Explore the vast range of titles available.

We study a continuous-time principal—agent model in which a risk-neutral agent with limited liability must exert unobservable effort to reduce the likelihood of large but relatively infrequent losses. Firm size can be decreased at no cost or increased subject to adjustment costs. In the optimal contract, investment takes place only if a long enough period of time elapses with no losses occurring. Then, if good performance continues, the agent is paid. As soon as a loss occurs, payments to the agent are suspended, and so is investment if further losses occur. Accumulated bad performance leads to downsizing. We derive explicit formulae for the dynamics of firm size and its asymptotic growth rate, and we provide conditions under which firm size eventually goes to zero or grows without bounds.

This paper presents a new method for the analysis of moral hazard principal-agent problems. The new approach avoids the stringent assumptions on the distribution of outcomes made by the classical first-order approach and instead only requires the agent's expected utility to be a rational function of the action. This assumption allows for a reformulation of the agent's utility maximization problem as an equivalent system of equations and inequalities. This reformulation in turn transforms the principal's utility maximization problem into a nonlinear program. Under the additional assumptions that the principal's expected utility is a polynomial and the agent's expected utility is rational in the wage, the final nonlinear program can be solved to global optimality. The paper also shows how to first approximate expected utility functions that are not rational by polynomials, so that the polynomial optimization approach can be applied to compute an approximate solution to nonpolynomial problems. Finally, the paper demonstrates that the polynomial optimization approach extends to principal-agent models with multidimensional action sets.

Most research linking compensation to strategy relies on agency theory economics and focuses on executive pay. We instead focus on the strategic compensation of nonexecutive employees, arguing that while agency theory provides a useful framework for analyzing compensation, it fails to consider several psychological factors that increase costs from performance-based pay. We examine how psychological costs from social comparison and overconfidence reduce the efficacy of individual performance-based compensation, building a theoretical framework predicting more prominent use of team-based, seniority-based, and flatter compensation. We argue that compensation is strategic not only in motivating and attracting the worker being compensated but also in its impact on peer workers and the firm's complementary activities. The paper discusses empirical implications and possible theoretical extensions of the proposed integrated theory.

We consider discrete-time dynamic principal-agent problems with continuous choice sets and potentially multiple agents. We prove the existence of a unique solution for the principal's value function only assuming continuity of the functions and compactness of the choice sets. We do this by a contraction mapping theorem and so also obtain a convergence result for the value function iteration. To numerically compute a solution for the problem, we have to solve a collection of static principal-agent problems at each iteration. As a result, in the discrete-time setting solving the static problem is the difficult step. If the agent's expected utility is a rational function of his action, then we can transform the bi-level optimization problem into a standard nonlinear program. The final results of our solution method are numerical approximations of the policy and value functions for the dynamic principal-agent model. We illustrate our solution method by solving variations of two prominent social planning models from the economics literature.

The paper analyzes dynamic principal-agent models with short period lengths. The two main contributions are: (i) an analytic characterization of the values of optimal contracts in the limit as the period length goes to 0, and (ii) the construction of relatively simple (almost) optimal contracts for fixed period lengths. Our setting is flexible and includes the pure hidden action or pure hidden information models as special cases. We show how such details of the underlying information structure affect the optimal provision of incentives and the value of the contracts. The dependence is very tractable and we obtain sharp comparative statics results. The results are derived with a novel method that uses a quadratic approximation of the Pareto boundary of the equilibrium value set.

Conjugate duality relationships are pervasive in matching and implementation problems and provide much of the structure essential for characterizing stable matches and implementable allocations in models with quasilinear (or transferable) utility. In the absence of quasilinearity, a more abstract duality relationship, known as a Galois connection, takes the role of (generalized) conjugate duality. While weaker, this duality relationship still induces substantial structure. We show that this structure can be used to extend existing results for, and gain new insights into, adverse-selection principalagent problems and two-sided matching problems without quasilinearity.

Despite its central role in the theory of incentives, empirical evidence of a trade-off between risk and incentives remains scarce. We reexamine this trade-off in a workplace lab environment and find that, in line with theory, principals increase fixed pay while lowering performance pay when the relationship between effort and output is noisier. Unexpectedly, agents produce substantially more in the noisy environment than in the baseline despite weaker incentives. In addition, principals’ earnings are significantly higher in the noisy environment. We show that these findings can be accounted for when agents maximize a non-CARA utility function or when they exhibit loss aversion. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2017.2914 . This paper was accepted by Uri Gneezy, behavioral economics.

This paper analyzes the case of a principal who wants to provide an agent with proper incentives to explore a hypothesis that can be either true or false. The agent can shirk, thus never proving the hypothesis, or he can avail himself of a known technology to produce fake successes. This latter option either makes the provision of incentives for honesty impossible or does not distort its costs at all. In the latter case, the principal will optimally commit to rewarding later successes even though he only cares about the first one. Indeed, after an honest success, the agent is more optimistic about his ability to generate further successes. This, in turn, provides incentives for the agent to be honest before a first success.

Using a gift-exchange experiment, we show that the ability of reciprocity to overcome incentive problems inherent in principal–agent settings is greatly reduced when the agent’s effort is distorted by random shocks and transmitted imperfectly to the principal. Specifically, we find that gift exchange contracts without shocks encourage effort and wages well above standard predictions. However, the introduction of random shocks reduces wages and effort, regardless of whether the shocks can be observed by the principal. Moreover, the introduction of shocks significantly reduces the probability of fulfilling the contract by the agent, the payoff of the principal, and total welfare. Therefore, our findings demonstrate that random shocks place an important bound on the ability of gift exchange to overcome principal–agent problems. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2177 . This paper was accepted by John List, behavioral economics.

We test for three-way complementarities among information technology (IT), performance pay, and human resource (HR) analytics practices. We develop a principal-agent model examining how these practices work together as an incentive system that produces a larger productivity premium when the practices are implemented in concert rather than separately. We assess our model by combining fine-grained data on human capital management (HCM) software adoption over 11 years with detailed survey data on incentive systems and HR analytics practices for 189 firms. We find that the adoption of HCM software is greatest in firms that have also adopted performance pay and HR analytics practices. Furthermore, HCM adoption is associated with a large productivity premium when it is implemented as a system of organizational incentives, but has less benefit when adopted in isolation. The system of three-way complements produces disproportionately greater benefits than pairwise interactions, highlighting the importance of including all three complements. Productivity increases significantly when the HCM systems \"go live\" but not when they are purchased, which can be years earlier. This helps rule out reverse causality as an explanation for our findings. This paper was accepted by Sandra Slaughter, information systems.