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Estimating a non-neutral production function: a heterogeneous treatment effect approach

Author

Listed:
  • Davide Antonioli

    () (Università degli Studi "G. D'Annunzio" di Chieti-Pescara)

  • Georgios Gioldasis

    () (Università degli Studi di Ferrara)

  • Antonio Musolesi

    () (Università degli Studi di Ferrara)

Abstract

This paper addresses the issue of estimating a production function that allows us to depart from the standard hypothesis of Hicks neutrality while also coping with the endogeneity of a dummy innovation variable. We consider specifications that relax Hicks neutrality, and we derive the testable conditions for common parametric approximations under which Hicks neutrality holds. The model is estimated through instrumental variables methods, allowing for a heterogeneous effect of innovation on the production process. The econometric analysis rejects Hicks neutrality and highlights three main features: i) a capital-saving technology of innovative with respect to non-innovative firms, ii) a locally progressive technical change and iii) fully heterogeneous technologies when comparing innovative to non-innovative firms.

Suggested Citation

  • Davide Antonioli & Georgios Gioldasis & Antonio Musolesi, 2018. "Estimating a non-neutral production function: a heterogeneous treatment effect approach," SEEDS Working Papers 0618, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Mar 2018.
  • Handle: RePEc:srt:wpaper:0618
    as

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    File URL: http://www.sustainability-seeds.org/papers/RePec/srt/wpaper/0618.pdf
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    References listed on IDEAS

    as
    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    2. Mairesse, Jacques & Mohnen, Pierre, 2010. "Using Innovation Surveys for Econometric Analysis," Handbook of the Economics of Innovation, Elsevier.
    3. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    4. Crepon, B. & Duguet, E. & Mairesse, J., 1998. "Research Investment, Innovation and Productivity: An Econometric Analysis at the Firm Level," Papiers d'Economie Mathématique et Applications 98.15, Université Panthéon-Sorbonne (Paris 1).
    5. Steedman, Ian, 1985. "On the 'Impossibility' of Hicks-Neutral Technical Change," Economic Journal, Royal Economic Society, vol. 95(379), pages 746-758, September.
    6. Bronwyn Hall & Francesca Lotti & Jacques Mairesse, 2009. "Innovation and productivity in SMEs: empirical evidence for Italy," Small Business Economics, Springer, vol. 33(1), pages 13-33, June.
    7. Jacques Mairesse, 2008. "Employment, innovation, and productivity: evidence from Italian microdata," Industrial and Corporate Change, Oxford University Press, vol. 17(4), pages 813-839, August.
    8. Angrist, J.D., 1991. "Linear Instrumental Variables Estimation Of Average Treatment Effects In Nonlinear Models," Harvard Institute of Economic Research Working Papers 1542, Harvard - Institute of Economic Research.
    9. Wooldridge, Jeffrey M., 2003. "Further results on instrumental variables estimation of average treatment effects in the correlated random coefficient model," Economics Letters, Elsevier, vol. 79(2), pages 185-191, May.
    10. Parisi, Maria Laura & Schiantarelli, Fabio & Sembenelli, Alessandro, 2006. "Productivity, innovation and R&D: Micro evidence for Italy," European Economic Review, Elsevier, vol. 50(8), pages 2037-2061, November.
    11. Ronald W. Jones, 1965. "The Structure of Simple General Equilibrium Models," Journal of Political Economy, University of Chicago Press, vol. 73, pages 557-557.
    12. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, January.
    13. Zvi Griliches, 1998. "R&D and Productivity: The Econometric Evidence," NBER Books, National Bureau of Economic Research, Inc, number gril98-1, July.
    14. Joshua D. Angrist, 1991. "Instrumental Variables Estimation of Average Treatment Effects in Econometrics and Epidemiology," NBER Technical Working Papers 0115, National Bureau of Economic Research, Inc.
    15. Pierre Mohnen & Bronwyn Hall, 2013. "Innovation and Productivity: An Update," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 3(1), pages 47-65, June.
    16. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    17. Antonio Musolesi & Jean-Pierre Huiban, 2010. "Innovation and productivity in knowledge intensive business services," Journal of Productivity Analysis, Springer, vol. 34(1), pages 63-81, August.
    18. Bruno Crepon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation And Productivity: An Econometric Analysis At The Firm Level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 7(2), pages 115-158.
    19. Giovanni Cerulli, 2014. "ivtreatreg: A command for fitting binary treatment models with heterogeneous response to treatment and unobservable selection," Stata Journal, StataCorp LP, vol. 14(3), pages 453-480, September.
    20. Daron Acemoglu, 2015. "Localised and Biased Technologies: Atkinson and Stiglitz's New View, Induced Innovations, and Directed Technological Change," Economic Journal, Royal Economic Society, vol. 0(583), pages 443-463, March.
    21. Kennedy, Charles & Thirlwall, A P, 1977. "Extended Hicks Neutral Technical Change-A Comment," Economic Journal, Royal Economic Society, vol. 87(348), pages 768-768, December.
    22. Hansen, Christian & Hausman, Jerry & Newey, Whitney, 2008. "Estimation With Many Instrumental Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 398-422.
    23. repec:fth:prinin:455 is not listed on IDEAS
    24. José Luis Montiel Olea & Carolin Pflueger, 2013. "A Robust Test for Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 358-369, July.
    25. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    26. Atkinson, Anthony B & Stiglitz, Joseph E, 1969. "A New View of Technological Change," Economic Journal, Royal Economic Society, vol. 79(315), pages 573-578, September.
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    More about this item

    Keywords

    Biased technical change; Hicks neutrality; Innovation; Productivity; Knowledge production function; CDM model; Instrumental variables; heterogeneous treatment effect;

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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