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Endogenous technological progress in a multi-sector growth model

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  • La Torre, Davide
  • Marsiglio, Simone

Abstract

This paper presents an endogenous growth model driven by human capital, where human capital can be allocated across three sectors: the production of the final consumption good, the educational sector and the production of technological capital (in the form of knowledge or ideas). In our model, which also includes public expenditure and population growth, labor augmenting technical progress is endogenous and this enriches the transitional dynamics of the economy. With respect to ideas-based growth models, we assume knowledge is produced according to a neoclassical technology, combining ideas and human capital. Such an assumption is motivated by empirical works showing the existence of significant decreasing returns in the creation of ideas at the aggregate level (as Kortum, 1993; and Pessoa, 2005) and of the weak relationship between some inputs of the knowledge production process (as the number of researchers) and the total factor productivity growth rate (as Jones, 2002). Under some general conditions, this economy exhibits the existence of a steady state equilibrium and an unstable multidimensional manifold. Numerical examples are provided to show the existence of stable arms.

Suggested Citation

  • La Torre, Davide & Marsiglio, Simone, 2010. "Endogenous technological progress in a multi-sector growth model," Economic Modelling, Elsevier, vol. 27(5), pages 1017-1028, September.
  • Handle: RePEc:eee:ecmode:v:27:y:2010:i:5:p:1017-1028
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    2. De, Supriyo, 2014. "Intangible capital and growth in the ‘new economy’: Implications of a multi-sector endogenous growth model," Structural Change and Economic Dynamics, Elsevier, vol. 28(C), pages 25-42.
    3. Stefano Bosi & Carmen Camacho & Thai Ha-Huy, 2023. "On the uniqueness of the optimal path in a discrete-time model à la Lucas (1988)," PSE Working Papers halshs-03920386, HAL.
    4. Bosi, Stefano & Camacho, Carmen & Ha-Huy, Thai, 2023. "Balanced growth and degrowth with human capital," Economics Letters, Elsevier, vol. 232(C).
    5. Marsiglio, Simone & La Torre, Davide, 2012. "Population dynamics and utilitarian criteria in the Lucas–Uzawa Model," Economic Modelling, Elsevier, vol. 29(4), pages 1197-1204.
    6. La Torre, Davide & Marsiglio, Simone & Mendivil, Franklin & Privileggi, Fabio, 2015. "Self-similar measures in multi-sector endogenous growth models," Chaos, Solitons & Fractals, Elsevier, vol. 79(C), pages 40-56.
    7. Marchese, Carla & Marsiglio, Simone & Privileggi, Fabio & Ramello, Giovanni, 2014. "Endogenous Recombinant Growth through Market Production of Knowledge and Intellectual Property Rights," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201413, University of Turin.
    8. Alberto Bucci & Simone Marsiglio, 2019. "Financial development and economic growth: long‐run equilibrium and transitional dynamics," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(3), pages 331-359, July.
    9. Oluwaseun Sharomi & Tufail Malik, 2017. "Optimal control in epidemiology," Annals of Operations Research, Springer, vol. 251(1), pages 55-71, April.
    10. Simone Marsiglio & Alberto Ansuategi & Maria Carmen Gallastegui, 2016. "The Environmental Kuznets Curve and the Structural Change Hypothesis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(2), pages 265-288, February.
    11. Bucci, Alberto & Florio, Massimo & La Torre, Davide, 2012. "Government spending and growth in second-best economies," Economic Modelling, Elsevier, vol. 29(3), pages 654-663.
    12. A. Bucci & C. Colapinto & M. Forster & D. La Torre, 2011. "Stochastic technology shocks in an extended Uzawa–Lucas model: closed-form solution and long-run dynamics," Journal of Economics, Springer, vol. 103(1), pages 83-99, May.
    13. Hui, Wang & Xin-gang, Zhao & Ling-zhi, Ren & Ji-cheng, Fan & Fan, Lu, 2021. "The impact of technological progress on energy intensity in China (2005–2016): Evidence from a geographically and temporally weighted regression model," Energy, Elsevier, vol. 226(C).

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