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A constrained nonparametric regression analysis of factor-biased technical change and TFP growth at the firm level

Author

Listed:
  • Marijn Verschelde

    (Ghent University and KU Leuven KULAK)

  • Michel Dumont

    (Federal Planning Bureau and Ghent University)

  • Bruno Merlevede

    (Ghent University)

  • Glenn Rayp

    (Ghent University and SHERPPA)

Abstract

Using firm-level data for Belgium, we study the validity of Hicks neutrality in several sectors that cover the spectrum of knowledge intensity. We find that Hicks neutrality is clearly not supported by the data in different sectors. The results are not sensitive to altering the specification of the technology by including firm age and R&D into the analysis. We also reject Hicks neutrality for a balanced sample, pointing to `within-firm' factor-biased technical change and we also find factor-biased technical change in the pre-crisis era, indicating that unobserved heterogeneity in demand does not drive the results. Overall, our results point towards low-skilled laboursaving and materials-using technical change. So far, this has received little attention and may be linked to ofshoring and global value chain networks. Finally, we show that nonparametric estimates of TFP change that allow for factor biases support the evidence of the recent slowdown in TFP growth in many manufacturing sectors in Belgium. Estimations of TFP and technical change are shown to be sensitive to the estimation method and the specification of the factor bias of technical change.

Suggested Citation

  • Marijn Verschelde & Michel Dumont & Bruno Merlevede & Glenn Rayp, 2014. "A constrained nonparametric regression analysis of factor-biased technical change and TFP growth at the firm level," Working Paper Research 266, National Bank of Belgium.
  • Handle: RePEc:nbb:reswpp:201410-266
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    1. repec:wfo:wstudy:58200 is not listed on IDEAS
    2. Emmanuel Dhyne & Catherine Fuss, 2014. "Main lessons of the NBB’s 2014 conference “Total factor productivity : measurement, determinants and effects”," Economic Review, National Bank of Belgium, issue iii, pages 63-76, December.
    3. Zha, Donglan & Kavuri, Anil Savio & Si, Songjian, 2017. "Energy biased technology change: Focused on Chinese energy-intensive industries," Applied Energy, Elsevier, vol. 190(C), pages 1081-1089.
    4. Laurens Cherchye & Thomas Demuynck & Bram De Rock & Marijn Verschelde, 2018. "Nonparametric identification of unobserved technological heterogeneity in production," Working Paper Research 335, National Bank of Belgium.
    5. repec:eee:energy:v:148:y:2018:i:c:p:896-903 is not listed on IDEAS
    6. repec:kap:iecepo:v:14:y:2017:i:2:d:10.1007_s10368-016-0371-1 is not listed on IDEAS
    7. Johanna Vogel & Kurt Kratena & Kathrin Hranyai, 2015. "The Bias of Technological Change in Europe," WWWforEurope Working Papers series 98, WWWforEurope.

    More about this item

    Keywords

    total factor productivity; factor bias; nonparametric estimation;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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