学术论文

Although federal judges are the ultimate arbiters of insider trading enforcement, the role of their political ideology in insider trading is unclear. Using the partisanship of judges’ nominating presidents to measure judge ideology, we first document that liberal judges are associated with heavier penalties in insider trading lawsuits than conservative judges. Next, we find that firms located in circuits with more liberal judges have fewer opportunistic insider sales. Cross-sectional analyses show that this deterrent effect is stronger when managers face a higher risk of insider trading lawsuits. Finally, we find that the SEC considers judges’ ideology when selecting litigation forums.

We show that attention constraints of decision makers function as barriers to financial inclusion. Using administrative data on retail loan screening processes, we find that loan officers exert less effort reviewing applicants from unattractive social or economic backgrounds and reject them more frequently than justified by credit quality. More importantly, when quasi-random workload variations tighten officer attention constraints, unattractive applicants receive even worse treatment—review-time halves and approval rates drop by approximately 40%—while attractive applicants are not affected. Our findings suggest that financial technologies that reduce information-processing costs may promote more balanced financial access.

Workers often possess characteristics such as soft skills that are important for teamwork but unobserved by managers. In this paper, we develop a teamwork model based on the econometric teamwork framework in Bonhomme [Bonhomme S (2021) Teams: Heterogeneity, sorting, and complementarity. Becker Friedman Institute for Economics Working Paper No. 2021-15, University of Chicago, Chicago] and stochastic blockmodels for binary outcomes (e.g., Bickel et al. [Bickel P, Choi D, Chang X, Zhang H (2013) Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels. Ann. Statist. 41(4):1922–1943]) when only team-level outputs are observed. Our model does not impose any functional form restrictions on the complementarity between workers with unobserved characteristics, which are modeled as latent types. We apply our model to a data set from a leading Chinese real estate company; the data contain the complete history of team assignments, team performances, and property details. We find that complementarities between different agent types are heterogeneous and cannot be captured by commonly used production functions. More specifically, workers with intermediate solo performance complement all other workers the most, whereas those with the best solo performance are not the best team players. Our results suggest that firms can boost productivity by redesigning teams without incurring additional hiring costs. Leveraging our complementarity estimates, our counterfactual experiments demonstrate that reorganizing teams could enhance overall team output by up to 26.6%.

Revenue management decisions often involve both offline and online decisions. Offline decisions are made first and establish the broad and long-term operational context in which online decisions are frequently and repeatedly made, often in real time. We consider a joint optimization of offline and online decisions. Specifically, we examine a setting in which the offline decision concerns the selection of product-design characteristics (e.g., price, capacity, return eligibility, and other characteristics) and the online decision concerns the dynamic assortment optimization over a selling season. Our formulation has many applications, including optimizing products’ return eligibility and determining product discounts, and a key feature of our model is its explicit consideration of complex return dynamics and accompanying financial implications. We formulate an optimization problem that combines the impact of both offline and online decisions on the expected revenue. To determine the product design, we reformulate the choice-based deterministic linear program, solve its continuous relaxation, and round the resulting solution. Using value function approximations enables us to obtain a dynamic assortment policy whose expected revenue is at least a constant fraction of the choice-based deterministic linear program. Combining these two results, we show that our approach provides an approximate solution to the joint optimization problem with performance guarantees. Numerical experiments based on real transaction data from a major U.S. retailer show that our method achieves 95%–97% effectiveness, an advantage of up to 18% over methods that disregard the interplay between offline and online decisions. This framework also yields a systematic quantitative measure of the relative importance of both offline and online decisions. Based on this measure, numerical experiments highlight the crucial role of product design, accounting for 94% and 85% of the observed variation in effectiveness across various methods in applications involving volume discount and return eligibility, respectively.

An influential line of research emphasizes that colonial legacy plays a key role in formal financial development. Can colonial legacy also shape informal finance? We investigate the impact of colonial legacy on informal financial development using a manually georeferenced data set within a credible empirical framework. In the 19th century, Europeans arbitrarily designed colonial borders that partitioned many ethnicities across multiple countries in Africa. Leveraging several spatial regression discontinuity designs across national borders and within British-French–partitioned Cameroon and a unique natural experiment where the same former British colony is compared with two otherwise similar areas with different exposures to French colonization, we discover that former British colonies today have better informal financial development than former French colonies. Exploring the channels, we find that places with a British colonial legacy maintain a style of social control that facilitates information flow, supports private enforcement and market interactions, and promotes strong legal cultures.

This paper studies estimation of and inference on dynamic nonparametric conditional moment restrictions of high dimensional variables for weakly dependent data, where the unknown functions of endogenous variables can be approximated via nonlinear sieves such as neural networks and Gaussian radial bases. The true unknown functions and their sieve approximations are allowed to be in general weighted function spaces with unbounded supports, which is important for time series data. Under some regularity conditions, the optimally weighted general nonlinear sieve quasi-likelihood ratio (GN-QLR) statistic for the expectation functional of unknown function is asymptotically Chi-square distributed regardless whether the functional could be estimated at a root- rate or not, and the estimated expectation functional is asymptotically efficient if it is root- estimable. Our general theories are applied to two important examples: (1) estimating the value function and the off-policy evaluation in reinforcement learning (RL); and (2) estimating the averaged partial mean and averaged partial derivative of dynamic nonparametric quantile instrumental variable (NPQIV) models. We demonstrate the finite sample performance of our optimal inference procedure on averaged partial derivative of a dynamic NPQIV model in simulation studies.

This paper examines the impacts of technology training and buyer-supplier relationship on technology adoption and quality upgrading. We randomly varied subjects of each training group across farmer–exporter clusters—farmers, exporters, both, or none—and provided training on Good Agricultural Practices (GAP). We find that training farmers enhances technology adoption and quality upgrading. Yet, the effects are much stronger when farmers and exporters are trained together. We document a plausible mechanism to explain this finding: joint training improves buyer-supplier relationship, which facilitates contract trade between farmers and exporters. We find no effect of GAP certification eligibility on technology adoption.

We study the role of endogenous trust in amplifying ideological bias. Agents in our model learn a sequence of states from sources whose accuracy is ex ante uncertain. Agents learn these accuracies by comparing their own reasoning about the states based on introspection or direct experience to the sources' reports. Small biases in this reasoning can cause large ideological differences in the agents' trust in information sources and their beliefs about the states, and may lead agents to become overconfident in their own reasoning. Disagreements can be similar in magnitude whether agents see only ideologically aligned sources or diverse sources.

Each year, millions of user inventors spend billions of dollars creating innovations for their own use and to satisfy their own needs. Many user entrepreneurs also commercialize their user innovations to the mass market to benefit others and generate financial return. However, because user innovations are inherently self-focused, they often fail to achieve adoption in the mass market, leading to significant social welfare losses. Extant research suggests deploying customer-focused design modifications to improve the diffusion of user innovations. As an alternative to this customer-focused perspective, the authors propose a novel communication strategy that evokes adopters’ creativity to increase adoption. In particular, presenting user innovations’ self-focused goals and related key attributes in a sequence that mismatches adopters’ default innovation learning sequences can evoke adopters’ creative thinking regarding the self-relevance of the user innovations, and in turn enhances adoption. Furthermore, deploying self-focused, user inventor solo (vs. customer-focused, open customer participation) enhancement strategy sustains (vs. attenuates) the proposed effects.