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Do firms learn by exporting or learn to export? Evidence from small and medium-sized enterprises

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Abstract

Using a matching approach, we compare the productivity trajectories of future export-entrants and matched nonentrants. Future exporters have higher productivity than do nonentrants before entry into international markets, which indicates self-selection into exports. More interestingly, we also observe a productivity increase among export-entrants relative to nonentrants before export entry. This might be explained by higher investments in physical capital prior to export entry. We find no evidence that the productivity gap between export-entrants and nonentrants continues to grow after export entry. Our results suggest that learning to export occurs but that learning by exporting does not. In contrast to previous studies on Swedish manufacturing, we focus particularly on small and medium-sized enterprises (SMEs).

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Notes

  1. Seminal articles are Bernard and Jensen (1995, 1999). The literature has been surveyed by Greenaway and Kneller (2007) and Wagner (2007).

  2. See, e.g., Van Biesebroeck (2005) and Damijan and Kostevc (2006).

  3. The surveys by both Greenaway and Kneller (2007) and Wagner (2007) arrive at this conclusion.

  4. In addition, Hansson and Lundin (2004) use a panel of Swedish manufacturing firms with 50 or more employees, but for the period from 1990 to 1999. When they employ a matching approach, they find no impact of exporting on productivity in export-entrants after export entry.

  5. Export promotion of SMEs and, in general, the question of how to support the internationalization of SMEs are subjects that seem to attract significant policy interest on the national as well as on the EU level. See, e.g., SOU (2008) and EC (2007).

  6. See Tables 1 and 3.

  7. Swedish firm-level data are protected by secrecy legislation and are not publicly available. However, it is possible for researchers to apply for access to such data for use in specified research projects. For more information about data accessibility, see http://www.scb.se/Pages/List____39370.aspx.

  8. We obtain the exporter productivity premium by transforming the estimate on \( \beta_{1} \) in Table 3, \( 100\left( {\exp \left( {\beta_{1} } \right) - 1} \right) \), which is the percentage differential in productivity between exporters and nonexporters (Halvorsen and Palmqvist 1980).

  9. Andersson et al. (2008) and ISGEP (2008) have recently estimated similar labor productivity export premia for Swedish manufacturing using the same type of data.

  10. See the surveys of this literature by Greenaway and Kneller (2007) and Wagner (2007).

  11. See, e.g., Wagner (2002), Girma et al. (2004), and De Loecker (2007).

  12. See Heckman et al. (1997, 1998), Dehejia and Wahba (1999, 2002), and Smith and Todd (2005).

  13. For a more detailed and technical presentation of matching methods, see, e.g., Heckman et al. (1998), Imbens (2004), and Smith and Todd (2005).

  14. See Heckman et al. (1997, 1998).

  15. Although the cross-sectional and the conditional DID matching estimator are presented as quite distinct, their similarity becomes apparent when considering how pretreatment outcomes can be employed in both approaches. In the conditional DID case, pretreatment outcomes are used in calculating the before–after differences, whereas in the cross-sectional version, they are used as right-hand-side conditioning variables. In a regression context, LaLonde (1986) refers to the latter approach (including pretreatment outcomes as right-hand-side variables) as an unrestricted DID estimator.

  16. For the DID approach, this condition must hold in both the pre- and the posttreatment period.

  17. This will most likely induce an upward bias in the estimated treatment effect because some of the firms will have the intention to start exporting and make necessary investments yet fail to enter the export market. This export failure should, had it been observable, be regarded as part of the causal effect of the decision to try to become an exporter. Note also that in the comparison group of firms that do not enter export markets during the observable time period we most likely have firms that have started to prepare themselves for export entry but where export sales have not yet begun. This source of unobservability will tend to induce downward bias in the estimated treatment effect. Since both phenomena are unobservable we have no possibility to assess their relative importance.

  18. For a discussion of the methodological implications of dynamic treatment assignment and suggested solutions, see Fredriksson and Johansson (2008) and Crépon et al. (2009).

  19. In the conditional DID specifications, pre-export labor productivity is used to calculate the before–after potential export entry differences. For the learning-by-exporting case, this means that “before” refers to \( {\text{LP}}_{t - 1} \), while “before” for the learning-to-export case refers to \( {\text{LP}}_{t - 3} \).

  20. It is admittedly arbitrary to assume that 3 years is a suitable pre-entry baseline for all firms, but the limited longitudinal dimension in the data leaves us with few options.

  21. A complete list of estimated propensity scores for all matching models applied is available on request.

  22. All matching estimates are based on PSMATCH2 for STATA, by E. Leuven and B. Sianesi. Stata do-files used to compute the empirical results are available from the authors on request.

  23. In general, the results show little sensitivity depending on the exact weighting regime. Estimates based on single nearest-neighbor matching and different bandwidths for the Epanechnikov kernel are available on request.

  24. The standard errors are calculated assuming independent observations, fixed weights, and that the variance of the outcome variable is the same within the treatment and within the comparison groups and does not depend on the estimated propensity score. The exact formula can be found in appendix B in Lechner (2001). Bootstrapping is a widely used alternative to calculate standard errors of matching estimators. One practical drawback with bootstrapping is that it tends to be quite expensive in terms of computation time. More importantly, Abadie and Imbens (2006, 2008) have shown that bootstrapping is generally not valid for matching methods due to the nonsmooth nature of commonly used matching estimators (e.g., nearest-neighbor matching).

  25. The standardized bias of a covariate is defined as the difference of the sample means in the treatment and the comparison group as a percentage of the square root of the average of the sample variance in the two groups. See Rosenbaum and Rubin (1985).

  26. Complete results can be found in Table 10 in the Appendix.

  27. Previous studies that have provided evidence for the learning-to-export hypothesis (conscious self-selection) are Alvarez and Lopez (2005), Bellone et al. (2008), and Lopez (2009). Using Chilean manufacturing plant data, Alvarez and Lopez (2005) show that an increase in investment before export entry raises the probability of exporting while controlling for other factors that might affect the probability of entry on the export market. Lopez (2009) finds that productivity and investment increase before plants begin to export. Bellone et al. (2008) argue that, due to the investments carried out prior to the benefits of sales in foreign markets, export-entrants may experience a (temporary) decrease in productivity before entry. They reveal a U-shaped productivity path among French manufacturing firms prior to export entry. The declining total factor productivity (TFP) of future exporters before entry appears to be caused by an increase in capital stocks. However, when they use labor productivity instead of TFP, they observe that productivity increases throughout the pre-entry period.

  28. Similar divisions can be found in, e.g., Bernard and Jensen (1999), Hansson and Lundin (2004), and Alvarez and Lopez (2005).

  29. Complete results can be found in Table 11 in the Appendix.

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Authors and Affiliations

  1. Growth Analysis, Studentplan 3, 831 40, Östersund, Sweden

    Kent Eliasson & Markus Lindvert

  2. Department of Economics, Umeå University, 901 87, Umeå, Sweden

    Kent Eliasson

  3. Growth Analysis, Box 574, 101 31, Stockholm, Sweden

    Pär Hansson

  4. Swedish Business School, Örebro University, 701 82, Örebro, Sweden

    Pär Hansson

Authors
  1. Kent Eliasson
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  2. Pär Hansson
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Correspondence to Pär Hansson.

Appendix

Appendix

See Tables 10 and 11.

Table 10 Cross-sectional matching estimates of the effect of export entry on physical capital per employee (learning-to-export specification)
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Table 11 Cross-sectional matching estimates of the effect of export entry on labor productivity for different export status combinations (learning-by-exporting specification)
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Eliasson, K., Hansson, P. & Lindvert, M. Do firms learn by exporting or learn to export? Evidence from small and medium-sized enterprises. Small Bus Econ 39, 453–472 (2012). https://doi.org/10.1007/s11187-010-9314-3

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