Abstract
We offer an empirical, econometric analysis of the impact of migration on the EU27’s NUTS2 regions in the period 2000–2007. We find that migration had no significant impact on regional unemployment in the EU, but affected both GDP per capita and productivity. A 1 percentage point increase in immigration to immigration regions increased GDP per capita by about 0.02 % and productivity by about 0.03 % on impact and by 0.44 % for GDP per capita and 0.20 % for productivity in the long run. For emigration regions an increase in the emigration rate leads to similar reductions of GDP per capita and productivity both on impact and in the long run. Since immigration regions are often regions with above average GDP, while emigration regions in Europe practically all have below average GDP, migration does not seem to promote convergence.
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Notes
We refer to the Central and Eastern European countries acceding to the EU in 2004/2007 as the EU 10 and to the countries that were EU member states already before 2004 as the EU15.
Dustmann et al. (2008) argue that immigration of a particular skill group used by an industry may also lead to the change in the technology used in that industry. An increase of unskilled workers might thus promote the use of labor intensive production methods, for example agriculture may produce more labor intensive crops if unskilled labor is plentiful.
Since population data from EUROSTAT sometimes disaccords with national sources we corrected for discrepancies using national sources in the critical cases of Poland, Slovakia and the Czech Republic. For the other countries EUROSTAT data is consistent with national sources and the stylized facts reported in the literature (see e.g. Facchini and Mayda 2008).
Such data can be obtained from the European Labor Force Survey. This data, however, is missing for all EU10 and some EU15 countries either for the early years or even the first half of our investigation period. Furthermore, in many countries the small sample size for foreign born and the repeated methodological changes to these data lead to substantial measurement error mirrored in often implausible jumps in the time series. Therefore—although this clearly limits the scope of our analysis—we cannot distinguish the effects of migration of different skill levels and/or different nationalities.
This is given as the sum of absolute changes in shares over sectors of employment as compared to the previous year on a crude sector breakdown which differentiates between employment in agriculture, manufacturing, construction, trade and restaurants and transport (as one group), financial services and real estate, and non-market services.
Note we exclude the French overseas territories as well as Ceuta and Melilla from the analysis to focus only on the European EU regions.
This table reports both the overall standard deviation as well the within standard deviation (which is the standard deviation of indicators after controlling for region specific averages for the years 2000–2007) used to identify migration effects in our estimates.
As pointed out above, here, due to data constraints, we have to exclude Germany, France, Greece, Ireland, Portugal and Bulgaria and the UK. This results in a drop in the number of observations.
This result is highly robust. In results not reported we also estimated the specifications with other instrumental variable techniques as well as without controlling for endogeneity. We also experimented with additional controls suggested by Elhorst (2003) (population growth, age, long term unemployment, sector specialization and wages) as well as with including lagged migration rates to account for potential delays in the effect of migration on unemployment. The only significant result was a negative impact of migration on the unemployment rate in uninstrumented equations. This specification, however, suffers from a reverse causality problem (i.e. migrants moving to low unemployment regions). Furthermore, in specifications with a larger set of explanatory variables, while leading to qualitatively similar results, test statistics often performed poorly due to the larger instrument set.
We also considered the potential impact of migration on youth and long term unemployment. These results indicated an insignificant impact of migration on youth unemployment but potentially a small but significant increase of long term unemployment. They, however, suffered from low test statistics for the instrument validity, so that we do not report them here.
Again this result is highly robust. Estimating specifications with other instrumental variable techniques and without controlling for endogeneity as well as including lagged values of the net migration rate leaves the significantly positive coefficient of contemporaneous migration untouched, while lagged values remained insignificant.
The long run effect on productivity is therefore smaller than that on GDP per capita on account of lower persistence of productivity.
As before, this result is robust to specifications using other instrumental variable techniques as well as without controlling for endogeneity. Including lagged values of net migration, however, renders both the contemporaneous as well as the lagged migration rate insignificant and makes the test statistics questionable.
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Acknowledgments
Research for this paper has been undertaken as part of the project “The Impact of the Single Market” commissioned by the European Commission, DG Regio. We thank the participants of the WIFO Workshop Regional Development, Integration and Mobility in the European Union in Vienna, the EU Real/CRENOS Workshop on Economic Development in Peripheral Regions in Alghero and of the ERSA conference in Barcelona as well as two anonymous reviewers for helpful comments. All mistakes remain in the responsibility of the authors.
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Huber, P., Tondl, G. Migration and regional convergence in the European Union. Empirica 39, 439–460 (2012). https://doi.org/10.1007/s10663-012-9199-2
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DOI: https://doi.org/10.1007/s10663-012-9199-2