学术论文

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.

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.

We analyze a single-factor setting in which there is private information regarding cash flows as well as their betas. Private information about betas, together with market makers’ risk aversion and mean betas’ nonnegativity, implies a nonlinear price schedule whose stochastic slope covaries positively with order flow when the expected factor payoff is positive and vice versa. We predict a negative relation between the covariance and expected returns and an attenuation of the beta anomaly in asset returns after accounting for this relation. Empirical tests confirm these predictions.

We uncover the major drivers of macro aggregates and the real exchange rate at business cycle frequencies in Group of Seven countries. The estimated drivers of key macro variables resemble each other and account for a modest fraction of the real exchange rate variances. Dominant drivers of the real exchange rate are orthogonal to main drivers of business cycles, generate a significant deviation of the uncovered interest parity condition, and lead to small movements in net exports. We use these facts to evaluate international business cycle models accounting for the dynamics of both macro aggregates and the real exchange rate.

We re-examine monetary policy spillovers to Emerging Market Economies (EME) in the form of capital flow reversals, using sectoral-level securities holdings data for Euro Area investors. In response to a surprise monetary tightening, active investors such as investment funds re-balance their portfolios away from EME, while more passive, long term investors such as insurance funds and banks exhibit no significant reaction on average. For active investors, the reallocation out of EME appears stronger under synchronized monetary tightening between the Fed and the ECB. However, these investors may even inject more capital to EME securities when the monetary tightening surprises contain positive news about the Euro Area economy. Issuers’ monetary–fiscal stability may explain the heterogeneous impact of these spillovers.

In contemporary data analysis, it is increasingly common to work with non-stationary complex data sets. These data sets typically extend beyond the classical low-dimensional Euclidean space, making it challenging to detect shifts in their distribution without relying on strong structural assumptions. This paper proposes a novel o ine change-point detection method that leverages classiers developed in the statistics and machine learning community. With suitable data splitting, the test statistic is constructed through sequential computation of the Area Under the Curve (AUC) of a classier, which is trained on data segments on both ends of the sequence. It is shown that the resulting AUC process attains its maxima at the true change-point location, which facilitates the change-point estimation. The proposed method is characterized by its complete nonparametric nature, high versatility, considerable exibility, and absence of stringent assumptions on the underlying data or any distributional shifts. Theoretically, we derive the limiting pivotal distribution of the proposed test statistic under null, as well as the asymptotic behaviors under both local and xed alternatives. The localization rate of the change-point estimator is also provided. Extensive simulation studies and the analysis of two real-world data sets illustrate the superior performance of our approach compared to existing model-free change-point detection methods.

Lenders are reluctant to finance firms' innovation activities because such activities tend to be opaque, with a high likelihood of negative outcomes that could hamper loan repayment. We posit that public credit registries (PCRs), which play an important role in credit information sharing in many countries, can facilitate financing by reducing adverse selection and moral hazard and increasing bank competition. Using the staggered establishment of PCRs in different countries and an international firm–patent data set, we find that credit information sharing positively affects firm innovation, especially in firms that experience a larger increase in bank debt financing after the establishment of a PCR. This finding is consistent with the notion that credit information sharing promotes firm innovation by easing bank debt financing frictions. We also find a stronger effect in countries that experience a large increase in bank competition after the establishment of a PCR—consistent with increased bank competition serving as a channel through which credit information sharing facilitates bank debt financing, thereby generating a positive effect on firm innovation. The positive effect is more pronounced when the established PCR has features that promote credit information sharing. It is also more pronounced for opaque firms and firms in innovation-intensive industries, indicating that credit information sharing helps to reduce financing frictions. Finally, we posit and find evidence that firm efficiency in transforming innovation inputs into outputs improves after the establishment of a PCR. Overall, our paper offers novel insights into how credit information sharing facilitates firm innovation.