IDEAS home Printed from https://ideas.repec.org/a/eee/jmacro/v50y2016icp259-272.html
   My bibliography  Save this article

International, intersectoral, or unobservable? Measuring R&D spillovers under weak and strong cross-sectional dependence

Author

Listed:
  • Mitze, Timo
  • Naveed, Amjad
  • Ahmad, Nisar

Abstract

In the theoretical and empirical growth literature, private and social returns to R&D have been identified as the key drivers of productivity gains and economic development. However, recently the debate on the relative importance of private vis-à-vis social returns has been reinforced by contributions in two emerging fields of the applied econometric literature, namely spatial panel modeling and the common factor approach, which stress the role of cross-sectional dependence as a source for a potential estimation bias linked to the measurement of returns to R&D. In this paper, we account for these methodical advances when estimating sectoral knowledge production functions for OECD countries under weak and strong cross-sectional dependence. By doing so, we are able to uncover technology- and trade-related R&D spillover channels associated with social returns to R&D, while effectively controlling for other types of productivity spillovers and latent macroeconomic shocks. Our results highlight the role played by international-intersectoral R&D spillovers for the social rate of return to R&D, while we get limited evidence for private returns to R&D once cross-sectional dependence is properly accounted for.

Suggested Citation

  • Mitze, Timo & Naveed, Amjad & Ahmad, Nisar, 2016. "International, intersectoral, or unobservable? Measuring R&D spillovers under weak and strong cross-sectional dependence," Journal of Macroeconomics, Elsevier, vol. 50(C), pages 259-272.
  • Handle: RePEc:eee:jmacro:v:50:y:2016:i:c:p:259-272
    DOI: 10.1016/j.jmacro.2016.10.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0164070416301148
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. 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.
    3. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    4. repec:ags:stataj:243191 is not listed on IDEAS
    5. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    6. Vasilis Sarafidis & Neville Weber, 2015. "A Partially Heterogeneous Framework for Analyzing Panel Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(2), pages 274-296, April.
    7. Frantzen, Dirk, 2002. "Intersectoral and International R&D Knowledge Spillovers and Total Factor Productivity," Scottish Journal of Political Economy, Scottish Economic Society, vol. 49(3), pages 280-303, August.
    8. López-Pueyo, Carmen & Barcenilla-Visús, Sara & Sanaú, Jaime, 2008. "International R&D spillovers and manufacturing productivity: A panel data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 19(2), pages 152-172, June.
    9. 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.
    10. Huasheng Song & Vincent Vannetelbosch, 2007. "International R&D Collaboration Networks," Manchester School, University of Manchester, vol. 75(6), pages 742-766, December.
    11. Henrik Braconier & Fredrik Sjöholm, 1998. "National and international spillovers from R&D: Comparing a neoclassical and an endogenous growth approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 134(4), pages 638-663, December.
    12. Zvi Griliches, 1998. "Issues in Assessing the Contribution of Research and Development to Productivity Growth," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 17-45 National Bureau of Economic Research, Inc.
    13. Jaffe, Adam B, 1986. "Technological Opportunity and Spillovers of R&D: Evidence from Firms' Patents, Profits, and Market Value," American Economic Review, American Economic Association, vol. 76(5), pages 984-1001, December.
    14. Harald Badinger & Peter Egger, 2015. "Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(1), pages 11-35, March.
    15. Coe, David T. & Helpman, Elhanan, 1995. "International R&D spillovers," European Economic Review, Elsevier, vol. 39(5), pages 859-887, May.
    16. Edward F. Blackburne III & Mark W. Frank, 2007. "Estimation of nonstationary heterogeneous panels," Stata Journal, StataCorp LP, vol. 7(2), pages 197-208, June.
    17. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    18. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    19. Edmond, Chris, 2001. "Some Panel Cointegration Models of International R&D Spillovers," Journal of Macroeconomics, Elsevier, vol. 23(2), pages 241-260, April.
    20. Bruno, Giovanni S.F., 2005. "Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models," Economics Letters, Elsevier, vol. 87(3), pages 361-366, June.
    21. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    22. 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.
    23. M. V. Posner, 1961. "International Trade And Technical Change," Oxford Economic Papers, Oxford University Press, vol. 13(3), pages 323-341.
    24. Pesaran, M. Hashem & Tosetti, Elisa, 2011. "Large panels with common factors and spatial correlation," Journal of Econometrics, Elsevier, vol. 161(2), pages 182-202, April.
    25. Nadiri, M Ishaq & Prucha, Ingmar R, 1996. "Estimation of the Depreciation Rate of Physical and R&D Capital in the U.S. Total Manufacturing Sector," Economic Inquiry, Western Economic Association International, vol. 34(1), pages 43-56, January.
    26. Mary O'Mahony & Marcel P. Timmer, 2009. "Output, Input and Productivity Measures at the Industry Level: The EU KLEMS Database," Economic Journal, Royal Economic Society, vol. 119(538), pages 374-403, June.
    27. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    28. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2011. "Weak and strong cross‐section dependence and estimation of large panels," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 45-90, February.
    29. Madsen, Jakob B., 2007. "Technology spillover through trade and TFP convergence: 135 years of evidence for the OECD countries," Journal of International Economics, Elsevier, vol. 72(2), pages 464-480, July.
    30. Drukker, David M. & Peng, Hua & Prucha, Ingmar R. & Raciborski, Rafal, 2013. "Creating and managing spatial-weighting matrices with the spmat command," Stata Journal, StataCorp LP, vol. 13(2), pages 1-47.
    31. Everaert, Gerdie & Pozzi, Lorenzo, 2007. "Bootstrap-based bias correction for dynamic panels," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1160-1184, April.
    32. Bart Verspagen, 1997. "Measuring Intersectoral Technology Spillovers: Estimates from the European and US Patent Office Databases," Economic Systems Research, Taylor & Francis Journals, vol. 9(1), pages 47-65.
    33. Eberhardt, Markus, 2012. "Estimating panel time-series models with heterogeneous slopes," Stata Journal, StataCorp LP, vol. 12(1), pages 1-13.
    34. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    35. Wolff, Edward N. & Ishaq Nadiri, M., 1993. "Spillover effects, linkage structure, and research and development," Structural Change and Economic Dynamics, Elsevier, vol. 4(2), pages 315-331, December.
    36. Jerry Coakley & Ana-Maria Fuertes & Ron Smith, 2002. "A Principal Components Approach to Cross-Section Dependence in Panels," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B5-3, International Conferences on Panel Data.
    37. Jeffrey Bernstein & Xiaoyi Yan, 1996. "Canadian-Japanese R&D spillovers and productivity growth," Applied Economics Letters, Taylor & Francis Journals, vol. 3(12), pages 763-767.
    38. M. Hashem Pesaran, 2015. "Testing Weak Cross-Sectional Dependence in Large Panels," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1089-1117, December.
    39. Goto, Akira & Suzuki, Kazuyuki, 1989. "R&D Capital, Rate of Return on R&D Investment and Spillover of R&D in Japanese Manufacturing Industries," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 555-564, November.
    40. repec:hal:journl:peer-00796743 is not listed on IDEAS
    41. 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.
    42. J. Elhorst, 2012. "Dynamic spatial panels: models, methods, and inferences," Journal of Geographical Systems, Springer, vol. 14(1), pages 5-28, January.
    43. repec:ags:stataj:119268 is not listed on IDEAS
    44. Bart Verspagen, 1997. "Estimating international technology spillovers using technology flow matrices," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 133(2), pages 226-248, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:ecmode:v:68:y:2018:i:c:p:229-238 is not listed on IDEAS

    More about this item

    Keywords

    Sectoral productivity; R&D spillovers; Cross-sectional dependence; OECD;

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:eee:jmacro:v:50:y:2016:i:c:p:259-272. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/inca/622617 .

    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 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.

    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.