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On the pro-trade effects of immigrants

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Abstract

This paper investigates the causal effect of immigration on trade flows using Italian panel data at the province level. We exploit the exceptional characteristics of the Italian data (the fine geographical disaggregation, the very high number of countries of origin of immigrants, the high heterogeneity of social and economic characteristics of Italian provinces, and the absence of cultural or historical ties with the countries where immigrants come from) coupled with the use of a wide set of fixed effects and an ‘instrument’ based on immigrants’ enclaves. We find that immigrants have a significant positive effect on both exports and imports, but much larger for the latter. The pro-trade effects of immigrants tend to decline in space, and even turn negative when large ethnic communities are located too far away from a specific province (via a trade-diversion effect). Moreover, while our data show inter-ethnic spillovers for exports, we find no evidence that networks between different ethnicities affect provinces’ imports. Finally, we provide evidence of a substantial heterogeneity in the effects of immigrants: the impact on trade tends to be larger for immigrants coming from low-income countries, for earlier waves of immigrants, and for least advanced provinces (Southern Italy).

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Notes

  1. See De Benedictis and Taglioni (2011) for an empirically oriented review of the literature on the gravity model in international trade, and Head and Mayer (2014) for a state of the art survey.

  2. Several studies have explored the role of ethnic networks in international trade since Rauch (2001). See, among others, Rauch and Trindade (2002), Epstein and Gang (2004), Felbermayr et al. (2010), Coughlin and Wall (2011), and Hiller (2013).

  3. The Italian dataset guarantees the most extensive countries’ coverage among those considered in the empirical literature, reducing the risk that the selection of specific countries may bias the estimates of the elasticity of trade to immigration.

  4. Colonial origins and linguistic proximity can both influence trade—and so they do in the traditional analyses of bilateral trade based on the gravity model [see Head et al. (2010), Helliwell (1999), Debaere et al. (2013) and De Benedictis and Taglioni (2011), Anderson (2011) and Head and Mayer (2014) for a review of the gravity model in international trade]—and immigration and, therefore, they can confound the relationship between immigrants and trade flows.

  5. To the best of our knowledge, the Italian provinces are the smallest geographical entities used so far to investigate the link between immigration and trade.

  6. For the unfamiliar reader, NUTS stands for Nomenclature of Territorial Units for Statistics and is a European Union geocode standard for referencing the subdivisions of countries for statistical purposes. There are three zonal levels, NUTS-1, NUTS-2 and NUTS-3, which for Italy correspond to the country, region (regione) and province (provincia) levels, which also correspond to the three main administrative units of the country.

  7. To be more precise, the mean area of Italian provinces is 2,816 square km with a coefficient of variation of 0.17, almost 57 times tinier than American states (162,176 square km, when Alaska and Washington DC are included), and more than 200 times smaller than Canadian provinces (606,293 square km when Nunavut, North-West and Yukon territories are excluded). These administrative units are also much smaller and more regular in size with respect to French metropolitan départements and Spanish provinces. The mean area of French départements is 5,666 square km with a coefficient of variation of 0.33 (when Corsica and overseas French regions are excluded), whereas the related figures for Spanish provinces are 10,118 square km with a coefficient of variation of 0.47 (excluding Ceuta and Melilla).

  8. More precisely, we consider 103 provinces until 2006 and 107 afterwards. The number of Italian provinces changed in recent times, as reported by ISTAT. In the mid 1990s the number of Italian provinces was 103. In 2001 the Sardinia autonomous region established 4 new provinces, that became operative during 2005. In 2004 the Italian Parliament established 3 new provinces that became operative in 2009. The total actual number of provinces is 110. Since our dataset does not include observations for the years after 2009, we do not consider these latter changes in the number of Italian provinces.

  9. The information of Extra-EU transactions is based on the “Documento Amministrativo Unico” (DAU), for the intra-EU exchanges the custom system has been replaced, since 1993, by the Intrastat standard. The original values of trade flows, in euros, have been converted in US dollars using the nominal exchange rate from the World Development Indicators (WDIs on-line database) in order to make them consistent with GDP data used in the gravity equations. The conversion is not influencing the results, since in the multivariate regression in Sect. 4 we use country time-varying fixed effects.

  10. Like all previous papers on the topic, we only consider legal immigrants. Direct information on the stocks of immigrants with illegal status is not available. However, Bianchi et al. (2012) using data on years in which there were immigration amnesties in Italy show a very high correlation between the number of illegal immigrants and the stock of legal immigrants already present at the province level.

  11. The coefficient of variation refers to the distribution of the province’s share of the total number of foreign residents by nationality.

  12. The Anderson and Wincoop (2003) specification of the gravity equation can be derived from micro-foundations, and results from an expenditure function that takes into account the fundamental role of general equilibrium effects in trade: aka, the multilateral resistance index. See De Benedictis and Taglioni (2011), Anderson (2011) and Head and Mayer (2014) on the theoretical foundation of the gravity equation.

  13. All provinces of Sardinia are omitted from the analysis in 2006. This depends on the fact that, as we said above, four new provinces were created in Sardinia and we do not have lagged values for the independent variables for 2006.

  14. See De Benedictis and Taglioni (2011) and Head and Mayer (2014) on this specific point of the gravity literature.

  15. In general, studies using a poisson specification or other nonlinear models adopt a much less richer set of fixed effects. Just to take two examples, Helpman et al. (2008) include separate importer, exporter and year fixed effects, while Eaton and Tamura (1994) include separate region, sub-continent and year fixed effects.

  16. The inverse hyperbolic sine (IHS) transformation consists of replacing \(X_{ijt}\) with \(ln(X_{ijt}+(X_{ijt}^2+1)^{1/2})\). In this case, as in the traditional logarithmic transformation, if the values of \(X_{ijt}\) are not too small, the coefficients of the covariates can be interpreted as elasticities.

  17. Like for all the attempts enumerated in Online-Appendix C, all these estimates are available from the authors upon request.

  18. The complete set of estimates is available from the authors upon request. We also estimated the benchmark specification on the whole sample but including province fixed effects to account for the specificity of Rome and Milan, and obtained estimates very close to those in column (5). We then preferred to omit from the sample these two provinces given their clear nature of outliers in the trade-immigration relationship (see Online-Appendix D).

  19. Estimates are available upon request.

  20. Immigrant stocks in the gravity equation are defined by province of residence.

  21. We thank an anonymous referee for having raised this issue.

  22. Distances are computed using the provinces’ centroids. More in detail, the spillover variables were computed by aggregating the stocks of immigrants of all provinces falling within the radius \(d\).

  23. The variable ‘same language’ is taken from Mayer and Zignago (2011). For any country pair it takes value 1 if a language is spoken by 9 % (or more) of the population in both countries and zero otherwise.

  24. Data inspection reveals that English, French, Arabic, Spanish and Portuguese are the most spoken languages in terms of country frequencies. The percentage of countries with yearly income less than 3,300 (12,000) US dollars (2002 value) are 52 (80), 85.7 (90.5), 60 (85), 60 (95) and 85.7 (85.7) for English, French, Arabic, Spanish and Portuguese languages, respectively.

  25. Indeed, Combes et al. (2005) only considered 1993, while Briant et al. (2009) took the average of trade flows over 3 years (1998, 1999 and 2000) for each département-country pairs. Hence, both these studies were unable to account for trading-pair unobserved heterogeneity.

  26. As we said above, in 2006 four new provinces were created (in Sardinia), raising the total number of provinces from 103 to 107. Hence, even fixing the base year at 1995, the instrument assumes value zero for these four provinces. We avoided imputing weights based on subjective assumptions, but checked the sensitivity to this issue by dropping observations for Sardinia after 2006, and did not obtain notable differences in the 2SLS estimates.

  27. Peri and Requena-Silvente (2010) estimate, for instance, the impact of immigrants on trade of Spanish regions, and include in the gravity equation both region-country and country-year fixed effects. First, they add one to both trade and the immigrant stock to retain observations with zeros in their logarithmic specification, and, on top of that, in the 2SLS specification using the imputed stock of immigrants as the excluded instrument, they omit trading-pair dummies from the first stage.

  28. In the IZA Discussion Paper’s version of this paper we only reported the estimates for the sample including Rome and Milan. When including these cities the elasticity of exports falls to 0.004 and ceases to be statistically significant (SE = 0.038), while the elasticity of imports is only marginally affected (0.548, SE = 0.053). This issue has been already discussed in Sect. 4.1 and Online-Appendix D.

  29. This explanation is only apparently in contrast with our specification of the gravity equation in which the stock of immigrants at time \(t-1\) affects trade at time \(t\). First, using region by country fixed effects we are exploiting within-region variation across provinces, and not only yearly changes in the stocks of immigrants. Second, yearly changes in the stock of immigrants do not necessarily refer to first-time immigrants, but also to immigrants moving from other provinces. The probability that immigrants might have transferred from other provinces, and that they already have a good knowledge of Italy, is higher for first-wave immigrants.

  30. The ratio between exports plus imports over GDP for the provinces located in the North of the country is about 0.51, while for the Center is 0.39, and for the South and Islands is 0.24—far below the country’s average 0.39.

  31. These interaction terms have been instrumented with interaction terms between macro-region dummies and the imputed stocks of immigrants.

  32. In the case of Italy reference years are 1991 and 2001; in our case we use the earliest data.

  33. Since the Docquier et al. (2009) provides the skill structure of immigrants located in Italy for each country of origin, one could also have included three different variables for low, medium and highly educated immigrants in the gravity equation. However, this is not possible since the correlation coefficients between the three different stocks of immigrants are very high in our estimation sample: 0.981 between low and medium educated immigrants, 0.947 between low and highly educated immigrants and 0.985 between medium and highly educated immigrants. The high correlation between the three stocks depends on the fact that the actual stocks by educational level are not provided by ISTAT and must be imputed by applying the 2001 distribution by skill provided by Docquier et al. (2009) (which is time and province invariant in our estimation period) to the observed total stocks of immigrants by country of origin reported by ISTAT in each year.

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Acknowledgments

The research project started while Massimiliano Bratti and Luca De Benedictis were visiting UC Berkeley in 2010. Massimiliano Bratti wishes to thank IRLE and CLE and Luca De Benedictis the ARE Department for their hospitality. Luca De Benedictis and Gianluca Santoni received financial support by the Italian Ministry of Education, University and Research under the grant PRIN 2009 “The international trade network: empirical analyses and theoretical models” (www.netrade.org). We thank Rosalia Alessi, Antonella Ciccarese, Elena Mazzeo, and Alessia Proietti for their generous help with the data, and Massimo Tamberi for the references on super-diversity in migration. We also thank two anonymous reviewers, Sylvia Kuenne, and all the people who gave comments on previous versions of the paper, in particular Antonio Accetturo, Paolo Buonanno, Tommaso Frattini, Hubert Jayet, Giovanni Pica and participants in seminar and conference presentations at the Polytechnic Institute of Milan, the Solvay Brussels School of Economics & Management, ERSA 2011 (Barcelona), ETSG 2011 (Copenhagen), ITSG 2011 (Milan), AIEL 2011 (Milan), SIE 2011 (Rome), ‘Brucchi Luchino’ 2011 (Rome), CIE 2012 (Granada) and EGI 2012 (Bari). A previous and partly different version of this paper circulated under the same title as IZA Discussion Papers 6628. The usual disclaimer applies.

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Authors and Affiliations

  1. DEMM, Università degli Studi di Milano, Via Conservatorio 7, 20122, Milan, MI, Italy

    Massimiliano Bratti

  2. DED, Università degli Studi di Macerata, Via Crescimbeni 20, 62100, Macerata, MC, Italy

    Luca De Benedictis

  3. IE, Scuola Superiore Sant’Anna, Piazza Martiri della Liberta, 33, 56127, Pisa, PI, Italy

    Gianluca Santoni

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  1. Massimiliano Bratti
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Correspondence to Luca De Benedictis.

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Bratti, M., De Benedictis, L. & Santoni, G. On the pro-trade effects of immigrants. Rev World Econ 150, 557–594 (2014). https://doi.org/10.1007/s10290-014-0191-8

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