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The shape of aggregate production functions: evidence from estimates of the World Technology Frontier

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Abstract

The article provides multifaceted evidence on the shape of the aggregate country-level production function, derived from the World Technology Frontier, estimated on the basis of annual data on inputs and output in 19 highly developed OECD countries in the period 1970–2004. A comparison of its estimates based on Data Envelopment Analysis and Bayesian Stochastic Frontier Analysis uncovers a number of significant discrepancies between the nonparametric estimates of the frontier and the Cobb–Douglas and translog production functions in terms of implied efficiency levels, partial elasticities, and returns-to-scale properties. Furthermore, the two latter characteristics as well as elasticities of substitution are found to differ markedly across countries and time, providing strong evidence against the constant-returns-to-scale (CRS) Cobb–Douglas specification, frequently used in related literature. We also find notable departures from perfect substitutability between unskilled and skilled labor, consistent with the hypotheses of skill-biased technical change and capital–skill complementarity. In the Appendix, as a corollary from our results, we have also conducted a series of development accounting and growth accounting exercises.

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Bibliographic Info

Paper provided by National Bank of Poland, Economic Institute in its series National Bank of Poland Working Papers with number 102.

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Length: 61
Date of creation: 2011
Date of revision:
Handle: RePEc:nbp:nbpmis:102

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Keywords: world technology frontier; aggregate production function; Data Envelopment Analysis; Stochastic Frontier Analysis; partial elasticity; returns to scale; substitutability;

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  1. Simar, L. & Wilson, P.W., 1998. "A General Methodology for Bootstrapping in Nonparametric Frontier Models," Papers 9811, Catholique de Louvain - Institut de statistique.
  2. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
  3. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
  4. RITTER, Christian & SIMAR, Leopold, 1994. "Pitfalls of Normal-Gamma Stochastic Frontier Models," CORE Discussion Papers 1994041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Simar, Leopold & Wilson, Paul W., 2002. "Non-parametric tests of returns to scale," European Journal of Operational Research, Elsevier, vol. 139(1), pages 115-132, May.
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Cited by:
  1. Jakub Growiec, 2013. "On the measurement of technological progress across countries," Bank i Kredyt, National Bank of Poland, Economic Institute, vol. 44(5), pages 467-504.

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