The agglomeration of exporters by destination


Precise characterization of informational trade barriers is neither well documented nor understood. Using Russian customs data, we document that regional 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-level exports using Russian data and find improvement over 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. Furthermore, our findings can be considered in the burgeoning literature refining transaction costs beyond the traditional iceberg cost.

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Fig. 1
Fig. 2


  1. 1.

    Cassey (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.

  2. 2.

    Le Gallo and Dall’erba (2008) find no significant difference in results if they use great circle distances or travel time by road from the most populous town.

  3. 3.

    Russia’s federal regions are somewhat similar in geographic scope to US 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.

  4. 4.

    These results are robust to using export volume instead of the number of exporting firms.

  5. 5.

    Korinek and Sourdin (2010) show that shipping companies quote transportation rates in cost per container.

  6. 6.

    Martin et al. (2008) find that agglomeration positively affects firm productivity. To address this issue, Koenig et al. (2010) introduce a total factor productivity (TFP) variable to prevent overestimation of spillovers. In our data, we are unable to construct such a variable; however, Koenig et al. show that including productivity, while significant, does not affect the coefficient on the spillover.

  7. 7.

    Koenig et al. (2010) write, “Results are coherent with a linear specification since the effect on starting to export of having one neighbor exporting the same product to the same destination compared to zero (0.072) is very similar to the effect of having two neighbors instead of one, and of having three neighbors instead of two.”

  8. 8.

    We do, however, acknowledge that there other are bilateral variables such as immigration. But we believe that these are of secondary importance because the patterns of regional Russian immigrants (or the 185 identified ethnic groups in Russia) to the 175 countries in the world are unlikely to be empirically relevant compared to the product of GDPs.

  9. 9.

    The estimates for a “naive” gravity equation are

    $$\begin{aligned} \log X_{ij}&= -\underset{(0.580)}{0.84}+\underset{(0.043)^{*}}{0.56}\log Y_i+\underset{(0.020)^{*}}{0.29}\log Y_j-\underset{(0.065)^{*}}{1.17}\log D_{ij}\\&N=2,\!985, \quad \hat{R^2}=0.15, \; \text{ RMSE}=2.45 \end{aligned}$$
    Table 2 OLS estimates on Russian data
  10. 10.

    A logistic regression similar to (2) except replacing the number of exporting firms with aggregate weight yields:

    $$\begin{aligned} E_{ij}&= \underset{(0.074)^{}}{0.567}+\underset{(0.002)^{*}}{0.230}\log W_{ij}-\underset{(0.008)^{*}}{0.764}\log D_{ij}-\underset{(0.004)^{*}}{0.329}\log Y_i+\underset{(0.003)^{*}}{0.076}\log Y_j\\&N=3{,}979{,}007\nonumber \end{aligned}$$


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The authors thank Thomas Holmes, Yelena Tuzova, Anton Cheremukhin, and seminar participants at Amherst College, Vasser College, Washington State University, Kansas State University, University of Scranton, University of Richmond, and the Midwest International Trade Conference and New York Economics Association annual meetings. Cassey thanks Qianqian Wang and Pavan Dhanireddy for research assistance, and Jeremy Sage for help with ArcGIS. Cassey also thanks the Western Regional Science Association and the editors of the Annals of Regional Science. Portions of this research are supported by the Agricultural Research Center Project #0540 at Washington State University. This manuscript received the Springer Award for best paper by an early career scholar at the 51st Annual WRSA Meeting, Kaui HI, February 2012.

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Cassey, A.J., Schmeiser, K.N. The agglomeration of exporters by destination. Ann Reg Sci 51, 495–513 (2013).

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  • L29