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The border effects in Spain: an industry-level analysis

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Abstract

A gravity-model approach is used to estimate the magnitude of the internal border (home bias) and external border (frontier) effects in Spain using industry-level trade flows. We find that the average border effects are about 30 and 10, respectively. Next we explore the variation in the industry-specific border effects. First, the border effects are larger in highly product differentiated industries. Second, the internal border effect is twice bigger for trade in intermediate goods than for trade in final goods. Third, conditioning on the geographic concentration of firms reduces significantly the internal border effect.

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Notes

  1. The external border effect or frontier effect has been studied by McCallum (1995), Helliwell (1996, 1998), Anderson and Smith (1999), Anderson and van Wincoop (2003), Okubo (2004), Gil et al. (2005).

  2. Wei (1996), Head and Mayer (2000), Nitsch (2000), Evans (2003) and Chen (2004). These papers all calculated domestic trade as gross output minus exports, a “rough” estimate of intra-national trade flows.

  3. Helliwell (1996), Wolf (2000), Hillberry and Hummels (2003), Millimet and Osang (2007) and Combes et al. (2005) find an internal border effect between 2 and 6 in Canada, USA and France. Djankov and Freund (2000), Poncet (2003) and Daumal and Zignago (2008) find an internal border effect between 11 and 20 in ex-former USSR, China and Brazil.

  4. Evans (2003), Hillberry (2002) and Chen (2004) also use industry value added as a measure of exporter´s economic size rather than origin-region GDP to control for industry location patterns.

  5. The OECD countries included in the study are: Australia (AUS), Austria (AUT), Belgium (BEL, Belgium and Luxemburg), Canada (CAN), Czech Republic (CZE), Denmark (DNK), Finland (FIN), France (FRA), Germany (DEU), Greece (GRC), Hungary (HUN), Iceland (ISL), Ireland (IRL), Italy (ITA), Japan (JPN), Korea (KOR), Mexico (MEX), Netherlands (NDL), New Zealand (NZL), Norway (NOR), Poland (POL), Portugal (PRT), Slovenia (SVK), Sweden (SWE), Switzerland (SWT), United Kingdom (UK), United States (USA).

  6. Llano (2004b) describes in detail the harmonization method, the estimation of non-available data, the debugging procedure for transport flows in physical units and the estimation of value/weight relations from international trade statistics.

  7. The great circle distance between i’s and j’s cities is calculated as follows. First we transform the latitude φ j and the longitude λ into radians (xπ/360). Second, the formula used to calculate the distance between the pair of cities is \( \Updelta_{ij} \equiv \lambda_{j} - \lambda_{i} , \) \( d_{ij} = \arccos \left[ {\sin \varphi_{i} \sin \varphi_{j} + \cos \varphi_{i} \cos \varphi_{j} \cos \Updelta_{ij} } \right]z, \) with z = 6,367 for km. Third, we calculate the population-weighted average distance between the cities by region and by country using the same formula \( D_{{r,r^{\prime}}} = \sum_{i \in r} w_{i} (\sum_{{i \in r^{\prime}}} w_{j} d_{ij} ),\,w_{i} = pop_{i} /pop_{r} \).

  8. Head and Mayer (2002) and Chen (2004) use the same approach in their analysis of the home country bias in Europe using industry-levels trade flows.

  9. Following Chen (2004), ehe weight-to-value measure is industry-specific and averaged across all region-country pairs, \( \sum_{i} \sum_{j} Q_{ij,k} /\sum_{i} \sum_{j} X_{ij,k} \) where Q ij,k is the weight of bilateral international exports X ij,k .

  10. Of course, it is delicate to compare results of the diverse studies because they use different methods and data. Therefore, we have to remain cautious concerning these comparisons.

  11. When we used aggregated trade flows and replicated the specification of Gil et al. (2005) using manufacturing sectors only, the magnitude of the external border effect was 15, a greater value than the one obtained using industry-specific trade flows.

  12. The importance in absolute terms of the energy industry (electricity, gas and water) within the country, together with the domestic nature of the Spanish distribution system, tends to magnify the external border effect (it is the largest one) and diminish the internal border effect (it is among the smallest ones), compared to any estimation that omits this important sector.

  13. A specific industry in a region could account for long international inflows and low levels of inter-regional outflows. In some cases, this trade structure could fit with the typical one-way-trade predicted by the Ricardian or the Heckscher-Ohlin-Vanek model. However, it could be just a consequence of intra-firm imports of products that would be distributed nationally through the internal network of the company.

  14. Regarding this point, it has been argued that small and weakly internationalised companies in Spain are fond of promoting international trade just when the national demand is weak, and vice versa.

  15. Chen (2004) defines the first group of factors as “behavioural responses to trade costs” and the second group as “trade costs”.

  16. Evans (2003) and Chen (2004) use the same approach.

  17. For a discussion, see Deardorff (1998)), Anderson and Van Wincoop (2003) and Feenstra (2002).

  18. The three measures are constructed using Spain as a geographic unit. IIT was constructed using international import and export (value and quantity) flows. R&D was obtained from Estadística de I + D (INE) and ADV was obtained from Encuesta Industrial de Empresas (INE). For the variable ADV the agriculture sector is excluded due to lack of information.

  19. We set the two endpoints of the elasticity range (2–6) to the minimum and maximum IIT index values (0.03 and 0.66), and used linear interpolation to assign elasticities to the intervening industries, based on their ITT index values (as in Evans 2003).

  20. Notice that the use of information on geographic concentration for 1999 and trade flows for 2000 alleviates the problem of endogeneity between the geographic location of firms and trade flows.

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Acknowledgments

F. Requena acknowledges financial support from the Spanish Ministry of Science and Innovation (project number ECO 2008-04059/ECON) and Generalitat Valenciana, project Prometeo/2009/098. C. Llano acknowledges financial support from the Education Department of the Regional Government of Madrid (project TransporTrade S2007/HUM/497).

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Authors and Affiliations

  1. Dpto de Economía Aplicada II, Facultad de Economía, Universidad de Valencia, Avda Tarongers s/n, 46022, Valencia, Spain

    Francisco Requena

  2. Dpto de Análisis Económico, Universidad Autónoma de Madrid, 28049, Cantoblanco, Madrid, Spain

    Carlos Llano

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  1. Francisco Requena
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  2. Carlos Llano
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Correspondence to Francisco Requena.

Appendix

Appendix

See Table 6.

Table 6 Descriptive statistics
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Requena, F., Llano, C. The border effects in Spain: an industry-level analysis. Empirica 37, 455–476 (2010). https://doi.org/10.1007/s10663-010-9123-6

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