Abstract
Out-migration concerns foreigners who decide to leave a country where they used to live. Taking advantage of the OECD bilateral IMS database, we analyze the short-run determinants of out-migration using a panel of Schengen countries between 1995 and 2011. We find that out-migration is counter-cyclical: foreign nationals tend to leave host countries with high unemployment, while they are likelier to stay in good times (i.e. low unemployment). Typically, a 10 % increase in the unemployment rate leads to a 5 % increase in out-migration. Thus, short-term economic fluctuations have the same qualitative effect as restrictive migration policies in economic downturns. However, we find mixed evidence for the role of economic cycles in the potential destination countries of those flows. Movers appear to be sensitive to unemployment changes in their country of origin, but they do not seem to be sensitive to business cycles in potential destinations.
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Source: OECD IMS database
Source: OECD IMS database and world development indicators
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Notes
Throughout the paper, we also use the terms “migration outflows” or “outward migration” when referring to out-migration.
See also Dustmann et al. (2011).
Return migration cannot be disentangled from migration towards a third country, as the data does not provide information about the new country of residence of the outward migrant.
We have also undertaken two alternative checks: first, we ran our regressions again on a sub-sample of countries where the correlation between the two is statistically significant at 5 % and found similar results than those shown in the paper. Second, we also checked the consistency of our results by dropping each country of residence, one by one, in order to be sure that our results are not driven by one single country. All these results can be provided upon request.
Countries which implemented Schengen after 1995 are included in the sample only once they entered the Schengen area.
One could also think about the ’net costs’ of moving back home, where net costs correspond to the (re)adaption costs minus the satisfaction from returning to one’s original habits, culture, family, and network which were left behind after the first move.
We have alternatively computed these variables for the 3 main destinations with very similar results.
It should be noted that the usual procedures to tackle multilateral resistance cannot be implemented because the destination country is not observable; one cannot tell whether someone leaving one country of residence, say A, is returning to the origin country B or going to a third country C. Nevertheless, as one of the anonymous referees pointed out, it could be that a shock in country C still affects the decision to leave country A. By adding origin-year fixed effects however, we control at least partially for this multilateral resistance term, as a shock in a third country should not have the same impact on out-migration, across migrants from different origins. In fact, since each unobserved destination is weighted rather differently by migrants from different origins, any shock from that destination should be captured differently through the origin-time fixed effect.
Their initial motivation was to capture the heterogeneity between stayers and movers.
Some would be tempted alternatively to resort to the Bertoli and Fernandez-Huertas Moraga (2013) technique, using the Pesaran CCE estimator to account for the resistance term. But again, this technique would have been perfectly suitable had we had data by destination. One would then have been able to measure how changes in opportunities in third countries could produce an impact on moving from one country to another observed country. As discussed earlier however, in our case, the structure of the bilateral data is completely different from that modelled by Bertoli and Fernandez-Huertas Moraga. Hence, it is difficult to see the value added that we would get out of using this estimator. And even if one can see conceptually how to obtain some value added out of this technique using our data structure, the method cannot be implemented here: as it is also shown in Beine et al. (2013), we have an unbalanced panel (the data on countries of residence and countries of origin are not reported every year). As this method makes use of mean values for each observed year, and of dependent and independent variables to produce the estimate we need, these means cannot be compared across time as they would not be composed of the same set of country reporters and/or origin countries every year.
Note however that once we consider countries which are quite far or very far from each other, distance appears to matter: in the "Appendix 3" of the paper, one of the tables presents the results using all the countries and nationalities reported in the OECD dataset; it shows that there is a negative and statistically significant impact of geographical distance.
Some might flag a potential reverse causality between unemployment variables and outflows. The mechanism goes this way: people from one country exiting, if the flow of exit is sufficiently large, might reduce unemployment there. If they return home, and if the size of the corresponding flow is relatively large, they would in turn increase unemployment at home. If this is true, then our coefficients on the unemployment variables would be underestimated in absolute values. This thought is very unrealistic in our case, however, since the number of outflow migrants is extremely small compared to the unemployed in the residence countries. The maximum level of bilateral outflows is 38,950 (Italian outflows from Germany in 1997). Outflows higher than 10,000 represent 2.7 % of all bilateral flows only. Outflows higher than 30,000 (15 observations) are only Italian outflows from Germany for different years. Despite this skepticism concerning the risk of reverse causality, we ran additional regressions using the lagged value of unemployment. Our results are similar and can be obtained upon request.
This argument is very similar to Ortega and Peri (2013) on migration inflows. They find a much higher migration elasticity to income in the Schengen area than in the world sample and argue that it is explained by the lack of migration restrictions within Schengen.
Austria, Belgium, Denmark, Finland, the United Kingdom, Italy, Luxembourg, the Netherlands, Norway, and Sweden.
All results are available upon request.
Results are available upon request.
Growth in residence countries becomes significant (and negative) when standard errors are clustered at the dyadic level
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Acknowledgments
We would like to thank Gianluca Orefice and the participants at the 4th OECD-CEPII Conference on Immigration in OECD countries for their useful advice and comments. The usual disclaimers apply.
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Appendices
Appendix 1: Results on EU countries
See Table 5.
Appendix 2: Results on EU15 sample
See Table 6.
Appendix 3: Results on world sample
See Table 7.
Appendix 4: Results on a subsample of countries where data reliability is high
See Table 8.
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Bazillier, R., Magris, F. & Mirza, D. Out-migration and economic cycles. Rev World Econ 153, 39–69 (2017). https://doi.org/10.1007/s10290-016-0267-8
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DOI: https://doi.org/10.1007/s10290-016-0267-8