NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH
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Bharat Chandar

Stanford University
579 Serra Mall
Stanford, CA

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
Institutional Affiliation: Stanford University

NBER Working Papers and Publications

October 2019The Drivers of Social Preferences: Evidence from a Nationwide Tipping Field Experiment
with Uri Gneezy, John A. List, Ian Muir: w26380
Even though social preferences affect nearly every facet of life, there exist many open questions on the economics of social preferences in markets. We leverage a unique opportunity to generate a large data set to inform the who’s, what’s, where’s, and when’s of social preferences through the lens of a nationwide tipping field experiment on the Uber platform. Our field experiment generates data from more than 40 million trips, allowing an exploration of social preferences in the ride sharing market using big data. Combining experimental and natural variation in the data, we are able to establish tipping facts as well as provide insights into the underlying motives for tipping. Interestingly, even though tips are made privately, and without external social benefits or pressure, more tha...
Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings
with Ali Hortaçsu, John A. List, Ian Muir, Jeffrey M. Wooldridge: w26389
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...
 
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