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A Non‐parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax

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  • Ioannis Bournakis
  • Mike Tsionas

Abstract

We develop a non‐parametric technique framework for estimating firm‐level Total Factor Productivity (TFP). Our paper has two major novelties: first, we propose a modelling of productivity with both firm‐idiosyncratic factors and aggregate shocks. Second, we apply the Bayesian Markov Chain Monte Carlo (MCMC) technique that offers a numerical integration of productivity outside the posterior overcoming the restrictive assumptions about the relationship between productivity and variable production inputs. We implement our methodology in a group of 4,286 manufacturing firms from France, Germany, Italy, and the UK (2001–14). The results show that: (i) aggregate shocks matter for firm TFP evolution. The global financial crisis of 2008 caused severe, albeit short, adverse effects on TFP; (ii) there is substantial heterogeneity across countries in the way firms react to changes in R&D and taxation. German and UK firms are more sensitive to fiscal changes than R&D, while the opposite is true for Italian firms. R&D and taxation effects are symmetrical for French firms; (iii) the UK productivity handicap continues for years after the financial crisis; and (iv) there are substantial knowledge spillovers among German and Italian firms.

Suggested Citation

  • Ioannis Bournakis & Mike Tsionas, 2024. "A Non‐parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 641-671, June.
  • Handle: RePEc:bla:obuest:v:86:y:2024:i:3:p:641-671
    DOI: 10.1111/obes.12594
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    1. Marta Guerriero, 2019. "The Labor Share of Income Around the World: Evidence from a Panel Dataset," ADB Institute Series on Development Economics, in: Gary Fields & Saumik Paul (ed.), Labor Income Share in Asia, chapter 0, pages 39-79, Springer.
    2. Jan De Loecker & Frederic Warzynski, 2012. "Markups and Firm-Level Export Status," American Economic Review, American Economic Association, vol. 102(6), pages 2437-2471, October.
    3. Bournakis, Ioannis & Mallick, Sushanta, 2021. "Do Corporate Taxes Harm Economic Performance? Explaining Distortions In R&D- And Export-Intensive Uk Firms," Macroeconomic Dynamics, Cambridge University Press, vol. 25(1), pages 5-27, January.
    4. Desiderio Romero-Jordán & Ismael Sanz-Labrador & José Félix Sanz-Sanz, 2020. "Is the corporation tax a barrier to productivity growth?," Small Business Economics, Springer, vol. 55(1), pages 23-38, June.
    5. Godsill, Simon J. & Doucet, Arnaud & West, Mike, 2004. "Monte Carlo Smoothing for Nonlinear Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 156-168, January.
    6. Pindyck, Robert S, 1982. "Adjustment Costs, Uncertainty, and the Behavior of the Firm," American Economic Review, American Economic Association, vol. 72(3), pages 415-427, June.
    7. Jan De Loecker, 2013. "Detecting Learning by Exporting," American Economic Journal: Microeconomics, American Economic Association, vol. 5(3), pages 1-21, August.
    8. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    9. Jan De Loecker & Pinelopi Koujianou Goldberg, 2014. "Firm Performance in a Global Market," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 201-227, August.
    10. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
    11. Gallant, A. Ronald & Giacomini, Raffaella & Ragusa, Giuseppe, 2017. "Bayesian estimation of state space models using moment conditions," Journal of Econometrics, Elsevier, vol. 201(2), pages 198-211.
    12. Diewert, Walter E & Wales, Terence J, 1987. "Flexible Functional Forms and Global Curvature Conditions," Econometrica, Econometric Society, vol. 55(1), pages 43-68, January.
    13. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    14. Amit Gandhi & Salvador Navarro & David A. Rivers, 2020. "On the Identification of Gross Output Production Functions," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 2973-3016.
    15. Gallant, A. Ronald & Golub, Gene H., 1984. "Imposing curvature restrictions on flexible functional forms," Journal of Econometrics, Elsevier, vol. 26(3), pages 295-321, December.
    16. Bournakis, Ioannis & Mallick, Sushanta, 2018. "TFP estimation at firm level: The fiscal aspect of productivity convergence in the UK," Economic Modelling, Elsevier, vol. 70(C), pages 579-590.
    17. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    18. Yair Mundlak, 1961. "Empirical Production Function Free of Management Bias," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 43(1), pages 44-56.
    19. Creal, Drew D. & Tsay, Ruey S., 2015. "High dimensional dynamic stochastic copula models," Journal of Econometrics, Elsevier, vol. 189(2), pages 335-345.
    20. Amil Petrin & Jagadeesh Sivadasan, 2013. "Estimating Lost Output from Allocative Inefficiency, with an Application to Chile and Firing Costs," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 286-301, March.
    21. O'Mahony, Mary & Vecchi, Michela, 2009. "R&D, knowledge spillovers and company productivity performance," Research Policy, Elsevier, vol. 38(1), pages 35-44, February.
    22. Drew Creal, 2012. "A Survey of Sequential Monte Carlo Methods for Economics and Finance," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 245-296.
    23. Kasahara, Hiroyuki & Rodrigue, Joel, 2008. "Does the use of imported intermediates increase productivity? Plant-level evidence," Journal of Development Economics, Elsevier, vol. 87(1), pages 106-118, August.
    24. Nicolas Chopin & Sumeetpal S. Singh, 2013. "On the Particle Gibbs Sampler," Working Papers 2013-41, Center for Research in Economics and Statistics.
    25. Jacob, Martin, 2021. "Dividend taxes, employment, and firm productivity," Journal of Corporate Finance, Elsevier, vol. 69(C).
    26. Francesco Franco & Thomas Philippon, 2007. "Firms and Aggregate Dynamics," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 587-600, November.
    27. William M. Gentry & R. Glenn Hubbard, 2005. ""Success Taxes," Entrepreneurial Entry, and Innovation," NBER Chapters, in: Innovation Policy and the Economy, Volume 5, pages 87-108, National Bureau of Economic Research, Inc.
    28. Chamley, Christophe, 2001. "Capital income taxation, wealth distribution and borrowing constraints," Journal of Public Economics, Elsevier, vol. 79(1), pages 55-69, January.
    29. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    30. Hu, Yingyao & Huang, Guofang & Sasaki, Yuya, 2020. "Estimating production functions with robustness against errors in the proxy variables," Journal of Econometrics, Elsevier, vol. 215(2), pages 375-398.
    31. Yoonseok Lee & Andrey Stoyanov & Nikolay Zubanov, 2019. "Olley and Pakes‐style Production Function Estimators with Firm Fixed Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(1), pages 79-97, February.
    32. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    33. David Greenaway & Richard Kneller, 2007. "Firm heterogeneity, exporting and foreign direct investment," Economic Journal, Royal Economic Society, vol. 117(517), pages 134-161, February.
    34. Slemrod, Joel, 1992. "Do Taxes Matter? Lessons from the 1980's," American Economic Review, American Economic Association, vol. 82(2), pages 250-256, May.
    35. Hahn, Jinyong & Liao, Zhipeng & Ridder, Geert, 2018. "Nonparametric Two-Step Sieve M Estimation And Inference," Econometric Theory, Cambridge University Press, vol. 34(6), pages 1281-1324, December.
    36. Ulrich Doraszelski & Jordi Jaumandreu, 2013. "R&D and Productivity: Estimating Endogenous Productivity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(4), pages 1338-1383.
    37. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    38. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    39. Wooldridge, Jeffrey M., 2009. "On estimating firm-level production functions using proxy variables to control for unobservables," Economics Letters, Elsevier, vol. 104(3), pages 112-114, September.
    40. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    41. Mark Girolami & Ben Calderhead, 2011. "Riemann manifold Langevin and Hamiltonian Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 123-214, March.
    42. Peter Goodridge & Jonathan Haskel & Gavin Wallis, 2018. "Accounting for the UK Productivity Puzzle: A Decomposition and Predictions," Economica, London School of Economics and Political Science, vol. 85(339), pages 581-605, July.
    43. Robert E. Lucas & Jr., 1967. "Adjustment Costs and the Theory of Supply," Journal of Political Economy, University of Chicago Press, vol. 75(4), pages 321-321.
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • H21 - Public Economics - - Taxation, Subsidies, and Revenue - - - Efficiency; Optimal Taxation
    • H25 - Public Economics - - Taxation, Subsidies, and Revenue - - - Business Taxes and Subsidies
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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