When does state equity investment in private firms spur productivity growth and innovation? I examine this question in China’s semiconductor sector by comparing firms backed by central and local government venture capital funds. Using administrative records, balance sheets, and industry data, I show that central funds select firms that rank higher nationally in productivity, while local funds invest disproportionately in less productive firms within their own jurisdictions. I also find that managers’ prior educational and employment ties to government fund managers, especially ties formed through prestigious academic institutions, predict investment.
Despite lower initial productivity, locally backed firms experience larger post-investment gains in revenue productivity, intellectual property output, manufacturing capacity, and government procurement. They also expand their boards and management teams more rapidly, adding directors and executives with government experience, graduate degrees, and technical expertise. These gains are strongest among firms without prior ties to government fund managers. Because connected firms already have more technically trained board members and more patents before investment, the findings suggest that decentralized government venture capital can promote growth in high-tech sectors by providing capital and managerial resources to initially weaker, less connected firms.
As semiconductors dominate recent industrial-policy debates, understanding their underlying economics is critical. In contrast with the existing literature that relies on indirect estimates of semiconductor manufacturing costs, this paper combines comprehensive product specification data with a commercial-grade engineering cost model, constructing a granular panel of over 77,000 CPU and GPU chip-quarter observations from 2015 to 2025. By reverse-engineering the engineering cost model through a series of regressions, we document several findings that challenge standard modeling assumptions: (i) vintage-capital effects are highly heterogeneous and can even increase costs; (ii) physical learning-by-doing confers a modest 1.9% quarterly cost reduction; and (iii) static scale economies flatten above a 70% capacity utilization threshold. We also find accrual-based depreciation accounting to be a critical source of biases that could dominate all of these estimates if not properly isolated from physical, cash-flow-based costs. Finally, we confirm the presence of persistent firm-level heterogeneity, which could be a major bottleneck for industrial subsidies.
This paper examines how large industrial policy programs shape firm political spending. We argue that firm-level lobbying rises when large, firm-specific policy rents are at stake, especially when the total size and allocation of benefits remain uncertain. Undistributed benefits also induce firms that had never lobbied to overcome fixed entry costs and begin investing in politics. Event studies of two landmark 2022 industrial policies, the CHIPS and Science Act and the Inflation Reduction Act, show that semiconductor firms increased lobbying expenditures by about 52% between 2018 and 2022, while green-energy firms increased lobbying by about 112%. In both industries, new lobbying firms (the extensive margin) and previously active firms (the intensive margin) each contribute substantially, with entry accounting for about 40% of the semiconductor increase and roughly two-thirds of the green-energy increase. The increase persists after enactment, reflecting firms' concerns over ex post subsidy allocation and policy uncertainty from political transitions. We further find that, controlling for project size and political contributions, each additional $1 million in lobbying is associated with $66 million in CHIPS Act subsidies for the average award. We propose a costly signaling model with entry costs of lobbying consistent with the observed patterns.