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COUPLES LABOUR SUPPLY RESPONSES TO JOB LOSS:
GROWTH AND RECESSION COMPARED*
by
MARK BRYAN
Department of Economics, University of Sheffield
and
SIMONETTA LONGHI
Department of Economics, University of Reading
We examine whether couples in the UK increase labour supply to
cushion the fall in earnings from a job loss, comparing periods of
growth and recession. We consider both male and female earners and
various dimensions of labour supply adjustment. We find evidence of
labour supply reactions, but they can be negative as well as positive,
particularly at the extensive margin. During the recession, household
reactions are either unchanged or couples increase their labour market
attachment, with bigger positive reactions and smaller negative ones.
People do not react in advance of job losses, suggesting that unem-
ployment is a surprise.
1INTRODUCTION
One advantage of living in a couple rather than alone is that economic risks
can be shared. When a single person loses their job they have to fall back on
personal savings, unemployment insurance or external support networks to
maintain a minimum level of consumption. When a member of a couple
loses their job, there is an additional margin of adjustment: the other partner
* Manuscript received 5.6.15; final version received 20.1.17.
This work is part of the project on “Understanding the Impact of Recession on Labour
Market Behaviour in Britain”, funded by the Economic and Social Research Council
(ESRC), award no. RES-062-23-3284. This work also forms part of a programme of
research funded by the ESRC through the Research Centre on Micro-Social Change
(MiSoC) (award no. RES-518-28-001). The support provided by ESRC and the Univer-
sity of Essex (where part of the work was completed) is gratefully acknowledged. This
work was based on data from the Quarterly Labour Force Survey, 1992–2011, produced
by the Office for National Statistics (ONS) and supplied by the Secure Data Service at
the UK Data Archive. Office for National Statistics. Social Survey Division and North-
ern Ireland Statistics and Research Agency. Central Survey Unit, Quarterly Labour Force
Survey, 1992–2011: Secure Access [computer file]. 3rd Edition. Colchester, Essex: UK
Data Archive [distributor], May 2013. SN: 6727. The data are Crown Copyright and
reproduced with the permission of the controller of HMSO and Queens Printer for
Scotland. The use of the data in this work does not imply the endorsement of ONS or
the Secure Data Service at the UK Data Archive in relation to the interpretation or
analysis of the data. This work uses research datasets which may not exactly reproduce
National Statistics aggregates. We are grateful to the Editor and two anonymous referees
for very helpful comments.
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C2017 The Authors. The Manchester School published by The University of Manchester and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
1
The Manchester School Vol 00 No. 00 00–00 Month 2017
doi: 10.1111/manc.12186
can increase their labour supply. In this paper, we investigate this household
insurance mechanism, with a focus on the role it played during the Great
Recession and its aftermath (2008–11), as compared to the preceding period
of economic growth (from 1992 until 2007).
While previous studies have investigated couples labour supply reac-
tions to job loss, few have explicitly considered recessions, which is precisely
when the number of job losses peaks and additional labour income is argu-
ably of most value to households. After falling from 9.9 per cent in 1992 to
5.3 per cent in 2007 just before the Great Recession, the UK unemployment
rate rose back to 8.1 per cent in 2011 (ONS, 2013). This represented a major
shock to households that affected both male and female earners. To our
knowledge, only one previous study (Harkness and Evans, 2011) has looked
at UK couples labour supply reactions during the Great Recession, and this
looked only at the first phase of the downturn (2008–9), which was domi-
nated by mens job losses.
1
We provide a longer view by considering the
recession and subsequent period of stagnation up until 2011. In addition,
ours is one of the first studies to also examine how men react to their female
partners job loss—previously the exclusive focus of the literature was on
how women react to their male partners job loss. Such a focus appears
unwarranted now that the vast majority of working couples contain two
earners. In contrast with the previous literature, we also explicitly compare
the behaviour of single and dual earner couples, examining their responses
along multiple dimensions. Previous work has emphasized the need to exam-
ine the dynamics and timing of labour supply responses, and thus we use
panel data from the UK Quarterly Labour Force Survey (LFS).
In the next section, we discuss the background and previous literature
on household responses to employment shocks. We discuss the data and
descriptive statistics in Section 3 and our methodology in Section 4. Section
5 discusses the results while Section 6 concludes.
2B
ACKGROUND
It has long been recognized that individuals and households can partially
insure themselves against the income shocks from job loss by running down
savings, borrowing or delaying purchase of durable goods (Attanasio et al.,
2005; Benito and Saleheen, 2013). But the household, as opposed to the
individual, benefits from an additional margin of adjustment: one member
of the household may be able to take on additional work to compensate for
anothers job loss. This labour supply reaction is termed the added worker
effect (AWE) and was identified as long ago as the 1940s (Gong, 2011).
1
The unemployment rate of men rose by 2.4 per cent points (pp) between 2008 and 2009, com-
pared with 1.3pp for women. Thereafter mens unemployment stabilised at 8.6%, while
womens unemployment rose by a further 0.8pp, reaching 7.3% in 2011.
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Numerous studies have investigated the AWE since the advent of large-
scale micro data in the early 1980s, focussing in particular on the response of
women to their partners job loss. While some studies conclude there is no
AWE (Layard et al., 1980; Maloney, 1991; Spletzer, 1997; Bingley and
Walker, 2001), others have found that women variously respond to a
partners job loss by looking for work (Lundberg, 1985; Mattingly and
Smith, 2010), starting work (Lundberg, 1985; Juhn and Potter, 2007;
Kohara, 2010; Mattingly and Smith, 2010) or increasing their work hours
(Gong, 2011; Harkness and Evans, 2011). For the UK, there is little evidence
for the AWE from previous studies, although most date from well over a dec-
ade ago. Layard et al. (1980) found that women with unemployed husbands
were less, not more, likely to work than (observationally) similar women
with employed husbands. Two other studies concluded that a husbands
long-term unemployment reduced womens transitions into work from inac-
tivity (McGinnity, 2002) and increased the likelihood of women choosing
not to participate (Bingley and Walker, 2001). The dominant explanation for
these reverse AWEs is the disincentive effect of means-tested benefits when
a partner loses their job. As Harkness and Evans (2011) note, the benefit sys-
tem in the UK has been reformed in recent years to make work pay; never-
theless, even in their study covering 2006–9 they still find a husbands
nonemployment is associated with a lower probability of his partner being in
work (although a partner already in work is more likely to increase her
hours).
There are a number of reasons why AWE estimates may depend on eco-
nomic context and thus vary across studies. Within a standard lifecycle
framework with no credit constraints, the AWE is predicted to be small or
nonexistent when job losses are known to be likely, e.g. as part of the inher-
ent risk associated with an occupation or industry. In this scenario, a job
loss is just a transient shock that does not change expected future earnings,
and so couples cover the temporary income loss by borrowing or dissaving.
However, the prediction changes if the job loss comes as a shock: in this case
it is new information that signals probable lower earnings in future (see Ste-
phens, 2002, who notes evidence that displaced workers suffer permanent
earnings losses), and it is optimal for the partner to permanently increase
their labour supply to compensate. If, in addition, couples face credit con-
straints and so cannot smooth consumption by borrowing, then even
expected job losses will lead partners to increase their labour supply
(although the increase will only last until the unemployed partner regains a
job).
Against this, there are a number of factors that may dampen reactions
to job loss: couples may be able to rely on other income sources, most obvi-
ously unemployment insurance (Cullen and Gruber, 2000); or the two
partners non-market time may be complementary so a job loss raises the
value of the other partners non-market time (Maloney, 1991). A job loss by
Couples Labour Supply Responses 3
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one partner may also be a sign of weakness in the local labour market, which
can lead to an opposing discouraged worker effect (DWE) whereby the other
partner is less likely to enter the labour market or find work (Layard et al.,
1980). And if there are labour market frictions individuals may not be able
to move into work straightaway and thus the measured reaction may be
muted or delayed.
Since the balance of these factors generally changes during recessionary
periods, we may expect to find that the estimated AWE differs between peri-
ods of growth and recession, and particularly the Great Recession beginning
in 2008 (Mattingly and Smith, 2010; Starr, 2014). First, workers can expect to
be unemployed for longer periods during a recession, so a job loss is less likely
to be a transient shock. For instance the Great Recession arguably led to a
structural change in the economy that caused near-permanent declines in
employment in particular sectors such as construction (Starr, 2014). Second,
credit is typically less available in recessions—and particularly during the
most recent downturn which was sparked by the credit crunch (Kamath et al.,
2011). Third, the Great Recession was not forecast, so couples are more likely
to have to resort to increased labour supply rather than savings to maintain
their consumption. Fourth, the UK recession disproportionately hit mens
jobs in its first phase (2008–9), thus the female partners of unemployed men
may have been able to take up the slack. However, opposing these four factors,
there may also be a larger DWE if the lower overall level of labour demand in
a recession reduces the likelihood of a partner finding work. Therefore, on
balance, the size of the net effect becomes an empirical question.
Only a handful of previous studies have compared recessions and peri-
ods of growth (only one for the UK). Juhn and Potter (2007), using US data
covering 1968–2005, found that the AWE was higher during periods when
the economy was moving into recession (although the difference from other
periods was not statistically significant). Mattingly and Smith (2010), com-
paring 2004–5 and 2008–9 in the USA, found that the AWE was larger dur-
ing 2008–9 (the recession); in particular women whose partners had lost
their jobs were more likely to succeed in finding work (possibly because they
were prepared to consider lower quality jobs). In one of the rare papers to
look at the AWE for both men and women, Starr (2014) compared the
employment rates of individuals with workless spouses against other individ-
uals, and looked to see how these rates changed over 2005–9 in the USA.
Consistent with a stronger AWE during the recession, the employment rates
of women with workless husbands rose during the recession, while employ-
ment fell for other women. Similar, but substantially weaker, effects were
found for men. Like the US studies, Parker and Skoufias (2004), who com-
pared the Mexican Peso crisis with a period of prosperity, also concluded
that the AWE was stronger during a recession. Most recently, a cross-
country EU study by Bredtmann et al. (2014) found that the AWE was
larger at higher levels of unemployment.
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For the UK, Harkness and Evans (2011), using data from 2006–9,
found that the women partnered with nonworking men were less likely to be
in work, but that this negative association was reduced during 2008–9 (the
recession). They found similar, but weaker relationships, using transitions
data, concluding that job retention among women whose partners lost their
jobs was higher in the recession than before. Compared to Harkness and
Evans study, we use more years of data (starting from 1995 and covering
the downturn until 2011), we examine mens reactions to their female
partners job loss, and we decompose labour market entry into job finding
and search.
2
3DATA AND DESCRIPTIVE STATISTICS
To analyse couples response to job loss over the business cycle we use the
quarterly UK LFS for the period 1992q2–2011q1. The LFS is a survey of
households which collects a large amount of individual and household char-
acteristics, with focus on labour market variables such as education, employ-
ment status, job search activities and job characteristics. The LFS interviews
every adult member of the household and allows us to match couples as well
as other adults living in the same household.
The LFS has a rotating panel structure in which all adults in each
household are interviewed for up to five successive quarters. This allows us
to analyse quarter-on-quarter changes in the working situation of each
member of the household. Our sample includes married or cohabiting cou-
ples who participated in the LFS for at least four consecutive quarters, and
in which both partners are of working age but at least 23 years old (23–64
for men and 23–59 for women). We restrict the sample to people aged 23 and
over to exclude individuals who may have a job but may still be completing
their education; educational qualifications therefore become a time-
invariant characteristic. As we wish to avoid potential complications arising
from the labour supply of other household members, we also exclude from
the sample those households in which other members—excluding the two
partners—work, either in a paid job or as self-employed. Finally, we exclude
those households that are workless for the whole observation period, since
they cannot be subject to employment loss. Overall our sample comprises 70
per cent dual-earner households (i.e. in which both partners work), 22 per
cent male breadwinner households (only the man works) and 8 per cent
female breadwinner households.
The survey asks questions on job search to both employed and unem-
ployed respondents. Hence, besides analysing the probability that the
2
We also restrict our attention to involuntary job losses, which are most relevant to our investiga-
tion of the AWE, whereas Harkness and Evans (2011) consider job losses in general. Volun-
tary and involuntary job losses may have different effects if, for example, a voluntary job
loss is the result of a joint household decision about who should work.
Couples Labour Supply Responses 5
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respondent finds a job following a job loss of the partner, we can also iden-
tify whether the respondent is actively searching for a job. This is likely to be
particularly important in periods of recession when it becomes harder to
find a (new) job. Furthermore, since such questions are also asked to
respondents who already have a job, we can analyse whether the impact of
the partners job loss differs among employed and jobless respondents. We
identify whether a person is looking for a job on the basis of their answers to
three types of questions and classify as searching those who: (i) are looking
for employment, either paid employment or are looking for business oppor-
tunities or taking steps to open their own business; (ii) have looked for work
in the last four weeks, and (iii) mention at least one method of job search.
We classify as inactive all those who are not working and not actively look-
ing for employment opportunities (according to criteria 1–3 above). Among
jobless respondents, we define as searching all those who are unemployed
(and therefore searching by definition), or who are classified as inactive, e.g.
because they are not readily available to start a job, but are actively
searching.
For respondents who already have a job we also analyse whether the
partners job loss is correlated with a change in working hours or with a dif-
ferent probability of quitting the current job voluntarily. We classify as vol-
untary quits those cases where the reason for leaving the previous job was:
resigned; gave up job for health reasons; took early retirement; retired (at or
after statutory retirement age); gave up job for family or personal reasons;
and other reasons. Throughout the analysis our main explanatory variable is
a dummy which has value one for all those respondents whose partner expe-
rienced an involuntary job loss (i.e. when the reason to leave the job was: dis-
missed; made redundant/took voluntary redundancy; or temporary job
finished), and zero for those whose partner did not experience any change or
quit their job voluntarily. Since the variable we use to identify the reason for
the job loss is available only from the second quarter of 1995, our empirical
models focus on this shorter time period, while the descriptive statistics use
the longest period, starting in 1992, wherever possible.
Figure 1 shows the proportion of employed LFS respondents who lose
their job involuntarily by the following quarter. Before the recession (2007–
8) there are only minor differences between men and women and a flatter
profile of job losses over time. However, with the onset of the recession the
proportion of job losses increases sharply for both men and women,
although the increase for men is much larger than the increase for women.
Following these increases, however, job losses return to near previous levels
so that the proportions in 2009 are similar or smaller than those in 1995.
Our investigation of the AWE exploits both the spike in involuntary job
losses that occurred post-2007 and the fact that households suffering job
losses during this period were facing a much tougher economic environment
than their counterparts in the preceding years.
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Descriptive statistics by type of household are shown in Table 1. In our
data dual earner couples represent the majority of households (about 70 per
cent of the total), with almost 22 per cent male breadwinner and less than 8
per cent female breadwinner households. Female breadwinner households
are on average older than the other types of households (the average age of
both the man and the woman is higher) and less likely to have dependent
children. In dual earner households both man and woman are more likely to
have higher qualification levels, male breadwinner households seem to be
characterized by higher education for men while the opposite may be true
for female breadwinner households (e.g. female breadwinners are more likely
to be qualified to National Vocational Qualification (NVQ) level 41than
their partners, and a relatively large share of men in these households have
no qualifications). Perhaps not surprisingly, homeownership is higher
among dual earner couples, while we do not find any relevant difference
among single earner households.
Women in female breadwinner households are paid comparatively less
per hour than those in dual earner households, are more likely to work part-
time, in temporary jobs and in the public sector. Men in male breadwinner
households are paid comparatively more per hour than those in dual earner
households (as well as women in female breadwinner households), are more
likely to work part-time, in temporary jobs and less likely to work in the
public sector.
Table 2 shows transitions across types of households. Proportionally
there are few transitions among working households, although they are rele-
vant numerically (and it should be borne in mind they are quarterly transi-
tions). Almost 89 per cent of male breadwinner households and about 83
per cent of female breadwinner households do not make any transitions.
Most transitions are from single to dual earner households: almost 13 per
FIG. 1. Quarterly Job Losses Over the Business Cycle
Couples Labour Supply Responses 7
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cent of female breadwinners and almost 9 per cent of male breadwinners.
Some 4 per cent (53.4 10.6) of female breadwinner households experience
a job loss, of which 15 per cent (50.6/4) transition to male breadwinner
households (consistent with AWE behaviour) while the remainder are work-
less. Among male breadwinner households, 2.5 per cent lose their jobs and
in 7 per cent (50.2/2.5) of these the male earner is replaced by the female.
There are essentially no transitions for dual earner households: about 2.48
per cent transition to male breadwinner, 1.27 per cent transition into female
breadwinner and 0.13 per cent transition into workless households.
When it takes longer to find a job, single earner households in which
the breadwinner loses his or her job will transition into workless households.
The last row of Table 2 focus on such workless households (those that are
TABLE 1
DESCRIPTIVE STATISTICS BY TYPE OF HOUSEHOLD (1992–2011)
Male breadwinner Female breadwinner Dual earner
Dependent children 0.683 0.341 0.517
Other dependants 0.017 0.017 0.011
Home owners 0.742 0.746 0.865
Womans characteristics
Age 39.6 46.5 39.1
NVQ level 410.196 0.256 0.329
NVQ level 3 0.121 0.105 0.139
NVQ level 2 1Apprenticeship 0.325 0.275 0.323
Other qualifications 0.139 0.141 0.102
No qualifications 0.218 0.224 0.107
Hourly wage (no self-employed) 8.71 9.30
Paid hours 28.6 29.8
Part-time 0.477 0.445
Temporary job 0.065 0.058
Public sector 0.415 0.397
Mans characteristics
Age 41.8 49.8 41.2
NVQ level 410.300 0.216 0.324
NVQ level 3 0.207 0.212 0.234
NVQ level 2 1Apprenticeship 0.230 0.228 0.253
Other qualifications 0.149 0.129 0.108
No qualifications 0.114 0.216 0.081
Hourly wage (no self-employed) 13.03 12.11
Paid hours 41.5 41.7
Part-time 0.067 0.036
Temporary job 0.045 0.033
Public sector 0.184 0.232
Observations 186,611 65,063 602,240
Observations
a
(wage—women) 17,401 169,121
Observations
a
(wage—men) 47,826 163,832
The number of observations pools all years and only includes household with information on all
characteristics shown in this table (with the exception of wages).
a
In the Labour Force Survey wage data are collected only in the first and fifth interview, thus reducing the
number of observations for which such data are available. Observations on the other characteristics pool
all five waves of data.
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workless at time t21—note that households that are workless over the whole
period of analysis are excluded). Among these, less than 60 per cent are still
workless in the following quarter (t): almost 27 per cent transition into male
breadwinner, about 10 per cent transition into female breadwinner and
about 4 per cent transition into dual earner households. Hence, workless-
ness, in our sample, is likely to be a relatively short-lived condition.
4M
ETHOD
The literature has used a variety of methods to estimate the effect of unem-
ployment on spousal labour supply, which can essentially be divided into
three types. Early studies (e.g. Layard et al., 1980; Bingley and Walker, 2001)
tended to use cross-sectional methods and analyse employment in levels; but
unless unemployment is endogenized (Cullen and Gruber, 2000; Bingley and
Walker, 2001) these methods are vulnerable to unobserved permanent differ-
ences between couples that are correlated with both the risk of unemploy-
ment and labour supply. Moreover, these studies identify the effect of the
state of ongoing unemployment rather than the shock of job loss.
3
The second type of study also analyses labour supply in levels but con-
trols for fixed effects (FE). The focus here is on the effect of job loss (rather
than unemployment in levels), consistent with the interpretation of the
AWE in the structural lifecycle model. The FE approach is well suited to
estimating a structural lifecycle model, which implies the existence of an
unobserved couple-specific effect (the initial marginal utility of wealth).
Stephens (2002) presents a structural model as the basis for FE estimates
TABLE 2
QUARTERLY TRANSITIONS ACROSS TYPES OF HOUSEHOLDS (1992–2011)
Male
breadwinner t
Female
breadwinner t
Dual
earners t Workless t Total Obs.
Male breadwinner
t21
88.83 0.18 8.66 2.34 100 209,758
Female breadwinner
t21
0.59 83.35 12.68 3.37 100 59,606
Dual earners t21 2.48 1.27 96.12 0.13 100 687,894
Workless t21 26.56 10.24 4.39 58.8 100 16,936
Total (per cent) 21.38 6.21 70.59 1.83 100
Observations 208,247 60,509 687,658 17,780 974,194
This pools all years and only includes households with information on contiguous years and with informa-
tion on household and individual characteristics shown in Table 1 (with the exception of wages and all
other job characteristics). Households that are workless for the whole period of observation are excluded.
3
McGinnity (2002) looks at the effect of the state of unemployment on the spouses transitions in
and out of the labour market. More recently Harkness and Evans (2011) included an analy-
sis of the relationship between mens non-employment and their partners employment.
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of womens annual leisure hours (defined as annual work hours subtracted
from total available hours). More recent studies in this spirit include Gong
(2011), who models labour force participation and full-time employment
using FE linear probability models (LPM). Hardoy and Schøne (2014) also
estimate FE specifications to explain employment (using a LPM) and
annual income, although their specifications are not derived from a struc-
tural model.
Rather than looking at labour supply in levels, the third type of study
focuses directly on transitions in employment or in participation (Parker
and Skoufias, 2004; Juhn and Potter, 2007; Mattingly and Smith, 2010;
Harkness and Evans, 2011; Bredtmann et al., 2014) or on changes in work
hours (Kohara, 2010; Benito and Saleheen, 2013). Equations are generally
specified as transitions models in reduced form, which allows for possible
departures (discussed earlier) from the standard lifecycle model, as well as
for alternative models based on social roles (see the discussion in Starr,
2014). As in the second type of study, unemployment is represented by a set
of job loss dummies. While the estimated coefficients in these studies do not
correspond to a well-defined structural AWE parameter, they do illustrate
the net effect of different factors affecting the size of the AWE.
Most recent studies are of the third type and we follow this reduced-
form approach in our analysis, estimating quarterly transition and change
models for the following outcomes: becoming active, where we distinguish
between job search, job finding and joining the labour force (starting a job
search or finding a job); and, for dual-earner households, job retention, on-
the-job-search and changes in hours worked. For simplicity, and to avoid sit-
uations in which both partners influence each others behaviour, we exclude
from the analysis the small proportion (less than 0.2 per cent) of cases in
which both partners experience an involuntary job loss within the same
quarter.
We start by focusing on male breadwinner households and by estimat-
ing models for the probability that the female partner enters the labour mar-
ket in response to the male partners job loss:
Ait5DX0
itb11 1X
1
k521
bk
12Pit1k1e1it (1)
The sample here includes only women who are inactive and not searching
for a job at t21: the dependent variable Ait is zero for women who are inac-
tive and not searching for a job both at t21 and tand one for those who are
inactive at time t21 but become active at time t. Here we define activity as
being either employed, self-employed, unemployed or engaging in job
search. Although information on job search of people other than the unem-
ployed is rarely available in surveys, we believe it is important to include it in
the dependent variable as a possible outcome of a partners job loss. Given
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the sample restriction (women inactive at t21), Ait can also be derived as the
first difference of activity status (there is either no change or a change from
inactivity to activity). But as we are modelling a transition in one direction
only, we avoid delta notation (Ait cannot take negative values in this sam-
ple).
4
By contrast, the control variables X
it
enter explicitly as first differences
(note that the parameters in our models have two subscripts: the first refers
to the model number and the second refers to the matrix of covariates). The
transitions approach effectively removes time-invariant unobserved couple
characteristics affecting employment levels.
In line with the third type of AWE study, focussing directly on the effect
of job loss on transitions, the three dummies Pit1kare for changes in the
employment situation of the partner. A spouse may react immediately to her
partners job loss, or even before if there is advance warning (Stephens,
2002; Gong, 2011). Alternatively it may take time for her to find a job if
there are labour market frictions. Stephens (2002) finds small increases in
wives labour supply before a job loss and larger, persistent increases begin-
ning with the job loss itself. There is some weak evidence that wives react
further in advance of plant closings than layoffs, which it is argued are less
publicized in advance.
We test whether there is a lagged response to partners job loss with a
dummy for whether the partner lost the job involuntarily between t22 and
t21(Pit21); whether there is an immediate response with a dummy for
whether the partner lost the job involuntarily between t21 and t(Pit); and
whether there is an anticipation effect by including a dummy for whether the
partner lost the job involuntarily between tand t11(Pit11). We look at the
effect of involuntary job loss only, since a voluntary job quit may not prompt
a response by the spouse (which would understate the true AWE) or the cau-
sality may be reversed if a husband is enabled to leave his job because his
wife has increased her labour supply (potentially overstating the true
AWE).
5
Some endogeneity may remain if some job losses recorded as invol-
untary are in fact voluntary (measurement error); or if unobserved couple
characteristics affecting the partners job transition probabilities (as opposed
to employment levels) are correlated with job loss (Spletzer, 1997).
We allow for differences between recession and growth periods by
including interactions between these three job loss dummies and a dummy
identifying whether the change happens in a period of recession (i.e. if tis
between 2008 and 2011). This allows us to test whether the coefficients bk
12
4
Similar reasoning applies to the outcomes of equations (2)-(4) and (6), The dependent variable
in equation (5) is the change in hours DHit.
5
While most studies focus on involuntary job losses, or moves from employment to unemploy-
ment, others look at any job loss, and indeed Mattingly and Smith (2010) argue that transi-
tions to inactivity should also be included because husbands may be discouraged from
looking for work or be forced retirees.
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are different in periods of growth and of recession (similar to Juhn and Pot-
ter, 2007; Mattingly and Smith, 2010 among others).
6
In X
it
we include individual and household variables to control for
wages and taste for leisure. We control for taste for leisure with one dummy
for whether there are dependent children, one for whether there are other
dependants and one for homeowners. We also include the square of age of
the individual respondent and of the partner (the linear age term becomes a
constant equal to one when first differences are taken) which, together with
the homeownership dummy, should control for income and wealth (as the
equations are gender-specific, we do assume that the income determination
process is the same across men and women). Time-invariant characteristics,
such as education, are not included as regressors because they drop out in
first differencing.
In X
it
we also include a dummy for whether year twas a year of reces-
sion or not and the unemployment rate at the level of Government Office
Regions for England, plus Scotland, Wales and Northern Ireland. The
regional unemployment rate is computed from the LFS using sample
weights and should help us correct for the general conditions of the labour
market in the region that may have an effect on the probability of finding a
job and on the DWE. The recession dummy and the regional unemployment
rates are not taken in differences since we want to isolate the impact of the
recession.
We also wish to examine whether labour market entry is manifested as
job search or job entry. Hence, we also estimate models similar to equation
(1) with slightly different dependent variables:
Fit5DX0
itb21 1X
1
k521
bk
22Pit1k1e2it (2)
Sit5DX0
itb31 1X
1
k521
bk
32Pit1k1e3it (3)
where Fit represents job finding and Sit represents job search. Similarly to
model (1), the samples for models (2) and (3) include only women who are
inactive and not searching at time t21. Fit is zero for women who are inac-
tive and not searching for a job at t21 and do not have a job at t(whether
they have begun searching or are still inactive); and one for those who are
6
The probabilityof finding a job partly depends on changes in labour supply and demand. Differ-
ences between supply and demand are likely to be relevant mostly when comparing periods
of growth and recession. Our models include a dummy for the recession period, which
should pick up most of these differences. Similarly, changes in the benefit system that took
place during the recession should be picked up by the recession dummy as we do not have
any evidence that such changes had differential impacts on those households in which one
of the partners experienced a job loss.
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inactive at time t21 but have a job at time t.Sit is zero for women who are
inactive at t21 and have either found a job or are still inactive at t; and one
for those who are inactive at time t21 and do not have a job but are actively
searching for one at time t.Pit1kand X
it
are the same as in equation (1).
We then estimate a second set of models with focus on female breadwin-
ner households by estimating models similar to those in equations (1)–(3) in
which the dependent variables Ait,Fit and Sit represent the mans (rather
than womans) reaction to the job loss of the female (rather than male) part-
ner (Pit1k). X
it
is exactly the same as in equations (1)–(3).
We complement the analysis of single earner couples with a third set of
models focusing on dual earner couples. Since in dual earner couples both
partners work, here we can observe different types of reactions compared to
the previous models and focus on: on-the-job search, changes in hours
worked and job retention. Once again, we start by investigating the womans
reaction to the mans job loss and estimate a model for on-the-job search.
Women may start looking for a new or an additional job as a response to
their partner job loss by looking for a higher paying job. Those working
part-time and who are not able to increase their working hours in their cur-
rent job may start looking for a full-time job. Hence:
OTJSit5DX0
itb41 1X
1
k521
bk
42Pit1k1e4it (4)
where OTJSit represents on-the-job search. Here we focus on the sample of
women who are employed (either in a paid job or as self-employed) at both
t21 and tand who are not engaging in on-the-job search at t21. Hence,
OTJSit is a dummy which is zero for women working and not engaging in
on-the-job search both at time t21 and tand one for women working and
not engaging in on-the-job search at t21 but working and engaging in on-
the-job search at t.Pit1kand X
it
are the same as in equations (1)–(3).
Rather than start searching for a new or an additional job, those who
already have a job may respond to their partners job loss by changing the
number of paid hours they work.
DHit5DX0
itb51 1X
1
k521
bk
52Pit1k1e5it (5)
where the sample includes women who work both at t21 and at t, either
engaging in on-the-job search or not, and who report information on hours
worked both at t21 and t(this additional restriction excludes the self-
employed, for which we have no data on hours worked). DHit is the change
in the number of paid hours between t21 and t.Pit1kand X
it
are the same
as in the previous models. Here we include both women who remain in the
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same job and those who change job since it is possible that the job change is
related to the desire for changes in working hours. It is plausible that women
who cannot easily change their working hours within the same job will move
to a new job offering the desired number of working hours. There are too
few job changes to allow us to have separate estimations for new jobs.
Finally we analyse job retention as people may be less likely to quit
their job voluntarily as a response to partners involuntary job loss:
Qit5DX0
itb61 1X
1
k521
bk
62Pit1k1e6it (6)
The sample here includes women who had a job at time t21 and who, at
time twhere either still employed or had lost or quit their job: Q
it
is zero for
women who work both at t21 and at tand one for those who work at t21
but voluntarily quit their job (into inactivity) by time t. Those who lost their
job involuntarily are coded as zero. In addition, since compared to prime
age workers, those who are close to retirement age may react in a systemati-
cally different way to a job loss of their partners, here we only include
respondents who are younger than 55 years of age. Pit1kand X
it
are the
same as in the previous models.
Finally, we analyse mens reaction to womens job loss in dual earner
couples. We do this by estimating equations (4)–(6) in which the dependent
variables OTJSit,DHit and Q
it
represent the mans (rather than womans)
reaction to the job loss of the female (rather than male) partner (Pit1k). X
it
is
the same as in equations (1)–(3).
All models discussed in this section are estimated using OLS since
unobserved heterogeneity cannot be removed by differencing in non-linear
models (logit or probit) and given the non-linearity the results would be
more difficult to interpret.
7
5RESULTS
5.1 Single Earner Couples
We begin by looking at male breadwinner households which have tradition-
ally been the focus of the AWE literature. Specifically we consider those
households in which the woman was not working and not searching for a
job at t21 and examine her response at time tto her partners job loss at
t21, tor t11. The results are reported in Table 3. The first two columns
show the impact that the mans job loss has on the probability that the
woman moves into a job or starts actively searching for a job, while the last
two columns separate the two actions (finding a job and starts searching).
7
Non-linear models are preferred for predicting probabilities but our focus is on marginal effects.
14 The Manchester School
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The combined labour market entry variable and job finding are both com-
mon outcomes in the literature, while the job search variable picks up unsuc-
cessful searches lasting at least a quarter (shorter, successful searches are
picked up in the job finding variable). Because longer periods of search are
more common in a recession, we do not compare search activity across
growth and recession periods, but only make descriptive comparisons of
search versus job finding within periods. The last row of each table shows
the mean of the dependent variables. This gives an idea of the probability of
the event itself.
The control variables generally have the expected signs (thus labour
supply appears lower among homeowners, perhaps a wealth effect)
although they are often not significant. There is strong evidence that
TABLE 3
WOMENS REACTION TO MENS JOB LOSS IN MALE BREADWINNER HOUSEHOLDS (ONLY MAN
WORKS AT t21)
Sample: women inactive at t21
(1)
Start searching or find a job
(2)
Find a job
(3)
Start searching
Mans job loss t21 0.009 20.009 0.018**
(0.012) (0.008) (0.009)
Mans job loss t0.002 20.016** 0.018**
(0.011) (0.008) (0.009)
Mans job loss t1120.009 20.013 0.004
(0.011) (0.008) (0.008)
Mans job loss t213Recession 0.020 20.020 0.040
(0.030) (0.014) (0.027)
Mans job loss t3Recession 0.046 0.030 0.015
(0.031) (0.022) (0.023)
Mans job loss t113Recession 0.033 0.019 0.014
(0.030) (0.022) (0.022)
Dage square of man 20.013*** 20.009*** 20.005***
(0.002) (0.002) (0.001)
Dage square woman 20.014*** 20.006*** 20.008***
(0.002) (0.002) (0.001)
Ddependent children 0.029* 0.020 0.009
(0.016) (0.014) (0.009)
Dother dependants 0.001 0.018 20.016
(0.026) (0.019) (0.018)
Dhome owners 20.017 20.004 20.013
(0.027) (0.022) (0.018)
Recession at time t20.013*** 20.012*** 20.002
(0.003) (0.002) (0.002)
Regional unemployment (021) 20.044 20.182*** 0.138***
(0.061) (0.049) (0.039)
Intercept 0.100*** 0.072*** 0.028***
(0.004) (0.003) (0.002)
Observations 71,882 71,882 71,882
Mean of dependent variable 0.089 0.056 0.034
Coefficients of linear models; robust standard errors in parenthesis.
* Significant at 10 per cent, ** Significant at 5 per cent, *** Significant at 1 per cent.
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regional economic conditions affect labour supply as expected: higher
regional unemployment is associated with more (unsuccessful) searching
and less job finding. We find no impact of a partners job loss in column
(1), indicating no AWE in terms of labour market entry (either in periods
of growth or recession).
8
However, we find differences between job finding
and job search. Column (3) suggests that women are more likely to start
searching (by 1.8pp) when the partner loses his job (either in the same
quarter or in the following quarter). In contrast the probability of moving
directly into a job—after a short search—is lower (by 1.6pp) in the same
quarter when the partner experiences the job loss. These effects are both
quite large when compared with the unconditional probabilities of starting
to search (3.4 per cent) and finding a job (5.6 per cent) reported in the last
row of Table 3.
This reverse AWE in terms of employment is consistent with some pre-
vious studies of the UK which argue that the means-tested benefit system
makes work less attractive to the spouse of an unemployed partner because
his benefits are reduced as her earnings increase (see McGinnity, 2002 for
the UK and Bredtmann et al., 2014 for UK and Ireland combined). It may
only be worth the woman working if she can earn enough to compensate for
benefit reductions—so while her partners job loss triggers an increase in
search, it takes longer to find an acceptable job.
We find no evidence of any anticipation effect for any of our outcomes
and no additional impact of the recession. Nevertheless, the recession
dummy confirms that it is harder for women to find a job during a recession,
while it does not seem to have any relevant impact on the probability to start
searching.
We next turn to female breadwinner households, which have rarely been
analysed in the AWE literature, and, to our knowledge, never for the UK.
Table 4 considers men who have no job and are not searching in t21 and
their reaction to their partners job loss. Compared with their female coun-
terparts, they are more sensitive to family structure: having dependent chil-
dren is strongly associated with higher labour supply and having other
dependents with less job entry. As for women, higher regional unemploy-
ment predicts more job search and less job finding. Column (1) suggests that
men are more likely to enter the labour market as a response to their
partners job loss (by 5.6pp), and the response appears with a lag. As for
male breadwinner households, reactions do not change in the recession, but
in contrast we find a conventional positive AWE rather than the reverse
8
Redundancy payments may ease the budget constraint and reduce the AWE. However, the small
or nonexistent impacts shown in Table 3 are not due to the receipt of redundancy payment;
the results do not change if we exclude from the analysis the small number of people who
have received any type of redundancy payment or if we exclude all women who become self-
employed.
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AWE. This may be because, compared with women in male breadwinner
households, men are more likely to move into full-time jobs with earnings
that exceed any benefit loss.
Columns (2) and (3) separate job finding and job search and show the
relationship between these two outcomes. Men are more likely to find a job
one quarter after their spouse loses her job; however, the effect disappears
during periods of recession (column (2)). It is also during a recession that we
see an increase in job search, reflecting an increase in the difficulty to find a
job. In all cases, however, men seem to respond with a lag. Also as in male
breadwinner households, we do not see evidence that couples react to job
losses ahead of time.
TABLE 4
MENS REACTION TO WOMENS JOB LOSS IN FEMALE BREADWINNER HOUSEHOLDS (ONLY WOMAN
WORKS AT t21)
Sample: men inactive at t21
(1)
Start searching or find a job
(2)
Find a job
(3)
Start searching
Womans job loss t21 0.056** 0.055** 0.001
(0.025) (0.022) (0.013)
Womans job loss t0.000 0.004 20.003
(0.023) (0.020) (0.014)
Womans job loss t11 0.021 0.009 0.013
(0.026) (0.021) (0.017)
Womans job loss t213Recession 0.045 20.069* 0.114*
(0.069) (0.042) (0.059)
Womans job loss t3Recession 0.088 0.013 0.075
(0.068) (0.049) (0.053)
Womans job loss t113Recession 20.058 20.039 20.019
(0.047) (0.035) (0.032)
Dage square of man 20.020*** 20.007** 20.013***
(0.004) (0.003) (0.002)
Dage square woman 20.014*** 20.011*** 20.003
(0.004) (0.004) (0.003)
Ddependent children 0.155*** 0.104*** 0.051*
(0.042) (0.037) (0.027)
Dother dependants 20.091* 20.074* 20.017
(0.051) (0.043) (0.032)
Dhome owners 20.026 20.016 20.011
(0.058) (0.043) (0.043)
Recession at time t20.003 0.001 20.004
(0.006) (0.005) (0.004)
Regional unemployment (021) 20.008 20.223** 0.215***
(0.128) (0.102) (0.083)
Intercept 0.114*** 0.083*** 0.031***
(0.008) (0.007) (0.005)
Observations 20,307 20,307 20,307
Mean of dependent variable 0.105 0.066 0.039
Coefficients of linear models; robust standard errors in parenthesis.
* Significant at 10 per cent, ** Significant at 5 per cent, *** Significant at 1 per cent.
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5.2 Dual-Earner Couples
Next we turn to look at dual-earner couples, beginning with womens reac-
tions to their male partners job loss. The first column of Table 5 shows the
impact that a job loss of the partner has on the probability of starting to
search for a new or an additional job; the second column shows the impact
on a change in hours worked; while the third shows the impact on the proba-
bility of giving up the job voluntarily. Women are more likely to begin on-
the-job search if their partner loses his job (we investigate below whether
this concerns full or part-time workers). The impact is contemporaneous
(women whose partners have just lost their job are 1.9pp more likely to
TABLE 5
WOMENS REACTION TO MENS JOB LOSS IN DUAL EARNER HOUSEHOLDS (BOTH MAN AND
WOMAN WORK AT t21)
Sample: women working at t21
(1)
Start searching
on-the-job
(2)
Change in hours
(3)
Voluntarily
quit
Mans job loss t21 0.002 20.119 0.018***
(0.006) (0.166) (0.006)
Mans job loss t0.019*** 20.043 0.009
(0.007) (0.169) (0.005)
Mans job loss t1120.002 20.034 20.000
(0.006) (0.161) (0.005)
Mans job loss t213Recession 20.003 0.624* 20.027***
(0.013) (0.332) (0.008)
Mans job loss t3Recession 0.003 20.388 20.006
(0.015) (0.344) (0.009)
Mans job loss t113Recession 0.007 0.505* 0.008
(0.013) (0.287) (0.011)
Dage square of man 20.005*** 0.014 20.001
(0.001) (0.026) (0.001)
Dage square woman 20.006*** 20.004 20.001
(0.001) (0.027) (0.001)
Ddependent children 20.015*** 20.144 0.039***
(0.003) (0.111) (0.005)
Dother dependants 20.001 20.050 20.006
(0.011) (0.307) (0.010)
Dhome owners 0.023* 20.069 20.009
(0.014) (0.300) (0.011)
Recession at time t20.003** 20.040 20.006***
(0.001) (0.025) (0.001)
Regional unemployment (0 21) 0.120*** 1.113* 20.030*
(0.024) (0.585) (0.018)
Intercept 0.037*** 20.124*** 0.022***
(0.001) (0.037) (0.001)
Observations 242,983 209,666 215,793
Mean of dependent variable 0.041 20.061 0.019
Coefficients of linear models; robust standard errors in parenthesis; hours worked are not available for the
self-employed.
* Significant at 10 per cent, ** Significant at 5 per cent, *** Significant at 1 per cent.
18 The Manchester School
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search) and there is no evidence that it differed during the recent recession.
We also find that women seem to increase the number of hours worked (by
around half an hour) during a recession (but not during the growth period),
both with a lag, and in anticipation of the partners job loss. This evidence
of positive AWEs on the intensive margin is consistent with evidence from
TABLE 6
WOMENS REACTION TO MENS JOB LOSS IN DUAL EARNER HOUSEHOLDS (BOTH MAN AND
WOMAN WORK AT t21)—WOMEN WORKING PART-TIME VS.FULL-TIME AT t21
(1)
Start searching
on-the-job
(2)
Change in hours
(3)
Voluntarily quit
Sample: women
working at t21 Part-time Full-time Part-time Full-time Part-time Full-time
Mans job loss t2120.004 0.007 0.265 20.394* 0.029*** 0.011*
(0.008) (0.009) (0.243) (0.224) (0.011) (0.006)
Mans job loss t0.023** 0.015 0.287 20.327 0.007 0.011*
(0.010) (0.010) (0.218) (0.250) (0.009) (0.006)
Mans job loss t11 0.004 20.007 0.058 20.058 20.002 0.002
(0.009) (0.008) (0.204) (0.237) (0.008) (0.005)
Mans job loss t21
3Recession
20.017 0.004 0.487 0.856** 20.048*** 20.013
(0.016) (0.019) (0.544) (0.418) (0.011) (0.009)
Mans job loss
t3Recession
0.033 20.019 20.656 20.112 20.006 20.007
(0.027) (0.017) (0.481) (0.482) (0.016) (0.011)
Mans job loss
t113Recession
0.009 0.004 0.210 0.670 20.001 0.015
(0.020) (0.017) (0.347) (0.452) (0.015) (0.015)
Dage square of man 20.004*** 20.005*** 20.001 20.005 20.002 20.000
(0.001) (0.001) (0.033) (0.039) (0.001) (0.001)
Dage square woman 20.004*** 20.007*** 20.078** 0.042 0.000 20.002**
(0.001) (0.001) (0.035) (0.041) (0.001) (0.001)
Ddependent children 20.008 20.018*** 20.152 0.127 0.047*** 0.041***
(0.009) (0.004) (0.223) (0.127) (0.012) (0.005)
Dother dependants 0.020 20.017 20.666 0.373 20.022 0.006
(0.016) (0.016) (0.435) (0.417) (0.020) (0.008)
Dhome owners 0.026 0.021 0.342 20.530 20.011 20.007
(0.020) (0.019) (0.365) (0.466) (0.019) (0.010)
Recession at time t20.002 20.003** 20.083** 0.020 20.009*** 20.004***
(0.002) (0.002) (0.035) (0.036) (0.001) (0.001)
Regional unempl.
(021)
0.189*** 0.053 2.823*** 0.947 20.033 0.000
(0.034) (0.033) (0.814) (0.829) (0.031) (0.019)
Intercept 0.029*** 0.044*** 0.220*** 20.516*** 0.030*** 0.013***
(0.002) (0.002) (0.050) (0.054) (0.002) (0.001)
Observations 115,792 127,160 100,059 109,591 100,403 115,363
Mean of dependent
variable
0.039 0.043 0.362 20.449 0.027 0.012
Coefficients of linear models; robust standard errors in parenthesis.
* Significant at 10 per cent, ** Significant at 5 per cent, *** Significant at 1 per cent.
Couples Labour Supply Responses 19
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the few previous studies that include dual-earner couples (e.g. Gong, 2011;
Harkness and Evans, 2011).
The last column of Table 5 also suggests that women are more likely
to quit their job voluntarily as a response to their partners job loss; a
lagged effect of 1.8pp. Again we see evidence of a reverse AWE at the
extensive margin, this time among dual-earner couples. One explanation
may be incentives in the means-tested benefit system as discussed, but the
results is also consistent with the two partners non-market time being
complementary, i.e. partners enjoy spending time together or they are more
productive in home production together than alone.
9
Previous evidence
about retirement behaviour and the intra-household effects of work hours
reduction policies suggests that complementary leisure leads people to
work less if their partners also work less (Blau, 1998; Goux et al., 2014).
Since in our models we only include people younger than 55, our results
suggest that this may apply also to younger couples.
10
However, the reces-
sion reduces the rate of voluntary quits. Women are 0.6pp less likely to quit
during recession and also on average less, not more, likely to quit as a
lagged response to partners job loss: the total lagged response in times of
recession is 20.9pp (51.8–2.7).
We may expect womens reactions to the partners job loss to differ
depending on the type of job she was working in at t21: we may see no
AWE for women working full-time (Bredtmann et al., 2014; Hardoy and
Schøne, 2014), while we may expect a bigger impact of partners job loss on
women working part-time (less than 30 hours per week). Table 6 replicates
the models in Table 5, but separately for women who were working part-
and full-time in t21.
The results suggest that it is indeed women working part-time who are
more likely to start engaging in on-the-job search as a reaction to their
partners job loss. This is not surprising since we also found a search AWE
among women in male breadwinner households (Table 3), and households
with a part-time worker are closer to being single-earner households than
are dual full-time households. In contrast, we find that hours responses are
driven by women in full-time work: curiously, they decrease their working
hours as a lagged response to the partners job loss during a period of
9
Although the job loss is involuntary and the transitions analysis should remove time-invariant
heterogeneity, there could be some upward bias from assortative mating if men are more
likely to lose their jobs tend to be married towomen who are more likely to quit their job.
10
Some support for a role for benefits is that the effect may be stronger among lower-income
households. Models estimated separately by mens education (not shown here but available
on request) show regression coefficients that are slightly larger for men with low—rather
than high—education. The results for women confirm that women with lowereducation are
much more likely to quit their job in response to a partners job loss than women with high
education.
20 The Manchester School
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growth, while hours increase if the partners job loss happens during a
recession.
11
Both part-time and full-time workers seem more likely to voluntarily
quit their job as a response to their partners job loss, but again it is part-
time workers who have the larger responses (2.9pp vs. 1.1pp). While women
working full-time show both a contemporaneous and a lagged response,
those working part-time show only a lagged response. In addition, for
women working part-time, the positive quit effect is reversed if the partners
job loss happens during the recession.
Finally we consider mens reactions to a womans job loss in dual
earner couples: Table 7 analyses men who have a job in t21 and their reac-
tion to their partners job loss.
Men who have a job do seem to react to a job loss of their partner by
starting to engage in on-the-job search, but not by changing the number of
hours worked. Moreover, the last column of the table suggests that, as for
women, on average mens attachment to the labour market may decrease,
not increase, when their partner loses her job: men are 1.3pp more likely to
voluntarily quit their job as a response to their partners job loss (but unlike
women there is no difference during the recession).
12
5.3 Sensitivity Analyses
There are two potential concerns related to our treatment of dynamic
effects.
13
The first is that by estimating the anticipated and lagged impact of
a partners job loss, we limit our analysis to couples who are in the sample
for at least four consecutive quarters. If those who are in the sample for at
least four quarters are not a random sample our results may be biased. To
check if this is the case we have re-estimated our models excluding the lags
and leads. We first re-estimate the models on the restricted sample of couples
who are observed for at least four consecutive quarters, and then on the full
sample including also those couples who have been in the sample for a
shorter period of time. The sign and magnitude of the coefficients does not
vary across specifications and the increase in the sample size for the model
based on the larger sample tends to increase the level of statistical signifi-
cance of our results.
11
The results are almost identical if we restrict the sample to women remaining in the same job, so
the findings are not driven by the different requirements of a new job.
12
Although the proportion of men working part-time is rather small (about 5%) for completeness
we have estimated the models separate for part-time and full-time workers (the results are
not shown here but available on request). We find some evidence that men working full-time
are more likely to start search for a job in response to the partners job loss and are more
likely to decrease their working hours during periods of growth. While men working part-
time are less likely to quit when the partner loses her job, those working full-time are more
likely to quit.
13
The results of the all sensitivity checks described here are available on request.
Couples Labour Supply Responses 21
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The second concern is that we may be underestimating any job finding
effects because our time window only extends back by one quarter. We there-
fore re-estimated our models excluding the lead (t11) and including an addi-
tional two-quarter lag of job loss (t22). We find similar results as previously
with no additional effect at t22, thus it does not appear that we are missing
large job finding effects because of our short time window.
6D
ISCUSSION AND CONCLUSIONS
We have examined how couples labour supply behaviour in the UK responds
to a job loss by one partner, comparing the period of growth of 1995–2007 to
the Great Recession and its aftermath of 2008–11. Unlike most previous stud-
ies, we have looked at the reactions of both women and men to their partners
TABLE 7
MENS REACTION TO WOMENS JOB LOSS IN DUAL EARNER HOUSEHOLDS (BOTH MAN AND
WOMAN WORK AT t21)
Sample: men working at t21
(1)
Start searching
on-the-job
(2)
Change
in hours
(3)
Voluntarily
quit
Womans job loss t21 0.003 20.249 0.001
(0.005) (0.169) (0.002)
Womans job loss t0.011* 0.165 0.013***
(0.006) (0.177) (0.004)
Womans job loss t11 0.008 20.177 0.002
(0.006) (0.169) (0.003)
Womans job loss t213Recession 0.000 0.001 0.001
(0.012) (0.029) (0.006)
Womans job loss t3Recession 0.013 0.006 20.001
(0.014) (0.030) (0.009)
Womans job loss t113Recession 0.020 0.120 20.001
(0.015) (0.123) (0.006)
Dage square of man 20.006*** 0.531* 0.001
(0.001) (0.279) (0.000)
Dage square woman 20.006*** 20.136 0.001
(0.001) (0.337) (0.000)
Ddependent children 0.011** 0.028 0.001
(0.005) (0.028) (0.002)
Dother dependants 0.000 1.140* 20.012**
(0.010) (0.639) (0.005)
Dhome owners 0.007 0.220 0.001
(0.011) (0.374) (0.004)
Recession at time t20.009*** 20.585 20.001
(0.001) (0.401) (0.000)
Regional unemployment (0–1) 0.239*** 0.368 0.034***
(0.023) (0.345) (0.009)
Intercept 0.035*** 20.232*** 0.004***
(0.001) (0.040) (0.001)
Observations 297,152 248,182 246,258
Mean of dependent variable 0.046 20.158 0.006
Coefficients of linear models; robust standard errors in parenthesis.
* Significant at 10 per cent, ** Significant at 5 per cent, *** Significant at 1 per cent.
22 The Manchester School
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C2017 The Authors. The Manchester School published by The University of Manchester and John Wiley & Sons Ltd.
job loss. We have investigated couples reactions along various dimensions
and tested whether couples react in advance of job losses or with a delay.
We find that women in male breadwinner households whose partners
lose their jobs are not more likely to enter the labour market than those
whose partner does not experience a job loss. However, this null effect
decomposes into a positive job search effect and a negative job entry effect.
These findings are consistent with previous UK studies that find little evi-
dence of a conventional AWE—if anything there appears to be a reverse
AWE such that women are less likely to join the labour market or find
employment when their partners lose their job (possibly because of incen-
tives in the benefit system). A possible explanation of our result is that a
partners job loss triggers the start of a job search by the woman, but she
then takes longer to find a job that pays enough to cover any loss of benefit.
For men in female breadwinner households (not analysed in previous
studies), the story is a little different. Here we do find an overall positive
AWE, which is reflected in increased job finding in the growth period and
increased but unsuccessful search in the recession. The fact that the AWE is
positive does not fit the benefit story, but this may be because men tend to
move into higher paying full-time work.
We also find evidence of labour supply responses at the other extensive
margin, voluntary quits among dual earner couples. Similar to male bread-
winner couples, the response is negative: it seems that a persons job loss
may be a trigger for the other partner to stop work too. One reason could
again be benefit incentives but the effect is also consistent with the idea that
couples non-market time is complementary.
14
Both men and women react
but the largest effects are among women working part-time. In contrast to
single earner households, these effects are reversed during the recession,
such that women working part-time are less likely to quit voluntarily follow-
ing their partners job loss (they are also less likely to quit overall in the
recession).
Both men and women who already have a job react to their partners
job loss by engaging in on-the-job search, again with the largest effects being
among part-time women. Women working full-time instead increase their
hours if the job loss happens during a recession (but decrease them in peri-
ods of growth).
While reactions are often delayed or contemporaneous, we find little
evidence that people act in advance of job losses, suggesting that unemploy-
ment typically comes as a surprise. Previous studies similarly found no evi-
dence of anticipatory effects (Gong, 2011) or only relatively small effects
(Stephens, 2002) but they looked at job losses a year ahead. We have
14
We do not have enough details on the reasons why workers quit their jobs, so a more thorough
analysis of the possible mechanisms behind these results is left for future research.
Couples Labour Supply Responses 23
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C2017 The Authors. The Manchester School published by The University of Manchester and John Wiley & Sons Ltd.
supplied additional evidence that even job losses in the next quarter do not
appear to be anticipated.
What role does the recession play in couples responses? We hypothe-
sized that the recession might increase couples recourse to the labour mar-
ket following a job loss, as has been found in some international (mainly
US) evidence. The results are somewhat mixed and depend on which margin
we look at. The clearest difference we find between recession and growth is
in (part-time) womens voluntary quit behaviour. Following their partners
job loss during the growth period, women are 1.8pp more likely to quit, but
during the recession they are 0.9pp less likely to quit their job. In other
words, the reverse (voluntary quit) AWE is overturned during recession as
households seek to maintain their labour market attachment (consistent
with previous UK evidence reported by Harkness and Evans, 2011).
On the other hand, among breadwinner households the AWE in terms
of overall labour market entry is the same in the recession as in the growth
period. The only difference (among female breadwinner households) is that
it is manifested as job finding during growth and (unsuccessful) search dur-
ing recession. Among dual-earner couples, while the job search response is
the same in both periods, women appear more likely to increase their hours
in a recession in response to their partners job loss.
Overall then, we find no evidence that AWEs were smaller (or the reverse
AWE bigger) in the recession: there was either no difference or households
reacted by increasing their attachment to the labour market. This does not
always mean that households were successful in restoring labour earnings fol-
lowing a partners job loss. As we have shown, the AWE may well show up as
an increase in search activity rather than job entry. Our findings illustrate the
importance of analysing the different potential margins of couples labour
supply adjustments. A fruitful avenue for future research would be to look at
dual earner couples in more detail. They have been largely ignored in the pre-
vious literature even though they now make up the bulk of households, and
their quitting behaviour has rarely been addressed. Much could be learned
from studies across different institutional contexts.
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