Petra Todd and Jere R. Behrman were awarded a National Science Foundation Social and Econonomic Science grant for “Using New Longitudinal Linked Data to Investigate the Determinants of Educational Attainment and Achievement in Mexico.”

Petra Todd and Jere R. Behrman were awarded a National Science Foundation Social and Econonomic Science grant for “Using New Longitudinal Linked Data to Investigate the Determinants of Educational Attainment and Achievement in Mexico.” This project will use newly available administrative test score and survey data to analyze the determinants of educational attainment and achievement of grade 6-12 Mexican students and to evaluate impacts of the Prospera conditional cash transfer (CCT) program.  This research will use a variety of methods to study the determinants of educational attainment and achievement. A major challenge in estimating test score determinants and analyzing the effects of policy interventions is sample selection, namely, that there is substantial grade repetition and drop-out at post-primary grade levels (grades 7-12), test scores are only available for children/youth attending school, and policy interventions potentially affect the composition of students attending school. We propose both structural and nonstructural approaches for analyzing grade progression and test score performance that account for sample selectivity and also for potential peer effects. We will also estimate a spatial demand model for schooling and a coordination game model of student effort choice. The specific aims are: (i) Estimate value-added models of test score dynamics to study how school quality and family inputs affect student performance and to examine how the Prospera program affects beneficiaries and nonbeneficiaries through peer effects. (ii) Develop and estimate a dynamic model of the determinants of student enrollment, achievement and grade progression, incorporating failure, grade retention and dropout. (iii) Estimate the demand for different types of schools, accounting for individual-specific choice sets (based on geographic location), and analyze how demand depends on school quality, distances to schools, family background, Prospera status and local labor market conditions affecting the returns to education and the demand for child labor. (iv) Develop and estimate a discrete choice dynamic programming (DCDP) model of student enrollment, study effort, drop-out, and working decisions and use the model to study the effect of varying Prospera incentive payments, modifying school quality and increasing schooling access. (v) Develop and estimate a strategic model of student effort choices within classrooms to study how the initial ability distribution influences effort choices and test score outcomes. All our analyses will allow for heterogeneous program and policy effects with respect to family background, indigenous status, gender and urbanization.