The enforcement of peer-to-peer (P2P) registration requirements in mid-2018 triggered a P2P market meltdown, highlighting the inherent challenge faced by Chinese market participants in distinguishing between genuine and fraudulent fintech firms. The difference-in-difference results suggest that the too-big-to-fail (TBTF) perception can effectively halve investor outflows and borrower outflows during periods of uncertainty. Dynamic analysis further validates the parallel-trend assumption and underscores the persistent influence of TBTF perception. Moreover, the empirical findings suggest that, in the face of a market downturn, fintech market participants become unresponsive to all other certification mechanisms, including venture capital participation, custodian banks, and third-party guarantees.

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

This paper represents the first attempt to show how heterogeneous shock experiences help explain the enduring scars on household future behaviors. Using a large-scale household survey with 15,652 observations combined with geospatial transportation big data, we identify a novel belief-updating mechanism through which crises may exert prolonged impacts on household asset allocation and consumption patterns. An increase in the duration of previous lockdown experience is associated with a 10.52% escalation in enhanced anxiety for future precautionary saving motivations. This experience-based learning perspective supports the resolution of long-lasting overreactions to negative shocks via belief revisions and extends to households’ consumption behaviors. The lingering effects continue to skew households' beliefs even when conditions improve. Additionally, households with different individual-based shock experiences may exhibit varying perceptions of external shocks, resulting in disparate belief revision processes.

This study examines the effects of the robotic subsidy policy in China’s manufacturing sector. The demand-side subsidy policy aims at encouraging manufacturing firms to invest in robotics by lowering the cost of purchase. Our difference-in-difference analysis reveals distributional impacts of municipality-level robot subsidies on manufacturing firms of different scales. Although the subsidy brings a 14.2% increase in the application of robot patents, the facilitated access to robotics has not transformed into new firm entries. Strikingly, new firm entry decreases by 23.5% after the policy implementation. On the other hand, robot subsidies have increased the revenue, total asset, and employment of larger manufacturing firms by 9.8%, 6.9%, and 6.7%, respectively. To interpret the mechanism, we develop a simplified framework incorporating financial frictions into a task-based model. The model reveals that idiosyncratic borrowing costs lead to an inefficient equilibrium by generally depressing automation adoption and creating automation dispersion across firms. Such ex-ante distortion results in a uniform subsidy disproportionately benefiting firms with better capital access, thus creating a trade-off in terms of efficiency: while the subsidy can enhance overall automation, it simultaneously exacerbates automation dispersion. To quantify the efficiency implications, we embed this simplified model into a dynamic heterogeneous-agent framework, calibrated to the 2010 productivity distribution, financial frictions, and robot density in the industrial sector in China. Our dynamic model reveals that a 20% robot subsidy narrows the gap between mean and optimal automation level by 22% percentage points, while raises automation dispersion by 49%. This results in a 1.23% increase in aggregate output at the cost of a 2.40% decline in TFP. This dynamic model proposes a novel mechanism that automation exacerbates capital misallocation by enlarging asset accumulation dispersion between workers and entrepreneurs. Controlling for this dynamic feedback could enhance the subsidy-induced output gain by an additional 26%

In recent decades, over 40% of dollar-denominated corporate bonds have been issued by non-US firms. Strikingly, these foreign issuers face an extra discount of 20 bps than their US counterparts. While standard risks fail to account for the discount, the Economic Policy Uncertainty index from Baker, Bloom, and Davis (2016) can explain a substantial portion of this discrepancy, consistent with uncertainty-based model calibrations. Moreover, such foreign discount (USA effect) dominates the dollar safety premium (USD effect). My findings highlight the foreign discount effect in interna- tional corporate bonds, particularly amidst escalating global economic instability and uncertainty.

How does information sharing affect consumers' usage of FinTech credit? Using a unique dataset of ``Buy Now, Pay Later (BNPL)" users from a large digital platform and exploiting a credit reporting policy change, we document that consumers significantly reduce their usage of BNPL credit when the BNPL lender becomes subject to credit reporting regulation. This reduction is more pronounced among borrowers with previous default records, who also become more disciplined in repayment behaviors, than those without such records. The decrease in BNPL usage also leads to a reduction in online consumption, supporting the financial constraint hypothesis. Our results suggest that information sharing can help alleviate overborrowing and overspending, with stronger effects observed among younger borrowers, those who previously consumed more, or those with credit cards. We also highlight the synergies between BNPL lending and Big Tech platforms' ecosystems, which imperfectly substitute for formal enforcement institutions.

This study empirically investigates the impact of dialect diversity on corporate investment efficiency under different levels of economic policy uncertainty. Our findings reveal that local dialect diversity enhances investment efficiency during stable periods, but this advantage significantly diminishes under high economic policy uncertainty. This reduction primarily arises from underinvestment and overly cautious decision-making by fragmented management during periods of turmoil. Further analysis reveals that this reduction is exacerbated by stronger internal governance, which emphasizes checks and balances, and mitigated by stronger external governance, which focuses on supervisory power. Our results remain robust when using alternative measures of main variables and employing topography as an instrumental variable.

We investigate the causal impact of a company's environmental and social (ES) risk on the borrowing costs of its peer firms (that share lending banks). Using a regression discontinuity design based on the voting outcomes of ES-related shareholder proposals in US public companies' annual meetings from 2005 to 2021, we find that the passage of ES-related proposals leads to an average increase of 38 basis points in the loan costs for peer firms in the subsequent year. The negative spillover is more pronounced for peers with lower bargaining power in their banking relations or having lower ex-ante ES scores, on credit lines rather than term loans, and during the earlier years, validating that banks indeed channel the spillover. Notably, the spillover is particularly significant if the peer firms locate in the same states as the focal firm, or when the proposals reflect a higher degree of disagreement between the proposing shareholders and the managers, or for loans issued by banks lacking prior incentives or expertise in pricing ES risks (``non-ES banks''). We interpret these findings as evidence that the passage of ES-related shareholder proposals releases new information related to peers' ES risks and especially raises the awareness of ES risks among non-ES banks, prompting them to adjust loan rates for their portfolio companies accordingly.

This study investigates how firms respond to restricted access to government information. Specifically, the Stop Trading on Congressional Knowledge (STOCK) Act, which limits the stock trading activities of government officials (hereafter referred to as politicians), reduces the willingness of politicians from federal executive branches to engage with firms. Utilizing this exogenous disruption in private communication, we employ a difference-in-differences approach to demonstrate that firms with significant government customers decrease the frequency of management forecasts more than other firms due to the STOCK Act. This reduction is more pronounced for firms where government sales are crucial to their performance and for those that serve as suppliers and government contractors. Further, the positive impact of the STOCK Act on voluntary disclosures is more significant for firms that ex-ante rely heavily on direct political engagements, as indicated by their discussions of political risk and political contributions, and for those expecting government support, as evidenced by higher competition levels within their industry. Conversely, the STOCK Act does not significantly affect the non-financial disclosures of these firms. Finally, consistent with findings on executive branch officers, our results indicate that congressmen are also involved in corporate communications and are effectively regulated on information exchange by the STOCK Act. Overall, these results justify the powerful supervisory impact of the STOCK Act on the U.S. government and capital market and help to facilitate a new U.S. government information disclosure policy for a fairer investment environment.

This paper explores the impact of political network on insider trading activities in China. We find that stronger political network discourages insider trading. Such effect is more pronounced among long-standing and high-level connections, and persists in the events of M&A and public policy announcement when insiders may make profitable informed trading. This finding points to new cost of being politically connected. In exploring the underlying mechanisms, we confirm that the muted insider trading is related to preferable financial and policy support, and are more pronounced for SOEs in provinces with stronger market force and legal enforcement.