JOB MARKET PAPER
Dynamic pricing models typically assume that consumers respond to marginal incentives. But how attentive are consumers to these incentives? I use a field experiment to assess the impact of dynamic pricing on residential electricity consumption and find strong evidence of inattention. I propose a model to interpret the results which suggests that the benefits of dynamic pricing may be substantively undermined by inattention. I also explore the role of automation in dynamic pricing, which holds the promise of reducing the cognitive choice frictions that cause inattention and lowering the effort cost of responding to price changes.
I report three primary findings. First, households---both with and without automation---significantly respond to a short term price increase by reducing consumption. Second, responses are very insensitive to the size of the price change. A price increase of 31 percent causes consumption to fall by 11 percent on average, whereas a price increase of 1,875 percent causes an average reduction of 13 percent. Third, automation causes responses that are five times larger than the average effect, but are still insensitive to the price level. The results suggest that households use simplifying heuristics when facing dynamic prices and that automation reduces effort costs, but does not resolve inattention. I apply the model to recover bounds on the price elasticity of demand and shed light on the potential attention costs of dynamic pricing.
"The Effect of Extreme Heat on Cognitive Ability: Evidence from High School Exit Exams" (with Maximilian Auffhammer and Catherine Wolfram)
This paper exploits plausibly exogenous daily weather variation to measure the reduced-form effect of outdoor temperature on student test scores. We study the California High School Exit Exam, a state-wide standardized test that measures aptitude in math and English-language arts. During the period we study, the exam featured real stakes since passing it was required to receive a high school diploma. We find a nonlinear relationship between maximum daily temperature and mean scores and pass rates in both subjects. The results show a significant negative impact for temperatures above 25C (77F) for the math assessment, but the results are insignificant for language arts. We also document heterogeneity by neighborhood income and find more pronounced effects for schools in areas with the lowest incomes.
WORK IN PROGRESS
"Experimentally Evaluating Machine Learned Targeting" (with Maximilian Balandat and Datong Zhou)
Characterizing treatment effect heterogeneity provides insights for understanding decision mechanisms, improving program efficiency through targeting, and extrapolating results beyond study settings. In this paper, we leverage machine learning techniques to develop a set of estimators that recover heterogeneity through individual treatment effects (ITEs)---average treatment effects by unit of treatment. We provide sufficient conditions under which our ITE estimates are unbiased, and develop variance estimates based on bootstrapping techniques. Since these ITE estimates are not causally identified, we verify their predictive performance causally using a two-stage field experiment in residential dynamic electricity pricing. In the first stage we use experimental pricing interventions to estimate ITEs and partition the sample by price responsiveness. In the second stage we randomly assign half of the sample to receive targeted pricing interventions based on the estimated responsiveness and the other half to a control group that receives non-targeted interventions. We find that targeting using the ITE heterogeneity causes a substantial increase in program efficiency. We further validate our method by verifying that the average treatment effect (ATE) estimate obtained by marginalizing the ITE estimates closely matches the causally identified experimental ATE with reduced variance. As a final application, we use the ITE heterogeneity to extrapolate the experimental findings to the broader population.
PAPERS FROM PREVIOUS RESEARCH INTERESTS
"Does Quantitative Easing Affect Market Liquidity?" (with Jens Christensen). 2016. FRBSF Working Paper 2013-26.
"Spectroscopy and Electrochemistry of Cytochrome P450 BM3-Surfactant Film Assemblies" (with Andrew K. Udit, Katherine D. Hagen, Peter J. Goldman, Andrew Star, Harry B. Gray, and Michael G. Hill). 2006. Journal of the American Chemical Society, 128(31), 10320-10325.
"Redox Couples of Inducible Nitric Oxide Synthase" (with Andrew K. Udit, Wendy Belliston-Bittner, Edith C. Glazer, Yen Hoant Le Nguyen, Michael G. Hill, Michael A. Marletta, David B. Goodin, and Harry B. Gray). 2005. Journal of the American Chemical Society, 127(32), 11212-11213.
"Do Fed TIPS Purchases Affect Market Liquidity?" (with Jens Christensen). FRBSF Economic Letter 2012-07 (March 5, 2012).
"Recent Trends in Small Business Lending" (with Elizabeth Laderman). FRBSF Economic Letter 2011-32 (October 17, 2011).
"Has the Treasury Benefited from Issuing TIPS?" (with Jens Christensen). FRBSF Economic Letter 2011-12 (April 18, 2011).