Employment Diversification and Urban Mobility Disparities: A Multi-scale Analysis of U.S. Core-Based Statistical Areas

The Economic Complexity Index (ECI), a metric traditionally utilized in international trade to correlate high complexity with lower income inequality, is evaluated here at the subnational level to determine if this relationship persists across diverse urban scales. By adapting the ECI to employment distributions across 121 Core-Based Statistical Areas (CBSAs) in five U.S. states—California, New York, New Mexico, Louisiana, and Mississippi—this study integrates Replica mobility data with American Community Survey socioeconomic indicators. The analysis reveals a significant reversal of international trends: CBSAs with the highest economic complexity demonstrate the greatest income inequality (Gini = 0.51, r = 0.42, p < 0.001), despite maintaining superior mobility efficiency through lower VMT per capita and reduced radii of gyration. Utilizing Principal Component Analysis and bootstrap-validated K-means clustering, we categorize regions into four distinct typologies—Prosperous Knowledge Hubs, High-Density Mixed Economies, Rural Resource-Dependent Regions, and Small Industrial Towns—each exhibiting unique trade-offs between complexity, mobility, and equity. This "complexity-inequality paradox" suggests a localized Simpson’s Paradox where skill-biased agglomeration and occupational polarization at the regional scale override the institutional mechanisms typically found at the national level. These findings indicate that employment-based ECI, coupled with high-resolution mobility analytics, provides a scalable diagnostic framework that challenges uniform development strategies in favor of data-driven regional policy.