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Abstract
The literature on trade facilitation has mostly focused on implications for trade volumes. However, recent theoretical contributions have emphasized that trade costs – such as transaction costs related to cross-border trade procedures – affect both the traded volumes of ‘old’ goods (the intensive margin) and the range of traded goods (the extensive margin). This article therefore tests whether trade facilitation affects the extensive margin by counting the number of 8-digit products that are exported from developing to EU countries, and using this as the dependent variable in an estimation. Moreover, it also tests whether the extensive margins in differentiated and homogeneous goods are affected in the same way by transaction costs. Estimation results suggest that if export transaction costs – proxied by the number of days needed to export a good – declined by 1%, the number of exported differentiated and homogeneous products would rise by 0.6% and 0.3%, respectively. Policy simulations further illustrate that if all countries were as efficient at the border as the most efficient country at the same level of development, the number of exported differentiated and homogeneous products would increase by 62% and 26%, respectively.
Acknowledgments
The paper was partly written while the author was visiting the Leverhulme Centre for Research on Globalisation and Economic Policy (GEP) at the University of Nottingham. The author thanks Richard Kneller, Inmaculada Martínez-Zarzoso, Patrik Gustavsson Tingvall, Fredrik Wilhelmsson, Yves Bourdet, Joakim Gullstrand, Karin Olofsdotter, and seminar participants at the Eleventh Annual Conference on Global Economic Analysis in Helsinki, June 2008; the Tenth Annual European Trade Study Group Conference in Warsaw, September 2008 and at Lund University for helpful comments and suggestions. Financial support from the Marianne and Marcus Wallenberg Foundation, and from the Jan Wallander and Tom Hedelius Foundation under research grant numbers P2006-0131:1 and W2009-0352:1 is gratefully acknowledged.
Notes
1. The hypothetical trading firm that is a private limited liability company, fully domestically owned with a minimum of 100 employees, is located in the country's most populous city but does not operate within an export processing zone (EPZ) or an industrial estate with special export or import privileges, and exports more than 10% of its sales to international markets. The good is assumed to be non-hazardous, not to include any military arms or equipment, not to require refrigeration or any special environment, nor any special phytosanitary or environmental safety standards, and to be shipped in a dry-cargo, 20-foot, full container load. Trade is assumed to take place by ocean transportation through the closest or main port to the most populous city (the port may be located in another city or country). All procedures from the conclusion of a sales contract until the good leaves the port of exit are included. The database contains data on all documents required for export; the number of days needed, and the official fees levied on the container. For more specifics, see World Bank (2007a), or Djankov, Freund, and Pham (2010).
2. For a few countries, there is no data for 2005, but data is available for 2006 or 2007. When this is the case, we choose to use the latter data to get as full a sample as possible. This is reasonable, given that there is very little time-series variation in the data (which implies, also, that nothing is gained from adding more years to the sample).
3. Examples of papers that have used disaggregated country-level trade data to measure the extensive (and sometimes intensive) margin include Dennis and Shepherd (2007), Cadot, Carrère, and Strauss Kahn (2007), Brenton and Newfarmer (2007), Evenett and Venables (2002), Flam and Nordström (2006), Baldwin and Di Nino (2006), Amurgo-Pacheco and Piérola (2008), Debaere and Mostashari (2005), Baldwin and Harrigan (2007), Hummels and Klenow (2005), Funke and Ruhwedel (2001), and Kehoe and Ruhl (2003). Other authors have used aggregated country-level trade data – Felbermayr and Kohler (2006, 2007) and Helpman, Melitz, and Rubinstein (2008) – or firm-level trade data: e.g. Bernard and Jensen (2004), Crozet and Koenig (2007), Bernard et al. (2007), and Andersson (2007).
4. Rauch's classification has been used in many contexts before. In the particular area of trade facilitation, Sadikov (2007) applies the so-called liberal classification, while Martínez-Zarzoso and Márquez-Ramos (2008) use the conservative categorization. For details on the classification, see Table A1 in the Appendix. Note that we use the conservative classification, but results do not change if we switch to the liberal classification. Note further that besides differentiated goods, Rauch distinguishes between two types of homogeneous goods – those that are traded on organized exchanges, and those for which there are reference prices that are quoted in trade publications. Since there does not seem to be a theoretical reason for treating these types of goods differently in the context of the Chaney (2008) model, we refer to both types of goods as homogeneous.
5. Estimated using STATA's Poisson command, with robust standard errors, clustered by country-pairs.
6. It should be pointed out that this empirical model for explaining the extensive margin does not rest on any solid theoretical foundation. However, the model outlined in Chaney (2008) does give a theoretical base for the hypothesis that trade transaction costs should affect differentiated goods more strongly than homogeneous goods.
7. Empirically, Imbs and Wacziarg (2003) have illustrated that diversification follows a U-shaped pattern, where countries first diversify when income per capita levels rise, but then start to specialize again at a later stage in the development process. Cadot, Carrère, and Strauss Kahn (2007) find the same pattern concerning export diversification. Concerning this, note that the inclusion of GDP and population means that we are de facto controlling for income per capita.
8. Including country-specific exporter effects is appealing, but they would capture everything that only varies by exporter, i.e. one could not measure the effect of cumbersome cross-border trade procedures separately. See Table A1 in the Appendix for all variable definitions and data sources.
9. They also use a different sample (for instance they use aggregate EU imports) and calculate the number of exported products per 2-digit chapter.
10. A formal Wald test on these two coefficients' equality yields a χ2 -statistic of 33.65, and a p-value of 0.000.
11. Note that these simulations only capture the direct effects of changes in trade transaction costs. Altered trade transaction costs could also affect trade through the multilateral resistance discussed above. We do not control for such effects. Note also that the regression coefficients themselves represent marginal effects: if export transaction costs declined by 1%, the number of exported differentiated goods would rise by 0.6%, and the number of homogeneous goods would increase by 0.3%.
12. We thank an anonymous referee for making this suggestion.
13. The correlation between the time and document variables is about 0.5.
14. We thank an anonymous referee for suggesting this type of separate regressions on finer product categories.
15. Note that, by design, all countries in the sample of exporters are eligible for GSP preferences. Here, we allow separate TF effects for those countries that are eligible for additional preferences as ACP countries, Mediterranean countries, LDCs or under the so-called drug regime: a special arrangement within the GSP where countries affected by the production and trafficking of illicit drugs have been offered additional benefits since 1991. For more on the various trade preference schemes, see Persson and Wilhelmsson (2007).