Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings

Bharat K. Chandar, Ali Hortacsu, John List, Ian Muir and Jeffrey Wooldridge

No 26389, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Field experiments conducted with the village, city, state, region, or even country as the unit of randomization are becoming commonplace in the social sciences. While convenient, subsequent data analysis may be complicated by the constraint on the number of clusters in treatment and control. Through a battery of Monte Carlo simulations, we examine best practices for estimating unit-level treatment effects in cluster-randomized field experiments, particularly in settings that generate short panel data. In most settings we consider, unit-level estimation with unit fixed effects and cluster-level estimation weighted by the number of units per cluster tend to be robust to potentially problematic features in the data while giving greater statistical power. Using insights from our analysis, we evaluate the effect of a unique field experiment: a nationwide tipping field experiment across markets on the Uber app. Beyond the import of showing how tipping affects aggregate market outcomes, we provide several insights on aspects of generating and analyzing cluster-randomized experimental data when there are constraints on the number of experimental units in treatment and control.

JEL-codes: C23 C33 C5 C9 C91 C92 C93 D47 (search for similar items in EconPapers)
Date: 2019-10
New Economics Papers: this item is included in nep-exp and nep-ore
Note: IO PE
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Citations: View citations in EconPapers (6)

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Working Paper: Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings (2019)
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