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Nonlinearities in Productivity Growth: A Semi-parametric Panel Analysis

Author

Listed:
  • Bity Diene

    (CERDI - Centre d'Études et de Recherches sur le Développement International - UCA [2017-2020] - Université Clermont Auvergne [2017-2020] - CNRS - Centre National de la Recherche Scientifique)

  • Mbaye Diene

    (UCAD - Université Cheikh Anta Diop de Dakar [Sénégal])

  • Théophile Azomahou

    (UNU-MERIT - UNU-MERIT - United Nations University - Maastricht University)

Abstract

We use country panel data spanning over 1998-2008 for both developed and developing countries to study the productivity growth when countries are close to the technology frontier. Relying on a semi-parametric generalized additive model, we estimate both reduced and structural forms for total factor productivity growth. We consider three measurements of frontier: the economy with the highest level of productivity growth, the world productivity growth and the productivity growth of the USA. We obtain a U-shape relation between productivity growth and the proximity to the world productivity growth. The relation between productivity growth and human capital displays an inverted U-shape form (res. U-shape) when the proximity to the highest productivity growth is used (res. the proximity to productivity growth of the USA). Total staff in R&D has an inverted W-shape effect on productivity growth. The share of R&D expenditure funded by government and from abroad impact positively the growth of productivity. However, the increase in government spending on R&D has a greater impact on productivity growth when the former is weak, and a smaller impact when R&D spending is already high. International trade has a positive effect on productivity growth. Specification tests show that our semi-parametric models provide a better approximation of the data compared to the parametric analogue, revealing a high degree of nonlinearity governing productivity growth.

Suggested Citation

  • Bity Diene & Mbaye Diene & Théophile Azomahou, 2013. "Nonlinearities in Productivity Growth: A Semi-parametric Panel Analysis," Post-Print halshs-00914557, HAL.
    • Handle: RePEc:hal:journl:halshs-00914557
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    Citations

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    Cited by:

    1. Danquah, Michael, 2018. "Technology transfer, adoption of technology and the efficiency of nations: Empirical evidence from sub Saharan Africa," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 175-182.
    2. Jorge Antunes & Goodness C. Aye & Rangan Gupta & Peter Wanke & Yong Tan, 2020. "Endogenous Long-Term Productivity Performance in Advanced Countries: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach," Working Papers 2020111, University of Pretoria, Department of Economics.
    3. Sakiru Adebola Solarin, 2017. "Testing for the Stationarity in Total Factor Productivity: Nonlinearity Evidence from 79 Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 8(1), pages 141-158, March.

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