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Good jobs, good pay, good health? The direct effects of job quality on health dynamics among older European workers

Golo Henseke LLAKES Research Centre, UCL Institute of Education,

g.henseke@ioe.ac.uk

First Draft, This version: 15/06/2015

Abstract:

Longer working lives require sustainable jobs. By applying an amended methodology from Adams et

al. (2003, 2004) to a dataset with longitudinal information on work and health for older people in 15

continental European countries, I provide new evidence on differential effects of job quality on a

range of physical and mental health outcomes. Job quality indicators are grounded in a recently

proposed multi-disciplinary approach that combines financial and non-financial aspects into a

comprehensive concept. To better control for section into jobs I utilise data on childhood

circumstances and healthiness – a novelty in this literature strand. My findings suggest significant

effects of intrinsic job quality on the occurrence of musculoskeletal disorders, incident depressive

symptoms, and the onset of poor health. A cross-national comparison reveals the most limited

health effects in Southern Europe and the widest range of job quality effects on health in

Scandinavia. Potential policy initiates to improve population health through intrinsic job quality will

need to take the existing institutional context into account.

Keywords: job quality, older workers, health dynamics, childhood circumstances, cross-national

comparison

JEL: I14, J81, C22

1 Introduction Despite improving morbidity and mortality, an almost universal access to health care, health and

safety regulations, and an overall decline in accidents at work, around a quarter of Europeans

believe their jobs to be pathogenic (Barnay 2014). The total costs from major work-related health

disorders are estimated to amount to about 4.5% of total GDP in the EU (EU-OSHA 2014). Extended

working lives, growing wage inequality, a largely unbroken trend to more intense work (Green et al.

2013), and increasingly common precarious employment relations (Standing 2011) have an impact

on job quality and potentially add to work-related health hazards. Almost 30% of older workers in

low quality jobs suffered from fair or poor health, compared to only 12% in high quality jobs in

Continental Europe (Figure 1). Impaired health is a main reasons for early retirement. In this study, I

analyse how job quality contributes to health inequalities in a general population of people aged 50+

in European countries. A better understanding of the potential health hazards of work will help to

inform on sustainable jobs to extend working lives in times of ageing populations in the different

policy contexts across Europe.

There is a rich body of multi-disciplinary research that explores the influence of work and working

conditions on health and well-being (Barnay 2014; Bassanini & Caroli 2014). Hazards at work,

material well-being, and psychosocial facets of job quality correlate with a range of stress-related

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physical and mental health disorders. This affects workers of every age, but evidence suggest

growing vulnerabilities to work-related health hazards over the life-course (M. K. Jones et al. 2013).

Emerging health inequalities are, however, moderated and shaped by the wider national policy

context (Bambra et al. 2014).

Figure 1: Self-reported health by intrinsic job quality amongst older European workers, 2004-2013

Base: Employed labour force aged between 50-74 years Austria, Belgium, Czech Republic, Denmark, Estonia, France,

Germany, Italy, Netherlands, Poland, Slovenia, Spain, Sweden, and Switzerland. Source: SHARE W1, W2, W4, W5, own

calculations

Despites decades of research in this field there is debate as to what extent the observed associations

reflect causal mechanisms. Workers do not randomly select into jobs but chose them based on their

skills, health, and tastes. The labour market positioning at any given time is the result of decisions

over the career, during childhood and in adolescence. A growing economic research strand has

started to deal with these selection mechanisms in order to identify the direct effects of work on

health in greater detail. However, this literature has so far looked mainly at general health

outcomes.

By analysing the effect of job quality on the health dynamics over time in a cross-national population

of older workers, my work makes several contributions to the growing body of economic research in

this area. Firstly, I utilise a modern, multi-disciplinary concept of job quality to analyse the different

pathways through which work can affect the health of older workers. Going beyond psychosocial job

quality allows me to analyse the effect of related, but often ignored, dimensions of job quality such

as earnings and job security. Secondly, unlike most previous work, I consider detailed health

outcomes covering both physical health, mental health disorders and functional disabilities rather

than a summary indicator of health or one specific disorder to better trace comorbidities. And

finally, I adopt a longitudinal model of the incidence of new health conditions that together with rich

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individual background information including data on childhood circumstances, can help to shed light

on the health effects of good and bad work.

Drawing on data from the Survey of Health and Retirement in Europe (SHARE), I can follow the life

situations of people aged 50 and older and their partners between 2004 and 2012 across 15

European countries. The survey provides not only biannually collected longitudinal data on health

and work, but also unique retrospective information on childhood circumstances and healthiness.

In all, my results suggest that causal effects on job quality cannot be ruled out. Psychosocial job

quality predicts the occurrence of musculoskeletal disorders, incident depressive symptoms and the

onset of bad health. Women seem more physically susceptible to poor intrinsic job quality, whilst

men struggle more with mental health outcomes. Job security and earnings do not predict health

innovations in the pooled data. The estimates suggests cross-national differences in the subsequent

health inequalities by working conditions. Distinguishing by type of welfare states reveals that

differences in job quality have the smallest direct health effects in southern European countries and

the most diverse direct impacts in Scandinavia. A finding that clearly contrasts with the established

cross-country patterns in income and skills inequality. Improving the intrinsic job quality by one

standard deviation is predicted to reduce the onset of work-related health outcomes. The estimated

average effects are similar to giving up a risky health behaviour trait in the pooled sample. But the

there are differences across the existing policy context – a point specifically relevant for EU wide

policy initiatives.

The remainder of this study starts with a review of the existing literature on working conditions and

health. Section three describes the conceptual model and the empirical implementation. The data

and variables are introduced in section four. Section five summarises the findings from the empirical

analysis, followed by discussion and conclusions in section 6.

2 Previous Literature Long-term exposure to adverse working conditions is thought to affect health mainly through

continued physiological stress responses. Even relatively minor environmental and intrinsic stressors

involve physiological arousal that leads to a wear and tear on the body, called allostatic load

(McEwen & Seeman 1999). Stressors are not restricted to jobs strains, but can also include stress

due to material deprivation, or hazardous environments. In the short-term, these bodily response

help to cope with stress, but when exposed to repeated or persistent stressors, the accumulation of

allostatic load causes dysregulation in the body and leads to health problems. High blood pressure

develops into hypertension and eventually coronary heart disease. Repeated muscle strain develops

into chronic pain (Gruenewald et al. 2012; Karlamangla et al. 2002; Seeman et al. 2001; Steptoe &

Kivimäki 2012). As consequence, chronic stress lowers the body’s resources to adapt and eventually

increases frailty and the risk of diseases and disorders (Seeman et al. 2014; Ganster & Rosen 2013).

Research on health effects of working conditions is commonly grounded in psychosocial theories of

work-related stress. Seminal conceptual contributions include Karasek (1979), Johnson et al. (1989),

and Siegrist (1996). Situations of job strain (combination of high demands with low control), iso-

strain (job strain in conjunction with a lack of social support) or effort-reward imbalance (lack of

reciprocity between work effort and received reward) are theorized to impact health and subjective

well-being. Empirical findings, mostly from epidemiological, sociological and psychological research,

largely confirm this conclusion for a range of health outcomes such as cardiovascular disease,

musculoskeletal disorders, gastrointestinal problems, disrupted immune response, body mass, or

psychological well-being including fatigue, depressive symptoms and emotional exhaustion (Backé et

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al. 2012; Häusser et al. 2010; Kivimäki et al. 2012; Niedhammer et al. 2014; Nieuwenhuijsen et al.

2010; Nixon et al. 2011; Vanroelen et al. 2009; Schütte et al. 2014).

Older workers are found to be more susceptibility to job-related stress (Fletcher et al. 2011 in US

data; Davies et al. 2014 for UK data; M. K. Jones et al. 2013 for European data; Ravesteijn et al. 2013

for Germand data; for a general review Bohle et al. 2009). When exposed to job-strain, older

workers face among others alleviated risks of developing depressive symptoms, disabilities, or

increasing frailty (Reinhardt et al. 2013; Siegrist et al. 2012; Kalousova & Mendes de Leon 2015).

Ravesteijn et al (2013) calculates that an increase in physical jobs demands by one standard

deviation over the ages 60-64 is equivalent to ageing 14 additional months, similarly working in a job

with low-control has equivalent health effects as ageing 2 years in German data. Tentative evidence

suggests differential dynamics of health by job type: cumulative exposure to adverse working

conditions seems to wear out people faster. Health inequalities thus widen over the working life

(Case & Deaton 2005; Gueorguieva et al. 2009; Kjellsson 2013).

More recently, job insecurity (Sverke et al. 2006) and precarious work (Benach et al. 2014; Benach &

Muntaner 2007) have attracted the attention of scholars as further sources of work-related stress.

Flexible work arrangements have progressively become more common in developed countries and

are advocated, in the form of downshifting, bridge jobs or gradual retirement, as key to extend

working lives beyond current normal retirement ages (Barnay 2014; Van Vuuren 2014). Downshifting

is thought to reduce job demands and help people to achieve a good work-life balance (Kennedy et

al. 2013). But older workers, particularly women, job-seekers and people from lower socio-economic

background, risk instead to move into precarious job roles that constitute own health hazards

(Benach & Muntaner 2007; Bohle et al. 2009; Loretto & Vickerstaff 2015). The adverse consequences

on physiological health and mental well-being of chronic job insecurity and precarious employment

are well documented (Caroli & Godard 2013; Green 2011; Llena-Nozal 2009; Virtanen et al. 2013;

Aerden et al. 2015).

Major differences in the quality of health care systems, labour market institutions, and work

organisation across European countries shape the wider context of work with potential impacts on

the degree of health inequalities. Existing empirical findings suggest that the more generous welfare

states in Scandinavian countries, for instance, correlate simultaneously with better population

health and less strenuous working conditions (Dragano et al. 2010; Lunau et al. 2015). A common

typology differentiates between Scandinavian, Corporatist, Liberal, Southern, and Eastern European

welfare states – ordered by their generosity and the level of social protection against adversaries

(e.g., Eikemo et al. 2008; Bambra 2011). But even the generous welfare states of the Nordic

countries with relatively small inequalities in income and education produce persistent, sizeable

health inequities (Brennenstuhl et al. 2012; Bergqvist et al. 2013; Mackenbach 2012). The few

existing studies on cross-country variations in health inequalities by job quality point to similar

conclusions (Gupta & Kristensen 2008; Bambra et al. 2014).

Building on (Bustillo et al. 2011) and the multi-disciplinary insights into the health-effect of working

conditions, Green and Mostafa (2012) and Green et al. (2013) propose a comprehensive concept of

objective job quality. Besides intrinsic, psychosocial aspects, their approach distinguishes

additionally between earnings, job prospects, and working time quality. Earnings capture the

material variation in job quality. Job prospects combine job security and prospects for future

development. Intrinsic job quality includes most of the commonly explored psychosocial stressors

and workplace hazards grouped into four subscales: skill use and discretion, social environment,

physical environment, and work intensity. Working time quality encapsulates job features that

contribute to a good work-life balance. The authors successfully validate their concept against a

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range of health and subjective well-being outcomes in cross-sectional data. So far, the concept has

not been used yet to study the onset of health conditions.

Though decades of research have explored the link between job quality and health, the evidence on

the causal effects particularly in a general populations of older workers has remained scarce. Two

key selection mechanisms hamper the inference of work-related health effects from observational

data (Ravesteijn et al. 2013). Firstly, there is a strong socio-economic gradient with respect to job

quality and health (Macmillan & Smith 2007; Stowasser et al. 2012; Galama & van Kippersluis 2013).

Workers choose specific jobs depending on to their own abilities, educational achievement, health,

and, potentially, taste. As a consequence people form better off backgrounds tend to be healthier

and employed in higher quality jobs (Landsbergis et al. 2014; Moncada et al. 2010). In fact, job

quality differences explain a substantial fraction of the socioeconomic health inequalities over the

life course (Bauer et al. 2009; Kaikkonen et al. 2009).

Socioeconomic inequalities in health and employment over the life-course can be traced to

childhood conditions (Mazzonna 2014; Case et al. 2005; Smith 2009b). Socioeconomic circumstances

during childhood and potentially even in utero determine the level of acquired skills, healthiness and

education with subsequent effects on the labour market trajectories and the history of adverse

working conditions and labour market disadvantage (Dragano & Wahrendorf 2014; Jerrim 2014;

Macmillan et al. 2013). Working conditions in the first job after full-time education are indeed found

to impact health later in life (Fletcher 2012).

Further, older workers potentially downshift or become economically inactive in reaction to their

health status (García-Gómez 2011; Disney et al. 2006; García-Gómez et al. 2013; Bound et al. 2010).

This health related selection in labour market positioning introduces a feedback of reverse causation

– declining health can lead to lower payer, lower pay can contribute to diminished health. How these

selection mechanisms are dealt with in the empirical analysis, matters for the estimated health

effects of work (Davies et al. 2014; M. K. Jones et al. 2013).

Secondly, the theory of compensating wage differentials suggests a trade-off between pay and risky

working conditions. Economic theory suggest that higher pay is thought to compensate workers for

otherwise not insured job hazards (Pouliakas & Theodossiou 2013 for review). Workers could use

the earnings premium of high risk jobs to offset work-related health impairments (e.g., Case &

Deaton 2005) or, more generally, change health-related behaviour in response to job quality. A

sedentary job might stimulate to exercise after work. High levels of work-related stress could lead to

increased alcohol consumption or an unhealthy diet. Initial blue collar employment seems, for

instance, to lead to more risky health behaviour (Kelly et al. 2012).

Two basic econometric strategies are commonly applied to deal with selection bias: instrumental

variables or individual effects to controls for unobserved heterogeneity. Scholars have proposed a

range of external instrumental variables such as the number of health and safety regulations across

countries (Cottini 2012; Cottini & Lucifora 2013), industry and country-specific stringency of

employment protection (Caroli & Godard 2013), variations in the local unemployment rate at time

of job choice (Kelly et al. 2012), or organisational measures of high performances human resource

(M. K. Jones et al. 2013). But IV strategies, though promising, remain hard to realise in this context.

Good external instruments are rare, most seem to have insufficient statistical power, and even if

they instruments strongly correlated with working conditions, effects would be identified for local,

not clearly, defined populations. Cottini and Lucifora (2013) and Jones et al. (2013), for instance, can

report a larger effect of working conditions on health outcome using IV, whereas the point estimates

in the remaining IV-studies decline compared to the baseline findings.

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Models with time invariant individual effects have been the method of choice for most scholars to

achieve consistent estimates. Dynamic panel data estimator with lagged dependent variable as

additional regressor to account for the persistency in health are commonly applied in the literature

(Butterworth et al. 2011; Fletcher et al. 2011; Gupta & Kristensen 2008; Llena-Nozal 2009; Robone et

al. 2011). Alternatively, fixed effects specifications or models in first differences are used to cancel

out time invariant individual effects (Ravesteijn et al. 2013; Green 2011; Llena-Nozal 2009). Fletcher

(2012) uses sibling fixed effects to control for common family background characteristics.

Adjusting for selection and state-dependent health changes the estimated effect sizes substantially.

Compared to baseline estimations without adjustments coefficients on job quality drop and on

occasion become insignificant. Clearly, selection and persistency in health explain most of the

association between job quality and health. However, in most of the existing studies health

dynamics are restricted to a first-order Markov process. The influence of longer lags is ignored.

Further information on initial conditions is usually gleaned from data at survey baseline. A practice

that has received criticism (Smith 2009a).

For the current study, I follow in the footsteps of the latter literature strand but add pre-labour

market information on healthiness and abilities. I adopt an identification design that was initially

developed to detect causal non-effect of SES on health (Adams et al. 2003; Adams et al. 2004). The

highly cited study, henceforth HWWA, applied a Granger causality framework to a longitudinal

survey of elderly American to test for the absence of direct SES impacts on the onset of a range of

health outcomes conditional on previous health. HWWA main advances were the exploration of

health innovations rather than levels and the insight that non-causal effects in a statistical

framework might imply absence of “true” causality. The authors propose parameter invariance as a

necessary condition for causal processes.

HWWA stimulated a huge controversy. SES was found to have no impact on the incidence of most

health outcomes apart from mental health conditions. Most criticism was levied against the

treatment (or lack thereof) of unobserved heterogeneity, the test of non-causality including the

proposed condition of time, and the assumption that health dynamics can be satisfyingly described

by a first-order Markov process.

More recently Stowasser et al. (2014; 2012) have revisited the approach. By adding childhood

information to the model and allowing for longer health lags, Stowasser et al. (2014) tackle two

areas of criticism: lack of rich health dynamics and omission of individual effects that potentially

confound the relation between SES and health. Their methodology provides a valuable starting point

for my analysis of job quality effects on health.

3 Methodology Drawing from the existing literature, I assess the effects of job quality on new health events

conditional on previous health and individual effects that potentially correlate with health

outcomes, labour supply and job quality. In doing so, I can account for at least part of the selection

bias that confounds estimations of job quality effects on health. Further by analysing the effect of

job-quality on health across several European countries I can test for parameter invariance under

several different policy regimes. The latter test might provide valuable insights into the (non-

)existence of cross-country differences in job-quality effects on health. It can also be informative

about the role of public policy as cause of health inequalities in a mature population.

Adapting the methodology in HWWA and the related literature, I specify the following dynamic

model of health incidences:

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𝑓(𝐻𝐼𝑖𝑡𝑗

|𝐻𝐼𝑖𝑡𝑘<𝑗

, 𝐻𝑖𝑡−1, 𝐽𝑄𝑖𝑡−1, 𝑋𝑖𝑡−1, 𝜗𝑖)

The model describes the incidence of health condition 𝑗 for individual 𝑖 conditional on past job

quality (𝐽𝑄𝑖𝑡−1), the instantaneous effects from concurrent health shocks 𝐻𝐼𝑖𝑡𝑘<𝑗

, past health

(𝐻𝑖𝑡−1), a set of socio-demographic variables (𝑋𝑖𝑡−1), and a time constant individual effect 𝜗𝑖. More

specifically:

JOB QUALITY: 𝐽𝑄𝑖𝑡−1

Following Green and Mostafa (2012), I consider earnings, intrinsic job quality, and job security at 𝑡 −

1 as indicators of overall job quality in 𝑡 − 1. These are the variables of interest. I expect that good

jobs protect people’s health and lower the risk to develop medical conditions. Additionally I include

information on job tenure and working hours which might affect health transitions and job quality.

PAST HEALTH, 𝐻𝑖𝑡−1

Health is highly persistence over time (Contoyannis et al. 2004; Jones et al. 2012). Including past

health in the empirical model controls for the state dependency (e.g. depressive symptoms in the

past make it more likely to develop depressive symptoms in the future) and comorbidities between

health conditions (e.g. increased risk of heart attack with history of hypertension; relation between

physical and mental health). The vector of past health conditions includes health behaviour,

measures of physical as well as mental conditions, and information on general health and functional

disabilities.

CONCURRENT HEALTH INNOVATIONS, 𝐻𝐼𝑖𝑡𝑘<𝑗

The onset of health conditions could result from other concurrent health shocks. To capture the

effect of contemporaneous health shock, I impose a unidirectional instantaneous causal chain of

health incidences flowing from potentially life-threatening acute conditions, to chronic

cardiovascular conditions, to musculoskeletal disorder, to depressive symptoms, functional

disabilities, health-related behaviour, and, finally, self-assessed health. The assumed causal chain

follows propositions in HWWA. Adams et al. (2004) report validity tests.

SOCIO-DEMOGRAPHIC COVARIATES, 𝑋𝑖𝑡−1

A vector of time varying and time constant covariates with potential influences on health and job

quality. It includes age, age square, a dummy variable to indicate whether he respondent has

reached the early retirement age, gender, indicator if born abroad, current marital status, cognitive

abilities, and country of residence.

INDIVIDUAL EFFECTS: CHILDHOOD AND FAMILY BACKGROUND

Let the individual effect 𝜗𝑖 be a representation of innate healthiness, behaviour, and abilities with

effects on both the labour market positioning and the trajectory of health. To approximate the

variable, I combine retrospective childhood information on healthiness, parental socioeconomic

status, educational attainment and academic performance. Further I include information on

premature parental mortality to capture potential genetic risk, the respondent’s smoking history to

control for past health-related behaviour and height which has been shown to reflect healthiness

and to correlate with labour market outcomes. In addition to dealing with individual heterogeneity,

measures of childhood health also enrich the possible dynamics in health transitions.

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The proposed dynamic model is comparable to Grossman’s specification of health as durable capital

good (Grossman 1972; Grossman 2000). Within this human capital framework, job quality can be

interpreted as a health (dis-)investment whose level is chosen by the individual to maximise life-

cycle utility (Fletcher et al. 2011; e.g., Case & Deaton 2005).

The health innovations are assumed to follow a Poisson distribution.

𝐻𝐼𝑖𝑡𝑗

= 𝑒ln(𝐸𝑡−1,𝑡)+𝛽0+∑ 𝐻𝐼𝑖𝑡𝑘𝑘<𝑗

𝑘=1 𝛼1𝑘+𝐻𝑖𝑡−1

′ 𝛼2+𝐽𝑄𝑖𝑡−1′ 𝛼3+𝑋𝑖𝑡−1

′ 𝛼4+𝜗𝑖𝛼5+𝑢𝑖𝑡

For chronic conditions, e.g. hypertension, diabetes or disability, 𝐻𝐼𝑖𝑡𝑗

indicates the onset, whereas for

acute conditions, e.g. heart attack or stroke, it indicates a new occurrence. The events are either

binary, e.g. occurrence of depression, or a count of multiple events, e.g., onset of additional

cardiovascular risk factors. The models are estimated using Poisson regression. In conjuncture with

the length of exposure, 𝐸𝑡−1,𝑡, measured by the duration between survey interviews, Poisson

regression is equivalent to a proportional hazard survival model. Because strict exogeneity is unlikely

to hold, e.g. downshifting in the follow-up period in response to an acute health incidence, I pool the

data and do not use a dedicated panel estimator.

To test for potential heterogeneous effects across time and countries, I include two sets of

interaction terms between lagged job quality and, firstly, country dummies and, secondly, period

dummies. Wald tests of joint significance are used to test against the null hypothesis of

homogenous parameters.

Despite best efforts it is conceivable that unobserved factors, such as taste and inter-temporal

preference correlate with the variables of interest and health dynamics. Further, with longer time

periods between observations the assumed causal chain of health innovations becomes more

restrictive and ignored job quality shocks could start to manifest in health differences. To deal with

potential model misspecification, violations of the Poisson process, and serial correlation in the

outcomes within individuals, I estimate the regressions using a Huber/White/Sandwich variance

estimator. Significant coefficients on lagged job quality measures might best be interpreted to

predict but not “structurally” cause the onset of new health conditions.

4 Data, variables and summary statistics

4.1 Dataset I use data from the Survey of Health, Ageing and Retirement in Europe. SHARE is a large longitudinal

representative probability sample that provides comprehensive and cross-nationally comparable

data on health, socioeconomic characteristics, and the labour market status including job quality

instruments of people aged 50 years and older and their partners (Börsch-Supan et al. 2005; Börsch-

Supan et al. 2013). SHARE was launched in 2004 and has been conducted in a biannual rhythm. To

date, the survey has collected information on more than 110,000 individuals from 20 European

countries and Israel over five waves. The first wave was fielded in 2004 in 11 northern, central and

southern European countries. For the second wave in 2006/2007, Poland and the Czech Republic

joined the survey. The third wave, SHARELIFE, supplemented the concurrent information from the

previous waves with retrospective life history data. The fifth and most recent wave covers the yeas

2012/2013. It includes along concurrent information also key retrospective data on childhood

circumstances. For more information on the data collection and survey design see the technical

reports (Börsch-Supan & Jürges 2005; Börsch-Supan et al. 2008; Schröder 2011; Malter & Börsch-

Supan 2013; Malter & Borsch-Supan 2015).

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The analysis requires longitudinal information on a wide range of covariates for people who were at

work at baseline. I make use of data from all five available waves for people aged 50-74 years. To

study health transition conditional, amongst others, on childhood information, I restrict the sample

to countries that have contributed to at least two consecutive waves and participated in waves three

or five. Wave three (SHARELIFE) contains details life history data, but only a very limited selection of

concurrent health measures and is therefore not directly available for the analysis of health

transitions. In total, my sample comprises of about 19,700 observations from roughly 14,100

different respondents in 14 countries with complete information on all variables. The countries

group into four distinct welfare state regimes: Denmark and Sweden belong to the Scandinavian

cluster, the central and western European countries Austria, Belgium, France, Germany,

Netherlands, and Switzerland represent Corporatist welfare states, Greece, Italy, and Spain are

examples of Southern European welfare states, and the Czech Republic, Estonia, Poland, and

Slovenia are classified as Eastern European welfare states. The exact sample sizes will differ across

regression models.

The average length between interviews was 2.8 years including the gap between waves 2 and 4. I’ll

conduct invariance tests to establish whether the estimated effect sizes shift between waves.

4.2 Variables

4.2.1 Job Quality SHARE includes measure of psychosocial working conditions and job security from the Job Content

Questionnaire (Karasek et al. 1998) and the effort-reward imbalance questionnaire (Siegrist et al.

2004), employment contract, pay and occupations. Following the concept outlined in Green et al

(2013) and Green and Mostafa (2012), I group the items into three key aspects of job quality:

intrinsic job quality, job security, and earnings. Scores are calculated in the complete pooled cross-

sectional dataset (N = 43,500) to guarantee sufficient observations in each category. Once derived,

the intrinsic job quality and job security scales are z-standardized.

Intrinsic job quality combines categorical items on skills and discretion with social support, physical

environment and work intensity. The included items cover the ISCO skill level, opportunity to

develop new skills, discretion over how to do the work, support in difficult situations, recognition of

work, adequate salary, job prospects, physical work demands, and time pressure. The psychosocial

measures are coded on Likert-scales. ISCO skill levels distinguish between four ordinal ranked groups

(academic, tertiary non-academic, upper secondary, lower secondary or below) which map to the

occupational major groups. I use multiple correspondence analysis (MCA) to derive the item weights

needed to calculate a scale of intrinsic job quality. MCA can be viewed as a generalization of

principal component analysis for categorical variables. The first two dimensions explain at least 82.5

% of the total inertia. After an inspection of the calculated weights, dimension one returns noise,

whilst dimension two retains the intrinsic job quality. The weights of the index follow plausible

patterns: opportunity for skill development, high skill level, recognition, social support and

particularly adequate salary contribute strongly to high levels of intrinsic job quality.

The index of job security is constructed from a Likert-item on perceived job insecurity and the

employment terms: permanent, temporary employment, or self-employment. Combing both

variables using MCA, explains at least at least 95.5 % of the total inertia in the first dimension. Again

the weights are intuitively plausible. Temporary employment diminishes job security. Employees in

secure, permanent position receive the highest index value.

SHARE contains a range of labour income questions, but the items suffer from non-response and

reporting error. Therefore, I use imputed values that are provided as data supplement to the survey

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waves. SHARE uses a multiple imputation procedure drawing on longitudinal information, a set of

concurrent variables and unfolding brackets to predict a range of plausible annual monetary values.

In the analysis, I use the first plausible value of annual net income from employment or self-

employment. Variables are converted to average monthly earning using information on the number

of months worked in the job and transformed to German prices in 2005 by purchasing power parity

conversion rates. In total, average monthly net earnings were around EUR 1,712 in the sample (Table

1). Approximately 13% of the income values were imputed.

Table 1: Job Quality

Variable Obs. Mean Std. Dev. Min Max

Intrinsic Job Quality 19707 -0.071 0.939 -4.288 1.815

Job Prospects 19707 0.053 0.922 -3.734 0.858

Real Net Pay 19707 1712.07 2186.06 -324.42 62812.70

Imputed Pay 19707 0.132 0.338 0 1

The distribution of job quality facets show sensible patterns across individuals and countries (see

Table 2). People with tertiary education score highest in all three dimensions, whilst workers with

lower secondary education or below found themselves in less secure jobs of lower quality with less

pay. Similarly, average intrinsic job quality declines as we move from Scandinavian to Eastern

European welfare state regimes. Net pay and job security peak in Corporatist regimes. Perhaps the

result of protective on insider focused labour markets. Job quality was generally lowest in Eastern

Europe. Job quality in southern European countries was located between the Corporatist and

Eastern European welfare states. The low average value of real pay in Scandinavia is partly resulting

from the high prevalence of part-time jobs. But even if differences in hours worked are account for

average net pay remains highest in Corporatist countries.

Table 2: Distribution of job quality

Intrinsic Job Quality Job Prospects Real Net Pay

Educational Attainment

Lower Secondary -0.335 -0.066 EUR 1,187.66

Upper Secondary -0.164 0.028 EUR 1,587.18

Tertiary 0.275 0.188 EUR 2,321.10

Welfare State Regime

Scandinavian 0.225 0.079 EUR 1,243.24

Corporatist -0.039 0.127 EUR 1,984.68

Southern -0.203 -0.027 EUR 1,423.34

Eastern -0.275 -0.522 EUR 505.68

Total -0.071 0.053 EUR 1,712.07

4.2.2 Health Indicators The full set of health indicates is given in Table 3 and health interventions are summarised in Table 4.

Some of the constructed variables may require further clarification. Acute conditions is a summary

score of previous diagnosis of heart attacks and strokes. Cardiovascular risk factor combines

previous diagnosis of hypertension, high blood cholesterol and diabetes. The index of

musculoskeletal disorders measures mostly mobility limitations resulting from impaired functioning

11

of the musculoskeletal system. It combines information on joint pain with 10 items on physical

difficulties such as walking, sitting, climbing stairs, or carrying and lifting. In wave 5, the item on joint

pain was split up and more thoroughly explored at the expense of some intertemporal consistency.

Depressive symptoms are measured on the EURO-D scale from 12 self-reported items. EURO-D has

been validated as a cross-national scale of mental health in elderly populations (Copeland et al.

2004; Prince et al. 1999). Values of four and above are indicative of major clinical depressions

(Castro-Costa et al. 2007; Larraga et al. 2006). The summary index of risky health behaviour captures

whether a respondent engages either never or only occasionally in moderate physical activity,

consumes alcohol daily or almost daily, and currently smokes. Functional disabilities measure the

difficulties a person has with self-care (ADL) and household management (IADL).

The occurrence of acute, potentially life threatening diseases is inferred from a dedicated item

battery. In cases respondent died after a heart attack or stroke, the cause of death from available

end of life interviews is coded as an acute incidence. The constructed binary variable indicates

whether any acute incidence has occurred or not. Innovations in cardiovascular risk and

musculoskeletal disorder code the onset of any new condition and limitation, respectively. Incident

depression indicates the development of severe depressive symptoms. Innovation in risky health

behaviour captures the development of potentially unhealthy behavioural habits. The onset of

functional disability is one if a respondent developed at least one ADL or IADL between survey waves

and zero otherwise. Similarly, the occurrence of poor or fair health captures the transition from

good or better health into less than good health. All health measures are self-reported.

Table 3: Prevalence of Health Conditions among older European workers

Variable Obs. Mean Std. Dev. Min Max

Acute Conditions 19707 0.059 0.241 0 2

Cardiovascular Risk Factors 19707 0.481 0.695 0 3

Musculoskeletal Disorders 19707 1.091 1.453 0 10

EURO-D Score 19707 1.988 1.957 0 12

Obese (BMI>=30) 19707 0.150 0.357 0 1

Underweight (BMI<18.5) 19707 0.007 0.083 0 1

BMI Missing 19707 0.014 0.118 0 1

Risky Health Behaviour 19707 0.546 0.686 0 3

Functional Disabilities 19707 0.098 0.585 0 13

Self-Reported Health

Poor/ Fair 19707 0.188 0.391 0 1

Good 19707 0.440 0.496 0 1

Very Good/ Excellent 19707 0.372 0.483 0 1

Table 4: Health Innovations

Variable Obs. Mean Std. Dev. Min Max

Acute health incident 19707 0.018 0.132 0 1

Cardiovascular Risk 19707 0.181 0.451 0 3

Musculoskeletal Disorders 19707 0.540 1.054 0 10

Incident Depression 19707 0.094 0.292 0 1

Risky Behaviour 19707 0.111 0.322 0 2

Functional disability 19705 0.051 0.220 0 1

Onset poor/ fair health 19703 0.098 0.298 0 1

12

4.2.3 Childhood and Family Background, socio-demographics, and further job

characteristics The full list of included covariates is given in Table 5. The derived variables were constructed as

follows.

The score of cognitive ability combines self-repots on writing and reading proficiency with test data

on orientation, memory functioning, verbal fluency, and numeracy. The categorical variables are

aggregated using weights derived from multiple correspondence analysis. The resulting index clearly

correlates with broad educational categories (spearman rank correlation coefficient of .47). The

strength of the correlation is similar to values obtained from the OECD Survey of Adult Skills using

more comprehensive cognitive skills assessments.

Premature parental mortality is an average of potential years of life lost. The variables averages the

number of potential years of life lost due to mortality before the age of 70 across both parents. If a

parent is still alive or deceased after the age of 70, his or her contribution is 0. If a parent died at the

age of 55, his or her potential years of life lost is 15. The reference age of 70 is in line with current

OECD values (OECD 2011).

Information on health and the living situation during childhood are collected retrospectively. For the

purpose of the survey, childhood was defined to include ages 0 to 15. Childhood health can be

thought to captures initial healthiness. Previous research has confirmed the validity of the measures

(Havari & Mazzonna 2011). The health conditions index is an average over the occurrence of a range

of acute and chronic health conditions such as respiratory problems including asthma, chronic ear

problems, difficulties seeing with eyeglasses, migraines, or psychiatric problems.

Self-reported academic position at age 10 combines two binary items which are one if respondent

reported above average performance in mathematics and languages relative to others at the age of

10.

The adjusted total annual net household income is the available household income minus the

individual income from employment/ self-employment. It captures the effect of non-labour income

on health and labour supply decisions. I use the first plausible imputed value to derive the variable.

Early retirement eligibility ages are taken from the Mutual Information System on Social Protection

(MISSOC). I derive a dummy variable that distinguishes between people above and below the age

threshold. The variable is thought to capture differences in the propensity to leave the labour force

and thus in the length of exposure.

Table 5: Summary Statistics Covariates

Variable Obs. Mean Std. Dev. Min Max

Educational Level

Lower Secondary (ISCED0,1,2) 19707 0.270 0.444 0 1

Upper Secondary (ISCED3,4) 19707 0.412 0.492 0 1

Tertiary (ISCED5,6) 19707 0.317 0.465 0 1

Cognitive Ability Score 19707 0.377 0.750 -6.03 1.52

Premature Parental Mortality 19707 2.951 5.537 0 46.5

Adult Height (in cm) 19707 170.3 9.2 100 203

Ever smoked 19707 0.537 0.499 0 1

Self-Reported Health during childhood

13

Poor/ Fair/ Volatile 19707 0.084 0.277 0 1

Good 19707 0.274 0.446 0 1

Very Good/ Excellent 19707 0.643 0.479 0 1

Health Conditions Index 19707 0.017 0.038 0 0.4

Missed School 1+ Month 19707 0.133 0.339 0 1

Self-Assessed Academic Position (age=10) 19707 0.755 0.792 0 2

1+ Shelf of books (age=10) 19707 0.700 0.458 0 1

Rooms per household member (age=10) 19707 0.792 0.394 0 11.25

Job tenure 19707 20.9 13.0 0.5 62.5

Average working Hours

0-19 hrs 19707 0.124 0.330 0 1

20-34 hrs 19707 0.185 0.388 0 1

35-42 hrs 19707 0.402 0.490 0 1

42-54 hrs 19707 0.202 0.401 0 1

55+ hrs 19707 0.087 0.282 0 1

Adjusted Total Annual Net HH Income 19707 23205.06 40501.1 0 2930197

Imputation Flag - Household Income 19707 0.625 0.484 0 1

Marital Status

Cohabiting 19707 0.059 0.236 0 1

Separate/ Divorced 19707 0.118 0.323 0 1

Widowed 19707 0.746 0.435 0 1

Never Married 19707 0.077 0.266 0 1

Age 19707 56.7 4.4 50 74

Above Early Retirement Age 19707 0.455 0.498 0 1

Female 19707 0.452 0.498 0 1

Foreign-born 19707 0.074 0.261 0 1

5 Results

5.1 General

5.1.1 Health inequalities by job quality Table 6 reports the risk ratios for the prevalence of health conditions by job quality in the population

of older workers.

Table 6: Predicted Risk Ratios by Bad vs. Good Jobs

Health Outcome RR SE

Acute Conditions 1.1733 0.1009

Cardiovascular Risk Factors 1.1247 0.0347 ***

Musculoskeletal Disorders 1.3242 0.0304 ***

EURO-D Score 1.2473 0.0279 ***

Obese (BMI>=30) 1.3225 0.0646 ***

Risky Health Behaviour 1.0507 0.0293

Functional Disabilities 1.3607 0.1206 **

Poor/ Fair Self-Reported Health 1.2798 0.0517 ***

14

Note: * p<0.05, ** p<0.01, *** p<0.001. Risk ratio compare the predicted prevalence of health conditions among people in bad (1st

quartile of job quality indicators) vs. people in good jobs (3nd quartile of job quality indicators). Derived from sets of Poisson regressions

with age, age^2, gender, period and a full set of country dummies. Regression use the provided cross-sectional survey weights.

There are clear and apparent health inequalities by job type among older workers in Europe.

Workers in low quality jobs are significantly more likely to suffer from a range of health conditions

than people in high quality jobs. The notable exemptions are acute conditions and risky health

behaviour. These general differences in the prevalence of ill-health are well documented in the

related literature. The findings confirm the commonly acknowledged association between health

and working conditions, but how much of these inequalities can be actually attributed to job quality

differences is less clear.

5.1.2 Incidence of health conditions Table 7 summaries the estimated average marginal effects of job quality facets on the incidence

rates of health innovations conditional on the full set of covariates. Average monthly earnings are

broken down into five groups that roughly correspond to the income quintiles in the sample.

Table 7: Average marginal effects of job quality on the incidence of health conditions

Variable Acute Cardio muscle Mental Behaviour Disability Poor Health

Intrinsic Job Quality 0.0002 -0.0038 * -0.0199 *** -0.0042 *** 0.0015 -0.0004 -0.0054 ***

0.0005 0.00 0.0035 0.00 0.0012 0.00 0.0011

Job Security 0.0001 0.00 -0.0013 0.00 0.0007 0.00 -0.0014

0.0004 0.00 0.0032 0.00 0.0012 0.00 0.0011

Earnings

EUR <200 0.0006 0.00 -0.0134 0.00 -0.0041 0.00 -0.0053

0.0017 0.00 0.0117 0.00 0.0038 0.00 0.0041

EUR 200-950 -0.0008 0.00 -0.0122 0.00 -0.0041 0.00 -0.005

0.0017 0.00 0.0122 0.00 0.0041 0.00 0.0041

EUR 950-1500 (Ref.) (Ref.) (Ref.) (Ref.) (Ref.) (Ref.) (Ref.)

EUR 1500-2400 0.002 0.01 -0.0193 0.00 -0.0013 -0.01 -0.0063

0.0023 0.01 0.0138 0.00 0.0045 0.00 0.0052

EUR >=2400 0.0031 0.0042 -0.0228 0.0012 -0.0047 0.0027 -0.0108 *

0.0049 0.0067 0.0152 0.0048 0.0054 0.0048 0.0055

Time Invariance 0.417 0.809 0.00 0.727 0.895 0.45 0.447

Country Invariance 0.000 0.000 0.00 0.000 0.001 0.00 0.000

Joint Effect 0.821 0.153 0.00 0.001 0.704 0.46 0.000

N 20124 20035 20035 19983 19983 18726 16400

Note: * p<0.05, ** p<0.01, *** p<0.001. Average marginal effects derived from Poisson regressions using the full set of covariates.

Musculoskeletal disorders, incident depressive symptoms and poor health link mostly clearly to job

quality. Acute conditions, changes in risky health behaviour or the occurrence of functional

limitations in contrast are not directly predicted by job quality differences in the pooled sample. The

findings also suggest an effect of intrinsic job quality on the development of cardiovascular risk

factors but the influence is not strong enough to translate into a statistically significant combined

effect of job quality on this conditions.

Intrinsic job quality is the most active facet of job quality. An improvement of intrinsic job quality by

a standard deviation for 1000 people would have prevented the development of approximately four

new cardiovascular risk factors, around 20 musculoskeletal disorders, four incident depressions, and

five complaints about poor health within a year of exposure. The health effects are comparable to

15

the predicted consequences of 1000 people giving up an unhealthy behavioural trait (e.g., smoking,

physical inactivity, daily drinking)

Job security and earnings are on their own not associated with health innovations. Further

investigation shows that this not due to strong links between quality, pay and security. Even when

entered as single indicator security and pay largely fail to predict changes in health.

Gender differences exist, but are small and do not alter the main conclusions (see Table 9 in the

appendix). Intrinsic job quality emerges again as the most significant health-related dimension of

job quality. The findings suggest women are more susceptible to physical health impairments,

whereas men are more likely to struggle with mental health as a result of poor intrinsic job quality.

There are few significant health effects of earning, but the pattern is inconclusive. Job security does

again not directly predict health innovations. Further, there is still no evidence for job quality effects

on the onset of acute conditions, functional disabilities or changes in unfavourable health behaviour.

In all, allowing for differential effects of job quality by gender confirms the previous findings.

Health shocks might cause people to leave the survey before follow-up; in extreme cases, because of

death after a fatal heart attack or stroke. However even non-fatal health shocks might reduce the

propensity to participate in the survey. In that case, non-response bias might occur as the collected

data stems from more healthy people. This is a general problem for health-related inferences form

observational data and not specific to my data set. The rich list of included covariates in the

empirical model should account for systematic non-response, but potential bias could still be

present. Applying the methodology developed in Verbeek and Nijman (1992) and outlined in Jones

et al. (2013), I test for the presence of non-random attrition. The test results suggest systematic non-

response is not a systematic issues in my analysis. Acute health shocks, as expected, correlate

statistically significantly with response behaviour, but the tests do not systematically reject the null

hypotheses of attrition at random among the remaining outcomes. Table 10 in the appendix

summarises the p-values. The estimated (non-)effects can thus not be attributed to non-response

bias.

Overall, job quality clearly predicts the onset of further musculoskeletal disorders, incident

depression and the transition into poor health among older workers. If one believes my covariates

are sufficient to condition out selection, different paces of health risk accumulation across jobs can

account, at least in parts, for the observed inequalities in the prevalence of health conditions. Effects

slightly differ by gender: job quality predicts the onset of cardiovascular risk factors among women

but is unrelated with depression and the other way round for men. None of the remaining

conditions is directly related to job quality in the pooled sample. But health dynamics and the

development of comorbidities over time could lead to spill-overs from one condition to another. A

closer inspection of the estimations results suggests, for instance, that musculoskeletal disorders at

baseline predict the incidence of acute conditions and the development of functional disabilities at

follow-up. However, a prediction of long-term effects of job quality requires an etiologically more

fully defined model and is beyond the scope of this study.

But does non-effect of job quality on some health outcomes allow us to conclude the absence of

direct structural causal effects? No. Even though the estimations largely confirm time invariance,

there is still substantial cross-country heterogeneity in the health response to job quality. Even

outcomes that do not directly respond to job quality in the pooled sample, appear to react to work

characteristics in some of the included country. The question arises what country specificities drive

these unequal health effects of job quality. What public policies help to protect older workers from

the adverse consequences of poor job quality across Europe?

16

5.2 Country Differences in Health Inequalities

5.2.1 Welfare State Regimes Potential differences in health inequalities by welfare state types can provide first, tentative insights

into the role of public policies in moderating the risks from poor job quality. By replacing the country

dummies with a set of welfare state regime dummies in the estimation models, I can evaluate the

health effects of job quality across regimes. This multilevel fixed effects strategy accounts for

general, not work related differences in the onset of diseases across the regimes, whilst allowing for

differential effects of job quality on health. Table 8 displays the p-values of joint significance tests

of job quality effects and of cross-regime differences.

Table 8: Wald tests of jointly significant job quality effects on health outcome

Scandinavian Corporatist Southern Eastern Difference across

regimes

Acute health incident

Cardiovascular Risk * * Musculoskeletal Disorders * *** *** *

Incident Depression * * **

Risky Behaviour ** *

Functional disability

Onset poor/ fair health ** *** * * Note: * p<0.05, ** p<0.01, *** p<0.001, p-values from tests of jointly significant marginal effects of job quality within welfare state

regimes. Last column reports p-value from Wald tests of jointly homogenous job quality effects between welfare states.

The estimates confirm previous empirical studies in their inconsistency: health inequalities do not

adhere to the proposed ranking of welfare states. My findings suggest that job-related health

inequalities are least pronounced in Southern Europe (1 in 7 conditions), followed by countries with

a Corporatist or Eastern European type welfare state (3 in 7), and finally Scandinavia (5 in 7).

In southern Europe, job quality facets predict the development of cardiovascular risk factors (high

earnings), incident depression (intrinsic job quality), and the onset of poor health (intrinsic job

quality). All estimates are borderline significant and cumulate therefore into insignificant joint

effects of job quality, with the exemption of transitions into poor health.

In Corporatist welfare states, intrinsic job quality predicts innovations in cardiovascular risk factors,

musculoskeletal disorders, mental health and poor overall health. The marginal effect of intrinsic job

quality on the onset of musculoskeletal disorders is notably larger than in the pooled sample. There

is also a significant protective effect of high earnings on the occurrence of musculoskeletal disorders.

In all, job quality is related with changes in the major work-related health components:

musculoskeletal limitations, depression and poor health.

For Eastern European welfare states my findings suggest, if statistically significant, the quantitatively

largest health differences by intrinsic job quality. Marginal effects on the development of

cardiovascular risk factors, musculoskeletal disorders, and incident depression are highly statistically

significant and larger than in the other country groups. Given these clear and pronounced health

inequalities, the non-effect of intrinsic job quality on poor overall health is even more surprising.

Intrinsic job quality is like in the other regimes the main driver of health inequalities, but findings

additionally suggest significant protective effects of job security on musculoskeletal disorders and

from high earnings on the transition into poor health.

17

The findings for Scandinavia stand out. Like in the other countries, the estimates disclose protective

effects of intrinsic job quality on musculoskeletal disorders, mental health and poor general health.

But in addition there are significant beneficial effects of job security on the incidence of acute health

conditions and risky health behaviour. Further, it is the only country group where high earnings are

not found to be protective in at least one instance. Instead, I find that low earnings reduce the

occurrence of cardiovascular risk factors compared to a medium-paid job. As a result of the multiple

pathways of job quality on health, Scandinavia emerges as the region with the most diverse direct

health effects of job quality on health including on potentially fatal acute conditions.

Overall, the results support musculoskeletal disorders, depression and overall poor health as major

health outcomes of poor job quality. Job quality effects on cardiovascular risk factors are more

mixed across regimes and strongest in Eastern Europe. Intrinsic job quality emerges as the most

health active facet of job quality across welfare state regimes. The patterns for earnings and job

security are erratic and inconclusive. The onset of functional limitations is the only health outcomes

that remains statistically independent of job quality across regimes. But despite differences in

details, welfare state regimes can only account for some of the country heterogeneity in job quality

effects uncovered in the pooled analysis. Much of the differences remain unexplained.

6 Discussion and conclusions Sustainable jobs are essential for longer working lives in times of population ageing. In this study, I

have investigated the effects of job quality on a range of physical and mental health outcomes in a

general population of European workers aged 50 and above. The study makes use of a modern

comprehensive concept of job quality which combines earning, job security and intrinsic job quality

into a multidimensional measure of working conditions. Selection effects complicate the inference. I

adopt a methodology pioneered by Adams et al. (2003) and refined by Stowasser et al (2014) to

address reverse causality and endogenous selection into jobs.

Three key findings emerge. Firstly, causal effects of job quality on diverse health outcome cannot be

ruled out. Intrinsic, psychosocial, job quality is the most important job quality facet for subsequent

health innovations. It consistently predicts the occurrence of musculoskeletal disorders, incident

depressive symptoms and the onset of poor health. Job security and earnings predict health

outcomes in a few instances within specific policy context but not across the whole sample.

Secondly, whilst men are prone to develop psychiatric disorders in response to poor job quality,

women seem more physically vulnerable to job quality difference with alleviated occurrence of

cardiovascular risk factors. Thirdly, a differential analysis by welfare state types sees the broadest

range of job quality effects on health in the Nordic countries, whereas in Southern Europe job

quality is less key for the development of health inequalities. The quantitatively largest health

inequities are observed in Eastern European countries.

Overall the results largely confirm patterns found in the related literature on socioeconomic

positioning, working conditions and health inequalities. The inconsistent evidence on the role of

financial resources on the formation of health mirrors earlier empirical findings on the SES-health

nexus. On one hand, the non-effect of earning could be a testimony to the effectiveness of universal

public health care systems to achieve a decoupling of health from financial resources. On the other,

it could result from attenuation bias as earnings are measured with error and/ or approximate

health-related consumption only poorly. The limited influence of job security seems to contrast with

other findings in the literature. However, it is possible that job security is less of an issue for a

population close to retirement. Furthermore, previous research focused more broadly on mental

18

well-being, whereas the current study explores the effect on potentially clinical depressive

symptoms among other health outcomes.

The apparent inconsistency in predicted health inequalities across welfare states has caused some

stir among researchers in social epidemiology. So far studies have mainly investigated education-

related health inequities, but my findings suggest that the patterns carries over to job quality.

Several explanations have been put forward to illuminate this puzzle including stronger health-

related job selection in the Nordic countries or larger behavioural differences between job types. It

also conceivable that the systematic reduction of competing risk in highly developed countries with

generous social protection, increase the relative importance of job quality as predictor of health. If

job quality is partly hard wired into jobs by design, there will be limited scope for change.

This brings me to the potential policy implications. If my effects reflect causal effects, an

improvement in intrinsic job quality could reduce the onset of range of health disorders and

potentially help people to maintain employment to extend their working lives. The German

government has, for instance, been debating an anti-stress act proposed by the trade unions to

reduce psychosocial stressors at work. Over the working life, a reduction in stress might in fact

reduce wear and tear and reduce health inequalities. However my finding suggest that the change in

job quality would need to be quite substantial to realize shits in population health. Targeting other

population wide health risks such as obesity or risky health behaviour might prove to be more cost-

efficient. A careful assessment of costs and benefits of job quality improvements might be need to

provide a more conclusive answer.

Acknowledgment:

"This paper uses data from SHARE Wave 5 release 1.0.0, as of March 31st 2015 (DOI:

10.6103/SHARE.w5.100), SHARE Wave 4 release 1.1.1, as of March 28th 2013 (DOI:

10.6103/SHARE.w4.111), SHARE Waves 1 and 2 release 2.6.0, as of November 29th 2013 (DOI:

10.6103/SHARE.w1.260 and 10.6103/SHARE.w2.260) and SHARELIFE release 1.0.0, as of November

24th 2010 (DOI: 10.6103/SHARE.w3.100).

The SHARE data collection has been primarily funded by the European Commission through the 5th

Framework Programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life),

through the 6th Framework Programme (projects SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5-

CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th Framework

Programme (SHARE-PREP, N° 211909, SHARE-LEAP, N° 227822 and SHARE M4, N° 261982).

Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842,

P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11 and OGHA 04-064) and

the German Ministry of Education and Research as well as from various national sources is gratefully

acknowledged (see www.share-project.org for a full list of funding institutions)."

19

7 Appendix

Table 9: Job Quality Effects on Health Innovations by Gender

Variable Acute

Conditions CV Risks

Musculoskeletal Disorders

Depressive Symptoms

Risky Behaviour

Functional Disabilities

Poor/ Fair Health

Intrinsic Job Quality

M 0.0009 -0.002 -0.0188 *** -0.0065 *** 0.0004 -0.0001 -0.0046 **

0.0007 0.0021 0.0043 0.0011 0.0017 0.0009 0.0016

W -0.0003 -0.0054 ** -0.0211 *** -0.0019 0.0024 -0.0008 -0.0059 ***

0.0004 0.0019 0.0048 0.0015 0.0014 0.0011 0.0015

Job Security

M 0 0.0025 0.0065 -0.0002 0.001 -0.0012 -0.0009

0.0006 0.0022 0.0041 0.0012 0.0017 0.0008 0.0015

W 0.0001 0.0012 -0.0085 -0.0015 0.0005 0.0012 -0.0018

0.0004 0.0019 0.0044 0.0014 0.0014 0.0011 0.0014

EUR <200

M 0.0019 0.005 0.0088 0.0023 -0.0048 -0.0027 -0.0021

0.0025 0.0065 0.0142 0.0034 0.0053 0.0031 0.0052

W -0.0006 -0.0021 -0.0336 * 0.003 -0.0032 -0.0021 -0.008

0.0018 0.0053 0.0156 0.0046 0.0043 0.0035 0.0052

EUR 200-950

M -0.001 -0.0021 0.0078 0.0074 -0.0017 -0.0033 0.003

0.0025 0.0067 0.016 0.0042 0.0061 0.0033 0.0058

W -0.0007 0.006 -0.028 0.0017 -0.0049 -0.0001 -0.0104 *

0.0017 0.0055 0.0157 0.0048 0.0043 0.0036 0.005

EUR 950-1500

M (omitted) (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

20

W (omitted) (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

EUR 1500-2400

M 0.0039 0.01 -0.0099 0.006 -0.004 -0.0043 -0.0018

0.0031 0.0073 0.0146 0.0038 0.0059 0.0034 0.0059

W 0.0001 0.006 -0.027 0.0005 0.0017 -0.0073 -0.011

0.0021 0.0068 0.0192 0.0054 0.0053 0.0038 0.0064

EUR >=2400

M 0.0066 0.0051 -0.0161 0.0022 -0.0077 0.0003 -0.011

0.0072 0.0081 0.0154 0.0043 0.0065 0.0042 0.0058

W -0.0017 0.0024 -0.0207 0.0036 0.0011 0.006 -0.0068

0.0026 0.0079 0.0218 0.0072 0.0065 0.0069 0.0076

Gender Invariance 0.275 0.269 0.038 0.083 0.441 0.313 0.245

Time Invariance 0.428 0.797 0.002 0.742 0.894 0.458 0.454 Country Invariance 0.000 0.000 0.001 0.000 0.000 0.000 0.000 Joint Job Quality (Men) 0.437 0.479 0.000 0.000 0.913 0.555 0.011 Joint Job Quality (Women) 0.969 0.065 0.000 0.693 0.386 0.227 0.001

N 20124 20035 20035 19983 19983 18726 16400

Table 10: Test results for non-response bias

Test: participation in t+1 Test: total number of waves in the panel

Chi p Chi p

Acute health incident

22.366 0.000 5.097 0.024

21

Cardiovascular Risk

0.425 0.515 2.291 0.130

Musculoskeletal Disorders

3.434 0.064 6.171 0.013

Incident Depression

8.323 0.004 0.448 0.503

Risky Behaviour 0.388 0.534 0.038 0.846

Functional disability

1.330 0.248 0.672 0.412 )* Greece data exempt

Onset poor/ fair health

0.248 0.618 0.091 0.762

22

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