Structural Behavioral Finance
Paper Session
Sunday, Jan. 8, 2023 10:15 AM - 12:15 PM (CST)
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Chairs:
Tarun Ramadorai, Imperial College London - Alberto G. Rossi, Georgetown University
Arbitration with Uninformed Consumers
Abstract
This paper studies the impact of the arbitrator selection process on consumer outcomes. Using data from consumer arbitration cases in the securities industry over the past two decades, where we observe detailed information on case characteristics, the randomly generated list of potential arbitrators presented to both parties, the selected arbitrator, and case outcomes, we establish several motivating facts. These facts suggest that firms hold an informational advantage over consumers in selecting arbitrators, resulting in industry-friendly arbitration outcomes. We then develop and calibrate a quantitative model of arbitrator selection in which firms hold an informational advantage in selecting arbitrators. Arbitrators, who are compensated only if chosen, compete with each other to be selected. The model allows us to decompose the firms’ advantage into two components: the advantage of choosing pro-industry arbitrators from a given pool, and the equilibrium pro-industry tilt in the arbitration pool that arises because of arbitrator competition. Selecting arbitrators without the input of firms and consumers would increase consumer awards by $60,000 on average relative to the current system. Forty percent of this effect arises because the pool of arbitrators skews pro-industry due to competition. Even an informed consumer cannot avoid this pro-industry equilibrium effect. Counterfactuals suggest that redesigning the arbitrator selection mechanism for the benefit of consumers hinges on whether consumers are informed. Policies intended to benefit consumers, such as increasing arbitrator compensation or giving parties more choice would benefit informed consumers but hurt the uninformed.The Supply and Demand for Data Privacy: Evidence from Mobile Apps
Abstract
Since December 2020, Apple has required all apps to disclose their data collection practices by filling out privacy “nutrition” labels that are standardized and easy-to-read. We web-scrape these privacy labels and first document the following stylized facts regarding the supply of privacy: (i) 80% of the data collected are used for purposes unrelated to app functionality; (ii) top data collectors tend to be developed by public firms and enjoy a larger market share and better ratings; (iii) games, news, shopping, and entertainment apps collect more data for advertising and marketing purposes. Second, augmenting privacy labels with weekly app downloads and revenues, we study how consumers react to the disclosure of data collection practices. We exploit the staggered release of privacy labels and use the nonexposed Android version of each app to construct the counterfactual. After privacy label release, an average iOS app experi- ences a 12-15% drop in weekly downloads and revenues when compared to its Android counterpart, with an even stronger effect for more privacy-invasive and substitutable apps. Consumers in the US, UK, and Canada respond more negatively, suggesting that they are most averse to data collection. We also observe adverse stock market reactions, especially among firms that harvest more data. Our findings highlight the lack of consumer awareness of firms’ data collection practices as a key barrier to privacy protection.The Endowment Effect and Collateralized Loans
Abstract
The unwillingness to exchange an endowed good for another is a classical finding in behavioral economics. We focus on how the endowment effect among individual borrowers interacts with collateral requirements of loans. Many loans are collateralized using the assets financed by the loans themselves, e.g. home loans, car loans, lease-to-own, many business-equipment loans. Other loans require providing existing assets as collateral. This paper employs evidence from a field experiment in Kenya to address the following questions: Does the endowment effect cause borrowers to prefer collateralizing with new rather than old assets? Is this because borrowers under-estimate how “attached” they will come to feel to the new asset in the future? A structural model is used to quantify the implications for consumer welfare.Discussant(s)
Hunt Allcott
,
Microsoft Research
Alessandro Gavazza
,
London School of Economics
Claudia Robles-Garcia
,
Stanford University
Giorgia Barboni
,
University of Warwick
JEL Classifications
- G4 - Behavioral Finance
- G5 - Household Finance