstützung durch den Staat führte und dadurch auch zu gesunkenen Preisen für landwirtschaftliche Produkte. Alle diese Faktoren haben viele Haushaltsmitglieder von Kleinbauern motiviert, wenn nicht gar gezwungen, nach neuen Einkommensmöglichkeiten außerhalb der Grenzen von Chiapas oder Mexiko zu suchen, Durch einen faktoriellen ANOVA-Test konnte gezeigt werden, dass die Annahmen bezüglich einer Aussetzung der staatlichen Unterstützung richtig sind. Die geringe wirtschaftliche und technische Unterstützung hilft den Kleinbauern nicht die Produktivität durch Investitionsmaßnahmen zu verbessern, sie hat außerdem nur unwesentliche Auswirkungen auf das kleinbäuerliche Einkommen. In der Tat zeigten die Ergebnisse keine Unterschiede im Einkommen von staatlich geförderten und nicht-geförderten Haushalten. Der Mangel an Entwicklung, die Marginalisierung und die Bodendegradation führten zu folgenden Migrationsströmen: zu den nahen städtischen Zentren (ländlich-städtisch), zu regionaler Migration zum benachbarten Bundesstaat Quintana Roo (Riviera Maya), zu den zentral gelegenen Bundesstaaten Mexico City und Guadelajara, zum nördlichen Bundesstaat Baja California (Tijuana), und (als eine relativ neue Migrationsbewegung) von den ländlichen Gemeinden aus dem Hochland nach North Carolina, Virginia, Florida und Kalifornien in den USA (ländlich-international). Die Hochlandregion, mit geringen Einkommen, einem hohen Anteil an anthropogen verursachter Bodendegradation, und einer starken Marginalisierung 211 der Bevölkerung sendet Migranten sowohl zu regionalen als auch zu internationalen Zielen. Die von den anderen Regionen abweichenden ökonomischen und sozio-demografischen Charakteristika (wie die indigene Herkunft) sind zu einer Hürde für die Arbeitssuche in den städtischen Gebieten Tapachulas geworden. Es ist sehr wahrscheinlich, dass eine weitere Bodendegradation die ländliche Wirtschaft in Gefahr bringt und größere Teile der Bevölkerung dazu bewegt nach besseren Lebens- und Arbeitsgebieten Ausschau zu halten, auch in neuen/erweiterten Zielgebieten. Die Ergebnisse zeigen, dass die Migranten durchschnittlich einen monatliche Betrag von $ 1.371 MXN ($105 USD) in der mittleren Höhenstufe und von $ 1.340 MXN ($ 103 USD) im Hochland an die Haushalte in Tapachula schicken, dies trifft für 25% der Haushalte in der mittleren Höhenstufe (ML) und im Hochland (HL) zu. Diese Geldzahlungen werden in erster Linie für Nahrungsmittel ausgegeben. Tatsächlich führt der Beitrag durch Geldzahlungen in den Empfänger-Haushalten nicht zu einer Verbesserung der Entwicklung der Haushalte. Im Gegenteil, sowohl in der untersuchten Region als auch in ganz Chiapas, untergräbt die Abwanderung, als ein Ergebnis aus existierenden Ungleichheiten und einer hohen Marginalisierung, die wirtschaftliche Entwicklung und verstärkt die Konditionen für Mangel an Entwicklung. Im Besonderen beinhaltet diese nachteilige Konsequenz der neoliberalen Globalisierung eine gestiegene Arbeitsunsicherheit, die Restrukturierung der Abwanderungsmuster der Chiapaneco Bevölkerung, eine Reduzierung der staatliche Unterstützung für die Kleinbauern-Landwirtschaft und mehr Umweltzerstörung. Die Abwanderung untergräbt die bisher bestehende Arbeitsmarktstruktur der Region, und schafft einen Arbeitskräftemangel. Dieser Mangel wird durch die Grenz- und Zeitarbeiter (meist saisonale Arbeit) aus Guatemala ausgeglichen. Paradoxerweise ist der plötzliche Anstieg der Abwanderung mit dem Aufkommen von neuen Einwanderungsmustern verknüpft, diese sind durch Armut und gefährliche Arbeitsbedingungen charakterisiert. Die Ergebnisse der Arbeit zeigen, dass fast die Hälfte der befragten Kleinbauern externe Arbeitskräfte aus Guatemala (30%) oder benachbarten Gemeinden (18%) einstellt. Bezüglich der Einstellung von guatemaltekischen Arbeitern gaben die Bauern an, dass die Hauptgründe einerseits aus einem Mangel an lokalen Arbeitern wegen der Abwanderung bestehen und andererseits, dass die Arbeiter aus Guatemala bessere und billigere Arbeitskräfte als die lokalen seien. Weitere Faktoren die zu einer starken Mobilität von landwirtschaftliche Saisonarbeitern aus Guatemala führen sind: (a) die relativ kurze Anreise aus den Grenzregionen in Guatemala, (b) ein relativ einfachen Erhalt von legalen 212 Arbeitsgenehmigungen und (c) die lange und ausgeprägte historische und kulturelle Migration zu den Kaffeeanbauregionen in Tapachula. Hierbei ist wichtig herauszustellen, dass die Beschäftigung von guatemaltekischen Arbeitern in kleinbäuerlichen Betrieben nicht notwendigerweise bedeutet, dass sich hier die wirtschaftliche Situation verbessert und modernisiert hat. Die temporäre Migration von guatemaltekischen Saisonarbeitern zu den Kaffeeplantagen in Tapachula ist kein neues Phänomen. Die Arbeitsmöglichkeiten, die Versorgung, die Löhne sowie die Lebensumstände und unter Umständen Gesundheitseinrichtungen, manchmal wird auch eine Kinderbetreuung für deren Kinder angeboten, die von den Kaffeebauern zur Verfügung gestellt werden sind vergleichsweise besser als in Guatemala. Außerdem erlaubt es die Versorgung von Essen und Wohnraum durch die Kaffeplantagen, den Saisonarbeitern fast ihr ganzes Einkommen zu sparen. Allerdings zeigen die Ergebnisse auch, dass die aktuellen Arbeits- und Lebensbedingungen nicht den Ansprüchen für menschenwürdige Arbeit (decent work), definiert von der (ILO) International Labour Organisation entsprechen. Diese Definition basiert auf den Prinzipien von Freiheit, Gleichheit, Sicherheit und Menschenwürde. Derartige Arbeitsbedingungen wie auf den Kaffeplantagen zieht die lokalen Arbeiter nicht an.Wenn man die Natur der Migration nach oder durch Mexiko näher betrachtet, sind sowohl legale als auch illegale grenzüberschreitende Migranten gleichermaßen zu einer verletzlichen Bevölkerungsgruppe geworden, die nach besonderer Aufmerksamkeit durch die Regierungen, die Entwicklungs(hilfe)institutionen und die Gesellschaft verlangen. Diese Forschungsarbeit setzt die Grundlage für die dialektische Verknüpfung von Bodendegradation mit der Reduzierung von ländlichen Ressourcen für Entwicklung und dem Anwachsen von Abwanderung. Sie zeigt die Bedeutung der Einbeziehung von geografischer Lage, sowie den natürlichen, klimatischen und topografischen Gegebenheiten bei der Entwicklung einer vergleichende Analyse-Plattform für eine Einschätzung und Auswertung der Einflüsse von Bodendegradation auf ländliche Entwicklung und Migration. 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L oc al id ad __ __ __ __ __ __ __ __ __ __ __ __ __ __ _M un ic ip io __ __ __ __ __ __ __ __ _E st ad o_ __ __ __ __ __ _ Fe ch a_ __ __ __ __ __ _ H og ar # __ __ __ __ __ 1 a 12 I nf or m ac ió n so br e lo s ha bi ta nt es d el h og ar ( in cl ui do je fe /a d el H ) y ot ro s hi jo s de la je fa /e q ue h an e m ig ra do ( lu ga re s, f ec ha s y ra zo ne s) N om br e (1 ) S ex o (2 ) R el ac ió n a la je fa /e (3 ) E da d na ci ó 1 2 3 4 5 6 7 8 9 (1 ) ( 9) (2 ) (5 ) (6 ) 1= M , S i 1= J ef e/ a 1= S ol te ro /a 1= P ri m ar ia ( 6) 2= F , N o 2= C on yu gu e 2= C as ad o/ a 2= S ec un da ri a (9 ) 3= H ijo /a 3= V iu do 3= T éc ni ca ( 12 ) 4= S ob ri no /a 4= U ni ón li br e 4= P re pa ra to ri a (1 2 5= T ío /a 5= D iv or ci ad o/ a 5= U ni ve rs id ad ( 17 ) 6= P ri m o/ a 6= S ep ar ad o/ a 6= E sc ue la p ar a ad ul to s (3 ) 7= A bu el o/ a 7= I le tr ad o 8= P ad re /M ad re 8= O tr o 9= H er m an o/ a 10 = C uñ ad o/ a 11 = N ie to /a D ep t. o f D ev el o pm en t E co n o m ic s, M i Un iv er si da d Au tó n o m a de Do ct o ra do en Es tu di o s de l D es ar ro llo 24 1 1= B aj a pr od uc tiv id ad a gr íc ol a 2= S ue lo e s m uy p ob re 3= N o ha y su fi ci en te ti er ra 4= F al ta d e ag ua /s eq ui a 5= B aj os s al ar io s 6= F al ta d e se rv ic io s/ in fr ae st ru ct ur a 7= B us ca r m ej or es o po rt un id ad es e co nó m ic as 8= N o ha y tr ab aj o 9= F al ta d e at en ci ón d el g ob ie rn o 10 = T ie ne p ar ie nt es /a m ig os e n aq ue l l ug ar 11 = In se gu ri da d/ vi ol en ci a 12 = O tr o 1= A gr ic ul to r 2= G an ad er o 3= F or es ta l 4= J or na le ro 5= C om er ci an te 6= T ax is ta /C ho fe r 7= A lb añ il 8= E m pl ea do S . p ub lic o (n o ag rí co la ) 9= E m pl ea do S . p ri va do ( no a gr íc ol a) 10 = H og ar 11 = E st ud ia nt e 12 = O tr o (e sp ec if ic ar ) 13 = N in gu no EN CU E ST A SO CI O - EC O N O M IC A, PR O D U CT IV A Y M IG R AT O R IA E l o bje tiv o de es ta en tr ev ist a es re co le ct a r in for m a ci ón so br e la s fam ili a s de es ta zo n a , la m a n er a en qu e tr a ba jan la tie rr a , lo s pr o bl em a s qu e en fre n ta y có m o ig ra r. La in for m a ci ón qu e n o s pr o po rc io n e se rv irá pa ra id en tifi ca r lo s pr o bl em a s qu e a fec ta n a U d. , su . To do lo qu e u st ed n o s di ga es co n fid en ci a l, es de ci r, n o se lo co n ta re m o s a n in gu n a o tr a pe rs o n a . L oc al id ad __ __ __ __ __ __ __ __ __ __ __ __ __ __ _M un ic ip io __ __ __ __ __ __ __ __ _E st ad o_ __ __ __ __ __ _ Fe ch a_ __ __ __ __ __ _ H og ar # __ __ __ __ __ te s de l h og ar ( in cl ui do je fe /a d el H ) y ot ro s hi jo s de la je fa /e q ue h an e m ig ra do ( lu ga re s, f ec ha s y ra zo ne s) (4 ) L ug ar d on de na ci ó (M un ic ip io y E st ad o) (5 ) E st ad o ci vi l (6 ) N iv el ed uc . (7 ) A ct iv id ad ec o. p ri nc ip al (8 ) In gr es o m en su al (9 )V iv e en e l ho ga r N o (7 ) (1 2) (9 ) 4= P re pa ra to ri a (1 2 5= U ni ve rs id ad ( 17 ) 6= E sc ue la p ar a ad ul to s (3 ) R es po n sa bl e: M . C. D av id Ec he Un iv er si ty o f K as se l D ep t. o f D e v e lo pm en t E co n o m ic s, M ig ra tio n an d Ag ric u ltu ra l P ol ic y Un iv er si da d Au tó n o m a de Za ca te ca s Do ct o ra do en Es tu di o s de l D es ar ro llo 1= B aj a pr od uc tiv id ad a gr íc ol a 3= N o ha y su fi ci en te ti er ra 4= F al ta d e ag ua /s eq ui a 6= F al ta d e se rv ic io s/ in fr ae st ru ct ur a 7= B us ca r m ej or es o po rt un id ad es e co nó m ic as 9= F al ta d e at en ci ón d el g ob ie rn o 10 = T ie ne p ar ie nt es /a m ig os e n aq ue l l ug ar 11 = In se gu ri da d/ vi ol en ci a la s fam ili a s de es ta zo n a , la m a n er a en qu e tr a ba jan la tie rr a , lo s pr o bl em a s qu e en fre n ta y có m o lem a s qu e a fec ta n a U d. , su fam ili a y L oc al id ad __ __ __ __ __ __ __ __ __ __ __ __ __ __ _M un ic ip io __ __ __ __ __ __ __ __ _E st ad o_ __ __ __ __ __ _ Fe ch a_ __ __ __ __ __ _ H og ar # __ __ __ __ __ te s de l h og ar ( in cl ui do je fe /a d el H ) y ot ro s hi jo s de la je fa /e q ue h an e m ig ra do ( lu ga re s, f ec ha s y ra zo ne s) (1 0) D on de vi ve ac tu al m en te (1 1) ¿C uá nd o em ig ró ? (1 2) ¿P or qu e em ig ró ? (g ) 242 13. Ocupación Secundaria del JH (ver por opciones en pregunta 7) 14. Origen (a) si es nativo pasar a la pregunta 19 15. ¿Cuánto tiempo tiene viviendo aquí? 16. ¿Dónde vivía antes? 17. ¿Por qué vino a vivir aquí? (b) 18. ¿Recibió algún apoyo para llegar aquí? (c) no pregunta 19 18.1 ¿De quién recibió apoyo para llegar aquí? (d) 18.2. ¿Cómo le ayudaron? (e) (a) (b) (c) (d) (e) 1= Nativo 1= Tierras baratas 1= Si 1= Padres 1= Conseguir tierra 2= Migrante 2= Mas empleo 2= No 2= Hermanos 2= Conseguir trabajo 3= Mejor salario 3=Hijos 3= Conseguir crédito 4= Mejores suelos 4= Otros parientes 4= Construir una casa 5= Mas seguridad 5= Amigos 5= Otro (esp) 6= Me trajeron (la familia/parientes) 6= ONG’s 7= Problemas con la familia 7= Gobierno 8= Quería ser independiente 8= Otro (esp) 9= Actividades económicas 10= Otro (esp.) CARACTERISTICAS Y USO DE LA PROPIEDAD AGRICOLA (P.A) 19. ¿De qué tipo es su Propiedad Agrícola? (a) 20. ¿Cuántas hectáreas tiene su P.A? ha 21. ¿En qué año consiguió su P.A? 22. ¿Cómo consiguió su P.A? (b) 23. ¿Cuál es el uso de su P.A? (c) 24. ¿Cómo era su P.A cuando Ud. llegó? (d) 25. ¿Qué tipo de suelo tiene principalmente? (e) 26. ¿Cómo describiría la calidad del suelo de su P.A? (f) 27. ¿Cómo es la topografía predominante en su P.A? (g) 28. ¿Tiene áreas pantanosas o inundadas con frecuencia? (h) 28.1 Si, ¿Cuantas hectáreas aproximadamente? ha 29. ¿Tiene áreas degradadas en las que ya no cultiva? (i) 29.1 Si, ¿Cuantas hectáreas aproximadamente ha 29.2 ¿ Porque cree que esta zona se encuentra degradada/erosionada? (j) 30. ¿tiene áreas de bosque? (k) 30.1 Si, ¿Por qué no las ha usado? (l) 30.2 ¿Qué piensa hacer con el bosque en el futuro? (m) (a) (b) (c) (d) (e) (f) (g) 1= Ejidal 1= Dotación Gob. 1= C. Anuales 1= Montaña 1ria 1= Negro 1= Muy Buena 1= Plana 2= Privada 2= Heredó 2= C. Perennes 2= Montaña 2ria 2= Rojo 2= Buena 2= Ondulada 3= Comunal 3= Compró 3= Ambos A/P 3= Acahuales 3= Amarillo 3= Regular 3= Quebrada 4= Rentada 4= Otro (esp) 4= Pastizal 4= Pastos 4= Grisáceo 4= Mala 4= Ladera 5= Bosque 5= Cultivos 5= Otro (esp) 5= P/O (h) (i) (k) 6= Acahuales 6= Combinados 6= P/Q 1= Si 7= Agostadero 7= Agostadero 7= O/Q 2= No 8= Otro (esp) 8= Otro (esp) 8= L/O 9= Otro (j) (l) (m) 1= Lluvias intensas 1= Provee leña y madera 1= Cortar y usar para c. anuales 2= Deforestación 2= No hay mano de obra 2= Cortar y usar para c. perennes 3= Sobrepastoreo/Ganadería 3= Mano de obra costosa 3= Cortar y usar para pasto 4= Uso excesivo de fertilizantes 4= Quiere recuperar el bosque 4= Dejarla en montaña como reserva de madera/árboles 5= Mal manejo de la tierra 5= Quiere recuperar el suelo 5= Manejar la regeneración natural de árboles maderables 6= Otro 6= Otro (esp) 7= Otro (esp) 24 3 U so d e la ti er ra 2 00 5 y 20 10 a sí c om o su bs id io s ex is te nt es 31 .¿ C uá nt as he ct ár ea s ti en e ac tu al m en te d e… .? 32 . E n 20 05 (o ha ce 5 añ os ) el á re a cu lti va da fu e: ( a) co n re la ci ón a 2 01 0 33 . ¿Q ué tip o de si st em a de pr od uc ci ón us a? (b ) 34 . ¿ R ec ib e al gú n su bs id io po r su s cu lt iv os ? (c ) S i 35 . ¿C ua nt o re ci be po r ha /a ño ? 36 . N om br e de l pr og ra m a y/ o in st it uc ió n ha C . A n u a le s S oy a M aí z S or go A jo nj ol í H or ta liz as O tr os ( es p) - - - C . Pe re n n es C af é M an go P lá ta no F ru ta le s O tr os ( es p) - - - P as ti za l B os qu e 1r io A ca hu al < 7 a ño s B os qu e 2r io > 7 a ño s S il vo pa st or il O tr os ( es p) - - - T ot al (a ) (b ) (c ) 1= M en or 1= R ie go 1= S i 2= M ay or 2= S ec an o/ te m po ra l 2= N o 3= Ig ua l 3= A m bo s 4= N o lo c ul tiv ab a 5= N o re cu er da 24 4 C A M B IO S EN LA PR O D U C C IO N Y PR EC IO S D E L O S PR O D U C TO S A ho ra q ui si er a pr eg un ta rl e so br e lo s ca m bi os e n su c os ec ha h ac e 5 añ os , l os in su m os y o tr as té cn ic as p ar a m an ej ar s us c ul ti vo s us ad os e n lo s úl tim os 1 2 m es es . (d ) (e ) 1= N o 1= N o 2= G al lin az a 2= F er til iz an te 3= E st er co la za 3= H er bi ci da 4= R em an en te s de c os ec ha 4= P es tic id a 5= H um us 5= F un gi ci da s 6= O tr o 6= O tr o (e sp ) C u lti v o C a m bi os en la co se ch a C a m bi o s en lo s pr ec io s U so de in su m o s N u ev as té cn ic a s 37 . P ro du cc ió n an ua l t / h a 38 . C on re la ci ón a l 20 05 la pr od uc ci ón fu e (a ) 39 . Si a u m en tó ¿P or q ué ? (b ) 40 . Si di sm in u yó ¿P or q ué ? (c ) 41 . Pr ec io s ac tu al es kg . 42 . P re ci os en e l a ño 20 05 43 . ¿ Q ué ti po de a bo no or gá ni co u só ? (d ) 44 . ¿ Q ué t ip o de pr od uc to s qu ím ic os u só ? (e ) 45 . ¿ U sa a lg un a té cn ic a es p. e n el m an ej o de es te c ul tiv o? ( f) C . A n u a le s S oy a M aí z S or go A jo nj ol í H or ta li za s O tr os ( es p) - - - C . Pe re n n es C af é M an go P lá ta no F ru ta le s O tr os ( es p) - - - (b ) 1= N ue va s pl an ta s 2= N ue va á re a de c ul tiv o 3= U so d e ab on os q uí m ic os 4= U so d e ab on os o rg án ic os 5= M ej or es s em ill as n o hi br id as 6= M ej or es s em ill as h ib ri da s 7= M ej or m an ej o 8= O tr o (e sp ) (a ) 1= M ay or 2= M en or 3= Ig ua l 4= N o sa be (f ) 1= N o 2= S em ill a G M 3= M IP 4= A so ci ac ió n de c ul tiv os 5= R ot ac ió n de c ul tiv os 6= P od as 7= O tr o (e sp ) (c ) 1= S ue lo v ie jo / m en os p ro du ct iv o 2= P la nt as v ie ja s/ m en os p ro du ct iv as 3= M al as h ie rb as 4= P la ga s 5= D es as tr es n at ur al es 6= O tr o (e sp ) 245 GANADERIA Numero y precio de los animales que mantiene en su P.A en 2005 y 2010 PRODUCTOS FORESTALES 51. ¿Recoge usted productos del bosque? 1= Si 2= No pregunta 52 51.1 ¿Qué productos recoge y cuál es su uso usted del bosque? En que los usa (a) 1. Maderas finas 2. Maderas corrientes 3. Frutas silvestres 4. Plantas medicinales 5. Semillas 6. Resinas 7. Leña 8. Animales silvestres 9. Otros - USO FUTURO DEL SUELO Percepciones para el uso de la propiedad agrícola (a) 1= Tumbar mas bosque/acahual para cultivos 2= Convertir área de pastos a cultivos 3= Convertir a sistema agrosilvopastoril 4= Rehabilitar áreas en descanso para ser cultivado otra vez 5= Otro _____________________________ (b) 1= Convertir área de cultivos a pastos 2= Dejar área de cultivos a descansar/regenerar bosque 3= Otro _____________________________ (c) (d) (e) (f) (g) (h) 1= Si 1= La ganadería es más rentable que cultivos 2= No 2= La ganadería es más fácil que cuidar/requiere menos mano de obra 3= No sabe 3= Hay más facilidades de crédito 4= Hay más asistencia técnica para el manejo de ganado 6= Para consumo familiar 7= Otro _____________________________ 46. # de unidades actuales 47. # de unidades en el 2005 48. ¿Qué Sistema de crianza usa? (a) 49. Precio actual por unidad 50. Precio unidad en el 2005 Bovino Porcino Ovino Équido Aves Conejos Otros Ud. va a… S/N ¿Cómo?(a)(b) 52. Aumentar el # de ha de cultivo (a) 62 53. Disminuir el # de ha de cultivo (b) 63 54 Dejar descansar la tierra (c) 55 Preservar el bosque/acahuales (d) 56 Introducir/cambiar a cultivos nuevos. (e) 57 ¿Qué cultivos? (esp) 58 Vender la parcela o parte de la misma (f) 59¿Cuántas hectáreas? ha 60 Aumentar el # de ganado (g) 61 ¿Por qué? (h) (a) 1= Uso/consumo del hogar 2= Venta 3= Regalar 4= Otro (a) 1= Estabulado 2= Libre pastoreo 3= Semiestabulado 246 MANO DE OBRA 64. ¿Cuál es la fuente principal de mano de obra en su finca? 1= Todos los miembros del hogar 2= Los hombres adultos en el hogar 3= Las mujeres adultas en el hogar 4= Jornaleros mexicanos 5= Jornaleros extranjeros 65. ¿En los últimos 12 meses uso jornaleros o mano de obra contratada para trabajar en la P.A? 1= Si 2= No Pregunta 75 66 Tipo de mano de obra Jornalero Contratado (por tarea) 67 ¿Cuántos trabajadores emplea? _____H _____M _____ H ______M 68 ¿Por cuánto tiempo uso este tipo de mano de obra? _________ días ________ meses 69 ¿Qué tipo de trabajo hacen principalmente? (a) _____________ ______________ 70 ¿Desde qué año empezó a emplear mano de obra? 71 ¿Cuánto pagó en promedio? _________$/día _________$/mes 72 ¿De dónde vienen los trabajadores principalmente? (b) 73 ¿Qué porcentaje de sus trabajadores son extranjeros? % % 74 ¿Por qué emplea trabajadores extranjeros? (c) (a) (b) (especifique) (c) 1= Tumbar montaña/bosque 1= Ejidos cercanos 1= Mano de obra más barata 2= Plantar o cosechar cultivos de ciclo corto 2= Otras partes del municipio 2= No hay trabajadores locales 3= Plantar o cosechar cultivos perennes 3= Otras municipios 3= Son mejor trabajadores que los locales 4= Limpiar cultivos o pasto 4= Otras estados 4= Otro 5= Para sacar madera/tablones 5= Centro América 6= Otro (esp) 6= Caribe 7= Sudamérica 8= Otro ACTITUDES, SATISFACCION, VULNERABILIDAD Y REMESAS 75. ¿Cómo considera su situación actual en comparación con la que vivía hace 5 años? 1= Igual pregunta 76 2= Mejor 3= Peor 75.1. ¿Por qué es mejor? 1= Tengo más tierra para cultivar 2= Mejor cosecha 3= Tengo más ganado 4= La situación económica ha mejorado 5= Hay más trabajo 6= Mejor salud 7= Mejor clima para los cultivos 8= Otro ______________________________________________ 75.2. ¿Por qué es peor? 1= Peores cosechas 2= Más plagas en las cosechas 3= No hay trabajo 4= La situación económica ha empeorado 5= Todo esta más caro 6= Más problemas de salud 7= Peor clima 8= Más contaminación ambiental 9=Incremento en la inseguridad 10= Conflictos con vecinos 11= Otros______________________________________________ 76. ¿En su opinión que es lo que más impide el mejoramiento de la producción agrícola y/o ganadera de su P.A.? 1= Falta de agua 2= Suelos pobres 3= Suelos erosionados/degradados 4= Precios de cultivos/animales son muy bajos 5= Falta de capital para nuevas técnicas y mejorar la producción 6= Corrupción autoridades ejidales 7= Falta de asesoramiento 8= Falta de créditos, ayuda del gobierno 9= Otro _____________________________ 77. En los últimos 5 años su P.A ha sufrido de: 1= Sequias 2= Inundaciones 3= Heladas 4= Plagas 5= Temblores/terremotos 6= Huracanes 7= Incendios naturales 8= Tala de bosques 9= Ninguno 247 10= Otro ______________________________________________ 78. ¿Ha disminuido la productividad de su P.A en los últimos 5 años? 1= Si 2= No pregunta 80 3= No sabe 79. ¿Por qué? 1= Suelos más pobres/degradados 2= Escases de Agua/sequia 3= Inundaciones 4= Plagas y enfermedades 5= Otro ___________________________________ 80. ¿Qué va a hacer con su P.A. en el futuro cuando la producción a disminuido considerablemente? 1= Nada 2= Seguir manejándola 3= Venderla 4= Venderla y emigrar 5= Arrendarla 6= Dejarla a los hijos/as 7= Other 81. ¿Cree Ud. que el Tratado de libre Comercio (TLC, NAFTA) le ha beneficiado o afectado? 1= Ha beneficiado 2= Ha afectado 3= No sabe pregunta 82 81.1 ¿Porque? _________________________________________ _________________________________________ _________________________________________ 82 ¿Existen problemas de contaminación en su PA o ejido? 1 No pregunta 83 2 No sabe pregunta 83 3 De ríos/pozos 4 Aire 5 Suelo 6. Otra ________________________________________________ 82.1. ¿Cuáles son las principales fuentes de contaminación? 1= Uso de agroquímicos 2= Aguas negras y basura de los poblados 3= Industria______________________________________________ 4= Actividad minera 5= Otras ________________________________________________ 83. ¿Piensa emigrar en el futuro? 1. Si 2. No pregunta 84 3. No sabe pregunta 84 83.1. ¿Cuándo piensa emigrar?__________________ 83.2. ¿Dónde piensa emigrar? _____________________ 83.3. ¿Cuál sería la principal razón para emigrar? 1= Baja productividad agrícola 2= Suelo es muy pobre 3= No hay suficiente tierra 4= Falta de agua 5= Inundaciones 6= Falta de servicios/infraestructura 7= Buscar mejores oportunidades económicas 8= No hay trabajo 9= Falta de caminos 10= Tiene parientes/amigos en aquel lugar 11= Inseguridad/violencia 12= Otro_____________________________________ 84. ¿Recibe remesas de algún miembro de su familia? 1= Si 2= No 84.1. Si, ¿Qué cantidad y en qué frecuencia? Semanal Quincenal Mensual Trimestral Semestral Anual 84.2 Cual es el uso principal de las remesas 1= Gastos/consumo familiar 2= Mantenimiento de la P.A 3= Contratar mano de obra 4= Comprar insumos agrícolas 5= Comprar más tierras 6= Educación 7= Otro ASISTENCIA TECNICA Y GUBERNAMENTAL 85. ¿Desde que trabaja en su P.A. ha recibido Ud. algún tipo de asistencia técnica de cualquier institución? 1= Si 2= No 86. ¿Ha recibido la ayuda de algún programa gubernamental para su P.A? 1= Si 2= No pregunta 91 87. Mencione el nombre del programa __________________________________________ 88. ¿En qué consistió la ayuda? 1= Charla/curso práctico; visito finca demostrativa 2 = Seminario/taller (teórica) 248 3 = Insumos como semillas, plantas, abono 4 = Información escrito; folletos, etc. 5 = fondos/crédito 6= Subsidio para los cultivos 7= Otro _____________________________________________________ 89. Cuanto tiempo tiene recibiendo la ayuda_______ 90. ¿Cuán útil le ha sido para mejorar su P.A? 1= Mucho 2= Poco 3= Nada 4= No sabe 91 ¿Ha recibido la ayuda de algún programa gubernamental para el manejo adecuado de sus bosques? 1= Si 2= No pregunta 93 92. Mencione el nombre del programa __________________________________________ 93. ¿Practica alguna forma de manejo de bosques? 1=Si 2=No pregunta 95 94 ¿Que programa forestal maneja? 1= Manejo comunal 2= Tala de árboles no deseados 3= Protección cierto tipo de arboles/regeneración natural 4= Protección parcial para provisión de servicios ambientales 5= Otra ____________________________________ CONDICIONES DE VIVIENDA Y SALUD 95. la casa en la que habita es: 1= Propia 2= Rentada 3= Prestada . ¿De qué está hecha su casa? (Observación directa) 96) Paredes 1= Blocks 2= Blocks y Madera 3= Madera 4= Otro _______________________________________________ 97) Techo 1= Zinc 2= Loza 3= Hojas de palma 4= Otro _______________________________________________ 98) Piso 1= Cemento 2= Tierra 3= Madera 4= Otro 99. ¿Tienen luz eléctrica? 1= Sí 2= No 100. ¿De dónde obtienen el agua que toman? 1= Tubería 2= Pozo 3= Río 4= Otro______________________________________________ 101. ¿Cómo considera la calidad del agua que toman? 1= Buena 2= Regular 3= Mala 102. ¿Tienen letrina? 1= Sí 2= No 103 ¿Cuáles son los principales problemas de salud que tienen o han tenido en los últimos tres meses usted y su familia? 1= Diarrea 2= Parasitosis 3= Dengue 4= Enfermedades de piel. 5= Enfermedades respiratorias 6= Ninguna 7= Otro _______________________________________________ 104. ¿Por lo general dónde se atienden cuando hay problemas de salud? 1= Hospital 2= Centro de salud 3= Dispensario médico 4= Curandero 5= Otro _____________________________________________ * * * ¡GRACIAS POR SU COOPERACION! ☺ 249 Annex 2: Socio-demographic, labour and living conditions survey for temporary Central American agricultural workers C ue st io na ri o so br e m an o de o br a in te rn ac io na l t ra ba ja nd o en e l s ec to r A gr íc ol a de T ap ac hu la y s us c on di ci on es 1 a 7 . D at os d el e nt re vi st ad o N o m br e R el a ci ón Es ta do ci v il (a) ** ** * 8. ¿ V iv e us te d co n su f am ili a ac tu al m en te ? (d ) 8. 1 ¿C om o es tá c om pu es to s u ho ga r? 9. ¿ V iv e us te d co n ot ra s pe rs on as f ue ra d e su f am il ia ? 9. 1 ¿Q ui én es ? (a ) ( b) ( d) (c ) 1= S ol te ro 1= M , S i 1= P ri m ar ia ( 6) 2= C as ad o 2= F , N o 2= S ec un da ri a (9 ) 3= V iu do 3= P re pa ra to ri a (1 2) 4= D iv or ci ad o 4= T éc ni ca ( 12 ) 5= U ni ón li br e 5= U ni ve rs id ad ( 17 ) 6= S ep ar ad o 6= E sc ue la p ar a ad ul to s (3 ) 7= I le tr ad o (0 ) 8= O tr o R es po n sa bl e: M . C . D a v id Ec he D ep t. o f D ev el o pm en t E co n o m ic s, M ig ra tio n a n d A gr ic u ltu ra l P o lic y U n iv er sid a d A u tó n o m a de Za ca te ca s D o ct o ra do en E st u di o s de l D es a rr o llo 25 0 in te rn ac io na l t ra ba ja nd o en e l s ec to r A gr íc ol a de T ap ac hu la y s us c on di ci on es la bo ra le s E st a do ci v il (a) G en er o (b ) Ed a d N iv el de ed u ca ci ón (c) Lu ga r de n a ci m ie n to Lu ga r de re sid en ci a a ct u a l (e ) 1= E sp os o/ a 2= H ij o/ a 3= S ob ri no /a 4= T ío /a 5= P ri m o/ a 6= A bu el o/ a 7= P ad re s/ a 8= H er m an o/ a 9= C uñ ad o/ a 10 = O tr os R es po n sa bl e: M . C . D a v id Ec he U n iv er sit y o f K a ss el D ep t. o f D ev el o pm en t E co n o m ic s, M ig ra tio n a n d A gr ic u ltu ra l P o lic y U n iv er sid a d A u tó n o m a de Za ca te ca s D o ct o ra do en Es tu di o s de l D es a rr o llo Lu ga r de re sid en ci a a ct u al 251 10. ¿Cuáles son las razones por las que emigro? 1= Desempleo 2= Pobreza 3= Delincuencia/inseguridad 4= Desastres naturales 5= Reunirse con la familia 6= Aventura 7= Otro_________________________________________ 11. Como inmigrante Ud. se encuentra en México: 1= Trabajando temporalmente 2= Residiendo 3= Visitando familiares/parientes 4= De paso 5= Otro 12. ¿Quien le ayudo para llegar a México? 1= Familiares cercanos 2= Parientes 3= Amigos 4= Nadie 5= Contratista 6= Otros 13. ¿Por cuánto tiempo ha permanecido en México desde su última visita? ______________________________________ 14. ¿Qué tipo de documento tiene para permanecer en México? 1= Visa 2= FM1registro 3= FM2 Inmigrante 4= FM3 no inmigrante 5= FM6 transmigrante 6= FME extranjero 7= FMTTV (turista, transmigrante, visitante 8= FMVL (visitante local) 9= FMTA (trabajador agrícola) 10= FMTF (trabajador fronterizo) 11= Inmigrado 12= Refugiado 13= Ninguna 14= Otro 15. ¿Cree usted que hay mas y/o mejores oportunidades en México que en su país de origen? 1= Si 2= No pregunta 17 3= Igual 16. ¿Qué oportunidad le brinda México? 1= Mejores salarios 2= Mayor oferta de trabajo 3= Mayor seguridad 4= Ninguna 5= Otro 17. ¿Qué problemas ha tenido en el viaje hasta llegar a México? 1= Asalto 2= Extorsión 3= Abuso sexual 4= Ninguno 5= Otro 18. ¿Ha recibido algún tipo de maltrato por autoridades mexicanas durante su viaje a México? 1= Si 2= No pregunta 19 18.1 Que tipo de maltrato______________________ 19. ¿Ha emigrado anteriormente a los Estados Unidos? 1= Si 2= No pregunta 22 20 ¿En qué año? _______________________ 21 ¿A Que ciudad?____________________ 22 ¿Piensa emigrar a los Estados Unidos? 1= Si 2= No pregunta 33 23 ¿Cuando?__________________________ 24 ¿A qué ciudad? _____________________ 25 ¿Cuánto tiempo piensa quedarse en los? E. U.__________________________________ 26 ¿Ciudades en las que hará escala antes de llegar a la frontera norte? Cd. 1_________________________________ Cd. 2 _________________________________ Cd. 3 _________________________________ 27 Ciudad fronteriza de paso _______________________________________ 28 ¿Quién/es le acompañaran durante su viaje? 1= Cónyuge 2= Hijo/a 3= Padre 4= Madre 5= Hermano/a 6= familia completa 7= Nadie 8= Otros migrantes 29. ¿Cuál es su propósito/ilusión de ir a los Estados Unidos? 1= Reunificación familiar 2= En búsqueda de trabajo 3= Ayudar económicamente a la familia en casa 4= Otro__________________________ 30. ¿Quien le recibirá en los Estados Unidos? 1= Familiares cercanos 2= Parientes 3= Amigos 4= Nadie 31. ¿Para su viaje a los E.U. usted contrató la ayuda de coyote/pollero? 1= Si 2= No 252 32. ¿De quien recibe apoyo para su viaje a Estados Unidos? 1= Familiares 2= Parientes 3= Amigos 4= Nadie 5= Otros__________________________________ 33. Porque no piensa migrar a los Estados Unidos 1= No hay necesidad económica 2= Es difícil cruzar la frontera 3= El trayecto hasta la frontera es muy peligroso 4= No tiene dinero suficiente para el viaje 5= Dificultades familiares 6= Le gusta vivir en México 7= No le interesa 8= Otro__________________________________ 34. Si vive y/o trabaja en México usted envía remesas 1= Si 2= No pregunta 36 35¿Cada qué tiempo? ¿Cuánto envía? 1= Semanal 2= Quincenal 3= Mensual 4= Trimestral 5= Semestral 6= Otro 36 a 43. Aspectos y Condiciones laborales Ocupación antes de migrar Tipo de empleo (a) Como fueron sus condiciones laborales (b) ¿Porque? (c) actividad agrícola principal (d) Cultivo principal en el que Trabaja (e) Salario mensual # hrs por semana Jornalero Agricultor (propio) Chofer Comerciante Albañil Empleado Privado Empleado publico Negocio propio Estudiante Desempleado Otro Ocupación actual Jornalero agrícola Agricultor (propio) Aserrador Jornalero ganadero Otro (a) (b) (c) (d) (e) 1= fijo 1= buenas 1= Tuvo un salario justo 1= Cosechar 1= Café 2= permanente 2= Regulares 2= Tuvo un salario injusto 2= Sembrar 2= Plátano 3= Malas 3= No tuvo remuneración 3= limpiar la propiedad agrícola 3=Maíz 4= Si tuvo seguro medico 4= Talar árboles 4= Soya 5= No tuvo seguro medico 5= Cuidar la P.A 5= Cacao 6= Tuvo horas extras no remuneradas 6= Mantenimiento de la P.A 6= Mango 7= Fue discriminado 7= Cuidar animales 7= Sorgo 8= Otro 8=Otro 8= Pastos 9= Milpa 10= Otro 253 44. ¿Qué tan complicado le ha sido conseguir trabajo en México? 1= Fácil 2= Difícil 3= Mas o menos 45. ¿Ha sufrido algún accidente o enfermedad relacionada a su trabajo? 1= Si 2= No pregunta 48 46 ¿De qué clase? ___________________________________ 47. ¿Cubrió su empleador los gastos médicos por la atención a su enfermedad/accidente? 1= Si 2= No CONDICIONES DE VIVIENDA Y SALUD 48. ¿De qué está hecha su casa? (Observación directa) a) Paredes 1= Blocks 2= Blocks y Madera 3= Madera 4= Otro ________________________ 49 b) Techo 1= Zinc 2= Hojas de palma 3= Otro _______________________ 50 c) Piso 1= Cemento 2= Tierra 3= Madera 4= Mosaico 51. ¿Cuántas habitaciones tiene su casa? 52. ¿Tienen luz eléctrica? 1= Sí 2= No 53. ¿De dónde obtienen el agua que toman? 1= Tubería 2= Pozo 3= Río 4= Vertiente 5= Otro________________________ 54. ¿Cómo considera la calidad del agua que toman? 1= Buena 2= Regular 3= Mala 55. ¿Tienen letrina? 1= Sí 2= No 56.¿le ha proporcionado su empleador algún seguro medico? 1= Sí 2= No 57. ¿Cuáles son los principales problemas de salud que tienen o han tenido en los últimos tres meses usted y su familia? 1= Diarrea 2= Parasitosis 3= Dengue 4= Enfermedades de piel. 5= Enfermedades respiratorias 6= Otros ___________________________ 58. ¿Por lo general dónde se atienden cuando tiene 1= Hospital 2= Centro de salud 3= Dispensario médico 4= Curandero 5= Ninguno 6= Otro ___________________________ GRACIAS POR SU COLABORACIÓN! Annex 3: The trip of my life for my life45 For travelers, there is nothing more fascinating than encounter, discover, and explore new lands and cultures. This time, my curiosity and passion for research and travel took me to the southern Mexican region of Chiapas; an evergreen region with an array diversity of life, ethnic groups and cultures. With many interesting places to visit here; first I wanted to start the trip visiting the Mexican-Guatemalan border, the city of Tapachula in the coastal region, San Cristobal de las Casas city in the mountainous region, the Selva Lacandona (Lacandona jungle), the ruins of Palenque, Montes Azules natural park, Montebello lakes etc. the list was pretty long. Thus I did, this is the summary of a high-risk trip through Mexico, from border to border accompanying transit-migrants in their journey for reaching the American Dream. Very early in the morning, I started the journey from Tapachula toward ciudad Hidalgo (Hidalgo City) located at the border. This small, thriving and bustling city had an impressive mix of indigenous and mestizo ethnic groups. The traffic and the noise increased as long as I got closer to the borderline, people offering different type of goods and services sprouted everywhere. The bridge over the Suchiate River that joints Mexico and Guatemala showed intense economic and touristic activities on the one side. On the other side, in the Guatemalan city of Tecún Umán, informal activities were in motion under the bridge. The human settlements on both sides of the border are forsaken, extremely poor, and are the shelter for criminal gangs who deal with drugs, weapons, human smuggling, kidnapping and human rights violations. The prime unregulated activity I spotted from top of the bridge was the entering of undocumented Central American Migrants into Mexican territory. Immigrants crossed the river swinging when the waters were low, otherwise there were several rafts made up by lumber attached to a truck tire tube ready to cross anyone for some Mexican pesos or Guatemalan quetzals. Human migration is one of the fields that interest me the most as scientific researcher. Thus, I decided to postpone the visit to the touristic attractions and instead to join the transit- migration journey to reach the so-called “American dream”, a 2200 km journey with innumerable pitfalls. The low security and authority at the south-border have made it easy for migrants to sneak into Mexico. The challenges started few kilometers away from the border, where the first police and military checkpoints are placed. Few of the migrants in the group have already mapped out the journey and were aware of the difficulties this represents for them and their families 45 This five day trip started on February 4th 2011 as part of a scientific research titled “Land degradation, small- scale farms’ development, and migratory flows in Chiapas. Case study: Tapachula”. 255 behind. The goal for that day was to reach Tapachula and stay in one of the casas del migrante scalabrini46 (migrant’s home). Here, food and shelter are freely supplied to migrants, it is also a space to team up and gather experiences from other migrants who have crossed or attempted to cross Mexico in the past. However, the terrible migration journey experiences lived by some migrants terrified us. The next morning we continued traveling northwards to Arriaga City in Oaxaca, where the train network that transports materials and goods from the south to other Mexican states begins. It was dark when we arrived to the train station, shadows walking in the darkness, waiting to jump the terrible and so called “la bestia” (the beast) or “tren de la muerte” (death train). Our presence stole the dream of some migrants resting along the railways, under old train wagons, and on top of the roof of small abandoned houses; everybody is afraid of everybody because members of the transnational criminal group “mara salvatrucha” or MS-13 could walk within us, they said. Robbery, violations, extortions, and killings are daily present in this vicinity, labeled by Mexican authorities as a high-risk zone. The board and the ride on the beast The “iron beast” offers a faster ride to northern states, but it also brings more danger for free- riders. Getting on board the train is not easy, experienced migrants say that in many cases the train does not stop and travels to a speed of 30 to 45 km per hour. The chances of missing the train, to fall, to be amputated or to die under the railways were always in our minds. Exhausted, hungry and thirsty, we prepared to spend the night near the railway tracks and away from other migrant groups. At 05:45 a.m. the whistle of the train alarmed everybody, we got close to the railways and start running to catch speed and be able of boarding the train. Other migrants clutching small back packs and large water bottles emerged from the bushes yelling !correle correle! (run, run). The dawn was bringing the first sunrays, which helped us to see the handles, doors and stairs of the wagons. The crowd was pushing and pulling trying to get on top of the beast, many did not make it, but I dodged the difficulties and found myself on board. It was a violent rush time which increased my adrenaline and tempered muscles and mind. In contrast with the beautiful surrounding landscapes we passed by, stories about the dangers brought about by smugglers, delinquents and as well as migration authorities shadowed the colorful journey. I arrived to Ixtepec in the state of Oaxaca, here I was told I need to swap trains, and take the one towards Apizaco in the state of Tlaxcala. The journey continued in a different train with the same limitations and risks. The night has fallen; the train maintained its speed, the breeze and the uncertainties kept us awake. It was a very quiet night; everybody was alert for any sudden event along the railroad. It was almost midnight and the train slightly reduced its speed, out the blue someone screamed !los maras!, the terror was awaiting us on the sides. The bright of the moon revealed several shadows armed with machetes, sticks, pistols and hand-made weapons. People jumped desperately from the train and ran, when few gangsters hopped in the train, other maras were waiting next to the track to catch migrants, therefore, choosing the right exit to escape was primordial. My group and I ran toward the end 46 A network of houses located along the main migration routes used by transit-migrants 256 of the train, to jump out the train without being seen and to hide behind the bushes. It seemed like a wolf pack hunting sheep. I guess around 50 people were caught; their clothes were stripped in order to find money and other valuable goods. We decided to stay hidden and quiet, because a slight move could catch the gangsters’ eye, who would not hesitate to shoot or come after us. The scenes I was hearing and seeing were brutal and difficult to describe here. I read daily in the newspapers about the risks, kidnappings and executions of transit- migrants during my time in Mexico. In this situation the tomorrow’s news were occurring right there in front of my eyes. Once, the gangsters got the migrants money and raped some of the young women, they retreated laughing, chanting the brutality they used to make everybody understand that this is their land. A mara loudly said, “for crossing through their territory there is price to pay, and that this was the price”. The final destination Early in the morning we slowly got out of the hideout, we went to assist the fallen. Tears, sadness, and fear were the common feelings among us. I wanted to quit the journey and go back with at least a third of the migrants who were thinking the same. However, I have traveled already around 900 km and I was not going to give in easily. The group gathered again to assess the situation and to decide new alternative routes. We knew there was a small town ahead, which has been controlled by the maras; therefore, we left the railroad tracks to walk through the crops and wait for the train behind that town. The temperatures in this sub- humid region rose to 30 °C making of the tiring walk unbearable. In the afternoon we hit the railroad, I sat under a mango tree while others were looking for water, food and shelter. There were no signs of the next train, and we were tired, we needed to recover the energies for the next day. On the next morning, we walked along the railroad until the train arrived; we jumped on the beast again toward Tierra Blanca and Cordoba Orizaba in Veracruz State. As the train rolled on, more migrants appeared along the tracks to jump onto the moving train. The intense midday tropical heat warmed the surface of the train so hot that was impossible to lie down or to touch it with bare hands. The knowledge regarding the routes to follow acquired by transit-migrants from other migrants who successfully reached the North was useless, because the organized crime, the intensification of illegal activities, and police controls have forced migrants to use new clandestine and in some cases more dangerous ways. Curiously, in one of the small towns located along the railroad, its inhabitants knowing the tough journey transit-migrants suffer, they were contributing to mitigate the problems by providing food (tortillas and beans) in plastic bags and small bottles of water. Migrants hanging on the sides of the train tried to get something from the extended arms of the villagers. Besides poverty and violence in this region, yet, hope and help prevail. We stepped off the train when this got close to Apizaco train station to continue by bus that safely would take us to Veracruz. The entire journey combined different transportation methods, train, busses, lorries, trucks, and long walks. Three days on the roads can grind the transit-migrants’ soul, moral, mind and body. We headed to another casa del migrante to spend the night and get food, in the same place we assessed the past journey and mapped out the further way. The 257 final destination and the crossing point was the city of Nuevo Laredo in the state of Tamaulipas. We had to travel to the east coast of Veracruz where migration controls are scattered, the selected transportation mean was by bus, although we were exposing ourselves to be caught by authorities of the National Migration Institute (Instituto Nacional de Migración). Migration is also a matter of luck, not all the busses are checked up in the migration controls. A half-day journey on the way to Veracruz we arrived to Tampico, a very touristic place with a history of internal and international immigration. I covered already two thirds of the journey, and still 700 km separated us from the final destination. In the next coming days we shortened this distance by traveling in busses, taxis, combies (public vans), walking and jumping on trucks and lorries. Five days after the kick-off on the Mexican-Guatemalan border, I arrived to the casa del migrante in Nuevo Laredo, where I shared my experiences with some of the workers and migrants. I heard other stories that mirrored my experiences; however, these stories were more terrifying and tragic. This was the end of my journey, but for the transit-migrants that accompanied me, a new journey with unknown dangers was about to start. Thousands of migrants have died crossing the Rio Grande in the Mexico-United States border, and the desert of Arizona. Therefore, only few will embrace their “American dream”, other will fall victims of the “Mexican-American nightmare” on the voyage. The transit-migration using Mexico as a transit corridor is in increase, the globalization of migration is attracting people far beyond Central America, in my journey I encountered migrants from South America (Ecuador, Colombia, Bolivia and Peru), Africa (Burkina Faso, Cameroon, Uganda), and Asia (Vietnam, China, Nepal, India and Pakistan). The vast number of transit-migrants brought about by the failure to promote development in the sending regions will increase as long as the socio-economic gaps between classes are widening. Notwithstanding, all the Mexican migration policies and controls to stop and reduce undocumented migrants traveling through Mexico have marginal effects, making migration more difficult. Migrants are one of the most vulnerable human groups and easy to target and exploit. Especially since the Mexican drug war to crack down the cartels got worse. These cartels have joined forces with smugglers (coyotes) and gangsters to use migrants as resource of income and as mules to transport their drugs. In several cases, transit-migrants have been shot to death or masscred for refusing to take part in their illegal activities. As it is the “San Fernando massacre” that occurred on August 22nd as of 2010, wherein 72 transit-migrant of Central and South America nationalities were executed by the Zetas drug cartel in the state of Tamaulipas. 258 Appendixes: Appendix 1: First hypothesis, SPSS results for Multiple Regression Analysis Descriptive Statistics Mean Std. Deviation N Income sq root 35.8241 17.34421 149 Gender .83 .375 149 elementary .5034 .50168 149 Middle school .1745 .38082 149 Illiterate .2617 .44107 149 Age 57.34 13.109 149 Main econ activity .10 .302 149 Farm size ha 1.4082 .84502 149 Degraded areas .54 .500 149 receive remittances .19 .392 149 Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 receive remittances, elementary, Farm size ha, Age, Gender, Degraded areas, Middle school, Main econ activity, Illiterate(a) . Enter a All requested variables entered. b Dependent Variable: Income sq root Model Summary(b) Model R R Square Adjusted R Square Std. Error of the Estimate 1 .631(a) .398 .359 13.88363 a Predictors: (Constant), receive remittances, elementary, Farm size ha, Age, Gender, Degraded areas, Middle school, Main econ activity, Illiterate b Dependent Variable: Income sq root 259 ANOVA(b) Model Sum of Squares df Mean Square F Sig. 1 Regression 17728.603 9 1969.845 10.219 .000(a) Residual 26792.984 139 192.755 Total 44521.586 148 a Predictors: (Constant), receive remittances, elementary, Farm size ha, Age, Gender, Degraded areas, Middle school, Main econ activity, Illiterate b Dependent Variable: Income sq root Coefficients (a) Model Unstandardized Coefficients Standar dized Coeffic ients t Sig. 95% Confidence Interval for B Correlations Collinearity Statistics B Std. Error Beta Lower Bound Upper Bound Zero- order Partial Part Tole ranc VIF 1 (Constant) 30.899 9.132 3.384 .001 12.843 48.954 Gender -6.223 4.686 -.135 -1.328 .186 -15.487 3.042 .251 -.112 -.087 .422 2.370 elementary -2.550 4.986 -.074 -.512 .610 -12.409 7.308 .128 -.043 -.034 .208 4.804 Middle school .414 5.490 .009 .075 .940 -10.441 11.270 -.037 .006 .005 .298 3.357 Illiterate -8.932 5.344 -.227 -1.671 .097 -19.499 1.635 -.171 -.140 -.110 .234 4.266 Age .305 .098 .230 3.120 .002 .112 .498 .239 .256 .205 .795 1.259 Main econ activity -31.843 6.027 -.554 -5.283 .000 -43.760 -19.926 -.464 -.409 -.348 .393 2.542 Farm size ha 3.310 1.415 .161 2.339 .021 .512 6.108 .148 .195 .154 .911 1.098 Degraded areas -9.865 2.475 -.284 -3.986 .000 -14.759 -4.972 -.243 -.320 -.262 .851 1.175 receive remittances .451 3.158 .010 .143 .887 -5.793 6.695 -.218 .012 .009 .850 1.176 a Dependent Variable: Income sq root 260 Coefficient Correlations (a) receive remitta nces elementar y Farm size ha Age Gender Degrad ed areas Middle school Main econ activity Illiterate Correlations receive remittances 1.000 -.027 .017 .148 -.018 -.131 .067 -.222 -.068 elementary -.027 1.000 .037 -.093 .092 -.110 .790 .017 .853 Farm size ha .017 .037 1.000 .032 .044 -.254 .082 .089 .080 Age .148 -.093 .032 1.000 .126 .010 .120 .137 -.162 Gender -.018 .092 .044 .126 1.000 -.107 .033 .735 .074 Degraded areas -.131 -.110 -.254 .010 -.107 1.000 -.075 -.008 -.193 Middle school .067 .790 .082 .120 .033 -.075 1.000 -.065 .733 Main econ activity -.222 .017 .089 .137 .735 -.008 -.065 1.000 .016 Illiterate -.068 .853 .080 -.162 .074 -.193 .733 .016 1.000 Covariances receive remittances 9.973 -.429 .074 .046 -.272 -1.025 1.156 -4.225 -1.144 elementary -.429 24.862 .262 -.045 2.159 -1.360 21.620 .499 22.720 Farm size ha .074 .262 2.003 .004 .290 -.890 .638 .760 .603 Age .046 -.045 .004 .010 .058 .002 .064 .081 -.084 Gender -.272 2.159 .290 .058 21.954 -1.238 .840 20.764 1.856 Degraded areas -1.025 -1.360 -.890 .002 -1.238 6.126 -1.018 -.117 -2.553 Middle school 1.156 21.620 .638 .064 .840 -1.018 30.144 -2.160 21.500 Main econ activity -4.225 .499 .760 .081 20.764 -.117 -2.160 36.328 .504 Illiterate -1.144 22.720 .603 -.084 1.856 -2.553 21.500 .504 28.562 a Dependent Variable: Income sq root Residuals Statistics (a) Minimum Maximum Mean Std. Deviation N Predicted Value .2744 55.9410 35.8241 10.94476 149 Residual -32.79732 51.38694 .00000 13.45488 149 Std. Predicted Value -3.248 1.838 .000 1.000 149 Std. Residual -2.362 3.701 .000 .969 149 a Dependent Variable: Income sq root 261 262 -4 -3 -2 -1 0 1 2 Regression Standardized Predicted Value -4 -3 -2 -1 0 1 2 Re gr es si o n St a n da rd iz ed Pr e di ct e d Va lu e Dependent Variable: Income sq root Scatterplot 263 Appendix 2: Second hypothesis, SPSS results for multifactorial ANOVA Between-Subjects Factors Value Label N Technical assistance 0 No 113 1 Yes 36 Government economic aid 0 No 45 1 Yes 104 Descriptive Statistics Dependent Variable: Income sq root Technical assistance Government economic aid Mean Std. Deviation N No No 34.4393 14.35128 43 Yes 35.0690 17.27761 70 Total 34.8294 16.16280 113 Yes No 22.8607 30.91567 2 Yes 39.8926 20.07309 34 Total 38.9464 20.56375 36 Total No 33.9247 14.97139 45 Yes 36.6460 18.28223 104 Total 35.8241 17.34421 149 Levene's Test of Equality of Error Variances(a) Dependent Variable: Income sq root F df1 df2 Sig. 1.246 3 145 .295 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+GTA+GEA+GTA * GEA 264 Tests of Between-Subjects Effects Dependent Variable: Income sq root Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Corrected Model 1021.277 3 340.426 1.135 .337 .023 Intercept 30854.611 1 30854.611 102.848 .000 .415 GTA 80.482 1 80.482 .268 .605 .002 GEA 550.195 1 550.195 1.834 .178 .012 GTA * GEA 474.524 1 474.524 1.582 .211 .011 Error 43500.309 145 300.002 Total 235743.000 149 Corrected Total 44521.586 148 Estimated Marginal Means 1. Technical assistance Estimates Dependent Variable: Income sq root Technical assistance Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound No 34.754 1.678 31.438 38.071 Yes 31.377 6.301 18.922 43.831 Pairwise Comparisons Dependent Variable: Income sq root 95% Confidence Interval for Difference(a) (I) Technica l assistance (J) Technical assistance Mean Difference (I-J) Std. Error Sig.(a) Lower Bound Upper Bound No Yes 3.377 6.521 .605 -9.511 16.266 Yes No -3.377 6.521 .605 -16.266 9.511 Based on estimated marginal means a Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments). 265 Univariate Tests Dependent Variable: Income sq root Sum of Squares df Mean Square F Sig. Partial Eta Squared Contrast 80.482 1 80.482 .268 .605 .002 Error 43500.309 145 300.002 The F tests the effect of Technical assistance. This test is based on the linearly independent pairwise comparisons among the estimated marginal means. 2. Government economic aid Estimates Dependent Variable: Income sq root Government economic aid Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound No 28.650 6.265 16.268 41.032 Yes 37.481 1.810 33.903 41.059 Pairwise Comparisons Dependent Variable: Income sq root 95% Confidence Interval for Difference(a) (I) Government economic aid (J) Government economic aid Mean Difference (I-J) Std. Error Sig.(a) Lower Bound Upper Bound No Yes -8.831 6.521 .178 -21.719 4.057 Yes No 8.831 6.521 .178 -4.057 21.719 Based on estimated marginal means a Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments). Univariate Tests Dependent Variable: Income sq root Sum of Squares df Mean Square F Sig. Partial Eta Squared Contrast 550.195 1 550.195 1.834 .178 .012 Error 43500.309 145 300.002 The F tests the effect of Government economic aid. This test is based on the linearly independent pairwise comparisons among the estimated marginal means. 266 3. Technical assistance * Government economic aid Estimates Dependent Variable: Income sq root Technica l assistance Government economic aid Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound No No 34.439 2.641 29.219 39.660 Yes 35.069 2.070 30.977 39.161 Yes No 22.861 12.247 -1.346 47.067 Yes 39.893 2.970 34.022 45.764 Pairwise Comparisons Dependent Variable: Income sq root 95% Confidence Interval for Difference(a) Government economic aid (I) Technical assistance (J) Technical assistance Mean Difference (I-J) Std. Error Sig.(a) Lower Bound Upper Bound No No Yes 11.579 12.529 .357 -13.185 36.342 Yes No -11.579 12.529 .357 -36.342 13.185 Yes No Yes -4.824 3.621 .185 -11.980 2.332 Yes No 4.824 3.621 .185 -2.332 11.980 Based on estimated marginal means a Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments). Univariate Tests Dependent Variable: Income sq root Government economic aid Sum of Squares df Mean Square F Sig. Partial Eta Squared No Contrast 256.211 1 256.211 .854 .357 .006 Error 43500.309 145 300.002 Yes Contrast 532.467 1 532.467 1.775 .185 .012 Error 43500.309 145 300.002 Each F tests the simple effects of Technical assistance within each level combination of the other effects shown. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means. 267 268 Appendix 3: Third hypothesis, SPSS results for crosstab and chi-square Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Degraded areas * Region 57 100.0% 0 .0% 57 100.0% Degraded areas * Region Crosstabulation Count Region Total LL ML HL Degrade d areas No 9 15 0 24 Yes 3 8 22 33 Total 12 23 22 57 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 26.367(a) 2 .000 Likelihood Ratio 34.376 2 .000 Linear-by-Linear Association 21.813 1 .000 N of Valid Cases 57 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.05. Interval by Interval Pearson's R Ordinal by Ordinal Spearman Correlation N of Valid Cases a Not assuming the null hypothesis. b Using the asymptotic standard error assuming the null hypothesis. c Based on normal approximation. 269 Symmetric Measures Value Asymp. Std. Error(a) Approx. T(b) .624 .087 .643 .086 57 Approx. Sig. 5.924 .000(c) 6.233 .000(c) 270 Appendix 4: Fourth hypothesis, SPSS results for crosstab and chi-square Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent hired labour force last year * outmigration of HH members 149 100.0% 0 .0% 149 100.0% Hired labour force last year * outmigration of HH members Crosstabulation outmigration of HH members Total No Yes hired labour force last year No Count 50 27 77 % of Total 33.6% 18.1% 51.7% Yes Count 42 30 72 % of Total 28.2% 20.1% 48.3% Total Count 92 57 149 % of Total 61.7% 38.3% 100.0% Chi-Square Tests Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi-Square .687(b) 1 .407 Continuity Correction(a) .435 1 .509 Likelihood Ratio .687 1 .407 Fisher's Exact Test .500 .255 Linear-by-Linear Association .682 1 .409 N of Valid Cases 149 a Computed only for a 2x2 table b 0 cells (.0%) have expected count less than 5. The minimum expected count is 27.54. Interval by Interval Pearson's R Ordinal by Ordinal Spearman Correlation N of Valid Cases a Not assuming the null hypothesis. b Using the asymptotic standard error assuming the null hypothesis. c Based on normal appr 271 Symmetric Measures Value Asymp. Std. Error(a) Approx. T(b) .068 .082 .825 .068 .082 .825 149 oximation. Approx. Sig. .411(c) .411(c) 272 Appendix 5: Fith hypothesis, SPSS results for crosstab and chi-square Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Labour conditions * Reasons 80 100.0% 0 .0% 80 100.0% Labour conditions * Reasons Crosstabulation Reasons Total Fair Salary Unfair salary No- health insurance Unpaid overtime Other Labour conditions Good Count 43 3 0 0 1 47 Expected Count 31.7 11.8 1.8 .6 1.2 47.0 % of Total 53.8% 3.8% .0% .0% 1.3% 58.8% Fair Count 11 17 2 1 1 32 Expected Count 21.6 8.0 1.2 .4 .8 32.0 % of Total 13.8% 21.3% 2.5% 1.3% 1.3% 40.0% Bad Count 0 0 1 0 0 1 Expected Count .7 .3 .0 .0 .0 1.0 % of Total .0% .0% 1.3% .0% .0% 1.3% Total Count 54 20 3 1 2 80 Expected Count 54.0 20.0 3.0 1.0 2.0 80.0 % of Total 67.5% 25.0% 3.8% 1.3% 2.5% 100.0% Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 55.376 (a) 8 .000 Likelihood Ratio 39.310 8 .000 Linear-by-Linear Association 14.586 1 .000 N of Valid Cases 80 a 11 cells (73.3%) have expected count less than 5. The minimum expected count is .01. Interval by Interval Pearson's R Ordinal by Ordinal Spearman Correlation N of Valid Cases a Not assuming the null hypothesis. b Using the asymptotic standard error assuming the null hypothesis. c Based on normal approximation. 273 Symmetric Measures Value Asymp. Std. Error(a) Approx. T(b) .430 .116 4.203 .608 .089 6.768 80 Approx. Sig. .000(c) .000(c) Series International Rural Development edited by Prof. Dr. Béatrice Knerr* Vol. 1 Tzu-Ying Li (2010):Organic farm-based tourism as an element of sustainable multifunctionality to support peripheral regions-Hualien, Taiwan as an example. Göttingen, ISBN 978-3-86955-254-5 Vol. 2 Wei Sun: Increase in Protein Demand for China’s Livestock Sector and Ist Implications for International Agricultural Trade. Göttingen, ISBN 978-3-86955-803-5 Vol. 3 Muhammad Qasim (2012): Determinants of Farm Income and Agricultural Risk Management Strategies. The Case of Rain-fed Farm Households in Pakistan’s Punjab Kassel, ISBN 978-3-86219-298-4 Vol. 4 David M. Eche (2013): Land Degradation, Small-Scale Farms’ Development, and Migratory Flows. The case of Tapachula / Chiapas Kassel, ISBN 978-3-86219-652-4 Vol. 1 and 2 Cuvillier Verlag, Göttingen, as of Vol. 3 kassel university press GmbH, Kassel *) Department of Development Economics, Migration and Agricultural Policy (DEMAP), University of Kassel Steinstr. 19 D-37213 Witzenhausen knerr@uni-kassel.de International Rural Development Edited by Béatrice Knerr 4 David Eche Land Degradation, Small-Scale Farms’ Development, and Migratory Flows The case of Tapachula / Chiapas ISBN 978-3-86219-478-0 This research evaluates the impacts of land degradation on rural development and migration, using a comparative-analysis platform and quantitative and qualitative approaches, based on data from empirical investigations in six rural communities of Tapachula, Chiapas. The results show that deforestation, heavy rains and extreme weather events are the main determinants of land degradation, and that land degradation, smallholder farms’ income and outmigration are highly correlated. In addition, they portray a new migration dynamic, from rural areas in the highlands directly to urban centers in the US, and demonstrate that the poverty marginalization context contributes substantially to global migration flows. Despite the harsh labour conditions and the poor economic basis in the area, temporary Guatemalan workers rapidly replace the out-migrated local labour force on coffee plantations and small farms, giving evidence of their life at the fringe of the globalized economy. David Eche obtained an Agriculture Engineer degree at the Pontifical University of Ecuador-Ibarra. He pursued a MSc. pro- gramme in International Organic Agriculture at the University of Kassel in Germany. With his research work, he won the prize for the best master thesis of the faculty. Subsequently, he joined a double PhD program offered by the Universidad Autonóma de Zacatecas, and the University of Kassel, wherein he graduated with academic distinctions. His main research interests focus on climate change, environmental degradation, migration, organic agriculture and rural development. 4 La n d D eg ra d at io n , S m al l- Sc al e Fa rm s’ D ev el o p m en t, a n d M ig ra to ry F lo w s D av id E ch e