This paper evaluates the reasoning and thinking abilities of large language models in economic problems. Economic reasoning requires models to possess the capabilities of concept understanding and association, basic numerical reasoning, and structured comprehensive reasoning. By constructing a perturbation test set based on the theory of comparative advantage, the efficient market hypothesis, and the prisoner’s dilemma, this study tests the economic reasoning abilities of large language models. The experiments reveal that when faced with counterintuitive scenarios, changes in narrative order, substitution of person names, or variations in information types, the reasoning accuracy of large language models drops significantly, indicating a reliance on superficial textual patterns rather than genuine logical reasoning. The findings suggest that current large language models lack robust reasoning abilities in economic reasoning tasks and are easily disrupted by surface-level textual features.
This study employs data from listed A-share firms from 2008 to 2022, combining digital disclosure and digital investment from firm annual reports to measure the level of firms’ digital transformation. We examine the effect and mechanism of economic policy uncertainty (EPU) on firms’ digital transformation from the perspective of information disclosure and actual innovation, and the empirical results show that: As the economic policy uncertainty rises, both the digital disclosure and the actual digital investment of enterprises increase significantly, which means the digital transformation strategy of enterprises has the characteristic of “talk-and-walk”, and this conclusion still holds after a series of endogeneity and robustness tests. The mechanism analysis indicates that EPU enhances digital transformation through the “human capital effect” “external attention effect” and “industrial competition effect”. The heterogeneity analysis reveals that economic policy uncertainty exerts differential impacts on the application levels of different types of digital technologies in enterprises, and the promoting effect of economic policy uncertainty on digital transformation is more significant in non-state-owned firms, enterprises with technical management layers and high-tech firms. Further research finds that the digital transformation behavior of firms also has the effects of risk aversion and peer effects. Therefore, in the context of the normalization of uncertainty, firms should actively carry out digital transformation to avoid risks and enhance their resilience. The government should stabilize the market’s expectations for the future and reduce the adverse impact of uncertainty on firms’ innovation activities.
With the rapid development of generative artificial intelligence technology, the readership of corporate annual reports has expanded from human readers to artificial intelligence (AI), making the limitations of traditional readability measurement methods increasingly evident. This study constructs an AI readability index for annual reports under large language models from three dimensions — Long-text capability, table parsing, and other factors — Using random sampling Q&A and a random forest model. Based on an analysis of companies listed in China’s mainland from 2004 to 2023, the results show that: 1) Significant differences exist between the AI readability index and traditional readability measures in terms of time trends, numerical distribution, industry characteristics, and regional distribution. 2) The AI readability of different types of companies exhibits heterogeneous performance, which is opposite to traditional readability trends. Non-state-owned enterprises, high-tech industries, and companies with higher accounting information quality demonstrate significantly better AI readability in their annual reports compared to others. 3) Compared to traditional readability, AI readability more effectively improves corporate information transmission efficiency. This study innovatively proposes the concept and measurement method of AI readability for annual reports, providing a new perspective and approach for readability research in the context of large language models, thereby enhancing the understanding of corporate information disclosure motivations and economic consequences. It also offers insights for companies to adjust their annual report writing paradigms to make them more AI-friendly, while providing important references for regulators and investors to accurately evaluate corporate information disclosure levels under large language models.
In the globalized economic system, supply chain resilience is crucial for the stable and sustainable development of enterprises and the security and stability of the domestic economy. In the current booming development of new quality productive force, whether and how to strengthen supply chain resilience is an important research topic. This article is based on the data of A-share listed companies in the new energy vehicle industry chain from 2015 to 2022, and uses a two-way fixed effect model to empirically test the impact of the development of new quality productive force on the resilience of enterprise supply chains. The research results indicate that the improvement of new quality productive force in enterprises has a significant promoting effect on the resilience of the supply chain, and the conclusion still holds after alleviating endogenous problems and undergoing robustness testing. Heterogeneity analysis shows that the improvement of new quality productive force in enterprises has a more significant promoting effect on supply chain resilience in high-tech enterprises, small enterprises, and midstream enterprises in the new energy vehicle industry chain. Mechanism analysis has found that the development of new quality productive force in enterprises promotes the improvement of supply chain resilience through two channels:Reducing operating costs and enhancing bargaining power. The research findings enrich existing research on new quality productive force and provide useful empirical evidence for enterprises to enhance supply chain resilience and promote high-quality development through new quality productive force.
Against the backdrop of the rapid development of the low-altitude economy and the absence of a well-established statistical accounting system, accurately identifying its industrial structure and economic linkage effects is of important practical significance. Within the existing input-output statistical framework, this study constructs an input-output table for the low-altitude economy based on a sector-splitting approach, disaggregating it into low-altitude manufacturing, low-altitude operations, low-altitude infrastructure and information services, and low-altitude supporting industries, and systematically estimates their industrial linkage characteristics and final demand effects. The results show that the influence coefficient of low-altitude manufacturing remains significantly above one over time, exerting a strong pulling effect on upstream high-technology manufacturing sectors such as communication equipment, general and special equipment, and electrical machinery, whereas the influence coefficients of low-altitude operations, low-altitude infrastructure and information services, and low-altitude supporting industries are generally lower, indicating that their industrial driving effects have not yet been fully released. The sensitivity coefficients of all four low-altitude sectors remain at relatively low levels, suggesting that the low-altitude economy has not yet been widely embedded in the regular production system of the national economy, although the gradual increase in sensitivity reflects the progressive expansion of application scenarios. From the perspective of final demand, the production-inducement coefficients of the low-altitude economy are generally low, with a demand structure dominated by capital formation, while final consumption demand is still in the cultivation stage and the role of exports remains limited. Overall, the low-altitude economy exhibits typical characteristics of an emerging industry, namely relatively strong supply-side driving capacity but insufficient demand-side penetration, and the industry as a whole is still in a development stage dominated by investment-driven construction and application-scenario incubation. This study provides a new statistical basis for the quantitative evaluation of the low-altitude economy and offers a methodological reference for input-output measurement of emerging integrated industries.
As China accelerates its modernization drive toward harmonious coexistence between humanity and nature, exploring the impact of the digital economy on urban ecological resilience is of great theoretical and practical significance for advancing high-quality urban development and facilitating the construction of an ecological civilization. Based on panel data of 282 prefecture-level and above cities in China from 2011 to 2021, this study scientifically measures the level of urban ecological resilience from three dimensions, namely resistance, adaptability, and recovery. Taking the “Broadband China” pilot policy as a quasi-natural experiment, we construct a multi-period difference-in-differences (DID) model to systematically investigate the impact of the digital economy on urban ecological resilience and its intrinsic mechanisms. The empirical results demonstrate that the digital economy significantly enhances urban ecological resilience, with the most prominent promoting effect on urban ecological recovery. This conclusion remains robust after a series of robustness tests, including propensity score matching-DID (PSM-DID), instrumental variable method, and double machine learning (DML). Mechanism tests show that the digital economy improves urban ecological resilience mainly through three transmission channels: the industrial structure effect, the financial development effect, and the green innovation effect. Among these channels, the financial development effect and the green innovation effect are significantly more effective in boosting urban ecological recovery than in enhancing urban ecological resistance and adaptability. Heterogeneity analysis indicates that the improvement effect of the digital economy on urban ecological resilience is more remarkable in eastern China, large-sized cities, and non-resource-based cities. From the perspective of urban ecological resilience, this study deepens the understanding of the economic and environmental effects of digital economic development, and provides new empirical evidence and decision-making basis for further advancing high-quality economic development and the construction of an ecological civilization.
Under the strategic backdrop of synergistically advancing the “Dual Carbon” goals and the high-quality development of the manufacturing industry, exploring viable pathways for carbon emission reduction in manufacturing enterprises holds significant practical implications. Intelligent manufacturing serves as a core engine to resolve the “growth-emission reduction” paradox, and its implementation and application have unlocked novel pathways for the low-carbon transformation of manufacturing enterprises. Utilizing the national Intelligent Manufacturing Pilot and Demonstration Policy as a quasi-natural experiment, this study constructs a double machine learning model based on data from China’s A-share manufacturing listed companies spanning 2010 to 2023. The results showed the following: 1) The intelligent manufacturing policy significantly improves enterprises’ carbon emission performance, primarily through the dual channels of “stimulating innovation” and “securing government subsidies”. 2) The heterogeneity test shows that the policy effect is more significant in high-competition industries, low-transparency enterprises and high-marketization regions. 3) A further analysis shows that the policy effect is more pronounced in he high-tech industries in eastern regions and central cities, revealing disparities between regions and urban locations. Based on this, suggestions such as differentiated policy design, technology adaptation subsidies and regional coordinated development are put forward to provide theoretical and empirical support for solving the“growth-emission reduction” paradox.
Based on a sample of Chinese A-share listed companies from 2011 to 2020, this study examines the impact of engaging in tax haven direct investment, a significant form of overseas investment, on corporate cash dividend policy and its underlying mechanisms. The findings reveal that tax haven direct investment significantly enhances both the level and the willingness of cash dividend payments. The mechanism analysis indicates that paying cash dividends serves not only to alleviate the agency conflicts induced by tax haven investment but also to convey positive performance signals to the market and improve analyst forecast quality. Consequently, firms with tax haven direct investment demonstrate a stronger tendency to increase cash dividend payments, which supports both the dividend agency theory and the dividend signaling theory. Furthermore, this positive effect is more pronounced for firms with a higher degree of internationalization, lower shareholding balance, or lower managerial ownership. Additionally, the impact of tax haven direct investment on cash dividend levels exhibits persistence. This study offers valuable insights for a deeper understanding of the economic consequences of tax haven direct investment and the motivations behind cash dividend distributions.
Since the 2008 global financial crisis, the regulation of shadow banking has become a central issue in both policy debates and academic research. This paper develops a multi-sector dynamic stochastic general equilibrium (DSGE) model incorporating both commercial banks and shadow banks. Based on a clear identification of the operational mechanisms of shadow banking in China, it evaluates the effectiveness of alternative macroprudential policies in regulating the sector. Impulse response results indicate that under contractionary monetary policy and capital regulation shocks, credit supply from commercial banks and shadow banks exhibits a substitution relationship, suggesting that shadow banking serves as a channel for regulatory arbitrage by commercial banks and thereby weakens the effectiveness of macroeconomic regulation. Building on this, the paper assesses three types of capital regulatory policies: i) narrow capital regulation targeting only on-balance-sheet activities of commercial banks; ii) broad capital regulation covering both on- and off-balance-sheet activities; and iii) symmetric capital regulation that jointly constrains both commercial banks and shadow banks. Policy simulation results show that extending narrow regulation to broad regulation remains insufficient to effectively contain shadow banking. Only symmetric capital regulation can significantly enhance the credibility of regulatory indicators, curb regulatory arbitrage through off-balance-sheet activities, and ultimately restrain the disorderly expansion of shadow banking. This study provides a theoretical reference for advancing the regulatory framework of shadow banking and improving the practice of macroprudential policy in China.
Based on stock data from China’s A-share listed companies from 2019 to 2024, we introduce a contagion measure of investor sentiment alongside traditional sentiment measurement methods, proposing a comprehensive approach to quantifying investor sentiment. Building on this framework, we systematically examine the impact of investor sentiment on idiosyncratic stock risk. Our findings indicate that investor sentiment significantly increases idiosyncratic risk, a conclusion that remains robust after controlling for macroeconomic factors. Mechanism analysis reveals that investor sentiment primarily amplifies idiosyncratic risk by enhancing stock liquidity and trading activity. Furthermore, positive sentiment has a more pronounced effect on idiosyncratic risk, a result partially explained by prospect theory. In terms of heterogeneity, investor sentiment exerts a stronger impact on the idiosyncratic risk of small-cap stocks and STAR Market stocks. This study contributes to the measurement of investor sentiment by proposing a novel approach and expands the research perspective on its influence on idiosyncratic stock risk. The findings hold important implications for stock market risk management and the rational guidance of investor behavior.
In the digital economy era, data have become a key production factor driving the transformation and upgrading of traditional industries as well as the cultivation of emerging industries. The market-oriented allocation of data factors can help break factor constraints and foster the development of firms’ new quality productivity. Treating the successive establishment of data trading platforms as a quasi-natural experiment, this paper uses a sample of A-share listed firms in Shanghai and Shenzhen from 2013 to 2022 to examine whether the market-oriented development of data factors promotes the development of firms’ new quality productivity. The results show that, first, the market-oriented development of data factors significantly promotes the development of firms’ new quality productivity. Second, it advances such development by enhancing firms’ risk-taking capacity, optimizing their transaction cost efficiency, and strengthening their digital technological innovation capability. Third, this promoting effect is more significant for firms with a higher degree of digital transformation, more abundant slack resources, and stronger commercial value creation capability. These findings extend the research frontier on new quality productivity and provide valuable implications for the government in advancing the market-oriented development of data factors.
This paper studies the optimal investment of single-division firms and multi-division firms under Knightian uncertainty based on the internal capital markets
