Skip to main content
SpringerLink
Log in

The network of migrants and international trade

  • Published:
Economia Politica Aims and scope Submit manuscript

Abstract

This paper investigates the relationship between international trade and migration with the specific aim of estimating direct and indirect effect of the latter on cross-border flows of both homogeneous and differentiated goods. Adopting a spatial econometric approach along with a gravity model set-up, we account for the role of ethnic communities in neighbouring countries on trade, and we propose a new way to define neighbours based on the intensity of links in the migration network. Our approach is particularly well suited to measure the indirect effect stemming from the presence of significant ethnic communities on trade through a “market familiarization” effect. Using data covering all countries between 1970 and 2000, we find a significant indirect effect of migration on trade, that depends on the chosen weight matrix.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. Although subsequent work has shown that the actual magnitude of this pro-trade effect is smaller than originally estimated (see Felbermayr et al. 2010), its existence and its specific importance for differentiated goods has always been confirmed.

  2. Rauch and Trindade (2002) evaluated the effect of the Chinese network alone, while Felbermayr et al. (2010) extended the analysis to all potential ethnic group, but one at a time.

  3. In this context a spatial spillover is defined as the relationship between a characteristic of a country and the outcome of another country located in its neighbourhood (LeSage 2014).

  4. The exact meaning of significant number of migrants is explained in Sect. 2.1.

  5. Similarly, one could let k be a migration neighbour of the destination country j. In such a case migrants from i to k should represents another indirect channel affecting trade from i to j. However, we have no theoretical and empirical reason to model this second type of network dependence.

  6. Second-generation Albanians also play a role, since some of the Italian migrants abroad can have family ties to Albania.

  7. Also Anselin and Arribas-Bel (2013) demonstrated by simulation experiments that fixed effects correctly remove autocorrelation only in some specific cases.

  8. Assuming two countries, i and h, let \(N_i\) be the total number of migrants from country i, \(N_h\) the total number of migrants to country h, \(N_k\) the overall total number of migrants and \(N_{ih}\) the observed number of migrants from i to h. Under the null hypothesis of random co-occurrence, i.e. country h hosts indifferently migrants from every origin country, the probability of observing X migrants is given by the hypergeometric distribution

    $$\begin{aligned} H(X|N_k,N_i,N_h) = \frac{{N_i \atopwithdelims ()X} {{N_k-N_i} \atopwithdelims (){N_h-X}}}{{N_k \atopwithdelims ()N_h}} \end{aligned}$$

    and we can associate a p value with the observed \(N_{ih}\) as

    $$\begin{aligned} p(N_{ih}) = 1 - \sum _{X=0}^{N_{ih}-1} H(X|N_k,N_i,N_h). \end{aligned}$$

  9. The share of links present in both networks has grown from 65% in 1960 to more than 70% in 2000. Sgrignoli et al. (2015) provides evidence pointing in the same direction.

  10. More in details, since we also need to account for the time index in the pooled model specification (see Sect. 3) the matrix that defines the set of our origin countries’ neighbours has dimension \(n^2*t \times n^2*t\) and it is constructed as follows: \(\mathbf W ^{M}_{Kr,t} = I_t \otimes \mathbf W ^M_{Kr}\), where \(\mathbf W ^M_{Kr} = \mathbf W ^M \otimes I_n\).

  11. All the data (except for the dummies) are in \(\log _{10}\).

  12. To compute direct and indirect impacts in the SDM specification for the rth explanatory variable, we compute the following partial derivative expression: \(\frac{\partial \mathbf T _{ij}}{\partial \mathbf X _{k,hk}} = S_{r}(\mathbf W ^{M}_{Kr})_{ij,hk} , \forall k = 1,\ldots , K\), where \(S_r (\mathbf W ^{M}_{Kr}) = V(\mathbf W ^{M}_{Kr}) (I_{n^2} \beta _k + \mathbf W ^{M}_{Kr} \gamma _k)\) is a \(n^2 \times n^2\) matrix, with \(V(\mathbf W ^{M}_{Kr}) = (I_{n^2} - \rho \mathbf W ^{M}_{Kr})^{-1}\). The presence of global spillovers can be seen by recognizing that \((I_{n^2} - \rho \mathbf W ^{M}_{Kr})^{-1} = I_{n^2} + \rho \mathbf W ^{M}_{Kr} + \rho ^2 \mathbf W ^{M,2}_{Kr} + \ldots\) For each explanatory variable in the model, the direct impact is the average of the values in the main diagonal of \(S_r(\mathbf W ^{M}_{Kr})\), while the total impact is determined as the sum of all the elements of the matrix, divided by \(n^2\). The indirect impact is simply the difference between the total and the direct effect: \(Indirect = Total - Direct = \frac{\sum _{ij}\sum _{hk}(S_r(\mathbf W ^{M}_{Kr}))_{ij,hk}}{n^2} - \frac{\sum _{ij}\sum _{hk} diag(S_r(\mathbf W ^{M}_{Kr})_{ij,hk})}{n^2}\).

  13. We use nominal values for trade data, as well as for GDP per capita. Besides being customary in the literature [see for instance Head et al. (2010)], the choice is motivated by the fact that price levels are part of the multilateral trade resistance term: hence, by properly taking into account the MTR, there is no need for any additional deflation.

  14. Santos Silva and Tenreyro (2006) warn against the use of log-linearized gravity models, because of the loss of information associated with discarding country-pairs with zero trade flows. For this reason, we have also performed a Poisson pseudo maximum likelihood (PPML) estimation: we find that both OLS and PPML yield a significant effect of migration on trade.

  15. Table 8 in the Appendix reports the standard errors from a one-way clustering approach on country pairs to control for auto-correlation and heteroskedasticity. The two different alternatives for correcting standard errors yield qualitatively similar results in terms of significance of the coefficients.

  16. This is in good agreement with the literature [see for instance Felbermayr et al. (2015)] and suggests that distance picks up the effect of formal and informal knowledge barriers.

  17. The estimation approach proposed in Kelejian and Piras (2014) to remove endogeneity applies only to SAR models.

  18. It obviously follows that in the spatial autoregressive models we do not include origin- and destination- fixed effects.

  19. \(\mathbf W ^{M}_{Kr,t}\), which is the \(n^2*t \times n^2*t\) network weight matrix described in Sect. 2.1, constructed on 1960 migration data, and therefore a time invariant matrix where data for 1960 is replicated t=4 times.

  20. The spatial weight matrix is computed as the inverse distance metric (1 / dist). We call this matrix \(\mathbf W ^{S}_{Kr,t}\), that is a \(n^2*t \times n^2*t\) matrix generated using a Kronecker product on a initial \(n * n\) inverse distance weight matrix.

  21. In order to make our work comparable with Behrens et al. (2012), who found this parameter to be negative in the Cliff–Ord (SARAR) specification, we also estimated that same model, finding a negative \(\rho\) coefficient as well. SARAR regression results are available upon request.

References

  • Aleksynska, M., & Peri, G. (2014). Isolating the network effect of immigrants on trade. The World Economy, 37(3), 434–455.

    Article  Google Scholar 

  • Altonji, J. G., & Card, D. (1991). The effects of immigration on the labor market outcomes of less-skilled natives. In Immigration, trade, and the labor market (pp. 201–234). University of Chicago Press.

  • Anderson, J. E. (1979). A theoretical foundation for the gravity equation. The American Economic Review, 69(1), 106–116.

    Google Scholar 

  • Anderson, J. E., & van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. American Economic Review, 93(1), 170–192.

    Article  Google Scholar 

  • Anderson, J. E., & van Wincoop, E. (2004). Trade costs. Journal of Economic Literature, 42(3), 691–751.

    Article  Google Scholar 

  • Anselin, L. (1988). Spatial econometrics: methods and models (Vol. 4). Berlin: Springer.

    Book  Google Scholar 

  • Anselin, L., & Arribas-Bel, D. (2013). Spatial fixed effects and spatial dependence in a single cross-section. Papers in Regional Science, 92(1), 3–17.

    Article  Google Scholar 

  • Anselin, L., Bera, A. K., Florax, R., & Yoon, M. J. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1), 77–104.

    Article  Google Scholar 

  • Anselin, L., & Le Gallo, J. (2006). Interpolation of air quality measures in hedonic house price models: spatial aspects. Spatial Economic Analysis, 1(1), 31–52.

    Article  Google Scholar 

  • Baier, S. L., & Bergstrand, J. H. (2009). Bonus vetus ols: A simple method for approximating international trade-cost effects using the gravity equation. Journal of International Economics, 77(1), 77–85.

    Article  Google Scholar 

  • Behrens, K., Ertur, C., & Koch, W. (2012). Dual gravity’: Using spatial econometrics to control for multilateral resistance. Journal of Applied Econometrics, 27(5), 773–794.

    Article  Google Scholar 

  • Bratti, M., Benedictis, L., & Santoni, G. (2014). On the pro-trade effects of immigrants. Review of World Economics, 150, 557–594.

    Article  Google Scholar 

  • Briant, A., Combes, P.-P., & Lafourcade, M. (2014). Product complexity, quality of institutions and the protrade effect of immigrants. The World Economy, 37(1), 63–85.

    Article  Google Scholar 

  • Burger, M., Van Oort, F., & Linders, G.-J. (2009). On the specification of the gravity model of trade: zeros, excess zeros and zero-inflated estimation. Spatial Economic Analysis, 4(2), 167–190.

    Article  Google Scholar 

  • Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2011). Robust inference with multiway clustering. Journal of Business and Economic Statistics, 29(2), 238–249.

    Article  Google Scholar 

  • Case, A. C., Rosen, H. S., & Hines, J. R, Jr. (1993). Budget spillovers and fiscal policy interdependence: Evidence from the states. Journal of public economics, 52(3), 285–307.

    Article  Google Scholar 

  • Cliff, A. D., & Ord, J. K. (1973). Spatial autocorrelation (Vol. 5). London: Pion.

    Google Scholar 

  • Cliff, A. D., & Ord, J. K. (1981). Spatial processes: Models and applications (Vol. 44). London: Pion.

    Google Scholar 

  • Cohen, J. P., & Paul, C. J. M. (2004). Public infrastructure investment, interstate spatial spillovers, and manufacturing costs. Review of Economics and Statistics, 86(2), 551–560.

    Article  Google Scholar 

  • Eaton, J., & Kortum, S. (2002). Technology, geography, and trade. Econometrica, 70, 1741–1779.

    Article  Google Scholar 

  • Egger, P. (2004). Estimating regional trading bloc effects with panel data. Review of World Economics, 140(1), 151–166.

    Article  Google Scholar 

  • Ehrhart, H., Goff, M. L., Rocher, E. & Singh, R. J. (2014). Does migration foster exports? Evidence from Africa. Policy research working paper 6739, World Bank.

  • Elhorst, J. P. (2010). Applied spatial econometrics: Raising the bar. Spatial Economic Analysis, 5(1), 9–28.

    Article  Google Scholar 

  • Elhorst, J. P., Lacombe, D. J., & Piras, G. (2012). On model specification and parameter space definitions in higher order spatial econometric models. Regional Science and Urban Economics, 42(1), 211–220.

    Article  Google Scholar 

  • Fagiolo, G., & Mastrorillo, M. (2014). Does human migration affect international trade? A complex-network perspective. PloS One, 9(5), e97331.

    Article  Google Scholar 

  • Feenstra, R. C. (2003). Advanced international trade: theory and evidence. Princeton: Princeton University Press.

    Google Scholar 

  • Feenstra, R. C., Lipsey, R. E., Deng, H., Ma, A. C. & Mo, H. (2005). World trade flows: 1962–2000. Working paper 11040, National Bureau of Economic Research. http://www.nber.org/papers/w11040. Accessed Apr 2014.

  • Felbermayr, G., Grossmann, V., & Kohler, W. (2015). Migration, international trade and capital formation: Cause or effect? Handbook of the Economics of Migration, 1B(18), 913–1025.

    Article  Google Scholar 

  • Felbermayr, G. J., Jung, B., & Toubal, F. (2010). Ethnic networks, information, and international trade: Revisiting the evidence. Annales d’Economie et de Statistique, 97(98), 41–70.

    Google Scholar 

  • Florax, R. J., Folmer, H., & Rey, S. J. (2003). Specification searches in spatial econometrics: The relevance of hendrys methodology. Regional Science and Urban Economics, 33(5), 557–579.

    Article  Google Scholar 

  • Genc, M., Gheasi, M., Nijkamp, P., Poot, J., et al. (2012). The impact of immigration on international trade: A meta-analysis. Migration Impact Assessment: New Horizons, 1, 301.

    Article  Google Scholar 

  • Glick, R., & Rose, A. K. (2002). Does a currency union affect trade? The time-series evidence. European Economic Review, 46(6), 1125–1151.

    Article  Google Scholar 

  • Gould, D. M. (1994). Immigrant links to the home country: Empirical implications for us bilateral trade flows. The Review of Economics and Statistics, 76(2), 302–316.

    Article  Google Scholar 

  • Greene, W. H. (2003). Econometric analysis. Boston: Pearson Education India.

    Google Scholar 

  • Head, K., & Mayer, T. (2014). Gravity equations: Workhorse, toolkit, and cookbook. In G. Gopinath, F. Helpman, & K. Rogoff (Eds.), Handbook of International Economics, chapter 3 (Vol. 4, pp. 131–195). Amsterdam: Elsevier.

    Google Scholar 

  • Head, K., Mayer, T., & Ries, J. (2010). The erosion of colonial trade linkages after independence. Journal of International Economics, 81(1), 1–14.

    Article  Google Scholar 

  • Head, K., & Ries, J. (1998). Immigration and trade creation: Econometric evidence from Canada. Canadian Journal of Economics, 31(1), 47–62.

    Article  Google Scholar 

  • Helliwell, J. F. (1998). How much do national borders matter?. Washington, DC: Brookings Institution Press.

    Google Scholar 

  • Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating trade flows: Trading partners and trading volumes. The Quarterly Journal of Economics, 123(2), 441–487.

    Article  Google Scholar 

  • Kelejian, H. H., & Piras, G. (2014). Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes. Regional Science and Urban Economics, 46, 140–149.

    Article  Google Scholar 

  • Kelejian, H. H., & Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. The Journal of Real Estate Finance and Economics, 17(1), 99–121.

    Article  Google Scholar 

  • Kelejian, H. H., & Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International Economic Review, 40(2), 509–533.

    Article  Google Scholar 

  • Lambert, D. M., Brown, J. P., & Florax, R. J. (2010). A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application. Regional Science and Urban Economics, 40(4), 241–252.

    Article  Google Scholar 

  • LeSage, J. P. (2014). What regional scientists need to know about spatial econometrics. The Review of Regional Studies, 44(1), 13–32.

    Google Scholar 

  • LeSage, J. P., & Fischer, M. M. (2016). Spatial regression-based model specifications for exogenous and endogenous spatial interaction. In R. Patuelli, G. Arbia (Eds.), Spatial econometric interaction modelling (pp. 15–36). Cham: Springer

    Google Scholar 

  • LeSage, J. P., & Ha, C. L. (2012). The impact of migration on social capital: Do migrants take their bowling balls with them? Growth and Change, 43(1), 1–26.

    Article  Google Scholar 

  • LeSage, J. P., & Pace, R. K. (2008). Spatial econometric modeling of origin–destination flows. Journal of Regional Science, 48(5), 941–967.

    Article  Google Scholar 

  • LeSage, J. P., & Pace, R. K. (2011). Pitfalls in higher order model extensions of basic spatial regression methodology. The Review of Regional Studies, 41(1), 13–26.

    Google Scholar 

  • LeSage, J. P., & Thomas-Agnan, C. (2015). Interpreting spatial econometric origin–destination flow models. Journal of Regional Science, 55(2), 188–208.

    Article  Google Scholar 

  • Mayer, T. & Zignago, S. (2011). Notes on CEPII’s distances measures: The GeoDist database. Working paper 25, CEPII.

  • Metulini, R., Patuelli, R., & Griffith, D. A. (2018). A spatial-filtering zero-inflated approach to the estimation of the gravity model of trade. Econometrics, 6(1), 9.

    Article  Google Scholar 

  • Özden, Ç., Parsons, C. R., Schiff, M., & Walmsley, T. L. (2011). Where on earth is everybody? The evolution of global bilateral migration 1960–2000. The World Bank Economic Review, 25(1), 12–56.

    Article  Google Scholar 

  • Parent, O., & LeSage, J. P. (2008). Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers. Journal of Applied Econometrics, 23(2), 235–256.

    Article  Google Scholar 

  • Patuelli, R., Linders, G.-J. M., Metulini, R. & Griffith, D. A. (2016). The space of gravity: spatially filtered estimation of a gravity model for bilateral trade. In R. Patuelli, G. Arbia (Eds.), Spatial econometric interaction modelling (pp. 145–169). Cham: Springer.

    Google Scholar 

  • Peri, G., & Requena-Silvente, F. (2010). The trade creation effect of immigrants: Evidence from the remarkable case of Spain. Canadian Journal of Economics, 43(4), 1433–1459.

    Article  Google Scholar 

  • Rauch, J. E. (1999). Networks versus markets in international trade. Journal of international Economics, 48(1), 7–35.

    Article  Google Scholar 

  • Rauch, J. E., & Trindade, V. (2002). Ethnic Chinese networks in international trade. Review of Economics and Statistics, 84(1), 116–130.

    Article  Google Scholar 

  • Riccaboni, M., Rossi, A., & Schiavo, S. (2013). Global networks of trade and bits. Journal of Economic Interaction and Coordination, 8(1), 1–24.

    Article  Google Scholar 

  • Santos Silva, J. A., & Tenreyro, S. (2006). The log of gravity. The Review of Economics and statistics, 88(4), 641–658.

    Article  Google Scholar 

  • Sellner, R., Fischer, M. M., & Koch, M. (2013). A spatial autoregressive poisson gravity model. Geographical Analysis, 45(2), 180–201.

    Article  Google Scholar 

  • Sgrignoli, P., Metulini, R., Schiavo, S., & Riccaboni, M. (2015). The relation between global migration and trade networks. Physica A: Statistical Mechanics and its Applications, 417, 245–260.

    Article  Google Scholar 

  • Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65(3), 557–586.

    Article  Google Scholar 

  • White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817–838.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics and Management, University of Brescia, Contrada Santa Chiara 50, 25122, Brescia, Italy

    Rodolfo Metulini

  2. Unipol Financial Group, Bologna, Italy

    Paolo Sgrignoli

  3. Laboratory for the Analysis of Complex Economics Systems, IMT School for Advanced Studies Lucca, Lucca, Italy

    Massimo Riccaboni

  4. Department of Economics and Management, School of International Studies, University of Trento, Trento, Italy

    Stefano Schiavo

  5. Département de Recherche Innovation et Concurrence, Observatoire Français des Conjonctures Économiques, Paris, France

    Stefano Schiavo

  6. Department of Management Stategy and Innovation, KU Leuven, Leuven, Belgium

    Massimo Riccaboni

Authors
  1. Rodolfo Metulini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Sgrignoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefano Schiavo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Massimo Riccaboni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rodolfo Metulini.

Appendix

Appendix

See Table 8.

Table 8 Gravity results with OLS and FE models, with and without instrumenting migration for reverse causality
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Metulini, R., Sgrignoli, P., Schiavo, S. et al. The network of migrants and international trade. Econ Polit 35, 763–787 (2018). https://doi.org/10.1007/s40888-018-0106-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40888-018-0106-6

Keywords

JEL Classification

Search

Navigation