Testing for Selection Bias

This paper uses the control function to develop a framework for testing for selection bias. The idea behind our framework is if the usual assumptions hold for matching or IV estimators, the control function identifies the presence and magnitude of potential selection bias. Averaging this correction term with respect to appropriate weights yields the degree of selection bias for alternative treatment effects of interest. One advantage of our framework is that it motivates when is appropriate to use more efficient estimators of treatment effects, such as those based on least squares or matching. Another advantage of our approach is that it provides an estimate of the magnitude of the selection bias. We also show how this estimate can help when trying to infer program impacts for program participants not covered by LATE estimates

Simple Tests for Selection: Learning More from Instrumental Variables

We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects. The tests allow researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. The tests are quite simple; undergraduates after an introductory econometrics class should be able to implement these tests. We illustrate our tests with two empirical applications: the impact of children on female labor supply from Angrist and Evans (1998) and the impact of training on adult women from the Job Training Partnership Act (JTPA) experiment

Simple Tests for Selection Bias: Learning More from Instrumental Variables

We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects. The tests allow researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. The tests are quite simple; undergraduates after an introductory econometrics class should be able to implement these tests. We illustrate our tests with two empirical applications: the impact of children on female labor supply from Angrist and Evans (1998) and the training of adult women from the Job Training Partnership Act (JTPA) experiment

Essays in Consumer Choice and Consumer Demand

In chapter 1, I study sales of larger packages with quantity surcharge. Sales of larger packages with quantity surcharges occur often in the consumer packaged goods industry. This phenomenon poses a challenge to rationalizing consumer behaviors because the same amount of an identical product can be bought at a cheaper price. I present evidence that consumers lose a considerable amount of money by purchasing quantity surcharged larger packages. I develop and estimate a structural econometric model that combines (i) rationally inattentive consumers with (ii) the address model of consumer demand in the product characteristics space. By simulating consumer demand using model parameter estimates, I decompose the contribution of information friction and preference heterogeneity over package sizes on sales of larger packages with quantity surcharges. The estimated model predicts that only 40% of sales of larger packages with quantity surcharges can be attributed to information friction. I suggest revenue-improving, nonlinear pricing schemes that preserve consumer welfare at the current level. Under the pricing schemes, retailers can raise their revenues by up to 18%, and the corresponding sales of larger packages with quantity surcharge triples. As a methodological contribution, I state and prove the theorem that allows estimating the Rational Inattention (RI) model as if estimating an augmented logit model. In chapter 2 (coauthored with Ali Hortacsu), we study the relation between logit demand systems and constant elasticity of substitution (CES) demand systems. We develop a characteristics based demand estimation framework for the Marshallian demand system obtained by solving a budget-constrained constant elasticity of substitution (CES) utility maximization problem. From our Marshallian CES demand system, we derive the same market share equation of Berry (1994), Berry et al. (1995)'s characteristics based logit demand system. Furthermore, our CES demand estimation framework can accommodate zero predicted and observed market shares by separating intensive and extensive margins, and allows a semiparametric estimation strategy that is flexible regarding the distribution of unobservable product characteristics. We apply the framework to scanner data on cola sales, where we show estimated demand curves can be upward sloping if zero market shares are not accommodated properly

Millennials and the take-off of craft brands: Preference formation in the U.S. beer industry

We conduct an empirical case study of the U.S. beer industry to analyze the dis-ruptive effects of locally manufactured craft brands on market structure, an increasingly common phenomenon in consumer packaged goods industries typically attributed to the emerging generation of adult millennial consumers. We document a generational share gap: millennials buy more craft beer than earlier generations. We test between two competing mechanisms: (i) persistent generational differences in tastes and (ii) differences in past expe-riences or consumption capital. Our test exploits a novel database tracking the geographic differences in the diffusion of craft breweries across the United States. Using a structural model of demand with endogenous consumption capital stock formation, we find that heterogene-ous consumption capital accounts for 86% of the generational share gap between millenni-als and baby boomers with the remainder explained by intrinsic generational differences in preferences. We predict the beer market structure will continue to fragment over the next decade, overturning a nearly century-old structure dominated by a small number of national brands. The attribution of the share gap to consumption capital shaped through availability on the supply side of the market highlights how barriers to entry, such as regulation and high traditional marketing costs, sustained a concentrated market structure

Millennials and the take-off of craft brands: Preference formation in the U.S. beer industry

We conduct an empirical case study of the U.S. beer industry to analyze the dis-ruptive effects of locally manufactured craft brands on market structure, an increasingly common phenomenon in consumer packaged goods industries typically attributed to the emerging generation of adult millennial consumers. We document a generational share gap: millennials buy more craft beer than earlier generations. We test between two competing mechanisms: (i) persistent generational differences in tastes and (ii) differences in past expe-riences or consumption capital. Our test exploits a novel database tracking the geographic differences in the diffusion of craft breweries across the United States. Using a structural model of demand with endogenous consumption capital stock formation, we find that heterogene-ous consumption capital accounts for 86% of the generational share gap between millenni-als and baby boomers with the remainder explained by intrinsic generational differences in preferences. We predict the beer market structure will continue to fragment over the next decade, overturning a nearly century-old structure dominated by a small number of national brands. The attribution of the share gap to consumption capital shaped through availability on the supply side of the market highlights how barriers to entry, such as regulation and high traditional marketing costs, sustained a concentrated market structure

Simple Tests for Selection: Learning More from Instrumental Variables

We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects (LATEs). Our setup allows researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. We show that it applies to the standard binary instrument case, as well as to experiments with imperfect compliance and fuzzy regression discontinuity designs, and we link it to broader discussions regarding instrumental variables. We illustrate the substantive value added by our framework with three empirical applications drawn from the literature