This course will focus on the evaluation of social programs and will provide a thorough understanding of randomized evaluations. Through a combination of lectures and case studies from real randomized evaluations, the course will focus on the benefits and methods of randomization, choosing an appropriate sample size, and common threats and pitfalls to the validity of the experiment.
In addition to the lecture sequences, the course also includes various case studies. The case studies explore the concepts and issues discussed in the lecture sequences and involve some readings, followed by discussion topics. The discussion topics include multiple choice questions open response assessments. Students will also take multiple choice tests during the course that will be linked to the lectures’ contents and the compulsory readings for the case studies.
Important note: this course relies, among others, on the materials used in J-PAL’s/MIT course “Evaluating Social Programs – JPAL101x”.
1. What is evaluation?
2. Theory of change and measurement
3. Why randomize?
4. How to randomize
5. Sample size and power
6. Threats and analysis
7. RCT: start to finish
(8. Cost-effectiveness analysis and scaling up) – if time allows
Duflo, E. (2005). “Women as Policy Makers: Evidence from a Randomized Policy Experiment in India”, Econometrica, Vol. 72, No. 5. (UNIT 2)
Banerjee, A. V. et al. (2011). “Pitfalls of Participatory Programs: Evidence from a Randomized Evaluation in Education in India”, American Economic Journal: Economic Policy, 2-1, 1: 30. (UNIT 3)
Duflo, E. (2011). “Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya”, American Economic Review 101: 1779-1734. (UNIT 4)
Miguel, E. and E. Kremer (2004). “Worms: Identifying the Impact on Education and Health in the Presence of Treatment Externalities”, Econometrica, Vol. 72, No. 1. (UNIT 6)
Davey, C, Aiken, AM, Hayes, RJ and Hargreaves, JR. (2015). “Reanalysis of health and educational impacts of a school based deworming program in western Kenya: a statistical replication of a cluster quasi-randomized stepped wedged trial”, International Journal of Epidemiology, 1–12. Available at http://ije.oxfordjournals.org/content/early/2015/07/21/ije.dyv128.full.pdf. An earlier version available at http://www.3ieimpact.org/media/filer_public/2015/01/07/rps_3_part_2_top_copy_reduced_size_1_7_15-top.pdf. (UNIT 6)
Hicks, J. H., Kremer, M., and E. Miguel, (2014). “Estimating deworming school participation impacts in Kenya: A Comment on Aiken et al.(2014b)”, Document available at http://www.3ieimpact.org/media/filer_public/2015/01/07/rps3_worms-3ie-pure-response_2014-12-22-part_2.pdf (UNIT 6)
Hicks, J. H., Kremer, M., and E. Miguel, (2015). “Commentary: Deworming externalities and schooling impacts in Kenya: a comment on Aiken et. al. (2015) and Davey et. al. (2015)”, International Journal of Epidemiology, 1-4. Available at http://ije.oxfordjournals.org/content/early/2015/07/21/ije.dyv129.full.pdf (UNIT 6)
Duflo, E., Glennerster, R. and M. Kremer (2007). “Using Randomization in Development Economics Research: A Toolkit”, en T. Paul Schults y John Strauss (eds.), Handbook of Development Economics, Elsevier Science Ltd.: North Holland, Vol. 4, págs. 3895-62. (Also available as CEPR Discussion Paper No. 6059).
Gertler, P. J., Martínez, S., Premand, P., Rawlings, L. B. and C. M. J. Vermeesch (2011). Impact Evaluation in Practice, World Bank. Document available at http://www.worldbank.org/pdt
Teerenstra, S. et. al. (2012). “A simple simple size formula for analysis of covariance in cluster randomized trials”, Statistics in Medicine 31, 2169-2178.