This paper examines how potential disruptions from generative artificial intelligence (AI) to firms' core businesses affect their cross-industry acquisition decisions. Using data on Chinese A-share listed firms, we employ a large language model to assess the potential positive and negative effects of generative AI on core businesses and construct a measure of net AI risk exposure. We measure cross-industry M&A using the semantic distance between acquirers' and targets' business descriptions. A difference-in-differences analysis exploiting the commercial diffusion of generative AI shows that more exposed firms acquire more distant targets, with an effect equivalent to 8.2% of the sample mean and robust to alternative specifications. The effect is stronger among firms with narrower innovation boundaries and more concentrated business portfolios, consistent with innovation capability reconfiguration and strategic risk diversification. It is also stronger among firms with smaller market shares, in more competitive industries, and in more marketized regions. Further analyses show that high-exposure firms acquire targets with higher AI patent shares and receive more favorable announcement-day market reactions to distant acquisitions. Our findings suggest that potential AI-related disruptions shape acquisition target selection and firms' business boundaries.

We study how expanding access to intellectual property (IP)-backed credit affects firm innovation and aggregate total factor productivity (TFP). Exploiting China's IP-backed financing pilot as a quasi-natural experiment, we use difference-indifferences estimates to show that the policy increases R&D participation, raises firm-level productivity, and reduces capital misallocation. To interpret these effects and quantify their aggregate implications, we develop a dynamic model of heterogeneous firms that borrow subject to collateral constraints backed by tangible assets and IP, and choose R&D investment and the accumulation of wealth. R&D raises future productivity and creates collateralizable IP. We estimate the model by the method of simulated moments, using the policy-induced shift in R&D participation to identify the change in IP pledgeability. The estimated model implies that a nationwide adoption of IP-backed financing would raise long-run TFP by 14%, with roughly two-thirds coming from static gains due to improved capital allocation and the remainder from dynamic gains driven by higher innovation that accumulate over time. Relative to a comparable expansion of lending backed by tangible collateral, IP-backed financing delivers larger productivity gains by easing constraints for high-productivity but low-wealth firms and by strengthening incentives to accumulate IP as collateral for future borrowing.

This study evaluates whether electric vehicles (EVs) in China have delivered on their “green” promise by improving air quality and reducing private ownership costs. Combining city–month data on EV adoption and air pollution with detailed vehicle-level transaction records, we find little evidence that rising EV adoption reduces concentrations of major pollutants or greenhouse gases. Both the traditional instrumental variable (IV) and the Bartik IV estimations show that the null effects persist after addressing potential endogeneity. The limited environmental gains are largely attributable to the continued dominance of coal in electricity generation and to a sizable rebound effect whereby lower operating costs increase vehicle usage. On the private side, although EVs reduce energy expenditures by roughly CNY 16,747, their faster depreciation leads to an average net loss of about CNY 18,175 over the ownership period relative to fuel vehicles (FVs). Counterfactual simulations show that EVs match the emissions of fuel vehicles when the coal share falls to about 55%, deliver measurable benefits below 49%, and require a reduction to around 31% once higher utilization is considered. The results show that the environmental benefits from EV adoption depend on a transition toward cleaner electricity generation.

This paper studies the unintended spillover effects of China's 2020 "Three Red Lines" policy – a macroprudential intervention aimed at deleveraging real estate developers – on employment in the upstream manufacturing firms. Exploiting variation in industries' exposure to the real estate sector via input-output linkages and employing a generalized difference-in-differences (DID) design on panel data of listed manufacturing firms (2017-2022), we find a significant decline in employment among upstream suppliers, while there is no substantial effect on downstream firms. We identify two transmission channels: a demand propagation channel, evidenced by falling sales and inventory overhang, and a balance sheet contagion channel, where liquidity strain is passed upstream via extended payables and a shift to riskier short-term debt. Furthermore, we show that downward nominal wage rigidity shaped the adjustment, as firms resorted to layoffs rather than wage cuts for ordinary workers. From a regional labor market perspective, the employment losses represent a net reduction at the aggregate level, as displaced workers are not absorbed by other local firms in the same city or province. Our study reveals a significant real economic cost of sectoral macroprudential regulation and clarifies how such shocks propagate through production networks and interact with labor market frictions.

Mergers and acquisitions (M&A) are widely regarded as a critical instrument for industrial consolidation and capacity rationalization. Utilizing a dataset of China’s major national and regional steel enterprises from 1998 to 2013, this study empirically evaluates the efficacy of M&A in resolving over1capacity. We document a striking heterogeneity in outcomes: while M&A generally alleviates overcapacity, this effect is exclusively driven by inter-provincial transactions; intra-provincial mergers show no significant impact. Mechanism analysis reveals that inter-provincial M&A facilitates the transfer of control rights, effectively insulating firms from local government intervention. In contrast, intra-provincial mergers are undermined by local intervention through mechanisms of "internal control" and "external defense." Furthermore, despite the reduction in capacity, we find that inter-provincial mergers suffer from incomplete post-merger integration. There is a persistent lack of governance coordination, and no convergence in capacity management performance is observed between the merging entities over time. These findings offer critical policy implications for mitigating local protectionism and enhancing integration efficiency to resolve industrial overcapacity.

This paper explores the impact of the Integrated Government Service Platform (IGSP) reform, a large-scale digital regulatory initiative in China, on firms’ investment efficiency. Using a firm-level panel dataset of Chinese listed firms, we find that the IGSP reform significantly enhances firms’ investment efficiency. Mechanism analyses indicate that the IGSP enhances firms’ investment efficiency by improving governance service quality and convergence of governance services across cities within a province, thereby reducing economic policy uncertainty and reinforcing market expectations. The heterogeneity analyses reveal that the effect is more pronounced for firms without political connections, those operating in high business volatility and high-tech industries, and regions with stronger informal institutions. Finally, the IGSP encourages firms to expand their investment activities to other cities within the same province. This finding is fully consistent with the underlying mechanisms identified in this study. Our findings provide novel evidence on the economic consequences of digital regulatory integration and offer policy implications for advancing service-oriented and efficiency-driven governance in developing economies.

We investigate how investors assess transition risks arising from the discretionary regulatory mandates for nature conservation. We focus on the UN Biodiversity Conference in Kunming, China, a policy shock signaling heightened political commitment to protection. During the conference, firms exhibited negative abnormal stock returns on average. Using a machine learning approach, we identify historical nature-related subsidies to capture firm-level variations in expectations of mandated conservation engagement, beyond measuring corporate environmental (ecological) impacts. This metric negatively correlates with returns during the conference, particularly for firms with high operational volatility, and those in regions with limited fiscal flexibility and stringent conservation pressures. Investor concerns primarily stem from policy uncertainty, without positive earning expectations from conservation. Our findings underscore the role of authoritative discretion in shaping nature transition risks.

This paper studies the role of the soft budget constraint in shaping firm dynamics and long-run economic growth. We develop a Schumpeterian model with two sectors: consumption and investment, and two types of firms: state-owned enterprises (SOEs) and privately owned enterprises (POEs). Within this framework, multi-product firms require fixed inputs of investment goods to operate — a resource that is also crucial for firm expansion, and the soft budget constraint allows subsidized firms to continue operating on loss-making product lines. This distortion raises the price of investment goods, suppresses firm dynamics, and dampens aggregate growth. To quantify the mechanism, the model is calibrated using Chinese firm-level data. Our quantitative exercise shows that eliminating the SOE soft budget constraint friction reduces the price of investment goods by 9.82 percent, increases the overall TFP growth rate by 0.12 percentage points, and generates a welfare gain of 4.94 percent.

This paper examines how traditional informal institutions shape preferences for old-age support and the emergence of initiative-based long-term financial planning. Using data from the China Health and Retirement Longitudinal Study combined with clan genealogy records, we estimate the causal impact of exposure to clan culture on old-age support preferences. We find that stronger exposure to clan culture in early life is associated with a higher likelihood of adopting modern preferences for old-age support, characterized by greater reliance on self-initiated savings and pension participation rather than dependence on children. The results are robust to instrumental variable estimation and placebo test. Importantly, the effect operates exclusively through exposure to clan culture during early life, while clan culture encountered in adulthood has no significant influence, particularly among individuals who relocate later in life. Mechanism analysis suggests that peer effects, generalized trust, income growth, and financial literacy jointly mediate the relationship between clan culture and old-age support preferences. We further show that the expansion of the formal pension system complements, rather than crowds out, the influence of clan culture, consistent with a process of co-evolution between informal and formal institutions. The effects are stronger among individuals with favorable attitudes toward clans, rural residents, and men. Finally, modern preferences induced by clan culture translate into higher pension participation and larger financial asset holdings. Overall, the findings highlight how informal institutions can shape economic preferences and facilitate the adoption of formal financial arrangements.

We study machine-learning forecasts and portfolio allocation for Chinese government bonds. Penalised linear regressions and gradient-boosted trees predict next-month total returns for eight maturity buckets. Their forecasts feed mean–variance, Black–Litterman, and risk-budgeting implementations with weight, duration, and trading-cost controls; convexity, tail-risk, and attribution constraints are analysed separately as theoretical extensions. From January 2019 through December 2025, the three optimised cash portfolios earn net excess-return Sharpe ratios of 1.94–2.26 and maximum drawdowns of 0.18–0.25%. Three long-only bond-selection variants attain Sharpe ratios of 1.39–1.70. A futures-hedged arbitrage variant attains 1.08 under an 80% hedge-P&L scaling and basis-cost assumption. A stylised 10-year futures overlay reduces rather than improves cash-portfolio Sharpe ratios.