Beyond baseline and follow-up : the case for more t in experiments
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.Download Info
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Paper provided by The World Bank in its series Policy Research Working Paper Series with number 5639.Length:
Date of creation: 01 Apr 2011
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Handle: RePEc:wbk:wbrwps:5639
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Keywords: Scientific Research&Science Parks; Science Education; Statistical&Mathematical Sciences; Disease Control&Prevention; Economic Theory&Research;Other versions of this item:
- McKenzie, David, 2012. "Beyond baseline and follow-up: The case for more T in experiments," Journal of Development Economics, Elsevier, vol. 99(2), pages 210-221.
- O12 - Economic Development, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
- C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-07 (All new papers)
- NEP-DEV-2011-05-07 (Development)
- NEP-ECM-2011-05-07 (Econometrics)
- NEP-EXP-2011-05-07 (Experimental Economics)
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