ReflecT Research Paper
13/002
Exposure to Single and Multiple Risks of
Youngsters in their Early Career in Britain:
Continuous or Cumulative Disadvantage?
© Ruud Muffels, March 2013
ReflecT
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Exposure to Single and Multiple Risks of
Youngsters in their Early Career in Britain:
Continuous or Cumulative Disadvantage?
ABSTRACT
A plethora of studies raised concerns about the rising inequality on the labour
market since the mid 1980s and especially also during the current crisis. In this
paper we examine how low-skilled young men and women of 16 to 35 years of age
fare with respect to their income and employment security over their career using
panel data for Britain covering the early 1990s to the late 2000s. The main question
is whether low-skilled youngsters are able to recover from their lower entry-level
of security or that their disadvantage accelerates due to being exposed to adverse
events and statuses over the career? A brief review of globalization, insideroutsider, tournament and stratification theories renders some support to the idea
of cumulative disadvantage. The novelty of the approach is in studying the longerterm or career effects of exposure to single and multiple life course risks. The single
risk results confirm the literature that low-skilled youngsters face continuous
disadvantage instead of cumulative disadvantage implying that they recover but
that the gap with the high skilled is never closed. The multiple risk models however
reveal a mixed picture of cumulative disadvantage with respect to employment
security and continuous disadvantage with respect to income security.
Keywords: cumulative disadvantage, employment security, income security, youth,
unemployment, single risks, multiple risks, discrete choice models
JEL Classification: J24, J28, J41, J42
1
1.
Introduction
A plethora of studies raised concerns about the rising inequality on the labour
market since the mid 1980s and especially also during the current crisis.
Globalization, insider-outsider, tournament and stratification theories suggest that
particularly low-skilled youngsters face high risks on income and employment
insecurity in their early career and that these risks might aggravate. Youngsters
carry high risks on unemployment in the downturn and working in a low-paid
insecure job in the upturn when employment rises. The main question addressed
therefore pertains to how the low-skilled youngsters fare over their career using
data over nearly two decades covering downturns as well as upturn periods. Are
they able to recover from their initial lower income and employment security level
or does their disadvantage accelerates due to being exposed to multiple adverse
events and statuses over the career? The study views the career or employment
trajectories of low-skilled young men and women of 16 to 35 years of age using
the British Household Panel Study data covering the early 1990s to 2008-2009. A
brief review of the literature learns that particularly in Europe the focus has mainly
been on researching single disadvantage at group-level based on data derived from
repeated cross-sections. Most studies view the short-term effects only yielding no
evidence on the longer-term or career effects of multiple or cumulative
disadvantage. For that reason we add to the literature in three ways. First, because
we examine individual growth paths or income and employment trajectories of lowskilled youngsters based on prospective data instead of using aggregated data at
group level derived from repeated cross-sectional evidence as in most studies (e.g.
Berthoud 2003). Second, because we distinguish single risk and multiple risk
transition state models, where the latter views the effects of the combination or
accumulation of events on security outcomes. By estimating fixed-effects panel
regression models we correct for unobserved (time constant) heterogeneity
associated with individual differences in personality traits, motivation and effort.
Third, because we focus not only on the impact of single and multiple events but
also on disadvantaged statuses (e.g. unemployment or disability) following the
event (job loss and health shock) implying that the occurrence and length of stay
in these statuses codetermine also the effects on career-long income and
employment security. In this way we obtain a better insight into the additive or
2
cumulative nature of inequality and disadvantage in Britain with respect to lowskill.
The relevance of the issue for policy is clear with a view to concerns voiced
especially in recessions about a lost generation of youngsters and the longer-term
scarring effects on their careers. Labour markets differ in the way they adjusted to
the current crises (see e.g. O’Reilly et al., 2011). The uncoordinated or liberal
labour markets of the UK and the US responded through downward wage
adjustment and job displacement whereas the dual labour market of the South
responded through instantaneous lay-offs of unprotected workers in non-standard
contracts. Youth unemployment rose therefore strongest in the Southern labour
markets with a high level of insider-outsider dualism. However, also in the UK,
notwithstanding its flexible labour market, youth unemployment rose unexpectedly
rather strong in the current crisis. Insight in the longer-term scarring effects of
single and multiple life course risks is therefore important to design successful
intervention strategies.
Outline
In section 2 we briefly review the literature, define our conceptual model and
formulate in the end some hypotheses. Then in section 3 we present some
descriptive information on how low-skilled youngsters fared during the last two
decades in Britain in terms of their level of income and employment security and
the life course risks they faced. Do the low skilled indeed face higher single and
multiple risks than the medium or higher skilled and is the position of youngsters
better or worse compared to senior people? Then in section 4 we present the
findings of our multivariate analyses showing to what extent low-skilled youngsters
face cumulative disadvantage or not? In section 5 we end with conclusions and
discussion.
2.
Theory, empirical literature and hypotheses
In the literature on inequality and the life course it is often argued that people
gifted with favorable traits and endowments and better access to resources profit
more from the fruits of economic growth and prosperity than the less gifted ones.
This idea coined by Merton (1988) as cumulative advantage (CA) means that a
3
favorable relative current position produces positive gains later on in life. It implies
that inequality tend to accumulate over time (DiPrete and Eirich, 2006). Likewise,
cumulative disadvantage (CDA) implies that people with a less favorable position
endure future losses due to being exposed to life course risks emerging from the
occurrence of adverse events or entry into disadvantaged statuses causing
shortage of resources and reduced earning capacities which tend to aggravate the
initial disadvantage. The idea is also known as the Matthew effect or phrased in
terms as the “rich get richer” and “the poor get poorer”. Life course risks such as
unemployment, a low paid job, disability or poor health are therefore not equally
spread among the population but are especially faced by people already more likely
to have fewer resources and therefore belonging to the lower echelons of the life
chance distribution (e.g. Dannefer, 2003).
Notwithstanding the existence of a rich economic and sociological literature
concerning the issue of rising income inequality since the mid 1980s, there is
according to DiPrete and Eirich (2006) scarcity in studies about the causes and
patterns of growth of advantage and disadvantage in a broader sense over time.
Viewing the existing literature a variety of causal factors for the growth in inequality
and disadvantage are distinguished from the literature. Many empirical studies in
the economic and sociological literature found evidence that the growth in
inequality is associated with the consequences of globalization such as the lower
demand for low skilled workers due to skill biased technical change and rising
shares of insecure jobs associated with flexibilisation trends and increased
competition (see Blossfeld et al., 2003, 2006, 2009; Muffels, 2008; Muffels &
Luijkx, 2008; Muffels & Wilthagen, 2012). In the welfare state (WS) literature the
role of globalization is said to be paralleled with a shift in Welfare State policies
from the 1990s on according to which governments aim at downsizing its
intervening and supporting role in the economy and to withdraw from market
interference implying a greater reliance on the market and therewith permitting
rising inequality (Leibfried & Mau, 2008). In the economic literature the insideroutsider theory gained support (Lindbeck & Snower, 1988) arguing that labour
market regulations and institutions raise the transaction costs associated with
hiring and firing because of which there is lack of mobility resulting in widening
wage gaps between insiders and outsiders (Nickell & Wadhwani, 1990). Likewise,
it is argued mainly by economists working on income dynamics that the tendency
of the market to invest in low-risk activities which were gainful in the past, might
4
also contribute to the process of rising inequalities (see Ryan, 2002). This
phenomenon known as path dependency implies that past states and events impact
on the future career. In the empirical economic literature there is abundant
evidence that the duration of past unemployment has a negative effect on future
re-employment chances, known as negative duration dependency although the
evidence is mixed and supported for only very particular groups (Belzil, 1995;
Steiner, 2001; Böheim and Taylor, 2000; Pries, 2004). Also in sociology there is
mounting interest for the career effects of past events and statuses and whether a
“bad start” has long-term consequences for the careers of youngsters on the labour
market (e.g. Blossfeld et al., 2006, 2009). In another branch of the economic and
sociological literature it is stated that particular adverse trigger events or
transitions such as unemployment or a temporary job have a “scarring” effect on
the future career from which it takes long to recover. Especially in the American
literature starting with Ruhm’s work (1991) a rising number of empirical studies
appeared, providing support to the evidence especially on the scarring effects of
(long-term) unemployment. In Europe, since the end of the 1990s the number of
studies on scarring by economists and sociologists increased strongly thanks to the
improved availability of longitudinal data (Dickens et al., 1999; Booth et al., 2002;
Scherer, 2004; Dekker, 2007; DiPrete 2006, Gangl, 2004, 2006; Muffels, 2008).
The evidence showed that e.g. part-time employment has a scarring effect on
future employment and wages though the evidence is mixed how strong the effect
actually is, and how long it takes for people to recover from the initial shock
(Gregory & Jukes, 2001; Gallie & Paugam, 2000; Manning & Petrongola, 2008;
Bardasi & Gornick, 2008; Fouarge & Muffels, 2009). Fouarge and Muffels (2009)
found weak evidence for scarring of part-time work in the Dutch and German
context controlling for unobserved heterogeneity but strong evidence for Britain
suggesting that social norms and public support for part-time work (especially in
the Netherlands but increasingly so in Germany) reduces the occupational
segregation of part-time jobs improving their quality and wage prospects. In
another strand of the literature psychologists point to another explanation of
cumulative disadvantage that is associated with the impact of hereditary or genetic
factors (ability, talent, inherited wealth) causing existing inequalities in initial
endowments to sustain and to be reproduced over time. In the social stratification
literature the focus has been on the impact of social or parental background
pointing to the influence of social class and social-cultural or social-economic
5
explanations for changing inequality trends (Goldthorpe, 2002, Blau & Duncan,
1969, Breen et al., 2009).
It is this line of reasoning that we also adopt in this paper though the focus is
not on explaining intergenerational inequality as in Blau and Duncan’s status
attainment model (inequality between fathers and sons and mothers and
daughters) but more so on intra-generational or life course inequality trends that
is between men and women belonging to the same generation but over their entire
career. Reference can also be made to the so-called status maintenance
(Hungerford, 2007) and tournament models (Rosenbaum, 1979) focusing on the
way status and mobility in the early career has enduring effects on the later career.
In Rosenbaum’s tournament model in each round people compete on particular
positions or available resources on the labour market but the winners of the
competition gain advantage over the losers in the next round implying that initial
advantage tend to accumulate over time.
Conceptual model
To arrive at a conceptual model we pay credit to an earlier study of Bielby & Bielby’s
(1992). They made a distinction between three models: cumulative advantage,
cumulative disadvantage and continuous disadvantage. The first two models are
already explained before. The latter model refers to the idea that when analysing
e.g. gender career differences these differences might persist over time but not in
a cumulative way. In that case the gender gap in career success is not widened
over time because women are equally exposed to particular life course risks as
men without widening the gender gap. Bielby & Bielby (1992) find little evidence
for cumulative disadvantage but more for continuous disadvantage; gender gaps
appeared persistent and did not decline. In our case it would imply that low skilled
workers are equally exposed to particular life course risks as the high skilled and
therefore not more prone to disadvantage than they initially were. In the case of
continuous disadvantage the main effects are negative but the interaction effects
between skill and the occurrence of the adverse event insignificant. In the case the
main effect is negative but the interaction effects positive, disadvantage is
declining over time and when the interaction effects are negative disadvantage
associated with the occurrence of adverse events tend to increase confirming the
existence of cumulative disadvantage. A similar approach was adopted in Berthoud
et al. (2003), though they used the cross-sectional data of the British Labour Force
6
Survey. They focused on the effects of multiple disadvantage on unemployment
for Britain by viewing the main and interaction effects of six forms of disadvantage.
Their findings suggest that the effects of multiple disadvantages on unemployment
reveal an additive instead of an exponential pattern that would imply the risks of
unemployment to be larger and larger as the number of disadvantages increases.
The additive pattern confirms that each disadvantage adds to increasing the risk
on unemployment but not in an accelerating manner. Our approach is similar in
that we also examine the effects of multiple disadvantage on employment but
different in that we focus on the effect of multiple disadvantage on changes in
income and employment security over time. A further difference associated with
the cross-sectional nature of the data is that they examine disadvantaged statuses
only whereas we also include events.
The causality in the model runs from preferences (work-leisure) and values
(economic and social) together with resources (human capital) to people’s
constrained and unconstrained choices on the labour market (events and statuses)
and then to the outcomes, that is income and employment security. Preferences
and constraints are added as controls together with education level (medium
versus high skilled), age and age squared to account for the non-linearity in the
relationship between age and income and employment security and the annual
unemployment rate to control for the impact of the business cycle.
3.
Data and measures
The paper uses data of the British Household Panel Study for the years 1991-2008.
The British panel was launched in 1991 with about 10,300 individuals in 5,500
households. All individuals are interviewed. Again, sample representativeness is
maintained by including split-offs and their new households. The British panel has
been augmented by booster samples for Scotland and Wales in 1991 and a new
Northern Ireland sample in 2001. In 2008, the latest year used in the paper, the
sample size was just over 14,000. The labour market, marital and demographic
history of individuals is constructed from the retrospective information gathered in
the second wave (1992), supplemented with labour market and demographic
information from the panel waves. A major change occurred in 2010 when the
7
BHPS panel was merged into the new United Kingdom Household Longitudinal
Study (‘Understanding Society”), which included a great many additional
questions, especially in the health area. The new sample size is about 100,000.
The sample used for this paper consists of youngsters from 16 years to 34 years.
The total sample of these youngsters covering 18 waves of data was 51,950. The
sample was not restricted to youngsters below 25 years of age because the length
of the period to which youngsters are exposed to risks of job insecurity during the
early career associated with the process of flexibilisation of the labour market
seems to be extending affecting youngsters also of 25 years and older.
3.1.
Measures
Dependent variables and low skill
We use a measure of household income security instead of wage income security
to include non-wage income sources because one of the research questions
concerned the role of policies in compensating people for the risks they face. We
take household income since income risks faced by one member might be
compensated by additional income earned by other members. Income security is
therefore defined as the income-to-needs ratio, the ratio of annual after tax
household income and a minimum household income threshold below which people
are considered to live in relative poverty. We took the income threshold as defined
by the OECD and used by the European Union which is set at 60% of the mean
equivalent household income. The equivalence scale used to compare households
is the so-called modified scale which equals 1 for the head, 0.7 for the partner and
0.3 for the children. For employment security we use a rather straightforward
continuous measure being defined as the number of weeks people are employed
in any given year ranging from one to fifty-two.
Low skill is defined according to three measures: low education derived from
the international education level classification called ISCED, low socio-economic
status derived from the international classification scheme called ISEI and low
occupational class derived from the international EGP scheme (Erikson &
Goldthorpe, 2002). The results turned out to be very similar across the three
measures for which reason we only report on the ISCED measure of low skill.
8
Life course risks

Life course risks are defined as adverse events and statuses: events are
unemployment, a health drop of 2 points or more on the health satisfaction
scale ranging from one to 7, job displacement or being laid off last year from
the employer due to being dismissed or declared redundant, becoming
disabled (‘health limits type or amount of work’), early retirement, divorce or
separation from marriage and the birth of another child. Disadvantaged
statuses are being long-term unemployed, having a bad health or being
disabled (health limitations for work), retired, lacking employer related
training, being underworked, occupying a temporary job and being a lone
parent or single.

A bad hours match is defined in the BHPS as that people are unable to work
the hours they wish, that is that they worked more (overworked) or less than
they
prefer
(underworked).
We
expect
a
negative
association
with
employment and income security only for being underworked because we
suspect that people who work longer hours have a stronger position on the
labour market and therefore being more employment secure. People working
more hours than they wish work possibly longer hours and therefore will earn
more income because of which they are more income secure.
Controls
Controls are added to control for the non-linear effects of age on income and
employment security and for the differences in education level and job tenure.
Because low skill is one of our variables of interest we included a dummy for
medium skill with high skill as reference group. The three levels are based on the
ISCED scores with ISCED levels 1 and 2a for the low skilled, 2b and 2c for
intermediate level and 3a,4,5 for the high skilled. We also included controls for
differences in preferences and values or life goals between people. In the BHPS a
number of questions were asked on the importance of certain aspects in life such
as success in a job, what people can afford, living an independent life, importance
of having children, a partner, friends, a good health, and owning a house. Based
on principal components analysis we constructed two variables measuring the
economic dimension (‘success in job’, to afford things and ‘living an independent
life’), and the social dimension (‘’importance of partner’, importance of children’
and ‘importance of friends’). We did not weigh the factors with the factor loadings
9
but just took the aggregated scores on the variables belonging to the two
dimensions.
4.
The empirical model
The idea of cumulative disadvantage requires knowledge about the pattern of
change or the functional or mathematical form of the relationship between
disadvantage and outcome over time. Disadvantage might refer to current
disadvantage or past disadvantage and might impact on the current outcome level
as well on its growth, resulting hence in many alternative functional specifications.
In their review of the cumulative advantage literature, Diprete and Eirich (2006)
distinguish various specifications such as path-dependent cumulative disadvantage
in which the current outcome is dependent on previous outcomes, cumulative
exposure in which the growth in outcome is dependent on the continuous exposure
to a particular status and the status-resource interaction model in which current or
past status affects through its impact on current resources the current outcome
level but not its growth. In sociology, life course oriented studies viewed the impact
of particular life events such as divorce, job loss or disability on future outcomes.
Within a life cycle or life course framework life events are critical for understanding
the ongoing diversification of life courses in Western societies. In the life course
framework not only the occurrence and frequency of events matter but also their
timing, duration and sequence. Since the focus is on cumulative disadvantage we
view the long-term impact of the combination of life course risks on peoples’ career
outcome. Since most people are exposed to one to three lifecourse risks and few
people to more than three risks during the observation period many combinations
tend to have very few observations. For that reason we did not examine the impact
of all possible combinations but viewed the impact of exposure to a variety of single
risks (single risk model) as well as to the combination or sum of risks over the
observation period (combination or multiple risk model).
The specification of the model
The formal (empirical) model according to this framework results in the formulation
of a single risk (1) and combination or multiple risk model (2):
10
Yit   0   1 zit   2 zit2  1 X it   2Cit  1sit Eit   2 sit Sit  i   it ,
E
S
e 1
s 1
Yit   0   1 zit   z  1 X it   2Cit  1sit  Eit   2 sit  Sit  i   it ,
2
2 it
(1)
(2)
where Yit represents the outcome variable being either income security or
employment security and where the two zit terms are derived from the Mincerian
human capital framework (Mincer, 1974). They measure the time varying human
capital variables such as age and age squared. The quadratic term makes the
equation non-linear with respect to zit. The vector X it contains the other time
varying human capital variables such as tenure, skill level, occupational class and
social class and the Cit vector the controls (age, age squared, gender, household
size, number of children, unemployment rate). The sit measures our skill status
variable which is interacted with each single event Eit and status Sit (single risk
model) but also with the sum of all life course events Eit and life course statuses
Sit (multiple risk model) to account for accelerating or cumulative effects over time,
where E and S point to the total number of events and statuses. The model can be
estimated as a fixed effects panel regression model in which the i represents the
unobserved heterogeneity part and finally  it the
disturbance
term. The
specification is a modified version of the status-resource interaction model
formulated by DiPrete & Eirich (2006). Models are estimated with the main effects
of disadvantage, represented by the various single risks, and the inclusion of
interaction terms between low skill and the single risks. For the ‘multiple risk model’
we add interaction terms between low skill and the number of events and statuses.
We focus on young people between the age of 16 and 35 and we ran separate
models for young males and females since we judged their career trajectories to
be very different for various reasons. One of these reasons are the differences in
career preferences (see Hakim, 2002) and caring duties by gender because of
which women face higher risks on fragmented careers and intermittent spells of
unemployment and of ending up in so-called fragmented or ‘patchwork careers’
(Mills & Blossfeld, 2006; Fenton and Dermott, 2006).
11
5.
Descriptive results
In this part we first present some descriptive evidence on the exposure of young
people to various single career or life course risks (single risk) and the combination
or accumulation of risks (multiple risk) by skill-level and gender.
Single risks by skill-level
To obtain a first insight into the exposure to disadvantage we present in Table 1
evidence on a variety of (in)security indicators: employment insecurity (>=39
weeks unemployed in last year), income insecurity (household income below 60%
of median household income), low pay (wage below 2/3 of median gross wage),
subjective job insecurity (score lower than 6 on scale from 1 to 10) and low
subjective well-being (below 4 on scale from 1 to 7). The low-skilled young
generation is more exposed to employment and income insecurity, low pay, and a
low SWB compared to the intermediate or high-skilled. Further, two remarkable
observations can be made. First, subjective job insecurity of the low skilled is about
equal to that of the high skilled which might be related to the low employment
protection levels (low EPL) of British workers. Second, three in four low-skilled
young females are exposed to low pay, meaning that their wage is below twothirds of the median wage of all male and female workers. Low skilled young
females also exhibit a much higher level of household income insecurity but also
employment insecurity. Two-thirds of the low skilled females is employment
insecure against only one in four of the high skilled.
Table
1
also
shows
the
prevalence
of
particular
events
such
as
unemployment, disability (health limitations at work), divorce or separation,
dismissal, a drop in health of two points of more on a scale ranging from one to
ten, a move into a non-standard job, which can be a temporary job or a move into
self-employment as an own account worker and the birth of a child.
12
Table 1: Descriptive information on outcome and risk
variables by skill-level
Skill-level (N=51,950)
Employment insecurity
(>=13 weeks unemployed)
Income insecurity
(<60%median eq. HH income)
Subjective job security
(<6)
Low-pay
(<0.67 median gross wage)
Low SWB (<=4)
Events
Unemployment event
Disability event
Health drop
Divorce/separation
Childbirth
Laid-off last year
Entry non-standard job
Statuses
Disabled (‘health limits work’)
LT Unemployed last year
Underworked
Training employer
Temporary job
Lone parent
Single
Gender
Low
Medium
High
All
Male
Female
0.39
0.66
0.31
0.41
0.18
0.24
0.29
0.39
Male
Female
0.36
0.58
0.19
0.27
0.11
0.14
0.20
0.28
Male
Female
0.25
0.21
0.23
0.19
0.26
0.22
0.24
0.20
Male
Female
Male
Female
0.27
0.74
0.28
0.36
0.27
0.50
0.25
0.24
0.08
0.21
0.23
0.20
0.21
0.42
0.25
0.24
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
0.07
0.08
0.05
0.05
0.07
0.08
0.01
0.02
0.08
0.11
0.05
0.02
0.07
0.07
0.04
0.07
0.03
0.03
0.06
0.07
0.01
0.01
0.05
0.07
0.03
0.01
0.08
0.09
0.03
0.07
0.02
0.03
0.06
0.06
0.01
0.01
0.07
0.08
0.02
0.01
0.06
0.07
0.04
0.07
0.03
0.04
0.06
0.07
0.01
0.01
0.06
0.08
0.03
0.01
0.08
0.08
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
0.12
0.13
0.10
0.04
0.34
0.37
0.13
0.07
0.08
0.05
0.04
0.22
0.15
0.08
0.06
0.07
0.02
0.01
0.24
0.24
0.22
0.19
0.11
0.10
0.03
0.10
0.19
0.13
0.05
0.07
0.02
0.01
0.17
0.14
0.37
0.36
0.08
0.09
0.01
0.06
0.21
0.15
0.07
0.08
0.03
0.01
0.24
0.23
0.25
0.22
0.10
0.09
0.03
0.11
0.19
0.13
Source: BHPS, 1991-2008
The prevalence of disadvantaged statuses
The incidence of these events is very low and ranges from less than one per cent
for divorce or separation to eight percent for females experiencing an
13
unemployment event or a health shock. The low skilled are two times more likely
to experience an unemployment, job displacement or disability event than the high
skilled showing high levels of inequality by skill-level in Britain.
However, the
incidence of having a disadvantaged status, as shown in Table 1, is substantially
higher, ranging from one per cent of the low-skilled females in long-term
unemployment last year, to thirty-four per cent of the low-skilled females being
underworked. Again, the likelihood of a disadvantaged status is far more
pronounced for the low-skilled compared to the high skilled. The likelihood of
becoming disabled (health impairment at work) is twice as high for the low-skilled
young men but in the case of long-term unemployment even five times as high for
this group. Low-skilled youngsters are also much less likely to receive a training
compared to the high skilled. Low-skilled females are five times less likely to
receive training than high-skilled females.
Multiple risks by skill-level
In Figures 2a and 2b we show the proportion of people combining two or more
events or three or more disadvantaged statuses such as an unemployment event
and a health drop, or working in a non-standard job, being underworked and
receive any training.
0.7
0.7
Males Low skill
0.6
Females Low skill
0.6
Females Medium skill
Males Medium skill
0.5
0.5
Females High skill
Males High skill
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
Cum LCE
(.=2)
Cum LCS
(>=3)
Cum LCR
(>=2,3)
Cum LCE
(.=2)
16-34
Cum LCS
(>=3)
Cum LCR
(>=2,3)
35-59
Cum LCE
(.=2)
Cum LCS
(>=3)
16-34
Cum LCR
(>=2,3)
Cum LCE
(.=2)
Cum LCS
(>=3)
Cum LCR
(>=2,3)
35-59
Figure 2: Cumulative life course risks (LCEV=events; LCST=statuses; LCR=risks)
by age, gender and skill-level
14
Most people experience any adverse event but when they experience an event,
most of the time it is the only one. Only 3% of young men and 5% of young women
experience 2 or more adverse events. For the low skilled it is about one per cent
more. The prevalence of multiple disadvantaged statuses is however much higher.
About one in three young men are exposed to three or more disadvantaged
statuses but among the low-skilled men the percentage is 10% higher. The young
low-skilled women however fare much worse over the career. More than 60% of
the low-skilled young women are exposed to at least three disadvantaged statuses
or multiple risks whereas this percentage is much lower for the low-skilled
generations after them, those between 35 and 60 years of age (less than 40%).
6.
Single and multiple risk model estimation results
The results of the GLS fixed effects panel regression model are presented in Table
2 to 4. In Table 2 and 3 we report on the findings of the single-risk models for the
life course events and the statuses, respectively. In Table 4 the findings for the
multiple-risk models are presented. The fixed-effects models study the factors
which explain the changes in security levels over time and, hence, over peoples’
careers as a function of changes in human capital endowments, changes in
employment or marital status and the experiencing of a multiple set of events.
Only time varying variables are withheld in the model. This implies that we correct
for unobserved heterogeneity caused by the impact of time constant unobserved
factors such as motivation, efforts, personality traits etc. These effects are
captured in the individual effects term of the model and separately estimated as
one of the variance components (σμ). Since we have data over eighteen years, the
youngsters at 16 years of age in 1991 are followed over their career up to the time
that they become thirty-five. For the seventeen years old and so forth the
observation period is shorter. The results confirm largely the picture obtained from
the descriptive results.
15
Single risk models
Table 2 presents the single risk model. The social and economic values exert hardly
any significant effect on employment and income security. There is a positive
employment and a negative income security effect of economic values (such as
striving for success in the career) for women. Social values (importance of family,
friends and children) have only a small negative effect on women’s employment
security. The human capital variables show strong negative effects, such as the
variable for low-skill and medium-skill, but also of job tenure on particularly
employment security. The latter finding is remarkable since it means that longer
tenure negatively affects the employment security of young males and females.
The investments in skill formation do apparently pay-off for the career with a view
to the higher level of income and employment security.
Life course events
The main effects of events on income and employment security show a mixed
picture. The experience of a disability event has a negative effect on employment
security but only for men and not for women. For young women an unemployment
event in the last year might even improve their employment security, because they
quickly regain employment compared to not-working women. Most young men and
women recover very quickly from being laid-off in the previous year by finding new
employment. Further inspection shows that the effect is negative for those who did
not regain employment and became unemployed. Childbirth has a negative effect
on young women’s employment security and especially on young women’s and
men’s income security.
Interactions of events with low skill
Most of the interaction effects of the events with low skill, shown in Table 2, are
insignificant except for the negative interaction effect of an unemployment event
on young males’ employment security and the positive interaction effect of
childbirth on income security. The negative interaction effect of unemployment
combined with an insignificant main effect in the males’ model merely shows that
the low skilled are more likely to experience such an event but not that the effects
are accelerating.
16
Table 2: Estimation results of single risk GLS panel regression
model on employment and income security of young people 16-34
years
Model I. Events interaction model
Employment security
Income security
Males
Females
Males
Females
Values
Social values
-0.375
Economic values
0.201
Human capital (ref.: high skilled)
Low skill
-3.114
Intermediate skills
-4.657***
Tenure current job
-0.454***
Events
Disability event
-1.088
Health drop
0.386
Unemployment event
0.208
Laid-off last year
1.643**
Entry non-standard job
-0.406
Divorced/Separation event
0.254
Child birth
-0.143
Interactions: Low-skill*Events
(ref. med/high skilled)
Events
Disability event
-1.425
Drop subjective health
0.279
Unemployment event
-3.351*
Being laid-off last year
-0.135
Entry non-stand. Job
-0.707
Divorce/separation event
-2.64
Child birth
-0.657
Constant
-64.278***
R2
0.173
N
22193
σμ
14.978
σє
12.894
Ρ
0.574
-0.708*
1.392***
0.015
-0.001
-0.02
-0.027
-8.448***
-7.005***
-0.579***
-0.212*
-0.302***
-0.003
-0.393***
-0.539***
-0.008**
0.434
0.436
2.899***
2.884***
-0.417
0.781
-4.450***
-0.059
-0.031
-0.082**
0.052
-0.003
0.520***
-0.512***
-0.003
-0.015
0.012
0.067
-0.077***
-0.440***
-0.367***
-0.674
-1.399
1.437
4.577
2.654
-2.838
0.662
-37.789***
0.113
26575
17.711
15.734
0.559
0.062
-0.059
0.116*
-0.066
-0.034
-0.272
0.224***
-1.929***
0.07
22372
1.238
0.927
0.641
0.01
0.006
0.031
0.167
0.044
0.153
0.218***
-0.592
0.096
26791
1.217
0.886
0.654
Source: BHPS, 1991-2008
The positive interaction effects of childbirth on income security combined with the
negative main effects show that low-skilled young people are compensated at least
partly for the initial negative income effects later in the career (declining
disadvantage).
Disadvantaged statuses
Most of the effects, shown in Table 3, are significant and strongly negative such as
for unemployment, disability and lack of employer-related training. However, with
17
one exception, that is for young women employed in a non-standard job.
Especially, among young women, a temporary job seems to be salient for
employment security compared to women not working, possibly because women
have low chances to obtain secure employment. Divorced or separated women are
able to improve their employment security possibly because they need to work for
their living after divorce but their household income security is negatively affected.
Unemployed, underworked and disabled young men and women show a significant
lower level of employment and income security over their career than youngsters
at work or youngsters working the hours they prefer.
Interactions of disadvantaged statuses with low skill
The interaction effects with disadvantaged statuses show again that most of the
interaction effects are insignificant or positive, except for the negative interaction
effect of low skill with in-living children for women’s employment security. Low
skilled young women with children face cumulative disadvantage with a view to
their
employment
security
chances
later
in
the
career
(main
effect
-
9.543+interaction effect -3.971=-13.151 weeks employed). However, they appear
able to recoup from the initial disadvantage in terms of household income security
later on in the career but only partly (main effect -0.306 +interaction effect
0.162=-0.144 or -14.4%). Hence, young women never fully recover from the
reduced income security after divorce. Positive effects are found for young men
lacking employer-related training showing that the initial disadvantage with respect
to employment security is partly compensated later in the career (-3.885+1.237=2.648 weeks). They also fully recover from the reduced income security due to lack
of training (-0.088+0.150=+0.062 or +6.2%). For young females the reduced
employment security (-6 weeks) and income security (-7%) due to lack of training
is not compensated later in the career. Again, the positive interaction effect with
a non-standard job shows that the low-skilled young women are for their
employment security very much dependent on non-standard work either as
temporary worker or as freelance or own-account worker.
18
Table 3: Estimation results of single risk GLS panel regression
model on employment and income security of young people 16-34
years
II.
Statuses interaction
model
Employment security
Males
Females
Values
Social values
Economic values
Human capital(ref.: high skilled)
Low skill
Intermediate skills
Statuses:
Tenure current job
Bad health
Div/separated (ref. married)
Long-term unemployed last year
Disabled
Underworked
No employment-related training
Have kids
Non-standard job
Lone parent
Single person
Interactions: Low skill*Statuses
Bad health
Divorced/Separation Status
LT unemployed last year
Disabled
Underworked
No Training
Inliving children
Non-standard job
Lone parent
Single person
Constant
R2
N
σμ
σє
Ρ
Income security
Males
Females
-0.302
0.116
-0.258
0.797**
0.025
-0.019
-0.008
-0.049**
-2.691*
-2.625***
-5.929***
-3.669***
-0.421***
-0.208***
-0.346***
-0.288***
-0.319*** -0.427***
-0.831
-0.787
0.499
2.985***
23.806*** -17.818***
-2.705***
-0.445
-5.326*** -7.549***
-3.885*** -5.656***
-1.176*** -9.543***
4.136***
6.830***
0.066
-2.123**
-3.570*** -3.784***
0.001
-0.026
0.264***
-0.002
-0.01
-0.306***
-0.267***
-0.145***
-0.088***
-0.097***
-1.068***
0.019
0.065
-0.693***
-0.221***
-0.059**
-0.119***
-0.066***
-1.090***
-0.032
-0.313***
-1.004***
0.031
-0.128
0.096
0.07
0.066*
0.150***
0.344***
-0.074
0.002
0.343***
-0.065
0.230**
0.102
0.111**
0.111***
0.062
0.162**
0.023
0.03
0.16
1.097***
0.246
27342
1.089
0.883
0.603
1.737***
0.337
32279
0.998
0.823
0.595
1.515
0.063
-5.759**
-4.053
3.078*
6.289***
-0.749
-0.434
-0.24
3.120***
1.237*
-1.231
-0.5
-3.971**
-3.181***
2.978**
-1.363
0.783
0.494
0.179
88.249*** -38.631***
0.422
0.33
27307
32243
12.024
14.681
12.772
15.283
0.47
0.48
Note: The models are estimated with inclusion of controls: age, age squared and annual unemployment rate
interacted with periood
Source: BHPS, 2001-2009
19
Multiple risk models
Table 4 summarizes the findings of the multiple-risk models. The accumulation of
life course events exerts a negative effect on income security of young males and
females but the interaction effects with low skill are positive suggesting declining
disadvantage. They are smaller in size and therefore do not fully compensate for
the initial income loss. The effects of the accumulation of disadvantaged statuses
on income and employment security are strongly negative. The interaction effects
with low skill are negative for young men’s employment security, meaning that
the initial disadvantage of the low skilled (-3 weeks) is aggravated (with another
-1.5 week). The positive interaction effects on income security show once again
that the initial disadvantage of the low skilled is declining over the career. The
multiple risks model combining the cumulative events and statuses model confirm
cumulative disadvantage on low skilled men’s employment security but reduced
disadvantage on income security.
Whereas the single risk model highlights
continuous disadvantage, the multiple risk model indeed highlights cumulating
disadvantage for low-skilled young men.
Table 4: Estimation results multiple-risk GLS panel regression models
on employment and income security, youngsters 16-34, BHPS
Employment security
Model I. Events
EVENTS
Lowskill*EVENTS
R2
Model II:
Statuses
STATUSES
Low
skill*STATUSES
R2
Model III: Risks
RISKS
Lowskill*RISKS
R
2
N
Income security
Male
Female
Male
Female
0.186*
-0.149
-0.059***
-0.064***
-0.25
0.19
0.028**
0.043***
0.258
0.155
0.056
0.083
-3.025***
-3.966***
-0.267***
-0.316***
-1.579***
0.53
0.134***
0.157***
0.329
0.251
0.151
0.255
-2.209***
-3.019***
-0.218***
-0.254***
-1.000***
0.441
0.112***
0.131***
0.309
0.228
0.135
0.223
27699
32555
28051
32982
Notes: * p<0.10, ** p<0.05, ***p<0.001; Models are estimated with the social and economic values, human capital variables
(low skill, medium skill, tenure) and the controls (age, age squared, interaction unemployment rate and period) included.
Source: BHPS, 1991-2008
20
7.
Conclusions
The conclusions of the paper are therefore very straightforward. Low-skilled young
British men and especially young British women are more than their better skilled
peers exposed to life course risks jeopardizing their income and employment
security over the career. Especially, low-skilled young women in Britain are
confronted with unfavourable employment perspectives and reduced income
security. However, we find no evidence whatsoever of aggravating disadvantage
over the career since hardly any significant negative interaction effects between
the events or the statuses and low skill were observed except for the employment
security of low skilled young women with children. The latter finding confirms
hypothesis 3 on the ‘scarring’ effects of disadvantage, not necessarily being
cumulative, and the gender-based inequality patterns. The findings also confirm
the literature, but contradict the first hypothesis on cumulative disadvantage, since
for most of the low-skilled young people on the labour market, the career
perspectives are characterized by continuous disadvantage. Peoples’ life chances
seem more affected by persistent sources of inequality embedded in social
stratification processes in society than by an accelerating likelihood of becoming
disadvantaged over the career due to a heightened exposure to life course risks
exerting an cumulative negative impact on people’s careers. This confirms
hypothesis 4 that most of the career effects are additive and not multiplicative.
However in the final part our multiple risk models show that the picture becomes
different if we depart from the single risk framework and consider the effects of
the accumulation of adverse life course events and statuses in a multiple-risk
framework. Exposure to a combination of various disadvantaged statuses results
for the low-skilled young men and women in a mixed picture of cumulative
disadvantage on employment security and continuous disadvantage on income
security.
The latter results provide various challenging avenues for public policy and
further scrutiny. The current crisis appear to have affected especially the position
of youngsters on the labour market, but in general seem to have widened the
dispersion in employment and income security or inequality, asking for public policy
to tackle the insider-outsider cleavage many countries confront. The methodology
21
adopted here using long-running panel-data appears particularly valuable for
evaluating the causes and consequences of the current crisis. The data of the
British Household Panel Study and the much larger “Understanding Society” data
provide a rich and unique data source to study these issues in further detail also
for the years after 2008 and to provide a better understanding of the causes and
severe consequences of the crisis for society.
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