Parameter Heterogeneity and Convergence Clubs: Shedding Light on the Human Capital Puzzle

Leonardo Becchetti and Giovanni Trovato ()
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Giovanni Trovato: Università di Roma “Tor Vergata”

A chapter in Economic Policy Frameworks Revisited, 2023, pp 159-193 from Springer

Abstract: Abstract We analyze the contribution of human capital and the labor augmenting component to per capita GDP using a finite mixture model. We find strong evidence of heterogeneity in sub-models explaining levels of real per capita GDP and extremely weak evidence of heterogeneity in those determining rates of growth, with lack of convergence across groups. Furthermore, by conditioning the country-specific cluster prior probability to the initial level of human capital, we account for the feedback effect between human capital and initial conditions. In this way, we provide an interpretation of the human capital puzzle and document that initial schooling rate levels strongly affect the dynamic of country-specific growth. We also show that the heterogeneous impacts of human capital and the labor augmenting component on GDP are crucially affected by differences in latent variables and, most significantly, by quality of education, legal structure, and property rights’ regulation.

Keywords: Human capital; Economic development; Convergence; Bivariate semi-parametric mixture; Parameter heterogeneity; Concomitant variable; Solow model; Convergence clubs; C14; C33; 040 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-031-36518-8_9

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DOI: 10.1007/978-3-031-36518-8_9

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