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The dynamics of exports and R&D in SMEs


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


  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.


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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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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).

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

JEL Classifications

  • F12
  • L25
  • C25
  • L26