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"PRIVATE INFORMATION"
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When Private Information Settles the Bill: Money and Privacy in Google’s Market for Smartphone Applications
We shed light on a money-for-privacy trade-off in the market for smartphone applications (“apps”). Developers offer their apps at lower prices in return for greater access to personal information, and consumers choose between low prices and more privacy. We provide evidence for this pattern using data from 300,000 apps obtained from the Google Play Store (formerly Android Market) in 2012 and 2014. Our findings show that the market’s supply and demand sides both consider an app’s ability to collect private information, measured by the apps’s use of privacy-sensitive permissions: (1) cheaper apps use more privacy-sensitive permissions; (2) given price and functionality, demand is lower for apps with sensitive permissions; and (3) the strength of this relationship depends on contextual factors, such as the targeted user group, the app’s previous success, and its category. Our results are robust and consistent across several robustness checks, including the use of panel data, a difference-in-differences analysis, “twin” pairs of apps, and various measures of privacy-sensitivity and app demand.
This paper was accepted by Anandhi Bharadwaj, information systems.
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PREFERENCES FOR TRUTH-TELLING
Private information is at the heart of many economic activities. For decades, economists have assumed that individuals are willing to misreport private information if this maximizes their material payoff. We combine data from 90 experimental studies in economics, psychology, and sociology, and show that, in fact, people lie surprisingly little. We then formalize a wide range of potential explanations for the observed behavior, identify testable predictions that can distinguish between the models, and conduct new experiments to do so. Our empirical evidence suggests that a preference for being seen as honest and a preference for being honest are the main motivations for truth-telling.
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ROBUST PREDICTIONS IN GAMES WITH INCOMPLETE INFORMATION
We analyze games of incomplete information and offer equilibrium predictions that are valid for, and in this sense robust to, all possible private information structures that the agents may have. The set of outcomes that can arise in equilibrium for some information structure is equal to the set of Bayes correlated equilibria. We completely characterize the set of Bayes correlated equilibria in a class of games with quadratic payoffs and normally distributed uncertainty in terms of restrictions on the first and second moments of the equilibrium action—state distribution. We derive exact bounds on how prior knowledge about the private information refines the set of equilibrium predictions. We consider information sharing among firms under demand uncertainty and find new optimal information policies via the Bayes correlated equilibria. We also reverse the perspective and investigate the identification problem under concerns for robustness to private information. The presence of private information leads to set rather than point identification of the structural parameters of the game.
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PRIVATE INFORMATION AND INSURANCE REJECTIONS
Across a wide set of nongroup insurance markets, applicants are rejected based on observable, often high-risk, characteristics. This paper argues that private information, held by the potential applicant pool, explains rejections. I formulate this argument by developing and testing a model in which agents may have private information about their risk. I first derive a new no-trade result that theoretically explains how private information could cause rejections. I then develop a new empirical methodology to test whether this no-trade condition can explain rejections. The methodology uses subjective probability elicitations as noisy measures of agents' beliefs. I apply this approach to three nongroup markets: long-term care, disability, and life insurance. Consistent with the predictions of the theory, in all three settings I find significant amounts of private information held by those who would be rejected; I find generally more private information for those who would be rejected relative to those who can purchase insurance, and I show it is enough private information to explain a complete absence of trade for those who would be rejected. The results suggest that private information prevents the existence of large segments of these three major insurance markets.
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Bargaining with evolving private information
I study how the arrival of new private information affects bargaining outcomes. A seller makes offers to a buyer. The buyer is privately informed about her valuation, and the seller privately observes her stochastically changing cost of delivering the good. Prices fall gradually at the early stages of negotiations, and trade is inefficiently delayed. The first-best is implementable via a mechanism, whereas all equilibrium outcomes of the bargaining game are inefficient.
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AN EFFICIENT DYNAMIC MECHANISM
This paper constructs an efficient, budget-balanced, Bayesian incentive-compatible mechanism for a general dynamic environment with quasilinear payoffs in which agents observe private information and decisions are made over countably many periods. First, under the assumption of \"private values\" (other agents' private information does not directly affect an agent's payoffs), we construct an efficient, ex post incentive-compatible mechanism, which is not budget-balanced. Second, under the assumption of \"independent types\" (the distribution of each agent's private information is not directly affected by other agents' private information), we show how the budget can be balanced without compromising agents' incentives. Finally, we show that the mechanism can be made self-enforcing when agents are sufficiently patient and the induced stochastic process over types is an ergodic finite Markov chain.
Journal Article
Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning
We study dynamic unstructured bargaining with deadlines and one-sided private information about the amount available to share (the “pie size”). Using mechanism design theory, we show that given the players’ incentives, the equilibrium incidence of bargaining failures (“strikes”) should increase with the pie size, and we derive a condition under which strikes are efficient. In our setting, no equilibrium satisfies both equality and efficiency in all pie sizes. We derive two equilibria that resolve the trade-off between equality and efficiency by favoring either equality or efficiency. Using a novel experimental paradigm, we confirm that strike incidence is decreasing in the pie size. Subjects reach equal splits in small pie games (in which strikes are efficient), while most payoffs are close to either the efficient or the equal equilibrium prediction, when the pie is large. We employ a machine learning approach to show that bargaining process features recorded early in the game improve out-of-sample prediction of disagreements at the deadline. The process feature predictions are as accurate as predictions from pie sizes only, and adding process and pie data together improves predictions even more.
Data are available at
https://doi.org/10.1287/mnsc.2017.2965
.
This paper was accepted by Uri Gneezy, behavioral economics.
Journal Article
When Salespeople Manage Customer Relationships
At many firms, incentivized salespeople with private information about customers are responsible for customer relationship management. Although incentives motivate sales performance, private information can induce moral hazard by salespeople to gain compensation at the expense of the firm. The authors investigate the sales performance–moral hazard trade-off in response to multidimensional performance (acquisition and maintenance) incentives in the presence of private information. Using unique panel data on customer loan acquisition and repayments linked to salespeople from a microfinance bank, the authors detect evidence of salesperson private information. Acquisition incentives induce salesperson moral hazard, leading to adverse customer selection, but maintenance incentives moderate it as salespeople recognize the negative effects of acquiring low-quality customers on future payoffs. Critically, without the moderating effect of maintenance incentives, the adverse selection effect of acquisition incentives overwhelms the sales-enhancing effects, clarifying the importance of multidimensional incentives for customer relationship management. Reducing private information (through job transfers) hurts customer maintenance but has greater impact on productivity by moderating adverse selection at acquisition. This article also contributes to the recent literature on detecting and disentangling customer adverse selection and customer moral hazard (defaults) with a new identification strategy that exploits the time-varying effects of salesperson incentives.
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FISCAL RULES AND DISCRETION UNDER PERSISTENT SHOCKS
This paper studies the optimal level of discretion in policymaking. We consider a fiscal policy model where the government has time-inconsistent preferences with a present bias toward public spending. The government chooses a fiscal rule to trade off its desire to commit to not overspend against its desire to have flexibility to react to privately observed shocks to the value of spending. We analyze the optimal fiscal rule when the shocks are persistent. Unlike under independent and identically distributed shocks, we show that the ex ante optimal rule is not sequentially optimal, as it provides dynamic incentives. The ex ante optimal rule exhibits history dependence, with high shocks leading to an erosion of future fiscal discipline compared to low shocks, which lead to the reinstatement of discipline. The implied policy distortions oscillate over time given a sequence of high shocks, and can force the government to accumulate maximal debt and become immiserated in the long run.
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