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Latent heterogeneity to evaluate the effect of human capital on world technology frontier

Author

Listed:
  • Mastromarco, Camilla
  • Simar, Léopold

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

Abstract
Although human capital has been recognized as playing important role in spurring productivity growth, its empirical effect remains ambiguous due to the possibility of latent heterogeneity. To reveal the impact of this important driver of economic growth, we propose an alternative empirical methodology, robust frontier in non parametric location-scale models for accommodating simultaneously the problem of model specification uncertainty, latent heterogeneity and cross-section dependence in modelling technical efficiency. We estimate a nonparametric frontier model to define the world technology frontier of 40 countries over the period 1970–2007. Conditional versions of the frontier enables us to investigate the effect of external factors on the production process. One of these factors will allow to measure the openness of the economy of the country and the second will be this latent factor of heterogeneity linked to the absorptive capability and hence to human capital of the country. We have to adjust the methodology to take into account the panel structure of our data set, i.e., handling the time dimension and the cross-sectional dependence affecting the process. Our findings prove that human capital plays an important role in accelerating the technological catch-up (increase in the efficiency) but not on the technological changes (shifts in the frontier). This result seems to confirm the theoretical hypothesis that countries benefit from new technology (technological catch-up) only when they have the ability to exploit it, hence only when they have high level of absorptive capability.

Suggested Citation

  • Mastromarco, Camilla & Simar, Léopold, 2021. "Latent heterogeneity to evaluate the effect of human capital on world technology frontier," LIDAM Reprints ISBA 2021009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2021009
    DOI: https://doi.org/10.1007/s11123-021-00597-x
    Note: In: Journal of Productivity Analysis, Vol. 55, p. 71–89 (2021)
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