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We consider a principal who periodically offers a fixed and costly nonmonetary reward to agents to incentivize them to invest effort over the long run. An agent's output, as a function of his effort, is a priori uncertain and is worth a fixed per-unit value to the principal. The principal's goal is to design an attractive reward policy that specifies how the rewards are to be given to an agent over time based on that agent's past performance. This problem, which we denote by P , is motivated by practical examples from both academia (e.g., a reduced teaching load) and industry (e.g., "Supplier of the Year" awards). The following "limited-term" (LT) reward policy structure has been quite popular in practice. The principal evaluates each agent periodically; if an agent's performance over a certain (limited) number of periods in the immediate past exceeds a predefined threshold, then the principal rewards him for a certain (limited) number of periods in the immediate future. When agents' outputs are deterministic in their efforts, we show that there always exists an optimal policy that is an LT policy and also, obtain such a policy. When agents' outputs are stochastic, we show that the class of LT policies may not contain any optimal policy of problem P but is guaranteed to contain policies that are arbitrarily near optimal. Given any ϵ > 0 , we show how to obtain an LT policy whose performance is within ϵ of that of an optimal policy. This guarantee depends crucially on the use of sufficiently long histories of the agents' outputs. We also analyze LT policies with short histories and derive structural insights on the role played by (i) the length of the available history and (ii) the variability in the random variable governing an agent's output. We show that the average performance of these policies is within 5% of the optimum, justifying their popularity in practice. We then introduce and analyze the class of "score-based" reward policies; we show that this class is guaranteed to contain an optimal policy and also, obtain such a policy. Finally, we analyze a generalization in which the principal has a limited number for rewards in any given period and show that the class of score-based policies, with modifications to accommodate the limited availability of the rewards, continues to contain an optimal solution for the principal. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4482. [ABSTRACT FROM AUTHOR]

Based on a representative sample of consumer financial transaction data, this paper studies the consumption and savings response to a 2015 permanent increase in the marginal income tax of the high-income taxpayers. Using difference-in-differences regressions, controlling for individual and time fixed effects, we show robust results that the affected consumers experienced little change in their spending. The pattern is prevalent across consumer demographics and is found even among consumers who are sophisticated, have high levels of debt, or register little income changes. Furthermore, the tax increase financed fiscal redistribution leads to a long-lasting increase in the consumption of the lower-income population. This paper was accepted by Lukas Schmid, finance. Funding: The authors gratefully acknowledge financial support from Social Science Research Thematic Grant in Singapore [Future-Proofing Singapore: An Economic Approach MOE2019-SSRTG-024]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4823. [ABSTRACT FROM AUTHOR]

We conducted a randomized, controlled trial with small- and medium-sized enterprises in Kenya to estimate the causal impact of an electronic payment (e-payment) technology on business finance. Using an encouragement design, we exogenously increased e-payment usage among a random subset of firms by relaxing adoption transaction costs and information barriers. Sixteen months after the intervention, we find that the e-payment technology increased access to mobile loans (in the number of loans as well as in the amount borrowed) by at least 50% (0.17 standard deviation), likely because of the reduction of information asymmetries brought by an increase in digital transactions. We find no effect of the e-payment technology on sales and profits, but we do find a reduction of sales volatility and precautionary investment, especially for smaller firms. This suggests that mobile loans help smaller firms cope with short-term negative shocks. We provide a stylized model of business finance that rationalizes these findings. This paper was accepted by Bruno Biais, finance. Funding: This paper was produced under the framework of the "Enabling Innovation and Productivity Growth in Low Income Countries (EIP-LIC/PO5639)" project, funded by the Department for International Development (DFID) of the United Kingdom and implemented by Tilburg University. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4821. [ABSTRACT FROM AUTHOR]

This paper examines mutual funds' dual role as both clients and shareholders of broker banks. Mutual funds are more likely to hold and significantly overweight stocks of their broker banks. In line with the portfolio decisions, fund voting is biased toward broker management in contentious proposals. Such voting bias is inconsistent with maximizing broker banks' shareholder value yet significantly affects voting outcomes and the presence of contentious proposals at the banks' shareholder meetings. Furthermore, we show that although client funds are rewarded with preferential initial public offering allocations from connected brokers for their voting support, fund managers' engagement in reciprocal practices is ultimately determined by the economic tradeoffs they face. Our study not only uncovers a new mechanism—being brokers' friendly shareholders—through which the two parties maintain their quid pro quo relationships but also raises a broader concern about governance of important financial institutions. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4820. [ABSTRACT FROM AUTHOR]

Disputes over transactions on two-sided platforms are common and usually arbitrated through platforms' customer service departments or third-party service providers. This paper studies crowd-judging, a novel crowdsourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich data set from the dispute resolution center at Taobao, a leading Chinese e-commerce platform, we aim to understand this innovation and propose and analyze potential operational improvements with a focus on in-group bias (buyer jurors favor the buyer, likewise for sellers). Platform users, especially sellers, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin and when jurors perceive that their in-group's interests are threatened. However, the bias diminishes as jurors gain experience: a user's bias reduces by nearly 95% as experience grows from zero to the sample median level. Incorporating these findings and juror participation dynamics in a simulation study, the paper delivers three managerial insights. First, under the existing voting policy, in-group bias influences the outcomes of no more than 2% of cases. Second, simply increasing crowd size through either a larger case panel or aggressively recruiting new jurors may not be efficient in reducing the adverse effect of in-group bias. Finally, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool. This paper was accepted by Vishal Gaur, operations management. Funding: S. A. Yang and A. Zhang acknowledge the support of the Hong Kong General Research Fund [Grant "Decentralizing Platform Governance: Innovations from China; Project 17614921]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4818. [ABSTRACT FROM AUTHOR]

This paper studies the effects of default risk on expected equity option returns. In the cross-section, expected delta-hedged equity option returns have a negative relation with default risk measured by credit ratings or default probability. In the time series, credit rating downgrades (upgrades) lead to a decrease (increase) in the firm's delta-hedged option return. Our results are consistent with a stylized capital structure model in which the negative relation between option returns and default risk is driven by firm leverage and asset volatility. This paper was accepted by Lukas Schmid, finance. Funding: A. Vasquez thanks the Asociación Mexicana de Cultura A.C. for financial support. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4796. [ABSTRACT FROM AUTHOR]

This paper studies whether credit ratings can alleviate the hold-up problem in the loan market. We exploit a refinement of the rating information produced by a certifier that rates private bank-dependent firms in France on a vast scale. The refinement causes some firms to receive a positive rating surprise that is not due to an improvement in firm fundamentals. We show that affected firms become less reliant on lenders that have greater ability to extract informational rents. These firms receive greater and less expensive bank credit from new and less informed lenders and invest more. We deduce that credit ratings reduce the monopoly power of informed banks, helping firms to expand their access to bank credit. This paper was accepted by David Sraer, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4779. [ABSTRACT FROM AUTHOR]

This paper studies network market problems in which firm(s)/platform(s) sets quality in addition to price. A well-established result in the network economics literature is that a profit-maximizing firm concerns only how quality is valued by the marginal consumer but not by inframarginal consumers, aka the Spence effect/distortion. For markets with strong network effects under which multiple market-tipping equilibria exist, I show that the validity of the previous result depends on the choice of the equilibrium selection criterion. Precisely, I show that all criteria commonly used in this literature give rise to the Spence effect, whereas the well-justified risk dominance criterion in game theory and its generalizations do not. Novel quality strategies are derived based on the latter criteria. This paper was accepted by Joshua Gans, business strategy. [ABSTRACT FROM AUTHOR]

One advantage of online retail is that a large number of products can be displayed at low cost. However, online retailers must decide which of these products to carry in inventory (stock items) and which to order from suppliers when a customer places an order (nonstock items). In this paper, we empirically investigate how carrying a product in inventory affects its online sales. We use data from a European furniture and interior design retailer consisting of daily sales transactions and inventory data covering 18 months. We use a quasi-natural experiment—random transitions of products in and out of inventory at the retailer's central warehouse—to estimate the causal effect of carrying inventory on sales. Our results show a strong and statistically significant increase in sales of, on average, 65% associated with having the product available in stock. More interestingly, this effect differs between products and is moderated by the price of the product: sales of more expensive products are less sensitive to the product being in stock. We use these results to draw insights on which types of items to carry in inventory. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4777. [ABSTRACT FROM AUTHOR]

This paper extends the methodology of statistically extracting latent factors in settings with return-predictive firm characteristics as conditional covariances (betas) between returns and factors. The main feature is that the pricing errors (alphas) are specified to be orthogonal to the affine-transformed firm characteristics as the betas with one component of pricing errors lying outside the space spanned by the firm characteristics. The specification is shown to make substantial differences with the extant literature as the zero pricing error hypothesis is strongly rejected for various models with commonly used firm characteristics. This paper was accepted by Agostino Capponi, finance. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4768. [ABSTRACT FROM AUTHOR]

This paper provides evidence from a natural experiment on the relationship between positive affect and productivity. We link highly detailed administrative data on the behaviors and performance of all telesales workers at a large telecommunications company with survey reports of employee happiness that we collected on a weekly basis. We use variation in worker mood arising from visual exposure to weather—the interaction between call center architecture and outdoor weather conditions—to provide a quasi-experimental test of the effect of happiness on productivity. We find evidence of a positive impact on sales performance, which is driven by changes in labor productivity—largely through workers converting more calls into sales and to a lesser extent by making more calls per hour and adhering more closely to their schedule. We find no evidence in our setting of effects on measures of high-frequency labor supply such as attendance and break-taking. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4766. [ABSTRACT FROM AUTHOR]

This paper studies how competition affects the role of reputation in encouraging sellers to exert effort. More competition disciplines sellers, but at the same time, it erodes reputational premia. This paper identifies whether one effect dominates the other using data from Airbnb. I exploit the introduction of a short-term rental regulation effective in San Francisco in 2017 that halved the number of short-term listings on the platform. I focus on hosts who are present on the platform before and after the regulation, and I identify a negative causal effect of the number of competitors on ratings about hosts' effort. I extend this result with two other measures of effort: hosts' response rate and response time. I confirm that hosts exert less effort when the number of competitors increases. The rate of responses within 24 hours decreases, and response time increases. This paper was accepted by Matthew Shum, marketing. Funding: This work was supported by the Ministerio de Economía, Industria y Competitividad (Spain) [Grant ECO2016-78632-P]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4758. [ABSTRACT FROM AUTHOR]

In this paper, we solve a class of two-stage distributionally robust optimization problems that have the property of supermodularity. We exploit the explicit worst case expectation of supermodular functions and derive the worst case distribution for the robust counterpart. This enables us to develop an efficient method to obtain an exact optimal solution to these two-stage problems. Further, we provide a necessary and sufficient condition for checking whether any given two-stage optimization problem has the supermodularity property. We also investigate the optimality of the segregated affine decision rules when problems have the property of supermodularity. We apply this framework to several classic problems, including the multi-item newsvendor problem, the facility location problem, the lot-sizing problem on a network, the appointment-scheduling problem, and the assemble-to-order problem. Whereas these problems are typically computationally challenging, they can be solved efficiently under our assumptions. Finally, numerical examples are conducted to illustrate the effectiveness of our approach. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported by the National Natural Science Foundation of China [Grant 71971187], and Hong Kong Research Grants Council [Collaborative Research Fund (C6103-20GF), General Research Fund (14210821, 16204521, 16212419)]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4748. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of information sharing between a retail platform and a manufacturer in a supply chain. We develop a stylized model salient to which is that the product's quality is a priori unknown to customers, who can infer it from customer-generated reviews. The platform, in turn, has access to private information concerning the relationship between quality and demand, and the manufacturer can choose to acquire said information to help determine the quality of its product accordingly. Our analysis yields three main insights. First, information sharing in and of itself induces the manufacturer to improve quality. Second, under a wholesale price contract, information sharing and product reviews together have a negative effect on product quality: When each firm is able to adjust its price in response to the quality signal, it benefits the manufacturer and hinders the platform. Consequently, the presence of reviews discourages the platform from sharing information, and the manufacturer tends to produce a lower-quality product. Finally, the negative effect of product reviews on the supply chain can be mitigated when the platform can share less accurate information or when the platform and manufacturer make a commission contract, rather than a wholesale price contract. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: D. Shin received financial support from the Hong Kong University of Science and Technology [grant IGN17BM09]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4746. [ABSTRACT FROM AUTHOR]

The rapid adoption of artificial intelligence (AI) technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision makers (DMs) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors, and the DM's cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, although its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4744. [ABSTRACT FROM AUTHOR]

This paper evaluates liquidity requirements and public liquidity injections in the context of financial crises, using a model that includes near-money assets. Some key effects are transmitted through liquidity premia and the price of liquid assets, producing a heterogeneous impact that depends on an agent's holdings of liquid assets. In addition, some agents prefer policies that do not fully relax their liquidity constraints because these policies are associated with higher prices. Liquidity requirements and liquidity injections have the opposite impact on liquidity premia and combining them leads to Pareto improvements that cannot be achieved by each policy separately. This paper was accepted by Gustavo Manso, finance. Funding: This work was supported by the Wisconsin Alumni Research Foundation. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4737. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to price bonds jointly issued by a group of countries—Eurobonds in the euro-area context. We consider two types of bonds; the first is backed by several and joint guarantees (SJGs), and the second features several but not joint guarantees (SNJGs). The pricing of SJG and SNJG bonds reflects different assumptions regarding the pooling of debtors' fiscal resources. We estimate fiscal limits for the six largest euro-area economies over 2008–2021 and deduce counterfactual Eurobond prices. For the five-year maturity, SNJG bond yield spreads would have been about three times larger than SJG ones over the estimation sample. Hence, issuing SJG bonds could result in gains at the aggregate level. Notwithstanding, our model also predicts that gains may temporarily vanish in periods of acute fiscal stress. We finally envision postissuance redistribution schemes, whereby gains stemming from the issuance of SJG bonds could be shared among participating countries; we argue that these schemes may alleviate the reduction in market discipline resulting from common bonds issuance. This paper was accepted by David Sraer, finance. Funding: This work has benefited from financial support from the Swiss National Science Foundation [Grant 182293]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4740. [ABSTRACT FROM AUTHOR]

Start-ups are increasingly using social media to signal quality and provide information to potential investors. However, the effectiveness of social media on venture capital (VC) financing is likely to be heterogeneous, differing by demographic and network characteristics of the founders. In this paper, we examine whether social media use can improve funding outcomes for firms founded by women and by other people also lacking connections to the investor network, two groups that face greater difficulties in securing VC financing. Using Twitter data and data on VC investment in start-ups from Crunchbase, we explore the interaction effect between Twitter usage and gender and between Twitter usage and the network constraint measure. Overall, we show that social media can mitigate some disparities in financing experienced by these firms through improving information access. We find that this effect is stronger for first-time entrepreneurs than for experienced ones, stronger for attracting new investors than repeat ones, and stronger in more competitive markets. Collectively, these results suggest that social media could primarily help women and less connected individuals obtain financing by alleviating information asymmetry between founders and investors. This paper was accepted by D. J. Wu, information systems. Funding: The authors thank Wharton Mack Institute of Innovation Management for funding support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4728. [ABSTRACT FROM AUTHOR]

This paper studies an emerging subscription model called ship-then-shop. Leveraging its predictive analytics and artificial intelligence (AI) capability, the ship-then-shop firm curates and ships a product to the consumer, after which the consumer shops (i.e., evaluates product fit and makes a purchase decision). The consumer first pays the up-front ship-then-shop subscription fee prior to observing product fit and then pays the product price afterward if the consumer decides to purchase. We investigate how the firm balances the subscription fee and product price to maximize its profit when consumers can showroom. A key finding is the ship-then-shop firm's nonmonotonic surplus extraction strategy with respect to its prediction capability. As prediction capability increases, the firm first switches from ex ante to ex post surplus extraction (by lowering fees and raising prices). However, if the prediction capability increases further, the firm reverts to ex ante surplus extraction (by raising fees and capping prices). We also find that the ship-then-shop model is most profitable when (i) the prediction capability is advanced, (ii) the search friction in the market is large, or (iii) the product match potential is large. Finally, we show that the marginal return of AI capability on the firm's profit decreases in search friction but increases in product match potential. Taken together, we provide managerially relevant insights to help guide the implementation of the innovative subscription model. This paper was accepted by Dmitri Kuksov, marketing. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4723. [ABSTRACT FROM AUTHOR]

Through repeated interactions, firms today refine their understanding of individual users' preferences adaptively for personalization. In this paper, we use a continuous-time bandit model to analyze firms that recommend content to multihoming consumers, a representative setting for strategic learning of consumer preferences to maximize lifetime value. In both monopoly and duopoly settings, we compare a forward-looking recommendation algorithm that balances exploration and exploitation to a myopic algorithm that only maximizes the quality of the next recommendation. Our analysis shows that, compared with a monopoly, firms competing for users' attention focus more on exploitation than exploration. When users are impatient, competition decreases the return from developing a forward-looking algorithm. In contrast, development of a forward-looking algorithm may hurt users under monopoly but always benefits users under competition. Competing firms' decisions to invest in a forward-looking algorithm can create a prisoner's dilemma. Our results have implications for artificial intelligence adoption and for policy makers on the effect of market power on innovation and consumer welfare. This paper was accepted by Dmitri Kuksov, marketing. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4722. [ABSTRACT FROM AUTHOR]

Highly skilled workers are not only a crucial and relatively scarce input into firms' productive and innovative processes, but are also a critical resource determining competitive advantage. An increasingly high proportion of these workers in the United States were born abroad and permitted to work on skilled worker visas. How do multinational firms respond when artificial constraints, namely, policies restricting skilled immigration, are placed on their ability to hire scarce human capital? This paper combines visa microdata and comprehensive data on U.S. multinational firm activity to demonstrate that firms respond to restrictions on H-1B immigration by increasing foreign affiliate employment at the intensive and extensive margins, particularly in China, India, and Canada. The most impacted jobs were R&D-intensive ones, but there is some evidence that non-R&D employment was also affected. The paper highlights a means by which firms can circumvent constraining policies and mitigate country-level risk, and it also suggests that, for the average multinational company (MNC), this means is imperfect; for every visa rejection, they hire 0.4 employees abroad. The most globalized MNCs are the most likely to respond to these restrictions by offshoring, highlighting that firm capabilities—in the form of prior internationalization—shape the decision and ability to offshore in response to skilled immigration restrictions; indeed, these firms hire 0.9 employees abroad for every visa rejection. More broadly, the paper provides evidence of a push factor for internationalizing knowledge activity: artificial constraints on resources result in firms circumventing restrictive policies in ways that may not be anticipated by policy makers. This paper was accepted by Alfonso Gambardella, business strategy. Funding: This work was supported by the Mack Institute for Innovation Management. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4715. [ABSTRACT FROM AUTHOR]

Unemployment risk influences workers' incentives to invest in firm-specific human capital. This paper investigates the impact of unemployment risk on chief executive officers (CEOs)' risk-taking incentive compensation. Exploiting state-level changes in unemployment benefits, we find that after unemployment insurance benefits become more generous, boards increase the convex payoff structure of CEO pay to encourage risk taking. The increase in CEOs' convexity payoff is stronger in firms with more independent and diverse boards, higher ownership of long-term shareholders, and in industries requiring highly skilled labor. Our findings suggest that boards internalize workers' interests in firms' risk-taking decisions and executive compensation is one mechanism used. This paper was accepted by David Simchi-Levi, finance. Funding: K. Zhang acknowledges financial support from the National Natural Science Foundation of China [Grants 72272099 and 71902115]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4714. [ABSTRACT FROM AUTHOR]

This paper provides empirical evidence on predictable time variations in out-of-sample bond return predictability. Bond return predictability is associated with periods of high (low) economic activity (uncertainty), which implies that violations of the expectations hypothesis are state dependent and linked to features of the business cycle. These state dependencies in predictability, established by introducing a new multivariate test for equal conditional predictive ability, can be used in real time to improve out-of-sample bond risk premia estimates and investors' economic utility through a novel dynamic forecast combination scheme that uses predicted forecasting performance to identify the best set of methods to include in the combined forecast. Dynamically combined forecasts exhibit strong countercyclical behavior and peak during recessions. This paper was accepted by Lukas Schmid, finance. Funding: This work was supported by the Danish Council of Independent Research [Grants DFF 7024–00020B and DFF 9033–00003B] and the Danish Finance Institute. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4713. [ABSTRACT FROM AUTHOR]

Exhibiting altruism toward and cooperativeness with others is a key ingredient for successful work relationships and managerial decision making. Rising political polarization creates a hazard because it ruptures this fabric and impedes the interaction of employees, especially across political isles. This paper's focus is to examine various behavioral-, belief-, and norm-based layers of (non)strategic decision making that are plausibly affected by polarization. I quantify this phenomenon via five preregistered studies in the context of Donald J. Trump, comprising 15 well-powered behavioral experiments and a diverse set of over 8,600 participants. To capture the pervasiveness of polarization, I contrast the findings with various political and nonpolitical identities. Overall, I consistently document strong heterogeneous effects: ingroup-love occurs in the perceptional domain (how close one feels toward others), whereas outgroup-hate occurs in the behavioral domain (how one helps/harms/cooperates with others). The rich setting also enables me to examine the mechanisms of observed intergroup conflict, which can be attributed to one's grim expectations regarding cooperativeness of the opposing faction, rather than one's actual unwillingness to cooperate. For the first time, the paper also tests whether popular behavioral interventions (defaults and norm-nudges) can reduce the detrimental impact of polarization in the contexts studied here. The tested interventions improve prosociality but are ineffective in closing the polarization gap. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by Deutsche Forschungsgemeinschaft [Grant EXC 2126/1– 390838866]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4701. [ABSTRACT FROM AUTHOR]

In this paper, I analyze the joint design of capital requirements and fair value reporting rules for financial institutions with illiquid assets. I specifically examine how prudential regulation aimed at solving agency problems affects financial institutions' incentives to use Level 2 versus Level 3 fair value reporting, as well as financial stability. Crucially, Level 3 reporting allows financial institutions to use their private information, whereas Level 2 fair values are only measured with public information. Interestingly, my analysis shows regulators may leave to financial institutions the discretion to report illiquid assets at Level 2 or Level 3. Financial institutions then report at Level 3 only if they have good private information about the assets' quality. Moreover, prudential rules that only rely on Level 2 fair values may be efficient at solving agency problems within financial institutions but may also decrease financial stability. By contrast, prudential rules that leave to financial institutions the discretion to report illiquid assets at Level 2 or Level 3 while relaxing capital requirements may increase financial stability. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by the H2020 European Research Council [Grant 669217]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4692. [ABSTRACT FROM AUTHOR]

This paper studies intertemporal asset pricing in network economies when distress shocks can propagate through the network, similarly to epidemic outbreaks. Two classes of equilibria exist. In the first, idiosyncratic shocks are diversifiable and do not affect valuations; the consumption capital asset pricing model applies. In the second, idiosyncratic shocks generate nondiversifiable long-run cascades of shocks (financial pandemics) that introduce a new risk premium component unexplained by traditional systematic factors. We derive closed solutions for asset prices as a function of the network properties and discuss their properties. After a structural break (1984), we find evidence of a network risk premium that is statistically and economically significant. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by Ministero dell'Istruzione, dell'Università e della Ricerca [Grant PRIN-2017TA7TYC] and Baffi CAREFIN. C. Tebaldi is a fellow of Baffi CAREFIN and Innocenzo Gasaprini Institute for Economic Research Centers. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4687. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the economic value of a blockchain application. In the context of asset-backed securities (ABS) issuance in China, where some ABS are issued with blockchain technology and others are not, we find that the use of blockchain significantly reduces the coupon yield at issuance. Compared with other ABS, those issued using blockchain technology experience a decrease of 31.4 basis points in the yield spread, which corresponds to a relative decrease of 13%. We further document that the effect of blockchain is more pronounced for ABS deals rated by less reputable credit rating agencies and agencies that rely more on issuers for their rating business, for revolving ABS, and for ABS with a larger number of underlying assets. We also find that the use of blockchain can reduce the level of retained interest and number of credit enhancement mechanisms. This paper contributes to the literature by providing a small-sample analysis of the economic value of a blockchain application in financial markets. This paper was accepted by Brian Bushee, accounting. Funding: X. Chen and Q. Cheng acknowledge funding provided by the Lee Kong Chian Professorship at Singapore Management University. This work was supported by Singapore Ministry of Education [Grant MOE-T2EP40120-0005]. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4671. [ABSTRACT FROM AUTHOR]

This paper studies how a tick size change affects market quality, price discovery, and the competition for liquidity provision by dealers and high-frequency trading firms (HFTs) in the U.S. Treasury market. Using difference-in-differences regressions around the November 19, 2018, tick size reduction in the 2-year Treasury note and a similar change in the 2-year futures eight weeks later, we find significantly improved market quality. Moreover, dealers become more competitive in liquidity provision and price improvement, consistent with the hypothesis that HFTs find liquidity provision less profitable in the smaller tick size environment. Last, we find a significant shift in short-run price discovery toward the cash market, which then reverses when the futures market tick size is reduced, suggesting that the finer pricing grid in the cash market allows traders to act on small information signals that are not profitable to exploit in the larger-tick futures market. Our findings suggest that reducing the tick size in tick-constrained and highly liquid markets like the Treasury market is on balance beneficial. This paper was accepted by Bruno Biais, finance. Funding: F. Ruela acknowledges support from the National Science Foundation Graduate Research Fellowship Program [Grant DGE-1746045]. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4663. [ABSTRACT FROM AUTHOR]

This paper studies the cost of capital effect of a major regulatory technology, or RegTech, event: the staggered implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system of the Securities and Exchange Commission in the period from 1993 to 1996. This event represents a largely exogenous shock to corporate information dissemination technologies, resulting in a considerable reduction in information acquisition costs for investors. Using a difference-in-differences research design, we show that the cost of equity capital declines substantially after a firm switches from paper filing to mandatory electronic filing in EDGAR. The effect is stronger for small firms and firms with low analyst coverage and low institutional ownership. We identify three channels through which EDGAR affects a firm's cost of capital: the liquidity, risk-taking, and corporate governance channels. EDGAR implementation also improves a firm's investment efficiency significantly. We find evidence that the marginal value of a firm's capital investment and cash is higher during the post-EDGAR period. This paper was accepted by Suraj Srinivasan, accounting. Funding: C. Lin acknowledges financial support from the National Science Foundation of China [Grant 72192841] and the Research Grants Council of the Hong Kong Special Administration Region, China [Project HKU R7054-18 and T35/710/20R]. S. Lai acknowledges financial support from the Ministry of Science and Technology, Taiwan [Grants 107-2410-H-002-038-MY3 and 110-2410-H-002-079-MY2] and the E. Sun Academic Award. X. Ma acknowledges research support from the University of Macau [Grants SRG2019-00158-FBA and MYRG2020-00259-FBA]. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022.4660. [ABSTRACT FROM AUTHOR]

How do firms address complex collective action problems effectively? Institutional and stakeholder research suggests that firms may avoid the tragedy of the commons by aligning the interests of critical proximate stakeholders in ways that governments cannot accomplish. This phenomenological paper investigates this possibility by analyzing Amazon rainforest preservation by Natura, a Brazilian cosmetics company. The results indicate that Natura internalized environmental externalities by linking ecologically conscious consumers with rural Amazonian communities. A differences-in-differences analysis compares forest preservation and fire activity in the municipalities that Natura entered with those in which it did not enter. Natura's impact is identified through an instrumental variable analysis using missing satellite images, which Natura relied upon to decide which municipalities to enter. Quantitative results tie Natura's entry into municipalities with forest preservation. Analysis of three mechanisms associates Natura's involvement with stakeholder decisions to cultivate diverse forest-generated crops instead of clearing the land for conventional agriculture. This study contributes to the management literature by suggesting how firms can address important global challenges, such as rainforest preservation, by investing in stakeholder capability development and by creating institutional arrangements in line with those envisioned elsewhere. This paper was accepted by George Serafeim, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the Clarkson Centre for Business Ethics [CAD 7,500.00] and Canada's Social Sciences and Humanities Research Council. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4884. [ABSTRACT FROM AUTHOR]

Immigration has proven to be a major force driving entrepreneurial dynamics. In this paper I investigate how the geographical distribution of immigrants within a given country, and in particular the presence of "enclaves," affects the relationship between immigration and entrepreneurship. I examine the impact of Polish immigration to Great Britain following the unprecedented migration wave caused by the European Union enlargement in 2004. I address omitted variable concerns by using information on the location of historical Polish military settlements and the occupational composition and growth of Polish immigration in Ireland to construct instruments for enclaves and location choices of immigrants. The econometric results indicate, on the one hand, that immigration does increase immigrant entrepreneurship, but not in existing immigrant enclaves. On the other hand, immigrant entrepreneurs outside enclaves tend to achieve worse growth outcomes than those in enclaves. Further analyses provide an explanation to these findings due to blocked labor markets and to the higher prevalence of "necessity entrepreneurship" outside of enclaves. These results offer new insights to understand the influence of geography on entrepreneurship in the presence of immigration. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4776. [ABSTRACT FROM AUTHOR]

Private sector innovation is critical to mitigating and adapting to climate change. This paper studies innovation in solar energy technology, a key source of clean energy that has experienced rapid price declines over the past decade. To understand the causes and effects of innovation, I estimate a dynamic structural model of competition among solar panel manufacturers. The model captures important features of the industry, including the role of government subsidies for solar adoption, and I employ a unique measure of technological progress that is observable and verifiable. The results produce two main insights. First, ignoring innovation by firms can generate biased estimates of the effects of government policy. Second, decentralized government intervention in a global market generates spillovers; a subsidy in one country causes international firms to innovate more, leading to lower prices and increased adoption elsewhere. This spillover underscores the need for international coordination by governments and the private sector to address climate change. This paper was accepted by Erica Plambeck, Special Section of Management Science on Business and Climate Change. Funding: The author acknowledges support from the U.S. Environmental Protection Agency [Science to Achieve Results (STAR) Fellowship Assistance Agreement FP-91769401-0] and Harvard [Joseph Crump Fellowship]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4662. [ABSTRACT FROM AUTHOR]

This paper shows how green investing spurs companies to mitigate their carbon emissions by raising the cost of capital of the most carbon-intensive companies. Companies' emissions decrease when the wealth share of green investors and their sensitivity to climate externalities increase. We show that the impact of green investors primarily governs companies' long-run emissions. Companies are further incentivized to reduce their emissions when green investors anticipate tighter climate regulations and climate-related technological innovations. However, heightened uncertainty regarding future climate risks alleviates green investors' pressure on the cost of capital of companies and pushes them to increase their emissions. Calibrated on U.S. data, our model suggests that, albeit effective, the impact of green investors remains limited given their current wealth share and practices. This paper was accepted by George Serafeim, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the Europlace Institute of Finance. T. De Angelis received funding from the Engineering and Physical Sciences Research Council [Grant EP/R021201/1], and P. Tankov received funding from the Finance for Energy Markets research initiative of the Institut Europlace de Finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4472. [ABSTRACT FROM AUTHOR]

Many countries have adopted payment for ecosystem services (PES) programs to reduce deforestation. Empirical evaluations find such programs, which pay forest owners to conserve forest, can lead to anywhere from no impact to a 50% reduction in deforestation level. To better understand the potential effectiveness of PES contracts, we use a principal–agent model, in which the agent has an observable amount of initial forest land and a privately known baseline conservation level. Commonly used conditional contracts perform well when the environmental value of forest is sufficiently high or sufficiently low, but can do arbitrarily poorly compared with the optimal contract for intermediate values. We identify a linear contract with a distribution-free per-unit price that guarantees at least half of the optimal contract payoff. A numerical study using U.S. land use data supports our findings and illustrates when linear or conditional contracts are likely to be more effective. This paper was This paper was accepted by Beril Toktay, Special Section of Management Science on Business and Climate Change. Funding: W. Dai Li was supported by a Stanford Interdisciplinary Graduate Fellowship. I. Lo was supported by an Environmental Venture Project Grant from the Stanford Woods Institute for the Environment. Supplemental Material: Data and the e-companion are available at https://doi.org/10.1287/mnsc.2021.4273. [ABSTRACT FROM AUTHOR]

Is it feasible to build desalination plants for the coproduction of salt and fresh water from U.S. seawater that could lead to a restructuring of supply chains for salt imports? As it is predicted that climate change will increase water stress worldwide, an increasing number of countries are using desalination plants to generate fresh water. In most such cases, residual concentrates must be disposed of, and the disposal cost is increasing as countries are becoming more environmentally conscious. Selective salt recovery can help to alleviate this issue as it reduces the need for concentrate disposal and generates additional revenue. To gain some insights into the costs and benefits of coproduction plants, we have collected data on current desalination practices and salt imports in the United States along with the manufacturing costs and energy requirements for coproduction plants. We have used this data to build an optimization model to determine an optimal number and location of coproduction plants in the United States and their potential markets for the sale of coproduced salt. In our analysis, we consider a different total number of coproduction facilities, and for each configuration, we evaluate the resulting net water cost and carbon emissions impact. Our results indicate that there exists the potential for building several coproduction plants in the United States that would be both financially competitive with existing desalination plants and lead to a reduction in carbon emissions. This information might be of use to both governments and businesses when they make decisions about the type of desalination facilities built and the implemented "polluter pays" policies. This paper was This paper was accepted by Beril Toktay, Special Section of Management Science on Business and Climate Change. Supplemental Material: Data are available at https://doi.org/10.1278/mnsc.2021.4109. [ABSTRACT FROM AUTHOR]

Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible and trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on Bitcoin (BTC) exchange rates against the U.S. dollar (USD) in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC–USD relative to other exchange rate pairs as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent arbitrage frictions associated with short-sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC–USD futures was beneficial to the Bitcoin spot market by making the underlying prices more informative. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Funding: The authors acknowledge financial support from the Global Risk Institute. P. Augustin acknowledges financial support from the Canadian Derivatives Institute and from the Canada Research Chair Program of the Social Sciences and Humanities Research Council Canada. The paper has benefited significantly from a fellow visit of P. Augustin at the Center for Advanced Studies Foundations of Law and Finance funded by the German Research Foundation, project FOR 2774, and from a visiting position of P. Augustin at the finance department of the University of Luxembourg facilitated through the Inter Mobility Programme of the Luxembourg National Research Fund. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4900. [ABSTRACT FROM AUTHOR]

In several transportation systems, vehicles can choose where to meet customers rather than stopping in fixed locations. This added flexibility, however, requires coordination between vehicles and customers that adds complexity to routing operations. This paper develops scalable algorithms to optimize these operations. First, we solve the one-stop subproblem in the ℓ 1 space and the ℓ 2 space by leveraging the geometric structure of operations. Second, to solve a multistop problem, we embed the single-stop optimization into a tailored coordinate descent scheme, which we prove converges to a global optimum. Third, we develop a new algorithm for dial-a-ride problems based on a subpath-based time–space network optimization combining set partitioning and time–space principles. Finally, we propose an online routing algorithm to support real-world ride-sharing operations with vehicle–customer coordination. Computational results show that our algorithm outperforms state-of-the-art benchmarks, yielding far superior solutions in shorter computational times and can support real-time operations in very large-scale systems. From a practical standpoint, most of the benefits of vehicle–customer coordination stem from comprehensively reoptimizing "upstream" operations as opposed to merely adjusting "downstream" stopping locations. Ultimately, vehicle–customer coordination provides win–win–win outcomes: higher profits, better customer service, and smaller environmental footprint. This paper was accepted by Chung Piaw Teo, optimization. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72288101, 52221005 and 52220105001]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4739. [ABSTRACT FROM AUTHOR]

Folklore suggests that policy gradient can be more robust to misspecification than its relative, approximate policy iteration. This paper studies the case of state-aggregated representations, in which the state space is partitioned and either the policy or value function approximation is held constant over partitions. This paper shows a policy gradient method converges to a policy whose regret per period is bounded by ϵ, the largest difference between two elements of the state-action value function belonging to a common partition. With the same representation, both approximate policy iteration and approximate value iteration can produce policies whose per-period regret scales as ϵ / (1 − γ) , where γ is a discount factor. Faced with inherent approximation error, methods that locally optimize the true decision objective can be far more robust. This paper was accepted by Hamid Nazerzadeh, data science. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4788. [ABSTRACT FROM AUTHOR]

Hospitals throughout the developed world are reimbursed based on diagnosis-related groups (DRGs). Under this scheme, patients are divided into clinically meaningful groups, and hospitals receive a fixed fee per patient episode tied to the patient DRG. The fee is based on the average cost of providing care to patients who belong to the same DRG across all hospitals. This scheme, sometimes referred to as "yardstick competition," provides incentives for cost reduction, as no hospital wants to operate at a higher cost than average, and can be implemented using accounting data alone. Nevertheless, if costs within a DRG are heterogeneous, this scheme may give rise to cherry-picking incentives, where providers "drop" patients who are more expensive to treat than average. To address this problem, regulators have tried to reduce within-DRG cost heterogeneity by expanding the number of DRG classes. In this paper, we show that even if cost heterogeneity is eliminated, such expansion will fail to completely eliminate patient cherry picking. In equilibrium, the market will bifurcate into two groups, one of which will continue to cherry-pick patients and underinvest in cost reduction, whereas the other group treats all patients. Furthermore, we show that DRG expansion is particularly problematic if hospitals are also able to "upcode" patients, that is, intentionally assign patients to a more resource-intensive DRG than needed to increase income. Upcoding increases within-DRG cost heterogeneity and amplifies cherry-picking incentives. We examine potential solutions involving yardstick competition based on input statistics. This paper was accepted by Carri Chan, healthcare management. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.4752. [ABSTRACT FROM AUTHOR]

The internet advertising market is a multibillion dollar industry in which advertisers buy thousands of ad placements every day by repeatedly participating in auctions. An important and ubiquitous feature of these auctions is the presence of campaign budgets, which specify the maximum amount the advertisers are willing to pay over a specified time period. In this paper, we present a new model to study the equilibrium bidding strategies in standard auctions, a large class of auctions that includes first and second price auctions, for advertisers who satisfy budget constraints on average. Our model dispenses with the common yet unrealistic assumption that advertisers' values are independent and instead assumes a contextual model in which advertisers determine their values using a common feature vector. We show the existence of a natural value pacing–based Bayes–Nash equilibrium under very mild assumptions. Furthermore, we prove a revenue equivalence showing that all standard auctions yield the same revenue even in the presence of budget constraints. Leveraging this equivalence, we prove price of anarchy bounds for liquid welfare and structural properties of pacing-based equilibria that hold for all standard auctions. In recent years, the internet advertising market has adopted first price auctions as the preferred paradigm for selling advertising slots. Our work, thus, takes an important step toward understanding the implications of the shift to first price auctions in internet advertising markets by studying how the choice of the selling mechanism impacts revenues, welfare, and advertisers' bidding strategies. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4719. [ABSTRACT FROM AUTHOR]

Battery swapping for electric vehicle refueling is reviving and thriving. Despite a captivating sustainable future where swapping batteries will be as convenient as refueling gas today, a tension is mounting in practice (beyond the traditional "range anxiety" issue): On one hand, it is desirable to maximize battery proximity and availability to customers. On the other hand, capacitated urban power grids may curb decentralized charging at a slow speed. To reconcile this tension, some cities are embracing an emerging infrastructure network: Decentralized swapping stations replenish charged batteries from centralized charging stations. It remains unclear how to design such a network or whether pooling charging demands will save costs or batteries. In this paper, we model this new urban infrastructure network. This task is complicated by non-Poisson swaps and by the intertwined stochastic operations of swapping, charging, stocking, and circulating batteries among swapping and charging stations. We tackle these complexities by deriving analytical models, which enrich the classical batched repairable-inventory theory. We next propose a joint location and repairable-inventory model for citywide deployment of hub charging stations, with a nonconvex nonconcave objective function. We solve this problem exactly by exploiting submodularity and combining constraint-generation and parameter-search techniques. Even for solving convexified problems, our algorithm brings a speedup of at least three orders of magnitude relative to the Gurobi solver. The major insight is twofold: The benefit of pooling charging demands alone is not enough to justify the adoption of the "swap-locally, charge-centrally" network; instead, the main justification is that faster charging accessible at centralized charging stations significantly reduces the system-wide battery stock level. In a broader sense, this work deepens our understanding of how mobility and energy are coupled toward enabling smart cities. This paper was accepted by Chung Piaw Teo, optimization. Funding: Y. Zhang acknowledges the support from the National Natural Science Foundation of China [Grants 71871023, 72271029, and 72061127001]. W. Qi acknowledges the support from the National Natural Science Foundation of China [Grants 72272014 and 72188101] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2019-04769]. N. Zhang acknowledges the support from the China Scholarship Council [202106030140]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4731. [ABSTRACT FROM AUTHOR]

This paper examines how a firm's choice of the type of experiment impacts on its potential exploitation of new technological opportunities. It does so in the context of the failure of successful firms (or disruption) where the literature has informally suggested that firms undertake errors in experimental choice. It is shown that firms will generically choose experiments that minimize false positives (a high-bar) or minimize false negatives (a low-bar) rather than strike a balance between the two. This is done to better inform decisions regarding the exploitation of technological opportunities. It is shown that these choices can differ between incumbents and entrants based on their fundamentals and because of the anticipation of competition between them. This paper was accepted by David Simchi-Levi, business strategy. [ABSTRACT FROM AUTHOR]

This paper studies the contribution of analysts to the functioning and failure of the market for initial coin offerings (ICOs). The assessments of freelancing analysts exhibit biases because of reciprocal interactions of analysts with ICO team members. Even favorably rated ICOs tend to fail raising some capital when a greater portion of their ratings reciprocate prior ratings. Ninety days after listing on an exchange, the market capitalization relative to the initial funds raised is smaller for tokens with more reciprocal ratings. These findings suggest that conflicts of interest help explain the failure of ICOs. This paper was accepted by Bruno Biais, Special Section of Management Science: Blockchains and Crypto Economics. Funding: A. Barth acknowledges financial support from the Chaire Fintech at University Paris Dauphine - PSL. A. F. Wagner acknowledges financial support from the University of Zurich Research Priority Program "Financial market regulation." This paper was initiated when A. F. Wagner was visiting the Center for Advanced Studies on the Foundations of Law and Finance, funded by the German Research Foundation under the project FOR 2774 at Goethe University Frankfurt. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.02928. [ABSTRACT FROM AUTHOR]

In this paper, we study a family of sales-based rebate mechanisms as an effective tool to implement price discrimination across a population of buyers with correlated heterogeneous valuations on indivisible goods and services. In order to implement such sales-based rebate mechanisms, the seller charges each buyer a fixed price at the time of purchase contingent on a rebate that is a function of the ex post sales volume to be realized at the end of the sales period. The seller declares both a price and a menu of rebates as a function of sales. We show that, when there is a common component of uncertainty in consumers' valuations (to which we refer as the quality of the product), such rebates enable a seller to effectively induce different expected net prices at different valuations. Importantly, this effective price discrimination over valuations is achieved keeping both the base price and the rebate uniform across all buyers. This uniformity of price and rebate across buyers is a key advantage of our proposed rebate mechanism, thereby providing a new mechanism for price discrimination in crowd-based markets. We use tools and techniques from game theory and variational optimization to provide insight into the economics of such mechanisms. In particular, we identify two mechanisms that are monotone functions of the sales volume that are easy to implement in practice and perform well when compared with the much more complex optimal mechanism. We provide a rigorous analysis of the optimal mechanism and discuss practical limitations in implementing the globally optimal design, further demonstrating the efficacy of our proposed monotone mechanisms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: This research was supported by a Vannevar Bush Fellowship from the Office of Secretary of State and Army Research Office [MURI Project W911NF-19-1-0217]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4691. [ABSTRACT FROM AUTHOR]

On-demand delivery through sharing platforms represents a rapidly expanding segment of the global workforce. The emergence of sharing platforms enables gig workers to choose when and where to work, allowing them to do so in a flexible manner. However, such flexibility brings notorious challenges to platforms in managing the gig workforce. Thus, understanding the incentive and behavioral issues of gig workers in this new business model is inherently meaningful. This paper investigates how the incentive mechanisms of sharing platforms—earnings, ratings, and penalties—affect the working decisions of gig workers and their nuanced relationships. To achieve this goal, we use data from one leading on-demand delivery platform with more than 50 million active consumers in China and implement a two-stage Heckman model with instrumental variables to estimate the impact of earnings, ratings, and penalties. We first show that better ratings motivate gig workers to work more. However, interestingly, when ratings are employed together with earnings, the two positive effects of ratings and earnings can be substitutes for each other. Second, we reveal that higher past penalties discourage workers from working more, whereas, interestingly, workers with higher past penalties tend to be more sensitive toward an increase in earnings. Finally, we conduct follow-up surveys to understand the underlying mechanisms of the observed moderating effects from both psychological and economic perspectives. The ultimate goal of this work is to provide managerial implications to help platform managers understand how earnings, ratings, and penalties work together to affect gig workers' working decisions and how to manage high- and low-quality workers. This paper was accepted by David Simchi-Levi, entrepreneurship and innovation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72192823, 72172169, 71821002, 91646125, 72071206, 72231011, 72025405, and 72088101] and Program for Innovation Research at the Central University of Finance and Economics. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4761. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient's health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient's health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients' dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer. This paper was accepted by Chung-Piaw Teo, optimization. Funding: G. Forchini acknowledges funding from Jan Wallanders and Tom Hedelius Foundation and the Tore Browaldh Foundation, funding from MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1], jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the EDCTP2 program supported by the European Union; and acknowledges funding by Community Jameel. D. Rizmie acknowledges partial funding from the MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1]. J. C. D'Aeth acknowledges funding from the Wellcome Trust [Reference 102169/Z/13/Z]. S. Moret acknowledges partial support from the Swiss National Science Foundation (SNSF) under [Grant P2ELP2_188028]. S. Ghosal was funded by the Imperial College President's PhD Scholarship. F. Grimm was funded by the Health Foundation as part of core staff member activity. This research was funded in whole, or in part, by the Wellcome Trust [Grant 102169/Z/13/Z]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4679. [ABSTRACT FROM AUTHOR]