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 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%

We use administrative registration records with information on the owners of all Chinese firms to document the importance of “connected” investors, defined as state-owned firms or private owners with equity ties with state-owned firms, in the businesses of private owners. We document a hierarchy of private owners: the largest private owners have direct investments from state-owned firms, the next largest private owners have equity investments from private owners that themselves have equity ties with state owners, and the smallest private owners do not have any ties with state owners. The network of connected private owners has expanded over the last two decades. The share of registered capital of connected private owners increased by almost 20 percentage points between 2000 and 2019, driven by two trends. First, state owned firms have increased their investments in joint ventures with private owners. Second, private owners with equity ties to state owners also increasingly invest in joint ventures with other (smaller) private owners. The expansion in the “span” of connected owners from these investments with private owners may have increased aggregate output of the private sector by 4.2% a year between 2000 and 2019.