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Assessing the nonlinear nature of the effects of R&D intensity on growth of SMEs: a dynamic panel data approach

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  • Paulo Nunes

    ()

  • Zélia Serrasqueiro
  • João Leitão

Abstract

Based on a sample of Portuguese SMEs for the period 1999–2006 and using two step estimation, namely probit regressions and dynamic estimators, this study makes two important contributions to the literature: (i) identification of a quadratic relationship between R&D intensity and SME growth that takes the form of a U, and so R&D intensity is a stimulating factor of SME growth for high levels of R&D intensity; being a restrictive factor of SME growth for low levels of R&D intensity; and (ii) the growth of SMEs seems to be dependent on the nonlinear effects associated with distinct levels of R&D intensity. The nonlinear effects identified suggest that Portuguese SMEs with high levels of R&D intensity more easily find an efficiency scale and are more dependent on internal financing and short-term debt as sources for funding growth, compared to the case of Portuguese SMEs with lower levels of R&D intensity. Copyright Springer-Verlag 2013

Suggested Citation

  • Paulo Nunes & Zélia Serrasqueiro & João Leitão, 2013. "Assessing the nonlinear nature of the effects of R&D intensity on growth of SMEs: a dynamic panel data approach," Journal of Evolutionary Economics, Springer, vol. 23(1), pages 97-128, January.
  • Handle: RePEc:spr:joevec:v:23:y:2013:i:1:p:97-128
    DOI: 10.1007/s00191-011-0258-9
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    References listed on IDEAS

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    1. Georgios Fotopoulos & Helen Louri, 2004. "Firm Growth and FDI: Are Multinationals Stimulating Local Industrial Development?," Journal of Industry, Competition and Trade, Springer, vol. 4(3), pages 163-189, September.
    2. Blandina Oliveira & Adelino Fortunato, 2006. "Firm Growth and Liquidity Constraints: A Dynamic Analysis," Small Business Economics, Springer, vol. 27(2), pages 139-156, October.
    3. 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.
    4. Chesher, Andrew, 1979. "Testing the Law of Proportionate Effect," Journal of Industrial Economics, Wiley Blackwell, vol. 27(4), pages 403-411, June.
    5. 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.
    6. Jovanovic, Boyan, 1982. "Selection and the Evolution of Industry," Econometrica, Econometric Society, vol. 50(3), pages 649-670, May.
    7. Marc Deloof, 2003. "Does Working Capital Management Affect Profitability of Belgian Firms?," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(3-4), pages 573-588.
    8. O'Mahony, Mary & Vecchi, Michela, 2009. "R&D, knowledge spillovers and company productivity performance," Research Policy, Elsevier, vol. 38(1), pages 35-44, February.
    9. Gomez, Jaime & Vargas, Pilar, 2009. "The effect of financial constraints, absorptive capacity and complementarities on the adoption of multiple process technologies," Research Policy, Elsevier, vol. 38(1), pages 106-119, February.
    10. Alex Coad & Rekha Rao, 2011. "The firm-level employment effects of innovations in high-tech US manufacturing industries," Journal of Evolutionary Economics, Springer, vol. 21(2), pages 255-283, May.
    11. Myers, Stewart C., 1977. "Determinants of corporate borrowing," Journal of Financial Economics, Elsevier, vol. 5(2), pages 147-175, November.
    12. Rian Beise-Zee & Christian Rammer, 2006. "Local User-Producer Interaction in Innovation and Export Performance of Firms," Small Business Economics, Springer, vol. 27(2), pages 207-222, October.
    13. Hall, Bronwyn H, 1987. "The Relationship between Firm Size and Firm Growth in the U.S. Manufacturing Sector," Journal of Industrial Economics, Wiley Blackwell, vol. 35(4), pages 583-606, June.
    14. Giarratana, Marco S., 2004. "The birth of a new industry: entry by start-ups and the drivers of firm growth: The case of encryption software," Research Policy, Elsevier, vol. 33(5), pages 787-806, July.
    15. Toke Reichstein & Michael Dahl & Bernd Ebersberger & Morten Jensen, 2010. "The devil dwells in the tails," Journal of Evolutionary Economics, Springer, vol. 20(2), pages 219-231, April.
    16. Francesca Lotti & Enrico Santarelli & Marco Vivarelli, 2003. "Does Gibrat's Law hold among young, small firms?," Journal of Evolutionary Economics, Springer, vol. 13(3), pages 213-235, August.
    17. Domenico Sarno, 2008. "Capital structure and growth of the firms in the backward regions of the south Italy," Applied Financial Economics, Taylor & Francis Journals, vol. 18(10), pages 821-833.
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    Cited by:

    1. repec:rom:mancon:v:11:y:2017:i:1:p:617-632 is not listed on IDEAS
    2. TENG Joe K.L. & HU Jiayu & WU Dazhong & MIXON Phillip A. & DUAN Chaojie (CJ, 2016. "Determinants Of Research And Development Intensity From A Network Perspective," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 11(3), pages 129-139, December.
    3. repec:eso:journl:v:47:y:2016:i:2:p:213-245 is not listed on IDEAS
    4. Olubunmi Ipinnaiye & Declan Dineen & Helena Lenihan, 2017. "Drivers of SME performance: a holistic and multivariate approach," Small Business Economics, Springer, vol. 48(4), pages 883-911, April.

    More about this item

    Keywords

    Diversification; Growth; R&D intensity; SMEs; C23; L11; L25; L26;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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