Constructing the Tech-Wage Inequality Atlas: A Multi-Country, Multi-Sector Panel Dataset

While Gravina & Foster-McGregor (2024) and others use panel data for specific regions or sectors, there is a striking lack of a harmonized, cross-country dataset that systematically links granular technology adoption metrics to wage dispersion, especially at the intersection of gender, race, skill, and firm size. This project would build the “Tech-Wage Inequality Atlas,” combining sources like EU KLEMS, World Bank Enterprise Surveys, and microdata from national labor force surveys, enriched by scraping platform and patent data for digital tech adoption rates. The Atlas would enable new empirical work on questions like: When does AI adoption help or hurt wage equality? How do sectoral and geographic differences moderate these effects? This infrastructure would enable researchers to test conflicting theories, explore under-studied contexts, and inform global policy debates. Its novelty lies in the scope, granularity, and the capacity to analyze technology-wage dynamics at unprecedented detail.

References:

1. Unraveling wage inequality: tangible and intangible assets, globalization and labor market regulations. Antonio Francesco Gravina, Neil Foster-McGregor (2024). Empirical Economics.
2. Unraveling wage inequality: tangible and intangible assets, globalization and labor market regulations. Antonio Francesco Gravina, Neil Foster-McGregor (2024). Empirical Economics.