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The impact of job loss on family dissolution


The impact of involuntary job displacements on the probability of divorce is analysed using discrete duration models. The analysis uses the sample of couples from the British Household Panel Survey and distinguishes between types of displacements. Results show that couples in which the husband experiences a job loss are more likely to divorce. Redundancies have small, positive, often insignificant and short-lived effects while dismissals and temporary job endings have larger positive impacts. This is consistent with the interpretation of redundancies as capturing negative income shocks while other types of job loss also convey new information about potential future earnings and match quality.

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  1. Several studies of the effects of job displacements on earnings have used plant closures as exogenous displacements (see for example Gibbons and Katz 1991 for the US and Doiron 1995 for Canada). There is some dispute about the treatment of plant closures as exogenous (Schwerdt 2007). In any case, information on plant closures is not available in the BHPS.

  2. The number of observations with displacements due to plant closures is not provided but the estimation results (some substantial quantitative effects with large standard errors) suggest that perhaps the number of such job losses is too small to yield sufficient precision in the estimates.

  3. Eliason uses propensity score matching to compare two samples of married individuals with one sample consisting of persons who have experienced a plant closure during the year 1987. Length of marriage is used as a matching variable but this variable only distinguishes between unions of less than 3 years.

  4. See also Eliason (2004) for a more detailed explanation of this model.

  5. A related strand of the literature considers the impact of earnings’ shocks generally on family consumption and production. See for example Browning and Crossley (2001) and Cullen et al. (2000).

  6. See Darity and Goldsmith (1996) for a review.

  7. Studies have also found negative impacts of unemployment on the well-being of spouses and children. Most of these papers are also found in other fields of study such as psychology and social sciences (see Strom 2003; Voydanoff 1990 and Kalil and Ziol-Guest 2007).

  8. Additional samples of 1,500 households in each of Scotland and Wales were added in 1999, and in 2001 a sample of 2,000 households was added in Northern Ireland, making the panel suitable for UK-wide research. These samples are included in our analysis.

  9. In our main sample, 14% of observations consist of cohabitations. The modeling of the duration of the union distinguishing by type of union is not straightforward unless one assumes independent shocks (or competing risks) and although an interesting extension, it is left for future work.

  10. We would ideally like to know the date at which individuals felt their marriage end, regardless of the legal date of divorce or separation but this is not easily defined.

  11. Less than 25 couples.

  12. A sensitivity analysis is conducted by constructing a binary variable for couples who disappear from the survey and re-appear with a different marital status. This variable is introduced in our main models and does not affect the sign and significance of job loss variables. Results are available on request.

  13. Those couples where the man reaches 65 during the survey period are dropped at the time the man reaches 65 and treated as right-censored. We use age 65 as an exogenous censoring device. Due to the presence of mandatory retirement, the role of job displacements for workers older than 65 is likely to be quite different than for younger individuals.

  14. The proposed alternatives are: self employed, in-paid employment (full time or part time), unemployed, retired from paid work, on maternity leave, looking after family or home, full time student/at school, long term sick or disabled, on a government training scheme, something else.

  15. In our main sample, 72% of households report the husband as the individual with the largest labour income.

  16. See Borland et al. (1999).

  17. Previous research suggests that spousal labour income may be endogenous to job displacement so the wife’s labour income is not part of the main model.

  18. There is a limited incidence of repeated job loss of the same type in the same year mostly involving temporary job endings. Specifically, out of all observations with either a dismissal or redundancy (1031 couple–year), 72 or 7% have more than one occurrence of the job loss. Not surprisingly, the number is a lot higher for temporary job endings (21%). Sensitivity analysis is conducted with the addition of dummies for the observations with multiple occurrences and results are very similar to those reported below. Details are available from the authors.

  19. This assumption rules out right censoring rules that are correlated with unobservables and makes the use of self-reported disability or early retirement as censoring variables problematic. In our sample, durations are right-censored when they reach the end date of the sample, when the husband’s age reaches 65 or due to attrition from the sample.

  20. With single spell data, it is very difficult to allow for correlations between the time-invariant unobserved heterogeneity and the covariates.The independence assumption which must be maintained with random effects has implications for the measurement of the impact of the job displacement variables. Specifically, in models with unobserved time-invariant random effects, any signal contained in a job loss variable must be independent of any initial unobserved match value. One can easily imagine violations of this assumption. A strong marriage may be harder to influence; hence the signalling effect of a dismissal may be lower for these couples. Again we stress that these results are used more as sensitivity analysis, in particular as a check on the specification of the baseline hazard.

  21. We are grateful to an anonymous referee for suggesting this approach.

  22. Adding a third lag reduces the sample to 27,667 observations.

  23. The third lags on the displacement variables are also jointly insignificant; a Wald test yields a χ 2(3) value of 1.60 which corresponds to a p value of 0.659.

  24. Charles and Stephens (2004) also found that the effects of job displacements were short run. In their specification, displacements in the previous 3 years were grouped and these had significant positive effects on the probability of divorce. There was no evidence of effects for those job losses that occurred in the previous 4 to 5 years. For job losses that occurred more than 5 years ago, a negative effect was found in the case of layoffs but no effects were detected for displacements due to plant closures. They interpret the long term effects from layoffs as an indication that the marriages involved survived a crisis and came out with a strengthened relationship. They also argue that the lack of effects in the medium term following a displacement can be perceived as evidence that the effects they do find (either from plant closures or layoffs) cannot be due to an omitted (time-invariant) variable. In the context of our paper, the omission of a time-invariant effect also cannot explain the effects of job displacements since results from the random effects model are virtually the same as those of the main model.

  25. We estimate models with three mass points but these did not converge easily; specifically, we had to omit the age variables and restrict the baseline hazard to get convergence. In all cases, the probability of the third mass point was between 0.011 and 0.012 and the results on the job loss variables were similar to those presented in Table 4 except for coefficients on the temporary job endings that became smaller and generally insignificant.

  26. We should add that results on education, nonlabour income and the wife’s employment status are sensitive to the treatment of the age variables. This is not surprising given the correlation in these variables. Since we do not care about these variables per se we choose the more flexible specification and include all regressors.

  27. Interactions with the baseline hazard were restricted to due to the short marriages in the flow sample.

  28. This was suggested by an anonymous referee.


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We thank participants of the 2007 Australian Labour Market Research workshop and the 2008 Australian Conference of Economists for their suggestions and comments. Special thanks to two anonymous referees and associate editor Deborah Cobb-Clark for valuable comments and suggestions. The BHPS data was provided by the Economic and Social Research Council’s Data-Archive at the University of Essex and is used with permission. The usual disclaimer applies.

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Correspondence to Silvia Mendolia.

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Responsible editor: Deborah A. Cobb-Clark



Table 6 Variable definition—additional regressors

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Doiron, D., Mendolia, S. The impact of job loss on family dissolution. J Popul Econ 25, 367–398 (2012).

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  • Job loss
  • Divorce
  • Marriage duration

JEL Classification

  • J12
  • J60
  • J63