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The male immigrant–native employment gap in Sweden: migrant admission categories and human capital

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Abstract

Despite having a celebrated labor market integration policy, the immigrant–native employment gap in Sweden is one of the largest in the OECD. From a cross-country perspective, a key explanation might be migrant admission group composition. In this study we use high-quality detailed Swedish register data to estimate male employment gaps between non-EU/EES labour, family reunification and humanitarian migrants and natives. Moreover, we test if differences in human capital are able to explain rising employment integration heterogeneity. Our results indicate that employment integration is highly correlated with admission category. Interestingly, differences in human capital, demographic and contextual factors seem to explain only a small share of this correlation. Evidence from auxiliary regressions suggests that low transferability of human capital among humanitarian and family migrants might be part of the story. The article highlights the need to understand and account for migrant admission categories when studying employment integration.

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Fig. 1

Source: Statistics Sweden

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Notes

  1. The labour market integration of refugees can also be impeded on an individual level through the long reach of traumatic experiences.

  2. An anonymous referee made the important point that location choice, and arguably also some of our socio-demographic controls, might be endogenous. For instance, a Stockholm dummy could pick up labour market differences but also transferability and ability through self-selection. This makes a clean interpretation of the county dummy complicated and might also affect our estimates for human capital and transferability. As our study is mainly descriptive, making natives and immigrants comparable with respect to local labour markets is our highest priority.

  3. An anonymous referee pointed out that an alternative strategy would be to include an interaction between education and migration admission category into our pooled regression framework. While we agree that this strategy has merit in an ordinary least squares framework, there are two main reasons why we followed the decomposition approach. First, the marginal effect of an interaction in a probit framework is neither equal to the marginal effect of the interaction term nor constant. Second, instead of interacting only education with admission category, our decomposition can be interpreted as a fully interacted model. Hence, while one can question if the additional effort is worthwhile, we chose the comprehensive approach. A discussion on the arising issue of scaling is given in “Methodology: determinants of employment and employment gap decomposition”.

  4. The contribution is based on weighing each gap. Therefore all contributions sum to 1. For details on the calculation of weights, see Yun (2004).

  5. We also conducted a range of technical robustness checks for decomposition analysis. Our results are robust to a non-pooled framework with either native or immigrant-specific coefficients. Moreover, switching the reference group or accounting for differences in the relative group size produces similar results. However, the contribution of human capital then lies between the Blinder–Oaxaca and Probit results.

  6. The employment rate of immigrants from the Middle East and Africa is 34 percentage points lower than for their native counterparts. For European immigrants the gap is only 14 percentage points.

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Funding

The data used in this study were funded by the Swedish call of the European Integration Fund (Grant No. IF 5-2012).

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Correspondence to Marc-André Luik.

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The authors declare that they have no conflict of interest.

Appendix

Appendix

See Tables 6, 7, 8, 9, 10, 11, 12, 13 and 14.

Table 6 Probit model of employment
Table 7 Employment rate by educational attainment
Table 8 Employment rate by education type
Table 9 Probit model of employment: pooled by intake category
Table 10 Non-linear decomposition analysis: detail
Table 11 Immigrant–native employment gap by source region: all men
Table 12 Immigrant–native employment gap by source region: male labour immigrants
Table 13 Immigrant–native employment gap by source region: male family immigrants
Table 14 Immigrant–native employment gap by source region: male humanitarian immigrants

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Luik, MA., Emilsson, H. & Bevelander, P. The male immigrant–native employment gap in Sweden: migrant admission categories and human capital. J Pop Research 35, 363–398 (2018). https://doi.org/10.1007/s12546-018-9206-y

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