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

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

Natural Field Experiments from The Field Experiments Website

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 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.

Date: 2019
New Economics Papers: this item is included in nep-ecm and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://s3.amazonaws.com/fieldexperiments-papers2/papers/00681.pdf

Related works:
Working Paper: Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:feb:natura:00681

Access Statistics for this paper

More papers in Natural Field Experiments from The Field Experiments Website
Bibliographic data for series maintained by Francesca Pagnotta ().