Essays in Industrial Organization and Applied Econometrics

Abstract

This dissertation comprises three essays on empirical industrial organization (IO) and applied econometrics. The first and third chapters focus on identification approaches in structural models, with the first chapter dedicated to addressing limitations in demand modeling, while the third chapter studies identification in a triangular two-equation system. The second chapter applies modern econometric tools to understand policy-related topics in IO. The first chapter deals with identification in structural demand modeling, and generalizes the current framework in the literature to achieve a more accurate estimation of differentiated products demand. Within the framework of Berry (1994) and Berry, Levinsohn, and Pakes (1995), the existing empirical industrial organization literature often assumes that market size is observed. However, the presence of an unobservable outside option is a common source of mismeasurement. Measurement errors in market size lead to inconsistent estimates of elasticities, diversion ratios, and counterfactual simulations. I explicitly model the market size, and prove point identification of the market size model along with all demand parameters in a random coefficients logit (BLP) model. No additional data beyond what is needed to estimate standard BLP models is required. Identification comes from the exogenous variation in product characteristics across markets and the nonlinearity of the demand system. I apply the method to a merger simulation in the carbonated soft drinks (CSD) market in the US, and find that assuming a market size larger than the true estimated size would underestimate merger price increases. Understanding consumer demand is not only central to studying market structure and competition but also relevant to the study of public policy such as taxation. In the second chapter, we examine household demand for sugar-sweetened beverages (SSB) in the U.S. Our goal is to understand the distributional effect of soda taxes across demographic groups and market segments (at-home versus away-from-home). Using a novel dataset that includes at-home and away-from-home food purchases, we study who is affected by soda taxes. We nonparametrically estimate a random coefficient nested logit model to exploit the rich heterogeneity in preferences and price elasticities across households, including SNAP participants and non-SNAP-participant poor. By simulating its impacts, we find that soda taxes are less effective away-from-home while more effective at-home, especially by targeting the total sugar intake of the poor, those with high total dietary sugar, and households without children. Our results suggest that ignoring either segment can lead to biased policy implications. In the final chapter, we show that a standard linear triangular two equation system can be point identified, without the use of instruments or any other side information. We find that the only case where the model is not point identified is when a latent variable that causes endogeneity is normally distributed. In this non-identified case, we derive the sharp identified set. We apply our results to Acemoglu and Johnson's (2007) model of life expectancy and GDP, obtaining point identification and comparable estimates to theirs, without using their (or any other) instrument.