Policymakers fear artificial intelligence (AI) will disrupt labor markets, especially for high-skilled workers. We investigate this concern using novel, task-specific data for security analysts. Exploiting variation in AI’s power across stocks, we show analysts with portfolios that are more exposed to AI are more likely to reallocate efforts to soft skills, shift coverage towards low AI stocks, and even leave the profession. Analyst departures disproportionately occur among highly accurate analysts, leaving for non-research jobs. Reallocating efforts toward tasks that rely on social skills improve consensus forecasts. However, increased exposure to AI reduces the novelty in analysts’ research which reduces compensation.
Young firms’ contribution to aggregate employment has been underwhelming. However, a similar trend is not apparent in their contribution to aggregate sales or aggregate stock market capitalization. We study the implications of the arrival of “low marginal – high average” revenue-product-of-labor firms in a stylized model of dynamic firm heterogeneity, and show that the model can account for a large number of facts related to the decline in “business dynamism”. We study the long-term implications of the decline in business dynamism on the economy by providing analytical results that connect the decline in dynamism to the eventual decline of consumption.
In this paper, we quantify the magnitude of R&D spillovers created by grants to small firms from the US Department of Energy. Our empirical strategy leverages variation due to state-specific matching policies, and we develop a new approach to measuring both geographic and technological spillovers that does not rely on an observable paper trail. Our estimates suggest that for every patent produced by grant recipients, three more are produced by others who benefit from spillovers. Sixty percent of these spillovers occur within the US, and many of them occur in technological areas substantially different from those targeted by the grants.
We examine innovation following the Great Depression using data on a century’s worth of U.S. patents and a difference-in-differences design that exploits regional variation in the crisis severity. Harder-hit areas experienced large and persistent declines in independent patenting, mostly reflecting the disruption in access to finance during the crisis. This decline was larger for young and inexperienced inventors and lower-quality patents. In contrast, innovation by large firms increased, especially among young and inexperienced inventors. Overall, the Great Depression contributed to the decline in technological entrepreneurship and accelerated the shift of innovation into larger firms.
We use comprehensive micro data in the French manufacturing sector between 1994 and 2015 to document the effects of automation technologies on employment, wages, prices and profits. Causal effects are estimated with event studies and a shift-share IV design leveraging predetermined supply linkages and productivity shocks across foreign suppliers of industrial equipment. At all levels of analysis – plant, firm, and industry – the estimated impact of automation on employment is positive, even for unskilled industrial workers. We also find that automation leads to higher profits, lower consumer prices, and higher sales. The estimated elasticity of employment to automation is 0.28, compared with elasticities of 0.78 for profits, -0.05 for prices, and 0.37 for sales. Consistent with the importance of business-stealing across countries, the industry-level employment response to automation is positive and significant only in industries that face international competition. These estimates can be accounted for in a simple monopolistic competition model: firms that automate more increase their profits but pass through some of the productivity gains to consumers, inducing higher scale and higher employment. The results indicate that automation can increase labor demand and can generate productivity gains that are broadly shared across workers, consumers and firm owners. In a globalized world, attempts to curb domestic automation in order to protect domestic employment may be self-defeating due to foreign competition.
Presenter: Xavier Javarel (The London School of Economics and Political Science)
Coauthors: Philippe Aghion (College de France and London School of Economics and Political Science, Fellow; CEPR; NBER), Celine Antonin (Sciences Po), and Simon Bunel (Banque de France)
How does common ownership affect innovation? We study this question using project-level data on pharmaceutical startups and their venture capital (VC) investors. We find that common ownership leads VCs to hold back projects, withhold funding, and redirect innovation at lagging startups. Effects are stronger where R&D costs are larger, consistent with common owners aiming to cut duplicate costs. Effects are also stronger where technological similarity is greater and preexisting competition is lower, consistent with common owners seeking market power for their surviving projects. Overall, common VC ownership appears to generate social benefits, via improved innovation efficiency, but also social costs.
Presenter:Luke Taylor (The Wharton School of the University of Pennsylvania)
Coauthors: Xuelin Li (University of South Carolina) and Tong Liu (Massachusetts Institute of Technology)