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The Agglomeration of Exporters by Destination

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The Agglomeration of Exporters by Destination The Agglomeration of Exporters by Destination Andrew J. Casseya aSchool of Economic Sciences, Washington State University, 101 Hulbert Hall, P.O. Box 646210, Pullman, WA 99164, USA. Phone: 509-335-8334. Fax: 509-335-1173. Email: [email protected]. Katherine N. Schmeiserb;∗ bDepartment of Economics, Mt. Holyoke College, 117 Skinner Hall; 50 College St, South Hadley, MA 01075, USA. Phone: 612-910-2538. Email: [email protected]. Abstract Precise characterization of informational trade barriers is neither well documented nor understood, and thus remain a considerable hindrance to development. Using spatial econometrics on Russian customs data, we document that destination-specific export spillovers exist for developing countries, extending a result that was only known for developed countries. This result suggests behavior responding to a destination barrier. To account for this fact, we build on a monopolistic competition model of trade by postulating an externality in the international transaction of goods. We test the model's prediction on region- and state-level exports using Russian and U.S. data. Our model accounts for up to 40% more of the variation than in gravity-type models without agglomeration. This finding has important development implications in that export policy that considers current trade partners may be more effective than policy that focuses only on the exporting country's industries. Keywords: agglomeration, distribution, exports, development, location, Russia JEL: D23, F12, L29 ∗Corresponding author Preprint submitted to. September 21, 2011 1 1. Introduction 2 One of the unsolved problems in economic development is the lack of knowledge about the 3 size and nature of barriers to trade, and how these barriers affect growth and income. Great 4 theoretical and empirical strides have been made by country-level and firm-level studies, yet the 5 precise characterization of barriers to trade continues to elude researchers. A better understanding 6 of the size and nature of barriers to trade may lead to better trade-focused development policy. 7 Using spatial econometrics on Russian customs data, we document that firms exporting to 8 the same destination are located near each other. This agglomeration is beyond the clustering of 9 exporters around domestic ports (Glejser, Jacquemin, and Petit 1980; Nuad´eand Matthee 2007) 10 or gross domestic product (GDP). We document that destination-specific export spillovers exist 11 in a developing country such as Russia. Previous empirical studies first established that export 12 spillovers exist and are destination specific, but the data are from developed countries such as 13 the United States (Lovely, Rosenthal, and Sharma 2005), France (Koenig 2009), and Denmark 14 (Choquette and Meinen 2011). We also document destination-specific exporter spillovers using 15 spatial econometrics instead of a logit estimator. Therefore our results confirm the previous work 16 using a different method, and extend it to developing countries. 17 Additionally, we develop a theory to account for this destination-specific agglomeration in a 18 trade setting. We assume there are economies of scale in the transaction cost of international trade 19 at the firm level. In principle, destination-specific export spillovers could work through the fixed 20 or variable costs of trade. Econometric results by Choquette and Meinen (2011) provide evidence 21 for export spillovers working through variable costs as well as fixed costs. Furthermore, though 22 Koenig, Mayneris, and Poncet (2010) do not find robust support for spillovers working through 23 the variable cost, they do find evidence of this when the total number of exporters is small. Our 24 Russian data shows that the number of exporters selling to the same country from the same region 25 in Russia is often small, and thus are more likely to have the destination-specific export spillovers 26 working through variable costs. Notwithstanding these results, both Choquette and Meinen and 27 Koenig et al. measure agglomeration exclusively as the ratio of similar exporting firms to total 28 firms within some geographic area. With this measure, they find that destination-specific export 2 29 spillovers work primarily through the fixed cost. We try alternative measures of agglomeration and 30 find that variables of group activity such as the aggregate region-country weight of shipments work 31 more through the variable costs of trade. 32 The Russian customs data show that, by far, most shipments are small in weight. As long as 33 shipping prices to exporters include a fixed cost per container (buying the container) or per desti- 34 nation (permit to unload) and are based on weight (Micco and P´erez2002), then there are shipping 35 cost savings in grouping small exports together on a boat or plane heading to the same destination. 36 Alternatively, aggregate export weight may be thought of as the result of an informational spillover 37 about foreign market access. The exact nature of the externality is not important for our model or 38 results. However, we do offer some initial evidence in distinguishing between the two. We test our 39 theory using region-level data from Russia and state-level data from the United States. 40 We find that using the region-level export data from Russia, our model with agglomeration 41 functioning through the variables costs of trade accounts for 10% more of the variation in export 42 share than the benchmark model. Using U.S. state export data, our model accounts for 40% more 43 of the variation. Therefore, we have uncovered empirical evidence of a barrier to trade large enough 44 for firms to respond to it by agglomerating and we provide theoretic justification for it. 45 In our model, an exporter may skip over a destination that has more appealing demand in order 46 to achieve decreased transactions costs in selling to a less preferred destination. All else the same, 47 this creates variation in the destination choices of otherwise identical firms in different regions. 48 Lawless (2009) documents such skipping of \preferred" destinations at the national-level, but her 49 evidence is inconsistent with the theory developed in Eaton, Kortum, and Kramarz (Forthcoming). 50 Our theory better accounts for the geographic pattern of exporting by creating a regional hierarchy 51 of export destinations that differs from the national hierarchy. 52 That exporter clustering by destination exists for both developed and developing suggests firms 53 are responding to destination-specific transaction costs of trade. This finding offers insight into 54 improvement of trade-focused development policy in that the policy should perhaps respect the 55 destination of exports in addition to the exporting country's industrial base and comparative ad- 56 vantage. 3 57 The transaction-level data that motivates our theory are from Russia. We choose to study 58 Russian exporters because Russia is a developing country that is geographically large and diverse. 59 It borders fourteen countries (with China and Ukraine being major trading partners), has four 60 coasts (Arctic Ocean, Pacific Ocean, Baltic Sea, and Black Sea) and diverse economic subregions. 61 Finally, the laws in Russia limit the movement of firms regionally. (See Appendix A for a more 62 detailed description). This allows us to model a firm’s export destination decision without also 63 modeling its location decision within Russia. Furthermore, though Russia has a unique history 64 that may affect development (relationship with former Soviet countries, corruption, and remnants 65 of central planning to name a few), Cassey and Schmeiser (2011) show that the Russian data 66 qualitatively and quantitatively match firm-level data sets from Colombia and France. Therefore 67 the development lessons from this work apply more generally than Russia alone. 68 The findings of Cassey and Schmeiser (2011) notwithstanding, because we are aware that Rus- 69 sian data may be different from other countries' data, we test the macro-level predictions of our 70 theory on the World Institute for Strategic Economic Research's (WISER) Origin of Movement 71 U.S. state export data (OM). These data contain export information for all 50 U.S. states and the 72 same 175 countries that we use with the Russian data. Furthermore, our U.S. data are a panel 73 whereas the Russian customs data are a cross-section. Having the time dimension allows us to 74 conduct robustness tests on the U.S. data that we cannot do with the Russian data. 75 2. Facts on the location & agglomeration of Russian exporters 76 In this section, we show that 1) exporters are clustered regionally given output and the location 77 of ports and 2) exporters shipping to the same destination are further clustered. The purpose of 78 this section is to establish these two agglomeration facts using a spatial econometrics method. Our 79 empirical method differs from that used by Koenig (2009) and Choquette and Meinen (2011), who 80 use a logit estimator to see if the odds that a firm exports to a particular country depends on the 81 ratio of other firms in the same area also exporting to that country to total firms, and also from 82 that of Lovely et al. (2005) who put an industry concentration variable on the left hand side of 83 a linear model with the difficulty of reaching an export destination on the right hand side. Our 4 84 interest differs from the previous work in that we use data from a developing country instead of 85 a developed country. Therefore, one way of interpreting the purpose of this section is establishing 86 the generality of the above-mentioned agglomeration facts across countries in various stages of 87 development. Because of the uniqueness of our data, we begin by describing them. 88 2.1. The Russian data 89 To document the existence of destination-specific agglomeration of exporters, we use 2003 data 90 from the Russian External Economic Activities (REEA) set reported by Russian customs.
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