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I am a Behavioral Economist, focusing on questions in Quantitative Marketing, Industrial Organization, and Public Economics. My main research focus is on the behavioral economics of firms: How should firms account for behavioral consumers? How do firms respond in practice? Are firms sometimes behavioral too? 

I obtained my PhD in Economics from the University of California, Berkeley.


If you are a PhD student in marketing or economics, at any university, and would like to talk, please sign up here.

If you are taking my class please sign up here for office hours. 




More than a Penny's Worth: Left-Digit Bias and Firm Pricing
The Review of Economic Studies Volume 90, Issue 5, October 2023, Pages 2612–2645 (Featured article)
[FINAL] [2019 version here, similar results - different data]

Firms arguably price at 99-ending prices because of left-digit bias—the tendency of consumers to perceive a $4.99 as much lower than a $5.00. Analysis of retail scanner data on 3500 products sold by 25 US chains provides robust support for this explanation. I structurally estimate the magnitude of left-digit bias and find that consumers respond to a 1-cent increase from a 99-ending price as if it were more than a 20-cent increase. Next, I solve a portable model of optimal pricing given left-digit biased demand. I use this model and other pricing procedures to estimate the level of left-digit bias retailers perceive when making their pricing decisions. While all retailers respond to left-digit bias by using 99-ending prices, their behavior is consistently at odds with the demand they face. Firms price as if the bias were much smaller than it is, and their pricing is more consistent with heuristics and rule-of-thumb than with optimization given the structure of demand. I calculate that retailers forgo 1 to 4 percent of potential gross profits due to this coarse response to left-digit bias.

Sophisticated Consumers with Inertia: Long-Term Implications from a Large Scale Field Experiment (with Klaus Miller and Navdeep Sahni)
Revise and Resubmit at The American Economic Review

Are inert consumers aware of their future inertia? This field experiment offers two million readers of a European newspaper auto-renewing and auto-canceling contracts. Consumers are inert, yet sophisticated about their inertia: Auto-renew takers are inert, yet offering auto-renewing contracts lowers subscriptions by 24% and reduces the number of subscribers by 10% over two years. We estimate that 70% of readers in the population are inert, and the majority of them are sophisticated; among auto-renew subscribers, only 6% are sophisticated. Our results highlight the impact of sophistication about future biases on consumer behavior and market outcomes.

Choice Architecture, Privacy Valuations, and Selection in Consumer Data (with Tesary Lin)
Revise and Resubmit at Marketing Science

[UPDATED! July 2024] 
Winner - EC'2023 Exemplary Empirical paper; 
                  Boston University Platform Symposium Alessnadro di Fioré 2023 best                            paper award

We investigate how the choice architecture used during data collection influences the quality of collected data, including volume (how many people share) and representativeness (who shares data). To this end, we run a large-scale choice experiment to elicit consumers’ valuation for their Facebook data while randomizing two common choice frames: default and price anchor. An opt-out default decreases valuations by 14% compared to opt-in, while a $0–50 price anchor decreases valuations by 53% compared to a $50–100 anchor. Moreover, in some consumer segments, the susceptibility to frame influence negatively correlates with consumers’ average valuation. As a result, conventional frame optimization practices that aim to maximize data volume may often result in less representative data. We demonstrate the magnitude of this volume-bias trade-off in our data and argue that it should be a key decision factor in choice architecture design.

Firms may try to maximize profits but fail for various reasons. I use a reform to argue that the only model of the firm consistent with the data is one with misoptimization due to insufficient knowledge of the world. A reform in Israel limited prices to end with 0 as the cent digit (e.g. 2.90 but not 2.99). Since consumers are left-digit biased, specifically causing demand to discretely fall by 5%-9% at round prices, optimal pricing implies bunching at just-below prices (e.g. 2.99 pre-reform or 2.90 post) and avoiding round prices (e.g. 3.00). In fact, before the reform, supermarkets set just-below prices for 45% of prices and rarely used round prices. If price-setting before the reform was driven by the correct model of demand, firms' response to the reform would have been to update immediately according to their beliefs and avoid round prices. However, pricing after the reform is inconsistent with their pre-reform revealed beliefs, setting 20% of clearly dominated prices for almost a year. Whether firms were optimizing a wrong model or making decisions in a model-free way, their knowledge had to be partial. Partial knowledge driven by incomplete learning can explain how firms behave suboptimally in a persistent way and challenges counterfactual exercises that rely on the assumption of model-based optimization. I suggest an approach of almost-optimization to calculate counterfactuals that are more likely to contain the truth.

We report the results of an online experiment studying preferences for giving and preferences for group-wide redistribution in small (4-person) and large (200-person) groups. We find that the desire to engage in voluntary giving decreases significantly with group size. However, voting for group-wide redistribution is precisely estimated to not depend on group size. Moreover, people’s perception of the size of their reference group is malleable, and affects their desire to give. These results suggest that government programs, such as progressive tax-and-transfer systems, can help satisfy other-regarding preferences for redistribution in a way that creating opportunities for voluntary giving do not.

Impacts of Home-Care Subsidies: Evidence from Quasi-Random Assignment (with Yuval Ofek-Shanny and Dan Zeltzer)
[Media: BFI Research Brief]
We study the impact of subsidizing home-based long-term care on recipients’ health and the labor supply of their working-age children. We use administrative data from Israel on the universe of welfare benefit applications linked with tax records of applicants and their adult children. To address the endogeneity of benefit recipients’ health status, we instrument for benefit receipt using the leniency of randomly assigned evaluators who assess the applicant’s functional status and determine benefit eligibility. We find that for compliers – applicants who receive subsidies only from more lenient evaluators – subsidizing home-based care has large adverse effects on recipient health but no detectable effects on the labor market outcomes of their children. The results are consistent with the crowd-out of self-care for the marginal recipient, highlighting the need to assess the heterogeneous effects of home-care subsidies.

Work in Progress

Goals, Expectations, and Performance (with Alex Wellsjo)

The Dynamics of Following Defaults (with Rawley Heimer and Alex Imas)

Behavioral Responses to GST and Firm Pricing (with Josh Dean)

Assistant Professor of Marketing and Willard Graham Faculty Scholar
University of Chicago
Booth School of Business

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