Latest Research Publications

We document an increase in market power for politically active firms during times of heightened policy uncertainty, when their information and influence advantage is greater. The effect is long-lasting and stronger for large politically active firms. We show that relatively large investments during high uncertainty periods serve as a potential mechanism for gains in market power. Industries populated with politically active firms experience lower business dynamism and import penetration, consistent with active firms leveraging investment timing to restrict competition. Results suggest that political activism is a likely contributing factor to the dominance of large firms over the last two decades.

This paper studies whether investor composition affects the sovereign debt market. We construct a data set of sovereign debt holdings by foreign and domestic bank, nonbank private and official investors for 101 countries across three decades. Compared with other investors, private nonbank investors absorb a disproportionate share of the debt supply, and their demand for emerging market debt is most price responsive. A counterfactual analysis of emerging market sovereigns shows a 10% increase in debt leads to a 5.8% yield increase but an outsized 8.4% increase without nonbank investors. We conclude that sovereigns are vulnerable to the loss of nonbanks.

The literature on psychological contracts has focused on employees’ perceptions of their employers’ obligations, but not on employees’ perceptions of their own obligations. Hence, perceived general obligation has seldom been theorized. This study argues that workplace support (i.e., from the organization, supervisors, and coworkers) and morally relevant traits (i.e., moral identity, conscientiousness, and agreeableness) predict perceived general obligation, that perceived general obligation predicts performance outcomes, and that the effects vary across cultures. Meta-analytic data collected from 148 samples (N = 45,671) provide preliminary support for the proposed relationships. I also examine the incremental validity of perceived general obligation in predicting performance outcomes beyond other correlates (e.g., normative commitment, positive and negative affect), the mediating role of perceived general obligation in its nomological network, and alternative models for linking the study variables. This study therefore illustrates the value of perceived general obligation in psychological contract research. (PsycInfo Database Record (c) 2025 APA, all rights reserved)

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.