Lecturas

Las lecturas obligatorias (marcadas con “*”) permiten una discusión informada en la clase. Las lecturas que serán presentadas en exposiciones también son obligatorias y están marcadas con “+”. El resto de las lecturas no serán cubiertas en clase, pero son ampliamente recomendables. En las sesiones de exposiciones se espera que el resto de la clase tenga el conocimiento suficiente sobre el material presentado para participar en la discusión.

* Obligatoria + Exposición (también obligatoria) · Recomendada

Semana 1

• Diseño y econometría
  • * Freedman, D. A. (1991). Statistical models and shoe leather. Sociological methodology, 291-313.
  • * Athey, S., & Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3-32.
  • Heckman, J. J. (2001). Micro data, heterogeneity, and the evaluation of public policy: Nobel lecture. Journal of political Economy, 109(4), 673-748.
    
  • Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation. Handbook of econometrics, 6, 4779-4874.
• Inferencia causal
  • CA, Capítulo 2 (Causality and No Causality)

Semana 2

• Métodos experimentales
  • * MHE, Capítulo 2 (The Experimental Ideal)
  • MT, Capítulo 4 (Potential Outcomes Model)
  • * Banerjee, A., Duflo, E., Goldberg, N., Karlan, D., Osei, R., Parienté, W., Shapiro, J., Thuysbaert, B. & Udry, C. (2015). A multifaceted program causes lasting progress for the very poor: Evidence from six countries. Science, 348(6236), 1260799.
• Transparencia y replicabilidad
  • * Christensen, G., & Miguel, E. (2018). Transparency, reproducibility, and the credibility of economics research. Journal of Economic Literature, 56(3), 920-80.

Semana 3

• Revisión de métodos de regresión
  • CA, Capítulo 3 (Social Experiments and Linear Regression)
  • * MHE, Capítulo 3 (Making Regression Make Sense)
  • MM, Capítulo 2 (Regression)
• Inferencia estadística
  • * MM, Capítulo 1 (Randomized Trials), Apéndice (Mastering Inference)
  • MT, Capítulo 2 (Probability and Regression Review)
• Errores estándar
  • * MHE, Capítulo 8 (Nonstandard Standard Errors Issues)
  • Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2023). When should you adjust standard errors for clustering?. The Quarterly Journal of Economics, 138(1), 1-35.
  • Cameron, A. C., & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. Journal of Human Resources, 50(2), 317-372.
  • Stock, J. H. (2010). The other transformation in econometric practice: Robust tools for inference. Journal of Economic Perspectives, 24(2), 83-94.

Semana 4

• Aplicaciones de métodos experimentales
  • Acampora, M., Casaburi, L., & Willis, J. (2025). Land rental markets: Experimental evidence from Kenya. American Economic Review, 115(3), 727-771.
  • Aggarwal, S., Aker, J. C., Jeong, D., Kumar, N., Park, D. S., Robinson, J., & Spearot, A. (2026). The dynamic effects of cash transfers to agricultural households. American Economic Journal: Applied Economics, 18(3), 254-282.
  • + Alger, I., Juárez, L., Juárez-Torres, M., & Miquel-Florensa, J. (2020). Do informal transfers induce lower efforts? Evidence from lab-in-the-field experiments in rural Mexico. Economic Development and Cultural Change, 69(1), 107-171.
  • + Arceo-Gomez, E. O., & Campos-Vazquez, R. M. (2014). Race and marriage in the labor market: A discrimination correspondence study in a developing country. American Economic Review, 104(5), 376-80.
  • + Ascencio, S. J., & Chang, H. I. (2024). Do primaries improve evaluations of public officials? Experimental evidence from Mexico. Political Behavior, 1-22.
  • Baird, S., McIntosh, C., & Özler, B. (2011). Cash or condition? Evidence from a cash transfer experiment. The Quarterly journal of economics, 126(4), 1709-1753.
    
  • Bjorvatn, K., Ferris, D., Gulesci, S., Nasgowitz, A., Somville, V., & Vandewalle, L. (2025). Childcare, labor supply, and business development: Experimental evidence from Uganda. American Economic Journal: Applied Economics, 17(2), 75-101.
  • Blattman, C., Emeriau, M., & Fiala, N. (2018). Do anti-poverty programs sway voters? Experimental evidence from Uganda. Review of Economics and Statistics, 100(5), 891-905.
  • + Bruhn, M., Karlan, D., & Schoar, A. (2018). The impact of consulting services on small and medium enterprises: Evidence from a randomized trial in Mexico. Journal of Political Economy, 126(2), 635-687.
  • Bulte, E., Di Falco, S., Kassie, M., & Vollenweider, X. (2025). Low-quality seeds, labor supply, and economic returns: Experimental evidence from Tanzania. Review of Economics and Statistics, 107(3), 845-852.
  • Clemens, M. A., & Lewis, E. G. (2026). The effect of low-skill immigration restrictions on US firms and workers: Evidence from a randomized lottery. American Economic Journal: Applied Economics, 18(3), 43-82.
  • + Davies, E., Deffebach, P., Iacovone, L., & Mckenzie, D. (2024). Training microentrepreneurs over Zoom: Experimental evidence from Mexico. Journal of Development Economics, 167, 103244.
  • + De La O, A. L., Fernández-Vázquez, P. & García, F. M. (2023). Federal and state audits do not increase compliance with a grant program to improve municipal infrastructure: A pre-registered field experiment. Journal of Development Economics, 162, 103043.
  • Dell’Acqua, F., Kogut, B., & Perkowski, P. (2025). Super Mario meets AI: Experimental effects of automation and skills on team performance and coordination. Review of Economics and Statistics, 107(4), 951-966.
  • Duflo, E., Dupas, P., & Kremer, M. (2015). Education, HIV, and early fertility: Experimental evidence from Kenya. American Economic Review, 105(9), 2757-97.
    
  • Dupas, P. (2011). Do teenagers respond to HIV risk information? Evidence from a field experiment in Kenya. American Economic Journal: Applied Economics, 3(1), 1-34.
    
  • Dupas, P., Jayachandran, S., Lleras-Muney, A., & Rossi, P. (2025). The negligible effect of free contraception on fertility: Experimental evidence from Burkina Faso. American Economic Review, 115(8), 2659-2688.
  • + Gertler, P. (2004). Do conditional cash transfers improve child health? Evidence from PROGRESA’s control randomized experiment. American economic review, 94(2), 336-341.
  • Hämäläinen, K., Simanainen, M., & Verho, J. (2025). Health effects of cash transfers: Evidence from the Finnish basic income experiment. Journal of Public Economics, 250, 105480.
  • + Hoyos, R. D., Attanasio, O., & Meghir, C. (2024). Targeting high school scholarships to the poor: the impact of a program in Mexico. Economic Development and Cultural Change, 72(4), 1747-1768.
  • Londoño-Vélez, J., & Querubin, P. (2022). The Impact of Emergency Cash Assistance in a Pandemic: Experimental Evidence from Colombia. The Review of Economics and Statistics, 1-27
  • Martínez A, C., Puentes, E., & Ruiz-Tagle, J. (2018). The effects of micro-entrepreneurship programs on labor market performance: experimental evidence from Chile. American Economic Journal: Applied Economics, 10(2), 101-24.
  • Mbabazi, E. G., Abdulai, A., Kikulwe, E. M., & Gotor, E. (2025). Information flow and the adoption of soil-improving and water conservation measures, and household welfare: Insights from a randomized controlled trial in Uganda. American Journal of Agricultural Economics.
  • Mousa, S. (2020). Building social cohesion between Christians and Muslims through soccer in post-ISIS Iraq. Science, 369(6505), 866-870.
    
  • Muralidharan, K., Niehaus, P., & Sukhtankar, S. (2025). Identity verification standards in welfare programs: Experimental evidence from India. Review of Economics and Statistics, 107(2), 372-392.
  • Poertner, M. (2023). Does Political Representation Increase Participation? Evidence from Party Candidate Lotteries in Mexico. American Political Science Review, 117(2), 537-556.
  • + Sadka, J., Seira, E., & Woodruff, C. (2024). Information and Bargaining through Agents: Experimental Evidence from Mexico’s Labour Courts. Review of Economic Studies, rdae003.
  • + Seira, E., Elizondo, A., & Laguna-Müggenburg, E. (2017). Are information disclosures effective? evidence from the credit card market. American Economic Journal: Economic Policy, 9(1), 277-307.
    
  • + Tagliati, F. (2022). Welfare effects of an in-kind transfer program: Evidence from Mexico. Journal of Development Economics, 154, 102753.

Semana 5

• Corrección por prueba de múltiples hipótesis
  • Anderson, M. L. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. Journal of the American statistical Association, 103(484), 1481-1495.
  • Angelucci, M., Karlan, D., & Zinman, J. (2015). Microcredit impacts: Evidence from a randomized microcredit program placement experiment by Compartamos Banco. American Economic Journal: Applied Economics, 7(1), 151-82.
  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B (Methodological), 289-300.
    
  • Brodeur, A., Lé, M., Sangnier, M., & Zylberberg, Y. (2016). Star Wars: The empirics strike back. American Economic Journal: Applied Economics, 8(1), 1-32.
  • * Romano, J. P., Shaikh, A. M., & Wolf, M. (2010). Hypothesis testing in econometrics. Annual Review of Economics, 2(1), 75-104.
  • Savin, N. E. (1984). Multiple hypothesis testing. Handbook of econometrics, 2, 827-879.
    
  • Shaffer, J. P. (1995). Multiple hypothesis testing. Annual review of psychology, 46(1), 561-584.
• LATE y variables instrumentales
  • MHE, Capítulo 4 (Instrumental Variables in Action)
    
  • * MM, Capítulo 3 (Instrumental Variables)
  • MT, Capítulo 7 (Instrumental Variables)
  • * Angrist, J. D. (2006). Instrumental variables methods in experimental criminological research: what, why and how. Journal of Experimental Criminology, 2(1), 23-44.

Semana 6

• Aplicaciones LATE
  • Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery: evidence from social security administrative records. The American Economic Review, 313-336.
    
  • Angrist, J. D., Imbens, G., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American statistical Association, 91(434), 444-455.
    
  • Crépon, B., Devoto, F., Duflo, E., & Parienté, W. (2015). Estimating the impact of microcredit on those who take it up: Evidence from a randomized experiment in Morocco. American Economic Journal: Applied Economics, 7(1), 123-50.
    
  • Devoto, F., Duflo, E., Dupas, P., Parienté, W., & Pons, V. (2012). Happiness on tap: Piped water adoption in urban Morocco. American Economic Journal: Economic Policy, 4(4), 68-99.
  • + De La O, A. L. (2013). Do conditional cash transfers affect electoral behavior? Evidence from a randomized experiment in Mexico. American Journal of Political Science, 57(1), 1-14.
    
  • + Gonzalez-Navarro, M., & Quintana-Domeque, C. (2016). Paving streets for the poor: Experimental analysis of infrastructure effects. Review of Economics and Statistics, 98(2), 254-267.
  • Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, part II: Using the marginal treatment effect to organize alternative econometric estimators to evaluate social programs, and to forecast their effects in new environments. Handbook of econometrics, 6, 4875-5143.
    
  • Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica (1986-1998), 62(2), 467.
  • Kling, J. R., Liebman, J. B., & Katz, L. F. (2007). Experimental analysis of neighborhood effects. Econometrica, 75(1), 83-119.
• Métodos basados en selección por observables
  • * CA, Capítulo 4 (Selection on Observables)
  • * MHE, Capítulo 3 (Sección 3.3)
  • * MT, Capítulo 5 (Matching and Subclassification)
• Aplicaciones de métodos basados en observables
  • Abadie, A., & Imbens, G. W. (2016). Matching on the estimated propensity score. Econometrica, 84(2), 781-807.
    
  • Angrist, J., Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants, Econometrica 66(2), 1998, 249-288.
  • Becerril, J., & Abdulai, A. (2010). The impact of improved maize varieties on poverty in Mexico: a propensity score-matching approach. World development, 38(7), 1024-1035.
  • Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of economic surveys, 22(1), 31-72.
    
  • Chang, A., Miranda-Moreno, L., Cao, J., & Welle, B. (2017). The effect of BRT implementation and streetscape redesign on physical activity: A case study of Mexico City. Transportation Research Part A: Policy and Practice, 100, 337-347.
    
  • Daoud, A., Conlin, C., & Jerzak, C. T. (2026). Chinese vs. World Bank development projects: Insights from earth observation and computer vision on wealth gains in Africa, 2002–2013. World Development, 202, 107328.
  • Dehejia, R. H., & Wahba, S. (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American statistical Association, 94(448), 1053-1062.
  • + Diaz, J. J., & Handa, S. (2006). An assessment of propensity score matching as a nonexperimental impact estimator evidence from Mexico’s PROGRESA program. Journal of human resources, 41(2), 319-345.
  • + Espinosa, V., & Rubin, D. B. (2015). Did the military interventions in the Mexican drug war increase violence?. The American Statistician, 69(1), 17-27.
  • García-Díaz, R., Sosa-Rubí, S. G., Serván-Mori, E., & Nigenda, G. (2018). Welfare effects of health insurance in Mexico: The case of Seguro Popular de Salud. PloS one, 13(7), e0199876.
    
  • Hashmiu, I., Adams, F., Etuah, S., & Quaye, J. (2024). Food-cash crop diversification and farm household welfare in the Forest-Savannah Transition Zone of Ghana. Food Security, 16(2), 487-509.
  • LaLonde, R. J. (1986). Evaluating the econometric evaluations of training programs with experimental data. The American economic review, 604-620.
  • Stabridis, O., & Salgado-Viveros, C. (2023). Efectos de género y etnicidad en la brecha salarial entre jornaleros agrícolas del noroeste mexicano. Frontera Norte, 35.
  • Wellalage, N. H., & Locke, S. (2020). Remittance and financial inclusion in refugee migrants: inverse probability of treatment weighting using the propensity score. Applied Economics, 52(9), 929-950.

Semana 8

• Diferencia en diferencias
  • CA, Capítulo 7 (Difference-in-Differences)
  • * MM, Capítulo 5 (Differences-in-differences)
  • * MT, Capítulo 9 (Difference in differences), secciones 1 a 5
  • Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly journal of economics, 119(1), 249-275.
  • * Card, D. (1990). The impact of the Mariel boatlift on the Miami labor market. ILR Review, 43(2), 245-257.
  • * Card, D., & Krueger, A. B. (2000). Minimum wages and employment: a case study of the fast-food industry in New Jersey and Pennsylvania: reply. American Economic Review, 0(5), 1397-1420.
• DID desfasado
  • * MT, Capítulo 9 (Difference in differences), sección 9.6
  • Baker, A. C., Larcker, D. F., & Wang, C. C. (2022). How much should we trust staggered difference-in-differences estimates?. Journal of Financial Economics, 144(2), 370-395.
  • Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
  • Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254-277.
  • Marcus, M., & Sant’Anna, P. H. (2021). The role of parallel trends in event study settings: An application to environmental economics. Journal of the Association of Environmental and Resource Economists, 8(2), 235-275.
  • * Roth, J., Sant’Anna, P. H., Bilinski, A., & Poe, J. (2023). What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature. Journal of Econometrics, 235(2), 2218-2244.
• ANCOVA
  • * McKenzie, D. (2012). Beyond baseline and follow-up: The case for more T in experiments. Journal of development Economics, 99(2), 210-221.
  • Rojas Valdes, R.I., Wydick, B., & Lybbert, T.J. (2021). Can Hope Elevate Microfinance? Evidence from Oaxaca, Mexico. Oxford Economic Papers.

Semana 9

• Aplicaciones de DID
  • Boruchowicz, C., Parker, S. W., & Robbins, L. (2022). Time use of youth during a pandemic: Evidence from Mexico. World Development, 149, 105687.
  • + Cabrera-Hernández, F., Padilla-Romo, M., & Peluffo, C. (2023). Full-time schools and educational trajectories: Evidence from high-stakes exams. Economics of Education Review, 96, 102443.
  • Campos, R. M., Esquivel, G., & Santillán, A. S. (2017). El impacto del salario mínimo en los ingresos y el empleo en México. Revista CEPAL.
  • + Chort, I., & Öktem, B. (2024). Agricultural shocks, coping policies and deforestation: Evidence from the coffee leaf rust epidemic in Mexico. American Journal of Agricultural Economics, 106(3), 1020-1057.
  • + Conti, G., & Ginja, R. (2023). Who Benefits from Free Health Insurance?: Evidence from Mexico. Journal of Human Resources, 58(1), 146-182.
  • Chen, H., Qian, W., & Wen, Q. (2021). The impact of the COVID-19 pandemic on consumption: Learning from high-frequency transaction data. AEA Papers and Proceedings, 111, 307-11.
  • Chen, L., Hasan, R., Jiang, Y., & Parkhomenko, A. (2024). Faster, taller, better: Transit improvements and land use policies. Journal of Development Economics, 171, 103322.
  • Clarke, D., & Mühlrad, H. (2021). Abortion laws and women’s health. Journal of Health Economics, 76, 102413.
  • Djourelova, M. (2023). Persuasion through Slanted Language: Evidence from the Media Coverage of Immigration. American Economic Review, 113(3), 800-835.
  • + Gutiérrez Vázquez, E. Y., & Parrado, E. A. (2016). Abortion legalization and childbearing in Mexico. Studies in family planning, 47(2), 113-128.
    
  • Haaga, T., Böckerman, P., Kortelainen, M., & Tukiainen, J. (2024). Effects of nurse visit copayment on primary care use: Do low-income households pay the price?. Journal of Health Economics, 94, 102866.
  • + Jurado, J. A., & Morales, J. S. (2026). Violence against journalists and freedom of the press: Evidence from Mexico. Journal of Development Economics, 182, 103775.
  • Qian, N. (2008). Missing women and the price of tea in China: The effect of sex-specific earnings on sex imbalance. The Quarterly Journal of Economics, 123(3), 1251-1285.
  • + Samaniego de la Parra, B., & Fernández Bujanda, L. (2024). Increasing the cost of informal employment: Evidence from Mexico. American Economic Journal: Applied Economics, 16(1), 377-411.
  • Wolfers, J. (2006). Did unilateral divorce laws raise divorce rates? A reconciliation and new results. American Economic Review, 96(5), 1802-1820.
  • Zhang, R., Li, Y., Zhang, A. L., Wang, Y., & Molina, M. J. (2020). Identifying airborne transmission as the dominant route for the spread of COVID-19. Proceedings of the National Academy of Sciences.

Semana 10

• Discontinuidades nítidas y difusas
  • CA, Capítulo 9 (Regression Discontinuity, Kink, and Bunching Designs)
  • MHE, Capítulo 6 (Getting a Little Jumpy: Regression Discontinuity Designs)
  • * MM, Capítulo 4 (Regression Discontinuity Designs)
  • * MT, Capítulo 6 (Regression Discontinuity)
  • * Manacorda, M., Miguel, E., & Vigorito, A. (2011). Government transfers and political support. American Economic Journal: Applied Economics, 3(3), 1-28.

Semana 11

• Aplicaciones de diseños con discontinuidades
  • Abdulkadiroğlu, A., Angrist, J., & Pathak, P. (2014). The elite illusion: Achievement effects at Boston and New York exam schools. Econometrica, 82(1), 137-196.
  • + Aguilar, A., Gutierrez, E., & Seira, E. (2021). The effectiveness of sin food taxes: Evidence from Mexico. Journal of Health Economics, 77, 102455.
  • + Alcocer, M., Skillman, R., & Torres-Beltrán, A. (2025). Female mayors and violence against women: Evidence from Mexico. Political Science Research and Methods, 1-8.
  • + Alix-Garcia, J., McIntosh, C., Sims, K. R., & Welch, J. R. (2013). The ecological footprint of poverty alleviation: evidence from Mexico’s Oportunidades program. Review of Economics and Statistics, 95(2), 417-435.
  • Anagol, S., & Fujiwara, T. (2016). The runner-up effect. Journal of Political Economy, 124(4), 927-991.
  • Angrist, J. D., & Lavy, V. (1999). Using Maimonides’ rule to estimate the effect of class size on scholastic achievement. The Quarterly Journal of Economics, 114(2), 533-575.
  • Bagues, M., & Campa, P. (2021). Can gender quotas in candidate lists empower women? Evidence from a regression discontinuity design. Journal of Public Economics, 194, 104315.
  • Bosch, M., & Schady, N. (2019). The effect of welfare payments on work: Regression discontinuity evidence from Ecuador. Journal of Development Economics, 139, 17-27.
    
  • Calonico, S., Cattaneo, M. D., Farrell, M. H., & Titiunik, R. (2019). Regression discontinuity designs using covariates. Review of Economics and Statistics, 101(3), 442-451.
    
  • + Cañedo, A. P., Fabregas, R., & Gupta, P. (2023). Emergency cash transfers for informal workers: Impact evidence from Mexico. Journal of Public Economics, 219, 104820.
  • Card, D., Dobkin, C., & Maestas, N. (2009). Does Medicare save lives? The quarterly journal of economics, 124(2), 597-636.
    
  • Carpenter, C., & Dobkin, C. (2009). The effect of alcohol consumption on mortality: regression discontinuity evidence from the minimum drinking age. American Economic Journal: Applied Economics, 1(1), 164-82.
    
  • Cook, T. D., & Wong, V. C. (2008). Empirical tests of the validity of the regression discontinuity design. Annales d’Economie et de Statistique, 127-150.
    
  • + Davis, L. W. (2008). The effect of driving restrictions on air quality in Mexico City. Journal of Political Economy, 116(1), 38-81.
    
  • + Davis, L. W. (2017). Saturday driving restrictions fail to improve air quality in Mexico City. Scientific Reports, 7, 41652.
  • + Del Valle, A., de Janvry, A., & Sadoulet, E. (2020). Rules for recovery: Impact of indexed disaster funds on shock coping in Mexico. American Economic Journal: Applied Economics, 12(4), 164-95.
  • + Del Valle, A. (2024). Saving Lives with Indexed Disaster Funds: Evidence from Mexico. American Economic Journal: Economic Policy, 16(2), 442-479.
  • + Dell, M. (2015). Trafficking networks and the Mexican drug war. American Economic Review, 105(6), 1738-79.
  • Duryea, S., Perez Ribas, R., Sampaio, B., & Trevisan, G. (2025). The impact of admission to a selective college on crime: Evidence from a tuition-free university in Brazil. Journal of Labor Economics.
  • + Goodwin, M. B., Gonzalez, F., & Fontenla, M. (2024). The impact of daylight saving time in Mexico. Applied Economics, 56(1), 22-32.
  • Kruse, H., & Myhre, A. (2025). Early retirement provision for elderly displaced workers. The Journal of the Economics of Ageing, 31, 100569.
  • Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of economic literature, 48(2), 281-355.
  • MacPherson, C., & Sterck, O. (2021). Empowering refugees through cash and agriculture: A regression discontinuity design. Journal of Development Economics, 149, 102614.
  • Makarin, A., Pique, R., & Aragón, F. (2020). National or sub-national parties: Does party geographic scope matter? Journal of Development Economics, 102516.
    
  • Moussa, W., Salti, N., Irani, A., Al Mokdad, R., Jamaluddine, Z., Chaaban, J., & Ghattas, H. (2022). The impact of cash transfers on Syrian refugee children in Lebanon. World Development, 150, 105711.
  • + Sierra, G. D. R., Martínez, A. A. G., Cruz, M. Á. M., & Barrientos, L. G. Z. (2024). The impact of subsidies on house prices in Mexico’s mortgage market for low-income households 2008–2019. Journal of Housing Economics, 63, 101970.
  • Sohn, H., & Lee, S. W. (2019). Causal Impact of Having a College Degree on Women’s Fertility: Evidence From Regression Kink Designs. Demography, 56(3), 969-990.
  • Takaku, R., & Yokoyama, I. (2021). What the COVID-19 school closure left in its wake: evidence from a regression discontinuity analysis in Japan. Journal of Public Economics, 195, 104364.
  • Tuttle, C. (2019). Snapping Back: Food Stamp Bans and Criminal Recidivism. American Economic Journal: Economic Policy, 11(2), 301-27.
  • Yu, S., Lui, K., Fiebig, D. G., Travadi, J., Homer, C. S. E., Sinclair, L., Scarf, V., & Viney, R. (2025). The impact of neonatal care on moderate-risk infants: Evidence from healthcare use in the first two years of life. Journal of Health Economics, 102, 102995.

Semana 12

• Control sintético
  • MA, Capítulo 8 (Synthetic Control)
  • * MT, Capítulo 10 (Synthetic Control)
  • * Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American statistical Association, 105(490), 493-505.
  • * Acemoglu, D., Johnson, S., Kermani, A., Kwak, J., & Mitton, T. (2016). The value of connections in turbulent times: Evidence from the United States. Journal of Financial Economics, 121(2), 368-391.

Semana 13

• Aplicaciones de control sintético
  • Abadie, A. (2019). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature.
  • Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495-510.
    
  • Abadie, A., & Zhao, J. (2026). Synthetic controls for experimental design. Review of Economics and Statistics, 1-39.
  • Absher, S., Grier, K., & Grier, R. (2020). The economic consequences of durable left-populist regimes in Latin America. Journal of Economic Behavior & Organization, 177, 787-817.
    
  • Alfano, V., Ercolano, S., & Cicatiello, L. (2021). School openings and the COVID-19 outbreak in Italy. A provincial-level analysis using the synthetic control method. Health Policy.
  • Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118.
  • aus dem Moore, N., Brehm, J., & Gruhl, H. (2025). Driving innovation? Carbon tax effects in the Swedish transport sector. Journal of Public Economics, 248, 105444.
  • + Boly, M., & Sanou, A. (2022). Biofuels and food security: Evidence from Indonesia and Mexico. Energy Policy, 163, 112834.
  • Botosaru, I., & Ferman, B. (2019). On the role of covariates in the synthetic control method. The Econometrics Journal, 22(2), 117-130.
    
  • Calderón, G., Robles, G., Díaz-Cayeros, A., & Magaloni, B. (2015). The beheading of criminal organizations and the dynamics of violence in Mexico. Journal of Conflict Resolution, 59(8), 1455-1485.
    
  • + Campos-Vazquez, R. M., & Esquivel, G. (2020). The effect of doubling the minimum wage and decreasing taxes on inflation in Mexico. Economics Letters, 109051.
    
  • + Campos-Vazquez, R. M., & Esquivel, G. (2023). The Effect of the Minimum Wage on Poverty: Evidence from a Quasi-Experiment in Mexico. The Journal of Development Studies, 59(3), 360-380.
  • + Cepeda-Francese, C. A., & Ramírez-Álvarez, A. A. (2023). Reforming justice under a security crisis: The case of the criminal justice reform in Mexico. World Development, 163, 106148.
  • Costa, F., Szerman, D., & Assunção, J. (2025). The environmental costs of political interference: Evidence from power plants in the Amazon. Journal of Public Economics, 242, 105314.
  • Geloso, V., & Pavlik, J. B. (2021). The Cuban revolution and infant mortality: A synthetic control approach. Explorations in Economic History, 80, 101376.
  • Grier, K., & Maynard, N. (2016). The economic consequences of Hugo Chavez: A synthetic control analysis. Journal of Economic Behavior & Organization, 125, 1-21.
  • + González-Rozada, M., & Ruffo, H. (2024). Do trade agreements contribute to the decline in labor share? Evidence from Latin American countries. World Development, 177, 106561.
  • Mitze, T., Kosfeld, R., Rode, J., & Wälde, K. (2020). Face masks considerably reduce COVID-19 cases in Germany. Proceedings of the National Academy of Sciences, 117(51), 32293-32301.
  • Moor, J., Ropicki, A., Asche, F., & Ceballos-Concha, A. (2026). An economic approach to mitigating spillover bias in synthetic control methods: An application to the price effects of fishing quotas. American Journal of Agricultural Economics.
  • Peri, G., & Yasenov, V. (2019). The Labor Market Effects of a Refugee Wave Synthetic Control Method Meets the Mariel Boatlift. Journal of Human Resources, 54(2), 267-309.
  • + Ventosa-Santaulària, D., Cepeda-Suárez, R., & Romero-González, A. (2025). The impact of special economic zones on border regions in Mexico. Bulletin of Economic Research, 78(1), 224-231.

Semana 14

• Inferencia causal, IA y aprendizaje automático
  • CA, Capítulo 5 (Causal Machine Learning)
  • * MLE, Capítulo 8 (Inference on Heterogeneous Effectz)
  • * MLE, Capítulo 9 (Optimal Policy Learning)
  • * Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11.
• Aplicaciones de IA y aprendizaje automático
  • Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.
    
  • Baiardi, A., & Naghi, A. A. (2021). The value added of machine learning to causal inference: Evidence from revisited studies. arXiv preprint arXiv:2101.00878.
  • Chetty, R. (2021). Improving equality of opportunity: New insights from big data. Contemporary Economic Policy, 39(1), 7-41.
  • Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. Econometrics Journal, 21(1), pp. C1–C68.
  • Chernozhukov, V., Demirer, M., Duflo, E., & Fernandez-Val, I. (2018). Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in India. Woring Paper No. w24678. National Bureau of Economic Research.
  • Cole, M. A., Elliott, R. J., & Liu, B. (2020). The impact of the Wuhan Covid-19 lockdown on air pollution and health: a machine learning and augmented synthetic control approach. Environmental and Resource Economics, 76(4), 553-580.
  • Dell, M. (2024). Deep Learning for Economists. arXiv preprint arXiv:2407.15339.
  • Naimi, A. I., Mishler, A. E., & Kennedy, E. H. (2017). Challenges in obtaining valid causal effect estimates with machine learning algorithms. ArXiv preprint 1711.07137.
  • Storm, H., Baylis, K., & Heckelei, T. (2020). Machine learning in agricultural and applied economics. European Review of Agricultural Economics, 47(3), 849-892.
  • Torrats-Espinosa, G. (2021). Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences, 118(7).
  • Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228-1242.
  • Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28

Otras lecturas sobre temas no cubiertos en el curso

Crítica a los experimentos

• Barrett, C. B., & Carter, M. R. (2010). The power and pitfalls of experiments in development economics: Some non-random reflections. Applied economic perspectives and policy, 32(4), 515-548.
  
• Barrett, C. B., & Carter, M. R. (2020). Finding our balance? Revisiting the randomization revolution in development economics ten years further on. World Development, 127, 104789.
  
• Hjort, J., Moreira, D., Rao, G., & Santini, J. F. (2021). How research affects policy: Experimental evidence from 2,150 Brazilian municipalities. American Economic Review, 111(5), 1442-80.
• Deaton, A., Case. (2019). Randomization in the tropics revisited: a theme and eleven variations. In Randomized controlled trials in the field of development: A critical perspective. Oxford University Press. Forthcoming.
  
• Ravallion, Martin. (2020). Should the randomistas (continue to) rule? National Bureau of Economic Research, Working Paper 27554.

Modelos estructurales en evaluación

• Abbring, J. H., & Heckman, J. J. (2007). Econometric evaluation of social programs, part III: Distributional treatment effects, dynamic treatment effects, dynamic discrete choice, and general equilibrium policy evaluation. Handbook of econometrics, 6, 5145-5303.
  
• Attanasio, O. P., Meghir, C., & Santiago, A. (2011). Education choices in Mexico: using a structural model and a randomized experiment to evaluate Progresa. The Review of Economic Studies, 79(1), 37-66.
  
• Duflo, E., Hanna, R., & Ryan, S. P. (2012). Incentives work: Getting teachers to come to school. American Economic Review, 102(4), 1241-78.}
• Hamilton, B. H., Hincapié, A., Miller, R. A., & Papageorge, N. W. (2018). Innovation and Diffusion of Medical Treatment. National Bureau of Economic Working Paper 24577.
  
• Keane, M. P. (2010). A structural perspective on the experimentalist school. Journal of Economic Perspectives, 24(2), 47-58.
• Keane, M. P., Todd, P. E., & Wolpin, K. I. (2011). The structural estimation of behavioral models: Discrete choice dynamic programming methods and applications. In Handbook of labor economics (Vol. 4, pp. 331-461). Elsevier.
  
• Low, H., & Meghir, C. (2017). The use of structural models in econometrics. Journal of Economic Perspectives, 31(2), 33-58.
• Ma, X., Lawell, C. Y. L., & Rozelle, S. (2020). Peer effects and the use of subsidized goods: A structural econometric model of a health promotion program in rural China. Working paper, Cornell University.
  
• Nevo, A., & Whinston, M. D. (2010). Taking the dogma out of econometrics: Structural modeling and credible inference. Journal of Economic Perspectives, 24(2), 69-82.
  
• Todd, P. E., & Wolpin, K. I. (2010). Structural estimation and policy evaluation in developing countries. Annu. Rev. Econ., 2(1), 21-50.
• Wolpin, K. I. (2013). The limits of inference without theory. MIT Press.

Evaluaciones de impacto a nivel de economía local (LEWIE)

• Taylor, J. E., Dyer, G. A., Stewart, M., Yunez-Naude, A., & Ardila, S. (2003). The economics of ecotourism: A Galápagos Islands economy-wide perspective. Economic Development and Cultural Change, 51(4), 977-997.
• Taylor, J. E., & Filipski, M. J. (2014). Beyond experiments in development economics: Local economy-wide impact evaluation. Oxford University Press.
• Taylor, J. E., Filipski, M. J., Alloush, M., Gupta, A., Rojas Valdes, R.I., & Gonzalez-Estrada, E. (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences, 201604566.

Combinación de metodologías no experimentales

• Cattaneo, M. D., Frandsen, B. R., & Titiunik, R. (2015). Randomization inference in the regression discontinuity design: An application to party advantages in the US Senate. Journal of Causal Inference, 3(1), 1-24.
  
• Donohue III, J. J., & Ho, D. E. (2007). The Impact of Damage Caps on Malpractice Claims: Randomization Inference with Difference‐in‐Differences. Journal of Empirical Legal Studies, 4(1), 69-102.
  
• Keele, L., Titiunik, R., & Zubizarreta, J. R. (2015). Enhancing a geographic regression discontinuity design through matching to estimate the effect of ballot initiatives on voter turnout. Journal of the Royal Statistical Society. Series A (Statistics in Society), 223-239.
• Levasseur, P. (2019). Can social programs break the vicious cycle between poverty and obesity? Evidence from urban Mexico. World Development, 113, 143-156.
• MacKinnon, J. G., & Webb, M. D. (2020). Randomization inference for difference-in-differences with few treated clusters. Journal of Econometrics.
  
• Parker, S. W., Saenz, J., & Wong, R. (2018). Health insurance and the aging: Evidence from the Seguro Popular program in Mexico. Demography, 55(1), 361-386.
• Sant’Anna, P. H., & Zhao, J. (2020). Doubly robust difference-in-differences estimators. Journal of Econometrics, 219(1), 101-122.

Impactos de largo plazo

• Athey, S., Chetty, R., Imbens, G. W., & Kang, H. (2019). The surrogate index: Combining short-term proxies to estimate long-term treatment effects more rapidly and precisely, National Bureau of Economic Research, Working Paper 26463.
• Hamory, J., Miguel, E., Walker, M., Kremer, M., & Baird, S. (2021). Twenty-year economic impacts of deworming. Proceedings of the National Academy of Sciences, 118(14).
• Dupas, P., Duflo, E. & Kremer, M. (2021). The Impact of Free Secondary Education: Experimental Evidence from Ghana. Stanford University Working Paper.
  
• Parker, S. W., & Vogl, T. (2018). Do conditional cash transfers improve economic outcomes in the next generation? Evidence from Mexico (No. w24303). National Bureau of Economic Research.

Ética

• Humphreys, M. (2015). Reflections on the ethics of social experimentation. Journal of Globalization and Development, 6(1), 87-112.
• Lewis, J. (2020). Experimental Design: Ethics, Integrity, and the Scientific Method. Handbook of Research Ethics and Scientific Integrity, 459-474.
• Rayzberg, M. S. (2019). Fairness in the field: The ethics of resource allocation in randomized controlled field experiments. Science, Technology, & Human Values, 44(3), 371-398.

Credibilidad e inferencia estadística

• Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature. 567, 305-307.
  
• Angrist, J. D., & Pischke, J. S. (2010). The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of economic perspectives, 24(2), 3-30.
  
• Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), 337-350.
• Leamer, E. E. (1983). Let’s take the con out of econometrics. The American Economic Review, 73(1), 31-43.
• Leamer, E. E. (2010). Tantalus on the Road to Asymptopia. Journal of Economic Perspectives, 24(2), 31-46.
• Nuzzo, R. (2014). Scientific method: statistical errors. Nature News, 506(7487), 150.
• Sims, C. A. (2010). But economics is not an experimental science. Journal of Economic Perspectives, 24(2), 59-68.
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129-133.

Discontinuidades geográficas

• Gonzalez, R. M. (2021). Cell Phone Access and Election Fraud: Evidence from a Spatial Regression Discontinuity Design in Afghanistan. American Economic Journal: Applied Economics, 13(2), 1-51.
• Keele, L. J., & Titiunik, R. (2015). Geographic boundaries as regression discontinuities. Political Analysis, 23(1), 127-155.
• Keele, L., & Titiunik, R. (2016). Natural experiments based on geography. Political Science Research and Methods, 4(1), 65-95.

Pliegues en la regresión

• Gamba, S., Jakobsson, N., & Svensson, M. (2022). The impact of cost-sharing on prescription drug demand: evidence from a double-difference regression kink design. The European Journal of Health Economics, 1-9.
• Card, D., Lee, D. S., Pei, Z., & Weber, A. (2017). Regression kink design: Theory and practice. NBER Working Paper 22781.
  
• Lurie, I. Z., Sacks, D. W., & Heim, B. (2021). Does the individual mandate affect insurance coverage? Evidence from tax returns. American Economic Journal: Economic Policy, 13(2), 378-407.

Inferencia por aleatorización

• Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2020). Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis. Econometrica, 88(1), 265-296.
  
• Athey, S., & Imbens, G. W. (2017). The econometrics of randomized experiments. In Handbook of economic field experiments (Vol. 1, pp. 73-140). North-Holland.
  
• Ho, D. E., & Imai, K. (2006). Randomization inference with natural experiments: An analysis of ballot effects in the 2003 California recall election. Journal of the American Statistical Association, 101(475), 888-900.
  
• Kerwin, J. T., & Thornton, R. L. (2020). Making the grade: The sensitivity of education program effectiveness to input choices and outcome measures. Review of Economics and Statistics, 1-45.