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  5. The Local Dynamic Effects of AI and Technological Change
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The Local Dynamic Effects of AI and Technological Change

Paper Session

Sunday, Jan. 5, 2025 8:00 AM - 10:00 AM (PST)

Hilton San Francisco Union Square, Continental Ballroom 1&2
Hosted By: American Economic Association
  • Chair: Erik Brynjolfsson, Stanford University

The Characteristics and Geographic Distribution of Robot Hubs in U.S. Manufacturing Establishments

Erik Brynjolfsson
,
Stanford University
Catherine Buffington
,
Census Bureau
Nathan Goldschlag
,
Census Bureau
J. Frank Li
,
University of British Columbia
Javier Miranda
,
University of Jena

Abstract

We use data from the Annual Survey of Manufactures to study the characteristics and geography of investments in robots across U.S. manufacturing establishments. We find that robotics adoption and robot intensity (the number of robots per employee) is much more strongly related to establishment size than age. We find that establishments that report having robotics have higher capital expenditures, including higher information technology (IT) capital expenditures. Also, establishments are more likely to have robotics if other establishments in the same Core-Based Statistical Area (CBSA) and industry also report having robotics. The distribution of robots is highly skewed across establishments’ locations. Some locations, which we call Robot Hubs, have far more robots than one would expect even after accounting for industry and manufacturing employment. We characterize these Robot Hubs along several industry, demographic, and institutional dimensions. The presence of robot integrators and higher levels of union membership are positively correlated with being a Robot Hub.

The Local Effects of Artificial Intelligence: Evidence from the Municipal Bond Market

Lefteris Andreadis
,
Bank of Greece
Eleni Kalotychou
,
Cyprus University of Technology
Manolis Chatzikonstantinou
,
Georgetown University
Christodoulos Louca
,
Cyprus University of Technology
Christos Makridis
,
Stanford University and Arizona State University

Abstract

Drawing on the universe of online job postings, coupled with municipal bond data between 2014 to 2022, we exploit plausibly exogenous variation in the issuance of bonds within the same county over time in response to the evolution of artificial intelligence (AI) and data analytics (DA) investments after controlling for all shocks that are common across states in the same year. We find that AI investments lead to significant declines in bond yields, meaning that municipalities can borrow at lower costs. Furthermore, these results are concentrated in counties with lower shares of larger superstar firms, consistent with the interpretation of positive spillover effects. We subsequently validate these results through an event study that compares job postings in counties before versus after the release of ChatGPT, leveraging counties' pre-existing exposure. Our results provide new evidence on the potential democratizing effects of AI investments at a local level.

Artificial Intelligence and Firms' Systematic Risk

Tania Babina
,
Columbia University
Anastassia Fedyk
,
University of California-Berkeley
Alex He
,
University of Maryland-College Park
James Hodson
,
AI For Good Foundation

Abstract

We provide direct evidence that firms’ investments in new technologies affect the com-position of firms’ risk profiles. Leveraging comprehensive data on firm-level artificial intelligence (AI) investments, we document that firms that invest more in AI experience increases in their systematic risk, measured by market beta. This is unique to AI: robotics, IT, organizational capital, and R&D investments do not display similar effects. Our results are consistent with AI investments creating growth options: AI-investing firms become more growth-like, and the effect on market betas concentrates during market upswings and periods of increased news and attention around AI advances.

AI Adoption in United States Hospitals

Avi Goldfarb
,
University of Toronto
Florenta Teodoridis
,
University of Southern California

Abstract

We examine adoption of artificial intelligence in US hospitals using two different data sources for adoption. First, we measure adoption of AI for hospital operations using the AHA hospital data set. Second, we examine adoption of AI for research through academic publication data. Third, we examine adoption of AI for clinical and other uses, through job posting data. Using panel data on adoption and hospital characteristics, we explore complementarity between the types of AI adoption and the role of within-hospital and local IT expertise in adoption decisions. Finally, we explore the correlation between adoption and changes in hospital-level outcomes.
JEL Classifications
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
  • J2 - Demand and Supply of Labor