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The dynamics of exports and R&D in SMEs

Abstract

A growing body of literature is exploring firm export and R&D activities. However, most studies examine the first one, whilst considering the second as an explanatory variable or vice versa. This paper contributes to this literature by exploring the joint dynamics of exports and R&D using data from a representative sample of small and medium-sized enterprises in Spanish manufacturing over the 1990–2006 period. The results confirm the existence of a strong interdependence between export and R&D activities. Indeed, engaging in export (R&D) activities will increase a firm’s chances of also engaging in R&D (export) activities. This, in turn, increases firms’ chances of succeeding in export (R&D) activities. Additionally, once we control for firm heterogeneity, strong persistence still remains in each activity due to true state dependence. The results are robust in the use of alternative measures of internationalization (i.e. imports) and innovative activities (product and process innovation).

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Fig. 1

Notes

  1. 1.

    We are grateful to an anonymous referee for drawing our attention to this point.

  2. 2.

    In contrast to firm-level studies, global-economy models of endogenous innovation and growth have long pointed out a strong interdependence between export and technological activities (Grossman and Helpman 1991). Additionally, endogenous growth theory associates productivity to decisions, such as investments in R&D and innovation (Romer 1990).

  3. 3.

    The threshold size used to define SMEs is 200 employees, rather than 250 as in Eurostat. The reason is that the sampling procedure in ESEE is different for firms with more than 200 employees. In the latter case, firms are surveyed exhaustively, resulting in a response rate of approximately 60% of the population.

  4. 4.

    We choose 7 years because it is the average period in which a firm is in the survey over the 1990–2006 period. When two potential spells are available for the same firm, the larger one is selected. The analysis has also been carried out for different thresholds, and the results (available upon request from the authors) do not differ significantly.

  5. 5.

    More specifically, it is 86.2 for non R&D performers (93.1–6.9) and 86.7 (97.3–10.6) for R&D performers.

  6. 6.

    These figures are obtained as 73.3–3.2 for non-exporters and 85.1–7.7 for exporters.

  7. 7.

    Explanatory variables are lagged one period to reduce simultaneity problems.

  8. 8.

    In model (1), even when \( u_{it} \) are assumed to be serially independent, the composite error term \( \left( {\mu_{i} + u_{it} } \right) \) is correlated over time due to the individual-specific time invariant μi term.

  9. 9.

    Specifically, \( Corr\left( {y_{it} ,y_{it - 1} } \right) = \rho = \sigma_{\mu }^{2} /(\sigma_{\mu }^{2} + \sigma_{u}^{2} ) \).

  10. 10.

    Average sales and capital stock might also be considered. We have decided not to include them because they are highly correlated with total factor productivity (TFP) proxy for productivity.

  11. 11.

    R&D activities carried out by foreign affiliates in the host market are playing an increasing role. See Moncada-Paternó-Castelló et al. (2011) for an overview on the trends of internationalization of R&D activities.

  12. 12.

    We have not included the mean values of age and foreign ownership because they show very little within-individual variation. Most variation in these two variables is across individuals.

  13. 13.

    As is well known, the common approach with a probit is a random effects model because there is not a sufficient statistic for a conditional fixed effect model. A basic assumption of the random effects (RE) model is that errors are not correlated with the regressors. To deal with this, we parameterize the effect by augmenting the RE model with the Mundlak specification to allow for individual effects that are correlated with the within-individual means of the regressors.

  14. 14.

    We tested the stability of results using different quadrature points in reported regressions. Some initial instability led us to increase the number to 24. It improves the accuracy of results at the cost of slowing down convergence.

  15. 15.

    A potential shortcoming of the Wooldridge (2005) approach is that it specifies a complete model for the individual unobserved effects (see Eq. 2), so that the estimates could be sensitive to mis-specification of this effect. We have also carried out estimations using the two-step method proposed by Heckman (1981) to deal with the initial conditions problem. The Heckman estimator of dynamic random effects probit model has been obtained using the redpace Stata command by Stewart (2006), and the results are fairly similar to those reported in the paper.

  16. 16.

    As pointed out by one referee, the estimates of the impact of past participation on current participation implied by the bivariate probit model are larger than those obtained from the estimation of random effects probit (Table 5), because the former does not control for individual heterogeneity. Hence, the latter possibly provides a more reliable estimate of the size of state dependence.

  17. 17.

    Collaboration with clients (44.4%) and with universities and technological centres (37.2%) are other relevant ways of technological collaboration. Of less importance are collaboration with competitors (3.8%) and joint-ventures (5.6%).

  18. 18.

    Some authors have argued that R&D measures could be biased towards underestimating the innovative activities of SMEs. Roper (1998) argues that large firms undertake frequent research activities that require a larger degree of formality (i.e., laboratories), in which cost accounting may be simpler. However, costs related to more informal innovative activities could be considered as general costs by smaller firms, and they would not be reflected as R&D investments.

  19. 19.

    Nevertheless, Aw et al. (2005) argue that many studies that failed to find evidence of learning-by-exporting may have neglected a potentially important element of the process of productivity change, namely the investments made by firms to absorb and assimilate knowledge and expertise from foreign contacts (due to exporting). These activities are probably more related to R&D.

  20. 20.

    Some recent papers have incorporated imports to explain efficiency heterogeneity across firms (e.g., Kasahara and Rodrigue 2008; Altomonte and Békés 2008; Castellani et al. 2010, for Chilean, Hungarian, and Italian plants/firms, respectively). Similarly to exports, the argument hinges on the existence of sunk start-up costs associated with import activities. In principle, the argument that sunk costs is related to import activity is less compelling than in the case of exports. However, these costs may arise when either client–supplier relationship is the result of a matching process after which both parts establish stable sourcing relationships (see, for example, Grossman and Helpman 2002) or importers incur complementary costs to adapt intermediate import (and incorporated technologies) to their production processes.

References

  1. Altomonte, C., & Bekes, G. (2008). Trading activities, firms and productivity. Mimeo: Hungarian Academy of Science.

    Google Scholar 

  2. Atkeson, A., & Burstein, A. (2010). Innovation, firm dynamics, and international trade. Journal of Political Economy, 118, 433–484.

    Article  Google Scholar 

  3. Aw, B. Y., Roberts, M. J., & Winston, T. (2005). The complementary role of exports and R&D investments as sources of productivity growth. NBER Working Papers No. 11774, Cambridge, MA: National Bureau of Economic Research.

  4. Aw, B. Y., Roberts, M. J., & Winston, T. (2007). Export market participation, investments in R&D and worker training, and the evolution of firm productivity. The World Economy, 30, 83–104.

    Article  Google Scholar 

  5. Aw, B. Y., Roberts, M. J., & Xu, D. Y. (2008). R&D investments, exporting, and the evolution of firm productivity. American Economic Review: Papers and Proceedings, 98, 451–456.

    Article  Google Scholar 

  6. Aw, B. Y., Roberts, M. J., & Xu, D. Y. (2011). R&D investments, exporting, and productivity dynamics. American Economic Review, 101, 1312–1344.

    Article  Google Scholar 

  7. Becker, S., & Egger, P. (2009). Endogenous product versus process innovation and a firm’s propensity to export. Empirical Economics. doi:10.1007/s00181-009-0322-6.

  8. Bernard, A. B., & Jensen, J. B. (1999). Exceptional exporter performance: Cause, effect, or both? Journal of International Economics, 47, 1–25.

    Article  Google Scholar 

  9. Bernard, A. B., & Jensen, J. B. (2004). Why some firms export. The Review of Economics and Statistics, 86, 561–569.

    Article  Google Scholar 

  10. Braunerhjelm, P. (1996). The relation between firm-specific intangibles and exports. Economic Letters, 53, 213–219.

    Article  Google Scholar 

  11. Bustos, P. (2011). Trade liberalization, exports and technology upgrading: Evidence on the impact of MERCOSUR on Argentinean firms. American Economic Review, 101, 304–340.

    Article  Google Scholar 

  12. Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. New York: Cambridge University Press.

    Book  Google Scholar 

  13. Cassiman B., & Golovko, E. (2007), Innovation and the export-productivity link. CEPR Discussion Papers No. DP6411, London: CEPR.

  14. Castellani, D., Serti, F., & Tomasi, C. (2010). Firms in international trade: Importers and exporters heterogeneity in the Italian manufacturing industry. The World Economy, 33, 424–457.

    Article  Google Scholar 

  15. Chamberlain G. (1984). Panel data, In Z. Griliches & M. D. Intriligator (Eds.), Handbook of Econometrics, vol. 2, Amsterdam: Elsevier.

  16. Cohen, W. M., & Klepper, S. (1996). Firm size and the nature of innovation within industries: The case of process and product R&D. The Review of Economics and Statistics, 78, 232–243.

    Article  Google Scholar 

  17. Cohen, W. M., & Levinthal, D. (1989). Innovation and learning: Two faces of R&D. Economic Journal, 99, 569–596.

    Article  Google Scholar 

  18. Constantini, J. A., & Melitz, M. J. (2008). The dynamics of firm-level adjustment to trade liberalization. In E. Helpman, D. Marin, & T. Verdier (Eds.), The organization of firms in a global economy. Cambridge: Harvard University Press.

    Google Scholar 

  19. Dosi, G., & Malerba, F. (Eds.). (1996). Organization and strategy in the evolution of the enterprise. London: MacMillan.

    Google Scholar 

  20. Geroski, P. A., Van Reenen, J., & Walters, C. F. (1997). How persistently do firms innovate? Research Policy, 26, 33–48.

    Article  Google Scholar 

  21. Girma, S., Görg, H., & Hanley, A. (2008). R&D and exporting: A comparison of British and Irish firms. Review of World Economics, 144, 749–772.

    Article  Google Scholar 

  22. Gkypali A., Tsekouras K., & von Tunzelmann N. (2011). Endogeneity between internationalization and knowledge creation of global R&D leader firms: An econometric approach using Scoreboard data. Industrial and Corporate Change, doi:10.1093/icc/dtr057.

  23. Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17, 109–122.

    Article  Google Scholar 

  24. Greenaway, D., & Kneller, R. (2007). Firm heterogeneity, exporting and foreign direct investment. Economic Journal, 117, F134–F161.

    Article  Google Scholar 

  25. Greene, W. (2008). Discrete Choice Modeling. In T. Mills & K. Patterson. The handbook of econometrics vol. 2, Applied econometrics, Part 4.2., ed, Palgrave, London.

  26. Grossman, G. M., & Helpman, E. (1991). Innovation and growth in the global economy. Cambridge: MIT Press.

    Google Scholar 

  27. Grossman, G. M., & Helpman, E. (2002). Integration versus outsourcing in industry equilibrium. Quarterly Journal of Economics, 117, 85–120.

    Article  Google Scholar 

  28. Hall, B. H. (2002). The financing of Research and Development. Oxford Economic Review of Economic Policy, 18, 35–51.

    Article  Google Scholar 

  29. Heckman, J. (1981). The incidental parameters problem and the problem of initial conditions in estimating a discrete time-discrete data stochastic process. In C. F. Manski & D. L. McFadden (Eds.), Structural analysis of discrete data with econometric applications (pp. 179–195). London: MIT Press.

    Google Scholar 

  30. Hirsch, S., & Bijaoui, I. (1985). R&D intensity and export performance: A micro view. Weltwirtschaftliches Archivies, 121, 238–251.

    Article  Google Scholar 

  31. Hummels, D., Ishii, J., & Yi, K. (2001). The nature and growth of vertical specialization in world trade. Journal of International Economics, 54, 75–96.

    Article  Google Scholar 

  32. Ito, K., & Lechevalier, S. (2010). Why some firms persistently outperform others: Investigating the interactions between innovation and exporting strategies. Industrial and Corporate Change, 19, 1997–2039.

    Article  Google Scholar 

  33. Kaiser, U., & Kongsted, H. C. (2008). True versus spurious state dependence in firm performance: The case of German exports. Empirical Economics, 35, 207–228.

    Article  Google Scholar 

  34. Kampik, F., & Dachs, B. (2011). The innovative performance of German multinationals abroad: Evidence from the European community innovation survey. Industrial and Corporate Change, 20, 661–681.

    Article  Google Scholar 

  35. Kasahara, H., & Rodrigue, J. (2008). Does the use of imported intermediates increase productivity? Plant-level evidence. Journal of Development Economics, 87, 106–118.

    Article  Google Scholar 

  36. Klepper, S. (1996). Entry, exit, growth, and innovation over the product life cycle. American Economic Review, 86, 562–583.

    Google Scholar 

  37. Kumar, N., & Siddhartan, N. S. (1994). Technology, firm size and export behaviour in developing countries: the case of Indian enterprises. Journal of Development Studies, 31, 289–309.

    Article  Google Scholar 

  38. Levinsohn, J., & Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. Review of Economic Studies, 70, 317–342.

    Article  Google Scholar 

  39. Lileeva, A., & Trefler, D. (2010). Improved access to foreign markets raises plant-level productivity… for some plants. Quarterly Journal of Economics, 125, 1051–1099.

    Article  Google Scholar 

  40. Mayer, T., & Ottaviano, G. (2008). The happy few: The internationalisation of European firms. New facts based on firm-level evidence. Intereconomics, 43, 135–148.

    Article  Google Scholar 

  41. Melitz, M. J. (2003). The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica, 71, 1695–1725.

    Article  Google Scholar 

  42. Moncada-Paternó-Castelló, P., Vivarelli, M., & Voigt, P. (2011). Drivers and impacts in the globalization of corporate R&D: An introduction based on the European experience. Industrial and Corporate Change, 20, 585–603.

    Article  Google Scholar 

  43. Mundlak, Y. (1978). On the pooling of time series and cross sectional data. Econometrica, 46, 69–85.

    Article  Google Scholar 

  44. Organization for Economic Co-operation and Development (OECD). (2002). OECD small and medium enterprise outlook. OECD, Paris.

  45. Ortega-Argilés, R., Vivarelli, M., & Voigt, P. (2009). R&D in SMEs: A paradox? Small Business Economics, 33, 3–11.

    Article  Google Scholar 

  46. Peters, B. (2009). Persistence of innovation: Stylised facts and panel data evidence. Journal of Technology Transfer, 34, 226–243.

    Article  Google Scholar 

  47. Roberts, M. J., & Tybout, J. R. (1997). The decision to export in Colombia: An empirical model of entry with Sunk costs. American Economic Review, 87, 545–564.

    Google Scholar 

  48. Rogers, M. (2004). Networks, firm size and innovation. Small Business Economics, 22, 141–153.

    Article  Google Scholar 

  49. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98, S71–S102.

    Article  Google Scholar 

  50. Roper, S. (1998). Under-reporting of R&D in small firms: The impact on international R&D comparisons. Small Business Economics, 12, 131–135.

    Article  Google Scholar 

  51. Salomon, R. M. (2006). Spillovers to foreign market participants: Assessing the impact of export strategies on innovative productivity. Strategic Organization, 4, 135–164.

    Article  Google Scholar 

  52. Stewart, M. (2006). Maximum simulated likelihood estimation of random-effects dynamic probit models with autocorrelated errors. The Stata Journal, 6, 256–272.

    Google Scholar 

  53. Van Beveren, I., & Vandenbussche, H. (2010). Product and process innovation and firms’ decision to export. Journal of Economic Policy Reform, 13, 3–24.

    Article  Google Scholar 

  54. Wagner, J. (2007). Exports and productivity: A survey of the evidence from firm-level data. The World Economy, 30, 60–82.

    Article  Google Scholar 

  55. Wakelin, K. (1998). Innovation and export behaviour at the firm level. Research Policy, 26, 829–841.

    Article  Google Scholar 

  56. Willmore, L. (1992). Transnationals and foreign trade: Evidence from Brazil. Journal of Development Studies, 28, 314–335.

    Article  Google Scholar 

  57. Wooldridge, J. M. (2005). Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics, 20, 39–54.

    Article  Google Scholar 

  58. Wright, M., Westhead, P., & Ucbasaran, D. (2007). Internationalization of small and medium-sized enterprises (SMEs) and international entrepreneurship: A critique and policy implications. Regional Studies, 41, 1013–1030.

    Article  Google Scholar 

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Acknowledgments

The authors are grateful to the Associate Editor, Marco Vivarelli, and two anonymous referees for their helpful comments and suggestions. Silviano Esteve-Pérez gratefully acknowledges financial support from the Spanish Ministry of Science and Innovation (reference ECO2011-27619/ECON) and Generalitat Valenciana (GVPROMETEO2009-098). Diego Rodríguez acknowledges financial support from the Spanish Ministry of Science and Innovation (reference ECO2010-18947) and the Micro-Dyn Project (VI Framework Program). This paper is a revised version of the Fundación de las Cajas de Ahorros (FUNCAS) Working Paper 467/2009. The usual disclaimer applies.

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Appendices

Appendix

See Tables 810.

Table 8 Definition of variables

Productivity (TFP)

We have followed the Levinsohn and Petrin (2003) semi-parametric estimation procedure in order to calculate Total Factor Productivity (TFP). As is well known, this procedure overcomes the so-called simultaneity bias that emerges in OLS estimates of production function. Labor is proxied with the number of effective hours worked. Capital stock of equipment goods in real terms is calculated by using the perpetual inventory formula: \( K_{t} = (1 - \delta )K_{t - 1} (P_{t} /P_{t - 1} ) + I_{t} \), where P is the price index for equipment, δ is the depreciation rate, and I is the investment in equipment. Finally, value added is deflated by using a double deflation procedure and taking advantage of the availability of price variations at the firm level: production is deflated by using individual price variations for output sold, while intermediate consumption uses individual price variations for intermediate consumption. The latter is calculated as a Paasche index, weighting the price variations of raw materials, energy and services purchased by surveyed firms.

Table 9 Trade, R&D and innovation (per cent of firms of each group)
Table 10 Robustness analysis: alternative measures of internationalization and innovative activities Univariate probit (dynamic random effects probit: Wooldridge (2005) estimator)

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Esteve-Pérez, S., Rodríguez, D. The dynamics of exports and R&D in SMEs. Small Bus Econ 41, 219–240 (2013). https://doi.org/10.1007/s11187-012-9421-4

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Keywords

  • SMEs
  • R&D
  • Exports
  • Binary choice panel data models

JEL Classifications

  • F12
  • L25
  • C25
  • L26