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Richard C Bishop, Kevin J Boyle, Richard T Carson, David Chapman, Matthew DeBell, Colleen Donovan, W. Michael Hanemann, Barbara Kanninen, Matthew Konopka, Raymond J Kopp, Jon A Krosnick, John A List, Norman Meade, Robert Paterson, Stanley Presser, Nora Scherer, V. Kerry Smith, Roger Tourangeau, Michael Welsh, Jeffrey M Wooldridge

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Bharat Chandar, Ali Hortacsu, John A List, Ian Muir, Jeffrey M Wooldridge

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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 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.