the impact of job loss on family dissolution australian conference of economists 1 october 2008...
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The impact of job loss on family dissolution
Australian Conference of Economists1 October 2008
Silvia Mendolia & Denise DoironUNSW - School of Economics
To examine the impact of job loss on the probability of divorce
To further our understanding of possible transmission channels:
Financial stress from the negative income shock
Psychological stress
Additional information on individual traits and revision of expectations on future value of match
Policy implications: Policies aimed at reducing the earnings’ shock from job losses may alleviate the former problem but they will be less effective if the latter impact is the main one
Objectives of the paper
Most of the previous literature focuses on costs of job displacements in terms of future employment probabilities and lost earnings. In these papers, family composition is ignored or treated as exogenous
Some of these papers distinguish different types of job losses (see Arulampalam, EJ 2001) and focus on the impact of layoffs vs. plant closure (see Gibbons and Katz, 1991 and Stevens, JLE 1997)
Recently, a few studies have taken a broader view of the impacts of job loss
Examples: Ercolani and Jenkins (1999) and Stephens (2004) study adjustments of wives’ labour supply; Clark and Oswald (1994) and Sullivan and von Wachter (2006) look at impacts on well-being and health
Only a few studies have looked at possible impacts on the probability of
divorce: Jensen and Smith (1990), Kraft (2001), Charles and Stephens (2004), Eliason (2004) and Blekesaune (2008). Results to date are inconsistent
Motivation and Background
Mostly derived from the pioneering work of Becker (1973, 1974)
The family is an expected utility maximizing unit
Two general causes for separation:
Meeting of a new partner with expected joint utility greater than the current match
“Surprises” may change initial expectations about the partner’s characteristics (see Weis and Willis, 1997) or alter the value of the partnership.
Economic models of divorce
Job losses can create immediate earning shocks that reduce the relative benefits of marriage/cohabitation. This generally relies on the job loss being unexpected
Job losses may impose pecuniary and non-pecuniary stress on the relationship
Job losses may also act as signals of the future monetary and non-monetary benefits of the match
One would not expect such effects in the case of exogenous job losses as exogenous displacements contain no information on the quality of the partner
The role of job loss in the decision to separate or divorce
Jensen and Smith (JPE, 1990) use a Danish data set and show that the probability of divorce increases following a man’s job loss (contemporaneous effect).
Charles and Stephens (JLE, 2004) use the PSID and show an increase in the probability of divorce following a husband’s job loss from layoffs (not from plant closure). This suggests a significant signalling effect.
Eliason (2004) uses a Swedish panel and finds a significant negative impact on family stability caused by displacements due to a factory closure. This suggests a strong impact from the earnings’ shock.
Kraft (Kyklos, 2001) uses the GSOEP 1987–1996 and show that a longer spell of husband’s unemployment increases the risk of separation.
Blekesaune (2008) uses the BHPS and shows that the probability of family dissolution increases after a man’s job loss, through a significant decrease in partner’s financial satisfaction
Results to date are few and contradictory regarding the transmission’s channels of the shock
Previous literature on job loss and divorce
Analysis of the causal effect of job loss on family dissolution, focusing on involuntary husband’s job displacement
Information on the reason for ending the employment spell is used, in order to control for possible job loss endogeneity
Analysis of multiple transmission channels, including the income shock, the psychological effect and the signalling role
Key points of this paper
The BHPS:
A nationally representative sample of the UK population, recruited mostly in 1991
An indefinite life panel survey; the longitudinal sample consists of members of the original households and their natural descendants
A rich source of information on demographic and household composition, employment and family characteristics
The family history data set: combination of retrospective histories and panel information.
We focus on couples in which men are aged 16-65 and in paid employment
Our analysis sample: 6,100 couples (40,662 observations)
Data – British Household Panel Survey
This information is derived from the family history dataset
Marriage includes cohabitation
If the two partners cohabitate before marriage, we consider the cohabitation starting date as the union starting date
Divorce includes separation
If there is a separation before the divorce, the date of separation is considered as the union end date
If a union ends, the partners are subsequently dropped from the analysis sample
We include marriages starting during the survey and second and later marriages
A sensitivity analysis is conducted in order to compare stock and flow samples including partnerships that began before/after the start of the survey (1991)
Variable definition: divorce and marriage
This information is derived from the work history dataset and the single waves job history file
In the first version of our model, we use one single job loss variable, including dismissals, redundancies and temporary job endings
Then, in order to investigate the different roles of job loss we use information on the reason for ending the employment spell
We consider involuntary job losses. These are separated by type: dismissals, redundancies and temporary job endings
Variable definition: job loss
Previous literature has not directly addressed the issue of potential job loss endogeneity. Previous papers’ explanations include:
the timing of the events and the use of panel data (see Jensen and Smith, JPE 1990, Kraft, Kylos 2001, Charles and Stephens, JLE 2004 and Blakesaune 2008)
the distinction between layoffs and plant closure (see Eliason, 2004 and Charles and Stephens, JLE 2004)
The information about plant closure is not available in the BHPS
Previous literature using the BHPS has relied on the distinction between different types of job loss (see Arulampalam, 2001) and the link with industry’s workforce growth rate (see Borland et al. 1999)
Variable definition: exogenous job losses (1/3)
Arulampalam (EJ, 2001) investigates re-employment probabilities and future earnings (using BHPS 1991-1997) and finds that redundancy is less stigmatising than other job losses.
We use redundancies as an exogenous measure of job loss
UK employment law allows three reasons for redundancy: total cessation of the employer's business (whether permanently or
temporarily), cessation of business at the employee’s workplace reduction in the number of workers required to do a particular job
In a redundancy situation, workers should be selected fairly, using objective criteria, and consultation rights apply in case of collective redundancies
Workers are entitled to receive redundancy payment if their tenure is greater than 2 years
Variable definition: exogenous job losses (2/3)
Previous literature using the BHPS (see Borland et al. and Taylor and Booth, 1996) has argued that the institutional system often blurs the distinction between redundancy and dismissal and that there is a risk of recall bias
Borland et al (1996) distinguish between displaced workers from industries with increasing/decreasing employment in an attempt to enforce some exogeneity over the cause of job loss
To minimize the likelihood of measurement error (respondents declaring redundancies in the case of dismissals) we also separate redundancies in industries with declining employment only
Variable definition: exogenous job losses (3/3)
Job loss can affect the family dissolution through more than one channel: The negative income shock The psychological stress and subsequent increase in family conflicts The signaling effect regarding partner’s characteristics
Redundancies will capture a negative income shock and a limited psychological stress
Temporary job endings will capture a negative income shock and a stronger psychological stress, due to the nature of the contract and marginal labour market attachment usually associated. These factors are also likely to have some signaling effect
Dismissals can be correlated with characteristics of the partner that also reduce the value or quality of the match. The impact of dismissals will capture effects from the negative earnings shock, the strongest psychological shock and possibly a signaling impact
The possibility of reverse causality is alleviated by considering job losses occurring in the year prior to the divorce
Transmission channels of the shock
We analyse the size of the income shock as an indication of effect coming from different types of job loss
People experiencing a dismissal experience the highest income shock with respect to the previous year (around 8%)
People experiencing a redundancy have a lower shock (this is also because of redundancy payments) and their subsequent earnings (one year after job loss) are also higher
People experiencing a temporary job ending have the lowest income shock (around 3%) and they are more likely to achieve wage gains one or two years after the shock
These findings are consistent with previous literature on these topics (see Arulampalam, EJ 2001 and Borland et al. 1999)
Income shock from different job losses
Couples are characterised by their “match quality” at the start of the relationship and this is an important predictor of the future stability of their union
We include differences between ages and education levels to capture the initial quality of the match
Income, education and the number of children are included to represent costs and benefits of the dissolution
Variable definition: other variables
Job loss in the analysis sample
Year Redundancy Temporary job ending
Dismissals
1991 129 39 18
1992 108 30 6
1993 113 41 14
1994 95 57 19
1995 68 44 10
1996 69 44 19
1997 82 44 16
1998 63 59 17
1999 68 56 19
2000 98 79 21
2001 208 144 27
2002 75 54 13
2003 50 43 4
2004 37 27 2
TOTAL 1273 761 205
Redundancy in the sample
0,00%
1,00%
2,00%
3,00%
4,00%
5,00%
6,00%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Divorce rate in the analysis sample
About 2% of marriages and cohabitations are dissolved each year
Couples who experience job losses have a slightly higher
divorce rate
The incidence of dissolution trends downwards over the length of the union
Divorce Rate
0,00%0,50%1,00%1,50%2,00%2,50%3,00%3,50%4,00%4,50%5,00%
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Job loss sample No job loss sample
Duration of marriages in the analysis sample
The percentage of short partnerships (less than 5 years) is high in both samples
Couples with job loss experience don’t have idiosyncratically lower levels of durability
Distribution of years of marriage
0,00%
2,00%
4,00%
6,00%
8,00%
10,00%
12,00%
14,00%
0 1 2 3 45-7
8-10
11-1
516-
2021-
2526-
3031-
3536-
4041-
45
No JL sample JL sample
Discrete time proportional hazards models hij = 1-exp{-exp{Xij’β + λj}} i=1,…..N, j=1,….Ti
h is the hazard rate: the probability of divorce at duration j conditional on the marriage having survived until j-1.
X includes job losses, education levels, income, number of children, woman’s employment status differences in age and education between partners.
λj is the baseline hazard which may depend on the duration j. Various specifications of λj are estimated .
Specifications of h containing unobserved time-invariant individual-specific effects (modeled as Gamma distributed) are also estimated. Flow and stock samples of marriages are treated separately to check for selection effects.
The model
The results are stable across different specification of the model:
We start by estimating a discrete logistic duration model and a discrete complementary log-log model
We consider several alternative when choosing a functional form of the baseline hazard (linearly depending on years of marriage, depending on years squared and cubic)
Then we incorporate unobserved heterogeneity (modelled as Gamma distributed)
The first specification of the model includes one job loss variable. Then, we distinguish between redundancies, dismissals and temporary job ending
A separate model is estimated including redundancies in declining industries, dismissals and temporary job endings
Results
Job loss significantly increases the probability of family dissolution
Results
Exp. Coeff.
Job loss 2.02 (0.35)**
Household income 0.79 (0.041)**
Number of children 1.16 (0.051)**
Difference in education level
0.99 (0.94)
Difference in age > 8 years 1.35 (0.046)**
Note: Standard error in parentheses. Sample size: 40,662 observations. + significant at 10%; * significant at 5%; ** significant at 1%. The baseline hazard linearly depends on years of marriage in this specification of the model. Similar results are found with other specifications. Coefficients are transformed to relative risk format. Standard errors are similarly transformed.
Job losses affect family dissolution through more than one channel: a negative income shock imposes stress on the relationship (redundancies) and new information is revealed regarding the value of the match (temporary job endings and dismissals).
Results
Exp. Coeff.
Dismissal 3.09 (1.11)**
Redundancy 1.48 (0.35)+
Temporary job ended 2.09 (0.60)**
Household income 0.82 (0.09)+
Number of children 1.14 (0.05)**
Difference in education level
0.98 (0.11)
Difference in age > 8 years 1.33 (0.20)+
Note: Standard error in parentheses. Sample size: 40,662 observations. + significant at 10%; * significant at 5%; ** significant at 1%. The baseline hazard linearly depends on years of marriage in this specification of the model. Similar results are found with other specifications. Coefficients are transformed to relative risk format. Standard errors are similarly transformed.
We also estimate a model in which redundancy and dismissals are grouped and separated from temporary job endings. The results are stable and confirm the existence of a signaling effect
The longer the partners have been together, the smaller the divorce probability: the hazard rate decreases over time.
Household non labour income decreases the probability of family dissolution.
The probability of divorce increases with the number of dependent children in the household.
Women in paid employment are less likely to divorce
Differences in age between partners (>8 years) increase the probability of divorce.
Other results
Separate estimation is conducted on the stock and flow samples (partnerships that begin before/after the start of the survey):
Older couples are more likely to divorce after a redundancy. They are more affected by the income shock. Signaling effect captured by dismissals is likely to be less relevant.
Younger couples are more likely to divorce after a dismissal as the signalling effect is more important. The income shock is less important in this sample, as there is a higher percentage of double-earners couples
Other results, Cont’d.
Job loss may affect marital stability through multiple channels:
a negative income shock
a psychological shock
a signal which leads to revised expectations on the spouse’s fitness as a partner
We find evidence of multiple channels of transmission:
The redundancy coefficient captures the first 2 elements
The effect of dismissals is higher and this is consistent with the hypothesis of an impact of job loss as a signal of future match quality
Conclusion
Further analysis of the transmission channels, focusing on financial expectations and general life satisfaction
Consideration of the role of social support and separating the impact of job loss in high unemployment areas
Consideration of the impact of job loss on children well being
Consideration of the impact of the female partner’s job loss
Extensions