This paper examines how profits from mutual funds’ participation in initial public offerings (IPOs) shape fund performance, investor flows, and market stability in China. Using comprehensive fund–IPO matched data from 2016 to 2023, we decompose fund returns into an IPO-lottery component and residual performance. At the aggregate level, IPO allocations add 2.05% to annualized excess returns; net of IPOs, excess return is −0.35% per year. At the individual level, the contribution of IPO profits varies substantially across funds and is most pronounced among mid-sized funds, inflating perceived managerial skill. Funds with higher IPO-driven gains attract greater inflows despite the absence of performance persistence, leading to capital misallocation. At the market level, IPO-profit-induced trading (PIT) predicts short horizon price run-ups that dissipate and reverse over subsequent months, while raising both total and idiosyncratic volatility. Overall, IPO profits temporarily enhance reported performance but erode market stability by propagating non-fundamental shocks through secondary markets.

We study the effect of China’s inland-favoring land policy on firm-level productivity by employing a research design combining difference-in-differences and regression discontinuity at the policy border. We find that the inland-favoring land policy decreased the firm productivity gap between developed (eastern) regions and underdeveloped (inland) regions. The relative changes are mainly due to slower eastern firm productivity growth rather than faster inland firm productivity growth. Eastern firms reduced their R&D expenditure and capital usage as a response to the policy.

This paper investigates how transparency in lawsuit information affects capital allocation and aggregate industrial production. Greater transparency enhances the availability of information about firms' fundamentals, which can influence resource distribution. We exploit regional variations in courts' compliance with mandated judicial document disclosures in China, implemented since 2014, as a natural experiment. For firms with initially high marginal revenue products of capital (MRPK), a 10-percentage-point increase in legal transparency results in a 4.4% increase in physical capital and a 7.9% reduction in MRPK, relative to firms with lower MRPK. Additionally, regions with higher transparency experience a rise in aggregate output. Further analysis differentiating firms by ownership type, public listing status, and industry-level contract intensity enhances the robustness of our findings.

This research study primarily examines the digital transformation’s internal mechanism promoting enterprises’ total factor productivity (TFP) based on the matching and learning mechanism. Afterward, this research article empirically examines the digital transformation’s influential mechanism on enterprises’ TFP, using the Chinese listed companies’ data on the “A” stock market for the time period ranging from 2007 to 2019. The major study findings are as follows: (1) the improvement of the digital transformation significantly increases enterprises’ TFP. The proposed conclusion remains robust after a series of robustness- and the endogeneity test. (2) Furthermore, mechanism analysis reveals that digital transformation effectively enhances enterprises’ TFP by eliminating resource misallocation in the industry. In addition to this, digital transformation relies on the mechanism of “learning by doing” to promote the technological innovation’s spillover effect; hence, effectively enhancing enterprises’ TFP. (3) Heterogeneity analysis demonstrates that the digital transformation’s impact on enterprises’ TFP is heterogeneous in the context of enterprise size, enterprise type, and enterprise ownership. Lastly, this study puts forward that government bodies should intensify the construction and investment in digital infrastructure, promote a series of institutional reforms, and support digital technological R&D practices.

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.

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%

Capital misallocation is a crucial factor that hinders the high-quality development of the capital market. Taking mergers and acquisitions (M&A) cases of Chinese listed companies from 2007 to 2019 as samples, this study finds that there is a mismatch between the target firm’s profit quality and the M&A premium. Moreover, based on the dual perspective of acquirer and target motivations, this study demonstrates that the target firm’s insufficient motivation to improve its information quality is the primary cause of a capital mismatch. Factors that can enhance the motivation of the target, such as improving financial services and facilitating labour flow, are the cure for capital misallocation. It is a crucial study to understand China’s capital misallocation and of great theoretical and practical significance to understand the combination of efficient markets and effective governments in emerging markets.

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.

We provide firm-level evidence on the real effects of shadow banking in terms of technological innovation. Firm-to-firm entrusted loans, the largest part of the shadow banking sector in China, enhance the borrowers’ innovation output. The effects are more prominent when the borrowers are subject to severer financial constraints, information asymmetry, and takeover exposures. A plausible underlying channel is capital reallocations from less productive but easy-financed lender firms to more innovative but financially less-privileged borrower firms. Our paper suggests that shadow banking helps correct bank credit misallocations and thus serves as a second-best market design in financing the real economy.

We estimate real losses arising from the cross-sectional misallocation of financial liabilities. Extending a production-based framework of misallocation measurement to the liabilities side of the balance sheet and using manufacturing firm data from the United States and China, we find significant misallocation of debt and equity in China but not the United States. Reallocating liabilities of firms in China to mimic U.S. efficiency would produce gains of 51% to 69% in real value-added, with only 17% to 21% stemming from inefficient debt-equity combinations. For Chinese firms that are large or in developed cities, we estimate lower distortionary financing costs.