理论论文

ABSTRACT We introduce the concept of subtle discrimination —biased acts that cannot be objectively ascertained as discriminatory. When candidates compete for promotions by investing in skills, firms' subtle biases induce discriminated candidates to overinvest when promotions are low‐stakes (to distinguish themselves from favored candidates) but underinvest in high‐stakes settings (anticipating low promotion probabilities). This asymmetry implies that subtle discrimination raises profits in low‐productivity firms but lowers them in high‐productivity firms. Although subtle biases are small, they generate large gaps in skills and promotion outcomes. We derive further predictions in contexts such as equity analysis, lending, fund flows, banking careers, and entrepreneurial finance.

We introduce the family of games with intertemporal externalities, where two disjoint sets of players play sequentially. Coalitions formed by the present cohort generate worth today. Moreover, today’s partition of players exerts an externality on the future; the worth of a coalition formed by future players is influenced by this externality. We adapt the classic Shapley axioms and study their implications in our class of games. They do not suffice to single out a unique solution. We introduce two values using the interpretation of the Shapley value as the players’ expected contributions to coalitions: the one-coalition externality value and the naive value. We state a relationship between these values and the Shapley value of an associated game in characteristic function form. Our main results characterize the two values by adding one additional property to the classic Shapley axioms. A property of equal treatment of contributions leads to characterizing the one-coalition externality value. A property of equal treatment of externalities characterizes the naive value.

Abstract We model a fishery with potential congestion, in which firms obtain public and private signals about the location of the densest fish stock. We analytically determine the regions of parameter space where greater precision of public and/or private information increases welfare, and we examine the effects of two types of information sharing. Using high-resolution data from the world’s largest fishery, we estimate the structural model. Point estimates imply that more precise private information raises welfare, whereas more precise public information has a negligible effect on welfare. Moreover, welfare is much more sensitive to changes in the precision of private information than to changes in the precision of public information. This difference reflects the fact that public information increases congestion more than private information does. We also find empirically that a small amount of information sharing can reduce welfare, whereas more extensive information sharing raises welfare, as do information clubs.

Abstract We introduce markup equilibrium—an extension of Walrasian equilibrium in which consumers pay a fixed percentage markup over producer prices. In quasilinear markets, markup equilibria exist despite nonconvexities. They are resource-feasible and envy-free, incur no budget deficit, and require little more communication and computation than ordinary Walrasian equilibrium. The associated markup mechanism is asymptotically incentive-compatible. We also introduce a Bound-Form First Welfare Theorem, which states that for any feasible allocation, the welfare loss compared to the first-best is bounded, using any price vector, by the sum of the resulting (i) budget surplus and (ii) rationing losses suffered by the participants. Using producer prices, this bound implies that any markup equilibrium with a small markup and few unallocated goods is nearly efficient.

This paper develops a macroeconomic model that combines an incomplete‐markets overlapping‐generations economy with a job ladder featuring sequential wage bargaining, endogenous search effort of employed and non‐employed workers, and differences in match quality. With these ingredients, our model provides a joint microfoundation for the three main inputs in aggregate production: capital, employment, and labor efficiency. The calibrated model offers a good fit to the empirical age profiles of search activity, job‐finding rates, wages, and savings. We use the model to analyze the impact of tax and transfer policies for labor market dynamics and aggregate economic activity via capital, employment, and labor efficiency channels. Lower unemployment benefits and a less progressive tax schedule bring about welfare losses for a newborn worker which are mainly driven by higher consumption risk and costlier search effort; both policies have differential effects along the age, income, and wealth dimensions.

Customs data reveal the heterogeneity and granularity of relationships among buyers and sellers, showing how more exports to a destination break down into more firms selling there and more buyers per exporter. We develop a quantitative general equilibrium model of firm‐to‐firm matching that builds on this insight to separate the roles of iceberg costs and matching frictions in gravity. In the cross section, we find matching frictions as important as iceberg costs in impeding trade, and more sensitive to distance. Because domestic and imported intermediates compete directly with labor in performing production tasks, our model also fits the heterogeneity of labor shares across French producers. Applying the framework to the 2004 expansion of the European Union, reduced iceberg costs and reduced matching frictions contributed equally to the increase in French exports to the new members. While workers benefited overall, those competing most directly with imports gained less, even losing in some countries entering the EU.

Using agile supply chains, fast fashion companies have been viewed as best practice examples in industries. Prior research focuses on how agility can equip such companies with strong sense-and-respond capabilities to identify and fulfill unpredictable customer demands. There is another powerful dimension of agility—the ability to create new products frequently—that has enabled recent market success of companies such as Shein and Temu, labeled by some industry press as ultra-fast fashion. These companies, relying on end-to-end digital technologies, have pushed this dimension of agility to unprecedentedly high levels, launching new products with great frequency and variety. The frequent product launches create freshness, stimulating latent demands of consumers and making these companies “ultra-fresh” fashion, a term we adopt in this paper. We seek to model this dimension of agility as an operational strategy for demand creation. Our model enables us to explore the effects through the lens of profit to the firm, consumer surplus, and environmental impact. Our analysis reveals that frequent product launches, high variety, and low prices—the three salient features of ultra-fresh fashion—are driven by both the product design agility and the customers’ interest for product freshness. The ultra-fresh strategy allows steady profit increase for the firm and benefits consumers in aggregate but may cause negative environmental impact if left unchecked. This paper was accepted by Jay Swaminathan, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06249 .

When organizations adopt artificial intelligence (AI) to recognize individuals’ negative emotions and accordingly allocate limited resources, strategic users are incentivized to game the system by misrepresenting their emotions. The value of AI in automating such emotion-driven allocation may be undermined by gaming behavior, algorithmic noise in emotion detection, and the spillover effect of negative emotions. We develop a game-theoretical model to understand emotion AI adoption, particularly in customer care, and analyze the design of the associated allocation policies. We find that adopting emotion AI is valuable if the spillover effect of negative emotions is negligible compared with resource misallocation loss, regardless of algorithmic noise and gaming behavior. We also quantify the welfare impacts of emotion AI on the users, organization, and society. Notably, a stronger AI is not always socially desirable and regulation on emotion-driven allocation is needed. Finally, we characterize conditions under which leveraging the AI system is preferred to hiring human employees in emotion-driven allocation. We also explore the alternative application of using emotion AI to monitor strategic employees and compare it with hiring a human manager for monitoring. Intriguingly, algorithmic noise may increase the profit of AI monitoring. Our work provides implications for designing, adopting, and regulating emotion AI.

Growing political polarization has reshaped the business landscape, shifting the mix of private and public politics companies face and requiring managers to develop more sophisticated nonmarket strategies. Yet the literature has largely continued to study strategies aimed at public and private politics in isolation. We analyze repertoires of nonmarket strategies and how they evolve over time in a formal game theoretic model of the policy life cycle, providing an integrated framework for studying public and private politics. Industry may resist activist campaigns or concede to them and may also lobby to block legislation when public politics is viable. The evolution of public opinion in a polarized world divides the game into two distinct phases, an initial phase of pure private politics and a subsequent phase with both public and private politics. Polarization delays social consensus and intensifies activism while increasing industry resistance to it. As public concern about an issue rises over time, industry resistance to activist campaigns weakens, and lobbying becomes costlier and less likely. We reveal important complementarities between strategies: The ability to lobby in the latter phase makes industry more resistant to activist campaigns during both phases of the life cycle. Our analysis demonstrates clearly and precisely how suboptimal strategic decisions may result from a failure to fully coordinate nonmarket strategy instruments.

We characterize how managers can curb manipulation by signaling information via stock repurchases in an environment with costly short selling and feedback effects from trading to firms’ access to capital. Without repurchases, manipulation coexists with informed trading at low shorting costs, reducing price informativeness and firm investment. Manipulation becomes less profitable as shorting costs increase, making prices more informative and boosting investment if speculators are less informed. Incentives to manipulate cease to exist at moderate short selling costs, but making shorting more costly reduces price informativeness and firm investment. Our model shows how contracts that precommit funding and condition future investment on stock prices can induce managers to eliminate manipulation through stock repurchases, facilitating optimal investment policies. An important policy implication is that when stock repurchases are allowed and shorting costs are low, short selling bans will typically reduce investment efficiency.

We examine a scenario in which an employee exerts productive effort to achieve a target but can also manipulate a performance measure to falsely claim target achievement. Although manipulation is detrimental from a societal perspective because of its negative externalities, the firm’s shareholders may benefit from undetected manipulation. We analyze the firm’s optimal implementation of internal control and its impact on the employee’s productive effort and manipulative behavior. We demonstrate that a profit-maximizing firm primarily utilizes internal control to enhance the employee’s performance pay contract; however, this does not necessarily reduce manipulation. We identify situations in which internal control reduces or aggravates manipulation and discuss factors that can complement internal control to combat manipulation.

We develop an economic model of a decentralized exchange with concentrated liquidity (e.g., Uniswap v3 and v4), with a particular focus on the economics of liquidity provision. We demonstrate that providing liquidity for a risky/risk-free asset pool is comparable to investing in a covered call, except that the call option therein is sold at intrinsic rather than market value. Hence, when providing liquidity, liquidity providers forgo the time premium of the call option in exchange for fees, and thus equilibrium liquidity provision decreases in the time premium. Finally, we provide an expression for equilibrium liquidity provision that is useful for empirical work.

In this article, we investigate how information about production externalities, such as ecolabels, can be presented to create market pressure on firms to reduce these externalities. Specifically, we explore whether integrating information on externalities with consumer product ratings into a single combined rating can generate pressure to reduce externalities from all consumers, not just environmentally conscious ones. Theoretically, we demonstrate the existence of an equilibrium where producers invest in both high product quality and low production externalities. We show that under separate ratings, this equilibrium depends on a high proportion of “green” consumers, whereas a combined rating achieves similar results without such a requirement. More broadly, our model suggests that bundling ratings can compel producers to address niche concerns more effectively. We experimentally validate this prediction across two studies, confirming that bundling ratings significantly increases producer investment in additional attributes, such as reducing externalities, while maintaining the rating system’s ability to incentivize high product quality.

“Superstar” markets, where a small portion of individuals earn disproportionately high incomes, are common in fields like entrepreneurship, sports, and entertainment. Participants in these markets face significant income uncertainty, which can deter entry or prompt early exit. To address this difficulty, we propose income pools: contracts where individuals agree to share a portion of future earnings with pool members if a specific salary milestone is achieved. To date, hundreds of income pool contracts have been signed in practice. This paper develops the first mathematical model to analyze such contracts, focusing on stability (i.e., pools where no agents leave or join). When constrained to creating a single pool, we show that a stable pool always exists with specific structural properties. Generally, stronger agents tend to be more “collaborative” and favor larger pools, whereas weaker agents are more “selfish” and prefer smaller pools. Next, we analyze pools that adhere to a maximum size (mirroring current practice) and show that stability persists. These pools typically require more weaker agents than strong ones and we find an interesting “Pareto dominance” result, whereby all agents in a stable pool prefer a particular unique stable pool. Finally, we study general partition structures, prove that a stable partition always exists under certain conditions, and provide an algorithm to construct such a partition. We conclude with a case study on data from 2,000 professional baseball players to demonstrate a 20%–30% increase in social welfare if players join income pools, under varying contract parameters.

Innovation contests incentivize the participants’ to exert effort toward combining (recombining) their existing knowledge to create solutions. In the current work, we consider the case of serial contests, where effort to create solutions for earlier contests can also expand the participant’s knowledge, which can then be valuable in future contests. We develop a novel framework that explicitly includes the generation and utilization of knowledge by participants in knowledge-driven serial innovation contests, and we analyze the implications of this framework for optimal incentive design. Analysis of our model reveals that the efforts expended by participants in a contest can depend on future rewards, especially when learning emerges as a “side effect” of execution effort (i.e., learning while doing). In fact, participants will exert effort in an earlier contest even when its associated reward is zero. In contrast, when explicit knowledge generation effort is feasible (i.e., learning before doing), the contest designer should increase the reward for the earlier contest to prevent participants from postponing their learning. Our model demonstrates that whether one should assign higher reward to the earlier or later contest depends on the mode of learning, the participant pool’s ex ante knowledge, and the transferability of learning from one contest to the next.

Choice models, which specify the consumers’ choice probabilities of options over a given choice set, are widely studied and applied in many fields. We propose a temporal tree representation of choice that covers all rational choice models. Compared with existing structural choice models, the tree representation exhibits two major advantages that address the key challenges of model identification. First, all rational choice models have a tree representation, and a subclass of tree representation (with set-dependent branching) has a one-to-one correspondence with the rational choice models. This bridges the gap in the existing characterizations of structural models, which are unidentifiable, impose uninterpretable conditions, or do not cover the entire space of rational choice models. Second, the tree representation allows for the flexibility of systematically specifying the choice model structure based on available knowledge and data. In particular, the number of parameters needed to specify a tree representation can be primarily determined by the sufficient knowledge level, which corresponds to a specific layer of the tree branching. The sufficient knowledge level can be empirically determined based on the amount of available data, which, in turn, determines the number of parameters needed for model estimation. Therefore, the tree representation allows for a natural way of data integration, avoiding misspecification from restrictive assumptions and overfitting for general models.

We model social media as collections of users producing and consuming content. Users value consuming content, but due to scarce attention, they may not value all content from other users. Users also value receiving attention, creating the incentive to attract an audience by producing valuable content but also through attention bartering—users mutually becoming each other’s audience. Attention bartering shapes substantially the patterns of production and consumption on social media, explains key features of social media behavior and platform decision making, and yields sharp predictions that are consistent with data we collect from #EconTwitter. We conduct Twitter and Instagram user surveys that yield additional direct evidence in support of attention bartering, and we discuss the implications of attention bartering for the design of social media platforms.

Risk pooling has become an increasingly critical tool for managing risks among corporations, institutions, states, and nations, with examples including multinational pooling, decentralized insurance schemes, catastrophe risk pooling, and burden sharing for nuclear accidents. Although there has been a rich literature on such practices, little is known from a theoretical viewpoint regarding the operational strategies of risk pools and, in particular, on issues about the effect of a pool’s expansion on each existing member’s welfare. This paper is the first to explore these issues by establishing different notions of consensus for the expansion of a risk pool: strong consensus, where both existing members and new candidates improve their risk measures due to the pool’s expansion, and weak consensus, which refers to the willingness of existing members to remain in the pool. Under optimal risk sharing for each pool, we show that its properties regarding expansion’s effects depend strongly on the underlying pricing rule. For instance, only with risk-adjusted equilibrium pricing are existing members willing to accept highly risky new members, whereas simple linear pricing excludes such members from the pool. Additionally, the impact of exogenous reinsurance on consensus is analyzed under both pricing rules. The established framework offers valuable managerial insights and policy implications, which we illustrate through two indicative case studies based on real data.

This paper introduces a second-price auction with a flexible reserve price, where the reserve price for the top bidder is not fixed but rather determined as a function of all losing bids. We show that when bidders are ex ante heterogeneous, a second-price auction with an optimally chosen flexible reserve price not only generates strictly higher expected revenue than that of a second-price auction with any fixed reserve price but is also revenue-maximizing among all mechanisms that implement discrimination-free social choice functions. We also provide an axiomatic characterization of all second-price auctions with a flexible reserve price. Finally, we discuss real-life implementation.

This paper investigates how autonomous vehicles (AVs) may reshape morning commute travel patterns and impact parking in a central business district (CBD). We develop a continuous-time game-theoretic traffic model that takes into account key economic deterrents to driving, such as parking fees and traffic congestion, and characterize the departure time and parking location (inside or outside the CBD parking area) patterns of commuters in equilibrium. Our analysis shows that all AV commuters may choose to park outside the CBD in equilibrium, increasing both vehicle hours and vehicle miles traveled as compared with the case with all human-driven vehicles. This change increases the total system cost and suggests a potential for CBD land use changes (e.g., repurposing CBD parking spots as commercial and residential areas) after the mass adoption of AVs. To reduce the total system cost, an urban planner may regulate commuters’ decisions by adjusting parking fees and/or imposing congestion tolls as a short-term measure, or adjusting infrastructure, for example, converting CBD parking spaces to drop-off spots for AVs. Our results indicate that these measures can reduce the total system cost substantially (e.g., up to 28.5% when calibrating our model to data from Pittsburgh).