Education, wages and job satisfaction

Cecilia Albert (Universidad de Alcalá) María Angeles Davia (Universidad de Castilla la Mancha)

January 2005

PROPOSAL FOR THE EPUNET 2005 CONFERENCE

Abstract

In this piece of work we intend to widen the already existent empirical evidence on the

relation between education, income and satisfaction. We explore satisfaction measures that

take into account both job satisfaction and some aspects of overall satisfaction with life. In

order to do so we create a synthetic satisfaction index and we estimate the link between

education and satisfaction taking into account the relation of both of them with wages through

a simultaneous equation system.

Amongst the most relevant results we would like to stress that those countries with lower

wages (those in the South of the EU-15) register a prominent relation between education and

satisfaction, and the link between education and job quality is so strong that the net effect of

education on satisfaction is positive. We have therefore found a very interesting difference

between northern and southern countries in Europe. In Southern countries the link between

education and satisfaction is more significant and positive even after introducing income in

the estimations. This responds to the strong positive impact of education on satisfaction via

wages, that is higher than the negative effect via expectations. Generally empirical evidence

(coming essentially from English speaking countries) points at a lower satisfaction for more

qualified individuals. This is not verified in Southern countries, particularly in Greece and

Portugal. In Nordic countries the pattern is more in line with the common evidence.

Spain does not register, however, the typical pattern in the link between education and

satisfaction in southern countries.

Keywords: satisfaction, wages, education, non monetary returns to education

JEL classification: J28

Contact address (and presenter): María A. Davia Facultad de Ciencias Económicas y Empresariales Universidad de Castilla La Mancha (UCLM) Pza de la Universidad, s/n 02071 Albacete, Spain mangeles.davia@uclm.es Tel (0034) 967 599200 (ext 2380) Fax (0034) 967 599220

Education, wages and job satisfaction

Cecilia Albert (Universidad de Alcalá) María Angeles Davia (Universidad de Castilla la Mancha)

January 2005

PROPOSAL FOR THE EPUNET 2005 CONFERENCE

1. Introduction

Research on job satisfaction is relatively recent and is increasingly arising an unusual

amount of interest (Gamero, 2003) from social researchers. It is a very interesting subject

since job satisfaction is one of the few variables economists may use to approach their

essential focus: the utility function. Moreover, it is crucial to forecast job turnover and a

signal of the effort workers are ready to make in order to increase their productivity in the job.

Empirical evidence about the link between education and satisfaction is not conclusive. A

higher educational level is expected to lead to high wages and good quality jobs, which are

positively related to job satisfaction. But education might also increase expectations about

both wages and job features. Expectations do not always come true, and this disappointment

is a source of dissatisfaction. This last perspective considers utility as dependant on the

distance between aspirations and outcomes: if the education level increases expectations and

these do not coincide with outcomes in the labour market the individual will finally feel

dissatisfied and a negative relation between education and satisfaction will be observed.

This last argument establishes a direct link between job match and job satisfaction:

should the worker feel her qualification is higher than the one required for the job, she will

feel unsatisfied as a result of unfulfilled expectations. Those workers with a good job match

should feel satisfied with their jobs. In this way, self-reporting of (under) overqualification

and job match may be considered as a sign of disappointment due to mismatches between

reality and expectations, whereas good matches between qualification and jobs should be

linked to a high level of satisfaction.

In this paper we intend to widen the empirical evidence about the link between education,

satisfaction and wages/income. To this aim, we will explore different measures of satisfaction

including both overall job satisfaction and some particular spheres of satisfaction. We will use

simultaneous equations and fixed effects estimations with instrumental variables to draw the

link between education and satisfaction taking into account the relation between both of them

and income.

2. Job satisfaction in the European Community Household Panel

The European Community Household Panel (hereinafter, ECHP) includes four overall

indicators of satisfaction: about the financial situation, housing situation, leisure time and the

economic activity. It includes as well a set of indicators about different elements of job

satisfaction. These refer to wages, hours of work, working times, type of job, job security,

employment conditions and time to travel to work. Our aim here is to construct a synthetic

indicator of satisfaction gathering all this information.

Self-perception of educational mismatch may be very related as well to satisfaction

variables. This variable is used to measure the fulfilment of individual expectations. Some

other variables in the ECHP contribute to this very same aim, such as the degree to which

education was useful in the achievement of the current job or the relation between initial

education or training and the job. All the measures of mismatch in the ECHP are self-reported

and, therefore, subjective.

In this paper we are initially using information of all the countries that conform the hard

core of the ECHP, and those where the information about satisfaction is gathered using the

same questionnaire. Unfortunately, the ECHP was interrupted in Germany, the United

Kingdom and Luxembourg from the third wave. In turn, a harmonised version of the national

panel surveys was developed. The relevant questionnaires did not include the same questions

about satisfaction and, when they were included, different wordings in the questions and

measurement scales were used, so that Eurostat (the European Statistical Office) has not

harmonised this part of the national questionnaires.

Moreover, the Swedish component of the ECHP, based on a national-wide cross-sectional

survey, does not allow us to make longitudinal studies. Therefore we have finally done

without Luxembourg, Germany, United Kingdom and Sweden. This means the remaining

eleven EU-15 countries are included in our study: Denmark, The Netherlands, Belgium,

France, Ireland, Italy, Greece, Spain, Portugal, Austria and Finland.

The ECHP is endowed with variables that contribute to explain both satisfaction and

wages: job features such as occupation, industry, size of the firm, public/private employer,

working hours, supervisory tasks, type of contract, job tenure and unemployment spells

before the current job, among others.

3. A first approach to the data-set: some descriptive analysis.

The following paragraphs are devoted to the description of the more relevant patterns

in the link between education and educational mismatch, wages and job satisfaction. We will

point at the diversity and similarities of job satisfaction indicators. In a first approach we will

focus on a representative variable of the many that indicate job satisfaction in the ECHP. We

will use here a pool of waves, not taking explicitly into account the longitudinal nature of this

dataset.

Figure 1 shows the average values of all the satisfaction indicators. It may be noticed

that those countries with a high average level of satisfaction register high satisfaction

simultaneously in all the different indicators, so that the ranking of countries in terms of

satisfaction keeps the same regardless the chosen indicator.

(figure 1)

The highest level of job satisfaction in all types of satisfaction (with wages, with job

security, with type of work, with working hours and working times, and with time to travel to

work) are registered in Denmark, The Netherlands, Austria and Ireland (though in the latter

country only in certain indicators). The opposite cases are the southern countries: Portugal,

followed by Greece, Italy and Spain. All these countries share a common pattern, though the

most extreme values are clearly in Portugal.

Satisfaction with non strictly employment related aspects (with economic activity,

with financial situation, with housing and with leisure) follows similar patterns, with the

maximum values in Denmark and The Netherlands. Austria and Ireland also register high

values and the lowest, again, appear in the southern countries.

When looking at the link between job satisfaction and education attainment (figure 2)

most dimensions follow a similar pattern (more education is related to higher satisfaction)

though with a diverse intensity. Figure 2 shows the patterns of overall job satisfaction and

education. The rest of job satisfaction items generate similar results.

(Figure 2)

The relation between satisfaction and wages shows a similar outcome: better paid

workers register higher satisfaction levels in all the items except with working times, working

conditions and time to travel to work. It is more pronounced in Greece, Spain and Portugal

than in the rest of the countries, whereas in the Netherlands, Finland and Belgium people with

disparate wages register similar satisfaction levels in these concepts. Satisfaction in other non

employment related dimensions has a lower correlation with wages than the overall

satisfaction with the job, and again is more distinct in Portugal, Greece and Spain than in

other countries.

(Figure 3)

Finally, figure 4 is aimed at going deeper in the link between the dynamics of wages

and satisfaction, which is something that hardly ever has been observed in the existent

empirical evidence. It may be seen that wage increases, both year-in-year and accumulated

along the period of observation, makes satisfaction grow. This increase is not proportional:

there is a positive but decreasing rate in the impact of wage increases on satisfaction growth.

Only in really outstanding cases an increase in hourly wages generates a reaction of the same

size in terms of satisfaction.

(Figure 4)

The reaction between satisfaction and hourly wage increases is hardly ever noticeable

in certain concepts such as satisfaction with the type of job, working times, working

conditions and housing.

4. Research hypotheses

Given the subjective nature of satisfaction it seems difficult to raise hypotheses about its

connection to other variables. Our hypotheses are based on empirical evidence to date and on

the different institutional frameworks in the countries we are studying. Thus, we classify them

into two groups:

A) Hypotheses on the relation between wages, satisfaction and education

The educational attainment contributes to a higher level of income, faster promotions and

the achievement of better jobs (Blanchflower and Oswald (1994)). Education contributes to a

higher level of autonomy, reduces routines in the job and enhances participation in the

relevant decisions of the firm, among many other aspects. This makes individuals with a

higher education attainment more prone to be satisfied, both with the job and with other

aspects not directly related to the job. Nevertheless, some pieces of evidence (such as Clark

and Oswald (1996)) find counterintuitive evidence: more educated individuals register a

lower level of satisfaction, even after controlling for income.

This result responds to different factors:

a) The effect of education on the individual expectations. Individuals with a higher level

of education have generally higher expectations that are more difficult to fulfil.

b) The negative effect due to the comparison with similar workers and differences in

wages across individuals with a similar level of education. The higher the level of education,

the more disperse incomes are.

c) The effect of past wages. Overall satisfaction with the job diminishes with the level of

education once income tends to stabilise.

d) Overqualified workers are less satisfied with their jobs.

B) Hypotheses regarding differences across countries.

The empirical analysis has shown so far that there are countries where, levels of education

are systematically below others: in southern countries levels of satisfaction in all aspects are

lower than in the north. Moreover, in two of the countries where wages are higher after

controlling for differences in acquisitive power (Austria, Finland and the Netherlands)

satisfaction levels are, in average, higher.

The outstanding correspondence between the ranking of countries as regards wages and as

regards satisfaction leads us to interpret, first of all, differences in satisfaction across countries

to differences in the level of wages. Nevertheless, although the average level of wages may

lead to an idea about what the average level of satisfaction will be, some of the institutional

differences across countries may help as well to infer which must be the distribution of job

satisfaction within countries. The national wide features to which we are going to pay

attention are labour institutions. We are aware that they are not the only characteristics to look

at when studying satisfaction, with sociological and psychological aspects also being relevant,

but we are not provided with tools to interpret the role of these other aspects in our labour

economics approach.

The first aspect to mention is centralization and coordination in collective bargaining. The

second refers to employment protection versus labour market flexibility.

As regards the first institution, if we agree with the hypothesis of relative income

compared to absolute income, in those labour markets where collective bargaining is

centralised or coordinated, the wage distribution will tend to be more compressed than in

those countries with de-centralised collective bargaining. In the first case, more qualified

workers may feel relatively unsatisfied if they do not observe a noticeable distance between

their wages and those of other lower educated workers, whereas lower educated may be

relatively favoured by collective bargaining. We expect therefore to find a more negative

effect of education on job satisfaction in countries with a centralised or coordinated collective

bargaining, such as Finland, Ireland, Portugal, and Belgium, versus France and Italy in the

opposite extreme. In these countries we expect more qualified workers to be relatively less

satisfied due to the wage compression resulting from the collective bargaining structure, as

long as the relative income effect is predominant over absolute income.

Another relevant institution is employment protection. In a country where permanent

employment is very protected, those outside the hard core of the labour market (i.e. temporary

workers) will tend to be relatively dissatisfied. Moreover, if wages are closely linked to

seniority, those countries where a strategy of flexibility in the margin of the labour market has

been implemented will tend to register the more marked differences in tenure and in wages

across age groups, which will exacerbate differences in job satisfaction across types of

contract and age groups. This should lead to stronger inequalities between types of contract

and age groups in Greece, Spain, Portugal and Italy than in the rest of the countries.

Finally, we could add a comment about the educational system, which has a relevant role

in the determination of overqualification problems in the labour market. The higher the

mismatch between supply and demand of qualifications, the higher the risk of workers being

unsatisfied due to the difficulties to make their expectations true. In southern Europe there has

been a strong education expansion, wich led to a fast increase in the qualification of the

labour supply. The demand for skills does not increase at the same pace as the supply of

qualified labour. Besides, the educational systems in these countries are strongly based on

general training and higher education, with vocational training having a secondary role. Both

circumstances contribute to educational mismatches and, therefore, to dissatisfaction.

Not only southern countries have an educational system based in general education.

France and Belgium share this feature, but educational expansion has not been as intensive

there as in southern countries. The educational system in the rest of the countries share both a

stronger effort on vocational training and smoother patterns of educational expansion, given

that they started from higher levels of educational attainment than Southern countries.

Finally, the distributive role of welfare regimes may contribute to explain the link

between wages and satisfaction. Spain, Greece, Portugal and Italy are the countries where the

tax and benefit system has the weakest distributive capacity. Northern countries are the

opposite case. This contributes to equality in the income distribution. Should workers look at

final disposable income and not at initial gross wages to determine their satisfaction, in those

countries with a stronger re-distributive welfare state, wages and satisfaction should be less

clearly connected. In northern countries, therefore, the correlation between wages and

satisfaction should be smoother than in the South. Nevertheless, we also are aware that family

networks and intergenerational solidarity in southern countries play a similar role as regards

income distribution, which might mitigate North-South differences in the wages-satisfaction

relation.

5. Constructing a satisfaction index

The set of satisfaction variables refer to seven aspects strictly related to jobs (wage,

working hours, working times, type of job, working conditions, travel to work) and four more

general issues (financial situation, housing, leisure, economic activity).This is rather unusual,

researchers dealing with satisfaction issues not being typically provided with such a wide

range of indicators. When more than one satisfaction indicator is available, usually

researchers use the one more linked to the problem they are studying and use the satisfaction

indicator directly as an ordered variable. This considerably reduces the range of multivariate

techniques that may be used to study relations between satisfaction and other variables,

namely, it hinders the treatment of endogeneity issues in the determination of satisfaction. As

long as we want to observe the net effect of education on satisfaction taking into account the

endogenous nature of wages, we need to compute a continuous index that gathers the

information from several dimensions and allows us to use quantitative techniques applied to

continuous variables.

We have developed a synthetic satisfaction index through multiple correspondence

analysis. We have summarised the trends of all the satisfaction indicators in a single

dimension. The eigenvalues vector, which defines the distance between each individual and

the centroid in the correspondence analysis, gathers this information through a continuous

variable with mean 0 and variance 1. This summary variable has no direct interpretation but

its values are correlated with the original satisfaction indicators. It will be used as a dependent

variable in the multivariate analysis. This practice, though rather unusual in the study of job

satisfaction, was used by Clark in 1996, who performed a factor analysis on several

satisfaction indexes and used the information of the first dimension of such analysis as a

dependant variable.

Table 1 shows how the satisfaction index finds the same ranking in satisfaction across

countries as the original variables.

6. Literature review on education, satisfaction and wages

The link between education and satisfaction is very controversial due to the apparently

paradoxical fact that more qualified individuals, even though achieve better jobs than non

qualified ones, are less satisfied with them. This may be the outcome of several forces

pushing in the opposite direction (see section 4). The negative sign in the relation between

satisfaction and education is only visible when differences in wages or objective job features

are controlled for (Clark, 1996; Clark and Oswald, 1996, among others). It is due to the fact

that more qualified workers are more prone to have higher expectations and to have their

expectations unfulfilled. It also responds to the fact that more qualified workers are more

prone to be overqualified, and educational mismatches are a source of dissatisfaction.

The educational mismatch is maybe the more relevant signal of quality in the job match.

Overeducation consistently generates dissatisfaction with the job (Allen and van der Velden,

2002), although does not necessarily lead to lower wages. Overqualified workers generally

achieve wages over the ones of correctly matched workers in the same jobs, but lower than

those who, having the same educational attainment, found a proper match in the labour

market.

If we want to estimate a net effect of education on satisfaction we will need to consider,

simultaneously, the effect of education on educational mismatch on the one hand and wages

on the other. The latter relation is also very interesting for researchers. Wages are essential in

the determination of satisfaction. The debate here focuses on the question on whether wages

determine satisfaction directly and, therefore, corroborates the absolute utility hypothesis or

whether it is the distance between current wage and a given threshold that is really

determining satisfaction, so that a relative utility hypothesis would rather be in place. The

theoretical implications of these results may be devastating, given that, if confirmed, they

would be pointing at utility as a relative concept, and not absolute, which is the traditional

interpretation.

There are two ways of approaching relative income. The first one is assuming that

individuals are comparing themselves with similar workers (Clark and Oswald, 1996). In this

case relative income is obtained from a mincerian equation of wages, the result of which is

included in the determination of satisfaction. The second one is thinking that the individual

defined in the past certain objectives in terms of wages so that, regardless what is happening

to other similar workers, she will want only to go beyond this level (as stated in the

preferences drift model). The latter is used by Lydon and Chevalier (2002), who use past

income as a proxy for expectations of workers. The higher the expectations, the more likely is

that the individual will be disappointed with her attainment in the labour market.

Endogeneity of wages in the determination of satisfaction (Lydon and Chevalier (2002)

being an exception) has hardly been treated in the empirical literature. It might happen,

particularly amongst women and youths, that those jobs which are expected to cause low

satisfaction will be simply not accepted, so that these groups will only work whenever jobs

are satisfactory and, therefore, are expected to register higher levels of satisfaction than men

and elder workers. This is a self selection issue: if an individual has the possibility not to

work, she will only accept jobs that will largely contribute to her utility (Clark , 1997).

Another argument for endogeneity of wages and satisfaction is due to the fact that more

satisfied workers will tend to make stronger efforts at work and will be, ceteris paribus, more

productive. Should this redound to her wages, we would find a positive feedback in the

wages-satisfaction connection.

Empirical evidence on satisfaction using panel data is quite new in Europe, due to the

recent availability of longitudinal data-sets. Some of the papers that take profit of the

econometric techniques with panel data are Clark et al (2004), Clark and Oswald, (2002),

Ferrer-i-Carbonell and Frijters (2004) and Senik (2004). All of them coincide in finding

strong differences between cross-sectional and longitudinal approaches to satisfaction. This

deals with the ability of fixed effects estimators to control for the different inherent baseline

satisfaction. These papers show how the results of the estimations change when control for

fixed effects is performed. Essentially, the longitudinal analysis implies that many of the

variables that are traditionally significant in the cross sectional analysis loose explanatory

power. The main difference between these papers and the present one is that they respect the

ordinal nature of the dependent variable, whereas we need to construct a continuous index in

order to control for the endogenous nature of wages. This control is something the former

papers lack.

7. The econometric model

In order to tackle the question of which is the net effect of education on satisfaction we

will study the link between the satisfaction index and education controlling for the eventual

educational mismatch a higher level of education may generate. In this way we intend to

observe a net direct effect of education on satisfaction in a context when the indirect effect via

wages and overqualification are taken into account. We therefore construct the following

model:

W = α0 + α1EDUC + α2MISMATCH + α3SATISFACTION + α4OTHER + π1 (1)

S = β0 + β1 EDUC + β2MISMATCH +β3WAGE + β4OTHER+π 2 (2)

We have estimated a system of two structural equations where each dependant

variable is an endogenous variable in the other equation. The main feature of these models is

that the disturbance term is correlated with the endogenous variables, violating the

assumptions of ordinary least squares. Further, since the dependant variables are explanatory

variables in the other equation, the error terms among the equations are expected to be

correlated. The application deployed here to estimate the system uses an instrumental variable

approach to produce consistent estimates and generalised least squares to account for the

correlation structure in the disturbances across the equations. For reference, see Greene (2003:

405-407).

Three least squares can be thought of as producing estimates from a three-step

process: Stage 1 consists on developing instrumented values for all endogenous variables.

These instrumented values can simply be considered as the predicted values resulting from a

regression of each endogenous variable on all exogenous variables in the system. Stage 2

consists of obtaining a consistent estimate for the covariance matrix of the equation

disturbances. These estimates are based on the residuals from a two stage least square

estimation of each structural equation. And finally, stage 3 consists on performing a GLS-type

estimation using the covariance matrix estimated in a second stage and with the instrumented

values in place of the right hand side endogenous variables.

The simultaneous determination of both equations will help us to determine the net

effect of education on satisfaction.

Control variables in both equations are gender, education, occupation (using ISEI, the

International Socioeconomic Index of Occupational Status as a proxy in order to save degrees

of freedom), industry, size of employer, public or private employer, working hours, type of

contract, supervisory nature of the job and previous unemployment spells.

In the wage equations there are some additional controls such as potential experience

in the market and tenure (both squared as well). In the satisfaction equation there are other

controls such as educational mismatch (via overqualification dummies and relation between

the job and initial training). Besides the family composition is also taken into account through

the combination of marital status and the presence of small children.

In a second step, we will not only control for the endogeneity of one of the most

relevant variables but also make profit of the longitudinal nature of the data set in order to

control for unobserved heterogeneity. The reason for this is that we expect certain unobserved

factors that contribute simultaneously to wages and satisfaction. Should these factors be

constant along time (such as, for instance, family background or work values) we might get

rid of their influence and reach more consistent estimators of the link between wage and

satisfaction. In order to do so we will control for fixed effects in lineal estimations of the

satisfaction index and we will tackle the endogeneity of wages via instrumental variables.

In a second stage, we have focused our attention on the dynamic nature of the data-set. This

allow us to estimate the model taking into account the unobserved time-invariant features of

the individuals that influence on their satisfaction levels and make them inherently more or

less satisfied with their jobs. We are in such a way controlling for the possibility that every

individual satisfaction function stems from a different baseline level. We have controlled for

this unobserved heterogeneity through a fixed effects model and we have introduced

instruments to tackle the possible endogeneity of wages. The chosen instrument has been the

log of the partner´s hourly gross wage. This variable was previously used by Lydon and

Chevalier (2002). Human Capital theory displays several arguments about how educational

levels, abilities and qualification of the individuals in a same household may be correlated.

We refer to this phenomenon as educational homogamy. Whatever knowledge of the labour

market an individual may acquire, may be used by his/her partner to achieve better jobs. They

share resources that help them accessing to similar quality jobs. All these arguments explain

the decision taken here as regards the instrumental variable. The cost of this decision is that

the sample for which we make the final estimation is somehow smaller than the original one,

since only individuals living in couples where both members report wages are included in the

model. That is why we perform an intermediate model to check if, only in the context of a

fixed effects estimation, the coefficients vary considerably between the total sample of

employed interviewees and the sub-sample.

As regards the way to control for endogeneity in a dynamic context, we have performed fixed

effects estimations with instrumental variables.

Consider an equation of the form1 :

itiitiititit ZitXYy νµδνµβγ ++=+++= 1

where yit is the dependent variable, Yit is an 1x g2 vector of observations on g2 endogenous

variables included as covariates, and these variables are allowed to be correlated with νit .

X1it is an 1 xk1 vector of observations on the endogenous variables included as covariates.

Zit =[Yit Xit]

γ is a g2 x 1 vector of coefficients

1 This definition has been borrowed from the Stata Manual.

β is a k1 x 1 vector of coefficients

δ is a K x 1 vector of coefficients, K = g2 + k1

Assume that there is a 1 x k2 vector of observations on the k2 instruments in X2it. The

order condition is satisfied if k2 ≥ g2. Let Xit = [X1it X2it]

there are several ways of tackling this in the context of panel data. Amongst them we have

used the within estimator, which fits the model after seeping out the µi by removing the panel

level means from each panel. This implies we assume µi to be fixed. Therefore, we assume

that µi may be correlated with the variables in Xit. If this assumption is true, then the results

are consistent. The price for using the within estimator is that it is not possible to estimate

coefficients on time –invariant variables, and all the inference is conditional on the µi in the

sample.

The reader will notice that the initial sample in the fixed effects estimations is smaller than

in the pooled sample for the cross sectional study in the first part. This is due to the fact that

the fixed effects estimation is only possible for those individuals with, at least, two

observation of wages whereas this condition does not hold in the cross-sectional pooled

estimations.

8. Results of the estimations

In the multivariate analysis of this paper we have approached the effect of education

on satisfaction through OLS estimations first and a system of simultaneous equations over a

pool of waves. The main dependant variable was the satisfaction indicator and the second clue

variable in the equation system was the logarithm of real gross hourly wages.

Table 2 shows three estimations for the total sample: the first is an OLS estimation

where satisfaction depends on education level and wages are not controlled for; the second

includes hourly wages, and the third is a system of simultaneous equations were the

endogeneity of wages is taken into account. Other covariates refer to initial training and

mismatches between the job and the worker, which contributes to measure the mismatch

between workers expectations and outcomes.

In the overall sample we may see that, in principle (column A), higher levels of

education are related to higher satisfaction. Nevertheless, the effect of education is the

outcome of two forces in the opposite direction. Education contributes to satisfaction

positively through wages (we will label it “wage effect”) and negatively via expectations

(“expectations effects”). When control for wages, (column B) they appear to be clearly

correlated with satisfaction, and the educational level expresses now only the “expectations

effect” and the negative well known negative sign for education appears. But when we take

into account the positive effect of education over wages through the simultaneous equation

system, the negative effect wipes out and we do not register any significance at all of

education itself. The wage effect is measured through wages and the expectations are

measured through job match quality and overqualification. And it seems that both opposite

forces are totally counterbalanced. More qualified individuals are more prone to be

overqualified and this mismatch contributes to dissatisfaction regardless the specification. The

same applies to education when has not been very useful to get the current job. Having

received any initial formal training also contributes to satisfaction. The effect of

overqualification and educational mismatch is not altered when wages are included in the

specification.

According to prior empirical evidence we find that certain personal and job

characteristics contribute to increase satisfaction. Amongst them, we may stress working in

the public sector, holding a permanent contract, living in couple, being healthy, being in a

supervisory position, amongst others. And some features are linked to lower levels of

satisfaction, such as being male, job tenure, working more than 40 hours per week, having

kids under 6 years old, and the regional unemployment rate (although the latter will register

different sign in different countries).

Last, the model over a pool of countries and waves perfectly follows the ranking of

countries in terms of wages and satisfaction: Northern countries and The Netherlands register

the top values, together with Ireland. France and Spain are somewhere in the middle and the

rest of southern countries register the lowest values in satisfaction.

The wage equation of the equation system shows the expected coefficients in all the

variables: wages are positively related to education, tenure, experience, permanent contracts,

supervisory positions, to the occupational status (measured through ISEI), to the industry

(compared to the primary sector) and to the size of the employer, amongst other factors.

We have afterwards repeated the analysis for all the countries in the sample and we

have noticed very interesting differences. There are several profiles in the link between

education and satisfaction. In the northern countries, the Netherlands, Belgium and

(surprisingly) Spain the effect of education on satisfaction is negative, even if wages are not

included in the set of explanatory variables. This reflects a very strong expectations effect,

even over the wage effect. This negative coefficient persists when wages are included in the

satisfaction estimation. In Austria and Ireland it seems that the wage effect and the

expectations effect are counterbalanced since initially the educational level does not seem to

be related to satisfaction. And last, in three of the four southern countries we observe a

persistent positive link between education and wages. This may confirm that the gap between

jobs for educated and uneducated workers is so large that differences in outcomes exceed

possible unfulfilled expectations or ill-adjusted jobs effects.

Wages are always positively related with satisfaction before their endogeneity is

controlled for. Afterwards in most cases they loose significance (Austria, Finland and

Denmark) or even become negative (The Netherlands, Belgium and Ireland). Nevertheless,

and again underlying North-South differences, wages gain significance in Italy and Greece

when their endogenous nature is taken into account.

As regards the role of education, once the wage has been “endogenized”, it tends to

maintain the negative coefficient in Denmark and Spain and its explanatory power decreases

in Belgium, France, Italy and Austria. The relevant coefficient remains positive in Greece and

Portugal. Therefore, in southern countries, with the exception of Spain, the expectations effect

is not as intensive as in the North, and the effect of education on wages and non-monetary

aspects of the job is so strong that the net effect of education is still positive. In fact, this

strong link between education and wages in Greece and Portugal is confirmed by the high

coefficient of educational level in the wage equation of the equation system.

Some other features of the countries in the sample (table 3) deserve attention as well.

In all the countries overqualified workers are less satisfied than correctly matched workers.

Nevertheless, job match measured through the utility of education for achieving the current

job is more relevant in the Netherlands, Belgium, Denmark, France and Italy than in the rest

of the countries, where this variable is hardly ever significant. Having received specific

training before the current job does register a negative sign consistently with the one for

education in Denmark, but positive in Spain and Belgium, so that we may interpret it as a

proxy for quality of job match. IN the rest of the countries2, possibly because of the high

correlation with the education attainment, this variable is not significant.

Last, we will mention some more peculiarities of the estimation for each country3: in

Denmark, in contrast with the traditional evidence, men are more satisfied with work than

women, everything else the same. This does not support prior evidence on the topic in other

countries. Usually it is said that women, although their job characteristics tend to be worse

than men’s, are more satisfied with their jobs, because their expectations about labour market

outcomes are lower. The different result for Denmark requires further attention, but we think

it is due to the fact that labour market participation is higher in Denmark than in other

countries and their expectations in terms of employment are similar to men’s. Nevertheless, it

2 This variable is not in the specification of the Netherlands, given that it was not in the Dutch questionnaire. 3 They are not shown in Table 3 for the sake of brevity, but can be requested from the authors.

is still a bit puzzling given that the gender wage gap in Denmark is not particularly lower than

in other EU countries. In Portugal we have a similar pattern, but this time it is difficult to

explain it via a high labour market participation of women. Nevertheless the gender wage gap

in Portugal in 2001 has been estimated by Eurostat at only 10%, the lowest but one at that

moment.

The traditional positive influence of working in the public sector does not hold in

Denmark, and in Belgium neither permanent workers not married ones are more satisfied than

the rest. In Italy and Belgium the regional unemployment rate has a negative link to

satisfaction whereas in the rest of the countries is either positive or non significant. The

positive sign in the unemployment rate may be explained with the same argument than the

positive sign for features such as being female or young: it refers to situations where access to

a job becomes more difficult and workers feel lucky for having a job, which increases

satisfaction. The opposite sign we find in Italy and Belgium may refer to very strong regional

disparities. In those regions with poorer employment expectations, employers do not need to

make jobs more attractive, so that they end up with poorer features and workers are

objectively more dissatisfied with them, even if they are relatively scarce. This is totally

plausible in the Italian case, where North and South labour markets are so different. The low

overall incidence of unemployment in Portugal, together with the usual long working week

contribute to variables related to unemployment and long working weeks not being significant

in this country.

In short, we find that, consistently with the descriptive analysis of section 3, countries

with average lower wages register a very pronounced relation between education, wages and

satisfaction. The impact of education over objective features of the job is so relevant that the

net effect of education on satisfaction is positive, even controlling for wages and job match

quality. Spain is an atypical southern country in this aspect.

We may think of different explanations for the different pattern in northern and

southern countries. On the one hand, we may assert that the wage level of the Nordic

countries is so high that the education “expectation effect” is stronger than the “wage effect”.

This might not deal with a higher wage level, but with a more equitable wage structure. If

workers tend to expect, given the collective bargaining system and the redistributive role of

the welfare state, that their educational level will not guarantee a considerable wage prima or

a noticeable gap with non so educated workers, it is understood that “expectations effect” are

over “wages effect”. Nevertheless, in countries like Portugal and Greece, and in Italy in a

lower extent, the redistributive role of the State is much smaller and the level of education

generates a noticeable wage prima. This may make “wage effect” stronger than “expectations

effects” and explain the persistently positive link between education and satisfaction.

The result for Spain is interesting and puzzling, and it deserves more attention. On the

one hand, the role of welfare state is as limited here as in other southern countries. On the

other hand, overqualification is not particularly more relevant in Spain than in other countries.

We think it might be due to a lower education prima in wages because of the incidence of

temporary employment, much higher than in other European countries (see table 4).

Temporary jobs are particularly concentrated amongst the younger cohorts, and at the same

time, due to the expansion of the education system, younger cohorts are by far more educated

than the elderly. Temporality amongst the more qualified alters the distribution of

opportunities and income by educational levels and may explain a milder connection between

education and satisfaction.

In a second stage we have included in the analysis the control for unobserved

heterogeneity. This reduces considerably the number of variables that remain significant in

the models. The first and most important variable affected by this change is education. Given

that it is almost time invariant, we are not able to detect changes in satisfaction due to changes

in education. In this case we must rely on the role of other variables in the model to have an

idea of the impact of education on satisfaction. Again, we have estimated the model both for

all the countries simultaneously and country –specific.

In the specification for the total sample (table 5) we may notice that wage remains

significant for explaining satisfaction, and even when we tackle endogeneity of this variable

via instrumental variables, it gathers even more significance. This result is in line with Lydon

y Chevalier (2002). Nevertheless, it is not consistent in the country-specific estimations, and it

will only be observed in Spain.

The education related variables loose explanatory power, but overqualification persist

in explaining dissatisfaction. Overqualified individuals, even controlling for unobserved time

invariant factors, are more dissatisfied with work and other spheres of life. This result persists

when controlling for endogeneity of wages. In the same line, though in the opposite direction,

individuals whose studies were useful and very related to the work achieved, tend to be

persistently more satisfied. Some other variables retain explanatory power, being age and

tenure. Both evolve in a parallel way and show that elder and more experienced workers tend

to be more dissatisfied. Having a permanent contract and a supervisory position is, at the

same time, a quite persistent indicator of job satisfaction. The same holds for enjoying a very

good health status.

Living in a couple and having small children are not relevant any more. This is, to a

certain extent, good for our purposes, because it means that the sub-sample we have used in

the instrumental variable estimation (people with couples who reported wages) does not

behave in a very different way than the total sample. This may also be noticed when

comparing results in column 2 and 3 of table 4, which are really similar to each other.

In short, when the possible effect of unobserved factors is controlled for, we may still

notice a positive effect of education on satisfaction via wages and a negative effect via

overqualification or mismatch between expectations and outcomes. The key variable in the

model, education, looses significance due to the fact that it is almost time-invariant during the

observation period (remember that the sample is conformed only by adults).

It is a well known fact that many of the variables that are traditionally relevant in the

empirical literature for explaining satisfaction are not relevant any more when panel data

estimations are performed. Moreover, in the case of satisfaction, controlling for fixed effects

is quite convenient due to the remarkable incidence of psychological aspects contributing to

the perception of the job, expectations and outcomes.

As regards national specific features, table 6 shows the coefficients of the key

variables in the model. When we look at the national sub-samples we find that education only

retains explanatory power in Belguim, and again it is wiped out when the endogenous nature

of wages is taken into account. Hourly Wage contributes to explain satisfaction in all the

countries, but again it looses significance when being substituted by the instrument in all

countries except in Spain. Again we notice that Spain has a special pattern as regards

satisfaction. Overqualification is also relevant for explaining satisfaction only in Denmark,

Belgium, France, Greece and Spain. Anyway it looses significance in the last specification.

The same happens with the variable indicating a studies being useful and very related to the

job achieved. Having received specific training before the job, which was directly correlated

with the level of education, also looses explanatory power.

And regarding other variables that do not appear in table 6, we just would like to stress

that tenure and type of contract are two of the very few variables that remain significant for

explaining differences in satisfaction when unobserved heterogeneity is controlled for.

In short, the longitudinal specification contributes to wipe out the differences between

north and south that we found in the cross sectional analysis. This may be indicating that there

are (cultural and psychological) unobserved features, of individuals from different countries,

that explain differences in the pattern of formation of expectations and satisfaction. For

instance, it is well known that people in southern countries with traditional welfare regime do

have a lower trust in institutions and in other people. This might be linked to lower levels of

job satisfaction. Once these features are controlled for, observable differences among

countries tend to blur.

9. Conclusions

The aim of this piece of work has been examining the links between education and

satisfaction taking into account the connection of both variables with the wage. We have tried

to obtain a coefficient that displays the “net” effect of this variable through the estimation of a

simultaneous equations system, with the dependent variables being satisfaction and education.

The paper has been essentially comparative and we have been able to see a range of

returns to education in terms of job satisfaction. We have detected a north-south pattern: in

Southern countries the link between education and satisfaction is more significant and

positive, even after the effect of wages and educational mismatch in the estimations is

included.

We have found a very interesting difference between northern and southern countries in

Europe. In Southern countries the link between education and satisfaction is more significant

and positive even after introducing income in the estimations. This responds to the strong

positive impact of education on satisfaction via wages , that is higher than the negative effect

via expectations. Generally empirical evidence (coming essentially from English speaking

countries) points at a lower satisfaction for more qualified individuals. This is not verified in

Southern countries, particularly in Greece and Portugal. In Nordic countries the pattern is

more in line with the common evidence.

Both groups of countries differ in the average level of wages and the institutional

framework. It is difficult to grasp which is the determinant of the differences in the link

between education and satisfaction: internal distribution of income (more egalitarian in

northern countries) or other institutional aspects.

It also seems a bit difficult as well to draw any conclusion about the results obtained as

regards the prevalence of the relative income hypothesis over the absolute income hypothesis.

The descriptive analysis showed that workers are more satisfied and better paid in average in

northern countries than in southern countries. This would corroborate the absolute income

hypothesis. But amongst northern countries, those with higher expectations face an equitable

income distribution (due to the redistributive role of the welfare system or the wage

compression related to the centralised collective bargaining system) that reduces wage prima

to education. In the meanwhile, in southern countries, where wages and satisfaction are lower

in average, returns to education via wages is more pronounced and, therefore, individuals who

are aware of being relatively better paid than their counterparts will be more satisfied.

Therefore the matter is not having a higher income in average, but knowing that, compared to

a reference individual (the workers in the same country4) wages are high, what makes

individuals more satisfied. This points at the hypothesis of relative income and utility as a

relative concept.

Finally, the control for unobserved heterogeneity completes the empirical analysis. It

confirms the positive link between education and satisfaction through better job achievements

(higher wages, permanent contracts, supervisory jobs, among other features). And it also

confirms the negative effect of education on satisfaction through the higher proneness to feel

overqualified. Nevertheless, this pattern is somehow smoothed when we simultaneously

control for unobserved heterogeneity and endogeneity in wages.

4 Moreover, and to a certain extent, we may affirm that the equation system we solve is contributing to look at both absolute and relative income. Taking into account the estimation procedure of simultanous equation,s not the real observed wage but the estimated wage is used in teh estimation of satisfaction. That means the estimated wage which is the average wage for individuals with the same set of characteristics as the respondent, what other authors call “reference group·.

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Heterogeneity in Reported Well-Being: Evidence from Twelve European

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L.C. Kaiser, (2002) Job satisfaction: a comparison of non-standard and self-employment

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Gamero Burón, C. (2003) Análisis económico de la satisfacción laboral. Tesis doctoral,

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Lydon, R and Chevalier, A. (2002) Estimates of the effect of wages on job satisfaction.

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Senik. C (2005, forthcoming). Income and well-being, what can we learn from subjective

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Figure 1. Percentage of individuals that report being very or totally satisfied with different aspects of jobs and in overall satisfaction. (per unit)

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Figure 2. Proportion of individuals very or totally satisfied with the relation to

activity (per unit).

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DK HOL BEL FR IRE ITA GR ES PT AT FIN

ISCED 5-7

ISCED 3

ISCED 0-2

Figure 3. Proportion of respondents very or totally satisfied with the activity they do

according to the position in the distribution of (gross hourly) wages.

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Figure 4. proportion of workers who improve their satisfaction compared to last

year against (hourly) wage changes in the same year.

satisfacción con la actividad

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Table 1.Average values of the synthetic satisfaction index from the multiple correspondence analysis. ALL DK HOL BEL FR IRE ITA GR ES PT AT FIN Average value of index 0 0.76 0.43 0.2 -0.14 0.52 -0.32 -0.48 -0.23 -0.65 0.8 0.18 Cronbach's Alpha 0.88 0.83 0.80 0.84 0.79 0.86 0.88 0.89 0.85 0.92 0.86 0.81 Total (Eigenvalue) 4.89 4.04 3.71 4.24 3.56 4.66 4.97 5.21 4.45 6.19 4.67 3.74 Component Loadings satisfaction with earnings 0.66 0.58 0.56 0.61 0.64 0.63 0.64 0.67 0.60 0.78 0.63 0.61 satisfaction with job security 0.62 0.51 0.46 0.56 0.54 0.56 0.68 0.74 0.61 0.74 0.61 0.46 satisfaction with type of work 0.73 0.68 0.65 0.71 0.62 0.71 0.77 0.82 0.73 0.79 0.76 0.69 satisfaction with work hours 0.72 0.66 0.65 0.69 0.51 0.71 0.75 0.77 0.70 0.85 0.73 0.60 satisfaction with times of days worked 0.72 0.64 0.65 0.66 0.62 0.66 0.72 0.72 0.69 0.82 0.72 0.58 satisfaction with conditions 0.69 0.63 0.58 0.70 0.67 0.70 0.68 0.71 0.66 0.79 0.72 0.63 satisfaction with distance to job 0.53 0.43 0.41 0.46 0.33 0.58 0.49 0.50 0.54 0.63 0.43 0.39 satisfaction with main activity 0.77 0.72 0.70 0.74 0.76 0.72 0.79 0.83 0.75 0.78 0.76 0.74 Satisfact with finances 0.67 0.60 0.59 0.62 0.61 0.65 0.68 0.70 0.63 0.66 0.64 0.58 satisfaction with housing 0.60 0.56 0.50 0.49 0.36 0.57 0.60 0.60 0.52 0.64 0.52 0.51 satisfaction with leisure time 0.58 0.61 0.56 0.53 0.44 0.62 0.52 0.37 0.52 0.73 0.56 0.52 Source: ECHP, (Eurostat)

Cuadro 2 Satisfaction estimations, OLS and simultaneous equations over a pool of waves. OLS (A) OLS (B) SIM EQ(C) EC SIM (D) Satisf Satisf Satisf Wages Satisfaction index 0.188*** Gross hourly wage (log) 0.239*** 0.090*** ISCED 5-7 0.027*** -0.065*** -0.010 0.215*** ISCED 3 0.016*** -0.013*** -0.001 0.083*** Overqualified -0.104*** -0.105*** -0.107*** 0.018*** Useful but not quite related -0.154*** -0.168*** -0.156*** 0.065*** Useful and quite related -0.101*** -0.117*** -0.102*** 0.067*** Useful and very related 0.081*** 0.054*** 0.080*** 0.054*** Initial specific training 0.139*** 0.130*** 0.129*** 0.001 “net” household income (000) 0.000*** 0.000*** 0.000*** Male -0.071*** -0.109*** -0.067*** 0.122*** Age 0.006*** 0.004*** 0.004*** 0.006*** 1-2 years of tenure -0.035*** -0.039*** -0.041*** 0.023*** 3-4 years of tenure -0.074*** -0.082*** -0.084*** 0.057*** 5-9 years of tenure -0.061*** -0.077*** -0.085*** 0.095*** 10 years of tenure or more -0.019*** -0.055*** -0.051*** 0.152*** Works over 50 hours /week -0.111*** -0.067*** -0.252*** Permanent position 0.207*** 0.178*** 0.188*** 0.067*** Supervisor 0.219*** 0.152*** 0.222*** 0.102*** Intermediate 0.079*** 0.058*** 0.083*** 0.041*** Public sector 0.182*** 0.158*** 0.158*** 0.203*** Married/living in couple -0.011** -0.019*** 0.034*** Kids under 6 years old -0.056*** -0.064*** -0.012** Previous unemployment spell -0.085*** -0.072*** -0.082*** -0.016*** Very good health 0.456*** 0.433*** 0.465*** Good health 0.139*** 0.117*** 0.203*** Average health -0.027 -0.039 0.041 Bad health -0.083** -0.076** -0.060* Regional unemployment rate -0.002*** -0.001*** -0.004*** Denmark 0.602*** 0.523*** 0.541*** 0.236*** The Netherlands 0.373*** 0.272*** 0.277*** 0.326*** Belgium 0.244*** 0.182*** 0.194*** 0.199*** France -0.066*** -0.073*** -0.100*** 0.063*** Ireland 0.493*** 0.461*** 0.473*** 0.038*** Italy -0.178*** -0.204*** -0.217*** 0.169*** Greece -0.527*** -0.450*** -0.484*** -0.234*** Portugal -0.366*** -0.244*** -0.344*** -0.427*** Austria 0.781*** 0.756*** 0.742*** -0.027*** Finland 0.239*** 0.241*** 0.223*** -0.060*** Intercept -0.626*** -0.890*** -0.689*** 0.841*** ISEI 0.007*** Manufacturing & mining 0.121*** Building 0.159*** Sales and hostels 0.069*** Transport 0.167*** Real state/ financial services 0.208*** Education, health, social services 0.122*** Other sectors 0.070*** Private, 5-19 employees 0.087*** Private, 20-49 employees 0.138*** Private, 50-99 employees 0.159*** Private, 100-499 employees 0.212*** Private, 500+ employees 0.261*** N 223830 220047 206771 206771 R2 0.30 0.30 0.29 0.64 Source: ECHP, waves 2 to 8, Eurostat. Year dummy controls omitted. Reference: Spanish woman with low education, less than one year of tenure, education was not useful to get her job, does not feel overqualified, works, at maximum, 40 hours per week, temporary position, non supervisory, single, without small children, suffering bad health, without previous unemployment spell, works in the primary sector in a private firm with less than 5 employees.

Table 3 Satisfaction estimations, different specifications. National sub-samples, pool of waves (cross-section)

OLS (A) OLS (B) SIM EQ(C) SIM EQ (D) DENMARK SATISF SATISF SATISF WAGES Satisfaction index 0.030** Gross hourly wage (log) 0.198*** -0.069 ISCED 5-7 -0.156*** -0.206*** -0.144*** 0.185*** ISCED 3 -0.093*** -0.106*** -0.102*** 0.075***

Overqualified -0.129*** -0.124*** -0.124*** -0.014*** Useful but not quite related -0.028 -0.039 -0.020 0.038 Useful and quite related 0.114 0.110 0.117 -0.002 Useful and very related 0.274*** 0.274*** 0.271*** -0.000 Initial specific training -0.198** -0.208** -0.189** 0.066*** N 11746 11565 9806 9806 R2 0.11 0.11 0.11 0.45 THE NETHERLANDS SATISF SATISF SATISF WAGES Satisfaction index -0.004 Gross hourly wage (log) 0.108*** -0.034 ISCED 5-7 -0.066*** -0.088*** -0.059** 0.118***

ISCED 3 -0.054*** -0.046** -0.047** 0.033*** Overqualified -0.121*** -0.125*** -0.120*** -0.003 Useful but not quite related -0.078** -0.080** -0.068* 0.017 Useful and quite related -0.112*** -0.116*** -0.092*** 0.056*** Useful and very related 0.108*** 0.098*** 0.117*** 0.073*** Initial specific training N 18943 18594 16787 16787 R2 0.06 0.06 0.06 0.46 BELGIUM SATISF SATISF SATISF WAGE Satisfaction index 0.139*** Gross hourly wage (log) 0.119*** -0.169

ISCED 5-7 -0.068** -0.110*** -0.029 0.179*** ISCED 3 -0.025 -0.035 -0.026 0.048*** Overqualified -0.137*** -0.132*** -0.117*** 0.005 Useful but not quite related -0.249** -0.248** -0.195 0.029 Useful and quite related -0.192 -0.200* -0.142 0.029 Useful and very related 0.079 0.071 0.142 -0.002 Initial specific training 0.184 0.184 0.156 0.014 N 7793 7676 7452 7452 R2 0.09 0.10 0.08 0.38 FRANCE SATISF SATISF SATISF WAGE Satisfaction index 0.095***

Gross hourly wage (log) 0.195*** 0.178*** ISCED 5-7 0.097*** 0.025 0.030 0.209*** ISCED 3 0.008 -0.004 -0.012 0.052*** Overqualified -0.123*** -0.118*** -0.114*** -0.008 Useful but not quite related -0.010 -0.039 -0.033 0.120*** Useful and quite related 0.043 0.019 0.018 0.061* Useful and very related 0.136** 0.107* 0.110* 0.072** Initial specific training 0.000 0.010 0.009 -0.015 N 11967 11792 11394 11394 R2 0.20 0.21 0.21 0.56

Table 3 Satisfaction estimations, different specifications. National sub-samples, pool of waves (cross-section). (cont.) OLS (A) OLS (B) SIM EQ(C) SIM EQ (D) IRLANDA SATISF SATISF SATISF WAGES

Satisfaction index 0.182*** Gross hourly wage (log) 0.143*** -0.286*** ISCED 5-7 0.040 -0.024 0.137*** 0.282*** ISCED 3 -0.045* -0.058** -0.018 0.107*** Overqualified -0.339*** -0.343*** -0.349*** 0.070*** Useful but not quite related 0.009 0.026 0.036 -0.089 Useful and quite related 0.093 0.117 0.101 -0.130 Useful and very related 0.224 0.234 0.244 -0.124 Initial specific training -0.033 -0.065 -0.016 0.173* N 9757 9577 9449 9449 R2 0.13 0.14 0.11 0.51

ITALIA SATISF SATISF SATISF WAGES Satisfaction index 0.116*** Gross hourly wage (log) 0.570*** 0.681*** ISCED 5-7 0.048** -0.114*** -0.282*** 0.258*** ISCED 3 0.060*** 0.004 -0.058*** 0.069*** Overqualified -0.064*** -0.063*** -0.064*** 0.005 Useful but not quite related 0.180* 0.153 0.126 0.057* Useful and quite related 0.190* 0.145 0.118 0.067** Useful and very related 0.438*** 0.375*** 0.363*** 0.038 Initial specific training -0.140 -0.134 -0.142 -0.015 N 19662 19335 18800 18800 R2 0.14 0.16 0.12 0.47

GREECE SATISF SATISF SATISF WAGES Satisfaction index 0.461*** Gross hourly wage (log) 0.425*** 0.653*** ISCED 5-7 0.356*** 0.209*** 0.083** 0.093*** ISCED 3 0.196*** 0.152*** 0.109*** 0.001 Overqualified -0.106*** -0.113*** -0.112*** 0.059*** Useful but not quite related 0.155 0.184 0.126 -0.110 Useful and quite related 0.215 0.212 0.180 -0.103 Useful and very related 0.276* 0.254 0.224 -0.124 Initial specific training -0.126 -0.149 -0.137 0.123 N 10814 10646 10352 10352 R2 0.25 0.27 0.23 0.14

SPAIN SATISF SATISF SATISF WAGES Satisfaction index 0.173*** Gross hourly wage (log) 0.298*** 0.060 ISCED 5-7 -0.038** -0.124*** -0.095*** 0.175*** ISCED 3 -0.026 -0.059*** -0.055*** 0.093*** Overqualified -0.083*** -0.072*** -0.083*** -0.001 Useful but not quite related -0.037 -0.053 -0.054 0.009 Useful and quite related 0.006 -0.015 -0.010 0.029** Useful and very related 0.231*** 0.191*** 0.227*** -0.002 Initial specific training 0.131*** 0.131*** 0.133*** -0.018 N 17899 17586 17369 17369 R2 0.15 0.16 0.14 0.56

Table 3 Satisfaction estimations, different specifications. National sub-samples, pool of waves (cross-section). (cont.)

OLS (A) OLS (B) SIM EQ(C) SIM EQ (D) PORTUGAL SATISF SATISF SATISF WAGES

Satisfaction index 0.590*** Gross hourly wage (log) 0.270*** -0.051 ISCED 5-7 0.137*** -0.049** 0.118*** 0.411*** ISCED 3 0.053*** -0.003 0.040** 0.124*** Overqualified -0.027*** -0.031*** -0.045*** 0.036*** Useful but not quite related -0.293 -0.245 -0.297 0.009 Useful and quite related -0.259 -0.241 -0.280 0.079 Useful and very related -0.072 -0.090 -0.083 0.050 Initial specific training 0.224 0.170 0.233 0.056 N 22252 21918 21596 21596 R2 0.08 0.09 0.05 0.13

AUSTRIA SATISF SATISF SATISF WAGES Satisfaction index 0.134*** Gross hourly wage (log) 0.245*** 0.049 ISCED 5-7 -0.018 -0.117*** -0.046 0.278*** ISCED 3 0.001 -0.023 -0.006 0.092*** Overqualified -0.159*** -0.151*** -0.159*** -0.000 Useful but not quite related -0.248** -0.284*** -0.274*** 0.117*** Useful and quite related -0.059 -0.081 -0.073 0.064* Useful and very related 0.199** 0.168* 0.179* 0.025 Initial specific training 0.040 0.047 0.059 0.012 N 12822 12627 12328 12328 R2 0.15 0.15 0.15 0.36

FINLAND SATISF SATISF SATISF WAGES Satisfaction index 0.132*** Gross hourly wage (log) 0.201*** -0.023 ISCED 5-7 -0.139*** -0.202*** -0.155*** 0.161*** ISCED 3 -0.131*** -0.142*** -0.138*** 0.035*** Overqualified -0.080*** -0.073*** -0.093*** -0.003 Useful but not quite related -0.020 -0.006 -0.035 -0.024 Useful and quite related -0.012 0.002 -0.023 -0.035 Useful and very related 0.151* 0.159* 0.155 -0.042 Initial specific training -0.029 -0.052 -0.034 0.075** N 11563 11374 8559 8559 R2 0.11 0.11 0.10 0.44 Source: ECHP, waves 2 to 8, Eurostat. Year dummy controls omitted. Other control variables: family per capita income (excluding that from the employee), sex, tenure, working week, contract, supervisory position, type of employer, industry, occupation, marital status, children under 6 years old in the household, health, regional unemployment rate, year dummies. Reference: Spanish woman with low education, less than one year of tenure, education was not useful to get her job, does not feel overqualified, works, at maximum, 40 hours per week, temporary position, non supervisory, single, without small children, suffering bad health, without previous unemployment spell, works in the primary sector in a private firm with less than 5 employees.

TABLE 4. DESCRIPTIVE OF SAMPLES

ALL DK HOL BEL FR IRE ITA GRE ES PT AT FIN

Satisfaction 0.01 0.73 0.42 0.17 -0.12 0.54 -0.28 -0.50 -0.23 -0.68 0.81 0.17 Hourly wage (log) 2.02 2.51 2.50 2.37 2.11 2.14 2.10 1.64 1.91 1.31 2.13 2.12 ISCED 5-7 0.23 0.38 0.13 0.45 0.31 0.25 0.12 0.32 0.33 0.09 0.08 0.42 ISCED 3 0.36 0.47 0.25 0.31 0.34 0.45 0.49 0.38 0.22 0.15 0.74 0.40 Overqualified 0.53 0.60 0.39 0.63 0.51 0.55 0.50 0.58 0.59 0.46 0.61 0.65 Useful but not quite related 0.04 0.03 0.04 0.07 0.03 0.03 0.03 0.01 0.09 0.00 0.07 0.09 Useful and quite related 0.17 0.16 0.15 0.29 0.15 0.17 0.16 0.07 0.19 0.13 0.21 0.23 Useful and very related 0.34 0.54 0.66 0.37 0.40 0.40 0.16 0.28 0.23 0.16 0.40 0.42 Initial specific training 0.58 0.73 1.00 0.73 0.59 0.60 0.35 0.35 0.56 0.29 0.69 0.74 “net” family income (000) 1445 1987 1752 1692 1432 1363 2463 783 1164 676 1306 1384 Male 0.53 0.49 0.57 0.52 0.48 0.50 0.53 0.54 0.57 0.55 0.56 0.45 Age 37.42 41.09 38.43 37.47 38.54 35.21 38.11 36.61 36.07 35.85 36.14 40.13 1-2 years of tenure 0.18 0.17 0.21 0.19 0.12 0.22 0.15 0.20 0.21 0.18 0.15 0.19 3-4 years of tenure 0.10 0.11 0.12 0.10 0.09 0.10 0.09 0.11 0.09 0.11 0.11 0.08 5-9 years of tenure 0.18 0.19 0.21 0.17 0.18 0.16 0.17 0.16 0.15 0.20 0.21 0.17 10 years of tenure or more 0.40 0.43 0.38 0.48 0.48 0.32 0.48 0.35 0.32 0.38 0.42 0.43 Works over 40 hours /week 0.23 0.21 0.16 0.30 0.21 0.22 0.19 0.22 0.26 0.32 0.21 0.21 Permanent position 0.83 0.91 0.90 0.89 0.87 0.82 0.88 0.77 0.63 0.80 0.92 0.83 Supervisor 0.09 0.16 0.12 0.11 0.11 0.12 0.08 0.05 0.07 0.04 0.10 0.15 Intermediate 0.15 0.14 0.15 0.22 0.23 0.17 0.16 0.07 0.17 0.06 0.24 0.17 Married/living in couple 0.79 0.96 0.92 0.90 0.88 0.63 0.75 0.70 0.68 0.71 0.75 0.94 Kids under 6 years old 0.19 0.25 0.19 0.26 0.21 0.19 0.17 0.17 0.15 0.19 0.16 0.22 Previous unemployment spell 0.25 0.22 0.16 0.24 0.17 0.22 0.35 0.31 0.42 0.22 0.19 0.19 Very good health 0.29 0.53 0.22 0.29 0.15 0.60 0.18 0.74 0.24 0.04 0.45 0.23 Good health 0.52 0.35 0.64 0.58 0.55 0.33 0.56 0.21 0.61 0.63 0.43 0.55 Average health 0.16 0.10 0.13 0.12 0.27 0.06 0.23 0.04 0.13 0.27 0.11 0.20 Bad health 0.02 0.01 0.01 0.01 0.02 0.00 0.03 0.01 0.02 0.05 0.01 0.02 Regional unemployment rate 9.34 5.15 3.99 9.28 11.13 9.21 10.74 11.14 18.23 6.00 4.19 15.04 ISEI 42.95 46.09 48.59 47.11 43.16 43.84 41.22 43.11 40.88 37.10 40.86 48.99 Manufacturing & mining 0.21 0.18 0.15 0.20 0.16 0.24 0.27 0.17 0.21 0.23 0.24 0.22 Building 0.07 0.06 0.06 0.04 0.04 0.06 0.05 0.08 0.10 0.12 0.09 0.05 Sales and hostels 0.16 0.11 0.15 0.09 0.11 0.18 0.12 0.21 0.19 0.20 0.19 0.13 Transport 0.06 0.06 0.06 0.06 0.07 0.06 0.06 0.06 0.05 0.04 0.06 0.07

Real state/ financial services 0.10 0.10 0.16 0.13 0.10 0.11 0.08 0.09 0.10 0.06 0.08 0.12 Education, health, social services 0.33 0.40 0.37 0.40 0.48 0.28 0.33 0.33 0.25 0.25 0.26 0.35 Other sectors 0.05 0.07 0.03 0.07 0.04 0.05 0.07 0.04 0.06 0.05 0.06 0.04 Previous unemployment spell 0.25 0.22 0.16 0.24 0.17 0.22 0.35 0.31 0.42 0.22 0.19 0.19 Public employer 0.34 0.44 0.30 0.38 0.56 0.31 0.37 0.38 0.24 0.24 0.29 0.43 Private, 5-19 employees 0.18 0.14 0.13 0.11 0.09 0.18 0.19 0.17 0.22 0.27 0.20 0.16 Private, 20-49 employees 0.11 0.10 0.10 0.09 0.08 0.12 0.10 0.09 0.13 0.12 0.13 0.09 Private, 50-99 employees 0.07 0.06 0.08 0.07 0.05 0.08 0.06 0.06 0.08 0.07 0.08 0.06 Private, 100-499 employees 0.10 0.11 0.15 0.13 0.09 0.14 0.08 0.04 0.10 0.08 0.12 0.11 Private, 500+ employees 0.08 0.10 0.20 0.15 0.07 0.06 0.05 0.03 0.07 0.03 0.09 0.06 1996 0.17 0.17 0.14 0.20 0.25 0.16 0.16 0.14 0.14 0.14 0.16 0.25 1997 0.15 0.15 0.15 0.19 0.12 0.16 0.15 0.14 0.14 0.15 0.16 0.24 1998 0.15 0.14 0.15 0.18 0.10 0.15 0.14 0.14 0.14 0.15 0.14 0.21 1999 0.13 0.10 0.16 0.03 0.10 0.13 0.13 0.13 0.15 0.16 0.14 0.12 2000 0.12 0.12 0.15 0.03 0.08 0.11 0.13 0.14 0.15 0.11 0.12 0.10 2001 0.13 0.13 0.12 0.16 0.08 0.10 0.12 0.15 0.15 0.16 0.12 0.09 N 143892 9806 16787 7452 11394 9449 18800 10352 17369 21596 12328 8559 Source: ECHP, waves 2 to 8, Eurostat.

Table 5. Panel estimation of satisfaction equations. Controlling for fixed effects. Total sample Total sample Sub-sample Sub-sample Fixed effects Fixed effects Fixed effects Fixed effects IV Gross hourly wage (log) 0.174*** 0.187*** 0.429*** ISCED 5-7 0.005 0.006 0.006 0.007 ISCED 3 -0.005 -0.003 -0.016 -0.014 Overqualified -0.046*** -0.045*** -0.042*** -0.040*** Useful but not quite related -0.062*** -0.064*** -0.031 -0.035 Useful and quite related -0.041** -0.045** -0.020 -0.027 Useful and very related 0.048** 0.045** 0.065*** 0.059*** Initial specific training 0.037* 0.039* 0.016 0.021 “net” family income (000) 0.000*** 0.000** 0.000** 0.000 Age -0.003* -0.010*** -0.011*** -0.021*** 1-2 years of tenure -0.051*** -0.052*** -0.056*** -0.058*** 3-4 years of tenure -0.110*** -0.113*** -0.125*** -0.130*** 5-9 years of tenure -0.154*** -0.157*** -0.175*** -0.180*** 10 years of tenure or more -0.172*** -0.176*** -0.178*** -0.184*** Works over 40 hours /week -0.066*** -0.032*** -0.027*** 0.019 Permanent position 0.129*** 0.125*** 0.145*** 0.138*** Supervisor 0.095*** 0.086*** 0.073*** 0.060*** Intermediate 0.058*** 0.054*** 0.054*** 0.049*** Public sector employee 0.057*** 0.056*** 0.056*** 0.054*** Married/living in couple 0.013 0.008 Kids under 6 years old -0.001 -0.001 -0.012 -0.013 Previous unemployment spell -0.048*** -0.041** -0.040** -0.030 Very good health 0.247*** 0.249*** 0.167*** 0.172*** Good health 0.071 0.073 -0.009 -0.005 Average health -0.004 -0.001 -0.093* -0.089 Bad health -0.055 -0.050 -0.137** -0.131** Regional unemployment rate -0.001 -0.001 -0.002 -0.002 Intercept 0.111 -0.798*** -0.726*** -2.012** N 86574 86754 65023 65023 F (Wald in the IV estimations) 51.36 570.9 47.19 5354.19 Source: ECHP, waves 2 to 8, Eurostat. Reference: Spanish woman with low education, less than one year of tenure, education was not useful to get her job, does not feel overqualified, works, at maximum, 40 hours per week, temporary position, non supervisory, single, without small children, suffering bad health, without previous unemployment spell, works in the primary sector in a private firm with less than 5 employees.

Table 6. Satisfaction estimations controlling for unobserved heterogeneity Denmark All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.347*** 0.263*** 1.272 ISCED 5-7 0.033 0.028 0.046 0.017 ISCED 3 0.034 0.025 0.038 0.002 Overqualified -0.117*** -0.103*** -0.105** -0.070 Useful but not quite related -0.090 -0.093 -0.085 -0.092 Useful and quite related -0.006 -0.008 0.016 0.015 Useful and very related 0.076 0.074 0.101 0.096 Initial specific training 0.062 0.058 0.042 0.026 F (Wald if IV included) 6.59 7.51 6.52 11057.6 Number of cases s 7825 7825 6970 6970 The Netherlands All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.075*** 0.083*** -0.518 ISCED 5-7 -0.038 -0.038 -0.027 -0.029 ISCED 3 -0.001 -0.002 -0.005 0.004 Overqualified -0.016 -0.014 -0.014 -0.027 Useful but not quite related -0.031 -0.032 -0.022 -0.009 Useful and quite related 0.001 -0.002 -0.004 0.027 Useful and very related 0.069*** 0.067** 0.065** 0.085 Initial specific training F (Wald if IV included) 7.12 7.29 6.47 5672.5 Number of cases s 11565 11565 10000 10000 Belgium All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.207*** 0.230*** -0.530 ISCED 5-7 0.191*** 0.185*** 0.181** 0.210 ISCED 3 0.150*** 0.152*** 0.153*** 0.158*** Overqualified 0.103*** 0.098*** 0.109*** -0.122* Useful but not quite related -0.136 -0.136 -0.048 -0.054 Useful and quite related -0.104 -0.103 -0.042 -0.051 Useful and very related -0.019 -0.017 0.001 -0.006 Initial specific training 0.017 0.013 -0.047 -0.037 F (Wald if IV included) 6.45 6.68 5.38 405.84 Number of cases s 4877 4877 3965 3965 France All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.147*** 0.137*** -0.148 ISCED 5-7 0.022 0.011 0.059 0.066 ISCED 3 -0.021 -0.018 -0.045 -0.049 Overqualified -0.052*** -0.052*** -0.047** -0.047** Useful but not quite related -0.034 -0.035 -0.094 -0.092 Useful and quite related -0.028 -0.027 -0.078 -0.079 Useful and very related 0.017 0.015 -0.037 -0.034 Initial specific training 0.035 0.037 0.084 0.077 F (Wald if IV included) 16.91 17.13 14.97 862.12 Number of cases s 7677 7677 6168 6168

Table 6. Satisfaction estimations controlling for unobserved heterogeneity (cont) Ireland All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.205*** 0.272*** 34.461 ISCED 5-7 0.040 0.027 0.044 -1.726 ISCED 3 0.007 0.004 -0.031 -0.753 Overqualified -0.031 -0.032 -0.065* -0.545 Useful but not quite related 0.260 0.269 0.257 0.673 Useful and quite related 0.216 0.221 0.229 0.914 Useful and very related 0.338 0.339 0.337 0.859 Initial specific training -0.233 -0.237 -0.238 -1.318 F (Wald if IV included) 6.29 6.48 5.47 27.66 Number of cases 4440 4440 3032 3032 Italy All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.306*** 0.342*** 1.717 ISCED 5-7 -0.011 -0.012 0.014 0.013 ISCED 3 0.034 0.038 0.063 0.076 Overqualified -0.015 -0.013 0.005 0.012 Useful but not quite related -0.038 -0.051 -0.013 -0.082 Useful and quite related -0.016 -0.035 0.023 -0.066 Useful and very related 0.123 0.104 0.151 0.061 Initial specific training 0.013 0.027 -0.035 0.033 F (Wald if IV included) 8.08 9.52 8.62 906.99 Number of cases 11028 11028 7567 7567 Greece All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.204*** 0.223*** -0.371 ISCED 5-7 0.139* 0.131* 0.017 0.041 ISCED 3 0.080 0.076 -0.075 -0.067 Overqualified -0.195*** -0.192*** -0.200*** -0.206*** Useful but not quite related 0.252 0.283 0.464 0.358 Useful and quite related 0.232 0.258 0.323 0.231 Useful and very related 0.252 0.280 0.317 0.222 Initial specific training -0.304 -0.333 -0.353 -0.257 F (Wald if IV included) 6.14 6.5 4.7 669.16 Number of cases 5569 5569 3510 3510 Spain All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.214*** 0.182*** 1.399*** ISCED 5-7 0.053 0.053 0.067 0.075 ISCED 3 -0.028 -0.029 -0.089 -0.098 Overqualified -0.043* -0.044** -0.020 -0.020 Useful but not quite related -0.099* -0.099* -0.011 -0.016 Useful and quite related -0.116** -0.119** -0.066 -0.084 Useful and very related 0.082 0.079 0.144** 0.138* Initial specific training 0.137*** 0.137*** 0.094 0.098 F (Wald if IV included) 8.15 8.54 6.37 294.16 Number of cases 8279 8279 5202 5202

Table 6. Satisfaction estimations controlling for unobserved heterogeneity (cont) Portugal All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.210*** 0.254*** 0.596 ISCED 5-7 0.053 0.040 0.053 0.039 ISCED 3 0.014 0.015 0.022 0.026 Overqualified 0.004 0.007 -0.002 0.003 Useful but not quite related -0.462** -0.464** -0.275 -0.279 Useful and quite related -0.430** -0.435** -0.231 -0.233 Useful and very related -0.392** -0.400** -0.183 -0.192 Initial specific training 0.478** 0.481** 0.273 0.275 F (Wald if IV included) 5.7 7.43 5.81 14334.83 Number of cases 10232 10232 6952 6952 Austria All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.044 0.141** 0.166 ISCED 5-7 -0.112 -0.115 -0.269 -0.270 ISCED 3 0.098 0.095 -0.106 -0.108 Overqualified -0.047** -0.047* -0.039 -0.038 Useful but not quite related -0.042 -0.042 0.219 0.218 Useful and quite related 0.035 0.034 0.285** 0.284** Useful and very related 0.185 0.184 0.412*** 0.411*** Initial specific training -0.088 -0.086 -0.317** -0.316** F (Wald if IV included) 7.56 7.32 6.03 9177.91 Number of cases 6961 6961 4910 4910 Finland All All sub-sample sub-sample ff.ee. ff.ee. ff.ee. ff.ee. & IV Gross hourly wage (log) 0.243*** 0.227*** -1.241 ISCED 5-7 -0.035 -0.034 0.010 0.035 ISCED 3 0.001 0.006 0.036 0.024 Overqualified -0.020 -0.019 -0.019 -0.020 Useful but not quite related 0.033 0.044 -0.037 -0.097 Useful and quite related 0.030 0.036 -0.058 -0.095 Useful and very related 0.075 0.082 -0.003 -0.039 Initial specific training -0.035 -0.043 0.025 0.063 F (Wald if IV included) 6.01 6.87 5.81 1130.28 Number of cases 8121 8121 6747 6747 Source: ECHP, waves 2 to 8, Eurostat. Year dummy controls omitted. Other control variables: family per capita income (excluding that from the employee), sex, tenure, working week, contract, supervisory position, type of employer, industry, occupation, marital status, children under 6 years old in the household, health, regional unemployment rate, year dummies. Reference: Spanish woman with low education, less than one year of tenure, education was not useful to get her job, does not feel overqualified, works, at maximum, 40 hours per week, temporary position, non supervisory, single, without small children, suffering bad health, without previous unemployment spell, works in the primary sector in a private firm with less than 5 employees.

Table 7. Average values, variables used in the longitudinal specification using instrumental variables todos DK HOL BEL FR IRE ITA GRE ES PT AT FIN Satisfaction 0.15 0.76 0.41 0.16 -0.12 0.62 -0.20 -0.28 -0.12 -0.63 0.90 0.22 Hourly wage (log) 7.15 7.21 7.28 7.07 6.83 7.01 9.16 6.54 6.81 6.16 6.88 6.86 Partner´s hourly wage (log) 6.85 7.20 7.16 7.05 6.81 6.97 6.87 6.51 6.78 6.13 6.83 6.85 ISCED 5-7 0.28 0.42 0.16 0.44 0.28 0.28 0.18 0.42 0.40 0.13 0.11 0.45 ISCED 3 0.36 0.44 0.26 0.30 0.37 0.41 0.51 0.30 0.20 0.12 0.74 0.35 Age 43.89 44.76 43.99 42.05 43.83 43.95 44.00 43.51 43.31 43.55 43.37 45.25 Previous specific training 0.65 0.75 1.00 0.72 0.58 0.64 0.40 0.40 0.67 0.34 0.68 0.77 1-2 years of tenure 0.11 0.15 0.14 0.09 0.06 0.14 0.07 0.09 0.12 0.09 0.09 0.11 3-4 years of tenure 0.07 0.10 0.09 0.06 0.06 0.08 0.05 0.07 0.06 0.07 0.07 0.06 5-9 years of tenure 0.16 0.17 0.20 0.14 0.14 0.15 0.12 0.14 0.13 0.18 0.16 0.16 10 years or more of tenure 0.61 0.50 0.52 0.68 0.69 0.54 0.73 0.62 0.58 0.61 0.62 0.61 Overqualification 0.52 0.60 0.38 0.59 0.49 0.54 0.50 0.56 0.56 0.47 0.59 0.62 Useful but not quite related 0.04 0.03 0.04 0.07 0.02 0.02 0.03 0.01 0.09 0.01 0.08 0.09 Useful and quite related 0.18 0.17 0.15 0.29 0.15 0.16 0.18 0.07 0.22 0.14 0.20 0.24 Useful and very related 0.40 0.55 0.68 0.35 0.39 0.46 0.18 0.32 0.31 0.19 0.39 0.44 Works over 40 hours /week 0.21 0.23 0.17 0.28 0.23 0.22 0.16 0.15 0.23 0.30 0.21 0.21 Permanent position 0.91 0.93 0.94 0.94 0.95 0.88 0.94 0.87 0.80 0.89 0.95 0.90 Supervisor 0.14 0.19 0.16 0.14 0.14 0.18 0.11 0.09 0.11 0.06 0.13 0.20 Intermediate 0.18 0.14 0.16 0.23 0.24 0.19 0.19 0.10 0.22 0.08 0.24 0.17 Public sector employee 0.44 0.47 0.34 0.44 0.63 0.46 0.51 0.58 0.37 0.34 0.39 0.48 Married/living in couple 1 1 1 1 1 1 1 1 1 1 1 1 Kids under 6 years old 0.17 0.19 0.17 0.20 0.16 0.25 0.18 0.18 0.20 0.14 0.11 0.16 Previous unemployment spell 0.19 0.20 0.15 0.20 0.13 0.18 0.21 0.23 0.32 0.18 0.15 0.15 Very good health 0.25 0.51 0.19 0.23 0.12 0.54 0.12 0.67 0.19 0.02 0.35 0.18 Good health 0.53 0.37 0.65 0.62 0.54 0.37 0.57 0.27 0.62 0.56 0.49 0.56 Average health 0.20 0.11 0.15 0.14 0.30 0.08 0.28 0.06 0.16 0.35 0.14 0.24 Bad health 0.02 0.01 0.01 0.02 0.02 0.00 0.03 0.01 0.02 0.05 0.02 0.02 Regional unemployment rate 7.93 4.88 3.60 8.34 9.67 7.63 8.99 7.93 15.47 4.83 3.85 14.99 “net” family income (000) 1773 2196 1830 1954 1647 1765 3092 1148 1647 941 1571 1712 N 65023 6970 10000 3965 6168 3032 7567 3510 5202 6952 4910 6747