Embed Size (px)
Citation preview
Job insecurity and life satisfaction: The
moderating influence of labour market
policies across Europe
Ewan Carr1*
Social Statistics, University of Manchester
ABSTRACT
This paper tests whether the association between perceived job insecurity
and life satisfaction is moderated by the generosity of national labour market
policies in Europe. Job insecurity is thought to influence well-being by
motivating anxieties about (i) the difficulties of finding another job and (ii)
alternative sources of non-work income. These components are related to
active and passive labour market policies, respectively. Generous policy
support is hypothesised to buffer the experience of job insecurity by lowering
the perceived difficulties of finding a similar job (active LMP) or by
guaranteeing a minimum income (passive LMP). The study combines data for
22 countries from the 2010 European Social Survey with contextual
information from Eurostat and the OECD. Based on a multilevel path analysis,
initial support for this hypothesis is found. Perceived employment insecurity
is negatively associated with life satisfaction but the strength of the
relationship is inversely related to the generosity of national LMP. The
harmful consequences of perceived insecurity are greater where LMP
expenditure is lower or where unemployment benefits replace a smaller
proportion of in-work income. No effect is found for employment protection
legislation (EPL). The analysis also highlights considerable heterogeneity in
the moderating influence of LMP. Whereas vulnerable workers are protected
by generous policy support, other groups see no effect. The limitations of the
study and directions for future research are discussed.
2
Introduction
Research has consistently shown perceived employment insecurity to be negatively
associated with individual health and well-being (e.g. Sverke, Hellgren, and Naswall
2002; De Witte 2007; Burchell 2005; Cheng and Chan 2008), and there is good
evidence indicating that labour market policies are themselves associated with
perceptions of labour market insecurity (e.g. Anderson and Pontusson 2007; Pacelli et
al. 2008; Clark and Postel-Vinay 2009). The moderating potential of labour market
policy, by contrast, has received little attention. Combining survey data from the 2010
European Social Survey (ESS 2010) with contextual information from the OECD and
Eurostat, this study tests whether the association between employment insecurity and
life satisfaction stronger in countries with less generous LMP provision.
Employment insecurity is an important research topic, not only because of the harmful
consequences for well-being2, but because many more employees worry about losing
their jobs than actually lose them (Dickerson and Green 2006). As Burchell (1994)
shows, the gap in psychological well-being between secure and insecure employees is
typically about the same size as the gap between employed and unemployed persons.
However, while perceived job insecurity is both harmful and widespread, particular in a
recession, not everyone is affected equally. At the individual-level, factors such as
social support (Lim 1996), family arrangements (Ertel, Koenen, and Berkman 2008) or
job control (Büssing 1999) can all moderate the experience of employment insecurity.
Fewer studies have considered contextual moderators, but it has been suggested that
economic conditions (such as unemployment or GDP growth) can also play a role (Carr,
Elliot, and Tranmer 2011). The scarcity of studies investigating contextual moderators
of insecurity is unfortunate, because research shows significant cross-national variation
in both the prevalence of employment insecurity (e.g. Böckerman 2004; Erlinghagen
2008) and the subsequent outcomes. As this study highlights, the country in which you
live can determine your experience of employment insecurity, even afters controlling for
individual characteristics. This has important implications for policy-making. While
cross-national variation in employment security is itself interesting, to be instructive for
policy requires some unpacking. What is it about living in a particular country that
explains the moderating effect? Why do employees in Scandinavia, when faced with a
similar threat, subsequently report lower levels psychological ill-health, compared to
those in Southern Europe?
This paper considers labour market policies (LMPs) as one potential moderating factor.
The study is in 4 parts. First, a discussion of the theoretical and analytical model is
given, detailing what is meant by ‘employment insecurity’ and the ways in which this is
2 Individuals who think they are likely to lose their job in the coming year are more likely, on
average, to report heightened anxiety and depression (Roskies, Louis-Guerin, and Fournier 1993;
Orpen 1993), impaired psychological well-being (De Witte 1999; Friesen and Sarros 1989;
Wilson, Larson, and Stone 1993; Kuhnert, Sims, and Lahey 1989) and problems sleeping
(Arnetz et al. 1988; Ferrie et al. 1998; Cobb and Kasl 1977; Withington 1989).
3
thought to relate to LMP. Second, an overview of the data and methods used, as well as
the measurement of the core concepts. The findings are presented third, before
concluding with a discussion.
Defining employment security
Job insecurity is a multidimensional concept that goes beyond the fear of imminent job
loss. Past studies have distinguished between ‘objective’ and ‘subjective’ or ‘cognitive’
and ‘affective’ dimensions. Objective insecurity refers to positions that are inherently of
limited duration, by the nature of the job (Witte and Naswall 2003; Pearce 1998). Such
contractual forms of insecurity include temporary, fixed-term or agency positions.
Subjective job insecurity, on the other hand, captures the individual’s own expectations
about becoming unemployed, the loss of job features (such as content, autonomy or
hours) and the consequences these changes may have. As Greenhalgh and Rosenblatt
(1984) put it, subjective job insecurity is derived from the objective threat “by means of
the individual’s perceptual processes, which transform environmental data into
information used in thought processes” (p. 440).
A further distinction can be made between ‘affective’ and ‘cognitive’ insecurity (Ashford
1989). Cognitive insecurity is the individual’s estimate of the probability they will lose
their job in the near future, whereas affective insecurity refers to worries or anxiety
about becoming unemployed. As Anderson and Pontusson (2007) put it, “affective job
insecurity boils down to the question ‘What happens to me (and my family) if I do lose
my job?’” (p. 4). A related distinction can be made between job insecurity and
employment insecurity, where the former is concerned with security in the current job
and the latter with employability in the wider labour market (Chung and Oorschot 2010,
7). This is an important distinction. An individual may think that job loss is imminent,
but if they feel confident of finding another position quickly, the consequences are likely
to be moderate. By contrast, for someone who thinks they are about to lose their job
and is pessimistic about finding another one – perhaps due to age, experience or skills
– the consequences are likely to be more severe. The term ‘employment insecurity’ is
often used interchangeably with the term ‘labour market insecurity’ (e.g. Anderson and
Pontusson 2007). For this discussion, both terms are understood as the extent to
which an individual can maintain a continuous employment career, which may include
changing employers or jobs (Pacelli et al. 2008; Dasgupta 2001). This will be
determined by both individual employability (e.g. skills, experience) as well as economic
and labour market conditions (e.g. unemployment or economic growth).
To date, most studies have considered cognitive employment insecurity, often referred
to simply as ‘job insecurity’ (e.g. Wichert 1999; Heaney, Israel, and House 1994). This
is perhaps because most large social surveys, if they address employment security at
all, tend only to include questions capturing the ‘perceived likelihood of becoming
4
unemployed in the near future’3, or the extent to which the respondent feels ‘their job is
secure’ (e.g. the 2004 and 2010 ESS). This study adopts a hybrid approach. As Chung
and Oorschot (2010) note, cognitive measures of employment insecurity are
problematic insofar as they include individuals who might lose their current job, but will
easily find another one. As they argue:
We believe that there is a problem categorizing this group of people as insecure,
for they will not go through a period where they are actually unemployed (p. 8).
For people to feel truly insecure, it follows, they must anticipate experiencing a non-
negligible period of unemployment. Consequently, this study operationalises
employment insecurity as workers who (a) do not feel that their job is secure, and (b)
think it would be difficult to find a similar job, were they to become unemployed.
Affective insecurity is addressed indirectly, as the well-being consequences of cognitive
insecurity. This presumes that the only mechanism by which cognitive insecurity can
influence subjective well-being is by motivating thoughts, worries and anxieties about
what job loss would entail, and that it is these concerns that are negatively associated
with well-being, (rather than the perceived probability of unemployment itself). Cognitive
insecurity, following this view, has no tangible manifestation (in the way that, say,
unemployment incurs a financial loss or change of routine). Rather, it is a subjective
assessment that leads to worries about the future (i.e. affective insecurity). Cognitive
insecurity does not exist outside the consciousness of the individual until it is
connected to the anticipated consequences (it is this connection that has such harmful
consequences). In practice, however, cognitive and affective insecurities are
inseparable. When evaluating the likelihood of job loss an individual will inevitably think
of the consequences (i.e. the connection referred to above is immediate).
Given this definition, affective insecurity is conceptualised here as the relationship
between cognitive insecurity and subjective well-being. This becomes important later,
when considering the moderating effect of LMP. These models regress the slope of the
association between cognitive insecurity and well-being on LMP. This is equivalent to
testing the association between affective insecurity and LMP.
Employment insecurity and labour market policy
This section sets out the theoretical basis for the study: why are labour market policies
expected to moderate the consequences of employment insecurity? At the outset it is
useful to distinguish between ‘direct’ and ‘moderating’ effects. Direct effects refer to
the association between LMPs and employment insecurity (i.e. insecurity is the
dependent variable) whereas the moderating effect is the extent to which LMPs change
the strength or direction of the association between insecurity and subjective well-being
(i.e. well-being is the dependent variable). Most existing studies emphasise the former,
3 This type of question is available in the European Working Conditions Survey (2005), the 2006
European Social Survey (2006) and the General Social Survey (Smith et al. 2008).
5
treating insecurity as the dependent variable and giving no explicit consideration to
subsequent outcomes (the connection is assumed, but not tested empirically). These
studies test whether national policy is able to explain individual variations in
employment insecurity. This section reviews this literature but also considers the
theoretical basis for the moderating effect.
First, it is necessary to define what is meant by ‘labour market policy’ (LMP). The
European Union defines LMPs as “public interventions in the labour market aimed at
reaching its efficient functioning and correcting disequilibria and which can be
distinguished from other general employment policy interventions in that they act
selectively to favour particular groups in the labour market” (Eurostat 2002, 4).
According to Robinson (2000) labour market policies as seek to balance 3 distinct
objectives: (i) the reduction of unemployment and inactivity; (ii) the reduction of public
expenditure or the costs of ‘welfare dependency’; and (iii) the reduction of income
poverty (p. 14). Data on LMP expenditure typically differentiate between active and
passive measures. Active labour market policies are interventions aimed at “the
improvement of the beneficiaries’ prospect of finding gainful employment or to
otherwise increase their earnings capacity” (OECD 2007, 14). This includes measures
to improve access to the labour market for the unemployed (Powell and Barrientos
2004, 88), investment in skills and qualifications (Rønsen and Skarðhamar 2009, 62)
and subsidised employment (Moncel, Ruivo, and Fravega 2004, 59). Passive labour
market policies include unemployment compensation schemes (unemployment
benefits, redundancy and bankruptcy compensation) and programmes for early
retirement (Moncel, Ruivo, and Fravega 2004, 58; OECD 2011). In summary, active
policies are those that help the inactive or unemployed into work whereas passive
policies seek to guarantee a minimum income for these individuals during
unemployment.
When considering the institutional determinants of employment insecurity it is
necessary to differentiate between ‘cognitive’ and ‘affective’ forms, since the
determinants of each will differ. Following Anderson and Pontusson (2007), affective
insecurity (the perceived consequences of job loss) can be decomposed into two
discrete variables: (i) the prospects of findings another similar job and (ii) access to
sources of income that are not related to employment. This is consistent with the notion
of ‘job dependency’, defined by Greenhalgh and Rosenblatt (1984) as a function of
occupational mobility (the ability to find a similar job) and income insecurity (“the
inability to meet living expenses without the income from the current job”) (p. 445).
These twin components of affective insecurity can be related to active and passive
LMPs, respectively.
Active labour market programmes aim to lower the barriers to re-employment, thus
reducing the perceived difficulties of finding another job. Essentially, this implies that
individuals’ perceived chances of finding a similar position is a function of the
effectiveness of activation schemes (e.g. training, counselling or job creation) and their
awareness of such schemes. Employees will worry less about the prospect of job loss if
they feel confident that effective activation support is readily available.
6
Income insecurity, the second component of job dependency, is thought to be
influenced by passive labour market policies (most notably, unemployment benefits).
Individuals worry about unemployment because of the loss of income this will incur.
Passive LMPs will reduce this worry insofar as they provide a replacement income
during unemployment. The more generous the benefit (i.e. the greater the proportion of
in-work income that is replaced) the less an individual will worry about unemployment,
since the consequences (e.g. the reduction in living standards) will be less severe.
Active and passive LMPs can be readily linked to the twin components of affective
insecurity, but the effect on cognitive insecurity is less clear. Simply put, there are few
plausible mechanisms by which active and passive LMPs, which typically target the
unemployed, can motivate the perceived risk of unemployment for current employees.
This is a conceptual argument, since it is hard to see how cognitive or affective
insecurities could exist in isolation, but is important nonetheless. Existing research
showing a link between LMP and ‘job insecurity’ tends to conflate cognitive and
affective forms (e.g. Lollivier and Rioux 2006). For example, the theoretical justification
cites affective insecurity but the empirical analysis is based upon cognitive insecurity.
Given the strict definition of cognitive insecurity as a probability, it it hard to justify a link
to active or passive policies, which benefit the unemployed. Rather, individual
assessments of cognitive insecurity are likely to depend on economic conditions
(e.g. the unemployment rate, or economic growth) or individual circumstances
(e.g. performance, skills or status).
There are two possible exceptions. The first is employment protection legislation (EPL),
an indicator that measuring regulations governing the ease with which employers can
hire and fire workers (OECD 2004). EPL can plausibly influence cognitive insecurity
because it will determine the ease with which employees can fired (i.e. cognitive
insecurity is likely to be inversely related to the strictness of employment legislation).
However, this naive interpretation is problematic. EPL is often limited to permanent
employees so can have a polarising effect. As Boeri, Börsch-Supan, and Tabellini (2001)
put it, “EPL concentrates the unemployment risk among outsiders” (p. 21). Stricter EPL
has also been associated with prolonged unemployment (Nickell 1997) and higher use
of temporary contacts (e.g. Dolado, García-Serrano, and Jimeno 2002; Polavieja 2006).
EPL might therefore increase affective insecurity, because by protecting the positions of
the currently employed, it makes it harder for labour market outsiders to find work (e.g.
Gautié 2011). It is plausible, therefore, that EPL might increase affective insecurity by
making it harder to find alternative employment.
A second plausible exception arises from ‘prospect theory’, as developed by Kahneman
and Tversky (1979), and specifically the notion of a ‘weighting function’ (p. 280). Put
Cognitive insecurity Affective insecurity
LMP
EPL
7
crudely, this states that the estimated probability of an event is not independent from
the expected gains or losses of such an event. Instead, the estimated probability
increases inline with the anticipated losses, such that individuals overestimate the
probability of severe outcomes and underestimate more minor consequences. With
regard to employment insecurity, this would imply that the supposed separation of
cognitive and affective insecurity is not only problematic in practice, but also is flawed
conceptually. Individuals who perceive job loss to be more harmful, following this idea,
are likely to overestimate the probability of losing their job. Therefore, since LMPs can
influence the perceived consequences of job loss (i.e. affective insecurity), they might
also influence cognitive insecurity, due to the non-independence of perceived
probabilities and perceived outcomes.
Having reviewed the mechanisms by which LMPs can influence employment insecurity,
both cognitive and affective, it would be useful to review existing evidence. Overall,
there is good evidence to support the direct association between labour market policies
and employment insecurity, although some studies find no effect and others emphasise
the economic climate, rather than the institutional framework, as the key determinant.
Using data for 15 OECD countries Anderson and Pontusson (2007) find more protective
employment legislation be associated with lower levels of cognitive insecurity, while
expenditure on active LMPs are associated with more positive assessments of
alternative employment prospects. Their findings support three mechanisms by which
LMPs can influence individual concerns about employment, consistent with the model
outlined above: (1) expenditure on active LMPs is shown to reduce labour market
(i.e. the ability to find another job); (2) more generous unemployment compensation
reduces worries about the income lost during unemployment (p. 18); and (3) more
restrictive employment protection legislation is associated with reduced levels of
cognitive job security. Notably, they find LMP expenditure to have no effect on cognitive
job insecurity.
Pacelli et al. (2008) find generous unemployment benefits (UB) to be positively
associated cognitive job security, although based on aggregate data. Clark and Postel-
Vinay (2009) find job insecurity to be negatively associated with the strictness of EPL,
and positively associated with the generosity of unemployment benefits, but only for
private sector workers. Based on data for 22 European countries, Chung and Oorschot
(2010) show the generosity of both passive and active labour market policies to be
negatively associated with perceived employment insecurity4. Finally, using the French
sample of the ECHP, Lollivier and Rioux (2006) found that workers felt more secure
when unemployment benefit levels are more generous.
Other studies are less supportive. A multilevel analysis of the 2004 European Social
Survey by Erlinghagen (2008) shows no association between social security spending
4 The study by Chung and Oorschot (2010) adopts a hybrid measure of ‘cognitive employment
insecurity’ that incorporates both cognitive (the perceived probability of job loss) and affective
elements (the difficulty of finding alternative employment). This measure forms the basis for the
operationalisation adopted in this paper.
8
and perceived job insecurity, having controlled for unemployment rates, GDP growth
rates and EPL. Some authors have suggested that instead of institutional factors, it is
labour market and macro-economic conditions that explain individual perceptions of
employment insecurity (e.g. Chung and Oorschot 2010; Erlinghagen 2008).
The moderating effect of labour market policies
Very few studies have explicitly considered the potential for labour market policies to
moderate employment insecurity, rather than predict it. In some ways, this doesn’t
matter. Affective insecurity was conceptualised above as the relationship between
cognitive insecurity and subjective well-being. Following this approach, ‘affective
insecurity’ is synonymous with ‘the consequences of cognitive insecurity’, and so our
understanding of the moderating effect of labour market policies can drawn upon the
existing literature on affective insecurity. In other words, LMP is expected to moderate
cognitive insecurity for the same reasons it is expected to predict affective insecurity.
One of the few studies to explicitly consider the moderating effect of LMPs is provided
by Burchell (2009), who tests whether national flexicurity 5 policies moderate the
association between cognitive insecurity and psychological well-being. Using data from
the European Working Conditions Survey (EWCS) and European Social Survey, Burchell
finds little evidence of any moderating effect. With few exceptions, the correlations
between subjective well-being and perceived job insecurity were no higher in countries
with higher levels of flexicurity policies than those that lacked flexicurity6.
The analytical model
Although this paper focuses upon the moderating effect of LMP it is necessary, for the
reasons discussed above, to take various direct and mediating pathways into account.
Figure 1 illustrates the overall model, labelling 8 key paths. ‘Between’ refers to
variables and relationships that operate at the country-level, whereas ‘within’ refers to
relationships varying within countries (i.e. by individual). The starting point of the study
is the negative association between cognitive insecurity and subjective well-being (path
5 Wilthagen and Tros (2004) defines ‘flexicurity’ in terms of 4 central themes. Firstly, employers
should have the flexibility to hire and fire staff in response to changes in demand and market
fluctuations. Secondly, employees must be protected from the welfare costs of such fluctuations,
so unemployment benefits must be sufficiently generous that job loss is not associated with
poverty in unemployment. Thirdly, active labour market policies should promote training and
employability to facilitate re-entry to work after job loss. Finally, flexicurity policies should be
maintained by a high level of trust and dialogue between the social partners (e.g. employers,
trade unions and government). Denmark and the Netherlands are cited as good practice
examples of flexicurity (Kok 2003), while Ireland, Italy and Spain are typically considered to be
low of flexicurity policies.
6 For the EWCS well-being was assessed using a scale measuring the perceived health
consequences of work (items included in the scale were headaches, stomach aches, heart
disease, stress, overall fatigue, sleeping problems, anxiety and irritability). For the ESS two
scales measuring ‘anxiety and depression’ and ‘restless sleep’ were considered.
9
1). As detailed below, a hybrid measure of ‘cognitive employment insecurity’ is adopted
(following Chung and Oorschot 2010), capturing both perceived job security and the
anticipated difficulties of finding similar job. This initial path is well established. A
wealth of evidence supports a negative relationship between job insecurity and
psychological or physical health (for a review, see Sverke, Hellgren, and Naswall 2002;
De Witte 2007; Burchell 2005; Cheng and Chan 2008). The contribution here is to test
whether this relationship varies cross-nationally, and moreover, whether this variation is
related to the generosity of labour market policy. This test is is represented by path 2 –
a cross-level interaction between LMP and insecurity (see below for implementation
details). Consistent with the above literature, the model also includes a link between
LMP and cognitive employment insecurity (path 3). While LMPs are most easily linked
to affective insecurity, it is hard to rule out their influence of cognitive insecurity,
particular with regard to EPL. Path 4 allows for a direct association between LMP
generosity and subjective well-being7.
Given the importance of labour market and macro-economic conditions in explaining
individual employment security (Erlinghagen 2008), path 5 allows a direct association
7 The generosity of labour market provision is likely to correlate highly with overall welfare
generosity (e.g. Scruggs 2006). Given evidence showing a positive relationship between national
welfare generosity and individual well-being (e.g. Pacek and Radcliff 2008; Kotakorpi and
Laamanen 2010) it is therefore reasonable to expect a direct association between LMP
generosity and subjective well-being.
Between
Within
1
23
4
5
6
8
7
Economic conditions
Labour market policy
Cognitive
insecurity
Age
Sex
Skills/education
Employment history
Health
Hours
Job control
Etc.
Subjective
well-being
Figure 1: The analytical model
10
between economic conditions and employment insecurity. Also, given evidence showing
that macro-economic conditions can directly influence individual well-being (e.g. Clark,
Knabe, and Rätzel 2010; Tella, MacCulloch, and Oswald 2003; Catalano and Dooley
1977; Brenner 1973) a path between economic conditions and well-being is also
included (path 6).
The remaining two paths (7 and 8) concern the influence of individual characteristics.
Past research has shown links between subjective well-being and attributes such as
age, income, marital status, education or personality (see Coombs 1991; Helliwell
2003; Kroll 2010), so these variables are included as controls. Furthermore, such
attributes can also influence employment insecurity directly (e.g. Anderson and
Pontusson 2007; Burgoon and Dekker 2010; Erlinghagen 2008). Perceived insecurity
has been linked to age (e.g. Mohr 2000; Sverke, Hellgren, and Näswall 2006; Hartley
1991), sex (Witte and Naswall 2003), employment history (Böckerman 2004), job
tenure (Green et al. 2001), industry sector (Aaronson and Sullivan 1998) and working
hours (Böckerman 2004). To reflect this, the model includes a direct path between
relevant individual attributes and perceived job insecurity.
As a further complication, the moderating influence of LMP is itself likely to depend on
the individual characteristics described above. For example, individual determinants of
employability (such as age, skills or experience) are likely to interact with active labour
market interventions, such that a weaker moderating effect is observed for more
mobile employees (i.e. those who are most optimistic about their chances of finding
another job, and so have least need for activation support). To take such ‘moderated
moderation’ effects into account the model is also estimated for various subgroups
separately
To reiterate, the primary focus of this paper is on the moderating influence of labour
market policies (i.e. path 2). Additional paths (e.g. 3, 5, 6 and 7) are included to control
for effects that would otherwise be problematic to ignore, but these relationships have
been addressed previously. This model can be condensed to the following hypotheses:
Path Hypothesis
1 Cognitive employment insecurity is negatively associated with
subjective well-being.
2
Labour market policy buffers the association between employment
insecurity and life satisfaction, such that the association is weaker
in countries with more generous provision
3 LMP generosity is negatively associated with perceptions of
employment insecurity.
4 LMP generosity is positively associated with life satisfaction
5
More favourable economic conditions (e.g. lower unemployment,
higher GDP growth) are negatively associated with perceived
employment insecurity.
6 More favourable economic conditions are positively associated
with life satisfaction.
11
Two limitations of this model are worth highlighting. First, it makes strong assumptions
regarding employees’ awareness of the level of support available. Similarly, it assumes
that eligibility and coverage is consistent across all employees in every region, sector
and workplace. Clearly, these assumptions are unlikely to universally true. Secondly, it
has been argued that financial loss is just one component of the psychological impact
of job insecurity (Burchell 2009, 368), and “with the greater affluence in more recent
times, the economic effects of unemployment are no longer the main mechanisms
accounting for the low well-being of the unemployed” (p. 368). Johada’s (1981; 1982;
1997) latent deprivation model states that employment provides not only financial
reward but also numerous latent functions, such as time structure, social contact,
collective purpose, identity and activity. Consequently, passive LMPs aimed at
maintaining income during unemployment address just one component of the
psychological impact of job insecurity. The loss of other, equally important, functions
will not be addressed.
Methodology
Data
This study uses data from the 5th round of the European Social Survey (2010)
combined with contextual information from the OECD (2012f), Eurostat (2012a) and
the European Commission (2012). The ESS is a biennial study of social attitudes and
values in Europe, with five rounds conducted between 2002 and 20108. It is suitable
here for several reasons. First, it is one of the few cross-national surveys to include
information on both perceived job security as well as the anticipated difficulties of
finding similar employment elsewhere9. Second, it includes information on ≈ 2000
individuals from 26 European countries, which is sufficient to allow the inclusion of
multiple contextual variables. Third, the fieldwork period for the 2010 survey (from mid-
2010 to early-2011) captures a volatile period in Europe, amid on-going pressures from
financial crisis of 2007, the subsequent recession, cuts to welfare expenditure and
persistently high levels of unemployment and labour market uncertainty. If labour
market policies are hypothesised to mitigate worries or anxieties about job security
then Europe in 2010/11 is a perfect test case.
The 2010 survey includes 50,781 individuals from 26 European countries. Given the
focus on perceived job insecurity this paper limits the sample to respondents for whom
information on job insecurity is available (i.e. those who are currently employed).
8 An alternative approach would be to pool multiple rounds of the ESS (e.g. 2004, 2006, 2008
and 2010). This, however, is problematic because (a) the measures of employment insecurity
are not consistent across all rounds and (b) given the huge changes in the labour market and
macro-economic environment in Europe between 2004 and 2010, the comparability of surveys
spanning these periods is questionable.
9 Earlier waves of the ESS (e.g. 2004, 2006) and other cross-national surveys (e.g. the European
Working Conditions Survey) provide information on cognitive job insecurity only.
12
Measurements
Subjective well-being, the main dependent variable, is measured using an 11-category
ordinal measure of reported life satisfaction, treated here as continuous. Respondents
were asked, “all things considered, how satisfied are you with your life as a whole
nowadays?” Responses were chosen from a card, where 0 represented ‘extremely
dissatisfied’ and 10 represented ‘extremely satisfied’.
The main explanatory variable is ‘cognitive employment insecurity’, a binary measure
which combines cognitive and affective assessments of insecurity. In 2010
respondents were asked whether the feel the statement “My job is secure” is very true,
quite true, a little true or not at all true10. As argued above, this alone is insufficient: an
individual may feel insecure (i.e. ‘not at all true’) but if they are confident of quickly
finding another job, their perceived probability of experiencing a period of
unemployment remains low. To feel truly insecure, it follows, individuals must both
perceive their job to be insecure and be pessimistic about their chances of finding a
similar one elsewhere. Fortunately the survey also asks respondents “how difficult or
easy would it be for you to get a similar or better job with another employer, if you had
to leave your current job?” Responses are chosen from a card on the range 0
(‘Extremely difficult’) to 10 (‘Extremely easy’). These two measures have been
dichotomised and combined. ‘High’ job insecurity refers to individuals who feel the
statement “My job is secure” is “not at all true” and who rate the difficulty of finding a
similar job as 2 or lower (i.e. very difficult). ‘Low’ job insecurity refers to everyone else. A
total of 1,499 respondents report ‘high’ job insecurity (approx. 8% of the sample). A
number of individual characteristics are included as controls. These include age,
gender, income, cohabitation status, divorce, subjective general health, religiosity11,
experience of discrimination, citizenship status, whether there are children in the
household, education and past experience of unemployment 12 . The models also
include a set of work-based controls13 and several measures of social capital14.
10 The questionnaire defines ‘secure’ as “the sense of an actual or implied promise/likelihood of
continued employment with that employer”.
11 A scale created by combining three items measuring religiosity: (1) Regardless of whether you
belong to a particular religion, how religious would you say you are?; (2) ‘Apart from special
occasions such as weddings and funerals, about how often do you attend religious services
nowadays?’; (3) ‘Apart from when you are at religious services, how often, if at all, do you pray?’.
All items are standardised and recoded such that higher values indicate greater religious
engagement. The Cronbach’s alpha coefficient for the scale is 0.85.
12 Has the respondent has ever been unemployed and seeking work for a period of more than
three months?
13 Work-related controls include occupational class, help from colleagues, opportunities for
advancement, variety in the content of work and a scale measuring work-family conflict.
14 The model includes a two measures of trust – both trust of other people and trust of institutions
– as well as a binary item asking respondents “Do you have anyone with whom you can discuss
intimate and personal matters?”
13
Eight measures of LMP are considered, using data from Eurostat (2012a) and the
OECD (2012f). These capture both passive and active support as well as the strictness
of employment protection legislation (see Table 1). Eurostat identifies 9 types of LMP.
For the purposes of this study, categories 2 to 7 are considered to be active
interventions. These include training, job rotation schemes, employment incentives,
supported employment and rehabilitation, direct job creation and start-up incentives.
Categories 8 and 9 are considered to be passive, and include out-of-work income
maintenance and early retirement schemes.
Three measures of passive LMP are considered: (1) public expenditure on passive
LMPs, (2) the replacement rate of unemployment benefits and (3) the typical duration
of unemployment benefits15. Following Vis (2007) and Hudson and Kuhner (2007), all
expenditure data have been standardised by the national unemployment rate (i.e. total
expenditure as a percentage of GDP × 100 divided by the standardised unemployment
rate)16. Measures of UB replacement rates are included for both the initial period as
well as longer spells of unemployment (60+ months).
Table 1: Measures of labour market policy
Type Measure Source
Passive 1. Expenditure on passive labour market policies (% of GDP) Eurostat
2. Short-term replacement rate of unemployment benefits OECD
3. Long-term replacement rate of unemployment benefits OECD
4. The ‘typical’ duration of unemployment benefits MISSOC
Active 5. Expenditure on active labour market policies (% of GPD) Eurostat
6. Activation support (participents per 100 unemployed) Eurostat
EPL 7. Employment protection legislation for regular workers OECD
8. Employment protection legislation for temporary workers OECD
Two types of active support are considered: (5) expenditure on active labour market
programmes and (6) activation support. The latter refers to proportion of the
unemployed who are engaged in labour market activation schemes (total participants
15 Using data from the European Comission’s Mutual Information System on Social Protection
(MISSOC; 2012), this measure estimates the duration of unemployment benefits for a ‘typical’
worker, based on the eligibility criteria in each country. A typical worker is set as someone who
has been in work for at least 12 months and is aged 30 to 40 (where age criteria apply). This
measure is somewhat problematic, since it fails to capture some of the more nuanced eligibility
criteria (e.g. in Switzerland and Poland the criteria take local unemployment rates into account,
while in Italy extensions apply ‘in the event of a recession’). It provides an approximate indication
of the duration of benefits, but should be interpreted with caution in later models.
16 This is a better measure of LMP effort than ‘spending as a percentage of GDP’ because LMP
spending usually increases inline with levels of unemployment (e.g. OECD 2003; Armingeon
2005). The standardised measure represents the percentage of GDP that is spent on LMP per 1%
standardised unemployment.
14
per 100 unemployed). Measures of EPL for regular and temporary workers are also
included. Finally, several macro-economic measures are considered, including the GDP
growth (% change on previous year), the unemployment rate and the average change in
unemployment (for the previous 3 years).
Methods
This study implements a two-level path analysis model with random intercepts and
random slopes. The random intercept model is needed to represent the hierarchical
structure of the data (i.e. individuals nested within countries; Tom Snijders and Bosker
2011a). A multilevel approach is required since ordinary least squares regression is
unable to differentiate between variables at different levels of analysis and, by treating
both individual and country-level statistics as if they are measured at the same levels,
overlooks the clustering of individuals within countries. Using OLS regression to analyse
contextual effects would therefore lead to conclusions based on deflated standard
errors.
A path analysis model17 refers to structural parameters that represent the hypothesised
relationships between a set of observed variables, modelled in terms of systems of
equations (Kaplan 2009, 13)18. The advantage of path analysis here is the ability to
model the mediating pathways involving multiple dependent variables. The standard
random intercept regression model (e.g Tom Snijders and Bosker 2011a, 42) models
the association between a set of explanatory variables and a single dependent variable,
precluding any relationships between the explanatory variables themselves. The
simplicity of this model is attractive, but only insofar as we can assume that the
explanatory variables are unrelated19.
Given the theoretical model specified above, such assumptions are problematic.
Perceived insecurity has been shown to depend on macro-economic conditions, the
institutional framework (e.g. LMP) and a range of individual characteristics such as age,
education or tenure. To adopt a standard multilevel model would effectively fix these
relationships at zero. A better alternative is to model these relationships explicitly, using
path analysis, simultaneously estimating the determinants of employment insecurity
and subjective well-being, as well as calculating any the indirect (i.e mediating) effects.
Despite concerns raised about estimating mediation effects with cross-sectional data
(e.g. Maxwell and Cole 2007), it is felt that the bias introduced by omitting these
17 Also referred to as a simultaneous equation model.
18 See MacKinnon (2008) for an introduction.
19 In preparation for this paper, all models were first estimated using the simpler multilevel
modelling (MLM) approach. Reassuringly, the MLM models produce results that are entirely
consistent with the MSEM models.
15
pathways altogether is larger than the bias incurred from the reliance on cross-
sectional data20.
Two features of the model warrant explanation: (i) the implementation of the mediating
pathways and (ii) the implementation of cross-level interactions. The hypothesised
model includes several mediating effects. These are indirect pathways where a third
variable is placed between an explanatory and dependent variable in the hypothesised
causal pathway. For single-level models the techniques for assessing mediation are
well established (e.g. Baron and Kenny 1986; MacKinnon et al. 2002) but these
methods are inappropriate in a multilevel context (Preacher, Zhang, and Zyphur 2011).
Subsequently several approaches have been proposed for testing mediation in a
multilevel framework (e.g. Bauer, Preacher, and Gil 2006; Kenny, Korchmaros, and
Bolger 2003; Krull and MacKinnon 2001; MacKinnon 2008; Pituch, Stapleton, and
Kang 2006; Raudenbush and Sampson 1999; Zhang, Zyphur, and Preacher 2009).
These techniques are an improvement over the single-level approach, but remain
subject to two major limitations (Preacher, Zyphur, and Zhang 2010). First, multilevel
models cannot accommodate upper-level mediators or outcome variables (e.g. a 1-1-2
or 1-2-2 design) and, relevant for this discussion, in multilevel mediation models
involving linkages between pairs of level-1 variables (e.g. in a 2-1-1 design) the ‘within’
and ‘between’ components of these are effects are typically conflated.
That is, the effect of M on Y within clusters and the effect of M on Y between
clusters are implicitly constrained to be equal (Preacher, Zhang, and Zyphur
2011, 162)
More recently, Preacher, Zyphur, and Zhang (2010) have shown that these limitations
can be overcome by using a multilevel structural equation modelling (MSEM)
framework where the ‘between’ and ‘within’ parts of all variables are separated
“allowing for an examination of direct and indirect effects at each level, as well as
contextual effects across levels” (Preacher, Zhang, and Zyphur 2011, 163). This
approach has been shown to reduce bias in contextual effects, when compared to the
group mean-centred MLM approach (Lüdtke et al. 2008; Preacher, Zhang, and Zyphur
2011). This study adopts the approach recommended by Preacher, Zyphur, and Zhang
(2010) for testing 2-1-1 mediation effects21
The second issue relates to the estimation of the cross-level interaction effects –
whereby a level-2 variable (w) influences the relationship between two level-1 variables
(x and y). In a traditional MLM cross-level interactions are implemented as the product
of level-1 and level-2 variables (e.g. the product of LMP expenditure and employment
insecurity). In a MSEM framework (and specifically, in Mplus) such interactions are
instead estimated using random slopes. A random slope for the regression of x on y
20 Moreover, these mediating pathways are a secondary concern; the primary interest is the
moderating effect of LMP (i.e. the cross-level interaction between job insecurity and LMP). The
various mediating pathways are included only because they are problematic to ignore.
21 The Mplus syntax for the model is adapted from the template provided by (Preacher 2011).
16
(denoted s) allows this relationship to vary by cluster, and s is then regressed on the
contextual variable, w. It is then possible to calculate the effect of x on y at various
levels of w.
A final issue is survey weighting. The ESS guidelines state that the “data should always
be weighted” (Survey 2011, 4), and yet the application of survey weights to multilevel
models is an unresolved research problem (for an overview, see CMM 2011), This is
particularly true when using Bayesian estimation, as in this study. There are two main
approaches: design-based and model-based inference. Following Tom Snijders and
Bosker (2011b), this study adopts the latter approach, and includes in the model as
many of design variables (variables upon which the sample design is based) as possible.
As Snijders puts it:
If the model is specified correctly given all the design variables, i.e., the
residuals in the model are independent of the design variables, then the
sample design can be ignored in the analysis (Snijders 2012, 222)
All models are estimated in Mplus 6.1 with the Bayes estimator using the default
starting values and non-informative priors22. Models were initially estimated using a
robust maximum likelihood estimator (MLR), which gave results consistent with those
obtained using Bayesian estimation.
Findings
This section is in four parts. It presents (i) descriptive statistics, (ii) coefficients from the
base model, (iii) the moderating effect of LMP and (iv) the moderating effect by
subgroups. First, some descriptive statistics showing the associations between
employment insecurity, life satisfaction and LMP generosity at the country-level.
Descriptive statistics
At the individual level, the average score for life satisfaction 6.62 (across all current
employees; N = 19,124) and 7% of these respondents report ‘high’ employment
insecurity. However, these values vary considerably across Europe, as illustrated in
Figure 2. This plots the aggregate level of life satisfaction against the proportion of
respondents reporting high insecurity (both estimates are weighted), showing a
significant negative trend: average life satisfaction is lower in countries where more
people feel insecure (a correlation of -0.65).
22 Bayesian estimation in Mplus 6 uses a Markov chain Monte Carlo (MCMC) algorithm with the
Gibbs sampler. The priors are set as follows. For the intercepts of continuous dependent
variables (e.g. life satisfaction) a normal N(0, ∞) prior is used. For variances and residual
variances of continuous dependent variables an inverse Gamma IG(-1, 0) prior is used. For fixed
regression coefficients a normal N(0, ∞) prior is used. Models are estimated with 2 chains and
convergence is assessed based on the trace plots, parameter distributions and autocorrelation
plots. For details on the technical implementation of Bayesian estimation in Mplus, see T.
Asparouhov and Muthén (2010a).
17
Table 2 presents correlations between weighted aggregate levels of life satisfaction,
employment insecurity, LMP generosity and economic context. The table shows life
satisfaction to be negatively associated with employment insecurity (-0.65), and
positively associated with active and passive LMP expenditure (0.72 and 0.52,
respectively) and the long-term replacement rate (0.66). Employment insecurity is
negatively correlated with ALMP expenditure (-0.53) and the long-term replacement
rate (-0.63), but correlations with other variables are non-significant. As might be
expected, the various measures of LMP generosity are all highly correlated with one
another. And notably, no significant correlations are observed for EPL or economic
context.
Bulgaria
Croatia
Cyprus
Denmark
Estonia
Finland
France
Germany
GreeceHungary
Ireland
Israel
Netherlands
Norway
Poland
Portugal
Russia
Slovakia
Spain
Sweden
Switzerland
Ukraine
UK
5
6
7
8
9
Lif
e s
ati
sfa
cti
on
(a
ggre
ga
te)
0.00 0.05 0.10 0.15 0.20 0.25
Proportion of respondents reporting high employment insecurity
Linear prediction (R = 0.46)
Belgium
Source: European Social Survey (2010)
Slovenia
Czech Republic
2
Figure 2: Weighted aggregated life satisfaction against the weighted proportion of
respondents reporting employment insecurity
18
Table 2: Correlations between aggregate scores for life satisfaction,
employment insecurity, LMP and economic context
Lif
e s
ati
sfa
cti
on
Em
plo
yme
nt
inse
cu
rity
AL
MP
exp
en
dit
ure
PL
MP
exp
en
dit
ure
Lo
ng
-te
rm R
R
EP
L (
reg
ula
r)
Un
em
plo
yme
nt
tre
nd
GD
P g
row
th
Life satisfaction 1.00
(26)
Employment
insecurity
-0.65* 1.00
(26) (26)
Active LMP
expenditure
0.72* -0.53* 1.00
(20) (20) (20)
Passive LMP
expenditure
0.52* -0.39 0.77* 1.00
(20) (20) (20) (20)
Long-term
replacement rate
0.66* -0.63* 0.61* 0.57* 1.00
(22) (22) (19) (19) (22)
EPL for regular
workers
-0.30 0.09 -0.13 -0.07 -0.30 1.00
(18) (18) (16) (16) (18) (18)
Trend in
unemployment
-0.15 0.02 -0.31 0.02 -0.16 -0.05 1.00
(22) (22) (20) (20) (20) (17) (22)
GDP growth 0.30 -0.40 0.04 -0.17 0.36 0.11 -0.36 1.00
(23) (23) (20) (20) (21) (18) (22) (23)
Cell counts are shown in parentheses and significant correlations (at the
5% level) are indicated with an asterisk.
Base model
Table 3 presents coefficients from the ‘base model’ – a model that includes all
pathways discussed earlier except for the moderating influence of LMP. The model
includes 12,517 individuals from 22 countries 23 and is estimated using Bayesian
MCMC estimation in Mplus 6. The table presents the unstandardised coefficient, a one-
tailed p-value based on the posterior distribution24 and the 95% Bayesian credibility
intervals (CIs).
Overall, the findings are consistent with previous studies. Employment insecurity is
found to be negatively associated with year of education, institutional trust, permanent
job tenure, opportunities for advancement and variety at work. Conversely, higher
insecurity is observed for older workers, women, workers from ethnic minorities or in
lower occupational groups (skilled manual or elementary) and for workers who report a
long-term limiting illness. For life satisfaction, significant negative coefficients are
23 Four countries are omitted due to missing data on the contextual variables (GDP and
unemployment). The study focuses solely on respondents who are currently in paid employment.
24 For a positive estimate the one-tailed Bayesian p-value is proportion of the posterior distribution
that is below zero. For a negative estimate the p-value is the proportion of the posterior
distribution that is above zero (Muthén 2010).
19
observed for employment insecurity, work-family conflict, ‘belonging to an ethnic
minority group’, ‘experience of discrimination’ and low levels of work-based support.
High insecurity is associated with a 0.23 reduction in life satisfaction, controlling for
other variables in the model. The strongest determinant of life satisfaction is self-rated
health. Satisfaction among workers with poor health is 1.6 units lower than those with
very good health. The model shows life satisfaction to be positively related to
cohabitation, working hours, household income, religiosity, institutional and
interpersonal trust, living in a rural area and ‘opportunities for advancement’ and
‘variety’ at work. These effects are consistent with the existing literature.
At the country-level, economic conditions are shown to have little effect. For both GDP
growth (grow) and the trend in unemployment (uempav) the association with
employment insecurity and life satisfaction are non-significant. However, this is perhaps
explained by the large, negative association life satisfaction and the between-level
component of employment insecurity (i.e. the random intercept for insecurity; a
coefficient of -0.810). Mirroring the descriptive findings, this suggests that in countries
where a higher proportion of respondents feel insecure, the average level of life
satisfaction will be lower. It is possible, therefore, that the influence of economic
conditions is therefore ‘explained away’ by the aggregate level of insecurity. The
residual variance for employment insecurity (empsec) is highly significant, indicating
significant cross-national variation (consistent with the descriptive findings above). The
intra-class correlation of life satisfaction is 6.8% (0.193/0.193 + 2.664) indicating that
6.8% of the variation in life satisfaction is attributable to country differences, controlling
for other variables in the model.
Table 3: Coefficients for the base model
Coef. P-value
Lower
2.5%
Upper
2.5%
EMPSEC ON eduyrs -0.024 0.000 -0.036 -0.012
age 0.001 0.060 0.000 0.002
female 0.115 0.001 0.040 0.194
ethnic 0.161 0.019 0.007 0.315
trustint -0.191 0.000 -0.246 -0.138
union 0.057 0.111 -0.035 0.156
public 0.043 0.153 -0.041 0.127
perm -0.310 0.000 -0.402 -0.222
occup3 0.176 0.000 0.088 0.266
advance2 -0.380 0.000 -0.468 -0.291
advance3 -0.618 0.000 -0.716 -0.522
hamp0 0.207 0.000 0.110 0.302
variety4 -0.113 0.004 -0.200 -0.027
SATLIFE ON female 0.046 0.078 -0.019 0.109
age 0.000 0.207 -0.001 0.001
children -0.022 0.249 -0.086 0.043
ptnr 0.442 0.000 0.369 0.513
hours 0.004 0.007 0.001 0.007
income 0.090 0.000 0.076 0.104
eduyrs -0.001 0.400 -0.011 0.008
religgm 0.120 0.000 0.080 0.158
discrim -0.311 0.000 -0.433 -0.184
health2 -0.344 0.000 -0.415 -0.275
health3 -0.765 0.000 -0.861 -0.669
20
Coef. P-value
Lower
2.5%
Upper
2.5%
health4 -1.640 0.000 -1.843 -1.435
ethnic -0.129 0.034 -0.272 0.010
discuss 0.491 0.000 0.373 0.616
trustppl 0.102 0.000 0.087 0.117
trustint 0.371 0.000 0.324 0.418
divorce -0.113 0.004 -0.195 -0.030
hamp0 -0.041 0.186 -0.137 0.049
rural 0.077 0.010 0.014 0.141
occup3 -0.061 0.055 -0.136 0.013
helpwork -0.196 0.000 -0.271 -0.122
advance2 0.154 0.000 0.082 0.228
advance3 0.320 0.000 0.248 0.392
variety4 0.223 0.000 0.160 0.289
conflictgm -0.445 0.000 -0.488 -0.402
Residual
variance
satlife 2.664 0.000 2.598 2.732
EMPSEC ON cons -0.927 0.000 -1.237 -0.623
uempav 0.000 0.137 0.000 0.000
grow -0.038 0.194 -0.127 0.054
SATLIFE ON uempav 0.000 0.473 0.000 0.000
grow 0.037 0.240 -0.073 0.142
empsec -0.810 0.006 -1.450 -0.204
Residual
variance
empsec 0.152 0.000 0.076 0.343
satlife 0.193 0.000 0.101 0.435
Intercepts s -0.230 0.022 -0.422 -0.007
satlife 6.125 0.000 5.343 6.787
Ni 12,517
Nj 22
21
Overall, the findings from the base model are as expected, with the direction and
magnitude of most coefficients inline with previous research.
Moderation effects
Moderation effects – the extent to which LMP generosity changes the strength of
direction of the association between insecurity and life satisfaction – are the central
focus of this paper. They are tested here using a random slope: the individual-level
association between employment insecurity and life satisfaction is allowed to vary by
country, and this variation is regressed on the country-level measure of labour market
policy. Figure 3 summarises this model.
Figure 3: The moderation model
The ‘between-level’ refers to relationships that vary between countries (denoted by
subscript j), whereas the ‘within-level’ refers to those that vary at the individual level
(subscript i)25. s refers to the random slope between employment insecurityi and life
satisfactioni, which is regressed on the country-level measure of labour market policy
(LMPj). Nine measures of LMP are tested one by one. Other contextual variables
(i.e. unemployment and GDP growth) are kept in the model as controls.
Each model produces a large number of coefficients which, due to space limitations,
cannot be presented in full. This discussion will focus on the moderating effect of LMP
– i.e. the association between each contextual variable (LMPj) and the slope of the
regression between employment insecurity and life satisfaction (s). With the exception
25 In a MLM these might be referred to as level-2 and level-1, respectively.
Life satisfaction
Life satisfactionEmployment insecurity i
Economic conditionsj
Employment insecurity j j
Individual controlsi
s
s
Between-level
Within-level
LMP j
i
22
of this regression, the nine models are otherwise identical to the base model, and the
other coefficients do not change substantially.
Table 4: Moderation effects (association between slope and LMP)
Moderator Coef. P-value Lower
2.5%
Upper
2.5%
Total LMP expenditure 0.014 0.021 0.001 0.027
Active LMP expenditure 0.041 0.019 0.002 0.081
Activation support 0.010 0.030 -0.001 0.021
Regular EPL -0.155 0.211 -0.560 0.250
Temporary EPL -0.034 0.386 -0.283 0.226
Passive LMP expenditure 0.023 0.024 0.000 0.045
Short-term replacement rate 0.007 0.209 -0.011 0.026
Long-term replacement rate 0.012 0.007 0.003 0.020
Typical duration of UB 0.012 0.099 -0.007 0.030
Table 4 presents the unstandardised coefficients, Bayesian p-values and 95% credible
intervals for each of the moderation effects. Each row of the table represents a
separate model. Based on the 95% credible intervals, significant moderation effects
are observed for (a) total LMP expenditure, (b) active LMP expenditure, (c) passive LMP
expenditure, and (d) the long-term replacement rate. The interpretation of the
coefficients themselves isn’t straightforward (they represent the change in the slope of
the regression between employment insecurity and life satisfaction for a unit change in
each measure of LMP). To illustrate the direction of these effects, therefore, Figure 4
plots the change in life satisfaction associated with high employment insecurity (y-axis)
against the contextual measure of LMP (x-axis). A negative value on the y-axis indicates
that high insecurity is associated with a reduction in life satisfaction (the further below
0, the greater the reduction).
All plots show a positive gradient, indicating that employment insecurity is more harmful
(i.e. is associated with a larger reduction in life satisfaction) in countries where
expenditure is lower or unemployment benefits are less generous. For example, the plot
for ‘total LMP expenditure’ shows the strongest effects in Mediterranean countries
(Spain, Portugal and Greece) that spend a smaller proportion of GDP on labour market
interventions. All four plots show LMP generosity to have a buffering effect, consistent
with the central hypothesis of this study.
Most countries fall below the y = 0 horizontal, indicating that in most countries
employment insecurity is negatively associated with life satisfaction. However, for a few
(in particular, Austria, Belgium, Denmark and Ireland) these plots suggest a positive
association between employment insecurity and life satisfaction. This seems unlikely,
and further research is undoubtedly required. No matter how generous labour market
23
programmes are in these countries, it is hard to see how feeling insecure could
increase life satisfaction.
Another oddity is the position of Sweden on the plot for passive LMP expenditure. On
most measures – and more broadly, in the welfare regime literature (e.g. Esping-
Andersen 1990) – Sweden enjoys generous welfare provision, making its position at
the bottom of the scale for passive expenditure remarkable. One possible explanation
lies in the system of unemployment insurance in Sweden, which is split into basic and
income-related components. The latter requires workers to join an independent
‘unemployment fund’ which is tied to a trade union (see IAF 2012). The low level of
public expenditure in Sweden likely reflects this separation of public expenditure and
trade union provision (the Eurostat data excludes expenditure by trade unions).
Figure 4: Moderation effects
It is possible that labour market policy is not itself responsible for the moderating effect,
but rather, that changes in life satisfaction are motivated by other national
characteristics that just happen to be correlated with LMP generosity. To try and rule
this out, an additional set of country-level controls were entered into each model, one
by one, to check whether the moderating effect of LMP remained significant. Six
24
variables are considered: (1) income inequality26, (2) income poverty27, (3) trade union
density28 (4) turnout at last election29, (5) the proportion of households who have ‘great
difficulty’ making ends meet30 and (6) ‘healthy life years’ for men and women31. These
contextual controls are entered into each model, one by one, as a predictor of life
satisfaction and employment insecurity32. Overall, the results from these models do not
differ substantially from the earlier results, suggesting that the observed moderation
effects are robust to the influence of these country characteristics.
Moderated moderation
So far, the findings indicate that employment insecurity is negatively associated with
life satisfaction, but that the strength of this relationship depends on the generosity of
labour market policy, as hypothesised. However, this average effect is likely to mask
considerable heterogeneity, and LMP is likely to be more important for some workers
than others. Past studies show employment insecurity to be predicted by various
individual characteristics, such as age, sex, employment history, job tenure and
industry (see above). These attributes are associated with the twin components of
affective insecurity: (i) the anticipated difficulties of finding another job, and (ii) access
to alternative sources of income. Labour market programmes are likely to matter most
to individuals who have least control over these components (i.e. those who are most
pessimistic about their chances of re-employment, and who would struggle to maintain
their present income during unemployment). Labour market policies, therefore, are
likely to benefit vulnerable workers most, and be less important for highly mobile
workers (in the case of active LMPs) or workers who can readily access alternative
income (in the case of passive LMPs).
A further hypothesis, therefore, is that the strength of the moderating effect of LMP is
dependent on individual levels of employment insecurity. To test this, the above models
have been re-estimated for subsets of the sample, grouping on individual factors known
to predict employment insecurity. These groups are set out in Figure 5, which also
shows the proportion of respondents reporting insecurity in each group. These models
focus solely upon the 4 measures of LMP that had a significant moderating effect
(i.e. policy expenditure and the long-term replacement rate).
26 Gini coefficient (Eurostat 2012b)
27 Households at risk of poverty rate (60% median) after social transfers (Eurostat 2012c).
28 Percent of workers who belong to a trade union (Armingeon et al. 2012)
29 Percent of the electorate who voted (Armingeon et al. 2012).
30 Eurostat (2012d)
31 Eurostat (2012e)
32 This requires each moderation model to be estimated 7 times, for each contextual contextual
control (a total of 63 models). In each case, the only parameter that changes is the contextual
control, which replaces ‘GDP growth’ from the base model (to avoid having too many between-
level coefficients).
25
Figure 5: Aggregate levels of employment insecurity by subgroup
Group Categories Employment insecurity 95% credible intervals
(Weighted proportion) Lower Upper
Age < 40 0.049 0.041 0.056
> 40 0.086 0.077 0.096
Gender Female 0.083 0.074 0.093
Male 0.056 0.049 0.064
Trade union Member 0.074 0.060 0.088
Not member 0.068 0.062 0.075
Sector Public 0.073 0.062 0.084
Private 0.069 0.061 0.076
Breadwinner Main breadwinner 0.072 0.062 0.081
Not breadwinner 0.069 0.061 0.077
Industry Manufacturing 0.074 0.065 0.084
Services 0.063 0.055 0.072
Occupation White collar 0.061 0.054 0.068
Blue collar 0.091 0.078 0.103
Tenure Permanent 0.061 0.055 0.067
Temporary 0.116 0.097 0.135
As before, the focus is upon the moderating effect of LMP, and whether this varies by
group (e.g. for older vs. younger workers, or men vs. women). These models are
identical to those presented above, except that they are estimated for selected subsets
of the sample. The full set of coefficients (from all 64 models) is available on request.
The findings can be summarised as follows.
Overall, the grouped analysis shows substantial and consistent differences in the
strength of the moderating effect of LMP. For the most part, these differences are
consistent with expectations, with LMP generosity having a stronger moderating
influence for individuals in more vulnerable groups. The most consistent differences are
found for breadwinner status (the proportion of household income the respondent is
responsible for), industry (manufacturing or services) and job tenure (permanent or
temporary). For example, for individuals who provide a large proportion of the
household income (‘very large’ or ‘all’), all four measures of LMP have a significant
moderating effect, whereas no effect is observed for individuals providing a smaller
proportion33. In other words, the moderating influence of LMP generosity was only
significant for respondents who were the main breadwinner. A similar result is shown
for employment industry. For the three expenditure measures (total, active and passive)
a significant moderating effect is observed only for workers in manufacturing. However,
for benefit replacement rates the reverse is found, with significant effects observed
only for individuals in the service sector. The effects for job tenure are similarly
33 To recall, a non-significant effect is where the 95% Bayesian credible intervals for a coefficient
cross zero. For respondents who are not the main breadwinner, this was the case for all 4
measures of LMP.
26
contrasting. Whereas permanent employees derive no benefit from LMP34, a strong
buffering effect is observed for temporary workers for all four measures of LMP.
Moreover, the size of the moderating effect is larger for temporary workers than any
other group.
These grouped moderation effects are plotted in Figure 6. As before, the y-axis
represents the change in life satisfaction associated with ‘high’ employment insecurity
and the x-axis shows possible values of the contextual variable. All plots show a positive
slope, indicating that perceived insecurity has more severe consequences for life
satisfaction in countries with less generous LMP provision. However, much steeper
slopes are observed for temporary employees, those working in manufacturing or who
provide a large proportion of household income.
Figure 6: Moderation effects by subgroup
Further ‘doubly-moderated’ effects are observed for other individual characteristics, but
only with regard to the moderating effect of replacement rates. Replacement rates were
shown above to have the strongest moderating influence out of all LMP measures
considered. However, this seems to be entirely dependent on individual circumstances.
34 That is, the slope of the regression between employment insecurity and life satisfaction is not
significantly associated with the level of LMP generosity.
Active LMP
expenditure
−15 0 15
−1.0
0.0
0.5
x
y2 Not breadwinnerMain breadwinner
− 4 0 0 4 0
−1
01
x
−40 0 40−2
02
Total LMP
expenditure
Manu facturin
g
Services
Temporary
Permanent
−15 0 15
−1.5
01
−15 0 15
−1.5
01.5
−30 0 30
−1.0
0.0
0.5
Manufacturing
Services
Temporary
Permanent
Main breadwinner
Not breadwinner
Passive LMP
expenditure
−30 0 30
−1.0
0.00.5
−30 0 30
−2
01
Manufacturing
Services
Temporary
Permanent
Long-term
replacement
rate
−60 0 60
−1.0
0.0
1.0
Temporary
Permanent
−60 0 60
−1.0
0.0
0.5
Breadwinner
Not breadwinner
Breadwinner
statusIndustry Job tenure
27
Generous replacement rates can buffer the consequences of employment insecurity,
but only for older workers, who are not members of a trade union, who work in the
public sector, who are the main breadwinner, work in a white-collar job or who have a
temporary employment contract. On the other hand, UB replacement rates do not
significantly influence the experience of insecurity for young workers, union members,
workers who are not the main breadwinner, work in the private sector, in blue-collar
jobs or who have a permanent contract35.
These are preliminary findings, and should be interpreted as such. Certainly, the
moderating influence of labour market policy does appear to be unevenly distributed,
benefiting some workers and not others, but further analysis is required to understand
the precise nature of these differences.
Discussion
Perceived insecurity has harmful consequences for well-being, even if employees never
actually lose their job. This is particularly important during periods of economic
recession, such as the years since the 2007 financial crisis. Amidst increasing
unemployment and declining economic growth, many millions of people across Europe
will worry about job loss and what this might entail. Such anxieties have been linked to
depression (Roskies, Louis-Guerin, and Fournier 1993; Orpen 1993), impaired
psychological well-being (De Witte 1999; Friesen and Sarros 1989; Wilson, Larson, and
Stone 1993; Kuhnert, Sims, and Lahey 1989) and problems sleeping (Arnetz et al.
1988; Ferrie et al. 1998; Cobb and Kasl 1977; Withington 1989) 36 . Perceived
employment insecurity should therefore represent a priority for policy-makers. As past
research has shown, despite the pervasiveness of employment fears, the
consequences for health and well-being can be mitigated. The link between perceived
insecurity and individual well-being is remarkably robust, but strength of this
association depends on various individual, organisational and national circumstances.
Examples include social support (Näswall, Sverke, and Hellgren 2005; Lim 1996),
family circumstances (Ertel, Koenen, and Berkman 2008) or individual employability
(Fugate, Kinicki, and Ashforth 2004). So while insecurity is harmful, it is within the
scope of policy to prevent the worst outcomes.
This paper considered the extent to which labour market policies buffered the
association between perceived insecurity and life satisfaction. It has hypothesised that
perceived insecurity motivated well-being via concerns about (i) future employment
options and (ii) replacement income during unemployment, and that these concerns
could be offset by active and passive interventions, respectively. By reducing the
barriers to re-employment or guaranteeing a minimum income, labour market policies
were hypothesised to buffer the consequences of perceived insecurity.
35 The 95% credible intervals for the moderating effect cross zero when the model is estimated for
these groups alone.
36 For a review, see Sverke, Hellgren, and Naswall (2002), De Witte (2007), Burchell (2005) and
Cheng and Chan (2008).
28
Based on data for 22 countries from the 2010 European Social Survey, this hypothesis
was mostly upheld. Employment insecurity was negatively associated with life
satisfaction, but the negative association was stronger in countries with less generous
LMP measures. This was found for measures of LMP expenditure (active, passive and
total expenditure) and the long-term replacement rate of unemployment benefits. No
effect was found for employment protection legislation.
Beyond this average effect, however, the moderating influence of LMP was itself found
to depend on individual attributes such as age, industry, occupation and tenure
(amongst others). Simplifying slightly, LMP was more important (as a buffer of insecurity)
for individuals who were most vulnerable to employment insecurity (i.e. workers for
whom the consequences of job loss would be most severe). Older workers benefited
strongly from generous policy support, whereas no effect was found for younger
workers. Individuals who were responsible for the majority of household income were
also more receptive to generous LMP. Temporary employees benefited much than did
permanent workers. Other differences were less intuitive. Workers in the manufacturing
sector benefited more from LMP than did individuals in other sectors. This might reflect
the uneven impact of the recession across sectors, or fears about a lack of transferable
skills among these employees (i.e. the skills required to transfer into other sectors).
Overall, while LMP does appear to buffer the association between perceived insecurity
and life satisfaction, the effects are heterogeneous and further research is required to
unpick these differences.
With regard to the policy implications of this study, it appears that the type of policy
intervention is key. A range of measures were considered, including total expenditure
(as a proportion of GDP), the replacement rate and duration of unemployment benefits
and the protectiveness of employment legislation (EPL). While significant moderating
effects were observed for data on expenditure, the strongest and most consistent
moderating effect was observed for the long-term replacement rate. This raises two
questions. First, why did EPL not have any moderating effect? And second, what
explains the dominant effect of benefit replacement rates?
Employment protection legislation (EPL) is an indicator measuring the regulations
governing the ease with which employers can hire and fire workers (OECD 2012g).
Given this definition, the lack of moderating influence is perhaps unsurprising. Job
insecurity was earlier defined as comprising of cognitive and affective components,
capturing the probability of becoming unemployed and the anticipated consequences
(respectively). Whereas the link between cognitive insecurity and EPL seems
reasonable (namely, cognitive insecurity will be inversely related to the strictness of
employment legislation), that EPL should influence affective insecurity is less obvious.
EPL might determine the chances of becoming unemployed, but it is hard to see how it
might alter the experience of unemployment.
One possibility, discussed above, is that strict EPL actually increases affective insecurity,
by protecting the positions of current employees currently employed. EPL could,
therefore, increase affective insecurity by making it harder to find alternative
employment. This mechanism aside, however, it is hard justify the inclusion of EPL as a
moderator of employment insecurity. EPL can neither provide a replacement income,
29
nor make it easier to find another job (other than via the aforementioned mechanism).
Previous studies typically frame EPL as a predictor of cognitive insecurity (e.g. Anderson
and Pontusson 2007), which is consistent with this theoretical model. That EPL should
predict affective insecurity is less plausible, and this perhaps explains the non-
significant moderating effect.
The second question arises from the finding that, of all policy measures considered, the
strongest and most consistent buffering effect is produced by the UB replacement rate.
It is worth considering why this might be. The proposed theoretical model is predicated
on the assumption that workers are aware of the level of institutional support available.
This mechanism is entirely perceived: individuals are protected because thoughts about
the possibility of future unemployment are not so immediately associated with stress
and anxiety, as the individual can be confident that sufficient support will be available.
The model, in effect, assumes that ‘perceptions of LMP generosity’ and ‘expenditure on
LMP’ are synonymous. Consequently, replacement rates might have the most
influential moderating effect because, of the measures considered, they are the most
visible. More people are likely to be aware of the living standards of the unemployed
than will be informed about government expenditure of LMP. In other words,
replacement rates overlap are a better measure of ‘perceived unemployment support’
than are data on policy expenditure.
The policy message of this paper is relatively straightforward: increase the generosity of
unemployment benefits, so they replace a greater proportion of in-work income. As
shown here, this should have a buffering effect, reducing the the harmful
consequences of perceived insecurity. Increasing replacement rates is an attractive
option for two reasons. First, as shown above, the benefit will target workers who feel
most vulnerable to unemployment. Second, generous replacement rates (and LMP
more generally) are beneficial for non-recipients. Obviously, increasing the living
standards of the unemployed is worthwhile in itself, but increasing the generosity of
unemployment benefits will also protect current employees. In the same way that
perceived insecurity is harmful for workers who never lose their job, LMPs can benefit
individuals who never actually receive support. Raising the generosity of unemployment
benefits therefore offers a cost-effective way of targeting support at the most
vulnerable workers.
Limitations
This study suffers several limitations. First, life satisfaction is a simple measure that
overlooks the multi-dimensional nature individual well-being (e.g. Bérenger and Verdier-
Chouchane 2007; Ryff and Keyes 1995). There are also issues of combining cross-
cultural assessments of life satisfaction (e.g. Oishi et al. 1999) that aren’t adequately
addressed here. While life satisfaction has been shown to give reliable results (Krueger
and Schkade 2008; Wallace and Wheeler 2002), correlates well with other measures
30
(Smith 2004) and is used widely in the insecurity literature (e.g. Lim 1996; Green 2009)
future research should explore alternative measures of psychological well-being37.
Second, the analysis includes mediating pathways but relies exclusively on cross-
sectional data (e.g. from LMP to life satisfaction, via employment insecurity). Past
studies have shown that cross-sectional approaches to mediation can generate
substantially biased estimates (e.g. Maxwell and Cole 2007) and recommended using
longitudinal data that can distinguish the temporal ordering of the mediating pathway.
Unfortunately, such data are not available. There are no longitudinal European surveys
that include information on perceived employment insecurity38. More positively, when
the models were replicated using in a multilevel framework (i.e. ignoring the mediating
pathway) the substantive conclusions were unchanged. Moreover, the mediating
pathway is secondary concern – the primary interest is in the moderating effect of LMP
and so the paper’s conclusions do not rely on this mediating effect. Ultimately, a two-
level path analysis model was chosen, over a simpler multilevel model, to avoid the
bias that would be introduced by ignoring the direct pathways between contextual
variables and employment insecurity. It was felt that the bias introduced by ignoring the
determinants of perceived insecurity be greater than those introduced by using cross-
sectional data to study mediation.
Third, the model makes strong assumptions about individuals’ awareness of the
generosity of labour market support. This is discussed above. The awareness of LMP
provision might differ markedly between two countries, due to cultural differences, even
for similar levels of expenditure. It would be interesting, in future research, to include
individual perceptions of labour market support, in addition to the contextual
measures39.
Fourth, the operationalisation of labour market policy is quite crude, and overlooks
qualitative differences in the configuration of support. To establish ‘what works’ from a
policy standpoint, much greater disaggregation is required. Expenditure data are likely
to hide significant cross-national variation in how support is delivered (particularly with
regard to activation schemes) and the efficiency and effectiveness of expenditure. Little
qualitative data is available from Eurostat, and detailed local knowledge for 22
countries goes beyond the scope of this paper. This should be addressed in future
research.
37 Particularly since the substantive conclusions have been shown to depend on the choice of
outcome measure (Gundelach and Kreiner 2004).
38 There are very few sources of longitudinal data for Europe. The EU-SILC (2005) provides a
longitudinal sample, but lacks any measure of perceived job insecurity. The British Household
Panel Survey and the Germany Socio-economic Panel also follow respondents over time, but (at
least for the BHPS) also lack consistent measures of perceived employment insecurity (and
moreover, would limit the ability to compare variation in LMP generosity).
39 For example, questions that ask individuals about the type and level of support they could
access during unemployment. Such data are unavailable in the 2010 ESS.
31
A fourth potential issue is the small number of clusters included in the study. For the
base model 22 countries are included while the ‘moderation’ models consider as few
as 18. This number is limited by (a) the countries available in the 2010 ESS and (b) the
availability of reliable measures of LMP. Past studies has suggested various rules of
thumb for the number of clusters and individuals. Snijders and Bosker (1999) suggest
that multilevel models should not be used with fewer than 10 clusters, while Kreft
(1996) proposes a ‘30/30 rule’, requiring 30 clusters with at least 30 individuals per
cluster. Hox (1998) extends this, suggesting a minimum of 50/20 (50 groups with 20
respondents each) for models involving cross-level interactions. There are two reasons
to be optimistic. First, past research has indicated that the problem is most acute when
the number of individuals per cluster is small (Austin 2010; Rodriguez and Goldman
1995; Bell et al. 2010), which isn’t the case here (the minimum sample size for any
cluster ≈ 500). Second, there is evidence to suggest that using Bayesian estimation
can avoid the bias associated with a small number of clusters (e.g. Raudenbush and
Bryk 2002, 410). A simulation study by T. Asparouhov and Muthén (2010b) shows that
with fewer than 50 clusters a Bayes estimator (in Mplus) can obtain better estimates
and more accurate confidence intervals, compared to maximum likelihood estimators
(p. 23).
A final issue is the temporal uniqueness of 2010. While not a limitation itself, it is
important to frame the study within the social, economic and political context of the
survey period. Can these findings be generalised beyond 2010, or are they idiosyncratic
of a particular era? Unemployment rates across Europe remained high in 2010,
following the rapid increase in 2007/2008. GDP growth had recovered somewhat,
following declines in 2007/2008, but remained below pre-recession levels. That
national context matters too. The fieldwork period for the UK overlapped with the 2010
general election, which saw the Labour Party replaced with a Conservative-led coalition.
In Greece the debt crisis was well underway when interviews were carried out in 2011.
During the fieldwork period in Spain, the unemployment rate stood at around 22%,
almost double the European average.
In short, the aftermath of a global financial crisis is an unusual study period that is
likely to differ systematically from pre-recession years. This is particularly relevant when
considering the impact of labour market policy. Contextual data for 2010 might indicate
generous LMP provision, but if workers think an incoming government (e.g. the UK) or
compulsory budget cuts (e.g. Greece) might reduce provision, then expenditure data will
be an inaccurate reflection of ‘perceptions of LMP generosity’. Future research should
therefore seek to replicate this analysis for other years.
Conclusions
This study uses recent European survey data to test whether the association between
employment insecurity and life satisfaction is moderated by the generosity of labour
market policies at the country-level. The core hypothesis is upheld: the impact of
insecurity on satisfaction is weaker where LMP is more generous, although the strength
of this moderating effect depends on individual attributes.
32
The study’s key contribution to the literature is the bringing together of job insecurity,
labour market policy and subjective well-being in a single model. Several studies have
shown a link between LMP and employment insecurity (e.g. Clark and Postel-Vinay
2009), but the subsequent consequences for well-being are assumed. Other studies
consider the moderated consequences of job insecurity, but few address the role of
labour market policy. By linking institutional support, employment insecurity and life
satisfaction, empirically, this study has shown that LMP can not only influence
perceived insecurities (as demonstrated previously) but can also determine the
consequences for well-being.
The policy recommendations, set out above, are straightforward: increase the
generosity of unemployment benefits, since this will protect current employees (even if
they never actually receive the benefit) as well as enhancing the well-being of the
unemployed. Given the scarcity of similar studies, these findings should be interpreted
as preliminary. Future research should address the limitations detailed above, in
particular, by considering other years (besides 2010), other measures of well-being
(besides life satisfaction), alternative and more disaggregated measures of LMP
(besides expenditure) and alternative operationalisations of employment insecurity.
33
References
Aaronson, D., and D. G. Sullivan. 1998. “The decline of job security in the 1990s: Displacement,
anxiety, and their effect on wage growth.” ECONOMIC PERSPECTIVES-FEDERAL RESERVE BANK OF
CHICAGO 22: 17–43.
Anderson, Christopher J., and Jonas Pontusson. 2007. “Workers, worries and welfare states: Social
protection and job insecurity in 15 OECD countries.” European Journal of Political Research 46:
211–235.
Armingeon, K. 2005. “OECD, EU, and active labor market policy.”
Armingeon, Klaus, Romana Careja, Sarah Engler, Marlène Gerber, Philipp Leimgruber, and Panajotis
Potolidis. 2012. “Comparative Political Data Set III.”
Arnetz, B. B., S. Brenner, R. Hjelm, L. Levi, and I. Petterson. 1988. Stress reactions in relation to
threat of job loss and actual unemployment: physiological, psychological and economic effects of job
loss and unemployment. National Institute for Psychosocial Factors and Health.
Ashford, Susan. 1989. “Content, Causes, and Consequences of Job Insecurity: A Theory-Based
Measure and Substantive Test.” Academy of Management Journal 32: 803–829.
Asparouhov, Tihomir, and Bengt Muthén. 2010a. “Bayesian Analysis Using Mplus: Technical
Implementation.”
———. 2010b. “Bayesian Analysis of Latent Variable Models using Mplus.”
Austin, Peter. 2010. “Estimating Multilevel Logistic Regression Models When the Number of Clusters
is Low: A Comparison of Different Statistical Software Procedures.” The International Journal of
Biostatistics 6 (January).
Baron, Reuben, and David Kenny. 1986. “The Moderator-Mediator Variable Distinction in Social
Psychological Research: Conceptual, Strategic, and Statistical Considerations.” Journal of Personality
and Social Psychology 51: 1173–1182.
Bauer, Daniel, Kristopher Preacher, and Karen Gil. 2006. “Conceptualizing and Testing Random
Indirect Effects and Moderated Mediation in Multilevel Models: New Procedures and
Recommendations.” Psychological Methods 11: 142–163.
Bell, B., G. Morgan, J. Kromrey, and J. Ferron. 2010. “The Impact of Small Cluster Size on Multilevel
Models: A Monte Carlo Examination of Two-Level Models with Binary and Continuous Predictors.” In
Section on Survey Research Methods.
Boeri, T., A. Börsch-Supan, and G. Tabellini. 2001. “Would you like to shrink the welfare state? A
survey of European citizens.” Economic Policy 16: 7–50.
Brenner, M. H. 1973. “Mental illness and the economy.” Cambridge, Mass.
Burchell, B. 1994. “Who is affected by unemployment? Job insecurity and labour market influences
on psychological health.” In Social Change and the Experience of Unemployment, ed. D. Gallie, C.
Marsh, and C. Vogler, 188–212. Oxford: Oxford University Press.
———. 2009. “Flexicurity as a moderator of the relationship between job insecurity and psychological
well-being.” Cambridge Journal of Regions, Economy and Society 2: 365.
Burchell, Brendan. 2005. “The welfare costs of job insecurity: Psychological well-being and family
life.” Trends in social cohesion special issue: Reconciling labour flexibility with social cohesion –
facing the challenge 15: 71–108.
Burgoon, B., and F. Dekker. 2010. “Flexible employment, economic insecurity and social policy
preferences in Europe.” Journal of European Social Policy 20: 126–141.
34
Bérenger, V., and A. Verdier-Chouchane. 2007. “Multidimensional measures of well-being: standard
of living and quality of life across countries.” World Development 35: 1259–1276.
Böckerman, P. 2004. “Perception of job instability in Europe.” Social Indicators Research 67: 283–
314.
Büssing, André. 1999. “Can Control at Work and Social Support Moderate Psychological
Consequences of Job Insecurity? Results from a Quasi-experimental Study in the Steel Industry.”
European Journal of Work and Organisational Psychology 8: 219–242.
CMM. 2011. Weighting in MLwiN. Bristol, UK: Centre for Multilevel Modelling, University of Bristol.
Carr, E., M. Elliot, and M. Tranmer. 2011. “A multilevel analysis of the relationship between national
economic conditions, an individual’s job insecurity and well-being in Western Europe”, CCSR Working
Paper 2011-05, http://www.ccsr.ac.uk/publications/working/2011-05.htm
Catalano, R., and C. D. Dooley. 1977. “Economic predictors of depressed mood and stressful life
events in a metropolitan community.” Journal of Health and Social Behavior 18: 292–307.
Cheng, Grand H. L., and Darious K. S. Chan. 2008. “Who Suffers More from Job Insecurity? A Meta-
Analytic Review.” Applied Psychology: An International Review 57: 272–303.
Chung, Heejung, and Wim van Oorschot. 2010. “Employment insecurity of European individuals
during the financial crisis. A multilevel approach.”
Clark, Andrew, Andreas Knabe, and Steffen Rätzel. 2010. “Boon or bane? Others’ unemployment,
well-being and job insecurity.” Labour Economics 17: 52–61. doi:DOI:
10.1016/j.labeco.2009.05.007.
Clark, Andrew, and Fabien Postel-Vinay. 2009. “Job security and job protection.” Oxford Economic
Papers 61: 207–239. doi:10.1093/oep/gpn017.
http://oep.oxfordjournals.org/content/61/2/207.abstract.
Cobb, S., and S. V. Kasl. 1977. “Termination; the consequences of job loss.” DHEW (NIOSH)
Publication 77.
Comission, European. 2012. “Comparative Tables on Social Protection (MISSOC).”
http://ec.europa.eu/employment_social/missoc/db/public/compareTables.do.
Coombs, R. H. 1991. “Marital status and personal well-being: A literature review.” Family Relations:
97–102.
Dasgupta, S. 2001. “Employment security: conceptual and statistical issues.”
De Witte, H. 1999. “Job insecurity and psychological well-being: Review of the literature and
exploration of some unresolved issues.” European Journal of Work and Organizational Psychology 8:
155–177.
———. 2007. “Job insecurity: Review of the international literature on definitions, prevalence,
antecedents and consequences.” SA Journal of Industrial Psychology 31.
Dickerson, A., and F. Green. 2006. “How should we measure the fear of job loss?”
Dolado, J. J., C. García-Serrano, and J. F. Jimeno. 2002. “Drawing lessons from the boom of
temporary jobs in Spain.” The Economic Journal 112: 270.
ESS. 2006. “ESS Round 3: European Social Survey Round 3 Data.” Norway: Norwegian Social
Science Data Services - Data Archive and distributor of ESS data.
———. 2010. “European Social Survey Round 5 Data.” Norwegian Social Science Data Services,
Norway Data file edition 2.0.. http://ess.nsd.uib.no/.
EUROSTAT. 2005. “EU-SILC User Database Description.”
35
EWCS. 2005. European Working Conditions Survey, 2005 [computer file]. European Foundation for
the Improvement of Living and Working Conditions. Colchester, Essex: UK Data Archive [distributor],
May 2007, SN: 5639.
Erlinghagen, Marcel. 2008. “Self-Perceived Job Insecurity and Social Context: A Multi-Level Analysis
of 17 European Countries.” European Sociological Review 24 (April): 183. doi:10.1093/esr/jcm042.
http://esr.oxfordjournals.org/cgi/content/full/24/2/183.
Ertel, K. A., K. C. Koenen, and L. F. Berkman. 2008. “Incorporating home demands into models of job
strain: Findings from the Work, Family & Health Network.” Journal of occupational and
environmental medicine/American College of Occupational and Environmental Medicine 50: 1244.
Esping-Andersen, G. 1990. The Three Worlds of Welfare Capitalism. Oxford: Polity Press.
Eurostat. 2002. “Labour Market Policy. Database Methodology.”
———. 2012b. “Gini coefficient (Source: SILC).”
http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&plugin=1&language=en&pcode=tessi19
0.
———. 2012c. “At-risk-of-poverty rate by poverty threshold, age and sex (Source: SILC).”
http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ilc_li02&lang=en.
———. 2012d. “Inability to make ends meet (Source: SILC).”
http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ilc_mdes09&lang=en.
———. 2012e. “Healthy Life Years (from 2004 onwards).”
http://appsso.eurostat.ec.europa.eu/nui/show.do?wai=true&dataset=hlth_hlye.
Ferrie, J. E., M. J. Shipley, M. G. Marmot, S. Stansfeld, and G. D. Smith. 1998. “The health effects of
major organisational change and job insecurity.” Social Science & Medicine 46: 243–254.
Friesen, D., and J. C. Sarros. 1989. “Sources of burnout among educators.” Journal of Organizational
Behavior 10: 179–188.
Fugate, M., A. J. Kinicki, and B. E. Ashforth. 2004. “Employability: A psycho-social construct, its
dimensions, and applications.” Journal of Vocational behavior 65: 14–38.
Gautié, Jérôme. 2011. “France: Protecting the insiders in the crisis and forgetting the outsiders?.” In
Work Inequalities in the Crisis: Evidence from Europe, ed. Daniel Vaughan-Whitehead, 198–242.
Cheltenham: Edward Elgar Publishing.
Green, F., D. Campbell, A. Carruth, and A. Dickerson. 2001. “An analysis of subjective views of job
insecurity.”
Green, Francis. 2009. “Job Insecurity, Employability, Unemployment and Well-Being.”
http://ideas.repec.org/p/ukc/ukcedp/0918.html.
Greenhalgh, L., and Z. Rosenblatt. 1984. “Job insecurity: Toward conceptual clarity.” The Academy of
Management Review 9: 438–448.
Gundelach, P., and S. Kreiner. 2004. “Happiness and Life Satisfaction in Advanced European
Countries.” Cross-Cultural Research 38 (January): 359–386.
Hartley, J. 1991. Job insecurity: Coping with jobs at risk. Sage Publications Ltd.
Heaney, C. A., B. A. Israel, and J. S. House. 1994. “Chronic job insecurity among automobile workers:
Effects on job satisfaction and health.” Social Science & Medicine 38: 1431–1437.
Helliwell, J. 2003. “How’s life? Combining individual and national variables to explain subjective well-
being.” Economic Modelling (January).
http://linkinghub.elsevier.com/retrieve/pii/S0264999302000573.
36
Hox, J. J. 1998. “Multilevel modeling: When and why.” Classification, data analysis, and data
highways 2000: 54–85.
Hudson, J., and S. Kuhner. 2007. “Towards Productive Welfare? A Comparative Analysis of 23 OECD
Countries.” Australian Social Policy Conference 2007.
IAF. 2012. “IAF’s mission.” http://www.iaf.se/English/IAFs-mission/.
Johada, M. 1981. “Work, employment, and unemployment.” American Psychologist 36: 184–191.
———. 1982. Employment and unemployment: A social-psychological analysis. Cambridge, England:
Cambridge University Press.
———. 1997. “Manifest and latent functions.” In The Blackwell encyclopedic dictionary of
organizational psychology, ed. N. Nicholson, 317–318. Oxford, England: Blackwell.
Kahneman, D., and A. Tversky. 1979. “Prospect theory: An analysis of decision under risk.”
Econometrica: Journal of the Econometric Society: 263–291.
Kaplan, David. 2009. Structural Equation Modelling: Foundations and Extensions. Advanced
Quantitative Techniques in the Social Sciences Series. London: Sage Publications.
Kenny, D., J. Korchmaros, and N. Bolger. 2003. “Lower level mediation in multilevel models.”
Psychological Methods; Psychological Methods 8: 115.
Kok, W. 2003. “Jobs, Jobs, Jobs. Creating More Employment in Europe. Report of the Employment
Task Force.”
Kotakorpi, K., and J. Laamanen. 2010. “Welfare state and life satisfaction: Evidence from public
health care.” Economica 77: 565–583.
Kreft, I. G. G. 1996. “Are multilevel techniques necessary? An overview, including simulation studies.”
Kroll, C. 2010. “Different Things Make Different People Happy: Examining Social Capital and
Subjective Well-Being by Gender and Parental Status.” Social Indicators Research: 1–21.
Krueger, A., and D. Schkade. 2008. “The reliability of subjective well-being measures.” Journal of
Public Economics 82: 3–24. doi:10.1016/j.jpubeco.2007.12.015.
Krull, J. L., and D. P. MacKinnon. 2001. “Multilevel modeling of individual and group level mediated
effects.” Multivariate behavioral research 36: 249–277.
Kuhnert, K. W., R. R. Sims, and M. A. Lahey. 1989. “The relationship between job security and
employee health.” Group & Organization Management 14: 399.
Lim, V. K. G. 1996. “Job insecurity and its outcomes: Moderating effects of work-based and nonwork-
based social support.” Human Relations 49: 171.
Lollivier, S., and L. Rioux. 2006. “Do UI benefit levels or benefit duration have an impact on
perceived job security?”
Lüdtke, O., H. Marsh, A. Robitzsch, U. Trautwein, T. Asparouhov, and B. Muthén. 2008. “The
multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual
studies.” Psychological Methods 13: 203.
MacKinnon, D. P. 2008. Introduction to Statistical Mediation Analysis. London: Erlbaum Psych Press.
MacKinnon, D. P., C. M. Lockwood, J. M. Hoffman, S. G. West, and V. Sheeis. 2002. “A comparison of
methods to test mediation and other intervening variable effects.” Psychological methods 7: 83–
104.
Maxwell, S., and D. Cole. 2007. “Bias in cross-sectional analyses of longitudinal mediation.”
Psychological Methods 12: 23–44.
37
Mohr, G. B. 2000. “The changing significance of different stressors after the announcement of
bankruptcy: A longitudinal investigation with special emphasis on job insecurity.” Journal of
Organizational Behavior 21: 337–359.
Moncel, N., M. Ruivo, and E. Fravega. 2004. “A Work-Welfare Nexus at the Heart of the Social
Construction of Employment: New Perspectives on Labour Market Changes and Social Exclusion.” In
Wage And Welfare: New Perspectives On Employment And Social Rights In Europe, ed. B. Clasquin,
N. Moncel, M. Harvey, and B. Friot, 49–75. Manchester, UK: Peter Lang Publishing Group.
Muthén, B. 2010. “Bayesian analysis in Mplus: A Brief Introduction.”
Nickell, S. 1997. “Unemployment and labor market rigidities: Europe versus North America.” The
Journal of Economic Perspectives 11: 55–74.
Näswall, K., M. Sverke, and J. Hellgren. 2005. “The Moderating Effects Of Work-Based And Non-Work
Based Support On The Relation Between Job Insecurity And Subsequent Strain.” SA Journal of
Industrial Psychology 31: 57–64.
OECD. 2003. Employment Outlook. Paris: OECD.
———. 2004. “Employment Protection Regulation and Labour Market Performance.” In OECD
Employment Outlook. Paris, France: OECD.
———. 2007. “The Social Expenditure database: An Interpretative Guide.”
http://www.oecd.org/dataoecd/54/43/39091497.pdf.
———. 2011. “Glossary of Statistical Terms.” http://stats.oecd.org/glossary/detail.asp?ID=2020.
———. 2012f. “OECD.Stat (database).” http://stats.oecd.org/.
———. 2012g. “Employment Protection Legislation (OECD Employment and Labour Market Statistics).”
doi:10.1787/lfs-epl-data-en. http://www.oecd-ilibrary.org/employment/data/employment-
protection-legislation_lfs-epl-data-en.
Oishi, S., E. Diener, R. Lucas, and E. Suh. 1999. “Cross-Cultural Variations in Predictors of Life
Satisfaction: Perspectives from Needs and Values.” Personality and Social Psychology Bulletin
(January). http://psp.sagepub.com/cgi/content/abstract/25/8/980.
Orpen, C. 1993. “Correlations between job insecurity and psychological well-being among white and
black employees in South Africa.” Perceptual and motor skills 76: 885–885.
Pacek, A. C., and B. Radcliff. 2008. “Welfare policy and subjective well-being across nations: An
individual-level assessment.” Social Indicators Research 89: 179–191.
Pacelli, L., D. Devicienti, A. Maida, M. Morini, A. Poggi, and P. Versan. 2008. “Employment security
and employability: A contribution to the flexicurity debate.” Office for Official Publications of the
European Communities.
Pearce, J. L. 1998. “Job insecurity is important, but not for the reasons you might think: The example
of contingent workers.” Trends in organizational behavior 5: 31–46.
Pituch, K. A., L. M. Stapleton, and J. Y. Kang. 2006. “A comparison of single sample and bootstrap
methods to assess mediation in cluster randomized trials.” Multivariate Behavioral Research 41:
367–400.
Polavieja, J. G. 2006. “The incidence of temporary employment in advanced economies: why is Spain
different?.” European Sociological Review 22: 61–78.
Powell, Martin, and Armando Barrientos. 2004. “Welfare regimes and the welfare mix.” European
Journal of Political Research 43: 83–105. http://dx.doi.org/10.1111/j.1475-6765.2004.00146.x.
Preacher, K. 2011. “Mplus syntax files for single- and multilevel mediation models.”
http://www.quantpsy.org/pubs/syntax_appendix_081311.pdf.
38
Preacher, K. J., M. J. Zyphur, and Z. Zhang. 2010. “A general multilevel SEM framework for assessing
multilevel mediation.” Psychological Methods 15: 209.
Preacher, Kristopher J., Zhen Zhang, and Michael J. Zyphur. 2011. “Alternative Methods for
Assessing Mediation in Multilevel Data: The Advantages of Multilevel SEM.” Structural Equation
Modeling 18: 161–182.
Raudenbush, S. W., and R. Sampson. 1999. “Assessing direct and indirect effects in multilevel
designs with latent variables.” Sociological Methods & Research 28: 123–153.
Raudenbush, S., and A. Bryk. 2002. Hierarchical Linear Models: Applications and Data Analysis
Methods. Thousand Oaks, CA: Sage.
Robinson, P. 2000. “Active labour-market policies: a case of evidence-based policy-making?.” Oxford
Review of Economic Policy 16: 13.
Rodriguez, G., and N. Goldman. 1995. “An assessment of estimation procedures for multilevel
models with binary responses.” Journal of the Royal Statistical Society. Series A (Statistics in Society):
73–89.
Roskies, E., C. Louis-Guerin, and C. Fournier. 1993. “Coping with job insecurity: how does personality
make a difference?.” Journal of organizational behavior 14: 617–630.
Ryff, C. D., and C. L. M. Keyes. 1995. “The structure of psychological well-being revisited.” Journal of
personality and social psychology 69: 719.
Rønsen, Marit, and Torbjørn Skarðhamar. 2009. “Do welfare-to-work initiatives work? Evidence from
an activation programme targeted at social assistance recipients in Norway.” Journal of European
Social Policy 19: 61–77.
Scruggs, L. 2006. “The Generosity of Social Insurance, 1971-2002.” Oxford Review of Economic
Policy 22: 349–362.
Smith, C. 2004. “Social wellbeing in New Zealand and the correlates of life satisfaction.” In Social
Policy Research and Evaluation Conference.
Smith, Tom, Peter Marsden, Michael Hout, and Jibum Kim. 2008. “General social surveys, 1972-
2010 [machine-readable data file], Principal Investigator: Tom W. Smith; Co-Principal Investigator,
Peter V. Marsden; Co-Principal Investigator, Michael Hout; Sponsored by National Science
Foundation, Chicago: National Opinion Research Center [producer]; Storrs, CT: The Roper Center for
Public Opinion Research, University of Connecticut [distributor].”
Snijders, T., and R. Bosker. 1999. Multilevel Analysis: An Introduction to Basic and Advanced
Multilevel Modeling. London: Sage Publications.
Snijders, Tom. 2012. “Multilevel Analysis.”
http://www.stats.ox.ac.uk/~snijders/SpringSchool/MLB_new_S.pdf.
Snijders, Tom, and Roel Bosker. 2011a. Multilevel Analysis: An Introduction to Basic and Advanced
Multilevel Modeling. London: Sage Publications Ltd.
———. 2011b. “Survey Weights.” In Multilevel Analysis: An Introduction to Basic and Advanced
Multilevel Modeling, 216–244. London: Sage Publications Ltd.
Survey, European Social. 2011. “Weighting European Social Survey Data.”
Sverke, M., J. Hellgren, and K. Naswall. 2002. “No security: A meta-analysis and review of job
insecurity and its consequences.” Journal of Occupational Health Psychology 7: 242–264.
Sverke, Magnus, Johnny Hellgren, and Katharina Näswall. 2006. “Job insecurity: A literature review.”
Tella, R. D., R. J. MacCulloch, and A. J. Oswald. 2003. “The macroeconomics of happiness.” Review
of Economics and Statistics 85: 809–827.
39
Vis, B. 2007. “States of welfare or states of workfare? Welfare state restructuring in 16 capitalist
democracies, 19852002.” Policy & Politics 35: 105–122.
Wallace, K. A., and A. J. Wheeler. 2002. “Reliability generalization of the life satisfaction index.”
Educational and Psychological Measurement 62: 674–684.
Wichert, Ines. 1999. “Job insecurity and work intensification: The effects on health and well-being.”
In Job Insecurity and Work Intensification, ed. B. Burchell, D. Day, M. Hudson, D. Ladipo, R.
Mankelow, J. Nolan, H. Reed, I. C. Wichert, and F. Wilkinson. York: Joseph Rowntree Foundation.
Wilson, S. M., J. H. Larson, and K. L. Stone. 1993. “Stress among job insecure workers and their
spouses.” Family Relations 42: 74–80.
Wilthagen, T., and F. Tros. 2004. “The concept of flexicurity: a new approach to regulating
employment and labour markets.” Transfer: European Review of labour and research 10: 166.
Withington, J. 1989. Shutdown: the anatomy of a shipyard closure. Bedford Square.
Witte, H. De, and K. Naswall. 2003. “‘Objective’ vs ‘Subjective’ Job Insecurity: Consequences of
Temporary Work for Job Satisfaction and Organizational Commitment in Four European Countries.”
Economic and Industrial Democracy 24 (January): 149–188.
http://eid.sagepub.com/cgi/content/abstract/24/2/149.
Zhang, Z., M. J. Zyphur, and K. J. Preacher. 2009. “Testing multilevel mediation using hierarchical
linear models.” Organizational Research Methods 12: 695–719.
