Immigration and Regional Specialization in AI

Gordon Hanson

No 9a45d, SocArXiv from Center for Open Science

Abstract: I examine the specialization of US commuting zones in AI-related occupations over the 2000 to 2018 period. I define AI-related jobs based on keywords in Census occupational titles. Using the approach in Lin (2011) to identify new work, I measure job growth related to AI by weighting employment growth in AI-related occupations by the share of job titles in these occupations that were added after 1990. Overall, regional specialization in AI-related activities mirrors that of regional specialization in IT. However, foreign-born and native-born workers within the sector tend to cluster in different locations. Whereas specialization of the foreign-born in AI-related jobs is strongest in high-tech hubs with a preponderance of private-sector employment, native-born specialization in AI-related jobs is strongest in centers for military and space-related research. Nationally, foreign-born workers account for 55% of job growth in AI-related occupations since 2000. In regression analysis, I find that US commuting zones exposed to a larger increases in the supply of college-educated immigrants became more specialized in AI-related occupations and that this increased specialization was due entirely to the employment of the foreign born. My results suggest that access to highly skilled workers constrains AI-related job growth and that immigration of the college-educated helps relax this constraint. (Stone Center on Socio-Economic Inequality Working Paper)

Date: 2023-10-25
New Economics Papers: this item is included in nep-ain, nep-geo and nep-ure
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https://osf.io/download/65396ffa8a28b11240ffd447/

Related works:
Working Paper: Immigration and Regional Specialization in AI (2023)
Working Paper: Immigration and Regional Specialization in AI (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:9a45d

DOI: 10.31219/osf.io/9a45d

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