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Heterogeneous Treatment Effects in Impact Evaluation

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  • Eva Vivalt

Abstract

It is very important to know how much we can extrapolate from a study's results. This paper examines the issue using data from impact evaluations in development.

Suggested Citation

  • Eva Vivalt, 2015. "Heterogeneous Treatment Effects in Impact Evaluation," American Economic Review, American Economic Association, vol. 105(5), pages 467-470, May.
    • Handle: RePEc:aea:aecrev:v:105:y:2015:i:5:p:467-70
    Note: DOI: 10.1257/aer.p20151015
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    References listed on IDEAS

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    1. repec:pri:rpdevs:deaton_instruments_randomization_learning_all_04april_2010 is not listed on IDEAS
    2. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    3. Sylvain Chassang & Gerard Padro I Miquel & Erik Snowberg, 2012. "Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments," American Economic Review, American Economic Association, vol. 102(4), pages 1279-1309, June.
    4. Tessa Bold & Mwangi Kimenyi & Germano Mwabu & Alice Ng'ang'a & Justin Sandefur, 2013. "Scaling-up What Works: Experimental Evidence on External Validity in Kenyan Education," CSAE Working Paper Series 2013-04, Centre for the Study of African Economies, University of Oxford.
    5. Katherine Casey & Rachel Glennerster & Edward Miguel, 2012. "Reshaping Institutions: Evidence on Aid Impacts Using a Preanalysis Plan," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(4), pages 1755-1812.
    6. Eva Vivalt, 0. "How Much Can We Generalize From Impact Evaluations?," Journal of the European Economic Association, European Economic Association, vol. 18(6), pages 3045-3089.
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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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