IDEAS home Printed from https://ideas.repec.org/a/bla/obuest/v86y2024i3p641-671.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/obes.12594
    Download Restriction: no

    File URL: https://libkey.io/10.1111/obes.12594?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
    2. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Productivity and Performance: A GMM approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 331-344, April.
    3. Jamil, Nida & Chaudhry, Theresa Thompson & Chaudhry, Azam, 2022. "Trading textiles along the new silk route: The impact on Pakistani firms of gaining market access to China," Journal of Development Economics, Elsevier, vol. 158(C).
    4. Bruno Merlevede & Angelos Theodorakopoulos, 2018. "Productivity Effects of Internationalisation Through the Domestic Supply Chain: Evidence from Europe," Working Papers of VIVES - Research Centre for Regional Economics 627689, KU Leuven, Faculty of Economics and Business (FEB), VIVES - Research Centre for Regional Economics.
    5. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    6. Paul Schrimpf & Michio Suzuki & Hiroyuki Kasahara, 2015. "Identification and Estimation of Production Function with Unobserved Heterogeneity," 2015 Meeting Papers 924, Society for Economic Dynamics.
    7. Bruno Merlevede & Angelos Theodorakopoulos, 2021. "Productivity effects of internationalisation through the domestic supply chain," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 808-832, September.
    8. Dobbelaere, Sabien & Kiyota, Kozo & Mairesse, Jacques, 2015. "Product and labor market imperfections and scale economies: Micro-evidence on France, Japan and the Netherlands," Journal of Comparative Economics, Elsevier, vol. 43(2), pages 290-322.
    9. Bang, Minji & Gao, Wayne Yuan & Postlewaite, Andrew & Sieg, Holger, 2023. "Using monotonicity restrictions to identify models with partially latent covariates," Journal of Econometrics, Elsevier, vol. 235(2), pages 892-921.
    10. Simon Pröll & Giannis Karagiannis & Klaus Salhofer, 2019. "Advertising and Markups: The Case of the German Brewing Industry," Working Papers 732019, University of Natural Resources and Life Sciences, Vienna, Department of Economics and Social Sciences, Institute for Sustainable Economic Development.
    11. Gornig, Martin & Schiersch, Alexander, 2019. "Agglomeration economies and firm TFP: different effects across industries," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203597, Verein für Socialpolitik / German Economic Association.
    12. repec:zbw:inwedp:732019 is not listed on IDEAS
    13. Jingfang Zhang & Emir Malikov, 2023. "Detecting Learning by Exporting and from Exporters," Journal of Productivity Analysis, Springer, vol. 60(1), pages 1-19, August.
    14. Victor Aguirregabiria & Margaret Slade, 2017. "Empirical models of firms and industries," Canadian Journal of Economics, Canadian Economics Association, vol. 50(5), pages 1445-1488, December.
    15. Richter, Philipp M. & Schiersch, Alexander, 2017. "CO2 emission intensity and exporting: Evidence from firm-level data," European Economic Review, Elsevier, vol. 98(C), pages 373-391.
    16. James Harrigan & Ariell Reshef & Farid Toubal, 2018. "Techies, Trade, and Skill-Biased Productivity," NBER Working Papers 25295, National Bureau of Economic Research, Inc.
    17. Petrick, Martin & Kloss, Mathias, 2013. "Identifying Factor Productivity from Micro-data: The case of EU agriculture," Working papers 144004, Factor Markets, Centre for European Policy Studies.
    18. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
    19. Khanna, Rupika & Sharma, Chandan, 2021. "Do technological investments promote manufacturing productivity? A firm-level analysis for India," Economic Modelling, Elsevier, vol. 105(C).
    20. Li, Tong & Sasaki, Yuya, 2024. "Identification of heterogeneous elasticities in gross-output production functions," Journal of Econometrics, Elsevier, vol. 238(2).
    21. Dolores Añon Higón & Juan A. Daniel Bonvin, 2023. "Do digitalization spurs SMEs’ participation in foreign markets?," Working Papers 2307, Department of Applied Economics II, Universidad de Valencia.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:obuest:v:86:y:2024:i:3:p:641-671. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/sfeixuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.