Joseph LeCates

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Welcome

I am an Economist for the Joint Committee on Taxation, providing official, nonpartisan revenue estimates of tax legislation considered by Congress. I received my Ph.D. in Economics from Cornell University in 2012 and M.A. and A.B. in Economics from the University of Georgia in 2006. After receiving my Ph.D. I joined Analysis Group as an Associate consultant to work on complex legal and business issues using economic and statistical methods. I have designed and managed the implementation of quantitative analyses using a wide variety of statistical methods and provided interpretation of results in the context of economic models. In March 2015 I moved to the Joint Committee to pursue active policy-related work. I am enthusiastic about applying and adapting methods to link data availability to the data generating process for causal inference.

My professional and research interests center on the analysis of large and small scale data, in particular that of business and consumer behavior. In my case work at Analysis Group I have analyzed existing data as well as drafted and reviewed novel surveys to evaluate an array of behavior and outcomes in industries as diverse as packaged goods, pharmaceuticals, mobile technology, telecommunications, automotive components, and medical devices. My own research has investigated the impact of heaped responses in large, population-based surveys. Additionally, I developed a model of consumer learning across bundled goods and have investigated how physicians learn based on choice of therapies. More information about my research can be found on the Research page.

Current Research

"Accounting for Inaccuracies in Retrospective Data: A Monte Carlo Study of Smoking Cessation "

with Donald Kenkel (Cornell University and NBER) and Feng Liu (Shanghai University of Finance and Economics).

Abstract: Even when contemporaneous data do not exist, retrospectively collected information provides opportunities to analyze the determinants of past behaviors. However, such data are often plagued by "heaping, the tendency of survey responses to be concentrated at certain values due to recall bias. In the context of a discrete-time hazard model, heaping results in misclassification of a binary outcome, which may substantially bias estimated regression coefficients and marginal effects. We present a model of the heaping process in a discrete-time hazard setting with significant heaping in respondent recall: smoking cessation. The 2002 Current Population Survey Tobacco Use Supplement provides a basis for a Monte Carlo analysis to quantify the bias introduced from heaping and compare several methods proposed to account for such bias. Results suggest that the bias in estimated regression coefficients and marginal effects in a discrete-time hazard model of smoking cessation are modest: less than 7% difference for estimated coefficients and 5% difference for marginal effects. Methods intending to account for such bias typically performed worse than the naïve model that ignores heaping. However, additional simulations of a policy intervention suggest that under some circumstances the biases from heaping are substantial.