Unequal Access to AI Training and Labor Market Inequality in Chicago

Abstract

Income inequality in U.S. cities has grown alongside changes in the labor market, raising questions about how new technologies may be reshaping existing disparities. This paper examines whether unequal access to AI-related training is contributing to widening labor market inequality in Chicago. It focuses on how differences in access to in-demand skills, particularly those related to artificial intelligence, may shape wage outcomes and financial security for workers in similar occupations. Using occupational wage data from the U.S. Bureau of Labor Statistics and neighborhood-level indicators from the Chicago Metropolitan Agency for Planning, the analysis compares four occupations with varying levels of AI exposure and skill requirements: software developers, registered nurses, customer service representatives, and retail salespersons. The findings identify a pattern in which occupations more complementary to AI tend to have higher wages and higher skill, while jobs more exposed to automation face lower pay and greater uncertainty. These patterns are also associated with geographic differences in access to training and job opportunities across Chicago neighborhoods. The results suggest that unequal access to AI-related training may deepen existing economic and spatial inequalities, with important implications for workforce development policy and long-term household financial stability.