AI, Occupational Entry Barriers, and Upward Mobility
Year: 2026
Type: preprint
Abstract: Public choice has long analyzed occupational entry barriers and is increasingly concerned with artificial intelligence (AI), but it has not examined how the two relate. We develop a public choice theory of tolls to explain it. An entry barrier is a toll set by the incumbents who profit from it, charged on credentials rather than demonstrated skill, and slow to adjust when more workers need to pass through it. Because the toll is fixed while AI lowers the value of some experience built up in the occupations it exposes, the toll claims a larger share of the cost of moving up for workers leaving those occupations, so the upward-mobility premium from entering a high-barrier occupation should erode, most sharply for them. A high-barrier occupation is one restricted by a license or comparable requirement in the worker’s state, measured by a positive State Occupational Licensing Index Barrier Score. Mobility is the change in O*NET Job Zone, a five-tier scale of required preparation, between the occupation a worker leaves and the one entered. Using linked Current Population Survey records for 2010–2025, we compare moves into high-barrier and low-barrier occupations before and after 2023. Entering a high-barrier occupation was associated with 0.029 more Job Zone steps of upward mobility before 2023; afterward the premium falls by 0.018 to 0.041 steps, to roughly zero or below, and the decline is concentrated among workers leaving AI-exposed occupations. The evidence is associational, and the exposure gradient is less precisely estimated than the timing result. JEL Classification: D72; J44; J62; O33
Source: Research Square
Authors: Ting Zhang, TyLee Armstrong
Institution: University of Baltimore
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225653562