Research
Research
The Experimenter's Dilemma: Inferential Preferences Over Populations (Joint with Luca Rigotti and Alistair Wilson) [Feb 2025] [Revise and Resubmit at Experimental Economics]
Abstract: We examine the experimenter's preferences over different populations using statistical power under a fixed budget as the stand-in for the researcher's utility. We consider five populations commonly used in experiments by economists: undergraduate students at a physical location, undergraduate students in a virtual setting, Amazon MTurk "workers", a filtered MTurk subset from CloudResearch, and Prolific. Focusing on noise due to inattention, observation costs dominate the comparisons, with the larger online population samples superior to the smaller lab samples. However, once we factor in responsiveness to treatment, the lab samples have greater power than either MTurk or Prolific.
The Female Sensitivity Hypothesis: Evidence from Experimental Economics (Joint with Felipe A. Araujo and Lise Vesterlund) [May 2025] [Revise and Resubmit at The Review of Economics and Statistics]
Abstract: We assess the empirical evidence for the hypothesis that women more than men respond to changes in treatment. First, we examine whether the results of over two hundred experimental economics studies support the female sensitivity hypothesis. Second, using data from two studies (DellaVigna and Pope, 2022; Exley et al., 2025), we conduct over two hundred pairwise tests of the hypothesis. Both analyses show that gender is not predictive of responsiveness to treatment. We further examine how the hypothesis has been disseminated in the literature and find strong confirmation bias with the hypothesis predominantly being cited by studies that support it.
Can Temporary Affirmative Action Improve Representation? [Aug 2025] [Under Review]
Abstract: This paper investigates whether temporary affirmative action can break self-perpetuating cycles of inaccurate statistical discrimination through belief updating. Using an online experiment with nearly 1,000 employers, I study hiring in a domain where men and women perform equally but employers believe men outperform women. Employers are randomly assigned to unrestricted hiring or temporary quotas requiring at least one woman be hired initially, with quotas then lifted. Under unrestricted hiring, biases against women persist in both beliefs and hiring outcomes. Under temporary affirmative action, I observe sustained improvements in women's representation and employer beliefs even after the policy ends. Among employers with high initial bias, the policy increases women's post-quota hiring odds by 59\%. When performance evaluations are included in hiring models, they explain most of the treatment effect among highly biased employers, providing direct evidence that belief updating drives persistence. The results show that well-designed, time-limited quotas can act as targeted correctives, improving representation by addressing informational distortions that prevent fair evaluation of talent.
Well, Excuse Me! Replicating and Connecting Excuse-Seeking Behavior (Joint with Beatriz Ahumada, Yufei Chen, Kelly Hyde, Marissa Lepper, William Mathews, Neil Silveus, Lise Vesterlund, Taylor Weidman, Alistair Wilson, K. Pun Winichakul and Liyang Zhou) [Jan 2022]
Abstract: Excuse-seeking behavior that facilitates replacing altruistic choices with self-interested ones has been documented in several domains. In a laboratory study, we replicate three leading papers on this topic: Dana et al. (2007), and the use of information avoidance; Exley (2015), and the use of differential risk preferences; and Di Tella et al. (2015), and the use of motivated beliefs. The replications were conducted as part of a graduate course, attempting to embed one answer to the growing call for experimental replications within the pedagogic process. We fully replicate the simpler Dana et al. paper, and broadly replicate the core findings for the other two projects, though with reduced effect sizes and a failure to replicate on some secondary measures. Finally, we attempt to connect behaviors to facilitate the understanding of how each fit within the broader literature. However, we find no connections across domains.
Going Virtual: A Set-by-Step Guide to Taking the In-Person Experimental Lab Online (Joint with David Danz, Marissa Lepper, Lise Vesterlund and K. Pun Winichakul) [Sep 2021]
Abstract: This guide provides a detailed account of procedures for conducting traditional in-person laboratory experiments in a “virtual setting.” The main objective of these procedures is to maintain the control of traditional in-person lab studies when conducting studies over the internet. Using the participant pool of the in-person lab the key procedural steps include participants having their webcams on throughout the experiment, technical screenings, and attention pledges, playing pre-recorded instructions out loud, upholding clear experimenter roles and communication protocols when interacting with participants, and finally detailed and scripted procedures for managing participants throughout the session. The described procedures have been used for more than 100 sessions and have secured results that are indistinguishable from those from the in-person lab.
The Use of Irrelevant Information to Discriminate in Hiring (Joint with Beatriz Ahumada and Mallory Avery) [Data Collection Stage]
Does Affirmative Action Cause or Concentrate Under-confidence? (Joint with Mallory Avery) [Design Stage]
Incentive-Compatible Mechanisms for Multi-Worker Selection: Theory and Applications (Joint with Rachel Mannahan) [Modeling and Design Stage]