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Knowledge spillovers: On the impact of genetic distance and data revisions

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  • Deeken, Tim

Abstract

This paper assesses the robustness of the empirical results in Ertur and Koch (2007), who develop a model, which accounts for technological interdependence among countries through spatial externalities. The original version models interdependence via an interaction matrix based on geographic distance. In contrast, in this paper, data on genetic distance, defined as the time since two populations have shared a common ancestor, from Spolaore and Wacziarg (2009) is used to construct the interaction matrix. It is found that, whereas in the original model indirect spillovers from capital investment were insignificant, using genetic distance, these spillovers now have a significant effect on steady-state income per worker. However, the version of the model with an interaction matrix based on genetic distance implies an implausibly large capital share of income. In addition, the model is subjected to a further series of robustness checks. The original version relies on data from Penn World Table (PWT) Version 6.1. More recent versions are currently available, and the data has been extensively revised (Johnson et al., 2013). It is shown that results are in general not robust across different versions of the PWT. Furthermore, the estimation results are highly sensitive both to the measure used to model interaction between countries (genetic or geographic distance) and to the specific functional form on which the weights in the interaction matrix are based.

Suggested Citation

  • Deeken, Tim, 2015. "Knowledge spillovers: On the impact of genetic distance and data revisions," Working Paper Series in Economics 74, Karlsruhe Institute of Technology (KIT), Department of Economics and Business Engineering.
  • Handle: RePEc:zbw:kitwps:74
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    References listed on IDEAS

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    1. Lindner, Ines & Strulik, Holger, 2014. "The great divergence: A network approach," Center for European, Governance and Economic Development Research Discussion Papers 193, University of Goettingen, Department of Economics.
    2. Quamrul Ashraf & Oded Galor, 2013. "The 'Out of Africa' Hypothesis, Human Genetic Diversity, and Comparative Economic Development," American Economic Review, American Economic Association, pages 1-46.
    3. J. Derbyshire & B. Gardiner & S. Waights, 2013. "Estimating the capital stock for the NUTS2 regions of the EU27," Applied Economics, Taylor & Francis Journals, vol. 45(9), pages 1133-1149, March.
    4. Johnson, Simon & Larson, William & Papageorgiou, Chris & Subramanian, Arvind, 2013. "Is newer better? Penn World Table Revisions and their impact on growth estimates," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 255-274.
    5. Ashraf, Quamrul & Galor, Oded, 2008. "Human Genetic Diversity and Comparative Economic Development," CEPR Discussion Papers 6824, C.E.P.R. Discussion Papers.
    6. Manfred Fischer, 2011. "A spatial Mankiw–Romer–Weil model: theory and evidence," The Annals of Regional Science, Springer;Western Regional Science Association, pages 419-436.
    7. Natalia Ponomareva & Hajime Katayama, 2010. "Does the version of the Penn World Tables matter? An analysis of the relationship between growth and volatility," Canadian Journal of Economics, Canadian Economics Association, vol. 43(1), pages 152-179, February.
    8. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    9. Breton, Theodore R., 2012. "Penn World Table 7.0: Are the data flawed?," Economics Letters, Elsevier, vol. 117(1), pages 208-210.
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    More about this item

    Keywords

    knowledge spillovers; genetic distance; spatial externalities; technological interdependence;

    JEL classification:

    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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