We study how financial markets integrate news about pollution abatement costs into firm values. Using China’s automation of pollution monitoring, we find that firms with factories in bad-news cities---cities that used to report much lower pollution than the automated reading---see significant declines in stock prices. This is consistent with the view that investors expect firms in high-pollution cities to pay significant adjustment and abatement costs to become “greener.” However, the efficiency with which such information is incorporated into prices varies widely---while the market reaction is quick in the Hong Kong stock market, it is considerably delayed in the mainland ones, resulting in a drift. The equity markets expect most of these abatement costs to be paid by private firms and not by state-owned enterprises, and by brown firms and not by green firms.

A unique natural experiment in China – the city-level staggered introduction of admin-istrative approval centers (AAC) – reduces bank loan processing times by substantially speeding up the process of registering collateral without affecting credit decisions. Fol-lowing the establishment of an AAC, firms significantly reduce their cash holdings. State-owned enterprises are less affected. Cash flow sensitivity of cash holdings de-creases, as does the cash flow sensitivity of investment. The share of short-term debt increases, while inventory holdings and reliance on trade credit decrease. Defaults also decrease. These results suggest that timely access to credit has important implications on firms’ financial management.

Does large-scale privatization increase income inequality? To answer this question, we analyze the impact of China’s reform of state-owned enterprises on labor market outcomes in urban areas from 1992 to 2004, exploiting cross-prefecture variation in reform exposure stemming from initial differences in the employment shares of urban collective enterprises and state-owned enterprises. Our analysis reveals that workers in prefectures with higher exposure to the reform experienced a more rapid decline in employment and a slower increase in income, compared to those in less exposed areas. Further analysis shows that individuals with lower income and those with lower educational attainment experienced greater losses. A back-of-the-envelope analysis indicates that the reform contributed to more than 40% of the study period’s increase in income inequality.

The scale expansion of low-carbon industries and the green transformation of carbon-intensive industries are two sides of the same coin in achieving the “dual carbon” goals. However, research on transition finance supporting the upgrading of traditional existing carbon-intensive industries remains insufficient. The key to examining the effectiveness of transition finance lies in distinguishing whether the supported enterprises are engaging in greenwashing or green evolution. Based on data of Chinese A-share listed companies in the carbon-intensive industries, an empirical study is conducted and offers the following findings: (1) Transition finance not only does not increase greenwashing but also promotes comprehensive green innovation in carbon-intensive enterprises. (2) In terms of the influencing mechanism, transition finance exerts “resource effects” and “signaling effects,” promoting green innovation by improving debt maturity mismatch and attracting green institutional investors. (3) Heterogeneity analysis shows that the positive impact of transition finance on green innovation is particularly pronounced among enterprises in the eastern region, state-owned enterprises, and those with lower levels of managerial myopia. (4) Further industry spillover effects analysis reveals that transition finance empowers green innovation within industries though peer effects and competitive effects. The findings are essential for understanding the effectiveness of transition finance and offer valuable insights for policymakers.

This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

As digital technologies proliferate, firms increasingly leverage digital transformation strategically, necessitating new orientations attuned to digital technological change. This study investigates how digital orientation (DORI)- the philosophy of harnessing digital technology scope, digital capabilities, digital ecosystem coordination, and digital architecture configuration for competitive advantage – influences firms’ environmental, social, and governance performance (ESG_per). Analysis of Chinese A-share firms from 2010-2019 reveals DORI is associated with superior ESG_per, operating through the mediating mechanism of enhanced digital finance (DIFIN) as a fund-providing facilitator for sustainability initiatives. Additional analysis uncovers important heterogeneities – private firms, centrally owned state-owned enterprises, politically connected, and emerging companies exhibit the strongest DORI - ESG_per linkages. Prominently, the study findings are validated through a battery of robustness tests, including instrumental variable methods, and propensity score matching. Overall, the results underscore the need for firms to purposefully develop multifaceted digital orientation and furnishes novel theoretical insights and practical implications regarding DORI’s role in improving ESG_per.

Digital transformation (DT) is an ongoing and costly process that requires careful planning and the motivation of top executives (CEOs). This research analyze the CEOs compensation as a motivation to embrace DT by reducing agency issue. We determine the extent of DT through a textual analysis method and utilize data from Chinese publicly traded companies spanning the period between 2007 and 2020. Our study findings are threefold, (a) we observe a positive relationship between CEOs' pay gap and DT, highlighting the significant role CEOs compensation plays in encouraging CEOs to adopt digitalization, (b) we find that managerial shareholding significantly enhances this relationship, (c) we note that the relationship between CEOs pay gap and DT is more pronounced in state-owned enterprises compared to non-stateowned enterprises. Additionally, we discover through channel analysis that agency cost and audit quality mediate the relationship between CEOs pay gap and DT potentially by reducing the agency problem between CEOs and shareholders. These findings are vital for comprehending the pay practices and behaviors of corporate executives regarding digitalization in China. Importantly, the study results remain robust when considering instrumental variables (IV), propensity score matching (PSM), and alternative techniques.

This paper explores the ethical dimensions of firm-level digitalisation and its impact on ESG metrics during a decade (2010-2020) of rapid technological progress, focusing on Chinese-listed companies. Utilising a text-based index to measure digitalisation, we find that while digitalisation positively influences ESG ratings, supporting resource-based and dynamic capability theories, its relationship with corruption reveals complex dynamics. Surprisingly, corruption strengthens digitalisation’s positive impact on ESG, raising concerns about technology being used to enhance ESG appearances artificially. A distinct difference emerges between state-owned enterprises (SOEs) and non-SOEs; SOEs use digitalisation more ethically and are less influenced by corruption, indicating a more responsible approach to technology adoption. Through examining cash holdings, internal controls, and audit fees, we unpack how corruption influences the digitalisation-ESG nexus. These insights underscore the need for policy that encourages ethical digitalisation and highlight the potential role of SOEs in leading the charge towards sustainable and ethical digitalisation.

This study examines the enduring effects of China’s planned economy on contemporary social attitudes. By leveraging spatial disparities in the historical distribution of state-owned enterprises and external shocks such as the First Five-Year Plan and the Third Front Construction movement, we find that a one percentage point increase in the historical SOE proportion of industrial output corresponds with a 0.57% to 0.89% increase in the contemporary preference for state-owned sectors. The results are robust after controlling the contemporary SOE employment share, and this effect does not apply to the younger generation born after the marketization reform. Furthermore, we provide evidence that city-level state ownership preferences significantly impact the likelihood of SOEs receiving subsidies, with this effect notably amplified in cities governed by locally-born leaders, but the share of locally-born leaders has been trending down.

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.