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Ufuk Akcigit, Fernando Alvarez, Stephane Bonhomme, George M Constantinides, Douglas W Diamond, Eugene F Fama, David W Galenson, Michael Greenstone, Lars Peter Hansen, Uhlig Harald, James J Heckman, Ali Hortacsu, Emir Kamenica, Greg Kaplan, Anil K Kashyap, Steven D Levitt, John A List, Robert E Lucas Jr., Magne Mogstad, Roger Myerson, Derek Neal, Canice Prendergast, Raghuram G Rajan, Philip J Reny, Azeem M Shaikh, Robert Shimer, Hugo F Sonnenschein, Nancy L Stokey, Richard H Thaler, Robert H Topel, Robert Vishny, Luigi Zingales

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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.

Kentaro Asai, Seda Ertac, Ali Hortacsu, John A List, Howard Nusbaum, Lester Tong, Karen Ye

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People often demand a greater price when selling goods that they own than they would pay to purchase the same goods- a well-known economic bias called the endowment effect. The endowment effect has been found to be muted among experienced traders, but little is known about how trading experience reduces the endowment effect. We show that when selling, experienced traders exhibit lower right anterior insula activity, but no differences in nucleus accumbens or orbitofrontal activation, compared with inexperienced traders. Furthermore, insula activation mediates the effect of experience on the endowment effect. Similar results are obtained for inexperienced traders who are incentivized to gain trading experience. This finding indicates that frequent trading likely mitigates the endowment effect indirectly by modifying negative affective responses in the context of selling.