Measuring Human Capital with Productivity-Weighted Labor Force: Methodology and Projections for China, India, the United States, and the European Union

Marois, G. ORCID: https://orcid.org/0000-0002-2701-6286, Gietel-Basten, S., Crespo Cuaresma, J., Zellmann, J.G., Reiter, C. ORCID: https://orcid.org/0000-0002-1485-3851, & Lutz, W. ORCID: https://orcid.org/0000-0001-7975-8145 (2024). Measuring Human Capital with Productivity-Weighted Labor Force: Methodology and Projections for China, India, the United States, and the European Union. IIASA Working Paper. Laxenburg, Austria: WP-24-005

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Abstract

This working paper provides a comprehensive overview of the methodology used to calculate a standardized and internationally comparable productivity-weighted labor force (PWLF) measure that takes into account both the education structure of the population and the quality of the educational system. Education-specific weights are calculated with a Mincerian earnings function on pooled data from all IPUMS-I censuses containing information on education, labor force status, and income. The education parameters are interacted with the countries' average educational attainment to account for the dependence of returns to education on the number of workers sharing that education level. Country and time specific adjustment factors for education quality are derived from skills assessment surveys. To calculate the productivity-weighted labor force size, these adjusted weights are then applied to labor force estimates and projections. The analytical value of the PWLF is validated making use of prediction exercise for GDP growth applied to a panel dataset covering all countries of the world from 1970 to 2015 for which data are available. Finally, the paper provides a practical application by forecasting PWLF figures for China, India, the United States, and the European Union from 2020 to 2100. These forecasts are compared against other population indicators (total population size, working-age population, and labor force size), highlighting the importance of population heterogeneity in the analysis of demographic trends.

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