IDEAS home Printed from https://ideas.repec.org/a/jed/journl/v46y2021i1p53-84.html
   My bibliography  Save this article

Empirical Analysis of an Augmented Schumpeterian Endogenous Growth Model

Author

Listed:
  • Ejike Udeogu (a) , Uzochukwu Amakom (b) and Shampa Roy-Mukherjee (a)

    ((a) University of East London, United Kingdom; (b) University of Nigeria, Nigeria)

Abstract

This study conducts an empirical analysis of an augmented Schumpeterian endogenous growth theory using aggregate-level data from 1981 to 2017 for 31 OECD countries. Despite a considerable number of studies analysing endogenous growth, cross-country analyses utilising estimators robust to endogeneity-bias and controlling for the macroeconomic effect of institutions are still rare. In this paper, we employ a relatively consistent estimator to analyse an augmented neoclassical production function that links output per worker to capital accumulation, technological progress, and institutions. Our results from the extended system of generalised method of momentS estimation align with the mainstream consensus that capital accumulation and technological progress or innovation, in the form of R&D activities, determine the level of output per worker in the long run. But in addition, we find that effective institutions underlie the innovation effect. On average, the impact of R&D activities on output per worker is higher in countries with more effective institutions.

Suggested Citation

  • Ejike Udeogu (a) , Uzochukwu Amakom (b) and Shampa Roy-Mukherjee (a), 2021. "Empirical Analysis of an Augmented Schumpeterian Endogenous Growth Model," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 46(1), pages 53-84, March.
  • Handle: RePEc:jed:journl:v:46:y:2021:i:1:p:53-84
    DOI: 10.35866/caujed.2021.46.1.003
    as

    Download full text from publisher

    File URL: http://www.jed.or.kr/full-text/46-1/3.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.35866/caujed.2021.46.1.003?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
    ---><---

    References listed on IDEAS

    as
    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Emmanuel Duguet, 2006. "Innovation height, spillovers and tfp growth at the firm level: Evidence from French manufacturing," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(4-5), pages 415-442.
    3. Hall, Bronwyn H. & Mairesse, Jacques & Mohnen, Pierre, 2010. "Measuring the Returns to R&D," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 1033-1082, Elsevier.
    4. Christopher F Baum, 2006. "An Introduction to Modern Econometrics using Stata," Stata Press books, StataCorp LP, number imeus, December.
    5. Zvi Griliches, 1998. "Comparing Productivity Growth: An Exploration of French and U.S. Industrial and Firm Data," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 157-186, National Bureau of Economic Research, Inc.
    6. Michael Rothschild & Joseph Stiglitz, 1976. "Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information," The Quarterly Journal of Economics, Oxford University Press, vol. 90(4), pages 629-649.
    7. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    8. Zvi Griliches, 1998. "Productivity and R&D at the Firm Level," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 100-133, National Bureau of Economic Research, Inc.
    9. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    10. John Van Reenen & Rupert Harrison & Rachel Griffith, 2006. "How Special Is the Special Relationship? Using the Impact of U.S. R&D Spillovers on U.K. Firms as a Test of Technology Sourcing," American Economic Review, American Economic Association, vol. 96(5), pages 1859-1875, December.
    11. Zvi Griliches, 1998. "Interindustry Technology Flows and Productivity Growth: A Reexamination," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 241-250, National Bureau of Economic Research, Inc.
    12. Robert J. Barro, 1998. "Determinants of Economic Growth: A Cross-Country Empirical Study," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262522543.
    13. Charles I. Jones, 1995. "Time Series Tests of Endogenous Growth Models," The Quarterly Journal of Economics, Oxford University Press, vol. 110(2), pages 495-525.
    14. Ross Levine & Norman Loayza & Thorsten Beck, 2002. "Financial Intermediation and Growth: Causality and Causes," Central Banking, Analysis, and Economic Policies Book Series, in: Leonardo Hernández & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Se (ed.),Banking, Financial Integration, and International Crises, edition 1, volume 3, chapter 2, pages 031-084, Central Bank of Chile.
    15. Zvi Griliches, 1984. "R&D, Patents, and Productivity," NBER Books, National Bureau of Economic Research, Inc, number gril84-1, January-J.
    16. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 70(1), pages 65-94.
    17. Zvi Griliches, 1998. "Productivity Growth and R&D at the Business Level: Results from the PIMS Data Base," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 134-156, National Bureau of Economic Research, Inc.
    18. Dominique Guellec & Bruno Van Pottelsberghe De La Potterie, 2003. "The impact of public R&D expenditure on business R&D," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 12(3), pages 225-243.
    19. Link, Albert N, 1981. "Basic Research and Productivity Increase in Manufacturing: Additional Evidence," American Economic Review, American Economic Association, vol. 71(5), pages 1111-1112, December.
    20. Marios Zachariadis, 2003. "R&D, innovation, and technological progress: a test of the Schumpeterian framework without scale effects," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 36(3), pages 566-586, August.
    21. 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.
    22. Scherer, F M, 1982. "Inter-Industry Technology Flows and Productivity Growth," The Review of Economics and Statistics, MIT Press, vol. 64(4), pages 627-634, November.
    23. 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.
    24. Richard Blundell & Stephen Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
    25. Dietmar Harhoff, 1998. "R&D and Productivity in German Manufacturing Firms," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 6(1), pages 29-50.
    26. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588.
    27. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    28. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    29. Stephen R. Bond, 2002. "Dynamic panel data models: a guide to micro data methods and practice," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 141-162, August.
    30. Rebelo, Sergio, 1991. "Long-Run Policy Analysis and Long-Run Growth," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 500-521, June.
    31. Arellano, Manuel, 1989. "A note on the Anderson-Hsiao estimator for panel data," Economics Letters, Elsevier, vol. 31(4), pages 337-341, December.
    32. Hall, Bronwyn H. & Mairesse, Jacques, 1995. "Exploring the relationship between R&D and productivity in French manufacturing firms," Journal of Econometrics, Elsevier, vol. 65(1), pages 263-293, January.
    33. Jong-Rong Chen & Chih-Hai Yang, 2005. "Technological knowledge, spillover and productivity: evidence from Taiwanese firm level panel data," Applied Economics, Taylor & Francis Journals, vol. 37(20), pages 2361-2371.
    34. Jacques Mairesse & Bronwyn H. Hall, 1996. "Estimating the Productivity of Research and Development: An Exploration of GMM Methods Using Data on French & United States Manufacturing Firms," NBER Working Papers 5501, National Bureau of Economic Research, Inc.
    35. Philippe Cuneo & Jacques Mairesse, 1984. "Productivity and R&D at the Firm Level in French Manufacturing," NBER Chapters, in: R&D, Patents, and Productivity, pages 375-392, National Bureau of Economic Research, Inc.
    36. Dajin Li, 2002. "Is the AK model still alive? The long-run relation between growth and investment re-examined," Canadian Journal of Economics, Canadian Economics Association, vol. 35(1), pages 92-114, February.
    37. Stephen Bond & Dietmar Harhoff & John Van Reenen, 2003. "Corporate R&D and Productivity in Germany and the United Kingdom," CEP Discussion Papers dp0599, Centre for Economic Performance, LSE.
    38. Andrews, Donald W. K. & Lu, Biao, 2001. "Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models," Journal of Econometrics, Elsevier, vol. 101(1), pages 123-164, March.
    39. John W. Kendrick & Beatrice N. Vaccara, 1980. "New Developments in Productivity Measurement and Analysis," NBER Books, National Bureau of Economic Research, Inc, number kend80-1, January-J.
    40. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    41. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    42. George A. Akerlof, 1970. "The Market for "Lemons": Quality Uncertainty and the Market Mechanism," The Quarterly Journal of Economics, Oxford University Press, vol. 84(3), pages 488-500.
    43. Guido W. Imbens, 1997. "One-Step Estimators for Over-Identified Generalized Method of Moments Models," Review of Economic Studies, Oxford University Press, vol. 64(3), pages 359-383.
    44. Mohnen, Pierre & Lepine, Normand, 1991. "R&D, R&D spillovers and payments for technology: Canadian evidence," Structural Change and Economic Dynamics, Elsevier, vol. 2(1), pages 213-228, June.
    45. Liu, Zhenjuan & Stengos, Thanasis, 1999. "Non-linearities in Cross-Country Growth Regressions: A Semiparametric Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 527-538, Sept.-Oct.
    46. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    47. Stephen Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    48. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    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. Ugur, Mehmet & Trushin, Eshref & Solomon, Edna & Guidi, Francesco, 2016. "R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis," Research Policy, Elsevier, vol. 45(10), pages 2069-2086.
    2. Ejike Udeogu & Shampa Roy-Mukherjee & Uzochukwu Amakom, 2021. "Does Increasing Product Complexity and Diversity Cause Economic Growth in the Long-Run? A GMM Panel VAR Evidence," SAGE Open, , vol. 11(3), pages 21582440211, August.
    3. Hall, Bronwyn H. & Mairesse, Jacques & Mohnen, Pierre, 2010. "Measuring the Returns to R&D," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 1033-1082, Elsevier.
    4. Markus Eberhardt & Christian Helmers & Hubert Strauss, 2013. "Do Spillovers Matter When Estimating Private Returns to R&D?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 436-448, May.
    5. Jarle Møen & Helge Sandvig Thorsen, 2017. "Publication Bias in the Returns to R&D Literature," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 8(3), pages 987-1013, September.
    6. Capolupo, Rosa, 2009. "The New Growth Theories and Their Empirics after Twenty Years," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-72.
    7. Giuseppe Medda & Claudio Piga, 2014. "Technological spillovers and productivity in Italian manufacturing firms," Journal of Productivity Analysis, Springer, vol. 41(3), pages 419-434, June.
    8. Johanna Vogel, 2015. "The two faces of R&D and human capital: Evidence from Western European regions," Papers in Regional Science, Wiley Blackwell, vol. 94(3), pages 525-551, August.
    9. Christophe Pennetier & Karan Girotra & Jürgen Mihm, 2019. "R&D Spending: Dynamic or Persistent?," Manufacturing & Service Operations Management, INFORMS, vol. 21(3), pages 636-657, July.
    10. Martin Andersson & Hans Lööf, 2011. "Agglomeration and productivity: evidence from firm-level data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(3), pages 601-620, June.
    11. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    12. Robert Wieser, 2005. "Research And Development Productivity And Spillovers: Empirical Evidence At The Firm Level," Journal of Economic Surveys, Wiley Blackwell, vol. 19(4), pages 587-621, September.
    13. Bettina Becker, 2013. "The Determinants of R&D Investment: A Survey of the Empirical Research," Discussion Paper Series 2013_09, Department of Economics, Loughborough University, revised Sep 2013.
    14. Martin Andersson & Hans Lööf, 2009. "Learning‐by‐Exporting Revisited: The Role of Intensity and Persistence," Scandinavian Journal of Economics, Wiley Blackwell, vol. 111(4), pages 893-916, December.
    15. Scott, K. Rebecca, 2015. "Demand and price uncertainty: Rational habits in international gasoline demand," Energy, Elsevier, vol. 79(C), pages 40-49.
    16. Osvaldo Lagares, 2016. "Capital, Economic Growth and Relative Income Differences in Latin America," Discussion Papers 16/03, Department of Economics, University of York.
    17. Petreski, Marjan, 2009. "Analysis of exchange-rate regime effect on growth: theoretical channels and empirical evidence with panel data," Economics Discussion Papers 2009-49, Kiel Institute for the World Economy (IfW Kiel).
    18. Charles Mawusi, 2021. "Economic Uncertainty and Remittances to Developing Economies: A System GMM Approach," Working Papers hal-03147813, HAL.
    19. Vogel, Johanna, 2013. "Regional Convergence in Europe: A Dynamic Heterogeneous Panel Approach," MPRA Paper 51794, University Library of Munich, Germany.
    20. Czarnitzki, Dirk & Thorwarth, Susanne, 2012. "Productivity effects of basic research in low-tech and high-tech industries," Research Policy, Elsevier, vol. 41(9), pages 1555-1564.

    More about this item

    Keywords

    Endogenous Growth Theory; Capital Accumulation; Generalised Method of Moments (GMM); Institutions; Neoclassical Production Function; OECD; Research & Development; Technical Progress;
    All these keywords.

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

    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:jed:journl:v:46:y:2021:i:1:p:53-84. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/eccaukr.html .

    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: Sung Y. Park (email available below). General contact details of provider: https://edirc.repec.org/data/eccaukr.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.