International Review of Economics & Finance最新文献

This study examines the impact of the digital economy on sustainable green innovation in China, with a focus on the mediating roles of debt financing costs and resource allocation efficiency. Leveraging a comprehensive dataset of 24,741 firm-year observations from 2008 to 2022, we utilize fixed-effects regression models to analyze the relationships between the digital economy, debt financing costs, resource allocation efficiency, and sustainable green innovation. The results demonstrate that the digital economy positively influences sustainable green innovation, with this effect being partially mediated by reductions in debt financing costs and improvements in resource allocation efficiency. Specifically, the digital economy lowers financing costs and optimizes resource allocation, which collectively enhances green innovation sustainability. The findings remain consistent across various endogeneity and robustness checks, including instrumental variable approach, lagged effects, heckman two-stage method, sample adjustments, alternative measures, and model specifications. Additionally, heterogeneity tests reveal that the digital economy's positive effect is stronger for firms in the early and middle stages of their life cycle and for those operating in less-polluting industries. These insights contribute to the growing literature on digital technologies and sustainable development, offering valuable implications for managers and policymakers aiming to harness the digital economy for advancing sustainable green innovation.

This paper utilizes the trade gravity model under the “double-cycle” pattern and selects the trade and export panel data of China in general and 31 provinces and cities located on mainland China specifically to the U.S. from 2008 to 2022 to analyze not only the reasons for the emergence of trade friction shocks between China and the U.S. but also the effects of trade friction shocks on trade between China and the U.S. and the characteristics of each market's export potential. The results of the study reveal that the volume of China's secondary industry GDP and the U.S. economic freedom index play a positive role in promoting China‒U.S. trade, while the volume of the U.S. GDP, the U.S. population, the trade distance between China and the U.S., and the number of U.S.‒China trade barrier notifications impede China‒U.S. trade. Furthermore, on the basis of the measurement of the trade potential equation, the provinces and municipalities of China's mainland are classified into the U.S. trade and export markets, and ways and means are sought to improve the potential of China's trade and exports with the U.S. Moreover, on the basis of the trade potential equation, China in general and the mainland provinces and cities specifically are divided into export markets to the U.S. to find ways to increase China's trade and export potential to the U.S. and promote China's “outward circulation” economy.

This paper examines the impact of point-of-sale (POS) adoption on training to investigate firms' requirements of employees. We construct a two-sector general model and find that the adoption of technology drives firms to increase training activities and decrease their labor-income share. Based on the data of 2451 firms from China's Small and Micro-sized Enterprises Survey in 2015 with an instrumental variable approach, we document that a one year increase in POS use increases the likelihood of firm training by 18.5–32.6%. In addition, the added training results from the demand for professional competence rather than simple needs for operating the POS system. Correspondingly, POS service intensifies the polarization of internal wages and worsens workers' welfare. The plausible underlying mechanisms for this are technology use, financial constraints, and operational efficiency.

We evaluate the impacts of government spending and government investment on the house price dynamics in China during its Great Housing Boom. Government spending is defined as public expenditures on non-productive public goods and services, while government investment is defined as expenditures on productive public capital. By estimating a DSGE model which allows for potential non-separability between government spending and housing in household utility, and a policy feedback rule governing government investment, we find: (a) government spending exhibits a crowding-out effect on housing consumption, though empirically it does not affect the housing price much; (b) government investment, which exhibits a strong wealth effect on household income and then the demand for houses, affects the housing price positively and substantially; (c) both government spending and government investment are effective instruments for stimulating output, but given that government investment can inflate house prices unnecessarily, policy makers who aim to stimulate the economy without destabilising the housing market would be better off utilising government spending.

We examine the impact of low-carbon city policy on corporate cost stickiness. Using a sample of Chinese A-share firms from 2008 to 2022, we document that cost stickiness increases for firms in a city with a low-carbon policy using a difference-in-differences research design. The findings are robust to an entropy balance method, alternative samples, placebo tests, after accounted for other environmental regulations, and after control for various time trend effects. The mechanism analysis suggests that easing financial constraints and engaging in green investments are the underlying logic behind the impact of low-carbon city policy on corporate cost stickiness. Additional analysis shows that the effect is more salient for firms in traditional manufacturing industries, which are state-owned, or belong to high technology industries. The economic analysis shows that while a firm's productivity generally decreases due to cost stickiness increase, the implementation of the low-carbon city policy offers significant benefits to offset the productivity decrease.

This study explores the influence of digital inclusive finance on household poverty in China, utilizing data from the China Household Finance Survey. The results of the analysis demonstrate that advancements in digital inclusive finance have a substantial impact on reducing household poverty. Mechanism tests indicate that this reduction in poverty occurs indirectly through increased local entrepreneurial activity. Furthermore, heterogeneity analysis reveals that the poverty-reducing effects are more significant among rural households and in the central and western regions of China.

The Environmental Protection Tax Law was officially implemented, marking the initial establishment of China's green tax system. This paper aims to estimate whether the introduction of environmental protection tax will promote enterprises' green technology innovation. Based on data from listed enterprises from 2015 to 2021, this paper takes the implementation of environmental protection tax in 2018 as a quasi-natural experiment, and uses the DID method to evaluate the impact of the implementation of environmental protection tax on the technological innovation ability of enterprises. The results show that, after the implementation of environmental protection tax, the quantity of green patents increased significantly, while the quality of green patents decreased significantly. The analysis of mechanism channel reveals that environmental protection tax policy appears to increase the number of green patents putting regulatory pressure on enterprises and increasing R&D investment, while managerial myopia inhibits the improvement of green patent quality. The changes in the quantity and quality of green patents in large enterprises are more significant. It is also found that the quantity in coastal areas decreased significantly, while the quantity in inland areas markedly increased.

As a key input factor and necessary space carrier for the development of various industries in the city, the market allocation mode of industrial land has a great impact on the land use efficiency of the whole city. This paper objectively evaluates the actual effect of industrial land marketization policy on urban land use efficiency, and finds that industrial land marketization reform further reflects land value, releases land dividend, increases land transfer income of local governments, and expands funds for land consolidation and development, thus effectively improving urban land use efficiency. By using the Slack-Based-Measure (SBM) model to evaluate the effective decision-making unit to measure the land use efficiency, and using the spatial Durbin difference-in-difference method to estimate the effect of industrial land marketization policy on improving urban land use efficiency in 2007, the main conclusions are as follows: (1) After using the static spatial difference-in-difference model and the dynamic spatial difference-in-difference model, it is found that the industrial land marketization policy implemented in 2007 significantly improved the urban land use efficiency. (2) The moderating effect shows that fiscal pressure has a negative impact on the positive role of industrial land marketization in improving urban land use efficiency, and promotion incentives have a positive impact on the positive role of industrial land marketization in improving urban land use efficiency. (3) Mechanism analysis shows that the market-oriented reform of industrial land will improve the efficiency of urban land use by promoting the upgrading of industrial structure and curbing real estate investment. The above research conclusions provide a theoretical basis for optimizing the allocation mode, management system and revenue and expenditure structure of industrial land transfer, improving the long-term assessment system of local governments, promoting the optimization and upgrading of industrial structure, and improving the unsustainable development of cities caused by excessive real estate investment.

This study examines the impact and underlying mechanisms of financial technology (FinTech) on corporate labor demand by utilizing microdata from Chinese listed companies between 2011 and 2019. The results indicate that FinTech significantly enhances firms' overall labor demand and demand for skilled labor. Mechanistic analysis reveals that FinTech alleviates firms' financing constraints, increasing research and development (R&D) investments, which subsequently stimulates labor demand. Heterogeneity tests have revealed that the impact of FinTech on firms' labor demand varies depending on their stage of growth, ownership attributes, and level of financial development in the regions in which they are located. Furthermore, we reveal that FinTech can concurrently increase labor demand for workers with varying skill levels and educational backgrounds and increase the share of labor income. We provide new insights with theoretical and practical implications for studying the intrinsic linkages between financial and labor markets.

Little attention has been paid to how the information asymmetry, agency costs of free cash flow and the controller involvement in the board contribute to the difference in the performance between family and non-family firms following share repurchase announcements. This study aims to make clear the above associations. This study uses OLS multiple regressions to examine the difference in the operating and market performance between family and non-family firms over two years following the repurchase announcements in a sample that contains 3800 announcements over the period 2000 and 2017. We find that family firms have better performance than non-family firms. We also find that free cash flow is positively associated with performance, and the association between performance and free cash flow is weaker for family firms than for non-family firms. Furthermore, these findings become increasingly significant when family controllers have a lower degree of involvement. Our results indicate that family firms, especially those with a lower degree of controller involvement, prefer repurchases to mitigate information asymmetry and increase interest alignment.