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Increasing Inequality in Social Exclusion Occurrence: The Case of Sweden During 1979–2003


In this paper, we examine the risk of social exclusion among the Swedish population from a longitudinal perspective. In the empirical analysis, a person is considered socially excluded if he or she suffers from at least two of the following six welfare problems: chronic unemployment, economic problems, health problems, experiences of threat or violence, crowded housing and lack of interpersonal relationships. Our three main findings are as follows: There is no evidence that immigrants have been better integrated into Swedish society over time from the perspective of social exclusion risk. Instead, there are weak signs that their situation has become worse. Further, even though men are worse off than women as regards the odds for social exclusion, there are weak signs that their relative situation has improved over time. Finally, compared to couples without children, there is clear evidence that the odds for social exclusion for singles with children have increased over time and that the odds for social exclusion for couples with children have decreased over time. We can, therefore, conclude that among these groups, the inequality has increased over time. To be able to make these conclusions, we have fitted several specifications of a logistic regression model with random effects for panel data to our data set.

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  1. Having an immigrant background means that the person is born abroad or that both parents are born abroad.

  2. Being a Nordic immigrant means that the person comes from any of the countries Denmark, Finland, Iceland and Norway. In the year 2003, Nordic immigrants constituted about 26% of the stock of immigrants and about 22% of the inflow of immigrants.

  3. See Table 9 in the Appendix for regression models fitted to each wave in the panel. Of course, we cannot statistically compare parameter estimates from different models. On the other hand, there is no need to do so since we fit several regression models in this paper that are designed for cross-sectional data with a time dimension.

  4. This is because the first odds = exp(−5.395) × 0.996 implies the probability = odds/(1 + odds) ≈ 0.00450, and the second odds = exp(−5.395) × 0.996 × 34.392 implies the probability = odds/(1 + odds) ≈ 0.13457.

  5. This is because the first odds = exp(−5.395) × 1.232 × 4.811 × 2.979 implies the probability = odds/(1 + odds) ≈ 0.07420, and the second odds = exp(−5.395) × 1.232 × 4.811 × 2.979 × 34.392 implies the probability = odds/(1 + odds) ≈ 0.73379.

  6. To be more precise, that the results are not driven by characteristics among men or women is clear since virtually the same odds ratios in the two regression models are significant. See Table 10 in the Appendix.


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Correspondence to Miia Bask.

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This paper has benefitted from comments by an anonymous reviewer for the journal, Mikael Bask, Jarko Fidrmuc, David Gordon, Björn Halleröd and Kenneth Nelson as well as from presentations at various conferences and workshops. Further on, I am grateful to the Stanford Center for the Study of Poverty and Inequality at Stanford University for giving me the opportunity to finalize this paper during a longer research visit. The usual disclaimer applies.



See Tables 9, 10.

Table 9 Regression models for each wave in the panel
Table 10 Regression models for men and women

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Bask, M. Increasing Inequality in Social Exclusion Occurrence: The Case of Sweden During 1979–2003. Soc Indic Res 97, 299–323 (2010).

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