Dynamic Adverse Selection Research Articles

Dynamic Adverse Selection and Belief Update in Credit Markets

Dynamic Adverse Selection and Belief Update in Credit Markets

The Times They Are A-Changing: Experimenting with Dynamic Adverse Selection

We examine a common value dynamic matching environment where adverse selection accrues slowly over time. Theoretical best responses are therefore time varying, and the prior experimental literature suggests that sequential environments might lead to greater understanding of adverse selection in this dynamic setting. However, while a sophisticated minority in our experiment do condition on time and are close to a best response, the majority use a stationary response, even after extended experience. In an environment with persistent uncertainty, our results indicate that sequentiality is insufficient for the large majority of participants to recognize the effects of adverse selection. (JEL C78, C92, D82, D91)

Looking the Other Way: The Screening Role of (Weak) Internal Monitoring

Internal monitoring is a cardinal responsibility of the board of directors, many of which have been subject to relentless criticism of conceding too much power to the managers and allowing them too much latitude. We analyze the role of internal monitoring in a dynamic adverse selection model, in which managers have unobservable ability and must be given rents through a compensation contract in exchange for revealing their private information. Monitoring has two effects: an ex-post disciplining effect, whereby more intense monitoring limits the information advantage and the rents for managers with higher ability; and an ex-ante screening effect, whereby weaker monitoring allows firms to attract better managers. We show an optimal level of monitoring intensity that balances the disciplining effect and the screening effect exists, even when monitoring is intrinsically costless and can be made arbitrarily strong. We empirically test these predictions and find that less monitoring by the board is indeed associated with on average more capable managers, and the relationship between monitoring intensity and firm value is hump-shaped in the data. Although the lack of monitoring is often viewed as a failure of internal governance, our analysis suggests it is a plausible tactic for attracting and retaining talented managers.

Dynamic Adverse Selection and Belief Update in Credit Markets

We develop a dynamic model of debt contracts with adverse selection. Entrepreneurs borrow investment goods from lenders to run businesses whose returns depend on entrepreneurial productivity and common productivity. Entrepreneurial productivity is the entrepreneur's private information, and lenders construct beliefs about entrepreneurial productivity based on the entrepreneur's business operation history, common productivity history, and the terms of the contract. The model provides insights into the dynamic and cross-sectional relations between firm age and credit risk, persistency of the effects of a productivity shock, cyclical asymmetry of the business cycle, slow recovery after a crisis, and constructive and destructive economic downturns.

Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning

Pricing products such as used cars, houses, and artwork is often challenging, because each item is unique, and the seller, ex ante, lacks information about the demand for individual items. This paper develops a dynamic pricing model for products with significant item-specific demand uncertainty, in which a forward-looking seller learns about the item-specific demand through an initial assessment, as well as during the selling process. The model demonstrates how seller learning, through several mechanisms, can lead to the commonly observed downward trend in the prices of individual items. These mechanisms include the seller’s optimal adjustment of prices over time to account for the dynamic adverse selection of unsold items and the diminishing option value in future learning. The model is estimated using novel panel data of a leading used-car dealership. Counterfactual experiments show that the value of learning in the selling process is $203 per car. Conditional on subsequent learning in the selling process, the initial assessment further improves profit per car by $139. With the dealer’s net profit per car being about $1,150, these estimates suggest a potentially high return to taking an information-based approach toward pricing products with item-specific demand uncertainty. This paper was accepted by Juanjuan Zhang, marketing.

Dynamic Adverse Selection in Flood Insurance

The National Flood Insurance Program (NFIP) has been criticized for setting rates that inaccurately reflect flood risk, while on the other side of the market, studies have shown that homeowners typically hold inaccurate beliefs about their own risk. Despite cataloguing these sources of incomplete information, no research to date has studied asymmetric information in flood insurance. In this paper, I find evidence of adverse selection that emerges through dynamic changes in demand. I first show that experienced flood policy holders have higher claims conditional on rating factors relative to new purchasers. Initial buyers who drop their coverage have lower average costs than those that keep it. I show how this phenomenon can be understood as a form of dynamic adverse selection. To explore the possible sources and implications of dynamic adverse selection, I incorporate my empirical estimates into a model of insurance with learning to illustrate how much of flood insurance demand is driven by consumer-side risk type uncertainty. My findings suggest that 80% of initial take-up and two-thirds of consumer surplus stem from buyers’ uncertainty over their own risk relative to their knowledge after maintaining coverage for up to 15 years. In addition to their implications for flood insurance, my methods can be applied to understanding other markets that feature learning and dynamic demand processes.

Dynamic Incentives for Buy-Side Analysts

We develop a dynamic adverse selection model where a career-concerned buy-side analyst advises a fund manager about investment decisions. The analyst's ability is privately known, as is any information she learns over time. The manager wants to elicit information to maximize fund performance while also identifying and retaining high-skill analysts. We characterize the optimal dynamic contract, show that it has several features supported by empirical evidence, and derive novel testable implications. The fund manager's optimal contract both maximizes the value of information and screens out low-skill analysts by incentivizing the analyst to always provide honest advice.

Dynamic Adverse Selection and Liquidity

Does a larger fraction of informed trading generate more illiquidity, as measured by the bid--ask spread? We answer this question in the negative in the context of a dynamic dealer market where the fundamental value follows a random walk, provided we consider the long run (stationary) equilibrium. More informed traders tend to generate more adverse selection and hence larger spreads, but at the same time cause faster learning by the market makers and hence smaller spreads. This latter effect offsets the adverse selection effect when the trading frequency is equal to one, and dominates at larger frequencies.

Optimal monitoring in dynamic procurement contracts

Innovation partnerships are a public procurement procedure in the European Union which allows public authorities to bundle the development and supply of innovative goods in a single procurement project. I study optimal monitoring strategies for public authorities in innovation partnerships using a principal–agent model with dynamic adverse selection and moral hazard. I show that monitoring of investments in innovation can be substituted by monitoring of exogenous cost factors that become known during the production phase. Thus, innovation partnerships may also be beneficial instruments if innovative activity is hard to monitor.

Trading Dynamics with Private Buyer Signals in the Market for Lemons

We present a dynamic model of trading under adverse selection in which a seller sequentially meets buyers, each of whom receives a noisy signal about the quality of the seller’s asset and offers a price. We fully characterize the equilibrium trading dynamics and show that buyers’ beliefs about the quality of the asset can either increase or decrease over time, depending on the initial level. This result demonstrates how the introduction of private buyer signals enriches the set of trading patterns that can be accommodated within the framework of dynamic adverse selection, thereby broadening its applicability. We also examine the economic effects of search frictions and the informativeness of buyers’ signals in our model and discuss the robustness of our main insights in multiple directions.

Dynamic Adverse Selection and Liquidity

Dynamic Adverse Selection and Liquidity

Dynamic Adverse Selection: Time-Varying Market Conditions and Endogenous Entry

In this paper I analyze the effects of time-varying market conditions and endogenous entry on the equilibrium dynamics of markets plagued by adverse selection. I show that variation in gains from trade, stemming from market conditions, creates an option value and distorts liquidity when gains from trade are low. An improvement in market conditions triggers a wave of high-quality deals due to the preceding illiquidity and lack of incentives to signal quality. When gains from trade are high, the market is fully liquid; high prices and no delay in trade attract low-grade assets, and the average quality deteriorates. My analysis also reveals that illiquidity can act as a remedy as well as a cause of inefficiency: partial illiquidity allows for screening of assets and restores efficient entry incentives. I demonstrate model implications using several applications: early stage financing, initial public offerings, and private equity buyouts.

Dynamic adverse selection with a patient seller

This paper considers dynamic bilateral trade with short-term commitment. We show that, when the seller is more patient than the buyer, there exist systematic differences between the optimal selling and renting mechanisms. While the former consists of simple price-posting, the latter induces the buyer to choose between a secure- and a random-delivery contract. Allowing for mechanisms more general than price-posting reduces the seller's cost of learning the buyer's valuation in the renting case. Renting leads to more learning than selling but (unless the horizon is sufficiently long) only when general mechanisms are available. Our results contrast with the common view that the restriction to price-posting is innocuous and that informational asymmetries are more persistent under renting than under selling.

Investing in Trading Opportunities in Dynamic Adverse Selection

I analyse a market with asymmetric information, interdependent values and trade frictions. The frictions can be reduced at a cost, e.g. by increasing attention, search, lobbying or computing power. For some parameters, there is a unique equilibrium in which an increase in the gains from trade delays trade. A rise in the trading surplus may reduce the payoff of all types. The driving force is a novel feedback loop between the endogenous trading frictions and the signalling motive.

Signaling and Experimentation in Dynamic Markets

Consider the case of a seller that can signal its own product quality but faces uncertainty about the cost of signaling. If signaling reveals information about its cost which can be used in future trades, how does learning affect signaling incentives and the informativeness of signals for buyers? Learning can reduce the value of signaling by introducing noisiness in the interpretation of future signaling decisions--the dynamic adverse selection effect. We construct and characterize properties of equilibria in which intermediate seller's types experiment with signaling. These experimental signaling equilibria are ranked with more experimentation leading to lower welfare.

Inducing Environmental Disclosures: A Dynamic Mechanism Design Approach

This paper studies the design of voluntary disclosure regulations for a firm that faces a stochastic environmental hazard. The occurrence of such a hazard is known only to the firm. The regulator, if finding a hazard, collects a fine and mandates the firm to perform costly remediation that reduces the environmental damage. The regulator may inspect the firm at any time to uncover the hazard. However, because inspections are costly, the regulator also offers a reward to the firm for voluntarily disclosing the hazard. The reward corresponds to either a subsidy or a reduced fine, depending on whether it is positive or negative. Thus, the regulator needs to dynamically determine the reward and inspection policy that minimizes expected societal cost in the long run. We model this problem as a dynamic adverse selection problem with costly state verification in continuous time. Despite the complexity and generality of this setup, we show that the optimal regulation policy follows a very simple cyclic structure, which we fully characterize in closed form. Specifically, the regulator runs scheduled inspections periodically. After each inspection, the reward level decreases over time until a subsequent inspection takes place. If a hazard is not revealed, the reward level is reset to a high level, restarting the cycle. In contrast to the reward level, the mandated remediation level is constant over time. Nonetheless, when subsidies are not allowed in the industry, we show that the regulator should dynamically adjust this remediation level, which then acts as a substitute for a subsidy. Our analysis further reveals that optimal inspection frequency increases not only when the inspection accuracy decreases, but also when the penalty for not disclosing the hazard increases.

Dynamic adverse selection and the supply size

In this paper we examine the problem of dynamic adverse selection in a stylized market where the quality of goods is a seller׳s private information while the realized distribution of qualities is public information. We obtain that full trade occurs in every dynamic competitive equilibrium. Moreover, we show that if prices can be conditioned on the supply size then a dynamic competitive equilibrium always exists, while it fails to exist if prices cannot be conditioned on the supply size and the frequency of exchanges is high enough. We conclude that the possibility to condition prices on the supply size allows us to reach efficiency in the limit for exchanges becoming more and more frequent, while otherwise the welfare loss due to delays of exchanges remains bounded away from zero.

Information Sharing in a Manufacturer–Supplier Relationship: Suppliers' Incentive and Production Efficiency

While there have been vast discussions on the materialistic benefits of continuous improvement from the Toyota and Honda experiences, the academic literature pays little attention to information sharing. In this study, we construct a dynamic adverse selection model in which the supplier privately observes her production efficiency, and in the contractual duration the manufacturer obtains an informative but imprecise signal regarding this private efficiency. We show that despite the disclosure of proprietary information, information sharing may benefit the supplier; the supplier's voluntary participation is more likely to occur when the shared information is rather imprecise. On the other hand, our analysis also reveals that this information sharing unambiguously gives rise to an upward push of the production quantity, and may sometimes lead to an upward distortion that ultimately hurts the supply chain. We also document the non‐trivial impact of the timing of information sharing on the supplier's incentive to participate.

In This Issue

In This Issue

Analysis of a Dynamic Adverse Selection Model with Asymptotic Efficiency

This paper studies an infinite horizon adverse selection model with an underlying two-state Markov decision process. It introduces a novel approach that constructs the continuation payoff frontier exactly, as the fixed point of a functional operator. If the model supports an incentive-compatible first-best (ICFB) contract, the continuation payoff frontier can be efficiently constructed, and the principal's optimal contracts converge to ICFB contracts over time. The existence of an ICFB contract is implied by the common assumption of private values and is a fairly general scenario. The paper generalizes some key findings in the dynamic adverse selection literature to this scenario.