Using the staggered intercity but within-province deregulation of local banks in China as exogenous variations, we evaluate the effect of banking integration across geographical segmentation on capital misallocation. Based on an administrative data set comprehensively covering Chinese manufacturing firms, we find that for firms with initially high marginal revenue products of capital (MRPK), the integration increases physical capital by 19.3%, and reduces MRPK by 33.1% relative to low MRPK ffrms. Our findings are more pronounced for non-statedowned firms and firms with higher exposure to integrated banks. Integration also significantly increases the responsiveness of firms’ investments to deposit shock on other cities within the same province.

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.

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 order to clarify the effect and mechanism of servitization level and digital transformation on the performance of listed sporting goods manufacturing enterprises in China, the index of the degree of digital transformation is constructed based on the data of 31 sporting goods manufacturing enterprises listed on Shanghai and Shenzhen A shares and the New OTC Market in China, taking the proportion of service business income in enterprise operating income as the index of servitization level, by analyzing the semantic expression of national policy related to digital economy and collecting "digital" category keywords in enterprise annual report with the help of crawler technology, then, the influence of servitization level and digital transformation on enterprise performance is discussed, and whether digital transformation plays a moderating effect between servitization and enterprise performance is tested. The results show that the servitization level suppresses the performance of listed sporting goods manufacturing enterprises, and there is a "Servitization Paradox" phenomenon. The degree of digital transformation has a positive U-shaped impact on enterprise performance, and at the same time, digital transformation has a weak positive moderating effect on servitization level and enterprise performance.

This paper investigates the causal effects of air pollution on housing prices in China using a dataset of 65 cities from 2014 to 2021. We employ the central environmental protection inspection (CEPI) as a quasi-natural experiment for air pollution index to show a negative causal relationship between air pollution and housing prices. We also find that this causal relationship is more pronounced for less-developed, manufacturing-intensive and tourismrelied cities. Our results reveal the response to central environmental protection inspection on improvement in regional air pollution protection but its by-effects on housing markets, as the impact is limited if the inspection is conducted repeatedly, suggesting its unsustainability as a regulatory tool.

This paper investigates how domestic policy uncertainty stemming from discretionary fiscal policy disrupts the efficient capital allocation across firms. While fiscal policy represents the government’s reaction to economic conditions, its volatility presents firms with considerable uncertainty about conditions affecting their future profitability and consequently disrupts firms’ decisions on investment in the presence of capital adjustment costs. Using firm-level data from Chinese manufacturing industries spanning from 1998 to 2007, we find that reducing fiscal policy volatility leads to a decrease in the dispersion of marginal revenue product of capital, accounting for 8.9 percent of the observed improvement in capital allocation during the sample period. In addition to various fiscal reforms to curb fiscal policy volatility directly, policies contributing to lower capital adjustment costs and lower reliance of firms on government expenditure can alleviate the adverse effects caused by fiscal policy volatility.

Based on the matching data of China’s industrial enterprise database and enterprise patent database, this study investigates the effect and mechanism of government subsidy policy on the innovation performance of Chinese enterprises using the panel data matching and multi-period difference-in-difference model. Results show that the incentive effect of government subsidy policy on enterprises’ innovation performance is dominant, thus improving the innovation performance of Chinese manufacturing enterprises. However, the promotion effect on lowquality innovation is greater than that on high-quality innovation. Moreover, the government subsidy policy affects the innovation performance of enterprises by stimulating enterprises to increase their R&D investment, increasing the spending on rent-seeking and taking on more social responsibility. The government subsidy policy significantly affects the innovation performance of non-state-owned enterprises, small and medium-sized enterprises, and technology-intensive industries. The impact of government subsidizing policies varies regionally. The policy effect on enterprise innovation performance is significant in the eastern and central regions but not in the western and northeast regions.

Research has argued that state-owned enterprises (SOEs) should bear more social responsibility than other listed firms, because their own goals include maintaining social stability and promoting social welfare. In contrast with the privatization of SOEs observed in other countries, in China, some listed firms’ major shareholders have become SOEs in recent years. This transition offers a good opportunity to investigate the impact of ownership change on firms’ corporate social responsibility (CSR). Using the propensity score matching difference-in-differences method, we document that the CSR performance of these firms does not improve when their ownership structure changes, and it can even worsen. Our results remain robust to a series of tests. Further investigating the underlying economic mechanism, we uncover those political connections, bank financing, and government subsidies play critical roles in determining the negative effect of ownership structure change on public firms, which is consistent with the soft budget constraint framework. In an additional analysis, we find that CSR performance is poor for manufacturing industry firms after ownership structure change. After calculating the frequency of keywords appearing in the annual reports of such firms, we find them to be satisfied with their new SOE background after ownership structure change. Our paper provides a possible explanation for the phenomenon of SOEs becoming major shareholder of listed firms.

We examine the effects of China's 2009–2010 stimulus package for innovation differentials between state-owned firms (SOEs) and privately-owned firms (POEs). Using a unique dataset of Chinese manufacturing firms, we find that in the pre-stimulus period SOEs patent at a lower rate than POEs in the least inventive patent category, and at a comparable rate in the more inventive categories. Post-stimulus, SOEs patent at an even lower rate relative to POEs in the least inventive category, but significant, positive SOE-POE patent rate differentials emerge in more inventive patent categories. The stimulus disproportionately benefited SOEs with higher investment subsidies and lower finance costs—institutional support which we find mediates roughly 45 percent of all positive effects of state-ownership for innovation. Institutional support produces larger SOE-POE innovation differentials among firms in strategic sectors and located in high-marketization provinces, and for centrally controlled SOEs.

The improvement of production efficiency and the change of business model brought about by the deep integration of the digital economy and the real economy have become an important driving force for industrial transformation and upgrading. This paper explains the necessity of digital transformation of manufacturing, the trends, paths and six technologies of financial digital transformation. In the digital era, relying on data, scenarios, and algorithms to explore the essential logic of business, make predictions and decisions based on business insights, and put forward higher requirements for financial empowerment business. As an important way for enterprise management transformation and upgrading, the core goal of financial digital transformation is to take "data-driven" as the main line, promote transformation based on the two principles of industry-finance integration and in-depth scenarios, and build "value-creating" finance, that is, based on the integrated application of digital technology, so that finance can expand the functions of supporting strategy, assisting decision-making, empowering business, preventing and controlling risks, lean management, operational excellence, quality and efficiency, compliance supervision and other functions on the basis of basic transaction accounting functions, and promote and even lead the value creation functions of enterprises. The article points out that the manufacturing industry should take enhancing competitiveness as the direction, financial management as the center, and improving quality and efficiency as the goal to accelerate digital transformation. Introduced Midea Group's financial digital transformation practices and results. It is proposed that enterprises should vigorously promote the deep integration of big data, Internet, cloud computing, Internet of Things, artificial intelligence, blockchain and the real economy, accelerate the digital transformation of finance, and inject new impetus into the high-quality development of enterprises.