Contractual Structure and Endogenous Matching in Partnershipso

We analyze optimal contracts and optimal matching patterns in a simple model of partnership where there is a double-sided moral hazard problem and potential partners differ in their productivity in two tasks. It is possible for one individual to accomplish both tasks (sole production) and there are no agency costs associated with this option but partnerships are a better option if comparative advantages are significant. We show that the presence of moral hazard can reverse the optimal matching pattern relative to the first best, and that even if partnerships are optimal for an exogenously given pair of types, they may not be observed in equilibrium when matching is endogenous, suggesting that empirical studies on agency costs are likely to underestimate their extent by focusing on the intensive margin and ignoring the extensive margin.Endogenous matching, partnerships, contractual structure

Learning by Doing vs. Learning from Others in a Principal-Agent Model

We introduce learning in a principal-agent model of stochastic output sharing under moral hazard. Without knowing the agents' preferences and technology the principal tries to learn the optimal agency contract. We implement two learning paradigms - social (learning from others) and individual (learning by doing). We use a social evolutionary learning algorithm (SEL) to represent social learning. Within the individual learning paradigm, we investigate the performance of reinforcement learning (RL), experience-weighted attraction learning (EWA), and individual evolutionary learning (IEL). Overall, our results show that learning in the principal-agent environment is very difficult. This is due to three main reasons: (1) the stochastic environment, (2) a discontinuity in the payoff space in a neighborhood of the optimal contract due to the participation constraint and (3) incorrect evaluation of foregone payoffs in the sequential game principal-agent setting. The first two factors apply to all learning algorithms we study while the third is the main contributor for the failure of the EWA and IEL models. Social learning (SEL), especially combined with selective replication, is much more successful in achieving convergence to the optimal contract than the canonical versions of individual learning from the literature. A modified version of the IEL algorithm using realized payoff evaluation performs better than the other individual learning models; however, it still falls short of the social learning's ability to converge to the optimal contract.learning, principal-agent model, moral hazard

Moral hazard and lack of commitment in dynamic economies

We revisit the role of limited commitment in a dynamic risk-sharing setting with private information. We show that a Markov-perfect equilibrium, in which agent and insurer cannot commit beyond the current period, and an infinitely-long contract to which only the insurer can commit, implement identical consumption, effort and welfare outcomes. Unlike contracts with full commitment by the insurer, Markov-perfect contracts feature non-trivial and determinate asset dynamics. Numerically, we show that Markov-perfect contracts provide sizable insurance, especially at low asset levels, and are able to explain a significant part of wealth inequality beyond what can be explained by self-insurance. The welfare gains from resolving the commitment friction are larger than those from resolving the moral hazard problem at low asset levels, while the opposite holds for high asset levels.Moral hazard ; Risk

Dynamic Financial Constraints: Distinguishing Mechanism Design From Exogenously Incomplete Regimes

We formulate and solve a range of dynamic models of constrained credit/insurance that allow for moral hazard and limited commitment. We compare them to full insurance and exogenously incomplete financial regimes (autarky, saving only, borrowing and lending in a single asset). We develop computational methods based on mechanism design, linear programming, and maximum likelihood to estimate, compare, and statistically test these alternative dynamic models with financial/information constraints. Our methods can use both cross-sectional and panel data and allow for measurement error and unobserved heterogeneity. We estimate the models using data on Thai households running small businesses from two separate samples. We find that in the rural sample, the exogenously incomplete saving only and borrowing regimes provide the best fit using data on consumption, business assets, investment, and income. Family and other networks help consumption smoothing there, as in a moral hazard constrained regime. In contrast, in urban areas, we find mechanism design financial/information regimes that are decidedly less constrained, with the moral hazard model fitting best combined business and consumption data. We perform numerous robustness checks in both the Thai data and in Monte Carlo simulations and compare our maximum likelihood criterion with results from other metrics and data not used in the estimation. A prototypical counterfactual policy evaluation exercise using the estimation results is also featured.National Science Foundation (U.S.)Templeton FoundationEunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.)Social Sciences and Humanities Research Council of CanadaBill & Melinda Gates Foundation (grant to the Consortium on Financial Systems and Poverty, University of Chicago

Computing Moral Hazard Programs With Lotteries Using Matlab

This paper provides a step-by-step hands on introduction to the techniques used in setting up and solving moral hazard problems with lotteries using Matlab. It uses a linear programming approach due to its relative simplicity and the high reliability of the available optimization algorithms.moral hazard, linear programming, matlab

A Social Network Model of COVID-19

I construct a dynamic social-network model of the COVID-19 epidemic which embeds the SIR epidemiological model onto a graph of person-to-person interactions. The standard SIR framework assumes uniform mixing of infectious persons in the population. This abstracts from important elements of realism and locality: (i) people are more likely to interact with members of their social networks and (ii) health and economic policies can affect differentially the rate of viral transmission via a person’s social network vs. the population as a whole. The proposed network-augmented (NSIR) model allows the evaluation, via simulations, of (i) health and economic policies and outcomes for all or subset of the population: lockdown/distancing, herd immunity, testing, contact tracing; (ii) behavioral responses and/or imposing or lifting policies at specific times or conditional on observed states. I find that viral transmission over a network-connected population can proceed slower and reach lower peak than transmission via uniform mixing. Network connections introduce uncertainty and path dependence in the epidemic dynamics, with a significant role for bridge links and superspreaders. Testing and contact tracing are more effective in the network model. If lifted early, distancing policies mostly shift the infection peak into the future, with associated economic costs. Delayed or intermittent interventions or endogenous behavioral responses generate a multi-peaked infection curve, a form of ‘curve flattening’, but may have costlier economic consequences by prolonging the epidemic duration

No bank, one bank, several banks : does it matter for investment?

This paper examines whether financial constraints affect firms’ investment decisions for older (larger) firms. We compare a group of unbanked firms to firms that rely on formal financing. Specifically, we combine data from the Spanish Mercantile Registry and the Bank of Spain Credit Registry (CIR) to classify firms according to their number of banking relations: one, several, or none. Our empirical strategy combines two approaches based on a common theoretical model. First, using a standard Euler equation adjustment cost approach to investment, we find that single-banked firms in our sample are most likely to exhibit cash flow sensitivity while unbanked firms are not. Second, using structural maximum likelihood estimation, we find that unbanked firms have a financial structure which is close to credit subject to moral hazard with unobserved effort, whereas single-banked firms have a financial structure which is more limited, as in an exogenously imposed traditional debt model. Firms in the unbanked category do not rely on bonds, equity, or formal financial markets, but rather on other firms in a financial or family-tied group (with either pyramidal or informal structure). We are among the first to document the importance of such groups in a European country. We control for reverse causality by treating bank relationships as endogenous and/or by appropriate stratifications of the sampl