Developing artificial intelligence (AI) technology requires data. In many domains, government data far exceed in magnitude and scope data collected by the private sector, and AI firms often gain access to such data when providing services to the state. We argue that such access can stimulate commercial AI innovation in part because data and trained algorithms are shareable across government and commercial uses. We gather comprehensive information on firms and public security procurement contracts in China’s facial recognition AI industry. We quantify the data accessible through contracts by measuring public security agencies’ capacity to collect surveillance video. Using a triple-differences strategy, we find that data-rich contracts, compared to data-scarce ones, lead recipient firms to develop significantly and substantially more commercial AI software. Our analysis suggests a contribution of government data to the rise of China’s facial recognition AI firms, and that states’ data collection and provision policies could shape AI innovation.

We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and laborthe task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of productiondue to automation and new taskscan be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.

Data is nonrival: a person’s location history, medical records, and driving data can be used by many firms simultaneously. Nonrivalry leads to increasing returns. As a result, there may be social gains to data being used broadly across firms, even in the presence of privacy considerations. Fearing creative destruction, firms may choose to hoard their data, leading to the inefficient use of nonrival data. Giving data property rights to consumers can generate allocations that are close to optimal. Consumers balance their concerns for privacy against the economic gains that come from selling data broadly. (JEL C80, D11, D21, D83, E22, K11, O34)

The US government is a major producer of economic and financial data, statistics, analysis, and forecasts that are gathered, compiled, and published as public goods for use by citizens, government agencies, researchers, nonprofits, and the business community. There is no market transaction in the publication and dissemination of these government data and therefore no market-determined value. The purpose of this paper is to outline and augment our understanding of the value of government data for business decision-making. We provide an overview of the topic, including results from government reports and a private sector survey. We then provide concrete examples of how these government data are used to make business decisions focusing on three sectors: automotive, energy, and financial services. Examples of new initiatives by the federal government to open access to more data, exploiting technology advances associated with the internet, cloud storage, and software applications, are discussed. With the significant growth in the digital economy, we also include discussion and insights around how digital platform companies utilize government data in conjunction with their privately generated data (or "big data") to foster more informed business decisions.