| ADVANCED COURSES AND WORKSHOPS: |
- Workshop: Impact Evaluation of Population, Health and Nutrition Programs. MEASURE Evaluation and Instituto Nacional de Salud Pública de México (INSP), July-August 2004, Cuernavaca, Mexico.
- Workshop: Population and Development, with emphasis on reproductive Health. PARTNERS and Consejo Nacional de Población de México (CONAPO), May 2003, Hidalgo, México.
- Stanford Workshop on Formal Demography, May 2008, Palo Alto, California.
- Mini course in “Computation of Economics”, Kenneth Judd, April 2011, Penn State University.
- Colloquium: Freedom and Development, Liberty Fund and Charles G. Koch Charitable Foundation, September 2011, Arlington, Virginia.
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PRESENTATIONS & OTHER PROFESSIONAL ACTIVITIES:
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- Poverty and Economic Policy Network 5th General Meeting, June 2006, Addis Ababa (participant)
- Southern Demographic Association Annual Meeting, October-November 2008, Greenville (presenter)
- Latin American and Caribbean Economic Association Annual Meeting, October 2009, Buenos Aires (presenter)
- The Cornell-PSU Macro Workshop, April 2010, Cornell University (participant)
- Pacific Conference for Development Economics (PAC-DEV) , March 2011, Berkeley University (presenter)
- 10th Journées Louis-André Gérard-Varet Conference in Public Economics, June 2011, IDEP France ( presenter)
- Trade Policy for Growth, October 2011, IGC trade
group and the World Bank trade research group (participant)
- Referee for Journal of International Economics
- Member of the Royal Economics Society
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THESIS ABSTRACT:
Essay 1: Learning Gains among College Entrance Exam Repeat Takers in Turkey: Disentangling Selection and Learning Effects (job market paper, with Kala Krishna and Cemile Yavas)
Every year, around 1.5 million students take the centralized college entrance exam (ӦSS) required for college placement in Turkey. About two thirds of these students are repeat takers. Using data on exam scores, high school GPA, and background characteristics on a random sample of 115,000 students who took the ӦSS in 2002, we estimate the learning (in terms of score improvement) predicted to occur upon retaking the exam. Measuring the improvement of repeat takers can have important policy implications if retaking allows less prepared students to become more prepared and compete on a level playing field once they get into college.
Our innovative estimation strategy provides a solution to the lack of panel data. It is inspired in a dynamic model of the decision to retake where students know their own (unobserved) ability, exams have a random element, and the system is in steady state. We define learning as an exogenous shock drawn from a distribution that can vary by background and unobserved ability. Cumulative learning between the first and the nth attempt is estimated as the mean difference between the score observed in the nth attempt and the predicted score in the first attempt, where the latter considers that selection into retaking is endogenous. We model GPA and exam scores as functions of observables and include unobservables through a factor structure where ability drives the correlation between error terms. We estimate the system in the sub-sample of first timers, which is selection and learning free, to get unbiased estimates of the marginal effect of observables on score. The mean difference between observed and predicted GPA among nth time takers approximates the effect of unobservables on their predicted initial score. We identify important cumulative learning gains among repeat takers, and more so for the less advantaged. This suggests that setting high standards for passing the college entrance exam while allowing students to retake without penalties or monetary costs may help disadvantaged students close the academic gap before they go to college.
Essay 2: Affirmative Action in Higher Education in India: Targeting, Catch Up, and Mismatch at IIT-Delhi (with Kala Krishna)
Affirmative action policies in higher education are used in many countries to try to socially advance historically disadvantaged minorities. Although the underlying social objectives of these policies are rarely criticized, there is intense debate over the actual impact of such preferences in higher education on educational performance and labor outcomes. Most of the work on this subject uses U.S. data where clean performance indicators are hard to find. Using a remarkably detailed dataset on the 2008 graduating class from the Indian Institute of Technology (IIT) in Delhi we evaluate the impact of affirmative action policies in higher education on minority students focusing on three central issues in the current debate: targeting, catch up, and mismatch. In addition, we present preliminary evidence on labor market discrimination. We find that admission preferences effectively target minority students who are poorer than the average displaced non-minority student. Moreover, by analyzing the college performance of minority and non-minority students as they progress through college, we find that scheduled caste and scheduled tribe students, fall further behind in their relative ranking compared to others in the major, which is the opposite of catching up. We also identify evidence in favor of the mismatch hypothesis: once we control for selection into majors, minority students who enroll in more selective majors as a consequence of admission preferences seem to actually earn less than they would have had if they had chosen a less selective major. Finally, although there is no evidence of discrimination against minority students in terms of wages, we find that scheduled caste and scheduled tribe students are more likely to get worse jobs, even after controlling for selection.
Essay 3: Employment Protection Legislation in India and its Effects on Productivity in the Organized Manufacturing Sector (with Sean Dougherty and Kala Krishna)
It is well known that India's formal Employment Protection Legislation (EPL) is among the strictest in the world. However, since industrial relations fall under the joint jurisdiction of central and state governments there is great variation in labor regulation standards across states, which offers a nice natural experiment setting. Using plant-level data from the Annual Survey of Industries (a repeated cross section) for all the fiscal years between 1998-99 and 2007-08, as well as a smaller unbalanced panel of firms, we evaluate the impact of laxer labor laws on total factor productivity (TFP) and labor productivity in the Indian organized manufacturing sector.
We show that labor laws differentially affect industries according to their labor intensity as the percentage change in unit cost due to more stringent EPL is higher the higher the labor intensity. Thus, identification of the effect of EPL follows from a difference-in-difference estimator that exploits the variation in labor regulations by state, as well as variation in industry-specific characteristics related to labor usage. Our study is particularly important for two reasons. First, it is the first study for India to evaluate the effect of labor regulation on plant-level productivity. Second, it relies on a comprehensive measure of labor market regulations which collects information on formal and informal amendments to the IDA as well as to other seven areas of industry regulation related to labor hiring and firing. This index overcomes the limitations of Besley and Burgess' (2004) index, so often used in the literature. Our results show that, as predicted, firms in labor intensive industries benefited the most from pro-employer reforms in their states. On average, firms in labor intensive industries and in flexible labor markets have TFP residuals 14% higher than those registered for their counterparts in states with stricter labor laws. The evidence presented suggests that the high labor costs and rigidities imposed through Indian federal labor laws are limited by labor market reforms at the state level.
Essay 4: Signaling Creditworthiness in Peruvian Microfinance Markets: The Role of Information Sharing
Microfinance institutions (MFI) originally emerged to serve poor clients excluded from formal credit markets. Credit information systems that expose a personalized credit relationship with an MFI to a larger market may reduce screening costs for other lenders, improving borrowers' access to credit. Despite the potential importance of credit information systems for the alleviation of credit constraints faced by the poor, too little is known about their specific effects in microfinance markets. In this essay, I measure the effects of an MFI's unilateral decision to increase the amount of information shared with other lenders. Using data from FINCA, one of the leading MFIs in Peru, I evaluate the effect of FINCA's decision to share information on individual outstanding debt records (positive information), in addition to default information (negative information), on borrowers' access to credit. I identify a credit expansion effect for some borrowers in FINCA who looked more creditworthy after their positive records were exposed, suggesting that other lenders targeted FINCA's clients with good records of repayment. My results suggest that this credit expansion effect actually hurt FINCA through higher default rates as its better clients were skimmed off. This suggests that universal information sharing among lenders in microfinance markets may not arise endogenously as an equilibrium outcome but would instead require coordinated efforts. My study is particularly important for two reasons. First, it departs from previous efforts that concentrate on universal increases of credit information. Moreover, it measures the implications of this decision using micro-level data.
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