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Beyond baseline and follow-up : the case for more t in experiments

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  • McKenzie, David

Abstract

The vast majority of randomized experiments in economics rely on a single baseline and single follow-up survey. If multiple follow-ups are conducted, the reason is typically to examine the trajectory of impact effects, so that in effect only one follow-up round is being used to estimate each treatment effect of interest. While such a design is suitable for study of highly autocorrelated and relatively precisely measured outcomes in the health and education domains, this paper makes the case that it is unlikely to be optimal for measuring noisy and relatively less autocorrelated outcomes such as business profits, household incomes and expenditures, and episodic health outcomes. Taking multiple measurements of such outcomes at relatively short intervals allows the researcher to average out noise, increasing power. When the outcomes have low autocorrelation, it can make sense to do no baseline at all. Moreover, the author shows how for such outcomes, more power can be achieved with multiple follow-ups than allocating the same total sample size over a single follow-up and baseline. The analysis highlights the large gains in power from ANCOVA rather than difference-in-differences when autocorrelations are low and a baseline is taken. The paper discusses the issues involved in multiple measurements, and makes recommendations for the design of experiments and related non-experimental impact evaluations.

Suggested Citation

  • McKenzie, David, 2011. "Beyond baseline and follow-up : the case for more t in experiments," Policy Research Working Paper Series 5639, The World Bank.
  • Handle: RePEc:wbk:wbrwps:5639
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    References listed on IDEAS

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    10. Fafchamps, Marcel & McKenzie, David & Quinn, Simon & Woodruff, Christopher, 2012. "Using PDA consistency checks to increase the precision of profits and sales measurement in panels," Journal of Development Economics, Elsevier, vol. 98(1), pages 51-57.
    11. Duflo, Esther & Glennerster, Rachel & Kremer, Michael, 2008. "Using Randomization in Development Economics Research: A Toolkit," Handbook of Development Economics, in: T. Paul Schultz & John A. Strauss (ed.), Handbook of Development Economics, edition 1, volume 4, chapter 61, pages 3895-3962, Elsevier.
    12. Nicholas Bloom & Benn Eifert & Aprajit Mahajan & David McKenzie & John Roberts, 2013. "Does Management Matter? Evidence from India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(1), pages 1-51.
    13. Glewwe, Paul & Kremer, Michael & Moulin, Sylvie & Zitzewitz, Eric, 2004. "Retrospective vs. prospective analyses of school inputs: the case of flip charts in Kenya," Journal of Development Economics, Elsevier, vol. 74(1), pages 251-268, June.
    14. Suresh de Mel & David McKenzie & Christopher Woodruff, 2009. "Returns to Capital in Microenterprises: Evidence from a Field Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(1), pages 423-423.
    15. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
    16. Fafchamps, Marcel & McKenzie, David & Quinn, Simon & Woodruff, Christopher, 2014. "Microenterprise growth and the flypaper effect: Evidence from a randomized experiment in Ghana," Journal of Development Economics, Elsevier, vol. 106(C), pages 211-226.
    17. Marcel Fafchamps & David McKenzie & Simon Quinn & Christopher Woodruff, 2011. "When is capital enough to get female microenterprises growing? Evidence from a randomized experiment in Ghana," CSAE Working Paper Series 2011-11, Centre for the Study of African Economies, University of Oxford.
    18. John Gibson & David McKenzie, 2014. "The Development Impact of a Best Practice Seasonal Worker Policy," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 229-243, May.
    19. Edward Miguel & Michael Kremer, 2004. "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities," Econometrica, Econometric Society, vol. 72(1), pages 159-217, January.
    20. Jishnu Das & Stefan Dercon & James Habyarimana & Pramila Krishnan, 2007. "Teacher Shocks and Student Learning: Evidence from Zambia," Journal of Human Resources, University of Wisconsin Press, vol. 42(4).
    21. Ashenfelter, Orley & Card, David, 1985. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 648-660, November.
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    23. de Mel, Suresh & McKenzie, David J. & Woodruff, Christopher, 2009. "Measuring microenterprise profits: Must we ask how the sausage is made?," Journal of Development Economics, Elsevier, vol. 88(1), pages 19-31, January.
    24. Miriam Bruhn, 2011. "License to Sell: The Effect of Business Registration Reform on Entrepreneurial Activity in Mexico," The Review of Economics and Statistics, MIT Press, vol. 93(1), pages 382-386, February.
    25. McKenzie, David J, 2004. "Aggregate Shocks and Urban Labor Market Responses: Evidence from Argentina's Financial Crisis," Economic Development and Cultural Change, University of Chicago Press, vol. 52(4), pages 719-758, July.
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    More about this item

    Keywords

    Scientific Research&Science Parks; Science Education; Statistical&Mathematical Sciences; Disease Control&Prevention; Economic Theory&Research;
    All these keywords.

    JEL classification:

    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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