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Productive capabilities: An empirical analysis of their drivers

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  • Christian Daude
  • Arne Nagengast
  • Jose Ramon Perea

Abstract

Recent contributions to the growth and trade literature have argued that the structure of an economy, as measured by its productive capabilities, is a key determinant for inter-country differences in development. Productive capabilities have been shown to be highly predictive of future economic growth, yet the country-level variables associated with them remain relatively unknown. In this paper, we empirically explore what variables are systematically associated with productive capabilities using a model averaging framework that can handle a very large number of potential explanatory variables without the need for arbitrary model selection. In order to estimate our dynamic panel specification, we propose a novel Bayesian averaging of classical estimates procedure based on the simple and efficient bias-corrected least squares dummy variable estimator. Our baseline and robustness analysis consider a large number of variables, sample periods and model priors. We find that there is persistence (as measured by the lagged dependent variable) and that variables, such as commodity terms of trade, energy availability, government consumption, capital per worker, arable land and capital inflows show a strong and robust association with capabilities.

Suggested Citation

  • Christian Daude & Arne Nagengast & Jose Ramon Perea, 2016. "Productive capabilities: An empirical analysis of their drivers," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 25(4), pages 504-535, June.
  • Handle: RePEc:taf:jitecd:v:25:y:2016:i:4:p:504-535
    DOI: 10.1080/09638199.2015.1073342
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    Cited by:

    1. Kang-Kook Lee & Trung V. Vu, 2020. "Economic complexity, human capital and income inequality: a cross-country analysis," The Japanese Economic Review, Springer, vol. 71(4), pages 695-718, October.
    2. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    3. International Monetary Fund, 2016. "Uruguay: Selected Issues," IMF Staff Country Reports 2016/063, International Monetary Fund.
    4. Keneck-Massil, Joseph & Nvuh-Njoya, Youssouf, 2021. "Did colonisation matter for comparative economic complexity?," Economics Letters, Elsevier, vol. 203(C).
    5. Elvis Korku Avenyo & Fiona Tregenna & Erika Kraemer-Mbula, 2021. "Do Productive Capabilities Affect Export Performance? Evidence from African Firms," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 33(2), pages 304-329, April.
    6. Izquierdo, Alejandro & Llopis, Jimena & Muratori, Umberto & Ruiz, José Juan, 2016. "In Search of Larger Per Capita Incomes: How To Prioritize across Productivity Determinants?," IDB Publications (Working Papers) 7511, Inter-American Development Bank.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • F43 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Economic Growth of Open Economies
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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