IDEAS home Printed from
   My bibliography  Save this paper

Productive Capabilities: An Empirical Investigation of their Determinants


  • Christian Daude


  • Arne Nagengast

    (Deutsche Bundesbank)

  • José Ramón Perea



Recent contributions to the growth 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 their country-level determinants have remained unknown. In this paper, we empirically explore their determinants using a model averaging framework that can handle a very large number of explanatory variables without the need for 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 LSDV estimator. Our baseline and robustness analysis consider a large number of variables, sample periods and model priors. We find that the existing stock of capabilities (as measured by the lagged dependent variable), commodity terms of trade, energy availability, government consumption, capital per worker, arable land and capital inflows show a strong and robust association with capabilities. Le concept de complexité économique a été soulevé comme un facteur déterminant des différences du développement entre les pays. La complexité d'une économie est liée à la disponibilité de la connaissance productive, ou le stock existant des capacités productives. Dans cet article, nous explorons empiriquement ses déterminants. Conscient du grand nombre de variables explicatives qui peuvent affecter les capacités, et en conséquence de la complexité d'une économie, nous choisissons d'estimer nos spécifications par une estimation bayésienne périodique, qui considère toutes les combinaisons possibles de spécifications. Cette fonctionnalité est appropriée lorsqu’il y a un grand nombre de variables explicatives et les critères de sélection du modèle ne sont pas connus avec certitude. Notre analyse de robustesse fournit une étude exhaustive des variables qui tiennent une relation significative avec les capacités productives, à travers toutes les combinaisons de modèles possibles, les échantillons de périodes et la probabilité à priori. Parmi ces variables, les termes de l’échange sur les matières de premières, la disponibilité de l’énergie, la consommation publique, le capital par travailleur, les terres arables et les flux de capitaux montrent un effet relativement important et robuste sur les capacités.

Suggested Citation

  • Christian Daude & Arne Nagengast & José Ramón Perea, 2014. "Productive Capabilities: An Empirical Investigation of their Determinants," OECD Development Centre Working Papers 321, OECD Publishing.
  • Handle: RePEc:oec:devaaa:321-en

    Download full text from publisher

    File URL:
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    2. Enrique Moral-Benito, 2012. "Determinants of Economic Growth: A Bayesian Panel Data Approach," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 566-579, May.
    3. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    4. Antonio Ciccone & Marek Jarociński, 2010. "Determinants of Economic Growth: Will Data Tell?," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(4), pages 222-246, October.
    5. Juan Blyde & Christian Daude & Eduardo Fernández-Arias, 2010. "Output collapses and productivity destruction," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(2), pages 359-387, June.
    6. Glüzmann, Pablo Alfredo & Levy-Yeyati, Eduardo & Sturzenegger, Federico, 2012. "Exchange rate undervaluation and economic growth: Díaz Alejandro (1965) revisited," Economics Letters, Elsevier, vol. 117(3), pages 666-672.
    7. Corden, W Max & Neary, J Peter, 1982. "Booming Sector and De-Industrialisation in a Small Open Economy," Economic Journal, Royal Economic Society, vol. 92(368), pages 825-848, December.
    8. Anke Hoeffler & Paul Collier, 2005. "Democracy and Resource Rents," Economics Series Working Papers GPRG-WPS-016, University of Oxford, Department of Economics.
    9. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    10. Daron Acemoglu, 2002. "Directed Technical Change," Review of Economic Studies, Oxford University Press, vol. 69(4), pages 781-809.
    11. Sanjaya Lall, 2000. "The Technological Structure and Performance of Developing Country Manufactured Exports, 1985-98," Oxford Development Studies, Taylor & Francis Journals, vol. 28(3), pages 337-369.
    12. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    13. Anna Jankowska & Arne Nagengast & José Ramón Perea, 2012. "The Product Space and the Middle-Income Trap: Comparing Asian and Latin American Experiences," OECD Development Centre Working Papers 311, OECD Publishing.
    14. Christian Daude, 2012. "Development Accounting: Lessons for Latin America," OECD Development Centre Working Papers 313, OECD Publishing.
    15. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    16. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
    2. International Monetary Fund, 2016. "Uruguay; Selected Issues," IMF Staff Country Reports 16/63, International Monetary Fund.
    3. Alejandro Izquierdo & Jimena Llopis & Umberto Muratori & José Juan Ruiz, 2016. "In Search of Larger Per Capita Incomes: How To Prioritize across Productivity Determinants?," IDB Publications (Working Papers) 7511, Inter-American Development Bank.

    More about this item


    Bayesian model averaging; capabilities; economic complexity; exports; growth;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oec:devaaa:321-en. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.