1. Miriam Juárez-Torres 1/ Do Agricultural Programs Lead Land- Use and Land-Cover Change in Developing Countries? Evidence of the Procampo Program Impact on Land use Change in Mexico 1/ Research Economist at Banco de Mexico, Mexico. Email.: mjuarez@banxico.org.mxmjuarez@banxico.org.mx

2. Changes in land cover and land use have a complex nature and usually are multifactorial processes. Local and regional factors interact in specific contexts to lead decision making on land use. In México, the structure of land use has dramatically changed. During the 1990’s and 2000’s decade an extensive process of agricultural conversion has performed across the country. Between 1993 and 2000, the cultivated area in Mexico increased 2.57 million of hectares at the expense of the wild land-cover and also of previously cleared areas (SAGARPA, 2012). Motivation Introduction Previous LiteratureModel Application/Results Conclusions 1

3. Land Cover in Mexico in 2010 Introduction Previous LiteratureModel Application/Results Conclusions 2 Agriculture Forest Water bodies Scrubland Other vegetation Grassland Rainforest No vegetation Urban area Source: CONAGUA

4. Annual growth rates of cover land by regions (Percentages) Introduction Previous LiteratureModel Application/Results Conclusions 2

5. The Procampo Program Introduction Previous LiteratureModel Application/Results Conclusions 2 PROCAMPO remains as the largest agricultural program in Mexico with 2.7 million of supported producers which represents 73% of farmers. Although payments are not directly linked to production decisions, Procampo has indirectly influenced production decisions of farmers, in particular in poor producers. The objective of this research is to quantify the effect of Procampo as a main driver in agriculture intensification in Mexico. Most of the studies that examine forest conservation are focused on protected areas; thus, programs such as payment for environmental services are analyzed but their findings usually apply for limited areas. The main contribution of this study relies on the analysis of the effects of a geographically broad based program, which allows to identify differentiated patterns across regions.

6. The Procampo Program Introduction Previous LiteratureModel Application/Results Conclusions 2

7. Variables related with the distribution of the Ejidos by State Introduction Previous LiteratureModel Application/Results Conclusions 2

8. Municipality Marginalization Index Introduction Previous LiteratureModel Application/Results Conclusions 2

9. Introduction Previous LiteratureModel Application/ResultsConclusions Data and Sources National Institute of Economics and Geography (INEGI). Three transition periods for land-use data: 1988-1990, 1996-1999 and 2007- 2009 Sociodemographic variables (population) Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food of Mexico (SAGARPA) Areas supported by PROCAMPO at municipality level (1994-2013) National Agrarian Register Procede program and changes on land tenure. Balanced panel of land at municipality level using the land-use and land-cover maps of INEGI. 13

10. Introduction Empirical FindingsModel Application/Results Conclusions The Model 6

11. Introduction Previous LiteratureModel Application/ResultsConclusions Estimations for the two-limit Tobit model, panel data random effects 14

12. Introduction Previous LiteratureModel Application/ResultsConclusions Marginal effects calculated using Delta-Method 14

13. Introduction Previous LiteratureModel Application/Results Conclusions 22 Procampo program has a positive and significant influence in agriculture intensification. In particular, the support allocated for grains and perennial in both cycles (fall-winter and spring-summer) have a positive and significant influence in agricultural intensification. Poverty (municipality marginalization index) has net positive and statistically significant effect on agriculture intensification. Procede program of regularization of land tenure has a negative and significant effect on agriculture intensification. Regions show a differentiated behavior in terms of agriculture intensification, Center and South show stronger patterns of agriculture intensification.