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The Geography of Trade in the European Single Market

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The Geography of Trade in the European Single Market The Geography of Trade in the European Single Market Shawn W. Tan∗ The World Bank Draft (October 2016) Abstract This paper uses a unique dataset of freight shipments between 270 regions in 28 countries of the European Single Market to examine how trade responds to spatial frictions. We find that aggregate trade falls rapidly over short distances as distance to destination increases. The sharp reduction in trade is driven by the fall in the extensive margins – the total and average number of shipments. And less so by the intensive margins: average value and average price reduce gradually, and average quantity does not change as distance increases. Border effects are present and affect aggregate trade largely through the extensive margins. We show that trade in intermediate inputs and the co-location of firms can explain why trade reduces rapidly at such short distances. We find that when the industrial demand of a sector at the destination is higher, the regions are more likely to trade with each other. JEL Classification: F10, F15, R10, R12, R40. Keywords: Distance, border effects, intermediate inputs, firm co-location, European Union. ∗Shawn Tan is an economist in the Trade and Competitiveness Global Practice at the World Bank and can be contacted at swtan [at] worldbank.org. He thanks Jelena Kmezic for her research assistance. 1 1 Introduction Trade should move freely within the European Single Market (ESM), unimpeded by tariffs and trade barriers. Yet, internal trade flows are still impeded by spatial frictions. These spatial frictions associated with distance and borders can exert a force on trade flows and reduce the flows between countries in Europe, and between the regions in each country. While many papers demonstrate how distance and borders reduce trade in Europe, they do not examine how and why these spatial frictions reduce trade. This paper not only demonstrates that spatial frictions reduce trade within the ESM, but also which components of trade are reduced most by spatial frictions. In addition, this paper will show how trade in intermediate goods and the co-location of firms can explain the strong negative effect of spatial frictions on trade. A unique dataset is used in this paper that captures freight shipments in 28 countries within the ESM. The data provides origin-destination detail about shipments between 270 European regions at the NUTS-2 level for 13 sectors.1 To our knowledge, this is the the best available data documenting sector-level inter-regional trade in Europe: it captures not only international trade flows between 28 countries but also inter-regional trade flows between the 270 regions. The detailed data allow us to decompose trade flows into the intensive and extensive margins of trade and to examine how spatial frictions reduce these components of trade. We find that spatial frictions exert a strong negative effect on trade in the ESM. Aggregate trade falls rapidly over short distances as the distance to destination increases. The sharp reduction in trade is driven largely by the fall in the extensive margins, where the total and average number of shipments drop after 250 km and remain flat thereafter. In contrast, the intensive margins are less affected by distance: average value and average price decrease gradually, and average quantity does not change as distance increases. Thus the relationship between aggregate trade and distance within the ESM over short distances are driven largely by shipments to nearby customers (the extensive margin) and not the value of shipments to customers (the intensive margin). Border effects are also present in the ESM. Trade flows within national borders and 1The NUTS (Nomenclasture of Units for Territorial Statistics) is the standard used in the by the EU to refer to different subdivisions of countries for statistical purposes. There are three NUTS levels: NUTS-1 refers to major economic regions with a population of at least 3 million people; NUTS-2 refers to basic regions with a population of at least 800,000 to 3 million people; and NUTS-3 refer to small regions with about 150,000 to 800,000 people. 2 within regional borders are higher than trade flows that cross these borders. Aggregate trade flows are 5.75 times higher for shipments within the same country and 10 times higher for shipments within the same region, compared to shipments that cross the national or regional borders. A large proportion (66 to 92 percent) of the own-region and own-country border effects occurs through the extensive margins. In contrast, the intensive margins play a smaller role in how spatial frictions affect trade. We explore the hypothesis that trade in intermediate inputs and the co-location of firms explain why aggregate trade and the extensive margins reduce at such short distances. To lower trade costs, firms are more likely to locate near firms that produce their inputs. Producing and consuming firms may choose to co-locate in the same region or cluster spatially in neighboring regions. Regions with matching production structures, where a region consumes the products of the other region, are more likely to trade with each other. Using probits to test this hypothesis for each sector, we find that when the industrial demand of a sector at the destination is higher, the probability of the two regions trading are higher even after controlling for the supply and demand for the goods. The results are robust to different measurements of industrial demand at the destination. The paper contributes to two strands of literature. The first strand relates to the estimation of border effects, and in particular the border effects in Europe. The literature started with McCallum (1995) who looked at U.S.-Canada trade flows and show that there is a ‘border puzzle’, where internal trade flows are higher than trade flows that cross the national border. Many papers have estimated the border effects in Europe using two types of trade flows: international (country to country) trade flows and regional (region to region/country) trade flows. Head and Mayer (2000), Nitsch (2000), Chen (2004), and Minondo (2007) use international trade flows and find that border effects exist for a variety of European countries. However, the border effects estimated in these papers may suffer from poor identification as each country has only one internal trade flow — trade with itself — to identify the border variable, compared to an estimation using regional trade flows that will have more than one internal trade flow. As a result, the estimated border effects using international trade flows may be a factor of the geographical aggregation of the data and the border effects may reduce when finer geographical data is used (Wolf, 2000; Hillberry, 2002; Hillberry and Hummels, 2003). The set of papers that use regional trade data to estimate border effects in Europe is small due the unavailability of regional trade flows data.2 Many papers have used 2This issue is not limited to Europe as data on trade flows within a country is limited. 3 the same dataset that covers Spanish regions trading with each other and with other European countries to estimate the border effects for Spanish regions (Gil-Pareja et al., 2005; Ghemawat et al., 2010; Requena and Llano, 2010; Llano-Verduras et al., 2011; Garmendia et al., 2012). These papers use freight data to construct regional trade flows but the country coverage is limited: while the data contains trade flows between Spanish regions, trade flows between these regions and other European countries are at the national level. Helble (2007) uses freight data between regions in France and Germany and Kashiha et al. (2016) use data on shipments of wine from eight European countries to the U.S. to estimate the border effects in Europe. This paper will be the first to estimate the border effects in Europe using a comprehensive dataset that covers trade flows between 270 regions in 28 European countries. This paper is closest to the work by Hillberry and Hummels (2008) who use data on freight shipments from the U.S. Commodity Flow Survey. They show that aggregate trade falls dramatically over a short distance, which is largely driven the extensive margins. They also show the state borders impede cross border trade. More importantly, the authors show that border effects exist for trade that crosses the finest geographical unit – a five digit zip code – highlighting that the geographical aggregation of data cannot fully explain the presence of border effects. One can imagine a reductio ad absurdum scenario where border effects may be present for trade between city blocks. The second strand of literature relates to the examination of the reasons behind the border effects and what drives the relationship between aggregate trade and spatial frictions. One explanation is the role of intermediate inputs and the co-location of firms.3 Firms locate close to each other and the input-output linkages between these firms can increase the trade of intermediate inputs, thereby increasing the estimated border effects. Hillberry and Hummels (2008) demonstrate that the trade in intermediate inputs can explain this relationship. In her estimation of border effects for seven EU countries, Chen (2004) also finds some evidence that the spatial co-location of firms increase border effects. While Hillberry and Hummels (2008) have data at a finer geographical level within the U.S., this paper can examine the issue over a wider geographical scope with data that has trade flows within and between countries. 3There are other possible reasons to explain the border effects. Anderson and Van Wincoop (2003) show that large border effects may be an artifact of omitted variable bias, but recent studies that find border effects in Europe have accounted for this estimation bias.
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