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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.


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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.

Consumer inertia, the tendency to remain inactive, is a robust and well-documented phenomenon. However, if consumers are aware of their future inertia they can act to mitigate its effects on their outcomes. Using a large-scale randomized field experiment with a leading European newspaper we investigate consumer response to inertia-inducing subscription contracts. We study in the same setting both the actual inertia and the inertia consumers anticipate before it actually takes place. We vary the promotional subscription price, the duration, and whether the contract automatically renews by default, or not, after the promotional period. Indeed, we find strong inertia. Roughly half of auto-renewal contract takers continue to a full pay subscription after the promotional period, relative to the auto-cancel contract takers who rarely renew. Those added auto-renewal subscribers do not use their subscription to access the newspaper. However, consumers preempt inertia; 24%-36% of potential subscribers avoid subscribing on the first weeks after being offered an auto-renewal contract. Further, the share of subscribers over the two years after the promo is 10\% lower due to being offered the auto-renewal contract. Overall, even though auto-renewal generates a higher revenue in the short term, auto-renewal and auto-cancel are revenue equivalent after one year, but with fewer subscribers in auto-renewal.  Using a simple mixed-type model we quantify inertia, the share of inert readers, and the share of sophisticated readers who are aware of it. Our estimates suggest that half of the readers are inert. At most 41% of these inert individuals are unaware of their future inertia. Their inertia is equivalent to a 72% monthly chance of not cancelling an unwanted subscription. Finally, we show that counterfactual targeting of contract types to maximize revenue or subscriptions does not pick up sophistication. Our results highlight the often-ignored first order outcomes of offering inertia-inducing contracts: lower take up in the short- and long-run driven by sophisticated consumers.

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.

Work in Progress

New Model and Evidence on Goal-Setting as a Motivating Tool (with Alex Steiny Wellsjo)

Is Left-Digit Bias Intentional (with Peter Jones)

Effect of subsidized in-home care on elders mortality and children’s labor supply (with Yuval Ofek-Shanny and Dan Zeltzer)

Choice Architecture, Privacy Valuations, and Data Selection (with Tesary Lin)

Assistant Professor of Marketing

University of Chicago

Booth School of Business

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