Ethnic Residential Segregation: A Family Matter? An Integration of Household Composition Characteristics into the Residential Segregation Literature

Abstract

The ethnic residential segregation literature seldom considers household characteristics, despite their importance for residential mobility. This study offers a first step to amend this lacuna by focussing on the relationship between marital status and the presence of children on the one hand and the extent to which ethnic majority households live segregated on the other. We investigated this association with data from the 2011 Belgian Census. We performed a conditional logit model on a sample of households formed by young adults of Belgian origin living in the metropolitan areas of Antwerp (N = 11,241), Brussels (N = 6690), Charleroi (N = 3483), Ghent (N = 7825) and Liège (N = 5873). It appeared that households with children are less likely than childless households to live in diverse neighbourhoods. Considering partnership status, we find that singles are the most likely to live in diverse neighbourhoods. Amongst the couples without children, those couples in legal cohabitation are less likely to live in diverse neighbourhoods than married or other unmarried couples, while married couples with children are less likely to do so when comparing to unmarried couples with children, both legally cohabiting and others. We, therefore, conclude that it is important to consider (the interaction between) partnership status and the presence of children when studying ethnic residential segregation.

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Notes

  1. 1.

    Dissimilarity scores were calculated for the following ethnic groups: Turkish (from 65% to 51%), Moroccan (from 53% to 42%), Tunisian (from 53% to 44%), Algerian (from 54% to 47%), Bulgarian (from 66% to 52%), Slovakian (from 73% to 46%) and Polish (from 56% to 36%).

  2. 2.

    Selection based on these factors was made possible by a link between the Census of 2001 and the Census of 2011.

  3. 3.

    Origin, in our study, is based on the heads’ own nationality and country of birth, along with the nationality and country of birth of both parents. Heads of household with Belgian nationality who were born in Belgium to parents who were also born there and who also have Belgian nationality are regarded as being of Belgian origin. The Belgian household members of households with mixed origins (i.e. households in which one head of household is of non-Belgian origin and the other is of Belgian origin) are also included. No separate analyses were performed on this sub-sample.

  4. 4.

    Although the word ‘choice’ might imply that people are free to choose the neighbourhoods in which they wish to live, conditional logit modelling allows the inclusion of constraints as well.

  5. 5.

    The tests of the assumptions showed that the nonlinearity of the association between the percentage inhabitants with a migration background and the log odds of the dependent would be better modelled using another association than the currently used second-order association in Ghent (i.e. a third-order association) and Antwerp (i.e. a log transformation). However, sensitivity analyses showed that these more and less optimal models lead to the same conclusions. It is therefore preferred to show the results of these second-order associations for all five cities due to theoretical, comparability and simplicity reasons.

  6. 6.

    Sensitivity analyses with the percentage of neighbourhood inhabitants of migration background in 2001 yielded similar results. These results are available upon request.

  7. 7.

    Northern European countries are Denmark, Finland, Iceland, Norway and Sweden; Southern European countries are Andorra, Cyprus, Gibraltar, Greece, Italy, Malta, Portugal, San Marino, Spain and Vatican City State; Western European countries are Austria, France, Germany, Ireland, Liechtenstein, Luxembourg, Monaco, Switzerland, the Netherlands and the UK.

  8. 8.

    Although people with roots in either of these geographical regions are not regarded as being of migration background, they are not included in the analyses in order to avoid further complication.

  9. 9.

    We also ran separate analyses for homeowners and renters. However, we decided to drop this variable as it did not significantly alter the results. These results are available upon request.

  10. 10.

    Including the third-order association between the percentage inhabitants of migration background and the log odds leads to results in line with the other cities.

  11. 11.

    Coefficients specific to particular household types are acquired by multiplying the log odds of the main effect by the corresponding interaction coefficient. This must be performed for the first-order and second-order coefficients separately.

  12. 12.

    For these analyses, we changed the reference category for the household-type variable. The results are not shown, but are available upon request.

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Appendix

Appendix

The analogous graphs for Antwerp, Charleroi, Ghent and Liège.

See Figs. 7, 8, 9 and 10.

Fig. 7
figure7

Results of Model 2, linking % MB inhabitants and household type to the predicted probabilities of living in a given neighbourhood, for Antwerp

Fig. 8
figure8

Results of Model 2, linking % MB inhabitants and household type to the predicted probabilities of living in a given neighbourhood, for Charleroi

Fig. 9
figure9

Results of Model 2, linking % MB inhabitants and household type to the predicted probabilities of living in a given neighbourhood, for Ghent

Fig. 10
figure10

Results of Model 2, linking % MB inhabitants and household type to the predicted probabilities of living in a given neighbourhood, for Liège

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Coenen, A., Verhaeghe, P. & Van de Putte, B. Ethnic Residential Segregation: A Family Matter? An Integration of Household Composition Characteristics into the Residential Segregation Literature. Eur J Population 35, 1023–1052 (2019). https://doi.org/10.1007/s10680-018-09514-9

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Keywords

  • Ethnic residential segregation
  • Self-segregation
  • Ethnic majorities
  • Life-cycle mobility
  • Conditional logit modelling