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Productivity of Nations: a stochastic frontier approach to TFP decomposition

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  • Fernando Garcia
  • Jorge Oliveira Pires

Abstract

This paper tackles the problem of aggregate TFP measurement using stochastic frontier analysis (SFA). Data from Penn World Table 6.1 are used to estimate a world production frontier for a sample of 75 countries over a long period (1950-2000) taking advantage of the model offered by Battese & Coelli (1992). We also apply the decomposition of TFP suggested by Bauer (1990) and Kumbhakar (2000) to a smaller sample of 36 countries over the period 1970-2000 in order to evaluate the effects of changes in efficiency (technical and allocative), scale effects and technical change. This allows us to analyze the role of productivity and its components in economic growth of developed and developing nations in addition to the importance of factor accumulation. Although not much explored in the study of economic growth, frontier techniques seem to be of particular interest for that purpose since the separation of efficiency effects and technical change has a direct interpretation in terms of the catch-up debate. The estimated technical efficiency scores reveal the efficiency of nations in the production of non tradable goods since the GDP series used is PPP-adjusted. We also provide a second set of efficiency scores corrected in order to reveal efficiency in the production of tradable goods and rank them. When compared to the rankings of productivity indexes offered by non-frontier studies of Hall & Jones (1996) and Islam (1995) our ranking shows a somewhat more intuitive order of countries. Rankings of the technical change and scale effects components of TFP change are also very intuitive. We also show that productivity is responsible for virtually all the differences of performance between developed and developing countries in terms of rates of growth of income per worker. More important, we find that changes in allocative efficiency play an important role in explaining differences in the productivity of developed and developing nations, even larger than the one played by the technology gap.

Suggested Citation

  • Fernando Garcia & Jorge Oliveira Pires, 2004. "Productivity of Nations: a stochastic frontier approach to TFP decomposition," Econometric Society 2004 Latin American Meetings 292, Econometric Society.
  • Handle: RePEc:ecm:latm04:292
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    1. Robert E. Hall & Charles I. Jones, 1999. "Why do Some Countries Produce So Much More Output Per Worker than Others?," The Quarterly Journal of Economics, Oxford University Press, vol. 114(1), pages 83-116.
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    6. Paul W. Bauer, 1988. "Decomposing TFP growth in the presence of cost inefficiency, nonconstant returns to scale, and technological progress," Working Papers (Old Series) 8813, Federal Reserve Bank of Cleveland.
    7. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
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    1. Alexander Bilson Darku & Stavroula Malla & Kien C. Tran, 2016. "Sources and Measurement of Agricultural Productivity and Efficiency in Canadian Provinces: Crops and Livestock," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 64(1), pages 49-70, March.
    2. Michele BOLDRIN & Mariacristina DE NARDI & Larry E. JONES, 2015. "Fertility and Social Security," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 81(3), pages 261-299, September.
    3. Sabrina Auci & Laura Castellucci & Manuela Coromaldi, 2021. "How does public spending affect technical efficiency? Some evidence from 15 European countries," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 108-130, January.
    4. Ceyhun Elgin & Selman Çakır, 2015. "Technological progress and scientific indicators: a panel data analysis," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 24(3), pages 263-281, April.
    5. Araujo, Jair Andrade & Feitosa, Débora Gaspar & Silva, Almir Bittencourt da, 2014. "Latin America: Total factor productivity and its components," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), December.
    6. Giampaolo Garzarelli & Stephen M. Miller & Yasmina R. Limam, 2016. "Output Decomposition in the Presence of Input Quality Effects: A Stochastic Frontier Approach," Working Papers 613, Economic Research Southern Africa.
    7. Almas Heshmati & Sun Peng, 2012. "International Trade And Its Effects On Economic Performance In China," China Economic Policy Review (CEPR), World Scientific Publishing Co. Pte. Ltd., vol. 1(02), pages 1-26.
    8. Víctor Giménez & Diego Prior & Emili Tortosa-Ausina, 2018. "The impact of efficiency on the economic growth of emerging economies: The case of Colombia," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 36(85), pages 86-100, November.
    9. Tamara Rudinskaya & Elena Kuzmenko, 2019. "Investments, Technical Change and Efficiency: Empirical Evidence from Czech Food Processing," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 11(4), December.
    10. Ibrahima Amadou Diallo, 2015. "The Effects of Real Exchange Rate Volatility on Productivity Growth," Eastern European Business and Economics Journal, Eastern European Business and Economics Studies Centre, vol. 1(2), pages 66-84.
    11. Yasmina Rim Limam & Stephen M. Miller & Giampaolo Garzarelli, 2016. "Decomposition of Output Growth in the Presence of Input Quality: A Stochastic Frontier Approach," Working papers 2016-13, University of Connecticut, Department of Economics.
    12. Pablo Coto-Millán & Xose Luís Fernández & Miguel Ángel Pesquera & Manuel Agüeros, 2016. "Impact of Logistics on Technical Efficiency of World Production (2007–2012)," Networks and Spatial Economics, Springer, vol. 16(4), pages 981-995, December.
    13. Maria Alice Móz Christofoletti & Humberto Francisco Silva Spolador, 2011. "Income convergence among Brazilian states after the economic openness in the 1990s," ERSA conference papers ersa10p172, European Regional Science Association.
    14. Loaiza Quintero, Osmar Leandro & Franco Vásquez, Liliana Yaned, 2012. "Un estudio acerca de los determinantes de la productividad y la ineficiencia técnica en la industria colombiana, 1992-2007 [Determinants of productivity and technical inefficiency in Colombia’s man," MPRA Paper 47736, University Library of Munich, Germany, revised 20 Jun 2013.
    15. Kluge, Jan & Lappoehn, Sarah & Plank, Kerstin, 2020. "The Determinants of Economic Competitiveness," IHS Working Paper Series 24, Institute for Advanced Studies.
    16. Agüeros Manuel & Casares-Hontañón Pedro & Coto-Millán Pablo & Ángel Pesquera Miguel, 2015. "Technical Efficiency of the Generation of Knowledge in the European Union (2003-2010)," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 27(27), pages 7-16, March.
    17. Cassiano Bragagnolo & Humberto F. S. Spolador & Geraldo Sant’Ana de Camargo Barros, 2010. "Regional Brazilian Agriculture TFP Analysis: A Stochastic Frontier Analysis Approach," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 11(4), pages 217-242.
    18. Bannor, Frank & Dikgang, Johane & Gelo, Dambala, 2021. "Agricultural total factor productivity growth, technical efficiency, and climate variability in sub-Saharan Africa," EconStor Preprints 231310, ZBW - Leibniz Information Centre for Economics.
    19. Pontus Mattsson & Jonas Mansson & William H. Greene, 2018. "TFP Change and its Components for Swedish Manufacturing Firms During the 2008-2009 Financial Crisis," Working Papers 18-27, New York University, Leonard N. Stern School of Business, Department of Economics.

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    More about this item

    Keywords

    Total factor productivity; stochastic frontiers; technical change; technical efficiency; allocative efficiency; scale efficiency; convergence;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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