Loss and labour
Gender differences in labour market performance of surviving relatives in
the Netherlands
Katja Chkalova
Centraal Bureau voor de Statistiek (CBS), The Netherlands
k.chkalova@cbs.nl
September 20, 2013
This paper analyses the gender differences in labour market trajectories of
surviving
partners
in
the
Netherlands. Using
panel
data
of
Statistics
Netherlands, the sequence of labour market transitions of survivors of
bereavement in the five years after losing their partner are reconstructed;
their labour market trajectories are examined; and a sequence typology is
developed. For that end I am using the alternative method for computing
differences amongst sequences which was introduced into social sciences by
Cees
Elzinga
survivors are
followed
more
by
likely
cluster
to
analysis.
continue
their
The
results
show
participation
or
that
male
to
start
working shortly after the event. Women, on the other hand, are more likely
to withdraw from the labour market or to stay inactive.
Keywords:
gender;
labour
market
participation;
events
[1]
widowhood;
life
course
1 Introduction
Gendered labour market outcomes manifest in the level of participation, but
even more so in its continuity and intensity (Daly, 2000). As the level of
women labour market participation in the Netherlands slowly approaches the
males', the intensity and continuity of participation remains significantly
different as women remain working part time en mass and are more likely to
interrupt
vulnerable
their
careers
(Brakel
and
fragmented
female
at
al.,
labour
2010).
market
The
causes
participation
of
are
more
often
linked to the life course perspective. Life course events, such as the
birth of a child, are often seen as main causes of interruptions or changes
in the female’s labour market careers when often having no effect or the
opposite
effect
on
male
careers
(Mol,
2008).
The
persistent
gendered
division of unpaid labour, elaborated by Hochschild (Hochschild, 1989), may
be one of the major explanations of these gendered labour market outcomes.
This
is
especially
the
case
when
young
children
are
present
in
the
household. The gap in labour market participation between men and women
starts widening just after 25th year of age: the age when families are
formed, children arrive and the quantity of unpaid labour in the household
sharply rises.
Losing a life partner is a life event that may have severe consequences on
many life domains, including labour market participation. Consequences of
survivorship such as personal grief over losing a partner, loss of income
and increase in care duties can contribute to the weakened position of
these individuals after such an event. Survivors of bereavement usually
come up as a particular subgroup in poverty research or in studies on
social security benefits (Van Eekelen & Vleeming, 2008). However, most
studies
with
survivors
as
research
objects
focus
on
the
financial
consequences of the death of the life partner and the likelihood of poverty
as a consequence of widowhood (Corden et al., 2008; Namkee, 2005) while
labour market participation of the survivors has hardly been investigated.
The
lack
of
interest
for
this
particular
group
is
mainly
because
it
concerns a subpopulation that usually is of an advanced age and hence is of
no special interest for labour supply studies. Another important reason for
leaving this particular group out of research is the small size of this
subgroup; which leaves survivors of bereavement invisible in surveys. The
only study, I could find, that examined the effect of the partner’s death
on the labour market supply (Haardt, 2007), found noticeable effect on
female survivors and no effect on widowers. However, the mentioned results
[2]
of that study were not statistically significant due to a very small case
numbers examined.
There is a field of research analysing labour market involvement of widows
as
a
viable
adaptive
option
to
cope
with
problems
of
widowhood
by
increasing women's economic, social and psychological resources (Morgan &
Leslie, 1980; Pai & Barrett, 2007). This line of research investigates the
possible positive effect of paid labour on well-being of widowed. I could
find one study that analysed the labour market participation of widows
finding that about 17 per cent of widows who were unemployed at the times
their
husband
died
entered
paid
labour
(Morgan
&
Leslie,
1984).
Unfortunately, this study cannot be of any help in my current research as
it is somewhat outdates considering the fact that the female labour market
participation has changed dramatically since the 80’s. In addition, the
widowers’s labour market behaviour has been left out of consideration in
this study. Another study I could find regarding labour market involvement
of widowed is dated from 1972 (Pihlblad et al. 1972), reporting tendencies
for
widows
widowers
to
tend
adjust
to
to
reduce
lower
incomes
expenditures.
by
I
seeking
cannot
employment,
take
this
whereas
results
into
account as this paper concerns widowed aged 65 and over and is out of date
considering great changes in the female labour market participation.
Another
line
of
research
that
can
contribute
to
the
theory
building
regarding gendered labour market outcomes focusses on gender differences in
emotional distress as a consequence of widowhood. There are strong evidence
that
widowers
have
more
excessive
detrimental
consequences
than
widows
(Lee & DeMaris, 2007; Nieboer et al., 1999; Stroebe et al., 2001; Stroebe,
1998; Umberson et al., 1992). This gendered vulnerability is linked to the
fact that widowhood is not the same event for men and women. The loss of a
spouse means primarily the loss of marriage benefits, which differ greatly
between men and women (Umberson et al., 1992) making the emotional distress
followed
by
widowhood
between
genders
incomparable.
By
applying
this
argument to the labour participation, one can argue that difference of
marriage
benefits
leaves
survivors
with
different
issues
they
need
to
overcome to continue their labour market participation.
Besides physiological effects of survivorship, there is a reported effect
of severe financial consequences for widows as opposed to widowers during
the
20th
century
in
the
Netherlands.
This
was
due
to
the
so-called
breadwinner model that was common at the time. Women usually withdrew from
the labour market after marrying and became financially dependent on their
husbands. In case of early death of their spouse, women often had to rely
[3]
on social security benefits or had to appeal to their families or to
charity
(McGarry
&
Schoeni,
2000).
On
the
other
hand,
men,
the
breadwinners, had little financial disadvantage after losing a partner. On
the contrary, husbands often experienced an increase in disposable income
as
fewer
persons
Bruggink
&
te
were
dependant
Riele,
2007).
on
This
the
same
is
why
income
social
(Namkee
Ahn
assistance
2005,
in
the
Netherlands was usually focused not on the widowers but on the well-being
of widows, who needed assistance to be able to support themselves (SER,
2006).
Over
the
past
occurred
decades,
that
Individualisation,
however,
impacted
on
changing
family
demographical
widows'
and
economical
financial
structures
and
an
changes
circumstances.
increase
in
women
labour market participation all contributed to the financial independence
of women, and therefore widows. The breadwinner model is consequently no
longer the standard family structure. Most women participate in the labour
market even after giving birth (Leufkens, 2009, Brakel et al., 2010). This
results in a blurring of traditional roles within the family. The twoincome family structure has become the norm since then in the Netherlands
(Moonen & Kösters, 2011), thereby equalizing the financial consequences of
survivorship between genders. However, in most households the women still
remain secondary earners as they usually work part time (Brakel at al.,
2010).
In spite of more universalistic financial risk distribution of widowhood
between genders, there are few factors that can lead to gendered outcomes
on the labour market. Firstly, the majority of Dutch women occupy part time
jobs that are often more suited to combine work and care, compared to the
more rigid full time jobs man usually have. This may make
women more
resilient when it comes to continuing labour market participation under
unusual circumstances. Moreover, it is more accepted for Dutch women to
temporarily
Whereas
most
or
permanently
Dutch
male
reduce
workers
work
still
hours
have
due
a
to
hard
personal
time
matters.
accepting
and
getting part time appointments (van Beek et al., 2010). Secondly, since
most women already are responsible for the major part of the unpaid labour
in their households, they will not experience care as added pressure after
losing their partner and will be able to adapt to the new situation more
easily compared to males who are more inexperienced to household labour.
At
the
present,
little
is
known
about
the
labour
market
behaviour
of
survivors in general and the possible gendered effect it may have on the
labour market outcomes. Considering the fact that other life course events
[4]
often lead to gendered labour market outcomes and hence contribute to a
persistent
gender
desirable,
not
gap,
only
empirical
from
the
evidence
position
of
about
the
this
phenomenon
academics
and
is
policy
researchers, but also from the position of politicians who are currently in
the process of reshaping the Dutch social security system.
[5]
2 Research aims
The main question of this research is whether and to what extent the labour
market outcomes of survivors of bereavement are gendered. The aims of this
research are primarily explorative. In particular I am interested in the
type of trajectories survivors of bereavement adopt after the loss of their
life
partner,
labour
and
market
whether
gender
trajectories
the
is
a
significant
survivors
follow
determinant
after
the
of
what
event
of
widowhood.
Survivors of bereavement are not a homogeneous group of people. The only
binding factor between the individuals in our database is the widowhood
event itself. A working person at the end of the research period that
passed through a series of transitions is fundamentally different in my
eyes from a person who worked all along. For these reasons I am making a
distinction between widowed persons who have worked prior to widowhood, and
survivors who were inactive prior to this life event.
As I already mentioned in the introduction, the main issues survivors of
bereavement face in terms of paid labour concern the loss of income on one
hand, and the increased household load on the other hand. The first issue
concerns mostly women who are more dependent on their spouse for income and
hence experience a loss in disposable income after becoming a widow in a
greater extent. The later issue concerns mostly men who are often not as
experienced
in
combining
the
household
and
care
tasks
with
paid
job
compared to their wives. Therefore I expect the reaction to this strain to
be gendered. Considering this and taking into account gendered outcomes
when it comes to other life events, I assume that men are less likely to
choose for staying home compared to women and vice versa.
Hypothesis 1 Women are more likely to follow the trajectory work –
event - inactive.
Hypothesis 2 Man are more likely to follow the trajectory work –
event – work.
I
expect
employment
women
as
to
they
be
are
more
flexible
showing
more
when
it
comes
fragmented
to
durability
employment
records
of
in
general and hence are more at ease interrupting their careers.
Hypothesis 3 Women are more likely to follow the trajectory work –
event – fragmented career path.
[6]
The
argumentation
above
applies
to
survivors
who
worked
prior
to
the
widowhood. When it comes to survivors with starting position inactive, I
still expect women to make career switches more often compared to man.
Hypothesis 4 Women are more likely to follow the trajectory inactive
– event – fragmented career path.
Hypothesis 5 Women are more likely to follow the trajectory inactive
– event – work.
As it comes to trajectories where survivors of bereavement were and stay
inactive after the event, I expect men to make this transition more often.
As primary earners, men are expected to work while their wives take care of
the household. Men who did not work in the first place, are presumably
unemployed or disabled with limited chances returning to the paid labour.
Building on this breadwinner model, I expect inactive men more likely to
stay inactive compared to their female counterparts who stay at home for
other reasons then their limited chances on the labour market.
Hypothesis 6 Man are more likely to follow the trajectory inactive –
event - inactive.
Considering the dominant breadwinner ideal of a man that should be working
and if he doesn’t participate, it is solely for the reasons of limited
chances
on
the
labour
market,
I
expect
that
other
covariates
(age,
household configuration, presence of children in the household and personal
income) will have greater explaining power for widows compared to widowers.
Hypothesis 7 Age, household configuration and personal income have a
greater
explaining
power
for
routes
widowers
[7]
the
widows
take
compared
to
3 Research design, concepts and definitions
In this research I am using the Social Statistical Database of Statistics
Netherlands. This database contains information from many registers as well
as data from sample surveys that are linked through a unique key. This
statistical database enables us to select persons who lost their partner in
the
period
of
2003,
2004
and
2005
and
acquire
statistics
about
these
groups. Such as the monthly labour market position from 2002 up to 2010 and
other demographic and socio-economic characteristics of these individuals.
In the Netherlands, in the years 2003, 2004 and 2005, 39.8 thousand persons
in the age category 18-54 lost their partner. These individuals form my
research population. To construct labour market transitions I used monthly
indicators of employment one year before the death of the spouse and five
years
after
the
widowhood
event.
Consequently,
I
have
constructed
the
sequence of labour market states for the total period of 73 months for all
individuals: 12 months before the widowhood, the month when the event took
place and 60 months after the event. To make a distinction between the
persons who were working prior to the widowhood event en persons who were
inactive prior to the event, I separated persons who worked more than 6
months prior to the event and persons who were not. These groups were
examined separately.
Over 60 per cent of almost 40 thousand examined survivors have not changed
their labour market status in the period we examined. Almost 70 per cent of
these
non-changers
have
worked
continuously
in
that
period
of
time.
Survivors who did change their labour market position experienced between
more than two transitions on average. The most dynamic person in our data
set changed his labour market position 29 times in 73 months.
In the state distribution plot (see plot 1) the impact of widowhood is
visible. Around the 13th month of the sequence, the month of the death of
the life partner shows a break in the state distribution. However, the
static picture in plot 1 does not give us any idea about which workers
became inactive and which inactives entered the labour market. It also
fails to show us what paths and trajectories are common amongst these
groups. This is why I apply sequence analysis at this point. Sequence
analysis makes it possible to calculate distances between the sequences.
Using the measured distances I could consequently develop sequence typology
with cluster analysis.
[8]
When one talks about calculating distances between the sequences, one is
actually trying to find a measure of how the sequences differ from one
another. In other words, I need to calculate similarities between distinct
sequences by measuring them. The most common way to compute the distances
amongst the sequences is Optimal matching (OM). This method was introduced
into
social
sciences
by
Abbot
and
Tsay
(Abbott
&
Tsay,
2000).
OM
is
originated from biology where it is commonly used to analyse strings of
DNA. The OM computes the minimal amount of the so-called substitution or
deletion costs: the costs that are required to make one sequence identical
to
another
sequence.
sequence
In
2003,
by
inserting
Elzinga
or
proposed
deleting
alternative
certain
states
methods
for
into
a
expressing
differences amongst sequences in social sciences (Elzinga, 2003) which take
into
consideration
the
particular
sequence
attributes.
Considering
the
objective of this research I choose LCS (longest common subsequence) method
to
calculate
the
distances.
This
method
calculates
the
longest
common
subsequence between every pair of sequences. An important detail of this
calculation is that subsequences do not necessarily consist of consecutive
parts
of
a
sequence
(string).
After
calculating
the
longest
common
subsequence between every pair of sequences I developed sequence typology
using cluster analysis.
To get more insights into who ends up where, I applied logistic regression
with gender, ethnicity, age, presence of children in the household and
personal
income
as
independent
variables.
For
all
clusters
I
used
the
regression model with covariates age category, gender, personal income,
presence of children and ethnic background with interaction effect gender
by personal income. Not every cluster benefits from this model. In some
clusters the included interaction effect had little to no impact compared
to other models. However, I have chosen to implement this model for every
cluster
to
keep
the
results
of
different
clusters
comparable
and
gain
maximum explained variance. The R square scores and other coefficients are
included in the appendix.
[9]
4 Results
The frequencies of the emerged clusters are shown in table 1. There were
four main clusters distinguished in each of two groups. The majority of the
group of persons who worked prior to the event, over 76 per cent, show
little
to
no
change
in
their
participation.
Almost
5
per
cent
show
fragmented paths of work with interruptions. And almost 19 per cent became
inactive just after the event or years later. The group who did not work
prior to the event show similar results. Three quarters have not changed
their inactive status. The remaining quarter entered the labour market just
after the event or years later. The fragmented paths did not show up as a
separate cluster in this group.
Applying regression analysis I determined the role of gender determining
the chances one will end up in one of the clusters. The results of the
analysis are attached in the appendix. The regression models come out with
R square varying from 0.08 to 0.17 for the clusters concerning no changes
and
clusters
event.
The
concerning
weakest
switches
models
are
in
the
the
career
ones
made
concerning
shortly
the
after
clusters
the
where
transitions were made later in the research period. The R square of this
type of clusters vary between 0.02 and 0.04. This minor explained variation
is of no surprise. It is hardly to be expected that widowhood will cause
the
labour
market
transitions
made
years
after
the
event.
Many
other
factors as another life event or particular circumstances could contribute
for a greater deal explaining the changes in participation years after the
widowhood. Besides minor explained variation most of the results of these
type of clusters show only few significant outcomes. For these reasons I
will leave these clusters out of consideration in the further discussion.
In
the
following
I
will
discuss
the
results
in
the
context
of
my
hypotheses. The overview of the findings by hypothesis is included in the
table below.
#
Hypothesis
1
Women
are
Results
more
likely
the
Supported
follow
the
Supported
follow
the
Not conclusive
to
follow
trajectory work – event - inactive.
2
Man
are
more
likely
to
trajectory work – event – work.
3
Women
are
more
likely
to
[10]
trajectory work – widowhood – fragmented
career path.
4
Women
are
trajectory
more
likely
inactive
to
–
follow
the
widowhood
Not conclusive
–
fragmented career path.
5
Women
are
more
likely
to
follow
the
Refuted
the
Refuted
trajectory inactive – widowhood – work.
6
Man
are
trajectory
more
likely
inactive
to
–
follow
widowhood
–
inactive.
7
Age, household configuration and personal
Not conclusive
income have a greater explaining power of
division of trajectory clusters of widows
compared to widowers
Hypothesis 1 Women are more likely to follow the trajectory work – event inactive.
The
trajectory
‘Work
–
event
–
inactive’
where
the
survivors
make
a
transition to inactivity shortly after the event, is almost 2 times more
likely to be taken by women than by man. This first hypothesis is therefore
supported by the results of the analysis. All other covariates also show a
significant contribution explaining why widowed end up taking this route
after losing a life partner. The chances ending up in this cluster are
greater with older age, with children present in the household and for
ethnical minorities. The greater income, on the contrary, has a negative
correlation with this cluster. This indicates that when put between the
financial need and the care duties survivors with better paid jobs are more
likely to choose for their jobs while workers with low paid jobs are more
likely to quit working. But when we examined the interaction effect with
gender, women show the reverse picture, where greater income makes widows
more likely to quit working. The explanation for this phenomenon could be
due
to
the
conceptualization
of
personal
income
in
this
research.
The
income variable I used in this analysis consists of income not only from
paid jobs, but includes also the component of pensions, social security
benefits and other sources of income. The assumption that men often are the
providers of the households with income consisted mainly from income from
paid labour, and women more often have marginal jobs complemented by other
sources of income such as pensions, can offer the explanation for this
[11]
interaction effect. While men are more depending on their income from paid
labour choose for their jobs, women can rely on other sources of income and
hence experience less financial strain, making them to be able to terminate
their participation after becoming a widow.
Hypothesis 2 Men are more likely to follow the trajectory work – event –
work.
This hypothesis is also supported by the outcomes of the analysis. Men are
more likely to end up in the cluster. However, factors as greater income
and age show higher score on Wald statistics hence playing a greater role
in explaining why widowed end up in this cluster compared to covariate
gender. In this cluster we also see the reverse effect of income when
interacted
with
gender
similar
to
the
results
in
follow
the
cluster
work-event-
inactivity.
Hypothesis 3
Women
are
more
likely
to
trajectory
work
–
widowhood – fragmented career path.
The
results
show
negative
correlation
with
gender
category
‘female’,
meaning that women are less likely to follow this fragmented career path
after widowhood. However, these results are not statistically significant.
Therefore, this hypothesis cannot be supported by the results nor it can be
refuted.
Hypothesis 4 Women are more likely to follow the trajectory inactive –
widowhood – fragmented career path.
The trajectory inactive – widowhood – fragmented career did not show up as
a
separate
cluster
in
my
analysis.
Hence
this
hypothesis
cannot
be
supported nor it can be refuted.
Hypothesis 5 Women are more likely to follow the trajectory inactive –
widowhood – work.
The cluster for this trajectory concerns inactive widowed who entered the
labour
market
short
after
the
event.
The
results
show
statistically
significant negative correlation with gender category ‘female’ meaning that
women are less likely to follow this career paths after widowhood compared
to
men.
This
hypothesis
is
therefore
refuted
by
the
results
of
the
analysis. Age has the highest scores on Wald statistics meaning it has
greater
explaining
power
in
this
cluster
[12]
compared
to
other
covariates
included in my analysis. Younger widowed have greater chances entering the
labour market after the event of widowhood compared to older widowed. The
income has also positive correlation with taking this career trajectory.
The higher personal income at the time of the event, the more chances the
survivor of bereavement will enter the labour market. Having children in
the household has a negative effect on the chances ending up in this
cluster.
Hypothesis 6
Man
are
more
likely
to
follow
the
trajectory
inactive
–
widowhood - inactive.
The results of the analysis refute this hypothesis as well. Women are more
likely
to
follow
this
path
compared
to
men.
Seemingly,
men
who
were
unemployed prior to the event tend to choose for paid labour when are put
into a position of choosing between staying home and paid labour. Having
children in the household have a positive effect on chances ending up in
this cluster, same as age. Covariate age in this cluster appears to be a
greater predictor compared to other covariates.
Hypothesis 7
greater
Age,
explaining
household
power
of
configuration
division
of
and
personal
trajectory
income
clusters
have
of
a
widows
compared to widowers.
To put this hypothesis on test, I have conducted logistic regression for
men and women separately without interaction effects. The results of this
analysis are included in the appendix. For the research population that
worked prior to the event, the covariates age and having a child in the
household at the time of the event have more predictive power for women,
while
income
and
origin
have
more
predictive
power
for
men.
For
the
population who were not working prior to the event, the gender differences
in predictive power of covariates differ amongst clusters. The cluster with
trajectories
inactive
-
event
–
working
short
after
the
event
the
covariates income and age have more predictive power for men while in the
cluster with trajectories inactive – event - inactive the same covariates
have more predictive power for women. The covariates origin and having
children in the household show no significant outcome for this group. Due
to this ambiguous results, this hypothesis cannot be supported nor it can
be refuted.
[13]
5 Conclusion
The majority of the survivors of bereavement don’t change their labour
market
status
after
distinguished
withdraw
the
groups
who
paid
labour
from
event
of
(re-)enter
after
widowhood.
the
the
labour
death
of
However,
market
their
there
(13%)
spouse
are
and
who
(9%).
The
distinction between these three groups is gendered. Men are more likely to
continue their participation or to start working after the event, while
women are more likely to withdraw from the labour market or stay inactive.
The causes of this gendered outcomes could besought in a strain survivors
of
bereavement
face
between
the
loss
of
income
on
one
hand
and
the
increased household load on the other hand. My assumption that men are less
likely to choose for later compared to women and vice versa is supported by
the data. Another notable result from the analysis is greater predictive
role
of
and
the
interaction
effect
with
personal
income.
This
could
indicate that women often complement their income from other sources than
paid labour compared to men, with less financial pressure as a result.
My hypotheses regarding surviving relatives who were inactive prior to the
widowhood are refuted by the data. My assumption was that men who were
inactive
labour
prior
market
to
the
event
compared
to
would
have
women.
This
more
seems
difficulties
not
to
be
entering
the
case.
the
One
possible explanation for these results could lie in the cause of death of
the
deceased
preceded
by
spouse.
a
period
If
the
of
death
of
the
sickness,
it
is
partner
possible
was
to
not
sudden
assume
that
but
the
surviving relative would take time off to nurse the dying partner; and
hence not be active shortly before becoming widowed. This means that the
reasons surviving relatives were not working prior to the widowhood event
were personal and have nothing to do with their chances on the labour
market.
My assumptions regarding greater explaining power of covariates on paths
women take, are only partially supported by the evidence. For surviving
relatives who were working prior to the event, the covariates age and
having
a
child
in
the
household
at
the
time
of
the
event
have
more
predictive power for women, while income and origin have more predictive
power for men. The covariates income and origin are often linked to the
chances on the labour market as men with low paid job, who are often low
educated,
and
men
of
foreign
origin
have
higher
risks
of
unemployment
(Dagevos, 2006). The greater predictive power the presence of children in
the household has on women when it comes to withdrawing from the labour
[14]
market supports my assumption that women will more often choose to stay at
home when are given the choice between keeping the job and the added care
pressure.
For the survivors who were not working prior to the event, the gender
differences in predictive power of covariates differ amongst clusters. The
covariates income and age have more predictive power for man who entered
the
labour
market
short
after
the
event.
The
greater
role
of
these
covariates could be linked to the chances men will find suitable job after
being inactive. However, the same covariates have more predictive power for
women who were and stay inactive. The assumption that the composition of
income differs between genders, could explain greater predictive power of
income on women with these trajectories. As women are more likely to have
other sources of income besides paid jobs, they are more likely to have
less financial strains and therefore have more liberty to choose to remain
inactive after becoming widowed compared to man.
All in all, the results show clear gendered outcomes of the labour market
performance of the surviving relatives. Age and personal income, both seem
to play a major role in the decision making process of survivors regarding
their participation on the labour market. The presence of children in the
household that represents the added pressure of care is as expected more of
a concern for women than men. On the other hand, the covariates linked to
the chances on the labour market such as income and origin have more
influence on male survivors.
[15]
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[18]
Appendix
Figure 1 State distribution plot of research population
Figure 2 State distribution plots of inactive survivors prior
to widowhood and working survivors prior to widowhood
[19]
Table 1
Group 1 working prior to the event
28.900
100%
Few or no changes (type 5)
22.040
76,3%
Work – event - inactive shortly after the event (type 8)
3.890
13,5%
Work – event - inactive years later (7)
1.550
5,4%
Work – event – fragmented career path (6)
1.420
4,9%
Group 2 inactive prior to the event
10.940
100%
Few or no changes (type 3)
8.240
75,3%
Inactive – event – working shortly after the event (type 2)
980
9,0%
Inactive – event – working two years later (type 1)
960
8,8%
Inactive – event – working 3-4 years later (type 4)
760
6,7%
[20]
Figure 3 State distribution plots of 4 trajectory types
(clusters) of inactive persons prior to the widowhood
[21]
Figure
4
State
distribution
plots
of
4
trajectory
(clusters) of working persons prior to the widowhood
[22]
types
Table 2 Nagelkerke R square of different regression models
Work – event Work – event –
Working prior to the
event
Work – event
inactive
Few or no
fragmented
- inactive
shortly after
changes
career path
years later
the event
0,119
0,027
0,016
0,150
0,135
0,035
0,017
0,168
Nagelkerke R Square
No interaction effects
Interaction
gender*income*
Inactive –
Inactive –
event –
event –
working
Inactive prior to the
working two
shortly after
Few or no
event – working
Inactive –
event
years later
the event
changes
3-4 years later
0,044
0,073
0,098
0,029
0,045
0,079
0,103
0,030
Nagelkerke R Square
No interaction effects
Interaction
gender*income*
* model used in the analysis
[23]
Table 3 Coefficient’s logistic regression for group working
prior to the event
Working prior to the
event
Few or no changes
odds ratios
Wald
Sig.
Work – event –
Work – event -
Work – event -
fragmented career
inactive years
inactive shortly
path
later
after the event
Exp(B) Wald
Sig.
Exp(B) Wald
Sig.
Exp(B) Wald
Sig.
Exp(B)
Man (reference cat)
(1) women
15,962
0,000
0,726
(reference cat)
102,550
0,000
(1) non-western
91,687
0,000
0,597
(2) western
17,744
0,000
0,808
(reference cat)
82,518
0,000
(1) 36-45
52,344
0,000
79,789
0,000
1,928
0,165
474,359
0,000
(1) 15.000-25.000 euro's
13,662
0,000
1,411
(2) 25.000-50.000 euro's
275,495
0,000
196,484
1,534
0,216 0,849
0,013
0,910
5,676
0,059
4,996
0,371
0,984
31,240
0,000
55,039
0,000
0,025 1,243 44,971
0,000
1,794
0,542 0,940 15,691
0,000
1,406
9,522
0,009
8,525
0,014
0,741
3,201
0,074 1,143
2,263
0,132
0,895
0,697
0,607
0,436 0,943
0,948
0,330
0,937
2,656
0,103 0,859 33,799
0,000
0,525
0,000
17,363
0,002
1,086
0,297
0,845
3,931 84,175
0,000 0,282 12,266
0,000
4,134 65,918
0,000 0,217
334,820
0,000
70,753
6,321
0,012
0,772
1,008
208,937
0,000
122,783
0,000
1,798
38,978
0,000
33,235
0,000
1,475
8,769
0,003
1,201
122,001
0,000
78,517
0,000
1,613
1,070 119,456
0,000
1,777
46,954
0,000
1,462
382,263
0,000
7,799
0,005
0,700
0,000
0,612 222,145
0,000
0,168
5,776
0,016
0,680 133,433
0,000
0,155
12,177
0,016
320,292
0,000
0,315 1,191
0,073
0,788
0,952
6,895
0,009
1,434
0,260 46,118
0,000 2,989
2,028
0,154
1,265 214,224
0,000
6,658
0,242 18,391
0,000 2,994
6,990
0,008
1,760 109,135
0,000
6,897
Indigenous population
< 35
years old
years old
(2) 45+
no children (reference
cat)
(1) children present
< 15.000
euro's
(reference cat)
144,63
1
3,491
0,062 0,752
(3) 50.000 euro's and
higher
female by income < 15.000
euro's
0,000
female by income 15.00025.000 euro's
female by income 25.00050.000 euro's
female by income 50.000
euro's and higher
[24]
Table 4 Coefficient’s logistic regression for group inactive
prior to the event
Inactive prior to the
event
Inactive – event –
Inactive – event –
working two years later the event
odds ratios
Wald
Sig.
Inactive – event –
working shortly after
Exp(B) Wald
working 3-4 years
Few or no changes
Sig.
Exp(B) Wald
Sig.
later
Exp(B) Wald
Sig.
Exp(B)
Man (reference cat)
(1) women
47,737
1,000
0,502
(reference cat)
15,519
2,000
(1) non-western
15,304
1,000
0,696
0,120
1,000
0,962
106,202
2,000
23,519
0,000
0,596
40,038
0,000
39,403
0,000
0,546
3,819
0,051
0,799
195,994
0,000
83,399
0,000
1,971
80,020
0,000
79,072
0,000
1,756
7,471
0,006
1,234
435,318
0,000
3,668
0,055
0,795
11,452
0,003
9,688
0,002
0,730
3,617
0,057
0,780
66,461
0,000
Indigenous population
(2) western
< 35
years old
(reference cat)
(1) 36-45
years old
(2) 45+
13,233
1,000
0,726
42,648
0,000
0,567
66,763
0,000
1,649
3,434
0,064
0,835
100,177
1,000
0,405 194,406
0,000
0,286 410,952
0,000
3,521
56,986
0,000
0,467
1,020
1,000
0,889
7,587
0,006
0,712
5,914
0,015
1,208
0,096
0,756
1,038
11,893
4,000
64,466
0,000
38,133
0,000
10,646
0,031
5,491
1,000
0,694
0,294
0,588
1,087
6,140
0,013
1,316
2,488
0,115
0,740
0,030
1,000
1,031
43,759
0,000
2,803
13,446
0,000
0,629
4,752
0,029
0,571
0,842
1,000
0,611
18,514
0,000
4,394
4,828
0,028
0,490
0,157
0,692
0,785
5,375
4,000
31,809
0,000
38,014
0,000
5,244
0,263
0,428
1,000
0,877
5,214
0,022
0,636
3,423
0,064
1,287
0,580
0,446
0,838
2,547
1,000
0,664
25,961
0,000
0,298
33,153
0,000
2,774
1,751
0,186
0,614
0,093
1,000
1,242
7,167
0,007
0,229
6,548
0,010
3,139
0,888
0,346
0,411
no children (reference
cat)
(1) children present
< 15.000
euro's
(reference cat)
(1) 15.000-25.000
euro's
(2) 25.000-50.000
euro's
(3) 50.000 euro's and
higher
female by income <
15.000
euro's
female by income
15.000-25.000 euro's
female by income
25.000-50.000 euro's
female by income 50.000
euro's and higher
[25]
Table 5 Nagelkerke R square of logistic regression by gender
Man
Work – event Working prior to
the event
Work – event –
Work – event -
inactive
Few or no
fragmented
inactive years
shortly after
changes
career path
later
the event
Nagelkerke R Square
0,073
0,044
0,020
0,093
Cluster frequencies
12.360
470
670
590
87,75%
3,30%
4,76%
4,19%
Inactive –
Inactive –
event –
Inactive –
event –
working
event –
Inactive prior to
working two
shortly after
Few or no
working 3-4
the event
years later
the event
changes
years later
0,046
0,153
0,128
0,022
Nagelkerke R Square
Cluster frequencies
290
340
1.340
170
13,59%
15,70%
62,65%
8,06%
Women
Work – event Work – event –
Work – event -
inactive
Few or no
fragmented
inactive years
shortly after
changes
career path
later
the event
Nagelkerke R Square
0,012
0,003
0,015
0,020
Cluster frequencies
9.680
950
880
3.300
6,43%
5,96%
22,29%
Working prior to
the event
65,32%
Inactive –
Inactive –
event –
Inactive –
event –
working
Inactive prior to
working two
shortly after
Few or no
working 3-4
event –
the event
years later
the event
changes
years later
Nagelkerke R Square
0,031
0,034
0,072
0,039
Cluster frequencies
670
650
6.900
590
7,57%
7,36%
78,38%
6,69%
[26]
Table 6 Coefficient’s logistic regression for group working
prior to the event by gender
Work – event –
Working prior to
the event, man
odds ratios
Few or no changes
Wald
Sig. Exp(B)
fragmented career
Work – event -
Work – event - inactive
path
inactive years later
shortly after the event
Wald
Sig. Exp(B)
Wald
Sig. Exp(B)
Wald
Sig. Exp(B)
Indigenous
population
(reference cat)
72,513
0,000
6,413
0,041
34,206
0,000
31,708
0,000
(1) non-western
54,410
0,000
0,531
6,409
0,011
1,449
22,446
0,000
1,859
21,490
0,000
1,845
(2) western
26,149
0,000
0,635
0,056
0,813
1,043
16,080
0,000
1,669
14,679
0,000
1,692
28,774
0,000
4,422
0,110
19,327
0,000
29,702
0,000
old
12,974
0,000
0,758
0,376
0,540
1,080
1,082
0,298
1,134
17,233
0,000
1,714
(2) 45+
28,759
0,000
0,667
1,426
0,232
0,857
14,346
0,000
1,546
29,577
0,000
2,012
11,960
0,001
1,388
0,694
0,405
0,874
13,510
0,000
0,549
0,827
0,363
0,873
444,474
0,000
132,143
0,000
20,964
0,000
351,962
0,000
14,358
0,000
1,425
3,691
0,055
0,745
0,859
0,354
0,860
8,301
0,004
0,692
267,715
0,000
3,889
80,514
0,000
0,288
13,861
0,000
0,593 213,719
0,000
0,172
190,392
0,000
4,109
61,187
0,000
0,227
7,966
0,005
0,633 128,504
0,000
0,157
< 35
years old
(reference cat)
(1) 36-45
years
no children
(reference cat)
(1) children
present
< 15.000
euro's
(reference cat)
(1) 15.000-25.000
euro's
(2) 25.000-50.000
euro's
(3) 50.000 euro's
and higher
[27]
Work – event –
Working prior to
fragmented career
Work – event -
the event, women Few or no changes
path
inactive years later
odds ratios
Wald
Sig.
Exp(B) Wald
Sig.
Exp(B) Wald
Sig.
Work – event - inactive
shortly after the event
Exp(B) Wald
Sig.
Exp(B)
Indigenous
population
(reference cat)
43,398
0,000
1,440
0,487
26,694
0,000
18,086
0,000
(1) non-western
41,988
0,000
0,640
0,537
0,464
1,101
25,149
0,000
1,798
16,733
0,000
1,378
3,105
0,078
0,899
0,785
0,376
0,896
3,306
0,069
1,236
2,359
0,125
1,111
57,940
0,000
5,850
0,054
7,485
0,024
95,842
0,000
old
39,961
0,000
0,732
3,153
0,076
1,179
7,225
0,007
0,777
61,935
0,000
1,596
(2) 45+
54,318
0,000
0,703
0,007
0,931
0,992
4,004
0,045
0,837
93,650
0,000
1,748
15,597
0,000
0,801
1,836
0,175
0,856
21,633
0,000
0,495
62,779
0,000
1,619
14,196
0,007
9,242
0,055
17,335
0,002
20,591
0,000
3,109
0,078
1,081
1,603
0,205
0,900
6,373
0,012
0,801
0,037
0,848
1,010
0,106
0,744
1,014
4,230
0,040
0,842
7,551
0,006
0,785
5,741
0,017
1,127
0,016
0,899
0,990
6,327
0,012
0,645
2,221
0,136
1,236
0,628
0,428
1,074
(2) western
< 35
years old
(reference cat)
(1) 36-45
years
no children
(reference cat)
(1) children
present
< 15.000
euro's
(reference cat)
(1) 15.000-25.000
euro's
(2) 25.000-50.000
euro's
(3) 50.000 euro's
and higher
[28]
Table 7 Coefficient’s logistic regression for group inactive
prior to the event by gender
Inactive prior
to the event,
Inactive – event – Inactive – event –
working two years
working shortly
later
after the event
man
Inactive – event –
working 3-4 years
Few or no changes
later
Exp(B
odds ratios
Wald
Sig. Exp(B)
Wald
Sig. Exp(B)
Wald
Sig.
)
Wald
Sig. Exp(B)
Indigenous
population
(reference cat)
0,297 0,862
2,615 0,271
2,149 0,341
5,182 0,075
(1) non-western
0,052 0,819 1,036
2,606 0,106 0,775
0,114 0,736 1,040
1,897 0,168 1,293
(2) western
0,179 0,673 0,910
0,221 0,638 0,904
2,149 0,143 1,270
2,105 0,147 0,637
< 35
years old
(reference cat) 29,816 0,000
135,209 0,000
153,805 0,000
0,792 0,673
(1) 36-45
years old
(2) 45+
1,114 0,291 0,844 43,573 0,000 0,355 30,278 0,000 1,994
0,685 0,408 1,196
25,199 0,000 0,415 134,930 0,000 0,126 147,629 0,000 5,001
0,073 0,787 1,062
no children
(reference cat)
(1) children
present
0,314 0,575 1,151
0,188 0,664 0,895
0,009 0,923 0,982
0,001 0,970 0,987
< 15.000
euro's
(reference cat) 12,115 0,017
86,865 0,000
51,497 0,000
12,492 0,014
(1) 15.00025.000 euro's
4,495 0,034 0,713
3,483 0,062 1,354
1,666 0,197 1,161
3,441 0,064 0,696
0,483 0,487 1,135 65,817 0,000 4,188 27,701 0,000 0,488
5,272 0,022 0,543
0,547 0,459 0,671 27,841 0,000 6,694
0,162 0,687 0,781
(2) 25.00050.000 euro's
(3) 50.000
euro's and
higher
[29]
8,773 0,003 0,376
Inactive prior
to the event,
Inactive – event – Inactive – event –
working two years
working shortly
later
after the event
women
Inactive – event –
working 3-4 years
Few or no changes
later
Exp(B
odds ratios
Wald
Sig.
Exp(B) Wald
Sig.
Exp(B) Wald
Sig.
)
Wald
Sig.
Exp(B)
Indigenous
population
(reference cat) 25,660 0,000
41,247 0,000
103,348 0,000
19,850 0,000
(1) non-western 25,079 0,000 0,547 40,510 0,000 0,441 103,214 0,000 2,222 19,371 0,000 0,580
(2) western
< 35
0,027 0,869 0,979
3,457 0,063 0,775
4,792 0,029 1,210
2,129 0,145 0,811
years old
(reference cat) 81,236 0,000
84,267 0,000
294,399 0,000
83,401 0,000
(1) 36-45
years old
15,206 0,000 0,662 13,992 0,000 0,668 45,334 0,000 1,615
(2) 45+
79,370 0,000 0,391 81,192 0,000 0,381 277,444 0,000 3,260 76,153 0,000 0,371
7,120 0,008 0,746
no children
(reference cat)
(1) children
present
1,766 0,184 0,839
7,226 0,007 0,682
6,542 0,011 1,246
0,009 0,925 1,012
< 15.000
euro's
(reference cat) 19,148 0,001
12,285 0,015
66,295 0,000
28,685 0,000
(1) 15.00025.000 euro's
14,885 0,000 0,608
9,750 0,002 0,674 44,524 0,000 1,710 12,358 0,000 0,625
4,292 0,038 0,672
1,781 0,182 0,784 22,124 0,000 1,807 15,438 0,000 0,350
0,393 0,531 0,747
0,013 0,908 0,952
(2) 25.00050.000 euro's
(3) 50.000
euro's and
higher
[30]
2,276 0,131 1,591
2,478 0,115 0,322