學術論文

With the rapid growth of omnichannel retailing and the takeaway delivery economy, the classic point-to-point mode for on-demand delivery is deficient in delivery capacity, coverage area, dispatching efficiency, and courier safety assurance. Inspired by the success of Dabbawala, a historical Indian company for lunch delivery, we propose a novel public on-demand delivery service system that uses the public transit network to satisfy stochastic delivery demands. In particular, the proposed system includes a radial public transit network for intermediate transshipment, as well as couriers with e-bikes for terminal pick-up and drop-off. Our research aims to generate system design that minimizes the sum of penalty costs from lost sales and operational costs associated with courier terminal delivery distance. Solving the integrated system optimization problem relies on incorporating operational details, especially the allocation strategies of the lines’ capacity and the couriers’ terminal traveling modes. For the former, we propose a novel flexible design, called dual long-chain design, to improve flexibility. For the latter, we propose an elegant approximation of optimal service region partitioning that minimizes the expected terminal delivery distance and the resulting costs, without compromising delivery timeliness. Leveraging the theoretical results of the operational strategies, we simplify the integrated optimization problem and propose an efficient approximation algorithm. Finally, we validate the advantage of the proposed system over classic point-to-point delivery in satisfying demands and reducing costs through extensive numerical experiments, providing managerial insights in handling massive on-demand delivery demands and utilizing the idle capacity of the public transit system.

Utilizing 1.54 million judicial judgments from enterprise-to-enterprise litigation between 2014 and 2019 in China, we provide evidence of municipal leaders exerting influence over the courts to favor enterprises connected to them. By leveraging variations in enterprise connections resulting from official turnover, we show that enterprises with connections to party leaders have higher chances of winning in business litigation than unconnected enterprises. We also examine the impact of the staggered roll-out of circuit courts, a top-down institutional reform, on cronyism in the courtroom. Our findings show that this reform has effectively reduced the judicial advantage enjoyed by connected enterprises by two-thirds. By contrast, the trial live-broadcasting reform increases visibility but is not associated with a reduction in the effect of political connections, suggesting that different forms of judicial bias require different monitoring approaches.

Many emerging economies employ preferential credit policies that target selected sectors. This paper quantifies the implications of such policies for aggregate productivity and welfare. Using Chinese firm-level data from 2009–2020, we first document that sectors with higher markups receive larger credit subsidies and exhibit higher revenue-based productivity. Motivated by these facts, we develop a multi-sector quantitative model with endogenously determined markups and calibrate it to match the distribution of sales both within and across sectors. We find that preferential credit subsidies raise aggregate productivity and welfare by reallocating market shares toward high-markup sectors. These gains persist in an extended framework with endogenous firm entry.

We examine whether and how common ownership affects Environmental, Social, and Governance (ESG) ratings—an important research question given the increasing use of these ratings in investment decisions and corporate evaluations. We find that companies with major shareholders in common with the rating agency (“sister firms”) tend to receive higher ESG ratings. When a company becomes a sister firm through a change in the rating agency's ownership structure, its rating from that agency is subsequently upgraded, whereas its ESG ratings from other agencies remain unchanged. Sister firms exhibit greater rating disagreements across agencies than other firms. The higher ESG ratings for sister firms are partly attributable to the transfer of immaterial positive ESG information through common owners. The common ownership effect is more pronounced when the owner can exert a greater influence on the rating agency. Moreover, sister firms with initially elevated ratings demonstrate poorer future ESG performance. Overall, our findings suggest that owners can affect ESG ratings of their portfolio companies in a way consistent with their influence and interest.

Trust is among the most critical factors in buyer-supplier relationships. In an effort to understand the origins of trust, power has become the focus of a burgeoning body of literature. However, research on the association between power and trust has been plagued by inconsistencies in terms of whose power and trust are being examined, which has led to confusion and hindered cumulative progress. We address this issue by disentangling the effect of the focal organization (actor effect) from the effect specific to its partner (partner effect) and accounting for both simultaneously. We further theorize and show that the partner's level of self-promotion communication about his or her own achievements and credentials moderates both actor and partner effects, thus adding knowledge about a contingency that accounts for a considerable degree of variation in the linkage between power and trust. Using multi-informant, dyadic survey data paired with archival information scraped from firms' webpages, we find that (i) actors low (vs. high) in power tend to place more trust (ii) while simultaneously eliciting higher levels of trust from their partners; however, (iii) these effects differ markedly depending on the partner's level of self-promotion communication. Our study offers a novel, integrative perspective on power and trust, and we elaborate on its important implications for understanding buyer-supplier relationships.

The structure of a special purpose acquisition company (SPAC) generates significant information asymmetry for public investors and provides a special role for its sponsors. Leveraging the unique characteristics of SPACs, this study reexamines the debate over the role of managerial network centrality in affecting M&A outcomes, namely, whether network centrality allows for more private benefits or rather adds value to firms and helps reduce information asymmetry. Specifically, we show that sponsors’ private equity (PE) and venture capital (VC) connections, measured by their network centrality, explain a large portion of postmerger return variation in the cross-section. A one-standard-deviation increase in sponsors’ network centrality leads to a 1.9% higher merger and acquisition success probability and a 1.3% higher postmerger monthly abnormal return. We attribute this outperformance of firms with high managerial PE network centrality to superior fundraising and deal-sourcing abilities. Moreover, this effect is particularly pronounced in a cold market and among SPAC sponsors with more “skin in the game.” Overall, we show that network connections reflect managers’ value-creation ability more than their rent-seeking capacity; these connections can add value to mergers and help alleviate information asymmetry and associated moral hazard issues, especially when managers’ private benefits are closely linked to postmerger firm performance.

Using data on Internet news reading, we measure fund-level attention to both aggregate and firm-specific news and relate it to fund portfolio allocation decisions. In the time series, we find that funds shift attention toward macroeconomic news during periods of high aggregate volatility. Those funds that exhibit stronger attention-reallocation patterns earn higher future returns. In the cross-section of fund portfolios, fund attention is positively related to stock holdings. Furthermore, fund attention to a stock increases the value-add of that position to the fund's performance. This relationship is stronger using fund attention to more value-relevant news articles.

We investigate investors' voluntary disclosure decisions under uncertainty about their information endowment. In our model, an investor may receive initial evidence about a target firm. Conditional on learning the initial evidence, the investor may receive additional evidence that helps them interpret the initial evidence. The investor takes a position in the firm's stock, then voluntarily discloses some or all of their findings, and finally closes their position after the disclosure. We present two main findings. First, the investor will always disclose the initial evidence, even though the market is uncertain about whether the investor possesses such evidence. Second, the investor's disclosure strategy of the additional evidence increases stock price volatility: they disclose extreme news and withhold moderate news. Due to the withholding of the additional evidence, misleading disclosure arises as an equilibrium outcome, where the investor's report decreases (increases) price despite their news being good (bad). These results remain robust when considering the target firm's endogenous response to the investor's report.

In this paper, we propose a Deep-DiD method that incorporates two deep neural networks in a difference-in-difference (DiD) framework to estimate heterogeneous treatment effects (HTEs). The dual-network architecture contains one neural network modeling HTEs as a nonparametric function of pretreatment features and another neural network capturing individual and time fixed effects. Through a series of simulations, we show that our method can uncover the true HTEs with high accuracy under various settings and demonstrates more robust estimation performance compared with existing methods like linear models and random forests. We apply this method to an empirical setting where a large video-sharing platform introduced a “Creator Signing Program” aimed at signing creators and motivating them to generate more high-quality video content. Leveraging a matched data set of signed and unsigned creators, we employ our Deep-DiD method to estimate the HTEs of the signing program. Our method can help the platform optimize creator selection by identifying creators with the highest-estimated treatment effects. Through out-of-sample tests, we show that creators selected by the Deep-DiD method experience substantially larger actual performance jumps than those selected by the platform. Creator selection based on the Deep-DiD method also consistently outperforms that based on linear models.