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Export spillovers in Hungary

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Abstract

Spillovers from peers in the immediate environment can encourage firms to engage in trade. This study examines whether there are spillover effects in exporting activity, using Hungarian product–country-level manufacturing trade data used from 1993 to 2003. Evidence suggests that exporting activity exhibits spillovers and benefits that are country and product specific. In addition, export spillovers exhibit considerable heterogeneity. Foreign-owned firms benefit from peers generally and domestic firms only from the agglomeration of domestic exporters. Spillovers are positively related to country distance and negatively to market size.

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Notes

  1. Dynamic trade participation decision is not easy to model. Except for the structural approach on export entry by Das et al. (2007), studies use reduced form models, e.g., Clerides et al. (1998) and model decision of engaging in trading via choice modeling. This study takes this latter route.

  2. In this paper, I will use terms good and product interchangeably.

  3. Note that these examples contain both fixed and variable cost elements.

  4. A third benefit is avoiding the following econometric problem. Simply using trade dummies would require the use of lagged dependent variables. Lagged dependent variables would control for the persistent nature of export behavior in the presence of fixed costs. However, in the case, we would like to use a fixed effects model, as I will in this paper, including lagged dependent variable would result in biased estimation. While there are econometric techniques developed for models with lagged dependent variables with fixed effects using dynamic panel data models (see Bond 2002), previous finding is, however, that GMM estimations on the Hungarian data show very unstable results with the starting points and lag structure being excessively important.

  5. Lagging the peer variable by one year also targets the reflection problem raised by Manski (1993) where the individual’s performance is explained by the average behavior of a group which the firm is part of.

  6. See, among many others, Marshall (1920), Henderson (2003), Puga (2010), Rosenthal and Strange (2004).

  7. The spatial sorting of heterogeneous firms has been investigated by Melitz and Ottaviano (2008) and Baldwin and Okubo (2006).

  8. See among many others, Ellison and Glaeser (1997), Barrios et al. (2003), Maurel and Sedillot (1999) or Duranton and Overman (2008)

  9. Another option would be to use spatial concentration indices as they allow for time variation. Calculating Ellison and Glaeser (1997) over Hungarian manufacturing industries shows only little variation over time; hence, sector dummies are sufficient.

  10. IE-HAS is the Institute of Economics of the Hungarian Economy of Sciences. CeFiG is a research project and community, Center for Firms in Global Economy, which is a joint effort of academic and researchers at Central European University and IE-HAS. For a detailed description of the dataset, see Békés et al. (2011).

  11. Merging the balance sheet data with the exports dataset is facilitated by the firms’ tax identifier. In the process, we omit, on average, 17 % of the trade volume each year, which corresponds to the 72 % of the firms observed in the trade dataset. The loss is due to omitting non-manufacturing firms and non-incorporated economic agents. We capture all manufacturing trade reported in the data.

  12. I will convert this information into a dummy variable, taking on the value of one if firm is more than 50 % foreign-/state-owned.

  13. That is, individual transactions are not observed but are summed up to product–country observations for each year for each firm.

  14. Trade with transition countries being previously, e.g., Czechoslovakia, Soviet Union, Yugoslavia cannot be captured in 1992.

  15. The distance between Hungary and the partner country is taken from CEPI’s GeoDist geography dataset.

  16. On the heterogeneity of firms in international trade see, e.g., Bernard et al. (2007), Mayer and Ottaviano (2008), Andersson et al. (2008) or Muûls and Pisu (2009)

  17. Given the estimation strategy, one actually needs temporal variation in firm distribution as well.

  18. See Chaney (2008) for modeling trade at firm level with gravity variables.

  19. The definition of spillovers are different from the ones used in the analysis of Koenig et al. (2010). They consider “all countries” rather than countries other than \(k\). This modification allows us to incorporate all spillover variable in a single regressions and test their difference within one model.

  20. I will include the value of trade to give weight to local information or export strategies.

  21. It is important to note that the distribution of the number of firms is not additive. As a firm can export to more than one country, it can appear more than once in the table.

  22. While in the literature, size often correlates positively with trade entry, the results on the state dummy are not as expected. However, the results is not stable over time; the coefficient gets negative if regression is carried out for the post- 1997 period. This reflects the results of the privatization literature, foreign investors cherry-picking the more productive firms. See, e.g., Brown et al. (2006).

  23. The result are analogous in an alternative specification when spillover variables are included separately. See Table 12 in the “Appendix.”

  24. The inference is reinforced by investigating the result when the country dimension is shut down entirely. Results are available at request.

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Authors and Affiliations

  1. Magyar Nemzeti Bank (MNB), Budapest, Hungary

    Péter Harasztosi

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  1. Péter Harasztosi
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Correspondence to Péter Harasztosi.

Additional information

The author is grateful for comments by the anonymous referees, Pamina Koenig, Cecilía Hornok, Gábor Békés, Miklós Koren, Gábor Antal, Álmos Telegdy and the participants of ETSG Lausanne, EEA Oslo and the Granada Workshop on International trade, Local spillover and Productivity of firms. Any remaining error is mine. Opinions expressed in the paper are those of the author and may not reflect the views of the institution he is affiliated with.

Appendix

Appendix

1.1 The impact of large or multi-site firms

There may be several problems related to large firms possibly operating several sites or at least a separate HQ.

To see the size of the potential bias when other plants are not within the same location, one can rely on another dataset. This data source comes from the annual labor survey (LFS) that covers all firms with at least 20 employees and a randomly selected set of small firms. In firms with at least 20 employees, one in ten employees is surveyed and the exact location of their workplace is duly noted.

I look at this data for all years in our sample. From this sample, one learns that only 7–8 % of firms have multiple sites, most multi-plant firms have two plants. On average, firms have 1.15 plants—so this is the maximum size of our bias. As for firms with more than one plant, the largest plant (which, in 80 % of the cases, is also the site of the firm’s headquarters) has 67 % of the employees.

Table 11 Within firm share of identified location in matched LFS sample for 2002
Full size table

In Table 11, the share of employment of a firm in the settlement is checked and in the microregion that I use as the identifier on the LFS sample. On a 2230 firm sample of 2002, it shows that 91 % of the firms are within the same municipality and also in the same microregion. In the case when a firm is located in more than one municipality, the one that I am able to identify holds 65–70 % of the firm’s employment. Finally, note that these figures mostly refer to firms with above 20 employees, and thus, whole economy figures are much smaller, since the majority of firms are small- and medium-sized enterprizes. This suggests that our biases due to multi-plant firms are probably small.

1.2 Additional tables and figures

See Tables 12, 13 and 14.

Table 12 Collected estimates when spillover variables are included separately
Full size table
Table 13 Estimations including value spillovers at the country–product level
Full size table
Table 14 Coefficients of same country, same product spillover
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Harasztosi, P. Export spillovers in Hungary. Empir Econ 50, 801–830 (2016). https://doi.org/10.1007/s00181-015-0965-4

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