學術論文

This study investigates how firms engage in information-seeking lobbying to address trade policy uncertainty. I argue that lobbying enables firms to gain early insights into forthcoming tariff actions, allowing them to strategically stockpile products likely to be targeted. Using shipping records of US firms during the 2018 US–China trade war, I find that lobbying firms increased imports of soon-to-be-tariffed products before tariff lists were publicly released, compared to non-lobbying firms. This selective stockpiling pattern disappeared after tariff announcements. Further analysis shows that lobbying firms were less likely to request tariff exemptions for products they had preemptively stockpiled, suggesting that information-seeking lobbying during policy formulation provides an additional benefit by reducing the need for costly government engagement during the implementation phase.

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 develop a transparent Bayesian framework to measure uncertainty in asset pricing models. By assigning a modified class of -priors to the risk prices of asset pricing factors, our method quantifies the trade-off between mean–variance efficiency and parsimony for asset pricing models to achieve high posterior probabilities. Model uncertainty is defined as the entropy of these model probabilities. We prove the model selection consistency property of our procedure, which is missing from the classic -priors. Acknowledging the possibility of omitting true asset pricing factors in real applications, we also characterize the maximum degree of contamination that the omitted factors can introduce to our model uncertainty measure. Empirically, we find that model uncertainty escalates during major market events and carries a significantly negative risk premium of approximately half the magnitude of the market. Positive shocks to model uncertainty predict persistent outflows from US equity funds and inflows to Treasury funds.

Why do seemingly similar firms show such different productivity? We argue that unresolved measurement problems, i.e., the persistent incongruity between economic transactions and their accounting representation, affect resource allocation. Our metric quantifies technical accounting terminology in firm disclosures to capture these unresolved measurement problems, UMPs, using over 90,000 10-Ks. A one standard deviation increase in UMP is associated with lower capital investment (6 percent), R&D (5 percent), and hiring growth (30 percent). We also find a reduction in total factor productivity (5 percent) and Tobin’s q (4 percent). Further, CEO compensation sensitivity to accounting performance decreases with UMP, whereas stock-based sensitivity remains unaffected. Our inferences continue to hold when we use a Bartik instrument, which exploits differential exposure to GAAP changes to isolate accounting-driven variation from the firm’s underlying economics. The results suggest unresolved measurement problems are a significant friction in resource allocation.

We examine whether mandatory tax information exchange agreements between governments have real effects on firms’ physical trade in tangible goods. We posit that some of the physical trade in tangible goods flowing through low-tax jurisdictions is intended to facilitate income shifting. As such, shocks to enforcement via mandatory information exchange agreements could cause firms to change the physical flow of goods. Using firm-level shipping container data, we find that adoption of bilateral tax information exchange agreements (TIEAs) between the U.S. and foreign jurisdictions is associated with significant decreases in the volume of imports by U.S. firms from those jurisdictions. We also find reallocation effects: U.S. firms increase imports from jurisdictions in the same subregion as the treated jurisdiction, resulting in minimal overall change in total imports. To our knowledge, ours is the first study to document a connection among enforcement-related tax disclosure, income shifting, and physical trade flows.

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.

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.

Although hospital-affiliated online health communities (OHCs) provide enormous potential for health promotion, their application can create uncertainty and complexities for existing off-line healthcare systems in terms of quality and equity concerns. Understanding how and why physicians’ off-line care quality and equity may change after joining an OHC is a critical yet underexplored question, particularly for patients with low socioeconomic status (SES), who are more likely to be impacted by such changes. This study seeks to quantify these effects using operation-level inpatient data along with the correlated activities of physicians and patients in an OHC sourced from a prominent hospital. Our empirical results show that physicians’ OHC participation is associated with a 3.87% (4.63%) reduction (increase) in the relative risk (safety) of mortality (recovery) for patients who engage in the community. To understand the mechanisms of change, we find that the enhancement of patient care continuity is a key mechanism through which OHC participation may improve off-line service quality. Study results also provide evidence of partial mediation for management and relational continuity in the process chains of OHC outcomes. Additionally, the impact of OHC participation is found to be far more pronounced for patients with low SES, suggesting that OHCs can promote the equity of off-line care quality. This study further tests how community activities along with the ladder of OHC interactions between physicians and patients affect off-line healthcare outcomes. It adds to the literatures on OHC, healthcare operations management, and public health as well as offers practical implications for service operations of online health platforms.

Digital platforms strive to filter out consumer reviews that are manipulated, which has become a common and increasingly vexing problem that can take anywhere from days to months to detect. Yet little is known about the consequences of such reviews on product market performance. Using data from the Apple App Store, we examine how the app’s ranking changes when it receives manipulated reviews that are later filtered out by Apple. Our findings reveal that both one-star and five-star manipulated reviews have a significant positive effect on app rankings within a week of posting. The positive effect of one-star manipulated reviews is particularly surprising, as it contradicts the expected effect of organic negative reviews and the intent behind using such reviews to harm competitors. We also explore how these effects evolve over time, shedding light on the role of filtering policies in mitigating distortions related to review manipulation. Results show that these effects become negative as platforms filter out manipulated reviews, but this process can take as long as six months to fully materialize. These findings highlight the need for digital platforms to increase their investment to promptly and accurately control review manipulation for the welfare of both businesses and users on the platform. To the best of our knowledge, this study is one of the first to empirically analyze the short-term and long-term effects of manipulated reviews on product sales, which provides crucial managerial implications for practitioners.