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.
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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! August 2023]
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.
Economics typically assumes that firms use a model of the environment to choose optimal actions. I use a reform that restricted the set of admissible prices to test this assumption. Specifically, a reform in Israel limited prices to end with X0 as the cents digits (e.g. 2.90 but not 2.99). When consumers are left-digit biased, demand drops at round numbers, hence optimal pricing prescribes bunching at just-below prices and avoiding round prices. Israeli supermarket chains respond to left-digit bias in the long-run and act as if they know this demand structure, setting just-below prices for 45% of prices. This response is consistent with awareness of the bias; However, it implies underestimation of its magnitude since estimated demand should lead to even higher shares of 99-endings. Further, following the reform, 20% of prices were round (e.g. 3.00). If firms were model-based their response to the reform would have been to update immediately according to their beliefs and avoid round prices; However, firms set clearly dominated prices for almost a year, re-learning something they seemed to know. Further, price changes at the product-store level were that 00-endings changed into 90-endings but 90-endings were absorbing. Together these findings suggest that firms learn in a model-free way, which may lead them to be model-free decision makers. Model-free incomplete learning can explain how firms behave sub-optimally in a persistent way and challenges counterfactual exercises that rely on the assumption of model-based optimization.
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.