Overcrowded Housing and Relationship Break-up


To what extent is there an association between crowding and relationship break-up? And if so, is it a causal relationship? Housing space may affect the probability of separation because of stress, lower subjective well being, and poor mental health, any of which could put pressure on the relationship with the partner and eventually cause a break-up. Using the Luxembourgish PSELL 2003–2011, we operationalize crowding with a subjective measure. We check for the following confounding factors: financial difficulties, home ownership, nationality, and type of household member. We find that there is no significant association between crowding and separation once confounders are taken into account, not to mention causality. Instead, home ownership turns out to be of the utmost importance in explaining the bivariate association between crowding and union dissolution.

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Fig. 1
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  1. 1.

    We investigate housing effects on the dissolution of marital and cohabitational relationships and use break-up, separation, and union dissolution interchangeably when referring to these kinds of dissolutions.

  2. 2.

    Some scholars use the net divorce/marriage ratio to compare countries’ divorce rates (i.e. the Crude Divorce Rate/Crude Marriage Rate). Unlike some statistics, it is not advisable to look at the net divorce/marriage ratio in Luxembourg since this figure might be misleading: One would think that Luxembourg would be in the ‘vanguard’ of countries when it comes to divorce [having a net divorce-to-marriage ratio of 67.5 in 2013 (Eurostat)]. The reason is that the marriage rate in the country is extremely low, and (or likely because) the country has exceptionally high immigration rates.

  3. 3.

    The housing cost overburden rate is the percentage of the population living in households that spent 40% or more of their equivalized disposable income on housing.

  4. 4.

    About half of the Luxembourgish population is of foreign descent, and about one-sixth of the population has a Portuguese passport (the biggest group of immigrants in the country).

  5. 5.

    Commuting to work in Luxembourg from another country is very common. The country is small and distances from towns like Trier, Thionville, or Arlon are close.

  6. 6.

    On the relationship between income and housing deprivation in Luxembourg, see Fusco (2015).

  7. 7.

    Note that this might be a proxy for financial stress since it is a subjective measure; a perception of the individual about their own situation.

  8. 8.

    Couples with a mixed background (whether it is e.g. education, age, ethnicity, or religion) have been found to be more likely to break-up than those from a non-mixed background (Janssen 2001). We assume the same applies to couples with heterogamous nationalities.

  9. 9.

    According to the diffusion hypotheses of Liefbroer and Dourleijn (2006), countries with a small proportion of cohabitors (in the Luxembourgish PSELL this proportion is 16%) show a ‘positive’ effect of cohabitation on the risk of union dissolution. This is due to the fact that cohabitors in such societies are a selected group with more modern values than married couples.

  10. 10.

    This is 1.6% in each wave (3.6% between-averages per wave), which is comparable to the wavely number of separations in the BHPS/Understanding Society Panels in the UK (about 1–2%).

  11. 11.

    We also considered including (poor) health of the household head into the model, but this can be a mediator. Hence, we decided to leave it out.

  12. 12.

    Because the logistic regression is a latent dependent variable model, the variance of the dependent variable is not fixed and the estimator for the regression coefficients requires an assumption about the distribution and the variance of the error terms in particular (Karlson et al. 2012; Mood 2010). The regression coefficients of the full and the reduced model (the model without confounders) are therefore a combination of the true regression coefficients and a rescaling factor.

  13. 13.

    Note that the concomitant variables are just control variables, in the sense they are included in both the reduced and the full model, but their residual is not calculated and included in the reduced model.

  14. 14.

    Without the kitchen, bathroom, outbuildings, or study/work rooms.

  15. 15.

    Meaning that the house is seen as a place of self-expression, which is made a place of one’s own, which provides security and stability (André et al. 2017).


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I would like to thank Matthias Kuepie and Laureen Vanni for their help with the data at various stages of the data manipulation and analyses. I would also like to thank Stéfanie André, Marie-Sophie Callens, Alessio Fusco, Amparo Nagore Garcia, Julien Licheron, Andrea Mercatanti, Javier Oliviera, and Alex Theloudis for their valuable comments and suggestions.


The author did not receive any funding for this study.

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Maike van Damme: Research affiliation at LISER, Living conditions department, 11 Porte des Sciences, L-4366, Esch-sur-Alzette, Luxembourg.



See Table 4.

Table 4 Descriptive statistics of the concomitant variables (between-averages per wave, analytical sample)

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van Damme, M. Overcrowded Housing and Relationship Break-up. Eur J Population 36, 119–139 (2020). https://doi.org/10.1007/s10680-019-09523-2

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  • Divorce
  • Housing
  • Crowding
  • Spuriousness
  • Union dissolution