Author : Daniel Ryan Yavorsky
Publisher :
ISBN 13 :
Total Pages : 108 pages
Book Rating : 4.:/5 (116 download)
Book Synopsis Consumer Search in the U.S. Auto Industry by : Daniel Ryan Yavorsky
Download or read book Consumer Search in the U.S. Auto Industry written by Daniel Ryan Yavorsky and published by . This book was released on 2020 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Consumers may become aware of products and their characteristics either passively or actively. While passive awareness may arise through conversations with friends or consumption of advertising, active awareness occurs through a process of canvassing sellers. Consumers often refer to this activity as browsing or shopping; economists refer to this activity as search. This dissertation begins with a summary of the current economic models of consumer search and the econometric methods used to estimate those models. An emphasis will be placed on models that assume consumers search sequentially for product fit, as opposed to non-sequential search or search for observable product characteristics such as price. The bulk of this dissertation then provides an empirical analysis of consumer search through which I demonstrate identification of -- and provide a first estimation of -- a key parameter in sequential search models, the standard deviation of product fit. I devote separate chapters to detailed explanations of the data acquired for the analysis; the model, its estimation, and identification of this key parameter; and important post-estimation analysis including an assessment of model fit, calculations of consumer surplus and price elasticities, and implementation of counterfactual analyses. The setting of my empirical analysis is the U.S. automotive market. I assemble a unique data set containing individual-level smartphone geolocation data that inform me about dealership visits which I combine with proprietary DMV registration data that inform me of new vehicle purchases. I model consumers' dealership visits and purchase decisions using a discrete choice model of demand with optimal sequential search for product fit. In these models, the benefit of searching is measured by the standard deviation of the product fit. This benefit is parametrically identified by the functional form of the match-value, but in practice it is difficult to jointly estimate along with consumer search costs. I use the distance a consumer must travel to visit a dealership as an exogenous search cost shifter, which enables me to identify and estimate the standard deviation of the product fit. My results show that the benefit provided by dealerships to consumers is substantial and that failure to estimate the standard deviation of the product fit leads to biased estimates of search costs and consumer surplus, as well as to inaccurate predictions regarding the number of searches that consumers conduct. These effects can impact managerial decisions, as demonstrated through my counterfactual analyses.
