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Hilton Atlanta, 304
Industrial Organization Society
The Industrial Organization of Financial Markets
Friday, Jan. 4, 2019 8:00 AM - 10:00 AM
- Chair: Vivek Bhattacharya, Northwestern University
Arbitration with Uninformed Consumers
AbstractWe examine whether firms have an informational advantage in selecting arbitrators in consumer arbitration, and the impact of the arbitrator selection process on outcomes. We collect data containing roughly 9,000 arbitration cases in securities arbitration. Securities disputes present a good laboratory: the selection mechanism is similar to other major arbitration forums; arbitration is mandatory for all disputes, eliminating selection concerns; and the parties choose arbitrators from a randomly generated list. We first document that some arbitrators are systematically industry friendly while others are consumer friendly. Firms appear to utilize this information in the arbitrator selection process. Despite a randomly generated list of potential arbitrators, industry-friendly arbitrators are forty percent more likely to be selected than their consumer friendly counterparts. Better informed firms and consumers choose more favorable arbitrators. We develop and calibrate a model of arbitrator selection in which, like the current process, both the informed firms and uninformed consumers have control over the selection process. Arbitrators compete against each other for the attention of claimants and respondents. The model allows us to interpret our empirical facts in equilibrium and to quantify the effects of changes to the current arbitrator selection process on consumer outcomes. Competition between arbitrators exacerbates the informational advantage of firms in equilibrium resulting in all arbitrators slanting towards being industry friendly. Evidence suggests that limiting the respondent's and claimant's inputs over the arbitrator selection process could significantly improve outcomes for consumers.
Search and Screening in Credit Markets
AbstractWe study the interaction of search and the approval process in credit markets. We use a unique dataset that details search behavior for a large sample of mortgage borrowers, and loan application and rejection decisions. Our data reveal substantial dispersion in mortgage rates and search intensity. However, in contrast to predictions of standard search models, we find a novel non-monotonic relationship between search and realized prices: borrowers, who search a lot, obtain more expensive mortgages than borrowers' with less frequent search. We provide evidence that this occurs because lenders screen borrowers' creditworthiness, rejecting unworthy borrowers. This approval process differentiates consumer credit markets from other search markets. Based on these insights we build a model that combines search and screening in presence of asymmetric information. The model rationalizes the facts in the data and reveals that search behavior is determined not only by consumer sophistication but also by the approval process that relies on the underlying distribution of borrower quality. We estimate the parameters of the model and study several counterfactuals. We find that using overpayment by consumers as a proxy for consumer unsophistication may be misplaced since it partly represents rational search in presence of rejections. Moreover, the presence of better models with big data, could endogenously lead to more severe adverse selection in credit markets. Finally, place based policies, such as the Community Reinvestment Act, may lead to higher prices in equilibrium that reflect the endogenous search response rather than increased credit risk.
Regulatory Interventions in Consumer Search Markets: The Case of Credit Cards
AbstractData on U.S. credit card markets display a large dispersion of interest rates at which consumers borrow. To understand this dispersion, we build a search model with two novel features: search effort/inattention and product differentiation. We calibrate the model to match statistics on the interest rate distribution that borrowers pay. The model fits these data well. Our analysis implies that low search effort accounts for almost all the dispersion in interest rates, whereas product differentiation is negligible. We use the calibrated model to study regulatory interventions in credit markets, such as caps on interest rates and higher lenders' entry costs.
University of Chicago
Einaudi Institute for Economics and Finance
Massachusetts Institute of Technology
- L1 - Market Structure, Firm Strategy, and Market Performance
- L2 - Firm Objectives, Organization, and Behavior