学术论文

We study whether access to local pollution information causes investors to make greener portfolio allocations, exploiting the rollout of air quality monitoring stations in India. Using a triple-differences framework on the trading records of 19 million investors, we show that retail investors’ holdings in “brown” stocks become more negatively related to local pollution after a nearby station appears. This effect is more pronounced on “alert” dates when air quality is reported to be harmful. The effect is strongest among tech-savvy investors likely “treated” by real-time pollution data, and younger investors, who may be more sensitive to environmental concerns.

We document a prominent yet underappreciated feature of the post-2000 Chinese economy: a vertical structure in which key upstream industries are dominated by state-owned enterprises (SOEs), while downstream industries are largely open to private competition. We develop a general-equilibrium model to analyze how this vertical structure—interacting with industrialization, globalization, and labor abundance—has shaped the Chinese economy. The framework offers new insights into SOE profitability, structural change, resource misallocation, and economic reform. First, upstream SOEs become more profitable as downstream private firms raise their productivity and face greater external demand during industrialization and globalization, helping explain the unprecedented prosperity of SOEs between 2002 and 2007. Second, reducing upstream market power would facilitate labor reallocation from agriculture to non-agricultural sectors, raising GDP and aggregate welfare. Third, preferential credit subsidies to SOEs can improve welfare by alleviating upstream under-supply; consequently, removing these subsidies without dismantling SOE monopoly power could reduce welfare. Quantitative analyses using firm-level data support the theoretical findings. We further show that this vertical structure can arise endogenously as an equilibrium outcome.

This paper investigates how local bureaucrats in China respond to minimum targets for air pollution control, which are part of a hybrid incentive contract that simultaneously specifies high-powered incentives for economic growth. Using a novel prefecture-day level dataset on air quality and applying a Regression Discontinuity Design, we find strong evidence that air quality tends to improve when the air quality target is doomed to fail, but deteriorates significantly once early fulfillment of the target is secured. We develop a theoretical model to rationalize these findings and highlight the role of uncertainty faced by local bureaucrats in shaping their responses. Further analyses explore how local economic pressure, the punishment loss for failing to meet the target, regional differences, and political turnovers affect bureaucratic strategic behavior after the target completion status becomes known. This study sheds light on how hybrid incentive contracts function in a multitasking context.

A platform matches a unit mass of sellers, each owning a single product of heterogeneous quality, to a unit mass of buyers with differing valuations for unit-quality. After matching, sellers make take-it-or-leave-it price-offers to buyers. Initially, valuations of buyers are only known to them and the platform, but sellers make inferences from the matching algorithm. The efficient matching is positive assortative, but buyer-optimal matchings are stochastically negative assortative when there are few low-value buyers (i.e., compared to lower-quality sellers, high-quality ones are matched to buyers with lower expected valuation). Although everyone trades, generating rents for the side lacking bargaining power results in inefficient matching.

Domestic outsourcing is known to reduce worker wages, but its effect on employment security — a key dimension of job quality — has not been studied. Using Brazil’s comprehensive employee–employer linked data, we find that outsourcing reduces exit from formal employment among cleaners and security guards during their first few years of tenure. The observed reduction in employment hazard is larger in cities with greater volatility in labor demand. The reduction is not attributable to differences in worker characteristics or differential exposure to local labor market shocks. The estimates suggest that outsourcing had larger positive effects on the net present value of worker earnings than implied by wage differentials alone. The patterns are consistent with a search-theoretic model in which outsourcing eases reassignment across firms.

Over the past decades, research on gender bias in leader evaluations has proliferated across multiple disciplines, significantly expanding contexts, outcomes, and theoretical perspectives examined. Despite these valuable contributions, the literature remains fragmented in explaining the persistent variability in how and why gender bias manifests, from severe penalties against women leaders in certain contexts to evaluative advantages in others. To resolve these discrepancies, we shift the focus from leaders to the motivated processes driving observer evaluations. We begin with an integrative review of research, revealing that observers, ranging from supervisors and subordinates to clients and investors, are not conduits of stereotypes but active evaluators whose motives shape how they selectively appraise women leaders. Drawing on motivated cognition theory, we develop a novel motive-driven process model that identifies three core directional motives: identity protection, value alignment, and resource dependence. Using this model, we integrate the literature by highlighting individual-level and contextual antecedents of each motive and explicating how motives drive distinct selective appraisal processes. By unpacking “why” and “how” observers evaluate women leaders through motivated processes, our model also offers targeted interventions that address observer motives rather than changing women’s behaviors, underscoring a pressing need to engage various stakeholders in addressing gender bias in leader evaluations.

In this paper, we characterize a forecasting model where forecasters cannot perfectly distinguish between the two persistent components (trends and cycles) in a dynamic setting. In this model, forecasters jointly update their beliefs about the two components: noisy information about one component is used to update beliefs about the other component. We present diagnostic empirical facts on forecasting behaviors and show that these facts are consistent with our model’s predictions while contradicting those of existing models in the expectation formation literature. To validate our model, we exploit the Federal Reserve’s 2012 adoption of explicit inflation targeting as a policy shock. Structural estimation reveals that this policy change altered the underlying data-generation process, and the corresponding changes in forecasting behavior indeed align with our model’s predictions. Finally, we revisit the standard Forecast Error-Forecast Revision regression approach in this literature. We examine its robustness within our enriched framework and reveal that trend-cycle confusion can interact with behavioral bias and generate horizon-dependent overreaction patterns documented in empirical studies.

A genealogical training process, in which senior (advisor) scientists mentor and train junior (advisee) scientists is one of the core organizational features of modern science. In this paper, we examine a key question faced by all junior scientists during their training: What impact does an advisee’s research agenda overlap with his or her advisor have on the advisee’s career-relevant performance outcomes? To answer this question, we constructed a novel, bibliometric-record-based data set on 11,289 U.S. biomedical scientists (advisees) who were trained in 5,632 principal investigator advisors’ labs between 1985 and 2009. We examined the relationship between advisor–advisee research overlap and an array of performance outcomes for emerging scientists, revealing a consistently positive relationship between high advisor–advisee research overlap and the junior scientist’s early-career funding outcomes. We further provide evidence that this positive relationship rests upon enhanced tacit knowledge transfer, as well as providing suggestive evidence for the boundary conditions of an intellectual independence imperative and potential competition between advisors and advisees. Taken together, these findings provide a more complete understanding of how advisor–advisee relationships shape new scientists’ performance during their early careers.

We study dynamic contracts that incentivize an agent to exert effort to increase the arrival rate of a Poisson breakthrough, where both the effort cost and the effort level at any time are the agent’s private information. Optimally, the principal offers a menu of contracts, each tailored to an agent type (with a different effort cost), specifying an initial payment, a contract deadline, and a payment-upon-arrival process over time. We first fully characterize the optimal contract menu in a two-type setting, where the agent is either a good (low-cost) or bad (high-cost) type. Specifically, the principal should hire the agent only if the breakthrough revenue exceeds a threshold. Above this threshold, if the bad agent’s cost is higher than another threshold, it is optimal to motivate only the good type to exert effort. The principal offers the good type a simple linear contract in which the payment-upon-arrival declines linearly over time until the deadline, whereas the bad type receives an initial payment and leaves immediately. The linear contract provides just enough incentive for the good agent to work. If the bad agent’s cost falls below the threshold, it becomes optimal to also motivate the bad agent to work using a linear contract, whereas offering the good agent a one-switch contract. The one-switch contract extends the linear form by allowing the payment-upon-arrival to take a single downward jump at a specific time before the deadline. The optimal contract structure extends to multiple-type cases, in which the one-switch contract becomes a multiple-switch contract. To obtain the entire menu of contracts, one only needs to solve a sequence of linear optimization problems together with a bisectional line-search, which is fast to compute and easy to interpret and implement.