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Productivity growth and biased technical change in French higher education

Author

Listed:
  • Carlos Barros

    (ISEG - Instituto Superior de Economia e Gestão - Technical University of Lisbon)

  • Jean-Pascal Guironnet

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

  • Nicolas Peypoch

    (UPVD - Université de Perpignan Via Domitia)

Abstract

This paper analyses the nature of technical change in the French labour market. Data Envelopment Analysis (DEA) is adopted to investigate productivity change in a sample of higher education leavers over the period 1999 and 2004. In a first step, the Luenberger Productivity Indicator (LPI) is used to estimate and to decompose produtivity change. Following LPI, a better productivity is found for the workers in Paris and the well-qualified occupations in France. In analysing the nature of the technical change by the concept of parallel neutrality, technical progress seems to have influenced all professions. In particular, biased inputs of human capital component benefit more for the well qualified professions with an upper increase of the efficiency scores for executives and teachers. Furthermore, some evidences show the key role of "learning by doing" in the worker's adaptation to technical change. Policy implications are then derived from our results.

Suggested Citation

  • Carlos Barros & Jean-Pascal Guironnet & Nicolas Peypoch, 2011. "Productivity growth and biased technical change in French higher education," Post-Print halshs-00657261, HAL.
    • Handle: RePEc:hal:journl:halshs-00657261
    DOI: 10.1016/j.econmod.2010.06.005
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    Cited by:

    1. Bogetoft, Peter & Heinesen, Eskil & Tranæs, Torben, 2015. "The efficiency of educational production: A comparison of the Nordic countries with other OECD countries," Economic Modelling, Elsevier, vol. 50(C), pages 310-321.
    2. Tomas Baležentis & Alfons Oude Lansink, 2020. "Measuring dynamic biased technical change in Lithuanian cereal farms," Agribusiness, John Wiley & Sons, Ltd., vol. 36(2), pages 208-225, April.
    3. Zhu, Weiwei & Zhu, Yaqin & Lin, Huaping & Yu, Yu, 2021. "Technology progress bias, industrial structure adjustment, and regional industrial economic growth motivation —— Research on regional industrial transformation and upgrading based on the effect of lea," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    4. Mathieu Bunel & Jean-Pascal Guironnet, 2011. "Earning Inequalities Between and Within Nests," Working Papers halshs-00868198, HAL.
    5. Jean-Pascal Guironnet & Matthieu Bunel, 2011. "Earning Inequalities Between and Within Nests: A Multilevel Modeling Approach Applied to the Case of France," Economics Working Paper Archive (University of Rennes & University of Caen) 201118, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    6. Tomas Baležentis, 2014. "Total factor productivity in the Lithuanian family farms after accession to the EU: application of the bias-corrected Malmquist indices," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(4), pages 731-746, November.
    7. Dayal Talukder & Love Chile, 2018. "Technological Innovation And Total Factor Productivity Growth Of Rice Production In Bangladesh In The Post-Liberalisation Era," Global Economic Observer, "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences;Institute for World Economy of the Romanian Academy, vol. 6(2), pages 50-70, December.
    8. Peng, Fei & Anwar, Sajid & Kang, Lili, 2017. "New technology and old institutions: An empirical analysis of the skill-biased demand for older workers in Europe," Economic Modelling, Elsevier, vol. 64(C), pages 1-19.
    9. Courtioux, Pierre & Lignon, Vincent, 2016. "A good career or a good marriage: The returns of higher education in France," Economic Modelling, Elsevier, vol. 57(C), pages 221-237.
    10. Dayal Talukder, 2011. "Are Private Providers more Productive and Efficient than Public Providers of International Education? Evidence from New Zealand," Oeconomics of Knowledge, Saphira Publishing House, vol. 3(4), pages 2-23, October.
    11. Birulin, Oleksii & Smirnov, Vladimir & Wait, Andrew, 2020. "The evolving nature of the college wage premium," Economic Modelling, Elsevier, vol. 93(C), pages 474-479.
    12. Dorocki Sławomir & Borowiec Monika, 2012. "The process of transformation of academic centres as a factor upgrading the quality of human capital in the regions of France," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 18(18), pages 15-27, November.
    13. Autant-Bernard, Corinne & Guironnet, Jean-Pascal & Massard, Nadine, 2011. "Agglomeration and social return to R&D: Evidence from French plant productivity changes," International Journal of Production Economics, Elsevier, vol. 132(1), pages 34-42, July.
    14. Assaf, A. George & Tsionas, Mike, 2018. "The estimation and decomposition of tourism productivity," Tourism Management, Elsevier, vol. 65(C), pages 131-142.
    15. Briec, Walter & Cavaignac, Laurent & Kerstens, Kristiaan, 2011. "Directional measurement of technical efficiency of production: An axiomatic approach," Economic Modelling, Elsevier, vol. 28(3), pages 775-781, May.

    More about this item

    Keywords

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    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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