专业化分工是企业成长的重要基础和一国经济增长的根本源泉,同时也是新形势下提高供给质量和供给效率的重要途径。深入探究国内市场专业化分工的内在机理,对于增强国内大循环内生动力至关重要。文章以“创建社会信用体系建设示范城市”政策作为社会信用环境的代理变量,借助双重差分法,实证考察了社会信用环境改善对企业专业化分工的影响。研究发现,社会信用环境改善显著促进了企业专业化分工,这主要是通过降低企业面临的外部交易成本而实现。异质性分析发现,当内部管控成本较高时,社会信用环境改善对企业专业化分工的促进作用有所减弱；而在非国有企业、制造业以及法治环境较好的地区,社会信用环境改善的专业化分工效应更为明显。进一步分析表明,社会信用环境改善对企业专业化分工的推动作用促进了企业全要素生产率的增长。文章揭示了社会信用环境对企业行为的影响,不仅为理解企业分工提供了一种新的视角,也丰富了信用经济学领域的相关研究。

As the world’s two largest economies, China and the US are currently experiencing political and economic friction. This conflict brings high uncertainty to financial markets. Assessing risk spillover effects in a sector level will help us to characterize international risk contagions. We construct a multilayer network to examine tail risk spillovers between China and the US and find that (1) the value of total connectedness rises amidst tensions but declines during reconciliations; (2) interlayer spillovers mainly manifest as extreme pulses instead of steady outflows, which implies a significant increase in the frequency and magnitude of interlayer spillovers requires vigilant monitoring; and (3) compared with the in-strength, the out-strength is more concentrated, which represents that some sectors may play the role of major interlayer transmitter in tail risk spillovers. Monitoring interlayer spillovers helps policymakers and investors respond to emerging systemic threats.

Using the data of Chinese listed firms from 2003-2020, this study applies a System GMM estimation approach to document that high local government ecological concerns increase a firm’s stock price crash risk. This finding remains consistent after addressing endogeneity issues and undergoing robustness checks. This study also reveals that the implementation of the new environmental protection law in 2015 mitigates the relationship between local government ecological concerns and stock price crash risk. Further analyses indicate that stricter environmental regulation and high subsidies, as well as enhanced corporate social responsibility and governance, can effectively alleviate the adverse effect of local government ecological concerns on stock price crash risk. In addition, we note that the influence of local government ecological concerns on stock price crash risk is more significant in the eastern region, heavily polluting industries, and non-SOEs. Lastly, the research identifies two potential channels through which local government ecological concerns can impact stock price crash risk by reducing the quality of information disclosure and intensifying investor disagreement.

Climate change shocks pose a threat to the stability of the financial system. This study examines the influence of climate risks on systemic risk in the Chinese market by utilizing extreme risk spillover network. Moreover, we construct climate risk indices for physical risks (abnormal temperature), and transition risks (Climate Policy Uncertainty). We demonstrate a significant increase in systemic risk due to climate risks, which can be attributed, in part, to investor sentiment. Furthermore, institutional investors can mitigate the adverse impact of climate risks. Our findings suggest that policymakers and investors need to exercise greater vigilance in addressing climaterelated adverse effects.

In this paper, we present an entropy-based approach to measure the asymmetry of stock returns. By applying this approach, we use the Bootstrap method that our asymmetry measure exhibits a significantly enhanced ability to detect asymmetry compared to skewness. Moreover, our empirical findings reveal that stocks characterized by higher upside asymmetries, as determined by our innovative entropy measure, exhibit lower average returns across a crosssection of stocks. This supports the conclusions drawn by Han et al. (2018). In contrast, when employing the three-moment skewness measure, the relationship between asymmetry and stock returns remains inconclusive within the Chinese market.

This paper addresses the dynamic tail risk in price-limited financial markets. We propose a novel censored autoregressive conditional Fr´echet model with a fiexible evolution scheme for the time-varying parameters, which allows deciphering the impact of historical information on tail risk from the viewpoint of different risk preferences. The proposed model can well accommodate many important empirical characteristics, such as thick-tailness, extreme risk clustering, and price limits. The empirical analysis of the Chinese stock market reveals the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited equity markets, providing a new tool for financial risk management.

As digital transformation strategies have emerged as a primary approach for enterprises to enhance their Total Factor Productivity (TFP), it is crucial to empirically examine the impact of these strategies on TFP. For this purpose, this study considers these transformation strategies as a quasi-natural experiment and employees a propensity score-weighted difference-indifferences methodology on data from Chinese firms listed on the A-share market between 2007 and 2020. The key findings include: (1) digital transformation has a significant positive influence on TFP; (2) Generalized boosted regression trees analysis reinforces this finding after controlling for other TFP determinants; (3) notably, non-state-owned and technology-intensive enterprises exhibit a more distinct enhancement in TFP following digital transformation. These results underscore the need for firms to increase investment in research and development capabilities and digital competencies.

This study examines the influence of ESG report textual similarity on stock price synchronicity within the Chinese A-share market. Using advanced textual analysis methods, including TF-IDF and LDA, we measure the textual similarity of ESG reports among industry peers. Our results reveal a positive association between ESG report textual similarity and stock price synchronicity, suggesting that ESG reports with high textual resemblance may not convey distinct market information. This research underscores the importance of textual distinctiveness in ESG reports and offers a fresh perspective on the role of non-financial information, particularly related to CSR, in stock pricing dynamics. By emphasizing the significance of ESG report textual distinctiveness, we contribute to the broader discourse on ESG disclosure behaviors and their implications for capital market efficiency.

Based on the panel data of listed manufacturing companies in China from 2010 to 2019, the artificial intelligence (AI) index is constructed using the industrial robot data provided by the International Federation of Robotics, and the two-way fixed effect model is used to test the impact of AI on the total factor productivity (TFP) of enterprises. The results show that AI significantly improves the TFP of manufacturing enterprises, and this conclusion remains valid after robustness tests and endogeneity processing. AI promotes TFP by improving the level of human capital and technological innovation, and management and operational levels positively regulate the promotional effect of AI on the TFP of enterprises. Compared with manufacturing enterprises in the central and western regions, AI boosts the TFP of those in the eastern region; compared with non-state-owned enterprises, AI boosts the TFP of state-owned enterprises; and AI significantly boosts the TFP of high-tech and non-high-tech enterprises.

We (re)evaluate the general-equilibrium effects of (environmental) policies from the perspectives of input-output networks and firm dynamics and heterogeneity. Using China’s carbon emission trading system (ETS) as an example, we find that ETS leads to more patent applications, especially the ones associated with low-carbon technologies in the targeted sectors. The effects are muted at the firm level due to selection effects, whereby only larger firms are significantly and positively affected. Meanwhile, larger firms occupy a small share in number but a large share of aggregate outcomes, contributing to the discrepancy between the effects of ETS at the individual firm and aggregate sector levels. The effects also diffuse in input-output networks, leading to more patents in upstream/downstream sectors. We build and estimate the first firm dynamics model with input-output linkages and regulatory policies in the literature and conduct policy experiments. ETS’s effects are amplified given input-output networks.