NBER Working Papers and Publications
|July 2020||A Graphical Lasso Approach to Estimating Network Connections: The Case of U.S. Lawmakers|
with , , : w27557
In this paper, we propose a new approach to the estimation of social networks and we apply it to the estimation of productivity spillovers in the U.S. Congress. Social networks such as the social connections among lawmakers are not generally directly observed, they can be recovered only using the observable outcomes that they contribute to determine (such as, for example, the legislators’ effectiveness). Moreover, they are typically stable for relatively short periods of time, thus generating only short panels of observations. Our estimator has three appealing properties that allows it to work in these environments. First, it is constructed for “small” asymptotic, thus requiring only short panels of observations. Second, it requires relatively nonrestrictive sparsity assumptions for identi...
|June 2019||On Testing Continuity and the Detection of Failures|
with : w26016
Estimation of discontinuities is pervasive in applied economics: from the study of sheepskin effects to prospect theory and “bunching” of reported income on tax returns, models that predict discontinuities in outcomes are uniquely attractive for empirical testing. However, existing empirical methods often rely on assumptions about the number of discontinuities, the type, the location, or the underlying functional form of the model. We develop a nonparametric approach to the study of arbitrary discontinuities — point discontinuities as well as jump discontinuities in the nth derivative, where n = 0,1,... — that does not require such assumptions. Our approach exploits the development of false discovery rate control methods for lasso regression as proposed by G’Sell et al. (2015). This framew...
|August 2017||Mostly Harmless Regulation? Electronic Cigarettes, Public Policy and Consumer Welfare|
with , , : w23710
Electronic cigarettes are a less harmful alternative to combustible cigarettes. We analyze data on e-cigarette choices in an online experimental market. Our data and mixed logit model capture two sources of consumer optimization errors: over-estimates of the relative risks of e-cigarettes; and present bias. Our novel data and policy analysis make three contributions. First, our predictions about e-cigarette use under counter-factual policy scenarios provide new information about current regulatory tradeoffs. Second, we provide empirical evidence about the role consumer optimization errors play in tobacco product choices. Third, we contribute to behavioral welfare analysis of policies that address individual optimization errors.