Randomized Control Trials (RCTs) in Development Economics

• Glennerster and Takavarasha (2013): Running Randomized Evaluations, chapters 1 to 3 (available on JSTOR).
• Fisher (1935): Design of Experiments, chapter II (available online).

Additional Related Readings:

• Angrist and Pischke (2015): Mastering Metrics, chapter 1 (available online).
• Gerber and Green (2012): Field Experiments, chapters 1 and 2.
• Jamison (2019): “The Entry of Randomized Assignment into the Social Sciences,” Journal of Causal Inference, 7(1).
• Parker (2010): “The Poverty Lab,” The New Yorker.

• Bruhn and McKenzie (2009): “In Pursuit of Balance: Randomization in Practice in Development Field Experiments,” American Economic Journal: Applied Economics, 1(4): 200–232.
• Duflo, Glennerster, and Kremer (2007): “Using Randomization in Development Economics Research: A Toolkit,” Handbook of Development Economics, Volume 4, 2007, Chapter 61, pages 3895–3962 (available from Elsevier or MIT/CEPR).
• McKenzie (2012): “Beyond baseline and follow-up: The case for more T in experiments,” Journal of Development Economics, 99(2): 210–221.
• Young (2019): “Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results,” Quarterly Journal of Economics, 134(2): 557–598.

Additional Related Readings:

• Gerber and Green (2012): Field Experiments, chapters 3 and 4
• Glennerster and Takavarasha (2013): Running Randomized Evaluations, chapters 4 to 7.

• Bruhn and McKenzie (2009): “In Pursuit of Balance: Randomization in Practice in Development Field Experiments,” American Economic Journal: Applied Economics, 1(4): 200–232.
• Athey and Imbens (2016): “Recursive partitioning for heterogeneous causal effects,” Proceedings of the National Academy of Sciences, 113(27): 7353- 7360.
• Wager and Athey (2018): “Estimation and inference of heterogeneous treatment effects using random forests,” Journal of the American Statistical Association, 113(523): 1228-1242.

Additional Related Readings:

• Glennerster and Takavarasha (2013): Running Randomized Evaluations, chapter 8
• James, Witten, Hastie, and Tibshirani (2021): “Tree-Based Methods.” In An Introduction to Statistical Learning, second edition.
• Chernozhukov, Fernandez-Val, and Melly (2013): “Inference on Counterfactual Distributions,” Econometrica, 81(6): 2205-2268.

• Young (2019): “Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results,” Quarterly Journal of Economics, 134(2): 557–598.
• Lee (2009): “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects,” Review of Economic Studies, 76(3): 1071-1102.
• Anderson (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.

• Ozier (2021): “Replication Redux: The Reproducibility Crisis and the Case of Deworming,” World Bank Research Observer, 36(1): pp. 101-130.
• Jakiela, Ozier, Fernald, and Knauer (2020): “Evaluating the Effects of an Early Literacy Intervention,” Journal of Development Economics, registered report conditionally accepted based on Stage 1 Pre-Results Review.
• James, Witten, Hastie, and Tibshirani (2021): “Linear Model Selection and Regularization.” In An Introduction to Statistical Learning, second edition.
• Leaver, Ozier, Serneels, and Zeitlin (2021): “Recruitment, effort, and retention effects of performance contracts for civil servants: Experimental evidence from Rwandan primary schools,” American Economic Review, forthcoming.
• Leaver, Ozier, Serneels, and Zeitlin (2018): “Power to the Plan” (available online on the World Bank’s Development Impact blog).

Additional Related Readings

• Brodeur, Lé, Sangnier, and Zylberberg (2016): “Star Wars: The Empirics Strike Back,” American Economic Journal: Applied Economics, 8(1): 1-32.
• Christensen and Miguel (2018): “Transparency, Reproducibility, and the Credibility of Economics Research,” Journal of Economic Literature, 56(3): 920-980.
• Coffman and Niederle (2015): “Pre-analysis Plans Have Limited Upside, Especially Where Replications Are Feasible,” Journal of Economic Perspectives, 29(3): 81-98.
• Olken (2015): “Promises and Perils of Pre-Analysis Plans,” Journal of Economic Perspectives, 29(3): 61-80.

• Every participant taking this course will receive a time-limited personal free license of STATA several days before the start of the Summer School
• Empirical exercises will be offered in parallel in R and Stata
• Participants should install the STATA software on their laptops for use during the practical sessions or should be comfortable working in R and have RStudio installed