Academic journal article Mankind Quarterly

Employment Rates for 11 Country of Origin Groups in Scandinavia

Academic journal article Mankind Quarterly

Employment Rates for 11 Country of Origin Groups in Scandinavia

Article excerpt

Numerous studies have reported very large differences in social status and related outcomes for different country of origin groups in Western countries (Fuerst, 2012; Fuerst & Kirkegaard, 2014; Jones & Schneider, 2010; Kirkegaard, 2014a, 2015; Kirkegaard & Becker, 2017; Kirkegaard & Fuerst, 2014). Detailed data has previously been reported for Denmark and Norway (Kirkegaard, 2014a; Kirkegaard & Fuerst, 2014). However, no data has yet been published (in English) for Sweden to the author's knowledge. Recently, a new edition of the annual publication Indvandrere i Danmark (Immigrants in Denmark) (Danmarks Statistik, 2016) was published. Each edition of the series has a particular focus, and the focus of the 2016 report is comparative analysis of immigrant outcomes in the Scandinavian countries. The purpose of this paper is to present the main data in English as well as some analyses of them.

Data and analyses

Danmarks Statistik (the Danish statistics bureau) collaborated with their Swedish and Norwegian analogues (Statistiska centralbyrån and Statistisk sentralbyrå), to compile matching data for employment rates for 2014. The data concern only foreign born (1st generation) immigrants aged 20-64, and cover a diverse set of 11 countries which they deemed to have sufficient numbers for comparison purposes.1 The data only cover persons with residence and thus work permits, not illegal immigrants. The employment rates are shown in Figure 1.2

The employment rates are similar across host countries with a mean correlation of .86. The rate for Somalis in Denmark is lower than those reported in Kirkegaard and Fuerst (2014). This probably has to do with the fact that the present numbers only concern foreign born persons, while the previously reported numbers concerned all persons from Somalia, including later generations who have higher employment rates.

Analysis 1 - Analysis of variance

Two regressions were run to see how host, origin and sex predicted the employment rate. The first model (n = 36) did not include sex and had an adjusted (multiple) R of .93 (R2 = 87%).3 Analysis of variance showed that origin was the most important predictor with an eta of .88 while host had an eta of .05. In the second model (n = 72), sex-disaggregated data were used and sex was added as a predictor. This model had an adjusted R of .93 (R2 = 86%). Analysis of variance etas were: origin .89, host .20 and sex .25. Thus, origin was still by far the best predictor, and the more complex model with disaggregated data was not superior to the simpler model. However, because sex itself was non-redundant and because it was based on more data points, the betas for the complex model are reported in Table 1. The output from the first model can be found in the supplementary materials.

The beta for each origin country can be regarded as an estimate of that country's human capital taken as composite variable consisting of cognitive, personality, interest, temperament, knowledge and skills (Jones & Potrafke, 2014). Such estimates will only be precise under strict assumptions, which are that migrants are equally representative of their country of origin with respect to human capital (no differences in migrant selectivity), and lack of host x origin interactions (equal opportunity for each immigrant group).

The host country predictor can be interpreted in multiple ways. One might regard it as a measure of integrational success. This could be, but then it should not affect the natives. Figure 1 shows that native Danes have lower employment rates than native Swedes and Norwegians. The employment rate of Danes in Denmark cannot easily be ascribed to integration efforts. For that reason, the host effects are more parsimoniously explained by system-wide effects such as slightly different ways of counting employment across countries or generosity of unemployment benefits.

An alternative analytic approach here is to recode employment rates as fractions of the native employment rate in that country. …

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