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Augmented Solow Model with Mincerian Education and Transport Infrastructure Externalities

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  • Tomasz Brodzicki

    () (University of Gdansk, Faculty of Economics, Sopot, Poland)

Abstract

According to Crescezni and Rodríguez-Pose (2008) backward European states and regions should follow balanced strategies in which infrastructure development is coordinated with policies aimed at developing human capital and the innovative potential of regions. In order to assess their postulates we extend the analysis of Carstensen et al. (2009) further augmenting the neoclassical Solow Model to incorporate Mincerian schooling externalities and infrastructure externalities in a single theoretical framework. Infrastructure is introduced into the model in a manner similar to exogenous Hicks-neutral technological change, raising the overall efficiency of an economy. The theoretical model has been empirically tested for a panel of European economies in the period 1999–2010. Econometric estimates for a balanced panel data model bring interesting results. The overall fit of the model is considerable. In accordance with our expectations, the macroeconomic returns to human capital accumulation and infrastru cture are positive and statistically significant for a full sample of countries. Externalities are stronger for CEE transition economies than for non-CEE countries. The infrastructure externality is positive and statistically significant for CEE states only when we control for the level of openness of an economy. Results obtained are robust when taken with the modifications of the baseline empirical model.

Suggested Citation

  • Tomasz Brodzicki, 2012. "Augmented Solow Model with Mincerian Education and Transport Infrastructure Externalities," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 6(2), pages 155-170, July.
  • Handle: RePEc:fau:aucocz:au2012_155
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    Citations

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    Cited by:

    1. Tomasz Brodzicki & Dorota Ciolek, 2015. "Territorial capital and Polish regional development. A neoclassical approach," Working Papers 1511, Instytut Rozwoju, Institute for Development.
    2. Mariya Neycheva, 2016. "Secondary versus higher education for growth: the case of three countries with different human capital’s structure and quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(6), pages 2367-2393, November.
    3. Tomasz Brodzicki, 2015. "Shallow determinants of growth of Polish regions. Empirical analysis with panel data methods," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 39, pages 25-40.
    4. Mariya Neycheva, 2015. "Impact of Secondary and Tertiary Education on Economic Growth: a Co-integration Model for Bulgaria," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 82-106.
    5. repec:spr:jsecdv:v:19:y:2017:i:1:d:10.1007_s40847-017-0033-3 is not listed on IDEAS
    6. Wei-Bin Zhang, 2013. "Education, Endogenous Human Capital, and Monetary Economic Growth with MIU Approach," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 7(2), pages 100-118, July.

    More about this item

    Keywords

    Economic growth; human capital; transport infrastructure; augmented neoclassical growth model; Mincerian schooling;

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • H52 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Education
    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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