THE BORDER EFFECT AND ROAD TRANSPORT METWORKS: EVIDENCE FROM EUROPE. [Preliminary version. Do not quote without authors permission]

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1 THE BORDER EFFECT AND ROAD TRANSPORT METWORKS: EVIDENCE FROM EUROPE [Preliminary version. Do not quote without authors permission] Henrik Braconier and Mauro Pisu OECD Abstract. Several studies have reported a large negative effect of national borders on the volume of trade based on great circle - or as-crows-fly distance measures. This paper provides new evidence on the border effect in continental Europe using actual road distances. A descriptive analysis of city pairs road distance indicate that, given the same great circle distance, the road distance between two cities located in the same country is around 10% lower than that between cities located in different ones. Gravity equation estimates show that using road-based internal and external distances reduces the border effect by around 15% on average. Overall the results point to the importance of road networks to explain trade patterns. Introduction Several studies have reported a large negative effect of national borders on the volume of trade, the so called border effect or home bias. McCallum (1995) was the first to provide evidence on this effect. Using data on Canadian provinces and US states, he found that the trade between Canadian provinces was more than 20 times larger than their trade with US states of similar economic importance and distance. Subsequent studies on North America, Europe or OECD countries have employed improved methodologies and found lower but still sizeable border effects. Estimates of the border effect, however, range widely from two to above ten. 1 In the literature, border effects on trade are estimated through gravity equations, where distance plays a central role as a proxy of trade costs. More specifically, using country-level data estimation of border effects require measurements of internal, i.e. within countries, and external, i.e. between countries, distance (Wei, 1996). Nitsh (2000) and Head and Mayer (2002) underline how border effect estimates are extremely sensitive to measurement errors in the distance variables. Large border effects could simply result from overestimating internal relative to external distance. 1. This means that a country will trade from two to ten times more with itself than with other countries of similar size and distance; see e.g. Helliwell (1995, 1997, 1998, 2000), Wei (1996), Hillberry (1999, 2001), Evans (1999, 2001), Wolf (1997, 2000a,b), Cyrus (2000), Helliwell and Verdier (2000), Nitsch (2000a,b), Head and Mayer (2000) and Anderson and van Wincoop (2001), de Serres et al.,

2 Previous studies have used different between- and within-country distance measures, virtually all based on the great circle distance between locations. External distances have often been computed using capitals or alternatively the most populous city in each country. The choice of cities is not important when countries are small and/or far from each other (in comparison with their size) or when the country s economic centre is actually close to the selected city. However, if these conditions are not met, computing between-country distances in this way will results in incorrect distance measures. The calculation of internal distance has relied on even rougher approximations such as taking one quarter of the distance to the nearest trading partner or assigning it a value as a function of the country s area. Head and Mayer (2002) propose alternative and mutually consistent between- and within-country distance metrics based on the population weighted average of the distance among a number of cities in each country. They show that using these distance measures the border effect drops significantly with respect to the one estimated using traditional distance measures as the later systematically underestimate internal distance. This paper provides new evidence on the border effect in Europe, using road rather than great circle distance measures. The intuition behind this is that roads may provide shorter connections among cities located within the same country than those between cities in different ones, given the same geodesic distance. As a result, road distances could help explain at least partly the large border effects reported so far. Differences between within- and between-country road distance may stem both from geography, as national borders often overlap with natural barriers, and from lower investment in international than national road links. 2 Furthermore, cross-national road connections may still be limited by the need for border checks. 3 This paper is also linked to the strand of research examining the trade effect of transport infrastructure in general and roads in particular. Limao and Venables (2001) find that infrastructure is an important determinant of transport costs, accounting for up to 60% of transport cost. However, they use an index that does not allow them to decompose the relative contribution of the different kinds of infrastructure. Egger and Larch (2007) concentrate on the trade effect of the length of national road and railway networks. They find that international trade is more sensitive to railways than roads. These results are based on the length of the networks however, and do not consider the qualitative aspects in terms of connectivity and its impact on the border effect. Road distances among XX European cities were extracted from Bing Maps Routing Services. We estimate a gravity equation using a dataset of bilateral trade flows at industry level among continental European countries. We focus on Europe as road freight accounts for around 75% of total freight traffic in the whole EU and on continental countries to avoid connections that partly rely on sea transport. 2. International coordination for building and managing infrastructure projects is notoriously difficult. 3. Notwithstanding the Schengen area, these effects could still persist in Europe. Road networks change only slowly and therefore may still reflect the erstwhile need of border checks. Also, countries maintain the right to reintroduce border checks in emergency situations. 2

3 Descriptive statistics for the city pairs indicate that the great circle distance systematically overestimates internal road distances. For the same great circle distance, the road distance between two cities located in the same country is around 10% lower than that between cities on the opposite side of national borders. Thus, everything else equal, road networks provide shorter and therefore cheaper connections within countries than between them. Gravity equation estimates show that using road-based internal and external distances reduces the border effect by around 15% on average, with significant variation across countries and industries. From a policy perspective these findings suggest that from an international trade perspective it may be beneficial to shift more road investment towards cross-national projects relative to purely domestic links. Several caveats apply, however. First the coincidence of national borders and geographic obstacles - in terms of rivers and mountains may render international links more costly than domestic ones. Compensating fully for such disadvantages by more infrastructure investment may not be efficient, however. Second, it is not obvious that more resources should be spent on cross-country connections rather than less on domestic links.. Finally, implementation issues in terms of inflated cost benefit analyses and political interference have to be addressed. 4 The rest of the paper is organised as follows: the next section explains the methodology and data we use. Section 3 describes the internal and external road distance of city pairs; the border effects obtained using great circle and road distance are presented in Section 4. Section 5 concludes. Methodology and data We follow previous studies and estimate a standard gravity equation (e.g. Evans, 1999; Hilberry, 1999, Nitsh, 2000; Chen, 2004). The popularity of the gravity model in international trade can be ascribed to its generality as it is robust to different theoretical specifications (e.g.: Anderson and van Wincoop, 2003). Using industry level data for a set of continental European countries, we estimate the canonical gravity model: (1) where X is the value (expressed in common currency) of exports of sector s from the origin (o) to the destination (d) country, Y os is the total production of sector s in the origin country and Y d is the GDP of the importing country; C od is a vector of bilateral characteristics affecting trade costs, which include a common language and adjacency dummy (the latter is one if the origin and destination countries share a border); ε ods is a classical error term. The border effect is captured by the coefficient of the dummy variable border, which is one when o = d. 5 In this case, the dependent variable measures domestic trade (X oos ). As in previous studies, this is computed as sector s total output minus its total exports to the world. This approach to measuring the border effect when trade data at sub-national level, such regions, are missing was proposed by Wei (1996) and 4. See Flyvbjerg (2009) and Short and Kopp (2005) for instance. 5. The common language and adjacency dummies are also set to one when o = d. 3

4 subsequently applied, inter alia, by Evans (1999), Nitsh (2000), Head and Mayer (2000) and Chen (2004). A positive and significant β 1 measures the preference for a country to trade with itself in excess to what is predicted by standard explanatory variables. The industry level production data (ISIC Rev. 3) come from the OECD STAN data base. They refer to the year This data was merged with bilateral trade data at the same industry level coming also from the OECD. The set of countries we consider focuses on continental Europe (17 in all), as shipping goods from or to non-continental European involves sea routes. 6 We follow the approach of Head and Mayer (2000), Chen (2004) and Mayer and Zignago (2005) and compute internal and external distance as weighted averages of the distance between major cities. However, in contrast to previous studies we use road instead of great circle distance. Road distances were extracted from the Bing Maps Route Service for 22 continental European countries. This service enables to compute the road distance between any two locations on a map providing either addresses or coordinates. The data were extracted during October 2012 and refer to the distance that minimises the travel time. Cities longitude and latitude were taken from the World Gazetteer website along with cities population, which are used as weights. The distance between any two countries is computed as following: where i and j index the cities in the origin and destination countries respectively. Internal distances are computed analogously. Our set of cities includes the largest ten cities in each country. We drop cities on islands, however, so as to avoid links combing road and sea transport and replace them with the next mainland city in terms of population. All in all, this affects one or two cities in Greece, Italy, Portugal and Spain. Internal and external distance Figure 1 shows the road and great circle distance (in logarithm) for all city pairs in our dataset. On average road distance exceeds great circle distance by around 29%. 7 The mean hides considerable variation across city pairs, however. For the bottom 5% of city pairs, road routes are less than 14% longer than great circle distance whereas for the top 5% they are more than 51% longer. (2) 6. Hence, we have excluded the islands - Iceland, Ireland and the United Kingdom plus Finland, Norway and Sweden as the latter are mainly connected to continental Europe by sea. 7. This percentage is computed as log difference between road and great circle distance. 4

5 Log of road distance Figure 1. Intercity road and great circle distance in continental Europe Log of great circle distance The use of great circle distances as a proxy for transport costs in equation (1) might result in inflated border effects if great circle distances systematically overestimate the internal-to-external distance ratio. To investigate this issue, we regress city-pair road distance (in log) on the great circle distance (in log) and a within country dummy, which is one, if the two cities are located in the same country. The basic specification of Table 1 (column one) suggests that for a given great circle distance, within-country intercity road links are indeed more than 6% shorter than links between cities in different countries. In column two we add the size of the departure and arrival cities as larger cities are likely to have better and more direct road links. The coefficients of cities size are negative and significant as expected but the within-country dummy coefficient is virtually unchanged. The specification in column three controls country-level unobservable effects using origin and destination dummies. The difference in the length of road links between cities located in the same countries and those in different ones rises to around 10%. Table 1. Difference between road and great circle distance within and between countries (1) (2) (3) Within country dummy ** ** ** [-4.656] [-4.533] [-6.197] Log of great circle distance 0.974** 0.975** 0.959** [ ] [ ] [ ] Log of population of departure city * ** [-2.275] [ ] Log of population of arrival city * ** [-2.090] [ ] Constant 0.459** 0.539** 0.670** [0.029] [0.040] [0.035] R-squared N Origin and destination country fixed effects No No Yes 5

6 LVA EST LTU ITA GRC BGR DNK CZE POL ROU HUN SVK ESP PRT NLD CHE LUX SVN BEL DEU FRA AUT Note: The dependent variable is the log of road distance; robust clustered standard errors by origin-destination country pairs in brackets; ** denotes significant at 1% level, * at 5%. The Figure 2 shows country specific estimates of the within-country dummy. These are obtained by interacting origin country dummies with the within-country dummy - using the specification with origin and destination country fixed effects. Estimates of the length difference between national and international road connections range from above 15% in the Baltic countries, Italy, Greece and Bulgaria to around 5% for Germany, France and Austria. For Austria the within-country effect is not significant at 5% level. As we will see in the next section, this cross-country variation explains part of the difference in the strength of the border effect among countries. Figure 2. Country-specific road-distance difference between national and international trips Note: This figure shows country specific estimates and the 95% confidence interval of the road length difference between national and international road connections; they are obtained using the specification in Table 1 column three and interacting the within-country dummy with origin country dummies. Recent studies emphasise how time is an important component of total transport costs. On a general level, lengthy shipping times translate into higher costs because of the need of larger inventory, higher depreciation and changing market conditions (Deardorff, 2001). Time-related trade costs might explain part of the border effect: if, everything else equal, crossing national borders lengthens the shipping time, then countries will trade less internationally and more domestically. Recent empirical evidence confirms the importance of time-related costs in international trade. Comparing air and ocean shipping, Hummels and Shaur (2012) find that customers are willing to pay a substantial premium to save delivery time. Their estimates suggest that a day in transit is worth between 0.6 and 2% of the value of the good. Djankov, Freund and Pham (2010) find that countries with long customs delays experience lower trade volumes, with the largest reductions taking place in the most time sensitive products. Evans and Harrigan (2005) show that products for which time delivery is important will be imported from locations close to the final demand. Closer to the subject of this paper, Combes and Lafourcade (2005) using a comprehensive data set of domestic road transport shipments for France are able to decompose total road 6

7 transport costs into distance- and time-related components. Their results show that timerelated trade costs matter. They accounted for around 63% of total road transport costs in 1998 and for slightly less than half of the 38% decline in total road transport costs from 1978 to We repeat the same exercise of Table 1 using however travel time as dependent variables and controlling for road rather than great circle distance. The results, shown in Table 3, indicate that travel time (speed) is around 5% lower (higher) for national than international trips, ceteris paribus. Overall, these results suggest that domestic shipments are characterised by lower both distance- and time-related trade costs than international ones. Our estimate is likely to be lower bounds as the time-travel data we use do not account for potential time losses due to border checks. The travel time difference between national and international road links we observe is likely to be determined by the different quality - and therefore speed limits of roads domestic and international freight traffic use. For instance, national shipments might take place for comparatively longer stretches on motorways than international ones, as roads that cross borders are unlikely to be motorways. Table 2. Regression of travel time Within country dummy * [0.020] Log of road distance 0.886** [0.006] Log of population of departure city [0.001] Log of population of arrival city [0.001] Constant 4.210** [0.044] R-squared N Origin and destination country fixed effects Yes Note: The dependent variable is the log of travel time; robust clustered standard errors by origin-destination country pairs in brackets; ** denotes significant at 1% level, * at 5%. Figure 3 shows country specific estimates analogous to those in Figure 2. Traveltime differences between domestic and international road trips are even more heterogeneous across countries than road-length differences. 8 For Luxemburg and the Netherlands and less so for the Czech Republic they are event estimated to be positive and highly significant, suggesting that travel time for national trips is on average higher than for international ones. (1) 8. The coefficient of variation of travel time difference is 1.9. The one for road length difference is

8 Figure 3. Country-specific travel time difference between national and international trips Note: This figure shows country specific estimates and the 95% confidence interval of the travel time difference between national and international road connections; they are obtained using the specification in Table 2 and interacting the within-country dummy with origin country dummies. Empirical results on the border effects The gravity equation model (1) was estimated using alternatively the great circle or road distance measures (Table 3). 9 Standard errors are clustered by origin-destination pairs to account for potential autocorrelation of the error term within each corigindestination pair. Column one and two report the estimates from specifications including industry dummies only. The border effect estimated either using either great circle distance or road distance suggest that domestic trade is around 4.5 times [=exp(1.5)] as large as trade with other countries, ceteris paribus. The elasticity of industrial production in the origin country and GDP of the destination country are not far from unity, as expected. The adjacency dummy is positive and significant whereas common language is not significant. In equilibrium, trade depends in addition to income and trade costs on origin and destination prices, which are unobserved. They are likely to bias estimates as they depend on trade barriers (Anderson and Wincoop, 2003). Column three and four therefore control for unobservable price indices by including origin and destination dummies interacted with industry fixed effects (Feenstra, 2002). 10 Estimates differ from those presented in the first two columns underlining the importance of these omitted variables. 1. Correcting for the bias attributable to price indexes, the border effect decreases by around 15% when road distance is used instead of its geodesic counterpart (column 9. The border effect was estimated only for the subset of countries used in the previous section (17 in all) for which trade and production data are available. 10. The inclusion of these variables prevents the estimation of the coefficient of industrial production and GDP. 8

9 three and four). This suggests that the border effect estimated in previous studies were partly overestimated because road networks provide, ceteris paribus, shorter and therefore cheaper links within countries than between them. The border effect estimated when using road distance is still large, however. The point estimate suggests that a continental European country tends to trade nearly four times [=exp(1.316)] more with itself than with a foreign one, everything else equal. The adjacency effect also drops by about 30% suggesting again that this effect results at least in part from mis-measured distances, as observed by Head and Ries (2002). Table 3. Estimation of the average effect: basic specification (1) (2) (3) (4) Home 1.712** 1.702** 1.460** 1.316** [0.21] [0.21] [0.25] [0.25] Log Y os 0.898** 0.895** [0.02] [0.02] Log Y d 0.894** 0.890** [0.03] [0.03] Log of geodesic distance ** ** [0.10] [0.11] Log of road distance ** ** [0.10] [0.11] Adjacency 0.604** 0.591** 0.336** 0.295** [0.13] [0.13] [0.11] [0.11] Common language [0.15] [0.15] [0.13] [0.13] Observations R Fixed effect Ind Ind Orig-Ind; Orig-Ind; Dest-Ind Dest-Ind Note: Robust clustered standard errors in brackets; ** denotes statistical significance at 1%; * at 5% Chen (2004) reports heterogeneous border effects for a set of continental European countries. This heterogeneity could be explained at least partly by national road networks providing - to a different extent across countries - better connections within countries than between them. Figure 2 shows indeed that the length difference between national and international road links is not the same across countries. Thus in some countries the border effects might be larger just because the difference between national and internal road connections is larger. Figure 3 shows that there is a negative relationship between country-specific estimates of the border effect using great circle distance and the country-specific estimates of the within-country coefficient of road length regressions (shown in Figure 2), which capture the difference in the length between national and international road links. The correlation coefficient between the two variables is Switzerland, Slovenia, France, Luxemburg and Austria are somewhat outliers as their estimated border effect is larger than what could be expected by the difference between within- and between-country road links length, suggesting again that other cost components not linked to trip lengths also affect the border effect. 9

10 Border effect (geodesic distance) Figure 4. Relationship between border effects and within- versus between-country road length difference EST GRC DNK SVK CHE ITA CZE POL ESP SVN HUN LUX FRA AUT NLD DEU BEL Within country effect (road length regressions) Note: This figure shows the relationship between the country-specific border effect (y axis) estimated using the specification in column three of Table 2 and the country specific withincountry dummy (x axis) shown in Figure 2. Some of the countries shown in Figure 2 are missing as they were not used in the gravity equation estimation because of missing trade and production data. Figure 5 depicts country-specific border effects obtained using road circle distance. 11 The largest and significant border effects are estimated for Estonia and Greece. Other things equal, domestic trade for these countries is more than 13 times higher than what standard gravity equation variables predict. The border effect is lowest and not significant for the Netherlands, Belgium and Germany. Smaller countries seem to exhibit larger border effects, but not always Luxemburg being the most notable exception. This is consistent with Anderson and van Wincoop (2001) who maintain that small countries should have a larger border effect than large ones because a small drop in international trade will lead to a comparatively larger domestic-trade increase in the fomer than in the latter. Although road distance lowers the country-specific border effects, it does not affect their relative size. The rank correlation coefficient between the country-specific border effects estimated using road and great circle distance is Chen (2004) found similarly that different distance metrics affect the magnitude but not the relative importance of the country-specific border effects. This is suggestive again that other factors not depending on distance determine the border effect. 11. These estimates are obtained using the specification with industry dummies interacted with origin and destination country fixed effects. The coefficient estimates of the other variables included in the regression, i.e. distance, adjacency and common language, are similar to those shown in column four of Table 2, the difference being less than 5%. 10

11 Figure 5. Country specific border effects Note: This figure shows the point estimates and 95% confidence intervals of countryspecific border effects using road distance. The specification in column four of Table 3 was used. The 95% confidence intervals were computed using robust clustered standard errors. Further evidence of the impact of road distance on the border effect can be gleaned from industry-specifc estimates. The gravity equation was the re-estimated by interacting the home dummy. We also interact all other right-hand-side variables with industry dummies intere as theory suggests that the elasticity of trade with respect to anyone of them e.g. let say distance - is given by the elasticity of substitution between foreign and domestic products and the elasticity of trade costs with respect to distance. There is evidence that these two elasticities are dissimilar across industries. Broda and Weinstein, (2006) and Imbs and Méjean(2009) show that the elasticity of substitution between domestic and foreign varieties is hetetogeneous across product categories and industries whereas the elasticity of trade costs with respect to distance depends on their transportability or value-to-weight ratio. Forcing all industries to have the same distance coefficient can therefore bias the border effect. For industries with high elasticity of trade costs relative to distance the border effect will be biased upward, as the home coefficient will capture some of the distance-related trade costs. Table 4 report the estimated border effect for each industry using great circle or road distance. The border effect is always larger when great circle distance is used, when significant. The only exception is Electricity and Gas, where the border effect is virtually the same when using either of the two distance metrics. This can be explained by the low transportability of Electricity and Gas. For this industry transport costs are then unrelated to the length and quality of road links. The general drop in the border effect using road distance seen in Table 4 should be stronger for those industries whose products can be easily transported by roads. That the border effect is unchanged only for Electricity and Gas is evidence that the decline experienced by the other industries is genuinely related to the different degree of connectivity road networks provide within and between countries. 11

12 Table 4. Industry specific border effect estimates Mining and quarrying Food products, beverages and tobacco Textiles, textile products, leather and footwear Wood and products wood and cork, except furniture; articles straw and plaiting materials Pulp, paper, paper products, printing and publishing Coke, refined petroleum products and nuclear fuel Chemicals and chemical products Rubber and plastics products Other non metallic product s Basic metals Fabricated metal products, except machinery and equipment Machinery and equipment n.e.c. Office, accounting and computing machinery Electrical machinery and apparatus n.e.c. Radio, television and communication equipment and apparatus Medical, precision and optical instruments, Geodesic distance Home Distance Adjacency Common language Road Geodesic Road Geodesic Road Geodesic distance distance distance distance distance distance Road distance ** ** 1.064** 1.030** [0.71] [0.74] [0.33] [0.35] [0.38] [0.39] [0.42] [0.42] ** ** 0.549** 0.482* 0.591* 0.602* [0.52] [0.53] [0.21] [0.22] [0.20] [0.20] [0.29] [0.29] ** ** [0.65] [0.66] [0.19] [0.20] [0.19] [0.19] [0.23] [0.23] 1.430** 1.340** ** ** 0.845** 0.832** [0.38] [0.38] [0.20] [0.20] [0.18] [0.19] [0.22] [0.22] 1.807** 1.639** ** ** 0.353* [0.36] [0.37] [0.18] [0.19] [0.17] [0.18] [0.20] [0.20] ** ** 0.964* 0.899* [0.83] [0.84] [0.46] [0.49] [0.40] [0.41] [0.49] [0.49] ** ** 0.351* * 0.352* [0.45] [0.44] [0.18] [0.19] [0.17] [0.17] [0.18] [0.17] 1.470** 1.366** ** ** [0.33] [0.34] [0.28] [0.31] [0.21] [0.22] [0.21] [0.21] 1.670** 1.507** ** ** [0.43] [0.44] [0.21] [0.23] [0.21] [0.22] [0.28] [0.28] ** ** [0.34] [0.36] [0.19] [0.20] [0.16] [0.16] [0.20] [0.20] 2.167** 2.020** ** ** [0.32] [0.33] [0.17] [0.18] [0.16] [0.17] [0.17] [0.17] 1.229** 1.163** ** ** [0.25] [0.25] [0.15] [0.16] [0.13] [0.13] [0.12] [0.12] ** ** 0.603** 0.600** [0.55] [0.56] [0.22] [0.24] [0.22] [0.23] [0.27] [0.27] 1.079** 0.926* ** ** [0.35] [0.37] [0.19] [0.21] [0.19] [0.20] [0.20] [0.20] 1.620** 1.533* ** ** [0.60] [0.60] [0.19] [0.21] [0.19] [0.20] [0.24] [0.24] 1.296** 1.138* ** **

13 watches and clocks Motor vehicles, trailers and semi Other transport equipment Manufacturing n.e.c. and recycling Electricity, gas, steam and hot water supply [0.44] [0.45] [0.20] [0.21] [0.18] [0.19] [0.20] [0.20] 0.871** 0.804* ** ** [0.33] [0.34] [0.18] [0.19] [0.17] [0.17] [0.19] [0.19] 1.208* ** ** [0.52] [0.53] [0.25] [0.27] [0.26] [0.27] [0.29] [0.29] 0.963* 0.807* ** ** * 0.543* [0.37] [0.38] [0.18] [0.18] [0.18] [0.18] [0.21] [0.21] 4.014** 4.020** ** 1.916** 1.979** 1.966** [0.81] [0.85] [0.56] [0.60] [0.58] [0.57] [0.55] [0.55] Note: These estimates show coefficient estimates and robust clustered standard errors (in brackets) of the explanatory variables interacted with industry dummies. The model include origin and destination fixed effects interacted with industry dummies. 13

14 Conclusions References (To be completed) De Serres, A., P. Hoeller and C. de la Maisonneuve (2001), "The Width of the Intra-european Economic Borders", OECD Economics Department Working Paper, No. 304 Imbs and Méjean (2009), Elasticity Optimism, CEPR Discussion Paper No