Lost jobs, broken marriages

J Popul Econ (2012) 25:13651397DOI 10.1007/s00148-011-0394-4

ORIGINAL PAPER

Lost jobs, broken marriages

Marcus Eliason

Received: 27 November 2007 / Accepted: 28 September 2011 /Published online: 15 November 2011 Springer-Verlag 2011

Abstract This paper examines the impact of both husbands and wives jobdisplacement on the risk that the marriage ends in divorce. Using Swedish-linked employeeemployer data, all married couples in which one of thespouses lost his or her job because of an establishment closure in 1987 or 1988and a comparison sample were identified. Over a 12-year period, the excessrisk of divorce among couples in which the husband was displaced was 13%and statistically significant. The estimated impact of wives job displacementswas of almost the same size, but not statistically significant.

Keywords Displaced workers Divorce Plant closureJEL Classification J12 J63 J65

1 Introduction

The aim of this paper is to study the impact of job displacement on the risk ofdivorce. Although economists interests in the consequences of job displace-ment and unemployment have generated an extensive body of research, onlya few studies have addressed the impact on marital stability or the risk ofdivorce. However, without also considering the non-economic consequences of

Responsible editor: Christian Dustmann

M. Eliason (B)The Institute for Labour Market Policy Evaluation (IFAU),P.O. Box 513, SE-751 20, Uppsala, Swedene-mail: [email protected]

M. EliasonDepartment of Economics, University of Gothenburg, Gothenburg, Sweden

1366 M. Eliason

job displacement, such as marital instability and ill-health, one cannot obtaina complete picture of the overall welfare loss caused by such events. Theseaspects of job displacement cannot be regarded as of secondary importance.In fact, divorce has been ranked as the most stressful life event and personalillness as the fifth most stressful life event after the death of a family member(Miller and Rahe 1997).

By linking administrative individual data to establishment data, all mar-ried couples in which at least one of the spouses lost a job because of anestablishment closure in Sweden in 1987 or 1988 were identified, as well asa control group containing a large sample of couples who were not affected bythese closures. To some extent, a closure can be viewed as a quasi-experimentsince all employees are laid off regardless of their personal characteristics andbehaviour. Using plant closures or other mass layoffs as a strategy to handlethe selection issues that are otherwise associated with job loss is common inthe displaced worker literature. Following the seminal study by Jacobson et al.(1993), a number of studies have examined the earnings impact of job dis-placements due to mass-layoffs (e.g., Eliason and Storrie 2006; Hijzen et al.2010; Couch and Placzek 2010; Huttunen et al. 2011).1 Most of these studieshave found that job displacements are followed by long-lasting earnings losses,although the European and especially the Nordic studies generally have foundsmaller losses than the US studies over both the short term and the long term.

Recently, economists have also become increasingly interested in usingmass-layoffs, especially business closings, to study the impact of job displace-ment on non-economic outcomes, such as mortality (Eliason and Storrie2009a; Sullivan and von Wachter 2009; Browning and Heinesen 2011), mor-bidity (Browning et al. 2006; Eliason and Storrie 2009b, 2010; Browning andHeinesen 2011), disability pension (Rege et al. 2009a, b), fertility (Huttunenand Kellokumpu 2010; Del Bono et al. 2011), childrens school performance(Oreopoulos et al. 2008; Coelli 2011; Rege et al. 2011), and criminality (Regeet al. 2009a, b).

I will follow this methodological strategy to estimate the causal effect of jobdisplacement on the risk of divorce while avoiding contamination by eitherreverse causality (i.e., marital problems caused the job loss) or selection bias(i.e., people who have lost their jobs have certain traits that render them lesslikely both to keep a job and to keep a marriage intact).2,3 Most previous

1See von Wachter (2009) for a review of the literature.2Attewell (1999) found that the effect of marital break-up on the risk of subsequent job loss weresimilar in size to the effect of job loss on subsequent divorce.3The focus on workers displaced because of an establishment closure instead of on unemploymenthas some implications for the interpretation of the results. As Stephens (2002) recognises,displaced workers do not necessarily experience unemployment. Because of advance notices orotherwise foreseen displacements, they may have found new jobs without suffering through aperiod of unemployment. However, unemployed workers have not necessarily been displaced;they may have quit voluntarily (perhaps to return to school) or may have come to the end ofseasonal employment. These types of unemployment may not require any adjustments within thefamily that could reduce marital stability.

Lost jobs, broken marriages 1367

studies in this area have focused on the impact of unemployment on the risk ofdivorce (e.g., Jensen and Smith 1990; Starkey 1996; Kraft 2001; Hansen 2005)and found evidence for at least an immediate increase in the risk of maritaldissolution. However, the design of these studies does not clearly answer thequestion of whether job loss or unemployment causes marital instability ordivorce but only of whether an association exists. A recent exception is Rege etal. (2007), who examined the impact of Norwegian husbands job displacementdue to plant closure on marital dissolution. They found that the married menwho lost their jobs because of plant closures during 19952000 were 11% morelikely to be divorced by 2003 than the married men working in stable plants.Their results also suggest that the destabilising impact on marriages could notbe explained by unexpected reductions in earnings.

Another exception is Charles and Stephens (2004) who examined the impactof job loss on divorce by cause of job loss. They found that job losses dueto plant closures had no effects on the probability of divorce, whereas othertypes of job losses had a positive impact on the likelihood of divorce.4 Theyspeculated that a job loss due to a plant closure does not affect the divorcedecision because such an event does not reveal any information about non-economic traits that are relevant to the spouse, since all employees are laid offregardless of their individual characteristics in these situations.

Nevertheless, a job displacement may affect marital stability and increasethe risk of divorce through several other channels. From an economic perspec-tive, job loss may unexpectedly affect a partners earnings capacity and causethe other spouse to reconsider his or her initial choice of a marriage part-ner.5 However, most marriages and divorces in contemporary industrialisedsocieties do not seem to be the result of utility-maximising behaviour in theconventional economic sense (Frey and Eichenberger 1996; Weiss 1997).

Another set of mechanisms that may contribute to an adverse impact ofjob loss on marital stability and the risk of divorce is related to the stressassociated with job loss. In family stress theory, a stressor event, such as ajob loss, will depending upon the familys coping resources and the familysperception of the event produce a crises or a resolution (Hill 1949).6 In thecase of a crisis, the level of adaptation will also depend upon the familyscoping resources, perception of the crisis, coping strategies, and additionalstressors (McCubbin and Patterson 1983). A job loss may operate as a stressorin various ways. It may produce financial strain, the extent of which woulddepend on, among other things, the re-employment possibilities, the eligibilityfor unemployment insurance, the other spouses income, and the duration of

4Similarly, Charles and Stephens (2004) did not find that spousal disability affected the divorcerisk.5Weiss and Willis (1997) and Bheim and Ermisch (2001) have studied the impact of unexpectedfinancial changes on the risk of divorce.6Prior research has used family stress theory and, more specifically, the ABC-X model as aframework for analysing the effects of unemployment on marital stability (e.g., Voydanoff 1983;Starkey 1996).

1368 M. Eliason

unemployment. Even without financial strain, a job loss may produce psycho-logical distress because a job, aside from income, also provides such elementsas social networks, time structure, and identity.7 Psychological problems arenot necessarily limited to the job loser either but may also be transmitted tothe spouse.8 Certain maladaptive individual coping strategies, such as alcoholabuse and violent behaviour, may also operate as additional stressors.9

The next section gives a brief overview of the macroeconomic environmentduring the study period and of the nature of marriage and divorce in theSwedish context. Section 3 presents the data, the empirical method, andsome descriptive statistics. Section 4 presents the results by starting withthe estimated effects of job displacement on earnings, non-employment, andunemployment for this particular sample. The section continues with the mainanalyses of the impact of job displacement on the risk of divorce, followed bya subgroup analysis and a supplementary analysis of how the impact of jobdisplacement varies with time. Finally, Section 5 summarises and concludesthe study.

2 Background: the Swedish context

2.1 The labour market

Table 1 summarises the Swedish labour market from 1985 to 1999. In the mid-to late 1980s, when the job displacements examined in this study occurred,Sweden experienced remarkably low unemployment rates. The unemploy-ment rate had been falling since 1983 and was down to 1.5% in 1989, whereasthe employment rate during the same period rose continually from 79.0% to82.9%. During the years that followed, Sweden experienced the most severerecession since the 1930s. The unemployment rate rose to 8.2% in 1993 andstayed at approximately this level until 1997, whereas the employment ratefell by ten percentage points to 72.6%. Thus, the displaced married men andwomen faced a very buoyant labour market at the time of the job loss andhad good opportunities to find new jobs before they had to face the impendingrecession.

Sweden has been internationally recognised for its high proportion ofwomen in the labour force. During these years, the labour force participationwas almost as high for women as it was for men and peaked at 82.3% in 1990after rising for decades.10 The increase was then halted by the economic crisis

7Bjrklund and Eriksson (1998) reviewed the Nordic literature on the effect of unemployment onmental health.8See Jones (1992) for a review of the literature on wives reactions to their husbandsunemployment.9Catalano et al. (1993) showed that unemployment is associated with alcohol abuse, and Kyriacouet al. (1999) showed that domestic violence is more prevalent among the unemployed.10However, it should be noted that women were mainly working part time.


Table 1 The Swedish employment, unemployment, and labour force participation rate during19851999

Year Employment (%) Unemployment (%) Labour force (%)Men Women Total Men Women Total Men Women Total

1985 83.5 76.9 80.3 2.8 2.9 2.8 86.3 79.8 83.11986 83.3 78.5 80.9 2.4 2.5 2.5 85.7 81.0 83.41987 83.6 79.2 81.4 2.1 2.1 2.1 85.4 80.9 83.21988 84.2 80.1 82.2 1.7 1.7 1.7 85.7 81.5 83.71989 85.1 80.7 82.9 1.4 1.5 1.5 86.4 81.9 84.21990 85.2 81.0 83.1 1.7 1.6 1.6 86.6 82.3 84.51991 82.7 79.3 81.0 3.3 2.6 3.0 85.6 81.4 83.51992 78.3 76.3 77.3 6.3 4.2 5.2 83.5 79.6 81.61993 73.0 72.1 72.6 9.7 6.6 8.2 80.9 77.2 79.11994 72.3 70.6 71.5 9.1 6.7 8.0 79.5 75.7 77.71995 73.5 70.8 72.2 8.5 6.9 7.7 80.3 76.1 78.21996 73.2 69.9 71.6 8.5 7.5 8.1 80.0 75.6 77.81997 72.4 68.9 70.7 8.5 7.5 8.0 79.1 74.5 76.81998 73.5 69.4 71.5 6.9 6.0 6.5 79.0 73.9 76.51999 74.8 70.9 72.9 5.9 5.2 5.6 79.5 74.8 77.2

Labour Force Surveys, Statistics Sweden

and subsequently fell to 73.9% in 1998. Similarly, the female employment ratefell from 81.0% in 1990 to 69.4% in 1998.

2.2 Marriage and divorce

Sweden has been internationally recognised not only for the high participationof women in the labour force but also for its high divorce rate and low marriagerate. Figure 1 depicts the crude marriage and divorce rates from 1985 to 1999.Although the marriage rate decreased dramatically during the 1970s and early1980s, the marriage rate decreased at a much slower pace during the periodof this study (see Andersson and Guiping 2001). Except for the spike in 1989,which was due to changes in the eligibility of widow(er)s pensions, the rate

Fig. 1 The crude marriage and divorce rates (i.e., the number per 1,000 population) between1985 and 1999

1370 M. Eliason

varied between 5.2 marriages per 1,000 population in 1988 to 3.6 per 1,000population ten years later.

The crude divorce rate has been relatively stable during the same timeperiod and has varied only between 2.1 and 2.5 divorces per 1,000 population.However, it is important to note that the crude measure, which accounts for thedivorce rate by population instead of by marriages at risk, conceals a somewhatincreasing trend in the divorce rate.

The relatively high rate of marriages ending in divorce can probably belargely attributed to a liberal divorce legalisation. From 1973 to 1974, whenthe liberalising changes accepting unilateral and no-fault divorces came intoeffect, the divorce risk doubled. The court must still approve a divorce, butit cannot refuse a divorce application. If both spouses agree to divorce andjointly request it and if neither of them has custody of a child younger than16 years or if the couple has lived apart for at least 2 years, then the courtcan grant the divorce immediately. Otherwise, a reconsideration period of atleast six months must elapse before a new application can be submitted andbefore the divorce can be subsequently granted. Upon the dissolution of themarriage, all economic ties between the spouses are terminated. The courtgrants alimony only under exceptional circumstances, although disagreementsabout ancillary questions (e.g. maintenance issues or custody of children) canprolong the divorce process. On request, however, the court can grant thedivorce immediately and make decisions regarding the ancillary questionslater.11

3 Data

3.1 The samples

The data used in this study is a linked employeeemployer data set containingall married couples in which at least one of the spouses was displaced becauseof an establishment closure in 1987 or 1988 and a random sample of otherwisecomparable couples. To create the data set, four registers (i.e., the RegisterBased Labour Market Statistics; the Longitudinal Database for Education,Income and Occupation; the Income and Wealth Register; and the HospitalDischarge Register) were merged to obtain information on the couples duringthree pre-displacement years and 12 post-displacement years.12 One can linkvarious registers to one another because each resident and each establishment

11For more details on Swedish divorce legislation, see Jnter-Jareborg (2003).12Income taxation and the administration of the universal Swedish welfare state provide thesource for many of the variables in these registers. The employer files all wage payments to thetax authorities. Because nearly all transfers in the Swedish welfare state, such as sickness andunemployment benefits, are subject to taxation, the National Social Insurance Board also filesincome statements on these transfers (along with non-taxable social assistance payments).


in Sweden has a unique identity number (i.e., a civic registration number oran organisation number). Moreover, since the obligatory income statements,which are filed with the taxation authorities by the employer, contain boththe employees civic registration number and the establishments organisationnumber, one can also link all employees to the establishments that employedthem. This feature of the data enabled the identification of both the closingestablishments and the employees who lost their jobs.

The samples of married couples were constructed in four steps. First, allclosing establishments with at least 10 employees were identified by thedisappearance of their identity numbers from the administrative registers.The problem of false firm deaths (i.e., cases in which the disappearanceof an identity number was due to, for example, a change of owner) empha-sised in Kuhn (2002) was eliminated or at least greatly reduced by StatisticsSwedens extensive examinations and corrections of the establishment num-bers (Statistics Sweden 2005).

In the second step, the employees at these establishments were identified.In administrative data, one can observe separations between employees andemployers, but usually no distinction can be made between quits and layoffs.Thus, it is necessary to define displacements as separations in connection tothe closures. However, one can question whether selection bias truly doesnot exist in plant closure studies, since there is reason to assume that thoseemployees with better outside options will be more likely to quit before theactual shutdown. On the other hand, the firm will be more likely to lay off itsless valuable employees first during a preceding downsizing period. Thus, itis essential to identify not only the employees who are laid off at the time ofthe actual shutdown but also the employees who depart earlier because of theimpending closure. The case study evidence in Pfann and Hamermesh (2008)indicates that this issue is important.

Therefore, if a closing process was deemed to last longer than one yearbased on the size of the establishment and the employee flows during the3 years prior to the closure, then not only the employees who separated fromthis establishment in the same year as the shutdown but also the employeeswho separated in the preceding year were included, and in a few cases thosewho separated in the year before that as well (i.e., a 3-year-window). However,most of the closing processes were considered to last no more than a calendaryear.13 As a sensitivity test, all estimations were performed also using a samplecontaining only those employees who separated within the calendar yearpreceding the closure (i.e., a 1-year window).

In the third step, two comparison groups were constructed that com-prised random samples of married men and women who were employed inNovember of 1986 at non-closing establishments with at least 10 employees.However, these employees could have been displaced in any subsequent year.

13This procedure has previously been applied in Eliason and Storrie (2006, 2009a, 2009b, 2010)and Eliason (2011).

1372 M. Eliason

Correspondingly, the displaced employees could have experienced multipledisplacements.

In the fourth step, all of the married employees were linked to their spouses.This was possible because married couples are taxed jointly in Sweden andbecause Statistics Sweden collects the administrative records from the NationalTax Board.14 Therefore, the same information could be obtained for bothspouses in each couple. This procedure resulted in two samples: 1) a sampleof married couples in which all of the husbands were employed at baseline, butsome of them were displaced; and 2) a sample of married couples in which allof the wives were employed at baseline, but some of them were displaced.Henceforth, I will refer to the two samples as the male sample and thefemale sample.

A few sample restrictions were applied before arriving at the final samplesused in the empirical analyses. To fully appreciate the basis of these restric-tions, it should be noted that many conditioning baseline variables containinformation that was measured 2 to 3 years prior to the job displacement. Thefirst restriction excluded the couples who had missing information in any ofthe three baseline years.15 The second restriction excluded those who werenot married in all three baseline years. This latter restriction ensures thatthe baseline variables correspond to information about the two spouses as amarried couple. It also implies that the establishment closures were unlikely tobe expected at the time that the couples became married. Finally, the sampleswere restricted to the couples in which both spouses were 20 to 64 years old asof December 31 in the selection year.

These restrictions reduced the number of couples in which the husband wasdisplaced from 4,227 to 3,692 and the number of couples in which the wifewas displaced from 3,318 to 2,786. The corresponding comparison groups werereduced from 51,899 to 46,990 and from 49,548 to 43,393.

3.2 The divorces

Divorces could not be directly observed in that data but only the maritalstatus of each spouse in each year. Therefore, a divorce was defined ashaving occurred if at least one of the spouses was registered as divorced. Thisdefinition would underestimate the number of actual divorces in the unlikelyevent that there were divorced couples in which both the former spousesremarried within the same calendar year of their divorce.

Table 2 shows the divorce rate for 12 post-displacement years in terms ofthe percentage and cumulative percentage for each of the populations definedin the previous section. From these raw figures, one can see that, on average,somewhat more than 1% of the marriages ended in divorce each year and that

14The taxation of wealth was abolished in Sweden on January 1, 2007.15In principle, this information would only be missing if either of the spouses were living abroadin that particular year.


Tab

le2

The

divo

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rate

inpe

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san

dcu

mul

ativ

epe

rcen

tage

s

Yea

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ale

sam

ple

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ale

sam

ple

Tot

alD

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aced

Non

-dis

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Non

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Cum

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Cum

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Cum

.%%

Cum

.%

01.

901.

901.

301.

301.

791.

791.

501.

501.

851.

851.

401.

401

1.64

3.54

1.17

2.47

1.69

3.49

1.22

2.72

1.67

3.52

1.19

2.59

21.

745.

271.

193.

661.

474.

961.

284.

001.

625.

141.

233.

823

1.08

6.35

1.20

4.86

1.69

6.65

1.14

5.14

1.34

6.48

1.18

5.00

41.

277.

621.

166.

021.

618.

271.

256.

391.

427.

901.

206.

205

1.27

8.89

1.11

7.13

1.01

9.27

1.07

7.46

1.15

9.06

1.09

7.29

61.

1410

.03

0.87

8.00

1.27

10.5

40.

878.

331.

2010

.25

0.87

8.16

71.

0711

.10

0.91

8.90

1.21

11.7

60.

929.

251.

1311

.38

0.91

9.07

81.

4812

.58

0.90

9.80

0.99

12.7

40.

9410

.19

1.27

12.6

50.

929.

999

1.19

13.7

60.

8310

.63

1.23

13.9

70.

8211

.01

1.20

13.8

50.

8210

.82

100.

9114

.68

0.74

11.3

70.

8914

.86

0.79

11.8

00.

9014

.76

0.76

11.5

811

0.93

15.6

10.

7012

.07

1.00

15.8

60.

7412

.54

0.96

15.7

20.

7212

.29

1374 M. Eliason

the risk of divorce decreased over time. The figures also indicate that thosecouples who were experiencing job displacements were more likely to divorce.An increased risk of divorce seems to be present regardless of whether thehusband or the wife lost the job.

3.3 The baseline variables

The baseline variables were drawn from three registers: the Register BasedLabour Market Statistics, the Income and Wealth Register, and the HospitalDischarge Register. The choice of variables included in the analyses was basedon the economic theory of marriage and divorce and on family stress theory.They can be categorised as socio-demographic/economic variables and asvariables corresponding to marital investments, match quality or homogamy,economic dependency/independency, coping resources, the establishment, andthe local labour and marriage market. All of the variables will be discussedbriefly below.

Demographic and socioeconomic variables These variables include thespouses ages (9 categories each), immigrant statuses, and education levels(4 categories each) as well as the couples county of residence (21 categories).

Marital investments This category includes the number of children within twoage ranges16 and an indicator of whether the couple owned a house. Marriage-specific capital should theoretically reduce the risk of divorce because itsvalue is higher within marriage. Prior empirical work has also found thatchildren, especially younger ones, reduce the risk of divorce (Andersson 1997).However, the presence and number of children could have a reverse effect on acouples perception of job loss because more children mean more dependantsto support. A positive correlation between marital investment and maritalstability could also be spurious. That is, since marital investments decrease invalue if the marriage dissolves, the possibility of divorce might lead less stablecouples to engage in more cautious investment behaviour (Becker et al. 1977).Svarer and Verner (2008) found this case to be true in their analysis of Danishcouples and also found that children actually destabilised marriages when theycorrected for this selection bias.

Match quality or homogamy Three dimensions of homagamy were measured:age, education, and ethnicity. Age homogamy is represented by a categoricalvariable with seven categories related to the difference in age between thespouses. Similarly, educational homogamy is represented by a categoricalvariable with four categories related to the spouses differences in terms of

16Because joint parenthood cannot be established in the data, the measure used in the analysisrepresents the minimum number of children reported for either of the spouses.


education levels. Ethnic homogamy was proxied with an indicator of whetherthe husband and wife were born in the same region of the world.

Prior studies have empirically established that positive assortative matingoccurs among couples (Weiss and Willis 1997; Mare 1991), and Becker (1973)theoretically shows that positive assortative mating over a large range ofpersonal traits is optimal.

Economic dependency/independency One categorical variable with five cat-egories representing the degree of one spouses economic dependency onthe other spouse was included, and measured as the wifes earnings relativeto the couples total earnings. Several studies have shown that higher rela-tive earnings from wives destabilise marriages (e.g., Jalovaara 2003; Kalmijnet al. 2007), although some studies suggest that this impact is confined to badmarriages (e.g., Sayer and Bianchi 2000).

Coping resources How a couple will perceive a job loss or any other adverselife event and how they will adapt to it depend on their coping resources.The couples financial situation (measured as their disposable family income,taxable wealth, and earnings) is likely to influence their perception of theseverity of the job loss. Several studies have also shown an inverse relationshipbetween the measures of socioeconomic status and the divorce risk (e.g.Jalovaara 2001, 2003).

A number of variables representing previous hardship in various dimensionswere also included. First, three variables measuring both spouses previousexperience of unemployment and whether the couple had received means-tested social benefits. However, the expected effect of previous hardship isunclear. On the one hand, previous hardship could have an accumulatedadverse impact, but on the other hand, it could also decrease a couplesperception of the current displacement as stressful.

Financial resources alone do not constitute the full set of coping resources.The spouses mental and physical health could clearly affect their ability tocope with subsequent adverse life events. However, little attention has beendevoted to how spouses health statuses affect marital stability and the risk ofdivorce, and the direction of causality is ambiguous. Some previous studieshave shown that husbands ill-health increases the risk of divorce (Jensenand Smith 1990; Svarer 2002), whereas other studies have found no effect ofdisability on the divorce risk, regardless of which spouse was disabled (Charlesand Stephens 2004). The spouses health statuses were measured both asincidence of hospital inpatient treatment with a discharge diagnosis of alcohol-related diseases/conditions, psychiatric conditions, or violence and as incidenceof hospital inpatient treatment regardless of diagnosis.17

17Inpatient stays for giving birth were excluded if there were no medical complications.

1376 M. Eliason

Regional characteristics In addition to 21 county indicators, four measuresof the characteristics of the local labour and marriage market were included.These measures were population size, unemployment rate, and the shares ofboth divorced and married persons. The latter two variables could be seen asmeasures of the availability of spousal alternatives. A low share of marriedmen/women would increase the opportunity to find a new potential spouseand, thus, decrease the search costs, which could threaten the stability ofmarriage (see South and Lloyd 1995).18

Establishment characteristics Although it can be argued that an establishmentclosure is a quasi-experiment, it is far from a true natural experiment becauseclosures do not, for example, occur randomly over regions or sectors. Thus, inaddition to the regional variables discussed previously, six variables measuringestablishment characteristics were included: economic sector (10 categories);number of employees; the proportions of employees who had completedcompulsory education, secondary education, and university education; and thefraction of female employees.

3.4 Empirical method

The exclusive focus on job displacements due to plant closures as a strategyto address the selection issues that are otherwise associated with job lossesshould not, which was also noted above, be interpreted as if having a naturalexperiment in hand. Although, any selection of which employees at a particularestablishment are laid off is eliminated or at least greatly reduced, one cannotignore that there is a non-random selection of which establishments aregoing out of business. Hence, it is still necessary to control for any baselinedifferences between the displaced and non-displaced couples.

A propensity score weighted estimator that is similar to those proposed inHirano and Imbens (2001) and Robins et al. (2000) was adopted. By propensityscore weighting one will ideally obtain a pseudo-sample in which the distribu-tion of observed characteristics is the same for the samples of exposed (i.e., dis-placed) couples and non-exposed (i.e., non-displaced) couples. The propensityscore (p) is the probability of exposure (Rosenbaum and Rubin 1983), whichwas estimated by a logit model: pi = Pr [Di = 1|Xi] = {1 + exp ( Xi)}1,where Di is an indicator taking a value of one if the husband/wife in couplei was displaced at baseline and zero otherwise, and Xi is a vector of baselinecovariates.

To estimate the effect on the displaced couples a weight defined aswi = Di + (1 Di) pi/ (1 pi) was assigned to each couple i (see Hiranoand Imbens 2001). Hence, all of the couples in which at least one of thespouses was displaced were assigned a weight equal to one, whereas each

18Although it is not as likely in secularised countries, a high marriage ratio could also indicate asocial norm that favours marriage, which implies higher social costs associated with divorce.


Table 3 Summary statisticsof the estimated propensityscores and the correspondingweights

The presented weights for thenon-displaced workers arenot normalised

Propensity score Propensity score weightsMean Min Max Mean Min Max

Male sampleDisplaced 0.16 0.01 0.73 1.00 1.00 1.00Non-displaced 0.07 0.00 0.70 0.08 0.00 2.30

Female sampleDisplaced 0.15 0.01 0.77 1.00 1.00 1.00Non-displaced 0.05 0.00 0.76 0.07 0.00 3.21

comparison couple j was assigned a weight equal to pj/(1 pj). Afterthe weights were normalised, as suggested in Hirano and Imbens (2001),they were used to estimate a weighted discrete-time logit model: hi (t) =[1 exp { (t) Zi Di}

]1, where h(t) is the hazard rate or the condi-tional probability of divorce, (t) is the baseline hazard function, Zi is a vectorof baseline covariates, and is the estimated effect of job displacement atbaseline. The latter was assumed to be constant over time. No time-varyingcovariates were included since they may mediate the effect of displacement onthe divorce risk and, therefore, bias the estimated effect of job displacement.

The choices of covariates included in Xi and Zi can produce four differentestimators. Including covariates in neither the estimation of the propensityscores nor the following discrete-time logit model will result in an unadjustedestimator. Including covariates only in the estimation of the propensity scoreswill result in a propensity score weighted estimator (PSW). If covariates,instead, are included only in the discrete-time logit model, this will result ina standard unweighted discrete-time logit model (DTL). Finally, includingcovariates in both estimations produces a propensity score weighted discrete-time logit model (PSW + DTL). As a check of robustness to the choice ofestimator the estimates from all four estimators will be presented.

3.5 Estimation of the propensity scores

The estimation of the propensity scores was performed separately for the maleand female samples, the two time windows, and for each of the subgroupsinvestigated in Section 4.3. The covariates included in the full model were allthose discussed in Section 3.3.19 For the sake of brevity, no estimation resultsare reported other than summary statistics for the propensity scores and thecorresponding weights for the male and female samples using the preferred3-year window.

Based on an assessment of these summary statistics in Table 3, it is evidentthat the samples of displaced and non-displaced couples are fairly similar withregard to the estimated propensity scores, although the non-displaced coupleshave a more positively skewed propensity score distribution. Moreover, since

19See also Table 4.

1378 M. Eliason

Tab

le4

Sam

ple

char

acte

rist

ics

for

the

disp

lace

d(D

=1)

and

non-

disp

lace

d(D

=0)

coup

les

Var

iabl

eM

ale

sam

ple

Fem

ale

sam

ple

Bef

ore

wei

ghti

ngA

fter

wei

ghti

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ew

eigh

ting

Aft

erw

eigh

ting

D=

1D

=0

SDM

D=

0SD

MD

=1

D=

0SD

MD

=0

SDM

Soci

o-de

mog

raph

icva

riab

les

Hus

band

sag

e20

24

year

s0.

4%0.

2%3.

30.

5%0

.30.

4%0.

2%4.

60.

5%0

.925

29

year

s3.

6%3.

1%3.

13.

8%0

.83.

8%2.

9%5.

23.

9%0

.730

34

year

s10

.6%

9.7%

2.9

10.8

%0

.711

.1%

9.5%

5.4

11.1

%0.

135

39

year

s15

.6%

16.4

%2

.415

.5%

0.2

14.6

%15

.9%

3.7

14.5

%0.

140

44

year

s17

.6%

19.4

%4

.517

.3%

0.9

19.4

%20

.3%

2.3

19.1

%0.

645

49

year

s14

.1%

15.5

%3

.814

.1%

0.2

16.3

%16

.0%

0.8

16.0

%0.

650

54

year

s12

.9%

13.5

%1

.912

.9%

0.1

12.5

%13

.2%

2.0

12.7

%0

.555

59

year

s14

.2%

12.9

%3.

614

.2%

0.1

12.8

%12

.2%

1.6

12.7

%0.

160

64

year

s11

.0%

9.3%

5.7

11.1

%0

.29.

2%9.

9%2

.59.

4%0

.8W

ife

sag

e20

24

year

s1.

6%0.

9%6.

61.

7%1

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7%0.

7%8.

91.

9%1

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29

year

s7.

0%6.

2%3.

47.

2%0

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6%5.

8%7.

27.

8%0

.830

34

year

s12

.6%

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%0

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.5%

0.0

12.2

%12

.6%

1.4

11.9

%0.

735

39

year

s17

.6%

18.0

%1

.217

.4%

0.4

17.0

%18

.2%

3.2

16.9

%0.

240

44

year

s17

.8%

19.8

%5

.217

.6%

0.4

20.4

%20

.5%

0.2

20.2

%0.

545

49

year

s13

.7%

15.0

%3

.713

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0.2

15.1

%15

.4%

0.8

15.0

%0.

450

54

year

s13

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12.4

%2.

613

.2%

0.1

11.7

%12

.7%

3.1

11.8

%0

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59

year

s11

.0%

10.2

%2.

911

.2%

0.5

10.2

%10

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0.0

10.4

%0

.560

64

year

s5.

6%4.

9%3.

35.

5%0.

24.

1%3.

9%1.

44.

2%0

.3H

usba

ndis

fore

ign-

born

13.9

%9.

9%12

.514

.0%

0.1

15.1

%9.

9%15

.815

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0.5

Wif

eis

fore

ign-

born

15.2

%11

.2%

11.8

15.2

%0

.218

.0%

10.6

%21

.418

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0.7

Hus

band

sed

ucat

ion

Unk

now

n4.

9%3.

1%9.

45.

0%0

.65.

1%3.

1%10

.65.

4%1

.3C

ompu

lsor

ysc

hool

40.5

%34

.5%

12.3

40.5

%0

.140

.5%

35.5

%10

.440

.9%

0.7

Seco

ndar

ysc

hool

38.8

%38

.0%

1.7

38.8

%0.

136

.5%

38.5

%4

.136

.1%

0.8

Uni

vers

ity

15.8

%24

.4%

21.

615

.7%

0.3

17.8

%23

.0%

12.

817

.6%

0.6


Tab

le4

(con

tinu

ed)

Var

iabl

eM

ale

sam

ple

Fem

ale

sam

ple

Bef

ore

wei

ghti

ngA

fter

wei

ghti

ngB

efor

ew

eigh

ting

Aft

erw

eigh

ting

D=

1D

=0

SDM

D=

0SD

MD

=1

D=

0SD

MD

=0

SDM

Wif

es

educ

atio

nU

nkno

wn

4.7%

3.5%

5.9

4.8%

0.6

4.9%

2.8%

11.1

4.9%

0.1

Com

puls

ory

scho

ol40

.7%

36.9

%7.

940

.8%

0.1

43.0

%32

.7%

21.2

43.2

%0

.5Se

cond

ary

scho

ol38

.9%

37.7

%2.

538

.8%

0.2

36.5

%39

.5%

6.2

36.4

%0.

2U

nive

rsit

y15

.7%

22.0

%1

6.0

15.7

%0.

115

.7%

25.1

%2

3.3

15.5

%0.

4M

arit

alin

vest

men

ts#

child

ren

aged

06

year

s0.

350.

360

.80.

360

.60.

320.

331

.80.

320

.5#

child

ren

aged

717

year

s0.

710.

754

.80.

710.

40.

720.

720

.00.

720.

2H

ouse

-ow

ners

76.6

%81

.6%

12.

276

.4%

0.6

76.9

%82

.5%

14.

176

.6%

0.7

Mat

chqu

alit

y/ho

mog

amy

Age

hom

ogam

yH

usba

nd6+

year

sol

der

18.4

%16

.2%

5.7

18.4

%0

.118

.2%

17.4

%2.

218

.2%

0.0

Hus

band

35

year

sol

der

28.3

%29

.1%

1.8

28.3

%0

.028

.7%

29.4

%1

.628

.9%

0.4

Hus

band

12

year

sol

der

26.6

%27

.4%

1.9

26.7

%0

.126

.5%

27.4

%2

.126

.7%

0.3

Sam

eag

e10

.7%

10.5

%0.

410

.8%

0.3

9.9%

10.5

%1

.99.

8%0.

4W

ife

12

year

sol

der

9.3%

10.3

%3

.59.

2%0.

510

.1%

9.6%

1.7

10.1

%0.

1W

ife

35

year

sol

der

4.8%

4.7%

0.3

4.7%

0.1

4.5%

4.2%

1.6

4.4%

0.5

Wif

e6+

year

sol

der

2.0%

1.7%

2.4

2.0%

0.5

2.1%

1.5%

3.9

2.0%

0.4

Edu

cati

onal

hom

ogam

yH

usba

ndhi

gher

educ

atio

n20

.5%

23.4

%7

.120

.4%

0.1

22.7

%20

.3%

5.9

22.3

%0.

9Sa

me

educ

atio

n51

.3%

51.6

%0

.551

.2%

0.3

50.1

%50

.8%

1.3

50.3

%0

.3W

ife

high

ered

ucat

ion

20.3

%19

.6%

1.7

20.2

%0.

118

.7%

24.2

%1

3.6

18.6

%0.

0E

duca

tion

unkn

own

8.0%

5.5%

10.1

8.2%

0.9

8.6%

4.8%

15.1

8.8%

1.0

Mat

chqu

alit

y/ho

mog

amy

Sam

ebi

rth

regi

on94

.3%

95.2

%3

.894

.3%

0.2

93.0

%95

.3%

9.5

93.2

%0

.6W

ife

sre

lati

veea

rnin

gsa

019

%25

.1%

23.3

%4.

125

.2%

0.3

15.8

%9.

9%17

.515

.8%

0.1

203

9%42

.0%

46.7

%9

.441

.5%

1.0

45.4

%47

.8%

4.9

45.3

%0.

3

1380 M. Eliason

Tab

le4

(con

tinu

ed)

Var

iabl

eM

ale

sam

ple

Fem

ale

sam

ple

Bef

ore

wei

ghti

ngA

fter

wei

ghti

ngB

efor

ew

eigh

ting

Aft

erw

eigh

ting

D=

1D

=0

SDM

D=

0SD

MD

=1

D=

0SD

MD

=0

SDM

405

9%28

.0%

27.7

%0.

828

.2%

0.5

29.8

%33

.5%

8.0

29.8

%0

.060

79%

3.8%

1.8%

11.7

3.8%

0.1

4.7%

4.2%

2.4

4.8%

0.2

801

00%

1.1%

0.5%

7.1

1.2%

1.4

4.3%

4.5%

0.9

4.4%

0.2

Cop

ing

reso

urce

sa

Hus

band

unem

ploy

ed12

.8%

4.6%

29.2

13.2

%1

.48.

7%6.

2%9.

68.

6%0.

5W

ife

unem

ploy

ed10

.8%

8.6%

7.5

10.8

%0

.312

.4%

7.8%

15.2

12.6

%0

.7So

cial

insu

ranc

ere

ceiv

er5.

2%3.

0%10

.95.

5%1

.35.

2%2.

6%13

.45.

3%0

.6D

ispo

sabl

ein

com

e56

5.6

581.

09

.156

4.4

0.7

567.

758

8.6

13.

556

6.5

0.8

Cou

ple

has

taxa

ble

wea

lth

9.6%

11.8

%7

.39.

6%0

.110

.3%

12.7

%7

.610

.5%

0.5

Hus

band

sea

rnin

gs47

2.5

505.

91

3.7

470.

50.

844

8.0

469.

48

.644

6.8

0.5

Wif

es

earn

ings

226.

323

7.4

7.5

225.

70.

424

8.5

278.

62

2.8

247.

60.

6H

ospi

talis

atio

nH

usba

nd:a

ny10

.9%

9.8%

3.5

11.0

%0

.310

.9%

10.2

%2.

211

.0%

0.6

Wif

e:an

y16

.8%

16.3

%1.

417

.0%

0.6

17.0

%15

.9%

3.2

17.1

%0

.1H

usba

nd:d

rugs

/vio

lenc

e0.

8%0.

4%4.

10.

8%0

.90.

9%0.

5%4.

00.

9%0

.1W

ife:

drug

s/vi

olen

ce0.

6%0.

5%1.

00.

6%0.

20.

6%0.

5%2.

20.

6%0

.2R

egio

nalv

aria

bles

Loc

alun

empl

oym

entr

ate

(log

s)3

.98

3.9

47

.23

.97

0.7

4.0

53

.95

18.

34

.07

3.2

Loc

alm

arri

age

rate

(log

s)0

.93

0.9

21

1.2

0.9

31.

10

.93

0.9

21

3.7

0.9

31

.1L

ocal

divo

rce

rate

(log

s)2

.72

2.7

49.

72

.71

0.8

2.7

02

.74

12.4

2.7

11.

7L

ocal

popu

lati

on(l

ogs)

10.4

310

.36

6.2

10.4

30.

010

.46

10.3

77.

510

.44

1.4

Est

ablis

hmen

tvar

iabl

esb

Shar

eof

empl

oyee

sw

ith

Com

puls

ory

scho

ol41

.9%

35.5

%30

.342

.2%

1.3

39.3

%28

.5%

48.5

39.6

%1

.0Se

cond

ary

scho

ol45

.5%

45.5

%0

.045

.3%

1.1

42.9

%43

.6%

3.6

42.8

%0.

7U

nive

rsit

y12

.6%

19.1

%3

2.9

12.5

%0.

417

.7%

28.0

%4

3.8

17.7

%0.

4Sh

are

ofw

omen

27.9

%30

.6%

11.

627

.9%

0.2

65.4

%68

.3%

11.

464

.6%

3.2

#of

empl

oyee

s65

.055

8.2

55.

363

.10.

263

.066

5.0

59.

761

.80.

1


Tab

le4

(con

tinu

ed)

Var

iabl

eM

ale

sam

ple

Fem

ale

sam

ple

Bef

ore

wei

ghti

ngA

fter

wei

ghti

ngB

efor

ew

eigh

ting

Aft

erw

eigh

ting

D=

1D

=0

SDM

D=

0SD

MD

=1

D=

0SD

MD

=0

SDM

Cou

nty

ofre

side

nce

Stoc

khol

mco

unty

22.0

%17

.9%

10.4

22.3

%0

.823

.3%

18.9

%10

.723

.3%

0.2

Upp

sala

coun

ty3.

2%3.

2%0.

03.

1%0.

43.

4%3.

2%1.

43.

3%0.

5S

derm

anla

ndco

unty

1.4%

3.1%

11.

61.

4%0.

20.

5%3.

1%1

9.7

0.5%

0.1

st

erg

tlan

dco

unty

4.0%

5.0%

4.7

4.2%

0.6

3.1%

4.7%

8.5

3.0%

0.6

Jnk

pin

gco

unty

2.5%

4.1%

9.2

2.5%

0.1

4.6%

4.0%

3.2

5.5%

4.2

Kro

nobe

rgco

unty

2.4%

2.3%

1.2

2.4%

0.2

1.4%

2.3%

6.7

1.3%

0.5

Kal

mar

coun

ty1.

1%2.

8%1

2.9

1.1%

0.1

0.7%

2.8%

15.

80.

7%0.

3G

otla

ndco

unty

0.9%

0.5%

4.0

1.0%

1.2

0.3%

0.6%

3.5

0.3%

0.5

Ble

king

eco

unty

1.9%

2.0%

0.8

1.9%

0.3

2.9%

2.0%

6.0

2.9%

0.4

Skn

eco

unty

8.7%

12.6

%1

2.8

8.7%

0.1

12.0

%12

.7%

2.1

12.2

%0

.5H

alla

ndco

unty

5.5%

3.0%

12.6

5.4%

0.8

3.1%

2.8%

1.6

2.8%

1.4

Vs

tra

Gt

alan

dco

unty

17.2

%16

.9%

0.7

16.6

%1.

618

.7%

17.2

%3.

918

.6%

0.3

Vr

mla

ndco

unty

3.5%

3.3%

1.4

3.5%

0.1

5.1%

3.1%

10.4

5.8%

3.1

re

bro

coun

ty4.

4%3.

3%5.

74.

6%1

.22.

7%3.

1%2

.42.

8%0

.1V

stm

anla

ndco

unty

4.2%

3.3%

5.1

4.4%

0.7

5.3%

3.1%

11.0

5.0%

1.8

Dal

arna

coun

ty4.

5%3.

2%7.

04.

5%0.

12.

6%2.

9%1

.82.

5%0.

8G

vle

borg

coun

ty2.

9%3.

4%2

.52.

9%0.

32.

7%3.

1%2

.32.

5%1.

2V

ste

rnor

rlan

dco

unty

1.6%

3.2%

10.

51.

5%0.

61.

2%3.

1%1

3.1

1.1%

0.6

Jm

tlan

dco

unty

0.4%

1.0%

7.4

0.4%

0.0

0.5%

1.2%

7.2

0.5%

0.0

Vs

terb

otte

nco

unty

2.1%

2.9%

5.2

2.0%

0.8

2.1%

2.9%

5.4

1.8%

1.9

Nor

rbot

ten

coun

ty5.

6%3.

1%12

.25.

8%1

.33.

6%3.

2%2.

33.

7%0

.4

1382 M. Eliason

Tab

le4

(con

tinu

ed)

Var

iabl

eM

ale

sam

ple

Fem

ale

sam

ple

Bef

ore

wei

ghti

ngA

fter

wei

ghti

ngB

efor

ew

eigh

ting

Aft

erw

eigh

ting

D=

1D

=0

SDM

D=

0SD

MD

=1

D=

0SD

MD

=0

SDM

Indu

stry

sect

orA

gric

ultu

re,f

ores

try,

and

fish

ing

3.8%

1.3%

15.6

3.9%

0.7

1.3%

0.4%

9.5

1.4%

1.8

Man

ufac

turi

ngan

dm

inin

g38

.6%

37.9

%1.

639

.0%

0.8

27.1

%15

.1%

29.9

28.6

%3

.5E

lect

rici

ty/w

ater

supp

ly;s

ewag

e/re

fuse

disp

osal

1.4%

2.4%

7.4

1.4%

0.3

0.4%

0.5%

1.8

0.4%

0.1

Con

stru

ctio

n11

.2%

8.2%

10.2

11.2

%0

.02.

4%1.

1%9.

82.

3%0.

8T

rans

port

,sto

rage

and

com

mun

icat

ions

20.8

%19

.1%

4.4

20.8

%0.

212

.9%

14.3

%4

.112

.5%

1.2

Rea

lest

ate,

rent

ing

and

busi

ness

acti

viti

es11

.8%

8.2%

12.0

11.7

%0.

213

.7%

6.6%

23.6

13.5

%0.

7E

duca

tion

and

rese

arch

1.0%

7.4%

32.

70.

9%0.

14.

4%14

.2%

34.

34.

3%0.

5H

ealt

han

dso

cial

wor

k2.

4%4.

5%1

1.9

2.4%

0.2

24.9

%35

.8%

23.

924

.7%

0.4

Com

mun

ity,

soci

alan

dpe

rson

alse

rvic

e4.

3%2.

8%7.

84.

1%0.

98.

3%4.

1%17

.48.

0%1.

4P

ublic

adm

inis

trat

ion,

defe

nce,

soci

alse

curi

ty4.

8%8.

2%1

3.7

4.7%

0.6

4.6%

7.8%

13.

44.

4%0.

7

The

disp

lace

dco

uple

sch

arac

teri

stic

sar

eno

taf

fect

edby

the

wei

ghti

ngan

dar

e,th

eref

ore,

not

repe

ated

.SD

Mis

the

stan

dard

ised

diff

eren

cein

mea

nsbe

twee

nth

etw

ogr

oups

.All

vari

able

sar

em

easu

red

inth

eye

arpr

eced

ing

the

job

disp

lace

men

tT

hedi

spla

ced

coup

les

char

acte

rist

ics

are

not

affe

cted

byth

ew

eigh

ting

and

are,

ther

efor

e,no

tre

peat

ed.S

DM

isth

est

anda

rdis

eddi

ffer

ence

inm

eans

betw

een

the

two

grou

ps.A

llam

ount

sar

ein

thou

sand

sof

SEK

.All

vari

able

sar

em

easu

red

inth

eye

arpr

eced

ing

the

job

disp

lace

men

tifn

otot

herw

ise

stat

eda M

easu

red

duri

nga

2-ye

arpe

riod

3to

2ye

ars

prio

rto

the

job

disp

lace

men

tbIf

poss

ible

mea

sure

din

the

year

prio

rto

the

clos

ing

proc

ess

and

othe

rwis

ein

1985


the propensity score is not near one for any observation, there are no corre-sponding weights that are unduly large for any comparison couples.

3.6 Descriptive statistics and balancing assessment

Table 4 presents the pre- and post-weighting means of the baseline variablesfor the main populations. To further assess whether the weighted samples arecomparable with respect to these variables, the standardised differences inmeans (SDM; i.e. the difference in covariate means between the displaced andthe non-displaced couples in percentage of the pooled standard deviation ofthat covariate before the weighting) are presented for the two samples beforeand after the propensity score weighting.

The largest pre-weighting differences in baseline characteristics betweenthe displaced and non-displaced couples correspond to differences in estab-lishment characteristics and spouses attained educational level. Both thedisplaced men and women were, on average, employed at much smaller estab-lishments with less educated workforces. As expected, the economic sector inwhich the establishments operated also differed greatly between the displacedand non-displaced employees (see Table 4). The displaced employees (bothmarried men and married women) also had more previous experiences ofunemployment and lower earnings.

These differences related mainly to the establishments characteristics re-peat the previously claimed need to adjust for baseline differences. Eventhough the problems associated with a non-random selection of who is laid offwithin a particular establishment are eliminated or greatly reduced in the caseof a closure, there is a non-random selection of which establishments that goesout of business, which may affect the distribution of such worker characteristicsthat are correlated with future divorce risk.

However, the weighting of the samples reduced the mean of the absolutevalues of the SDMs from 7.8 to 0.5 in the male sample and from 9.4 to 0.7 inthe female sample.20 Hence, the weighting process generated pseudo-samplesof non-displaced couples who were, on average, similar to the samples ofdisplaced couples.

4 Results

4.1 The impact of job displacement on earnings, non-employment,and unemployment

A natural point of departure would be to first make a convincing case thatthe job displacements produced a shock to earnings and changed the future

20Rosenbaum and Rubin (1985) judged a SDM of 20 to be substantial, whereas Normand et al.(2001) considered an SDM less than 10 to be small.

1384 M. Eliason

Fig. 2 The impact of job displacement on earnings in thousands of SEK with 95% confidenceintervals for married men (left) and married women (right)

employment situations of the couples. In these estimations the second stepdiscrete-time logit model was replaced with a fixed effect regression: yi,t =i + t + 11k=2 k Dki,t + i,t, where yi,t is the particular outcome of interestfor spouse i in year t, and the Dki,t s are a set of indicators for the numberof years relative to the displacement that allow the temporal impact of jobdisplacement to be estimated (i.e., k is the estimated impact k years after thedisplacement).21 The parameter i is the individual-specific fixed effect, t is atime-specific effect, and i,t is the error term.

To display the estimated impact on earnings, non-employment, and unem-ployment, the coefficients k are plotted in Figs. 2, 3 and 4. First, Fig. 2 presentsthe estimated effects of the job displacements of married men and womenon annual earnings in thousands of Swedish kronor (SEK). All amounts weredeflated to the 1999 values by using the consumer price index. It is clear thatthe married mens displacements generated earnings losses that in monetaryterms were much larger than those of women. In the year of the displacement,the relative drop in earnings corresponded to SEK 22,000 (8.8%) for themarried men and approximately SEK 11,000 (8.1%) for the married women.22

For the married men earnings dropped further to a gap of SEK 34,000 (14.1%)in the following year. Thereafter, the earnings gap slowly diminished in boththe male and female sample, although for the men, the gap was still as large asSEK 9,000 (4.9%) at the end of the 12-year follow-up period. For the women,the gap disappeared 8 years after the displacement.

Based on Fig. 3, it is apparent that these earnings losses can be explainedto a large extent by the displaced employees leaving paid employment. The

21The fixed effect estimator, which includes both the lagged and leading indicators of incidenceof job loss, has become standard in the displaced worker literature when examining wage andearnings effects (see Jacobson et al. 1993; Margolis 1999; von Wachter et al. 2009; Schmieder et al.2010; Couch and Placzek 2010).22The presented relative effects are calculated as k/

(E[ykD=1] k

).


Fig. 3 The impact of job displacement on non-employment (i.e., zero annual earnings) with 95%confidence intervals for men (left) and married women (right)

difference between the shares of displaced and non-displaced married menwho had zero annual earnings increased to 7.9 percentage points in the yearfollowing the displacement, whereas the corresponding difference for themarried women was 6.5 percentage points. For both the male and femalesample, the difference between the displaced and non-displaced employeesdiminished only slowly and at the end of the follow-up period there was aremaining statistically significant difference of 3.0 and 1.3 percentage points,respectively.

Figure 4 presents similar estimates of the impact on annually receivedunemployment insurance in thousands of SEK. There are obvious spikes in theyear of displacement and in the following year for both the men and women.These spikes corresponded to SEK 10,00012,000 (580%670%) and SEK8,0009,000 (330%350%), respectively. However, there was a rapid returnto smaller differences in the following 2 years (i.e., approximately SEK 3,000).Thereafter, there was no more than minor additional recovery for either thedisplaced men or the displaced women.

Fig. 4 The impact of job displacement on annually received unemployment insurance in thou-sands of SEK with 95% confidence intervals for men (left) and married women (right)

1386 M. Eliason

4.2 The impact of job displacement on the risk of divorce

After having established that job displacement inflicted both immediate andrather persistent earnings losses (though modest in an international context)as well as increased non-employment and unemployment among displacedmarried men and women, we can proceed to the main part of this paper, whichexamines whether job displacement also increased the risk of a subsequentdivorce.23 As a robustness check, estimates from all four estimators willbe showed in the main analysis. In all of the analyses, the estimates usingboth applications of the time window procedure that defines the displacedemployees will be displayed.

The main estimates are presented in Table 5. The unadjusted estimatoryields a statistically significant increase in the risk of divorce from displacementin all of the samples. For the preferred 3-year window, the excess risks ofdivorce among couples in which the husband or wife was displaced wereestimated to be 30% (HR, 1.30; 95% CI, 1.181.42) and 27% (HR, 1.27; 95%CI, 1.151.41), respectively.

Regarding the adjusted estimates, it is clear from the table that the choiceof estimator has a minimal impact on the estimates as long as one somehowadjusts for the baseline differences. Therefore, I will refer only to the propen-sity score weighted (PSW) estimator in the text. It is also clear that some ofthe unadjusted excess divorce risk is attributable to differences in the baselinesample characteristics since all of the adjusted estimates are considerablysmaller. For the male sample, both time windows yield a statistically significantexcess divorce risk from job displacement. Using the 3-year window, theestimated excess divorce risk is estimated to be 14% (HR, 1.14; 95% CI,1.031.26), whereas the corresponding estimate using the 1-year window isten percentage points higher (HR, 1.24; 95% CI, 1.111.39).24 For the femalesample, the 3-year window yields a statistically insignificant estimate of theimpact of job displacement (HR, 1.10; 95% CI, 0.981.23). However, the 1-yearwindow yields a statistically significant estimate also of the impact of wives jobdisplacement corresponding to a 16% excess divorce risk (HR, 1.16; 95% CI,1.021.32).

4.3 A subgroup analysis

In the previous section, it was established that the risk of divorce following jobdisplacement was increased during the 12-year follow-up period. The impact

23In an international context these losses are rather limited, although they are in line with thefindings of previous Nordic research (e.g., Eliason and Storrie 2006; Eliason 2011; Huttunen et al.2011).24The different estimates generated by using the two time windows suggest that, in lengthierclosing processes, those leaving early have a lower divorce risk (which is line with the hypothesisthat some workers with better outside options leave the closing workplace early for new jobs) orthat the longer window primarily picks up normal turnover that is unrelated to the closure.


Tab

le5

Est

imat

edim

pact

ofhu

sban

dsa

ndw

ives

job

disp

lace

men

ton

the

risk

ofdi

vorc

e

Thr

ee-y

ear

win

dow

One

-yea

rw

indo

wH

usba

nds

job

disp

lace

men

tW

ife

sjo

bdi

spla

cem

ent

Hus

band

sjo

bdi

spla

cem

ent

Wif

es

job

disp

lace

men

tH

R(9

5%C

I)H

R(9

5%C

I)H

R(9

5%C

I)H

R(9

5%C

I)

Una

djus

ted

1.30

(1.1

81.

42)

1.27

(1.1

51.

41)

1.51

(1.3

61.

67)

1.33

(1.1

91.

48)

PSW

a1.

14(1

.03

1.26

)1.

10(0

.98

1.23

)1.

24(1

.11

1.39

)1.

16(1

.02

1.32

)D

TL

b1.

17(1

.06

1.29

)1.

09(0

.98

1.21

)1.

27(1

.15

1.41

)1.

13(1

.00

1.27

)P

SW+

DT

Lc

1.13

(1.0

21.

25)

1.10

(0.9

81.

23)

1.26

(1.1

21.

41)

1.15

(1.0

21.

31)

The

esti

mat

edha

zard

rati

o(H

R)

isac

com

pani

edby

95%

conf

iden

cein

terv

als

(CI)

.All

esti

mat

ions

wer

epe

rfor

med

usin

gbo

thth

e1-

and

3-ye

arw

indo

wde

fini

ngth

ejo

bdi

spla

cem

ents

a The

prop

ensi

tysc

orew

eigh

ted

esti

mat

orw

itho

utfu

rthe

rco

vari

ate

adju

stm

ent

bT

heun

wei

ghte

ddi

scre

teti

me

logi

treg

ress

ion

c The

prop

ensi

tysc

ore

wei

ghte

ddi

scre

teti

me

logi

treg

ress

ion

1388 M. Eliason

was somewhat larger if the husband was the job loser. In this section, byperforming a subgroup analysis, the aim is to learn about how the impact ofjob displacement depended upon the couples marital investments, economicdependency/independency, and coping resources.

In multiple subgroup analyses, one would expect both more false negativeresults (i.e., a failure to reject the null hypothesis given that the alternativehypothesis is actually true) because of the smaller sample sizes and more falsepositive results (i.e., a rejection of the null hypothesis given that it is actuallytrue) because of multiple significance testing. Thus, the findings should beinterpreted with caution. Because of the reduced number of divorces in eachsubgroup only the propensity score weighted estimator without additionalcovariate adjustments was used.25 However, the estimates presented in Table 6contain those obtained by applying both the 1- and 3-year windows.

In the first subgroup analysis, the couples were divided into those with andthose without marital investments. Owning a house may obviously be viewedas a marital investment, which was why house ownership was included as acontrol variable in the previous analyses. However, a division based on houseownership is not meaningful here since more than three-quarters of the couplesowned a house. Hence, marital investments will be defined based only onwhether the couples had children.

A priori, it is not clear whether or not children would be expected to have aprotective effect. On the one hand, if children are seen purely as marriage-specific capital, the presence of children would require the shock to themarriage to be larger to result in a divorce. On the other hand, having childrencould increase the likelihood that the couple perceives the displacement as astressor because more children mean more dependants.

However, the estimates indicate that the protective effect was larger, atleast for the male sample. The estimated impact of the husbands displacementis statistically significant for those with no children, but not for those withchildren, and corresponds to an increased risk of divorce by 22% (HR, 1.22;95% CI, 1.001.49) using the 3-year window. For the female sample, the impactof job displacement is of similar size in both subgroups, and none of theestimates are statistically significant.

Traditionally, it has been suggested that womens increased economicindependence within marriages over time, which is the result of increasedlabour force participation, is an important factor behind the increasing divorcerates. However, the empirical support is inconclusive (see Sayer and Bianchi2000). Beckerian economic theory (Becker 1973, 1974) suggests that decreasedspecialisation within a marriage will decrease the gains from marriage and,therefore, increase the risk of divorce. Increased own earnings will also lowerthe barrier to divorce. However, two-earner families are the norm today. Both

25When the outcome event is rare and the exposure is more common, propensity score methodsare particularly useful as the number of covariates that can be conditioned on increases (Cepedaet al. 2003; Braitman and Rosenbaum 2002).


Tab

le6

Est

imat

edim

pact

ofhu

sban

dsa

ndw

ives

job

disp

lace

men

ton

the

risk

ofdi

vorc

e,by

subg

roup

s

Subg

roup

sT

hree

-yea

rw

indo

wO

ne-y

ear

win

dow

Hus

band

sjo

bdi

spla

cem

ent

Wif

es

job

disp

lace

men

tH

usba

nds

job

disp

lace

men

tW

ife

sjo

bdi

spla

cem

ent

HR

(95%

CI)

HR

(95%

CI)

HR

(95%

CI)

HR

(95%

CI)

Mar

ital

inve

stm

ents

Chi

ldre

n1.

09(0

.97

1.22

)1.

12(0

.98

1.28

)1.

16(1

.01

1.33

)1.

16(1

.01

1.35

)N

och

ildre

n1.

22(1

.00

1.49

)1.

08(0

.86

1.34

)1.

37(1

.10

1.71

)1.

16(0

.91

1.47

)E

cono

mic

depe

nden

cyE

qual

lyde

pend

ent

1.02

(0.8

41.

23)

1.04

(0.8

31.

29)

1.07

(0.8

41.

36)

1.06

(0.8

31.

36)

Une

qual

lyde

pend

ent

1.19

(1.0

61.

34)

1.12

(0.9

81.

28)

1.30

(1.1

41.

48)

1.19

(1.0

31.

38)

Cop

ing

reso

urce

sN

opr

evio

usha

rdsh

ip1.

18(1

.03

1.35

)1.

02(0

.87

1.20

)1.

25(1

.07

1.46

)1.

11(0

.93

1.32

)P

revi

ous

hard

ship

1.12

(0.9

71.

30)

1.18

(1.0

11.

39)

1.26

(1.0

61.

49)

1.21

(1.0

11.

46)

Fin

anci

alre

sour

ces

1.12

(0.9

91.

26)

1.05

(0.9

11.

21)

1.20

(1.0

51.

39)

1.08

(0.9

31.

27)

No

fina

ncia

lres

ourc

es1.

13(0

.95

1.36

)1.

24(1

.02

1.50

)1.

22(1

.00

1.49

)1.

32(1

.07

1.64

)

The

esti

mat

edha

zard

rati

os(H

R),

usin

gth

epr

open

sity

scor

ew

eigh

ted

esti

mat

orw

itho

utfu

rthe

rco

vari

ate

adju

stm

ent

(PSW

),ar

eac

com

pani

edby

95%

conf

iden

cein

terv

als

(CI)

.All

esti

mat

ions

are

perf

orm

edus

ing

both

the

1-an

d3-

year

win

dow

defi

ning

the

job

disp

lace

men

ts

1390 M. Eliason

spouses incomes will also have a positive effect on their living standards andprovide a buffer against economic uncertainty (e.g. uncertainty caused by jobdisplacement, as in this case).

A spouse is defined to be economically dependent on his/her partner if thespouses own earnings are less than 40% of the couples total earnings. Inequally dependent marriages, each spouses contribution will range from 40%to 60% of total earnings.

The estimates indicate that equal dependence within a marriage has abuffering effect. In all of the estimations, the impact of job displacement on therisk of divorce is minor and non-significant among the couples whose spousesare equally dependent. A supplementary analysis, which is not presented here,did not reveal any differential impact depending on whether the husband orthe wife was in a dependent position.26

Finally, the impact of job displacement on the risk of divorce for groupsof couples with varying degrees of coping resources was investigated. First, thesamples were divided based on whether the couples had suffered from previoushardships or difficulties. Couples experience of previous difficulties was nar-rowly defined as either spouses experience of either insured unemploymentor hospitalisation. The other measure of coping resources was equally nar-rowly defined as financial resources. The couples defined as lacking financialresources had either received means-tested social benefits or belonged to thelowest income quartile and also had no taxable wealth. In the female sample,both financial resources and the lack of previous difficulties seem to haveprotected the couple against the adverse effects of job displacement, whereasjob displacement significantly increased the risk of divorce both among thecouples lacking financial resources and among those who previously hadsuffered from unemployment or ill-health. However, in the male sample, thesecoping resources seem to have been of negligible importance to how the couplehandled the husbands current job displacement.

4.4 The impact of job displacement on divorce over time

In the analyses in the previous sections, the impact of job displacement onthe risk of divorce was assumed to be constant over time. This assumptionwas necessary to obtain enough precision to be able to empirically determinewhether the impact on the risk of divorce was statistically significant. Never-theless, it could be argued that such an assumption may not be valid. If jobdisplacement is viewed as a discrete event, one may expect the impact on therisk of divorce to be immediate only. However, a job displacement is morelikely to be a process that begins with the anticipation of displacement and isthen followed by the actual separation, possibly a period of unemployment,

26However, the few couples in which the wifes earnings constituted more than 60% of totalearnings did not allow the impact of job displacement to be estimated with any precision for thissubgroup.


Tab

le7

Est

imat

edti

me-

vary

ing

impa

ctof

husb

ands

and

wiv

esj

obdi

spla

cem

ento

nth

eri

skof

divo

rce

Tim

esi

nce

job

disp

lace

men

tT

hree

-yea

rw

indo

wO

ne-y

ear

win

dow

Hus

band

sjo

bdi

spla

cem

ent

Wif

es

job

disp

lace

men

tH

usba

nds

job

disp

lace

men

tW

ife

sjo

bdi

spla

cem

ent

HR

(95%

CI)

HR

(95%

CI)

HR

(95%

CI)

HR

(95%

CI)

13

year

s1.

21(1

.02

1.43

)1.

03(0

.85

1.25

)1.

23(1

.01

1.49

)1.

06(0

.87

1.33

)4

5ye

ars

0.85

(0.6

61.

08)

1.14

(0.8

91.

46)

0.98

(0.7

51.

28)

1.27

(0.9

71.

66)

5+

year

s1.

23(1

.06

1.42

)1.

13(0

.96

1.34

)1.

37(1

.16

1.62

)1.

18(0

.97

1.43

)

The

esti

mat

edha

zard

rati

os(H

R),

usin

gth

epr

open

sity

scor

ew

eigh

ted

esti

mat

orw

itho

utfu

rthe

rco

vari

ate

adju

stm

ent

(PSW

),ar

eac

com

pani

edby

95%

conf

iden

cein

terv

als

(CI)

.All

esti

mat

ions

are

perf

orm

edus

ing

both

the

1-an

d3-

year

win

dow

defi

ning

the

job

disp

lace

men

ts

1392 M. Eliason

reemployment, a period in which the person adapts to the new job, and soforth. In Section 4.1, it was also demonstrated that the displaced employeesin the current sample experienced long-lasting earnings losses (as shown in anumber of previous studies) and extended periods of non- and unemployment.

Furthermore, also a divorce is more accurately described as a process thanas a discrete event. This process probably begins with marital dissatisfaction orconflicts before proceeding towards separation and finally divorce. Therefore,the length of the transition from the initial trigger or stressor (i.e., job dis-placement) until the couple becomes legally divorced is an empirical matter.Following Charles and Stephens (2004), I will separate the estimated effect ofjob displacement into short-run (i.e., up to 3 years following the displacement),intermediate (i.e., 45 years), and long-run (i.e., more than 5 years) effects.In terms of the specification of the second-step discrete-time logit model, thecoefficient is replaced with a function (t) = 1 I (t 3) + 2 I (4 t 5) +3 I (t < 5), where I() is an indicator function, and t is the number of years sincedisplacement. The second step was again estimated without any additionalcovariates, and the first-step estimation of the propensity scores included thefull set of covariates discussed in Section 3.4. The resulting estimates arepresented in Table 7 for both samples and both time windows.

During the first 3 years following the husbands displacements, the hazardratio was estimated to 1.21 (95% CI, 1.021.43) using the 3-year window.The wives job displacements show no such short-run effect. Instead, thelargest estimated effect of wives displacements was found in the following2-year period (HR, 1.14; 95% CI, 0.891.46). In the male sample, there wasan opposite effect in these years, although it was not statistically significant.Coinciding with the deep recession there was an emerging long-term effectof job displacement also on the divorce risk. More than 5 years after thedisplacement, there was an excess divorce risk of 23% (HR, 1.23; 95% CI,1.011.49) among the couples whose husbands were displaced. The wives jobdisplacements had a long-term effect on the risk of divorce of similar size asthe intermediate effect and also not statistically significant.

5 Summary and conclusions

A job loss is, through various means, likely to change the conditions of mar-riage, which could affect marital stability and increase the risk of divorce. Froman economic point of view, a job loss may result in long-lasting earnings lossesand financial strain, which may lead to marital conflicts and increased maritalinstability. However, to obtain a better understanding of the relationshipbetween job loss and divorce, one also has to recognise that employment hasmeaning beyond a source of income. That is, having a job also affects lifestyle,social networks, and psychological well-being.

This paper examined both husbands and wives job displacements dueto establishment closures and extended the existing literature by analysingthe marital impact over a longer period (i.e., up to 12 years after the


displacement). The exclusive focus on displacements due to establishmentclosures is important because it reduces the selection problem (i.e., that thejob loss might have been caused by difficulties following a divorce and not viceversa, or that the person may possess characteristics that render both a jobloss and a divorce more likely). Moreover, the focus on job displacements alsoexcludes one potential explanation for why job losses in general and firings inparticular may increase the risk of divorce: since all of the employees at theworkplace are laid off, it is not reasonable to assume that the job displacementreveals any information about the spouses fitness as a mate. However, aperson may very well be fired because of personal characteristics that areunfavourable also within a marriage.

The estimates show a positive and statistically significant impact of jobdisplacement on the risk of divorce. During the 12-year follow-up period, therisk of divorce was elevated by 14% if the husband was displaced at baseline.This finding is in line with the estimates in Rege et al. (2007) of the effectof Norwegian husbands job displacement due to plant closure on the riskof divorce. It is notable that the estimates in both studies contradict thefindings in the one study that previously has investigated the impact of jobdisplacement due to plant closure. Charles and Stephens (2004) found that thedivorce risk did not increase following plant closures. Rather, the risk onlyincreased following other layoffs. They suggested that the information abouta spouses non-economic fitness as a mate drives the relationship between jobloss and divorce and that a job loss due to a plant closure does not reveal anysuch information because all of the employees are laid off regardless of theirindividual characteristics and behaviour. Therefore, they considered any effectof job losses due to plant closures as the effect that relates to purely economicconsiderations. However, other research has linked job displacements (dueto plant closures) to, for example, emotional distress and excessive alcoholconsumption. Hence, it seems obvious that job displacements may destabilisemarriages through other channels.

There is conflicting previous evidence with regard to whether wives joblosses and unemployment also affect marital stability and the risk of divorce.The findings in this study indicate that marriages are affected by both hus-bands and wives job displacements, although marriages are affected more soby husbands displacements. The similar findings by Hansen (2005) for Norwaymay suggest that the impact of wives job losses is specific to Scandinaviancountries, where the labour force participation among married women is high,and wives are expected to be self-supporting, even within marriage. However,a subgroup analysis revealed no effect of wives job displacement on marriagesin which the spouses were equally dependent.

A final finding is related to the timing of divorce. Husbands job dis-placements seem to have affected the divorce risk both in the short termand in the longer term but not in the intermediate term. It is possible thatless stable marriages were dissolved shortly after the displacement. Basedon previous research showing that displaced workers also suffer long-termearnings losses and increased risk of repeated job losses, it seems reasonable to

1394 M. Eliason

claim that job displacements may affect marriages even over the longer term.However, the finding that job displacement had no impact in the intermediateterm may suggest that the long-term effect depends on the general labourmarket conditions and that the subsequent deep recession caused more ofthe previously displaced employees to lose their jobs again. However, thewives job displacements showed a different time pattern. There was no excessrisk of divorce in the short term, whereas the divorce risk was elevated inboth the intermediate and long term although not statistically significant.Given the much smaller earnings losses following the wives displacements,the lack of an immediate adverse impact on marital stability is not surprising.A subgroup analysis also revealed that the increased divorce risk followingwives displacements was limited to the couples who had experienced previousdifficulties or who lacked financial resources. Thus, in the case of wivesdisplacements, the impact on the divorce risk appears to be caused by anaccumulation of difficulties that finally results in divorce.

In conclusion, job displacement is a multifaceted event that appears to beassociated with severe losses for some in both monetary and non-monetaryterms. However, for several reasons one must also recognise and under

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Lost jobs, broken marriages

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