学术论文

Over the past millennium, China has relied on the Confucian clan to achieve interpersonal cooperation, focusing on kinship and neglecting the development of impersonal institutions needed for external finance. In this paper, we test the hypothesis that the Confucian clan and financial markets are competing substitutes. Using the large cross-regional variation in the adoption of modern banks, we find that regions with historically stronger Confucian clans established significantly fewer modern banks in the four decades following the founding of China's first modern bank in 1897. Our evidence also shows that the clan continues to limit China's financial development today.

We develop a novel measure of volatility pass-through to assess international propagation of output volatility shocks to macroeconomic aggregates, equity prices, and currencies. An increase in country's output volatility is associated with a decrease in its output, consumption, and net exports. The average consumption pass-through is 50% (a 1% increase in output volatility increases consumption volatility by 0.5%) and it increases to 70% for shocks originating in smaller countries. The equity volatility pass-through is larger and in the order of 90%. A novel channel of risk sharing of volatility risks can explain our empirical findings.

Does a bank’s dependence on different external funding sources shape its voluntary disclosure of information? We evaluate whether economic shocks that increase the supply of bank deposits alter the cost–benefit calculations of bank managers concerning voluntary information disclosure. We measure information disclosure using 10-K filings, 8-K filings, and earnings guidance. As for the funding shock, we use unanticipated technological innovations that triggered shale development and booms in bank deposits. Further analyses suggest that greater exposure to shale development reduced information disclosure by relaxing the incentives for managers to disclose information to attract funds from external capital markets.

We study a risk-averse newsvendor problem where demand distribution is unknown. The focal product is new, and only the historical demand information of related products is available. The newsvendor aims to maximize its expected profit subject to a profit risk constraint. We develop a model with a value-at-risk constraint and propose a data-driven approximation to the theoretical risk-averse newsvendor model. Specifically, we use machine learning methods to weight the similarity between the new product and the previous ones based on covariates. The sample-dependent weights are then embedded to approximate the expected profit and the profit risk constraint. We show that the data-driven risk-averse newsvendor solution entails a closed-form quantile structure and can be efficiently computed. Finally, we prove that this data-driven solution is asymptotically optimal. Experiments based on real data and synthetic data demonstrate the effectiveness of our approach. We observe that under data-driven decision-making, the average realized profit may benefit from a stronger risk aversion, contrary to that in the theoretical risk-averse newsvendor model. In fact, even a risk-neutral newsvendor can benefit from incorporating a risk constraint under data-driven decision making. This situation is due to the value-at-risk constraint that effectively plays a regularizing role (via reducing the variance of order quantities) in mitigating issues of data-driven decision making, such as sampling error and model misspecification. However, the above-mentioned effects diminish with the increase in the size of the training data set, as the asymptotic optimality result implies.

In today’s environment characterized by business dynamism and information technology (IT) advances, firms must frequently update their enterprise information systems (EIS) and their use policies to support changing business operations. In this context, users are challenged to maintain EIS compliance behavior by continuously learning new ways of using EIS. Furthermore, it is imperative to businesses that employees of a functional unit maintain EIS compliance behavior collectively, due to the interdependent nature of tasks that the unit needs to accomplish through EIS. However, it is particularly challenging to achieve such a collective level of EIS compliance, due to the difficulty that these employees may encounter in quickly learning updated EIS. It is, therefore, vital for firms to establish effective managerial principles to ensure collective EIS compliance of a functional unit in a dynamic environment. To address this challenge, this study develops a research model to explain collective EIS compliance by integrating theoretical lens on social context and performance management context with social capital theory. It proposes that social context, an organizational environment characterized by trust and support, positively affects collective EIS compliance by developing business–IT social capital that enhances mutual learning between business and IT personnel. Furthermore, the performance management context, an organizational environment characterized by discipline and “stretch,” is seen to have a direct and beneficial effect on collective EIS compliance as well as an indirect, moderating effect on the causal chain among social contexts, business–IT social capital, and collective EIS compliance. General empirical support for this research model is provided via a multiple-sourced survey of managers and employees of 159 functional units of 53 firms that use EIS, as well as their corresponding IT unit managers. The theoretical and practical implications of these findings are discussed.

Large tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern data collection capabilities. Often such observations are taken over time, forming tensor time series. In this paper we present a factor model approach to the analysis of high-dimensional dynamic tensor time series and multi-category dynamic transport networks. Two estimation procedures are presented along with their theoretical properties and simulation results. Two applications are used to illustrate the model and its interpretations.

The online trading platform Alibaba provides financial technology (FinTech) credit for millions of micro, small, and medium-sized enterprises (MSMEs). Using a novel data set of daily sales and an internal credit score threshold that governs the allocation of credit, we apply a fuzzy regression discontinuity design (RDD) to explore the causal effect of credit access on firm volatility. We find that credit access significantly reduces firm sales volatility and that the effect is stronger for firms with fewer alternative sources of financing. We further look at firm exit probability and find that firms with access to FinTech credit are less likely to go bankrupt or exit the business in the future. Additional channel tests reveal that firms with FinTech credit invest more in advertising and product/sector diversification, particularly during business downturns, which serves as effective mechanisms through which credit access reduces firm volatility. Overall, our findings contribute to a better understanding of the role of FinTech credit in MSMEs.

警方包庇有组织犯罪的情况偶有所闻，但往往很难通过实证来研究。研究利用来自中国独特的性交易数据，证实地方警察或对一些有组织的卖淫活动提供庇护。具体而言，位于警力密度较高的社区内的桑拿浴室和按摩院，受到警方「保护」的机会较高，进而可以进行高风险、高罪责的卖淫活动。在地方警察「扫黄」期间，这种庇护效应更加明显，意味着选择性执法的存在。地方领导层的变化和中央政府纪律部队巡视则会显著减弱这种庇护效应。

Accessibility of Electric Vehicle (EV) charging stations is an important factor for adoption of EV, which is an effective green technology for reducing carbon emissions. Recognizing this, many governments are contemplating ideas for achieving EV adoption targets, such as constructing extra EV charging stations directly or offering subsidies to entice automakers to construct more EV charging stations. To achieve these targets, governments need to coordinate with automakers to ensure that the total number of charging stations is planned optimally. We study this coordination problem by considering the interactions among the government, automakers, and consumers, our equilibrium analysis yields three major results. First, both the government and the automaker should build extra EV charging stations when their construction costs are independent. Simultaneously, the government should offer a per-station subsidy to the automaker only when the adoption target and the construction cost are both high. However, when the construction costs are dependent, the government should delegate the construction to the automaker by offering a per-station subsidy. Second, when the government considers consumer purchase subsidy as an extra lever, we find that the purchase subsidy for consumers is more cost-effective than offering a per-station subsidy to the automaker. Third, the structure of the optimal government policy remains the same regardless of whether the government's goal is to improve EV adoption or consumer welfare. Our results can serve as guidelines for governments when contemplating coordination with automakers for the construction of EV charging stations to improve EV adoption as well as consumer welfare further.