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
Revise and Resubmit The Review of Economic Studies
[NEW VERSION] [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.
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.
Preferences for Giving Versus Preferences for Redistribution (with Johanna Mollerstrom and Dmitry Taubinsky)
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. People’s perception of the size of their reference group is malleable, and affects their desire to engage in giving. 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 cannot.