Industrial Policy
Paper Session
Sunday, Jan. 4, 2026 8:00 AM - 10:00 AM (EST)
- Chair: Hanming Fang, University of Pennsylvania and NBER
Decoding China's Industrial Policies
Abstract
We decode China’s industrial policies from 2000 to 2022 by employing large language models (LLMs) to extract and analyze rich information from a comprehensive dataset of 3 milliondocuments issued by central, provincial, and municipal governments. Through careful prompt engineering, multistage extraction and refinement, and rigorous verification, LLMs allow us to classify the industrial policy documents and extract structured information on policy objectives, targeted industries, policy tones (supportive or regulatory/suppressive), policy tools, implementation mechanisms, and intergovernmental relationships, etc. Combining these newly constructed industrial policy data with micro-level firm data, we document a set of facts about
China’s industrial policy that explore the following questions: What are the economic and political foundations of the targeted industries? What policy tools are deployed? How do policy tools vary across different levels of government and regions, as well as over the development phases of an industry? What are the impacts of these policies on firm behavior, including entry, production, and productivity growth? In addition, we explore the political economy of industrial policy, focusing on top-down transmission mechanisms, policy persistence, and policy diffusion
across regions. Finally, we document spatial inefficiencies and industry-wide overcapacity as potential downsides of industrial policies.
Technology Rivalry and Resilience Under Trade Disruptions: The Case of Semiconductor Foundries
Abstract
This paper studies the impact of industrial policies on technology competition and consumer welfare amid rising global trade disruption risks. Distilling key empirical features from novel data on the semiconductor foundry industry, I develop and estimate a dynamic oligopoly model that integrates step-by-step innovation, trade disruption risk, and industrial policies. While distortions from market power and technological externalities justify subsidies, their optimal levels depend on the magnitude of trade disruption risk: when the risk is low, the optimal subsidy rate remains low, as the welfare benefits are distributed globally, but the costs are borne exclusively by the subsidizing government. My quantitative model shows that a 35% trade disruption risk makes the 25% investment subsidy under the U.S. CHIPS Act optimal, resulting in a 6%welfare improvement for the U.S. The paper also analyzes the CHIPS Act’s restrictions on investments in rival countries, intended to secure technological leadership against their firms. Its efficacy depends on the strength of technology spillover restrictions and the scale of the rival home market secured for rival firms.Open Source Software Policy in Industry Equilibrium
Abstract
Open source software (OSS) is a form of public knowledge widely provided and relied on by the private sector. To study the effects of growing government involvement in this critical public good, I build a new empirical model where high-tech firms choose software inputs and developer labor in competitive equilibrium. For estimation, I create a new dataset of OSS and in-house investment for the global web development industry, where software choices are directly observable. I simulate counterfactuals to assess the global impact of China tightening its recent internet restrictions on cross-border OSS collaboration or increasing its financial support for domestic OSS. I find that stricter restrictions do little to boost domestic OSS investment. Instead, lost spillovers raise web development costs in China by $2 per dollar of disincentive and $7 globally. Heightened subsidies prove more effective at increasing domestic investment and cut global costs by $11 per dollar of subsidy—tripling if the US responds in kind.JEL Classifications
- L5 - Regulation and Industrial Policy