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. The
91 REEA data are shipment-level customs data of uniquely identified firms, providing value of goods
92 shipped (in 2003 USD), weight of shipment, exporter location within Russia, and destination coun-
93 try. See Cassey and Schmeiser (2011) for complete description of these data, as well as descriptive
94 statistics and stylized facts.
95 We only consider manufacturing exports because firms that exported natural resources were
96 necessarily located in the region endowed with those resources (Bradshaw 2008). In addition, we
97 only use manufacturing data so that we can later have a direct comparison with the OM data for
1 98 the United States.
99 From information on the customs form, we use data from firms who are making the export
100 decision—who may or may not be the firm that manufactured the final good. We do this because
101 we want to use the location of the exporter and not the producer (if they are different). Therefore,
102 we include manufacturers, distributors, and wholesalers who are coded as the principal agent in
103 interest. We do not include freight-forwarding firms because these firms could bias results if they
104 are located somewhere other than the true exporter’s location. Also, we eliminate observations in
105 which the “originating firm” is located outside of Russia since we believe they indicate re-exports
106 rather than true Russian exports, as well as to be consistent with the U.S. data.
107 To check for consistencies in the data, we refer to Cassey and Schmeiser (2011) who compare
108 the REEA data with product data from the United Nations and find close agreement. They
1Cassey (2009) shows that for the United States, manufacturing data are the only data that can be reliably used for the state of production of the export instead of the state where the export began its journey abroad, whereas agriculture and mining data are not reliable.
5 109 also compare the REEA data to statistics obtained with firm-level data from Colombia (Eaton,
110 Eslava, Kugler, and Tybout 2008) and France (Eaton et al. Forthcoming). The export patterns
111 are qualitatively and quantitatively similar. We are therefore confident that our data are not
112 greatly influencing or biasing our interpretation or generality of results, in particular towards any
113 geographic region.
114 We combine the REEA data with information about the the gross domestic products (GDP) of
115 each region using Russia: All Regions Trade & Investment Guide (CTEC Publishing 2004, 2006).
116 (We do not have data on the number or size of domestic firms.) We also use the World Economic
117 Outlook Database (IMF 2006) for GDP information for the 175 countries in our sample. Finally,
118 we calculate the great circle distance from the capital of each Russian region to the capital of each
119 country in the world.
120 Our data have two large drawbacks that limit the forthcoming empirical analysis. First, though
121 we have transaction-level data on shipments that we can match to unique firms, we are not able to
122 pinpoint exporter location by exact physical address. The best we can do is assign each exporter
2 123 to one of Russia’s 89 federal regions. This limitation forces us to conduct clustering tests on
124 the region-level (and therefore to later perform econometric tests of the model at the region-level
125 rather than the firm-level). See Appendix A for a detailed description of the political organization
126 of Russia and also for a table listing the regions. Second, we only have the customs data and
127 thus we are not able to match Russian firms with their characteristics. We do neither know the
128 employment, age, or total sales of the firms in our data nor do we know anything about firms that
129 are not exporting. Therefore we cannot compare the clustering of Russian manufacturing exporters
130 to that of the Russian manufacturing industry or subsectors therein, nor systematically exclude
131 other sources of firm clustering such as natural, labor, or infrastructural advantages. Given this
132 limitation, we instead show that Russian firms exporting to the same country are non randomly
133 located within Russian regions, controlling for variables such as distance, importer GDP, and
134 preferences of the importer for particular goods. This kind of clustering cannot be fully explained
2Russia’s federal regions are somewhat similar in geographic scope to U.S. states. The number of regions has decreased since 2003 because of mergers. It is common to study Russia at this level of geographic disaggregation. See Broadman and Recanatini (2001) for example.
6 135 without destination-specific export spillovers.
136 2.2. Russian exporters and exports
137 First, consider the number of exporting firms by region. Figure 1 shows that the regional
138 concentration of Russian manufacturing exporters is diverse. The majority of exporters are located
139 either around Moscow and St. Petersburg or the Kazakhstan/Mongolia/China border. Nine regions
140 did not have any reported exporting firms in 2003. Figure 1 shows that in Russia, as elsewhere,
141 many exporting firms are located near major ports. However, the figure also shows that there are
142 regions with many exporting firms that are not located near a major port. A map of Russia with
143 major rivers and railroads is in Appendix A.
144 The top destinations for Russian exports in 2003 were, in order: China, United States, United
145 Kingdom, Japan, Ukraine, Kazakhstan, Turkey, Netherlands, Germany, and Iran. Of these, only
146 Ukraine and Kazakstan were part of the Soviet Union. Additionally, Russia does not currently
3 147 have a Warsaw Pact nation as a top destination. Because we do not see evidence of favored trade
148 relations with former Soviet or Eastern bloc countries, we do not treat them any differently than
149 Western countries.
150 Federal laws in Russia severely limit the ability of Russian firms to change region (Botolf 2003).
151 Because of this, firms choose how much to export to each country in the world, taking their location
152 as given. Furthermore, because the Soviet Union did not trade with western countries, we believe
153 it is likely that the Soviet central planners chose firm locations for reasons other than the expansion
154 of trade (see Huber, Nagaev, and W¨org¨otter(1997) and Bradshaw (2008)). Other evidence that
155 international trade was not important in Soviet economic planning is that the large Pacific port of
156 Vladivostok, home of the Soviet Pacific Fleet, was closed to foreign vessels until 1991.
157 2.3. The geography of Russian exporters
158 To show that exporters are clustered, we first use the customs data to calculate the number of
159 firms in each region that export anywhere in the world. Figure 1 shows this as well as the location
3Soviet trade was with countries of similar ideology such as Yugoslavia and Poland.
7 8
Figure 1: Number of exporting firms, by region. Cut-offs correspond to quintiles so that there are an equal number of regions in each category. See Appendix A for a map of Russia with the regions labeled and railways indicated. 160 of ports, bodies of water, and neighboring countries, without modification for population or GDP.
161 Cut-offs for the groups are determined so that each group has the same number of regions.
162 Next, we weight the exporter count observations by regional GDP. We compare each region’s
163 exporter count to the overall mean. Figure 2 highlights regions with a concentration of exporters
164 far from the mean. Darker colors indicate regions whose exporter count, weighted by GDP, is much
165 larger than the mean. As seen in the figure, there exist regions in Russia that have statistically
166 significantly more exporters than expected based on regional GDP. Furthermore, many of these
167 export-concentrated regions do not have a major port. Figure 2 also shows there are regions
168 (weighted by GDP) with less exporting firms than the mean. These regions, indicated with the
169 lightest shades, are not nearly as far below the mean as the regions much larger than the mean.
170 We use this information to calculate the Moran’s I statistic for spatial autocorrelation. The
171 expectation of the statistic is −1/(89 − 1) under a GDP-weighted random distribution of exporters
∗ 172 to Russia’s 89 regions. With our data, the Moran’s I statistic for spatial autocorrelation is 0.16 (z−
173 score = 6.37) indicating that we reject spatial (GDP-weighted) randomness with 99% confidence.
174 Additionally, the clustering is not based on regions that have major ports. In Appendix C, we
175 conduct two other tests to confirm this result.
176 To see what the clustering is based on, consider a specific example: the location of Russian
177 firms that export to the United States and the location of Russian firms that export to Canada.
178 We choose these two countries because they have similar economies (though the United States is
179 much larger), distance from Russia, and consume similar goods (at the 2 digit level, six of the
180 top ten imported industries from Russia are the same). The Moran’s I for the location of Russian
∗∗∗ ∗∗∗ 181 exporters shipping to the United States is 0.04 (z = 1.93) and it is 0.03 (z = 1.68) for Canada.
182 Thus there is clustering for exporters to the United States and Canada with 90% confidence. To
183 see that it is not the same regions (and therefore the same firms within a region) that specialize in
184 exporting to both countries, see figure 3.
185 Figure 3 shows the deviation from the mean number of firms that export to the United States
186 and the mean number of firms that export to Canada. Notice that there are more regions in Russia
187 that have exporters shipping goods to the United States than Canada, but that is not surprising
9 ALL EXPORTERS
Figure 2: Deviations from the average number of exporting firms, by region, weighted by GDP. Moran’s I (89) = 0.16∗ (z − score = 6.37).
188 given the difference in GDP and that the United States is the second largest receiving country of
189 Russian exports. The second fact is that the regions where exporters shipping to the United States
190 are most concentrated (shaded black in figure 3 and indicating more than 2.5 standard deviations
191 from the mean) are not the same regions as those in which exporters to Canada are concentrated.
192 Therefore, Russian exporters to the United States are clustered in a different pattern than the
193 Russian exporters to Canada.
194 To support this claim, we perform a bilateral Moran’s I statistic for these two countries. The
195 statistic differences the number of exporters in each region i shipping to country c from the average
196 number of exporters also shipping to c and compares it to the difference between the number of
197 exporters in another region j shipping to country d from the average also shipping to d. Because
198 of its construction with respect to the other country d, the bivariate Moran’s I is not symmetric.
199 A statistic different from E(I) suggests a spatial pattern (clustering if positive) between exporters
10 UNITED STATES
CANADA
Figure 3: Deviations from the average number of exporting firms, by region, weighted by GDP. For the Unites States, Moran’s I (89) = 0.04∗∗∗ (z = 1.93) and for Canda, Moran’s I (89) = 0.03∗∗∗ (z = 1.68).
11
Table 1: Moran’s I for Location of Firms Exporting to Select Countries Canada China Germany Great Japan Poland United Ukraine Britain States Canada .0345∗ −.0012 .0243 .0313 .0300 .0196 .0194 .0340 China .0089 .0023 −.0448∗ −.0458∗ −.0147 −.0450∗ −.0375∗ −.0408∗ Germany .0249 −.0472∗ .0324∗ .0610∗∗ .0248 .0547∗ .0406 .0637∗∗ Great Britain .0215 −.5160∗ .0677∗∗ .0886∗∗∗ .0676∗∗ .0277 .0783∗∗ .0916∗∗∗ Japan .0221 −.0164 .0319 .0533∗ .0227 .0337 .0593∗∗ .0677∗∗ Poland .0146 −.0514∗ .0581∗ .0370 .0446 .0115 .0450 .0658∗∗ United States .0189 −.0367∗ .0413 .0571∗ .0525∗∗ .0307 .0369∗ .0751∗∗ Ukraine .0258 −.0426∗ .0593∗ .0801∗∗ .0626∗∗ .0520 .0773∗∗ .0220 Source: Cassey (Forthcoming) Notes: Observations on the diagonal are the Moran’s I for the location of exporters shipping to that country. Off-diagonals are the bivariate Moran’s I representing the degree of spatial correlation between the exports to the “row” countries and exports to the “column” coun- tries. Observations have been normalized with respect to regional GDP. Bold observations are the bivariate statistic where both countries have a statistically significant univariate Moran’s I.
∗, ∗∗, and ∗∗∗ indicate p-values less than .1, .05, and .01.
200 shipping to country c and those shipping to country d. The Moran’s I for Canada–U.S. is 0.0194 and
201 for U.S.–Canada it is 0.0189. Neither of these statistics is different from E(I) indicating that the
202 geographic location of exporters shipping to country c is statistically different from those shipping
203 to country d.
204 This pattern of exporter agglomeration holds when we expand the sample. Consider table 1,
205 taken from Cassey (Forthcoming). Observations on the diagonal are starred if there is statistically
206 significant clustering, indicating that Russian exporters are clustered around the destinations of
207 Canada, Germany, Great Britain, and the United States. The lack of stars on the off-diagonal
208 indicates the clustering pattern differs to these two countries if there are stars on the diagonal. (Stars
209 on the off-diagonal indicate the spatial autocorrelation of Russian exporters is the same for the two
210 countries.) Therefore, the pattern of clustering is not the same for seven of twelve possibilities (in
211 bold in table 1). One advantage of using the Moran’s I to find exporter agglomeration is that is is
212 easier to identify for which specific countries there is clustering. Our evidence contrasts somewhat
213 with that in Lovely et al. (2005) in that we find clustering around relatively open countries rather
214 than relatively closed countries that are difficult to export to. Perhaps this is a difference between
215 a developing country such as Russia and a developed country such as the United States or perhaps
216 this is due to Russia’s relationship with countries otherwise considered relatively closed.
12 217 To verify that this clustering by destination of exported shipments holds more generally, we
218 consider the location of the destination relative to Russian firms using the gravity equation. Though
219 the gravity equation takes several forms, we use the version derived by Anderson and van Wincoop
220 (2003). In this formulation, the exponent on exporter GDP, Yi, and importer GDP, Yj, must be
221 one, so we divide both sides by exporter and importer GDP. Aggregate region-country exports are
222 Xij and great circle distance are Dij. As recommended by Feenstra (2004, p. 161), we insert an
223 export-specific set of binary variables, Si, and an import-specific set of binary variables, Tj, to
224 account for the unobservable multilateral resistance terms. Using Ordinary Least Squares (OLS),
225 we estimate,
89 175 Xij X X log = − 7.72 − 1.32 log Dij + κiSi + δjTj + εij (1) Y Y (1.033)∗∗∗ (.110)∗∗∗ i j i=2 j=2 N = 2985, Rˆ2 = 0.58,RMSE = 2.03
∗∗∗ 226 where on the robust standard errors indicates the corresponding coefficient is significantly differ-
227 ent from zero at with 99% confidence. The regression results suggest that the overall explanatory
228 power of the gravity equation to account for Russian regional exports is low compared to the
2 229 R ≥ 0.7 common in country-level data sets that often do not use binary variables to increase the
4 230 goodness of fit.
2 2 231 We consider the relatively low adjusted R from using subnational data (compared to the R
232 on national data) in a gravity equation as evidence that something is missing from this equation
233 that applies to the regional level, but not at the geographic scope of the national level. Given that
234 many theories of trade predict a gravity equation-like relationship—Anderson (1979) and Chaney
235 (2008) for example—we have documented a weakness in existing theory applied to subnational
236 data. See Appendix D for the estimation of gravity nonlinearly to address the fact that only 19%
4The estimates for a “naive” gravity equation are
log Xij = − 0.84 + 0.56 log Yi + 0.29 log Yj − 1.17 log Dij + εij (0.580) (0.043)∗∗∗ (0.020)∗∗∗ (0.065)∗∗∗ N = 2985, Rˆ2 = 0.15,RMSE = 2.45
13 237 of our possible observations are nonzero.
238 To check the robustness of our results, we use a similar method to Koenig (2009) and Choquette
239 and Meinen (2011) on our Russian data. We estimate the following logistic regression:
Eij = 4.576 + 0.078 log Mij − 1.456 log Dij − 0.108 log Yi + 0.252 log Yj + εij (2) (.069)∗∗∗ (.004)∗∗∗ (.007)∗∗∗ (.004)∗∗∗ (.003)∗∗∗ N = 3, 979, 007
240 where Eij = 1 if a firm in region i exports to country j and Mij is the number of firms in region i
241 exporting to country j. These findings support our results using the Moran’s I, that agglomeration
242 exists and is destination-specific.
243 Using customs data from Russia, we have shown that Russian exporters are clustered but that
244 these clusters differ depending on the country receiving the exports. Next, we develop a theory to
245 account for this fact.
246 3. A theory of exporter agglomeration based on shipment destination
247 We develop a theory of trade where exporters agglomerate with other exporters shipping to the
248 same location due to international transaction costs. Our model is a monopolistic competition trade
249 model in the spirit of Melitz (2003) and Chaney (2008) but modified to incorporate agglomeration
250 as in Ottaviano, Tabuchi, and Thisse (2002) and Fujita and Thisse (1996).
251 The export agglomeration by destination we documented could be achieved either as a reduction
252 in the fixed costs of exporting or the variable costs of exporting. We base our firm-level theory on
253 the agglomeration being achieved through the variable cost because of evidence by Choquette and
254 Meinen (2011) of this mechanism. Koenig et al. (2010) finds non robust evidence of agglomeration
255 occurring through the variable cost as well, in particular when the number of firms in the area
256 exporting to the same country is small, as it often is in Russia. We therefore choose to explore this
257 avenue, while acknowledging that it is not the only path to take.
14 258 3.1. Motivation and evidence
259 Our theory may take a few forms in the real world. The first version is a traditional treatment
260 of an externality. As in Head, Ries, and Swenson (1995), agglomeration occurs within a region
261 because of knowledge spillovers that are limited in geography. In our case, the knowledge that is
262 spilled is about exporting to a specific country (because of good contacts, translation, or effective
263 bribes paid as a percent of shipment value, for example) regardless of the exporter’s product.
264 A second story is that firms benefit from economies of scale in transportation and shipments.
5 265 Because of containerization in rail, ship, and air transportation, there is a fixed size to the shipment.
266 As documented in Besedeˇsand Prusa (2006), most shipments are small.
267 This is true in our Russian data as well. Table 2 shows the quartiles for shipments by weight.
268 As the table shows, 50% of shipments weighed less than 185 kg (407 lbs) and 25% or less than 14
269 kg (31 lbs). Thus most shipments are small, with a few huge shipments.
Table 2: Weight of Transaction-level Shipments, by Quartile Quartile Weight (%) (kg) 25 14 50 185 75 4716 100 1,597,020,500 N 115,133
270 Because most shipments are small, exporters cannot fill a standardized container or a ship on
271 their own. Hence if firm A is exporting to China, firm B can add its goods to the shipment. Either
272 the transportation company would be willing to sell its remaining room to firm B below average
273 cost or firm A would be willing to sell its extra container room. Thus both firm A and B pay a
274 lower transportation cost than if they shipped alone. Micco and P´erez(2002) find evidence of this
275 by estimating that a doubling of trade volume weight from a particular port reduces transportation
276 costs on that route by 3–4%. They write: “In general, even though most of these economies of
5Korinek and Sourdin (2010) show that shipping companies quote transportation rates in cost per container.
15 277 scale are at the vessel level, in practice they are related to the total volume of trade between two
278 regions.”
279 Regardless of which of these motivating tales is the most reasonable, the modeling of them is the
280 same. The particular stories above should not be taken too seriously. For example, we know that
281 some countries require that imports in containers only come from one firm. But we think these two
282 stories exemplify the spirit of why we posit an externality in transaction costs. Nonetheless, though
283 not a focus of our paper, we do an initial test on the two competing mechanisms in section 4.4.
284 3.2. The model
285 Our model is a variation of Melitz (2003) as expanded by Chaney (2008). There are N non-
286 symmetric destination countries spread spatially across the world. We do not model the character-
287 istics of countries other than that each is a passive consumer of Russian goods and each differ in size
288 and location. The representative consumer in each country j has standard taste-for-variety utility
289 on the consumption of an aggregate good composed of the varieties of Russia goods available in
290 that county. Countries differ exogenously in their income endowment Yj and their spatial location.
291 They differ endogenously in the availability of Russian goods, their aggregate price index, and their
292 aggregate consumption.
293 There is a continuum of Russian exporters distributed spatially throughout Russia. These
294 exporters differ in manufacturing productivity in addition to their location in space. Firm produc-
295 tivity, ϕ, is drawn from a Pareto distribution and cannot be changed. Each firm, identified by their
296 productivity and the region where they are located, produces a unique good.
297 There is a per-unit cost of production wi that is common to all firms in a region, but differs
298 across regions. (We believe this is plausible given the relative lack of labor mobility within Russia.)
299 Because of the rigidness of Russian law, we assume that firms are not free to change location.
300 Finally, there is a fixed cost, fij, associated with exporting from each region i in Russia to each
301 country j in the world.
302 Given their productivity, the cost of production, and the fixed cost to export, a firm in region
303 i maximizes profits in each market j it sells to by choosing the price, pij(ϕ), and quantity, xij(ϕ),
16 304 in that market: x (ϕ) π (ϕ) = p (ϕ)x (ϕ) − w ij τ (A , ·) + f . (3) ij ij ij i ϕ ij ij ij
305 Notice there is a region-destination variable transportation cost, τij(Aij, ·). This cost is not
306 a constant, but a function of the agglomeration term Aij, among other variables such as physical
307 distance which will be specified later and are now represented with a dot. Turning τij into a function
308 of other exporter activities based on the activities of the entire region is the primary theoretical
309 contribution of this work. For technical purposes, the form of τij(Aij) can be very general, though
0 00 310 we think it economically reasonable to suppose τij(Aij) > 0, τij(Aij) < 0, and τij(Aij) > 0. If the
311 solution to (3) is not positive, then the firm does not export to j.
312 3.3. Equilibrium
Our equilibrium concept uses Bertrand competition between the monopolistic competitors.6
Bertrand competition gives the price for each firm’s output in each destination:
σ w p (ϕ) = i × τ (A , ·) ij σ − 1 ϕ ij ij
313 where σ > 1 is the elasticity of substitution in the utility function. Unlike the constant markup
314 common in Melitz-style models, the per-unit price charged by each firm in a destination also depends
315 on our agglomeration term Aij such that the greater the agglomeration, the lower the transaction
316 cost, and thus the lower the price.
317 From the zero profit condition of Bertrand competition, there exists a threshold productivity
318 for each region exporting to each destination. The equilibrium aggregate price index for country
319 j is given by the prices of firms from each region that are above the productivity threshold and
320 export to country j. Finally, equilibrium aggregate exports to country j from region i are:
−γ wiτij(Aij, ·) 1− γ Xij = YiYj (wifij) (σ−1) λ (4) θj
6Results are the same under Cournot competition because there is a continuum of goods.
17 321 where θj may be interpreted as a weighted trade barrier as in Chaney (2008) or multilateral resis-
322 tance as in Anderson and van Wincoop (2003), γ > σ−1 is the parameter on the Pareto distribution
323 of firm productivities, and λ is a constant.
324 Our agglomeration effect Aij shows up in equation (4) in two places. It shows up directly in P 1−γ/(σ−1) −γ−1/γ 325 τij(Aij, ·) but also indirectly in θj = i Yi(wifij) [wiτij(Aij, ·)] .
326 4. The benchmark gravity equation and our model
327 We derived our theoretical counterpart to gravity in equation (4). We take this prediction to
328 the Russian data at the region-level. To do this, we convert (4) into reduced form by taking logs,
γ γ log X = log Y Y −γ log τ (A , ·)+ 1 − γ − log w +γ log θ + 1 − log f +log λ. ij i j ij ij σ − 1 i j σ − 1 ij
329 We rewrite to get,
Xij log = α + β1 log τij(Aij, ·) + β2 log wi + β3 log θj + εij (5) YiYj
γ γ 330 where α = log λ, β1 = −γ, β2 = 1 − γ − σ−1 , β3 = γ, and εij = (1 − σ−1 ) log fij.
331 In order to estimate (5), we assume a functional form for how the agglomeration term, Aij,
332 relates to bilateral trade costs, τij. We assume trade costs are a function of physical distance, Dij,
333 and the agglomeration term, but nothing else. Often the gravity literature uses variables such as
334 common language, colonial history, location of overseas trade offices, and exchange rates as barriers
335 to trade. But these variables are not nearly as important in our data because there is not much
336 variation across regions within Russia. Therefore, while acknowledging some of these variables may
337 be of second-order importance, we assume:
−η τij(Aij, ·) = Dij × Aij . (6)
338 This technology’s modeling nests the gravity model by setting η = 0. Later, we test if η = 0.
339 We take (5) to our Russian data. We perform our experiments at the region-level rather than
18 340 the firm-level to document the increased explanatory power that the agglomeration term provides
341 for empirical trade flows. By doing so, we do not directly distinguish whether the agglomeration
342 works through the variable or fixed cost at the firm-level, since both would be realized as an increase
343 in aggregate exports. Also, we cannot directly distinguish what the correct agglomeration term is.
344 We consider two possibilities. The first is the number of exporters, similar to the agglomeration
345 measure used by Koenig (2009) and Choquette and Meinen (2011). The second agglomeration
346 measure we consider is aggregate weight of exports from each region to each country.
347 4.1. Agglomeration as measured by the number of exporters
348 We have data on Xij, Yi, Yj, Dij, and the number of exporters Mij. We do not have reliable data
349 on wi and fij, and therefore cannot calculate θj. We use a set of region-specific binary variables
350 Si to account for wi and any other unobserved unilateral region features. We use a set of country-
351 specific binary variables Tj to account for θj and any other unobserved unilateral country features.
352 We let the bilateral fixed cost be the error term.
89 175 Xij X X log = − 7.133 − 1.327 log Dij + .032 log Mij + κiSi + δjTj + εij (7) Y Y (.910)∗∗∗ (.111)∗∗∗ (.048) i j i=2 j=2 N = 2985, Rˆ2 = 0.58,RMSE = 2.03
353 The results indicate that the number of firms in region i exporting to country j is not statistically
354 different from zero in accounting for aggregate exports. This variable brings no additional explana-
355 tory power over standard gravity, which is reflected in the estimates being nearly identical to that
2 356 in (1) and identical adjusted R .
357 4.2. Agglomeration as measured by aggregate weight
358 As an alternative to measuring agglomeration by the number of exporters, we consider another
359 way of measuring agglomeration, aggregate weight. At the industry or product level, it must be
360 the case that an increase in shipment weight will cause an increase in shipment value. However,
361 this mechanical relationship between weight and exports is less strict across industries. That is,
19 362 the value of a shipment of computers and steel is not as strongly related to the combined weight
363 as either one or the other of these products. We choose to aggregate our data and focus on all
364 manufacturing industries at once and thus we are less concerned about a purely mechanical effect
7 365 driving our results than otherwise.
366 Let Wij be the aggregate weight of exports shipped from region i to country j. First consider
367 a logistic regression similar to (2) except replacing the number of exporting firms with aggregate
368 weight:
Eij = 0.567 + 0.230 log Wij − 0.764 log Dij − 0.329 log Yi + 0.076 log Yj + εij (8) (.074)∗∗ (.002)∗∗∗ (.008)∗∗∗ (.004)∗∗∗ (.003)∗∗∗ N = 3, 979, 007
369 Thus the estimate on aggregate weight is statistically significant and larger than the estimate on the
370 number of exporters from (2). Because of this evidence, we continue onto our preferred specification
371 of the gravity equation, which includes the agglomeration term as measured by aggregate weight.
89 175 Xij X X log = − 14.787 − .813 log Dij + .253 log Wij + κiSi + δjTj + εij (9) Y Y (.986)∗∗∗ (.101)∗∗∗ (.014)∗∗∗ i j i=2 j=2 N = 2985, Rˆ2 = 0.64,RMSE = 1.88
372 Compare (9) to the original gravity equation (1). In addition to our agglomeration term being
2 373 significant at the 99% level, the adjusted R improves from .58 to .64. Therefore our model with
374 an agglomeration term based on the aggregate weight of shipping costs accounts for 10.2% more
375 variation in export share than (1).
376 Because of our choice of how weight enters the transportation cost function (6), our model
377 predicts that the coefficient of physical distance Dij is equal to −γ and the coefficient on aggregate
378 export weight Wij is equal to γη. From these two predictions, we calculate η = 0.310, which is
379 economically significant. We conduct a nonlinear Wald test of whether η = 0. We reject that η = 0
7Koenig et al. (2010) does not find evidence of exporting clustering by industry, though they do find “spillovers on the decision to start exporting are stronger when specific, by product and destination.” But since we focus on aggregate exports, we do not report the results when industry is included.
20 380 with 99% confidence (F (1, 2755) = 46.35), so η is statistically significant as well. This suggests
381 our choice of technology is reasonable. Note that when we use the number of exporters as our
382 agglomeration term, η = 0.024 and we cannot reject that η = 0.
383 Another endorsement of our model comes from our estimate of γ. We cannot reject that γ = 1
384 with 99% confidence which agrees with Axtell (2001) who reports that γ is near one for many
385 countries. Furthermore, notice that the estimate for γ in (1) without the agglomeration term is
386 larger than Axtell.
γ 387 Using our estimate on γ and Chaney’s estimate for σ−1 , we calculate σ = 1.40. This is consistent
388 with the requirement that γ > σ − 1 and that σ > 1. At first, σ seems too low for an estimate
389 of the elasticity of substitution, since it implies very large markups in price over marginal cost.
390 Additionally our σ is low compared to the 3 to 8 range estimated by Hummels (2001), but Hummel’s
391 estimates are within a product category whereas ours is for all manufactured tradeables. Crozet
392 and Koenig (2010) estimates an average across trade-weighted industries of 2.25. Furthermore, as
393 argued by Rauch (1999), if there are informational costs to trade in the data that do not appear
394 as physical distance as in equation (6), then the estimate for σ will be low. When we use the
395 estimates from (7), σ = 0.34 indicating that goods are complements. We find this further evidence
396 that measuring agglomeration by aggregate weight is appropriate.
397 Though we derived our reduced form aggregate equation from firm-level theory, there is a
398 potential problem with the estimates in (7) due to the mechanical relationship between aggregate
399 shipment weight and export value. Though the relationship between export weight and value across
400 industries is far less strong than within industries, there still exists the possibility of an endogeneity
401 problem. To address this endogeneity concern, our approach is to follow Koenig (2009) and use
402 aggregate export weight from the previous year, 2002. Unfortunately, we only have the 2003 cross-
403 section of Russian customs data. Thus in order to proceed, we switch to U.S. state-level export
404 data for which we have a panel.
405 4.3. Application to U.S. state exports & robustness
406 Though we established the facts about agglomeration of exporters around the destination of
407 their shipments and motived our model using transaction-level customs data from Russia, there
21 408 is nothing in the reduced form equation (5) that requires customs data. Therefore, we take our
409 model to another regional export data set. The data set is the Origin of Movement export data for
410 U.S. states. We do this to check that our previous results are applicable to a wider set of countries
411 than Russia and to address the possible endogenity due to using aggregate shipment weight on the
412 right-hand side.
413 A detailed description of the OM data is in Cassey (2009). Because of his findings on data
414 quality, we limit our observations to manufactured exports only. We also restrict our sample to
415 2003 to match the Russian data. The same sample of countries is used except that Russia as a
416 destination replaces the United States. Also there are 22 observations which have positive export
417 sales but a weight of zero. We do not use these observations. Beginning with the benchmark gravity
418 equation in (1) and using OLS, we find:
50 175 Xij X X log = 2.21 − 2.05 log Dij + κiSi + δjTj + εij (10) Y Y (1.26) (.137)∗∗∗ i j i=2 j=2 N = 7061, Rˆ2 = 0.54,RMSE = 1.28
419 The estimated coefficient on distance is much larger than for the Russian data, and the constant
ˆ2 420 is not statistically significant at the 1% level. For us, the relevant statistic is R = .54.
421 Applying the U.S. data to (5) yields:
50 175 Xij X X log = −10.09 − .953 log Dij + .433 log Wij + κiSi + δjTj + εij (11) Y Y (.950)∗∗∗ (.102)∗∗∗ (.006)∗∗∗ i j i=2 j=2 N = 7061, Rˆ2 = 0.75,RMSE = 0.93
ˆ2 422 The R increases from .54 in the benchmark to .75 in our model, an increase of 40.2%. (The results
∗∗∗ 423 for the data pooled over 1997 to 2008 are essentially the same.) We estimate η = 0.454 . As
424 with the Russian data, we can confidently reject that η = 0 (F (1, 6835) = 81.87). Furthermore, the
425 estimated coefficients in equation (11) are similar to those using the Russian data in (9) whereas
426 the same is not true for the benchmark. Another encouraging result is that our estimate for γ is
427 again not statistically different from one, as before. Furthermore, our point estimate is close to
22 428 the 0.99 to 1.10 range for U.S. data reported in Axtell (2001) whereas the benchmark is too high.
429 Finally, with the U.S. data, we estimate σ = 1.5.
430 One concern with the results so far is that they are driven by the mechanical relationship
431 between export value and weight. Because our data is across manufacturing industries, we do
432 not think this relationship is strong. Nevertheless, we repeat the estimation using aggregate weight
433 lagged one year to 2002. This severs any mechanical relationship since past aggregate weight cannot
434 affect current export value in any way other than a reduction of the transaction costs of trade. We
435 lose some observations compared to 2003 because those state–country pairs did not trade in 2002.
50 175 Xij ∗ X X log = − 4.11 − 1.50 log Dij + .243 log lag Wij + κiSi + δjTj + εij (12) Y Y (1.177)∗∗∗ (.124) (.010)∗∗∗ i j i=2 j=2 N = 6617, Rˆ2 = 0.60,RMSE = 1.15
436 In this case, we find that our model accounts for 12% more variation in the data than the bench-
437 mark. The estimate on the aggregate weight term is statistically significant, though smaller than
438 previously. Therefore, we find that the agglomeration term is important even if it is lagged one
439 year.
440 Applying the OM state export data to our model strengthens the results achieved with the
441 Russian data since the U.S. data was not used in the spatial econometrics that guided the theory.
442 4.4. Explorations on clustering mechanism
443 Now that we have shown that agglomeration as measured by aggregate weight is a statistically
444 and economically significant variable in the gravity equation of trade, we do exploratory analysis to
445 determine which mechanism the agglomeration term works through. Our model cannot distinguish
446 agglomeration occurring because of an informational spillover about how to export to a specific
447 destination or economies of scale in shipping. However, we expect that savings from consolidat-
448 ing shipments to the same country would be more prevalent for small firms than big firms, who
449 presumably have their own distribution networks and can fill a container by themselves.
450 We re-estimate (9) for separate subsamples based on firm size. We split firms into size based
23 451 on total exports to the world.
89 175 Xij X X Small firms: log = − 22.03 − 0.55 log Dij + .325 log Wij + κiSi + δjTj + εij Y Y (1.737)∗∗∗ (.150)∗∗∗ (.036)∗∗∗ i j i=2 j=2 N = 851, Rˆ2 = 0.42,RMSE = 2.43 89 175 Xij X X Medium firms: log = − 21.40 − .744 log Dij + .400 log Wij + κiSi + δjTj + εij Y Y (1.628)∗∗∗ (.120)∗∗∗ (.030)∗∗∗ i j i=2 j=2 N = 1075, Rˆ2 = 0.45,RMSE = 2.40 89 175 Xij X X large firms: log = − 25.10 − .717 log Dij + .601 log Wij + κiSi + δjTj + εij Y Y (.777)∗∗∗ (.075)∗∗∗ (.019)∗∗∗ i j i=2 j=2 N = 2624, Rˆ2 = 0.53,RMSE = 2.54
452 These estimates show that the effect of the agglomeration term as measured by aggregate weight
453 is greater for large firms than medium or small firms, though the coefficient is still statistically
454 and economically significant for all sizes. This evidence suggests that destination specific export
455 agglomeration is working though an informational spillover rather than consolidation of shipments.
456 5. Conclusion
457 The theoretical and empirical international trade literature typically studies flows at the country-
458 level. However more recent papers such as Eaton et al. (Forthcoming); Bernard and Jensen (2004);
459 Tybout (2003); and Arkolakis and Muendler (2010) study firm-level export decisions. All of these
460 articles study the production and technology characteristics of the firm and their export decision—
461 what to export, how much to export, and to which countries. The findings from this work indicate
462 there are barriers to exporting whose precise characterization continues to elude researchers.
463 The work in Koenig (2009), Koenig et al. (2010), and Choquette and Meinen (2011) shows
464 export spillovers are important for firm export decisions in developed countries if those firms are
465 exporting to the same destination. We use the physical location of exporting establishments in
466 Russia to see if there is exporter agglomeration by destination in a developing country. Using the
467 Moran’s I statistic from spatial econometrics, we find there is agglomeration of exporters, not only
24 468 in general and around ports, but also in terms of the destination to which exports are shipped.
469 This evidence is supported by a logistic regression.
470 We believe this clustering of exporters by destination indicates firm behavior confronting economies
471 of scale in the transaction cost of trade to a specific destination. We posit that this agglomeration
472 of exporters by destination is an externality from how-to-export information spillovers or economies
473 of scale in shipping costs. We measure this agglomeration two ways: by the number of firms ex-
474 porting and by the aggregate weight of exported shipments. We find that the agglomeration term
475 as measured by aggregate weight increases the explanatory power of the gravity model of trade.
476 Using aggregate weight, our model’s prediction is verified with the Russian data and outside
477 sources, and our model accounts for more of the variation in export share than the benchmark
478 Anderson and van Wincoop (2003) or Chaney (2008) gravity-type model. We then test our model
479 on U.S. state export data and are able to account for 40% more variation in export share than the
480 benchmark.
481 We perform exploratory tests to find evidence through which mechanism the externality op-
482 erates. We find that the agglomeration term is more important for large firms than small firms,
483 indicating that destination-specific knowledge on how to export is important.
484 The documenting of agglomeration by destination and the confirmation of our model takes a step
485 toward understanding the size and nature of the international barriers to trade. Our combination
486 of localization techniques and externality modeling from industrial organization with the tools from
487 firm-level international trade models yields new empirical and theoretical insights into the nature
488 and size of barriers to international trade, and the behavior of firms to overcome these barriers.
489 The implications for trade-focused development is that policy perhaps should consider where extent
490 exports are operating when promoting exports rather than just particular industries or countries.
491 Acknowledgements
492 The authors thank Thomas Holmes, Yelena Tuzova, Anton Cheremukhin, and seminar participants
493 at Vasser College, Washington State University, Kansas State University, University of Scranton,
494 University of Richmond, and the New York Economics Association annual meeting. Cassey thanks
25 495 Qianqian Wang and Pavan Dhanireddy for research assistance, and Jeremy Sage for help with
496 ArcGIS. Portions of this research supported by the Agricultural Research Center Project #0540 at
497 Washington State University.
498 Appendix A. The political organization of Russia
499 In 2003, Russia had 89 federal regions. These regions were separated into 49 oblasts (provinces),
500 21 republics, 6 krais (territories), 10 okrugs (autonomous districts), 1 autonomous oblast, and 2
501 federal cities (Moscow and St. Petersberg). The divisions are based on ethnic groups and history.
502 Each region has equal representation in the Federal Assembly, though there are differences in
503 autonomy. However these differences are minor (and decreasing over time) because each region
504 is attached to one of eight federal districts administered by an envoy of Russia’s President. The
505 districts are designed to oversee regional compliance with federal laws.
506 Most taxes are set federally. In 2003, regions could set corporate property tax, gambling business
507 tax, and transport tax within bounds established federally (CTEC 2006, p. 955). Exports of goods
508 from Russia are subject to a value-added tax of 0% throughout Russia (p. 960), except on some
509 exports of oil and natural gas. Export customs duty rates are set federally as well (p. 961).
510 Appendix B. The location of products and ports
511 We show that the location of Russian exporters can neither be accounted for by the location of
512 export commodities nor the location of borders and ports.
513 Russia’s major manufacturing industries are: all forms of machine building from rolling mills
514 to high-performance aircraft and space vehicles; defense industries including radar, missile produc-
515 tion, and advanced electronic components, shipbuilding; road and rail transportation equipment;
516 communications equipment; agricultural machinery, tractors, and construction equipment; elec-
517 tric power generating and transmitting equipment; medical and scientific instruments; consumer
518 durables, textiles, foodstuffs, and handicrafts.
519 Russia’s largest export commodities are: petroleum products, wood and wood products, metals,
520 chemicals, and a wide variety of civilian and military manufactures. Products such as petroleum,
26 23
48 68 28 60 42 Kolpos Kassandras 52 67 77 84 5 14 15 9 Marmara Deniz 25 87 ^_ 49 4155 75 47 34 21 37 8 43 62 82 50 73 85 71 46 33 36 57 15 45 80 35 38 61 79 72 44 1 66 58 83 65 86 27 32 69 24 70 63 22 6 51 11 56 7 17 26 12 16 40 78 30 54 74 53 ^_ Capital Region Type 39
27 Rail Autonomous Region (1) 20 29 Autonomous Province (10) 65 31 City (1) 3 64 4 Territory (7) 81 10 18 13 Region (48) 76 Republic (21) 2 88 59
Gulf of Oman
Arabian Sea Ü Philippine Sea
Figure A.1: Russia’s Regions in 2003. The City of Moscow Region is included in the map only as the star and not listed separately. 521 wood, metals, and chemicals need to be processed to become manufactured goods. Therefore it
522 is possible that the location of exports is the same as the location of the refineries and processing
523 plants for these goods.
524 Figure 1 shows that though many exporters are located in the same region as a major port, there
525 are regions that are not close to a major port in the top quintile of the number of exporting firms.
526 The largest ports are Azov (Rostov, Black Sea), Kaliningrad (Kaliningrad, Baltic Sea), Nakhodka
527 and Vostochny (Primorsky, Sea of Japan, largest), Novorossiysk (Krasnordar, Black Sea, largest),
528 Primorsk (Leningrad, Baltic Sea, largest, lots of oil), and Saint Petersburg (Saint Petersburg, Baltic
529 Sea). Cassey (2010) documents that access to water is an important predictor of U.S. state exports.
530 Appendix C. Exports in the context of output
531 If external geography played no role in the location of exporters, the pattern of region-country
532 aggregate exports (Xij) would be identical to the pattern of regional production (Yi) with some
533 randomness. In this case, the destination of Russian sales would not matter for the location of
534 Russian exporters. To show that this does not hold, we conduct two simple tests. The first test is
535 to run the following regression:
log Xij = − 5.40 + 0.44 log Yi + εij (C.1) (0.386) (0.046)∗∗∗ N = 2985,R2 = 0.03,RMSE = 2.62
536 The index i runs over the set of Russia’s regions and the index j runs over the set of countries
∗∗∗ 537 in the world. Standard errors are robust and indicates the estimated coefficient is significantly
2 538 different from zero with 99% confidence. The extremely low explanatory power as given by the R
539 indicates that the pattern of production in Russia does not account for the pattern of exports in
540 the data.
The second test is to compare the ratio of exports from each Russian region to the ratio of GDP
from each region to Russia. We modify the familiar concept of location quotient to put Russian
28 exports by region into the context of overall economic activity,
X Y LQ = i i . i X Y
541 If the LQ is greater than 1 then the region’s export share to the world is larger than it’s share
542 of GDP. This indicates the region is relatively specialized in exporting in comparison to overall
543 economic activity. The strength of this approach is that it controls for the geography of economic
544 activity within Russia,
545 The average LQi is 1.454 with standard deviation 0.489. But though the average LQi is not
546 significantly different from one, the regions with the largest LQi are several times the standard
547 deviation. Figure C.1 shows the histogram of LQi. Though most region’s LQi is around 1, there
548 are many that are in the right tail of figure C.1. Thus there are regions in Russia that are heavily
549 concentrated in exporting compared to overall output and regions that do not export anything at
550 all.
551 Because we only consider manufacturing activity, we repeat the proceeding exercise by scaling
552 the regional GDP data by the share that is from industry. The results (not reported) are not
553 qualitatively different from before. Therefore, we have documented that Russian exporters are not
554 simply located in the geographic pattern of industrial economic activity.
555 Appendix D. Robustness
556 One feature of the Russian export data is the high frequency of zeros. There are 89 regions in
557 Russia and we have 2003 GDP data (IMF 2006) and distance for 175 countries. Therefore, there
558 are 15,575 possible observations. Of these, only 2991 have positive exports, or 19%. (We also lose
559 some observations because of missing regional GDP data from Russia.) Santos Silva and Tenreyro
560 (2006) show that (2) with OLS can bias estimates when there are many zeros in the data. We
29 40 30 20 Percent 10 0 0 10 20 30 40 LQ_exports_GDP
Figure C.1: LQ Histogram
30 561 follow them and use the poisson quasi-maximum likelihood estimator.
−1.04∗ 89 175 Xij ∗ (0.150) X X = exp −20.06 × log Dij + κiSi + δjTj × εij. Y Y (1.189) i j i=2 j=2 N = 15400, AIC = 526.00 −.804∗ .116 ∗ 89 175 Xij ∗ (0.119) (0.027) X X = exp −12.695 × log Dij × log Wij + κiSi + δjTj × εij. Y Y (1.231) i j i=2 j=2 N = 2985, AIC = 456.00
562 Though the estimates for the regression with the agglomeration term change quantitatively, the
563 are the same qualitatively. The agglomeration term is economically and statistically significant.
564 Importantly, the model with the agglomeration term has a lower Akaike Information Criterion
565 than the model without the term, indicating the model with agglomeration is preferred. However,
566 region-country observations of zero exports also have zero weight for those exports. Santos Silva
567 and Tenreyro do not consider the model where zeros occur on both the left and right hand side of
568 the regression.
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35 Table A.1: Geographic Regions
Region Type GDP Region Type GDP (millions) (millions) Adygeya Republic 352.8 Mordovia Republic 1281.7 Agin-Buryat Autonomous Okrug 77.8 Moscow Federal City 84742.3 Altai Krai 3130.4 Moscow Oblast 15517.4 Altai Republic 269.6 Murmansk Oblast 2834.3 Amur Oblast 1901.5 Nenets Autonomous Okrug 876.1 Arkhangelsk Oblast 3735.1 Nizhny Novgorod Oblast 7720.1 Astrakhan Oblast 1884 North Ossetia-Alania Republic 726.8 Bashkortostan Republic 54851.7 Novgorod Oblast 1356.1 Belgorod Oblast 2766.4 Novosibirsk Oblast 5797.2 Bryansk Oblast 1686.2 Omsk Oblast 4363 Buryat Republic 1685.9 Orenburg Oblast 4345.8 Chechen Republic 346.4 Oryol Oblast 1594.1 Chelyabinsk Oblast 7995.8 Penza Oblast 1706.1 Chita Oblast 1859.4 Perm Oblast 8058.3 Chukotsky Autonomous Okrug 11325.8 Permyakia Autonomous Okrug 4305.3 Chuvash Republic 1741.8 Primorsky Krai 1052.1 Dagestan Republic 2326.3 Pskov Oblast 9707.2 Evenkiysky Autonomous Okrug 141.8 Rostov Oblast 6366.7 Ingushetia Republic 167.4 Ryazan Oblast 2301.8 Irkutsk Oblast 5999 Saint Petersburg Federal City 15122.6 Ivanovo Oblast 1250.9 Sakha Republic 4526.4 Jewish Autonomous Oblast 298.3 Sakhalin Oblast – Kabardino-Balkar Republic 938.3 Samara Oblast 9541 Kaliningrad Oblast 1783.5 Saratov Oblast 4557.5 Kalmykia Republic 4770.8 Smolensk Oblast 1794.7 Kaluga Oblast 1852.9 Stavropol Krai 3823.1 Kamchatka Krai 1016.8 Sverdlovsk Oblast 11133.6 Karachayevo-Cherkessia Republic 412.5 Taimyrsky (Dolgano-Nenetsky) Autonomous Okrug 36269 Karelia Republic 1668.1 Tambov Oblast 1765.6 Kemerovo Oblast 5948.7 Tatarstan Republic 11075 Khabarovsk Krai 4253.7 Tomsk Oblast 3600.3 Khakassia Republic 997.4 Tula Oblast 2683.8 Khanty-Mansi Autonomous Okrug 26409.8 Tuva Republic 166.6 Kirov Oblast 2165.5 Tver Oblast 2573 Komi Republic 3941.4 Tyumen Oblast 2145.2 Koryaksky Autonomous Okrug 150.6 Udmurtia Republic 3393.1 Kostroma Oblast 1144.2 Ulyanovsk Oblast 2025 Krasnodar Krai 9573.7 Ust-Ordynsky Buryatsky Autonomous Okrug 145.4 Krasnoyarsk Krai 9548.8 Vladimir Oblast 2326.3 Kurgan Oblast 1386.9 Volgograd Oblast 4770.8 Kursk Oblast 2066.6 Vologda Oblast 3962.7 Leningrad Oblast 4595.1 Voronezh Oblast 3646.4 Lipetsk Oblast 3406.5 Yamalo-Nenets Autonomous Okrug 11325.8 Magadan Oblast 797.6 Yaroslavl Oblast 3626.9 Mari El Republic 853.2 Notes: In 2003, Russia had 49 oblasts (provinces), 21 republics, 6 krais (territories), 10 okrugs (autonomous districts), 1 autonomous oblast, and 2 federal cities (Moscow and St. Petersberg).
36