Following a period during which the two fields evolved separately, a consensus has emerged that competition and industrial policy are not inherently incompatible. This reflects broader intellectual shifts. Industrial policy is now viewed more favorably, not only for traditional development goals but also to strengthen technological capabilities for national security and secure global economic dominance. “Techno-nationalist” approaches to industrial policy may conflict with global technology diffusion efforts addressing issues like climate change (“techno-globalism”). Despite recent developments in the intersection of competition and industrial policy, there is a lack of evidence on how techno-nationalist and techno-globalist approaches interact with competition policy goals. This rticle fills this gap by empirically assessing the competitive effects of policy measures. We use a text-as-data approach, combining AI-driven document analysis with structured classification criteria. The data show that techno-globalist industrial policies are generally more pro-competitive than techno-nationalist ones, due to their broader scope and ability to lower entry costs. Moreover, we find that certain policy instruments are primarily associated with anti-competitive criteria, while others tend to exhibit predominantly pro-competitive features. Our results provide a fine-grained characterization of new industrial policy design in light of competition policy goals.

Data combination and analytics can generate valuable insights for firms and society as a whole. Multiple firms can do so by means of new technologies that bring the algorithm to the data (“algorithm sharing”) or, more conventionally, by sharing the data (“data sharing”). Algorithm-sharing technologies are gaining traction because of their advantages in terms of privacy, security, and environmental impact. We present a model that allows us to study the economic incentives generated by these technologies for both firms and a platform facilitating data combination. We find that, first, the platform chooses data sharing unless algorithm sharing’s analytics are sufficiently superior to those associated to data sharing. Second, we identify the properties of the analytics benefit function that ensure that algorithm sharing results in a higher total data contribution. Third, we highlight scenarios in which, in presence of data externalities, there can be a mismatch between the choice of the platform and the preference of a social planner.