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Economic performance and international trade engagement: the case of Portuguese manufacturing firms

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Abstract

By combining economic and financial data for Portuguese manufacturing firms with data on their exports and imports, we uncover some aspects of the relationship between international trade engagement and firms’ performances. In line with recent theoretical and empirical developments in the international trade literature: (i) we testify that Portuguese international trade is highly concentrated, especially on the import side, and both in inter- and intra-sector terms; (ii) we corroborate previous studies and theses according to which two-way traders outperform only importers, only exporters and above all domestic firms; (iii) we find that the greater the diversification of markets and goods (especially with regard to imports), the better the performance achieved by internationalised firms; (iv) we notice that the higher the intensity of firms’ international trade (especially imports), the better their performance; (v) we also present evidence that destination markets for exports and origin markets for imports are also important in explaining firm’s performance.

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Notes

  1. In most previous empirical works this limitation was mainly due to dataset limitations that prevented theoretical models from reaching the full spectrum of firms’ trading activities.

  2. For example, Amador and Opromolla (2010), who present the exports behaviour of Portugal-based firms in terms of both the decision to export or not and in terms of which products firms export and which markets they export to; and Handley and Limão (2011), who test for Portuguese firms the effects of trade policy uncertainty on export decisions)

  3. See Wagner (2011) for further details.

  4. The data was made available under the mandatory condition of censorship of any individual information. Data and its treatment (namely summary statistics) are available upon request.

  5. Before 2003, the INE uses the universe of firms employing more than 100 workers and a sample of all others. Since 2004, the INE has changed its methodology and works with the entire universe of Portuguese manufacturing firms. Before 2003, we used the only data available. The INE ensures the representativeness of data for that period.

  6. Our data includes 18 different sectoral types of traded goods.

  7. We do not have other useful data, such as: firms’ age, innovation output, labour composition (skilled and unskilled), educational level of the labour force and information on foreign affiliates of Portuguese multinational firms.

  8. The non-treated database comprised about 10,000 firms per year.

  9. Variables are deflated using 2-digit sector-level price indices provided by the INE; for capital stock, we use a unique deflator.

  10. LP is computed directly from the data, while TFP is obtained from Stata’s command “levpet”.

  11. As shown in the recent survey by Wagner (2011), the Chilean case is the only developing country already studied.

  12. Castellani et al. (2010) present a survey on this issue showing that conclusions are highly dependent on the number of employees of firms in the sample.

  13. Moreover, almost 20 % of our working database firms were “never exporters” during the 1996–2003 period.

  14. Moreover, of all large firms, only 13% are non-exporters – data available upon request.

  15. Theil index is a measure of inequality or concentration; higher Index values mean a higher concentration. We have also tested the use of a Gini coefficient with similar results.

  16. In our sample of Portuguese firms, the Theil index for trade is 55 % higher than for sales. For Italian firms, that difference was 4 % in 1993 and disappeared altogether in 1997. Italy is (to our knowledge) the only study with the same methodology.

  17. A more detailed explanation of these facts would involve the contribution of industrial organisation style models (e.g., Tirole 2003). However, such analysis is beyond the scope of this paper.

  18. Even taking into account the fact that the breakdown of data is not comparable.

  19. We considered at least 100 firms exporting to that market (to exclude some operations involving one firm and a single transaction).

  20. Selected by the absolute number of firms exporting to each destination.

  21. The discussion of the role imports may play in export performance is addressed by Serti and Tomasi (2008) and Silva et al. (2012) for example.

  22. In order not to lengthen the paper too much, the estimated pooled OLS results are not presented but are available upon request.

  23. Except when the dependent variable is the log of firms’ employees (this is applied in connected cases later on).

  24. There are important firm characteristics that would be appropriate to control for, such as firms’ age, the share of the intra-firm trade (e.g., Haller 2009), but they are not available in the database.

  25. However, given the simultaneity in the decisions on the dependent variable and on exporting/importing activities, an endogeneity problem may arise. These issues are discussed later in subsection 4.4.3.

  26. The use of such a comparison between OLS and FE estimations is in line with previous works (e.g., Castellani et al. 2010).

  27. In this group we included: the USA, Japan, Australia, New Zealand, South Korea, Singapore, Hong Kong, Canada, Israel, Taiwan, Switzerland, Kuwait, Oman, Qatar, UAE, Bahrain, Saudi Arabia.

  28. Especially with Spain, France and Germany, which are the main commercial partners and are close to Portugal.

  29. In 2003 the exports to those countries represented 0.6 % of all exported value and the firms involved accounted for 3 % of all exporting firms.

  30. We have also studied the importance of imports from Germany, finding that they always maintain their statistical relevance with positive premiums, even in the FE estimation. This suggests that imports from Germany, composed of technologically complex goods, machinery and similar inputs, need an adequate absorptive ability, which in turn requires higher TFP levels.

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Correspondence to Oscar Afonso.

Additional information

This work contains statistical data from the National Institute of Statistics of Portugal (INE). The data is used with the permission of the INE but does not imply the endorsement of the INE in relation to the interpretation or analysis of the statistical data.

Appendices

Appendix A

Table 23

Table 23 Sectoral theil index

Appendix B

Table 24

Table 24 Between sector concentration of exports and trade participation rates

Appendix C

Table 25

Table 25 Export intensive margin

Appendix D

Table 26

Table 26 Export growth (1996–2003) to the 10 most frequent destinations

Appendix E Toughest markets for exports (Difficult countries – DC)

Congo, Ecuador, Syria, Vietnam, Serbia, Iran, Gabon, Pakistan, Qatar, Sri Lanka, Ghana, Guatemala, Guinea, Bermuda, Benin, Uruguay, Mali, Libya, Kenya, El Salvador, Burkina Faso, Mauritania, Togo, Madagascar, Bangladesh, Nicaragua, Barbados, Oman, Bosnia, Sudan, Chad, Macedonia, Moldavia, Barbados, Liberia, Central African Republic, Kyrgyzstan, Haiti, Ethiopia, Honduras, Albania, Paraguay, Yemen, Azerbaijan, Uganda, Swaziland, Belarus, Kazakhstan, Niger, Botswana, Cambodia, Turkmenistan, Armenia, North Korea, Djibouti, Somalia, Uzbekistan, Rwanda, Samoa, Guam, Tonga, Malawi, Bhutan, Laos, Nepal, Iraq, Myanmar, Mongolia.

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Silva, A., Afonso, O. & Africano, A.P. Economic performance and international trade engagement: the case of Portuguese manufacturing firms. Int Econ Econ Policy 10, 521–547 (2013). https://doi.org/10.1007/s10368-012-0221-8

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