学术论文

We study whether legal liability protection helps companies to recruit and retain high-quality independent directors. We conduct difference-in-differences analyses exploiting the 1999 Ninth Circuit Court of Appeals Ruling on the Silicon Graphics case, which substantially raised the bar for filing securities class action (SCA) lawsuits as a shock. We document supporting evidence for the talent attraction hypothesis by showing improvements in newly recruited independent director quality following the ruling, but only for candidates who are previously not exposed to SCA litigation risk. The effects are stronger for firms facing greater litigation risk ex ante or smaller local supplies of director candidates. Results are more evident for experience-based quality dimensions. We also analyze a sample of voluntary independent director departures and find little support for the talent retention hypothesis, suggesting that more complex factors enter into a director’s continuation decision once a director is already exposed to SCA litigation risk. A policy implication is that liability protection can be useful in attracting more unexposed high-quality candidates to the pool of public boards but does little to attract high-quality candidates who are already in the pool of public firms.

We consider a stylized incentive management problem over an infinite time horizon, where the principal hires an agent to provide services to customers. Customers request service in one of two ways: either via an online or a traditional offline channel. The principal does not observe the offline customers’ arrivals, nor does she observe whether the agent exerts (costly) effort that can increase the arrival rate of customers. This creates an opportunity for the agent (i) to divert cash (that is, to under-report the number of offline customers and pocket respective revenues) and also (ii) to shirk (that is, not to exert effort), thus leading to a novel and thus far unexplored double moral hazard problem. To address this problem, we formulate a constrained, continuous-time, stochastic optimal control problem and derive an optimal contract with a simple intuitive structure that includes a payment scheme and a potential termination time of the agent. We enrich the model to allow the principal to either (i) dynamically adjust the prices for the services in both channels or (ii) monitor the agent. Both tools help the principal to alleviate the double moral hazard problem. We derive respective optimal strategies for using those tools that guarantee the highest profits. We show that the worse the agent’s past performance is, the lower the prices should be set and the more the principal should monitor the agent.

We study the data-integrated, price-setting newsvendor problem in which the price–demand relationship is described by some parametric model with unknown parameters. We develop the operational data analytics (ODA) formulation of this problem that features a data-integration model and a validation model. The data-integration model consists of a class of functions called the operational statistics. Each operational statistic maps the available data to the ordering decision. The validation model finds, among the set of candidate operational statistics, the ordering decision that leads to the highest actual profit, which is unknown because of the unknown demand parameters. This ODA framework leads to a consistent estimate of the profit function with which we optimize the pricing decision. The derived quantity and price decisions demonstrate robust profit performance even when the sample size is very small in relation to the demand variability. Compared with the conventional approach with which the unknown parameters are estimated and then the decisions are optimized, the ODA framework produces significantly superior performance in the mean, standard deviation, and minimum of the profit, suggesting the robustness of the ODA solution especially in the small-sample regime.

We study the reliable (uncapacitated) facility location (RFL) problem in a data-driven environment where historical observations of random demands and disruptions are available. Owing to the combinatorial optimization nature of the RFL problem and the mixed-binary randomness of parameters therein, the state-of-the-art RFL models applied to the data-driven setting either suggest overly conservative solutions or become computationally prohibitive for large- or even moderate-size problems. In this paper, we address the RFL problem by presenting an innovative prescriptive model aiming to balance solution conservatism with computational efficiency. In particular, our model selects facility locations to minimize the fixed costs plus the expected operating costs approximated by a tractable data-driven estimator, which equals to a probabilistic upper bound on the intractable Kolmogorov distributionally robust optimization estimator. The solution of our model is obtained by solving a mixed-integer linear program that does not scale in the training data size. Our approach is proved to be asymptotically optimal, and offers a theoretical guarantee for its out-of-sample performance in situations with limited data. In addition, we discuss the adaptation of our approach when facing data with covariate information. Numerical results demonstrate that our model significantly outperforms several important RFL models with respect to both in-sample and out-of-sample performances as well as computational efficiency.

We examine how parity employee representation (PER) on corporate boards affects firms’ capital investments. In Germany, PER is legally mandated for firms with more than 2,000 domestic employees, but not for those below this threshold. Exploiting this discontinuity, we show that PER has heterogeneous effects on firms operating in diverse environments. For firms experiencing positive growth, PER increases their responsiveness to investment opportunities, suggesting that employee participation increases firms’ ability to exploit growth options. The effect through growth options is particularly salient in situations in which growth options are highly valuable. In contrast, for firms experiencing negative growth, PER reduces investment responsiveness, suggesting that employees resist the exercise of abandonment options. This effect is more pronounced in firms with high labor intensity, in which employees are likely to have a strong voice. Furthermore, operational risk moderates the effects of employee representation––the positive effect through growth options is attenuated in high-risk firms and the negative effect through abandonment options is exacerbated, thereby suggesting that employee board representatives are less interested in pursuing growth where businesses are relatively risky, and they protect their constituents more forcefully in high-risk environments. Moreover, the positive effect of PER on exploiting growth options is attenuated by collective bargaining agreements, while its effect through abandonment options is little affected. Evidence from stock price behavior further supports the viewpoint that employee representation affects capital investment. Our findings are relevant to policymakers as well as to firms’ various stakeholders.

We demonstrate that a broad set of asset pricing factors/anomalies are significantly exposed to “noise trader risk,” and the noise trader risk is priced in factor premia. We first confirm that mutual funds’ flow-induced trading of factors are uninformed, as they generate a large price impact on factor returns, followed by a complete reversal. We then show that asset pricing factors are subject to flow-driven noise trader risk in that expected variation (covariation) of flow-induced noise trading strongly forecasts variance (covariance) of factor returns. Importantly, factor premia are higher when flow-driven noise trader risk is expected to be more salient.

Problem definition: Scant empirical research studies the impact of drug shortages on the quality of medical care in hospitals. We study the causal relationship between drug shortages and medication errors using a natural experiment: hurricane damage to factories that produce heparin, an essential medication used frequently in hospitals. Methodology/results: We collect data on medication errors from the U.S. Food and Drug Administration’s Adverse Event Reporting System and drug sales from IQVIA’s National Sales Perspective. Applying the synthetic control method, we find that hurricane-related heparin supply disruptions increased medication error rates by 152%. In addition, we find significant spillover effects. The disruption increased medication error rates of a substitute drug, enoxaparin, by about 114%. Managerial implications: Our study uses an exogenous event to show that medication supply chain disruptions may negatively impact hospitals’ quality of care. We contribute to the literature by empirically linking the effects of supply chain disruptions to downstream service quality. Our results show that commonly used measures to mitigate the impact of drug shortages, such as substituting medications, may be unsafe. We discuss several measures that hospital managers may consider implementing to mitigate the potentially harmful effects of drug shortages.

This study investigates the operations of a novel service model, mobile charging, in which an e-commerce platform operator dispatches trucks equipped with charging piles to provide charging services for customers’ electric vehicles with low battery levels. We model the platform-customer interaction with a Stackelberg game and explicitly characterize customers’ optimal charging decisions under the platform’s various service plans. In a general scenario that involves charging requests within a transportation network, we develop a joint optimization model for the platform’s pricing and service fulfillment, utilizing an elaborately constructed augmented network. In addition, we explore a localized subproblem where multiple orders are concentrated within a specific region. With a simplified model structure, we propose an approximation algorithm with provable performance guarantees and further theoretically evaluate the resource consumption and associated platform benefits for serving the orders in each region. The results enable the batching of neighboring demands within the general scenario as a specialized node, resulting in a streamlined network with fewer nodes. Additionally, we can enhance the algorithm’s efficiency within the general framework through strategic prioritization of node visitations, leveraging the analytical findings. Furthermore, the insights derived can offer recommendations for the deployment of mobile charging and the selection of target areas in the initial stages. Overall, our study provides comprehensive guidelines and valuable insights for mobile charging operations.

Are landlocked countries at risk from sea-level rise? We identify a new mechanism where natural disaster shocks influence countries’ macroeconomic performance through cross-border trade spillovers. Analyzing global data on climate disasters, infrastructure, trade, and the macroeconomy from 1970 to 2019, we find that climate disasters impacting ports, critical infrastructure for international trade, reduce imports, exports, and economic output in both the affected country and its major trade partner (both upstream and downstream) countries. The GDP effects on main upstream and downstream countries are as large as those in directly impacted countries: While directly affected countries offset climate disaster damages with increased government spending and investment, trade partners do not. Effective adaptation efforts, including building climate-resilient infrastructure and implementing disaster relief measures, must account for the cross-border spillover effects of climate disasters.