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What Makes a Satisfied Immigrant? Host-Country Characteristics and Immigrants’ Life Satisfaction in Eighteen European Countries

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Abstract

Based on the data from six waves of the European Social Survey collected from 18 European countries between 2002 and 2012, we aimed at explaining the variation in immigrants’ life satisfaction across countries, by focusing on host countries’ characteristics. By adopting the multi-level analysis, we examined the national-level traits from three aspects: namely, the climate of immigrant reception, the extent of public goods provision and the level of economic inequality. Our findings suggest that immigrants are likely to be more satisfied in countries that offer more welcoming social settings. However, this association is significant only when the social setting is measured by attitudes of the native-born towards immigrants, rather than by legal immigration regulations and policies. When taking into account the extent to which host country is able to provide public goods, country’s wealth levels seems not to matter for immigrants’ life satisfaction, whereas countries’ levels of human development is associated with an increase in immigrants’ life satisfaction albeit only at the 10% significance level. The role of economic inequality varies with immigrants’ own socio-economic statuses. On average, immigrants are less satisfied with their lives in host countries with higher levels of economic inequality. However, highly educated immigrants tend not to perceive economic inequality of the country as an obstacle of their satisfaction.

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Fig. 1

Source ESS 2002–2012 (rounds 1–6), authors’ calculations

Fig. 2

Source: http://www.mipex.eu/download-pdf

Fig. 3

Source: ESS 2002–2012 (rounds 1–6), authors’ calculations

Fig. 4

Source: http://hdr.undp.org/en/composite/trends

Fig. 5

Source: http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD

Fig. 6

Source: http://data.worldbank.org/indicator/SI.POV.GINI

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Notes

  1. In countries formerly belonging to the Soviet Union (e.g., Estonia and Ukraine), a part of non-native-born population are people who moved or were resettled during the Soviet Union era. We thus do not consider this kind of population as typical international migrants.

  2. Before 1980, migration flows varied substantially across the analysed countries. Southern European countries, Finland and Ireland were largely emigration countries that sent their citizens to the wealthier Northern and Western European countries or overseas. Whereas the UK or the Netherlands experienced substantial immigration flows from their former colonies, the rest of the Western receiving countries resorted to the recruitment of foreign labour force (Kogan 2007).

  3. We decided to deliberately focus on those who took an independent decision to migrate to another country and excluded migration of dependent children, due to our interest in immigrants’ life satisfaction in the economic dimension. However, releasing the restriction on the age of migration does not change results substantially. Relevant results are not provided in this paper but available upon request.

  4. The reason behind this decision is that we are not interested in the overall temporal trends of the macro-level variables. Rather, our focus is on within- and between-country differences in the dimensions discussed in the paper.

  5. For information on sample sizes for each country and wave, refer to Table 4 in “Appendix”.

  6. Life satisfaction is measured at the ordinal scale and hence ordered logistic regression is applicable. Since results of the ordered logit are rather similar to those of the OLS, but the latter set of results is easier to be interpreted, in the following, we present results of the OLS regressions. Results of the ordinal logistic regressions are available upon request.

  7. A question about happiness was also available in the ESS data and it was similarly measured by an 11-point scale. Results of the analysis using happiness as a dependent variable are similar to those presented here. They are available upon request.

  8. See http://www.mipex.eu/download-pdf.

  9. The factor analysis reveals one-factor solution with the Cronbach’s Alpha of 0.82.

  10. See http://hdr.undp.org/en/composite/trends.

  11. See http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD.

  12. See http://data.worldbank.org/indicator/SI.POV.GINI.

  13. In order to be able to cover the whole population, including currently unemployed and inactive respondents, we abstained from including the variable pertaining to respondents’ occupation. Besides, having education, occupational status and income in one equation would cause multicollinearity problems.

  14. The coding of income variable changes across waves 1–3 and 4–6 of ESS. We harmonized the coding of earlier waves to match that of the later waves (deciles of income distribution) relying on available transformation routine (http://www.talkstats.com/showthread.php/44664-European-Social-Survey-income-variable).

  15. We need to point out that variables, such as subjective health, feeling of safety, and sociability, are not strictly exogenous, as the causal relations between these variables and life satisfaction are controversial in the existing literature. By including them as control variables, however, we are interested only in the potential associations between these variables and the dependent variable.

  16. Among the native-born the difference in life satisfaction is even larger.

  17. For information on the within-country variation in all macro-level variables consult Table 5 in “Appendix”.

  18. Table 6 in “Appendix” presents the final model of the corresponding analyses for the native-born population.

  19. Models 3a and 3b, 5a and 5b as well as 6a and 6b incorporate different operationalisations of countries’ levels of public goods’ provision: HDI and GDP per capita respectively.

  20. Since this effect is neither substantial nor statistically significant, we do not include the MIPEX-variable into the subsequent multivariate analyses (models 5–6 in Table 1).

  21. These findings contrast the ones for the native-born (see Table 6 in “Appendix”), where coefficients for HDI and GDP per capita remain statistically significant in the final model.

  22. It does among the native-born as indicated by Table 6.

  23. Among the native-born population, the employed seem to be more satisfied with their lives than the inactive.

  24. Without controlling for income, education has a positive association with life satisfaction, in line with the finding in the existing literature.

  25. Such a trend is absent among the native-born populations.

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Authors and Affiliations

  1. Mannheim Center for European Social Research (MZES), Universität Mannheim, A5,6, 68159, Mannheim, Germany

    Irena Kogan & Jing Shen

  2. Federal Office for Migration and Refugees, Frankenstr. 210, 90461, Nuremberg, Germany

    Manuel Siegert

Authors
  1. Irena Kogan
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  2. Jing Shen
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  3. Manuel Siegert
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Correspondence to Irena Kogan.

Appendix

Appendix

See Tables 4, 5 and 6.

Table 4 Information on the sample size by country and survey year.
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Table 5 Basic descriptive information on macro-level variables.
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Table 6 Results of the multilevel OLS regression predicting life satisfaction among native-born population.
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Kogan, I., Shen, J. & Siegert, M. What Makes a Satisfied Immigrant? Host-Country Characteristics and Immigrants’ Life Satisfaction in Eighteen European Countries. J Happiness Stud 19, 1783–1809 (2018). https://doi.org/10.1007/s10902-017-9896-4

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