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Nonlinearities in productivity growth: A semi-parametric panel analysis

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  • Azomahou, Théophile T.
  • Diene, Bity
  • Diene, Mbaye
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    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.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X12000355
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    Bibliographic Info

    Article provided by Elsevier in its journal Structural Change and Economic Dynamics.

    Volume (Year): 24 (2013)
    Issue (Month): C ()
    Pages: 45-75

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    Handle: RePEc:eee:streco:v:24:y:2013:i:c:p:45-75

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    Web page: http://www.elsevier.com/locate/inca/525148

    Related research

    Keywords: R&D; TFP; Panel data; Nonparametric estimation; Reduced vs. structural form;

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