Latest Research Publications

We study the effects of the disclosure of critical audit matters (CAMs) on an auditor’s audit effort and an investor’s scrutiny effort decisions and on investment efficiency. Both the auditor and the investor can prevent a bad investment by respectively auditing and scrutinizing the firm’s financial reports to detect misstatements about the investment value. Investment efficiency is determined by the investor’s total mix of information. The disclosure of CAMs helps the investor assess investment risk and infer the auditor’s effort and thus enables the investor to fine-tune scrutiny effort, which can in turn adversely influence the auditor’s effort decision. We show when and why the disclosure of CAMs increases or decreases ex ante audit effort, ex ante investor scrutiny, and investment efficiency. Our analyses have both testable empirical implications and policy implications.

Job-based psychological ownership arises when workers develop personal feelings of possession over various aspects of a job. Drawing on conservation of resources and regulatory focus theory, the current research adopts a resource-based perspective to suggest a double-edged effect on job performance, mediated by three forms of territoriality (marking, defending, expanding) and information exchange and moderated by individual regulatory focus. With a multistep process in Study 1, the authors develop and validate a territorial expanding scale. Among 358 employee-supervisor dyads, Study 2 tests the proposed model; job-based psychological ownership prompts employees to engage in territorial marking, defending, and expanding. Territorial defending correlates negatively with information exchange, territorial expanding is positively related to it, and territorial marking has no relationship with information exchange. Information exchange is positively related to job performance. Job-based psychological ownership impedes job performance through increased territorial defending and reduced information exchange, especially among employees with a prevention focus. It enhances job performance through increased territorial expanding and increased information exchange, particularly if employees have a high promotion focus. These findings have notable implications for research and practice.

This paper investigates the short- and long-term impact of large-scale military investment on civilian industrial growth by focusing on China’s first attempt to modernize its military sector between 1861 and 1894. Panel data from 1858 to 1937 suggest that the program generated positive effects on civilian firm entry, but these effects appeared only after the government relaxed constraints on the entry of private firms. Long-term analysis shows that counties that received more military investment through the program, driven by plausibly exogenous ex ante political connections, had greater output in civilian industries in the 1930s. Analysis of the mechanisms suggest that the program boosted local economies through input–output linkages, human capital accumulation, and the rise of modern banks.

The Chinese Communist Party began decoupling its policies and practices from Maoist communist ideology more than four decades ago. Yet, why does Maoism continue to have an impact on the behavior of Party members? In this study, we argue that, although the influence of Maoist ideology has become weaker among younger Party members and Party members with higher educational attainment, such ideological decay is countered by a process of secondhand ideological imprinting. Based on data from 1,298 non-state-owned Chinese listed firms for 2000–2017, we find that firms with Party-member board chairs file fewer patent applications and are more likely to commit patent infringement. These effects are weaker if a board chair is younger or has higher educational attainment. Importantly, the moderating effect of young age is reduced as the presence of older Party-member corporate directors in a region becomes more prominent. However, the moderating effect of education appears to be unaffected by the presence of older Party-member directors. These findings generate fresh insights on the dynamics of ideological decay and persistence.

This paper estimates the returns to an additional year of advanced undergraduate education among students who would otherwise graduate with a bachelor’s degree using administrative student records merged with survey data on earnings from the largest public university in Singapore. We use a regression discontinuity design that exploits the fact that in some faculties, enrollment in the fourth (honors) year is governed by a grade-based eligibility rule. Students with average grades in their third year just above the threshold are significantly more likely to complete the fourth year and have higher earnings after graduation relative to students who narrowly missed the grade cutoff. The implied earnings return to the additional year of college is about 12 % six months after graduation. Linked administrative tax data confirm persistent gains in the longer run for at least 4 years after graduation. Moreover, the return remains substantial even after the university lowers the threshold to encourage more students to enroll in the fourth year, suggesting an effect not entirely driven by signaling of student ability.

We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD’s current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances.

Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.

A university uses both early-stage selection outcome (high-school affiliation) and late-stage admission test outcome (standardized test scores) to select students. We use this model to study policies that have been proposed to combat inefficient gaming in college admissions. Increasing university enrollment size can exacerbate gaming and worsen the selection outcome. Abolishing standardized tests for university admissions increases gaming targeting high-school admissions and worsens the selection outcome, whereas eliminating high-school ability sorting may improve the university selection outcome under some cost conditions of gaming. Committing to a lower-powered selection scheme can improve the selection outcome by reducing gaming behaviors.

New products are highly valued by manufacturers and retailers due to their vital role in revenue generation. Product life cycle (PLC) curves often vary by their shapes and are complicated by promotional activities that induce spiky and irregular behaviors. We collaborate with JD.com to develop a flexible PLC curve forecasting framework based on Bayesian functional regression that accounts for useful covariate information, including product attributes and promotion. The functional model treats PLC curves as target variables and includes both scalar and functional predictors, capturing time-varying promotional activities. Harnessing the power of basis function transformation, the developed model can effectively characterize the local features and temporal evolution of sales curves. Our Bayesian framework can generate initial curve forecasts before the product launch and update the forecasts dynamically as new sales data are collected. We validate the superior performance of our method through extensive numerical experiments using three real-world data sets. Our forecasting framework reduces the forecasting error by 5.35%–30.76% over JD.com's current model and outperforms alternative models significantly. Furthermore, the estimated promotion effect function provides useful insights into how promotional activities interact with sales curves.