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Span Econ Rev (2009) 11:51–74 DOI 10.1007/s10108-008-9043-9 REGULAR ARTICLE

Job satisfaction and disability: lower expectations about jobs or a matter of health? Ricardo Pagán · Miguel Ángel Malo

Published online: 10 June 2008 © Springer-Verlag 2008

Abstract This article focuses on the analysis of the reported differentials of job satisfaction for disabled and non-disabled individuals. Using the Spanish data of the European Community Household Panel during the period 1995–2001, we estimate a job satisfaction equation for each group and evaluate job satisfaction differentials through the Oaxaca-Blinder methodology. The results show that disabled individuals are more likely to be more satisfied in their jobs than non-disabled ones, but only after controlling for other variables. Oaxaca-Blinder decomposition shows the greater importance of the returns in job satisfaction for disabled people, which is supported by explanations based on the lower expectations about jobs of disadvantaged groups. Keywords

Job satisfaction · Disability · Spain

JEL Classification

J28 · I10

1 Introduction Traditionally, the literature about work and disability has been closely related to participation and/or the incentives to leave the labour force using disability pensions. On the other hand, labour market policy interventions have been closely associated with promoting labour market participation of people with disabilities. However, very

R. Pagán (B) Departamento de Estructura Económica, Universidad de Málaga, Plaza de El Ejido s/n, 28071 Málaga, Spain e-mail: [email protected] M. Á. Malo University of Salamanca, Salamanca, Spain

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little attention (either from academics or politicians) has been devoted to job quality of the working disabled (such as, for example, Blázquez and Malo (2005), who analyze the educational mismatch of people with disabilities). Here, we will focus on another aspect related to job quality, satisfaction. Indeed, satisfaction can provide a whole (subjective) valuation of the quality of the job. The objective of this article consists of analyzing the determinants of the job satisfaction differential between disabled and non-disabled workers. Previous literature has stressed that individuals with disabilities report lower levels of job satisfaction with respect to non-disabled workers. This literature is almost exclusively descriptive with very few exceptions (such as Uppal 2005). Some authors (such as Clark 1997) have proposed that disadvantaged groups in the labour market (women in Clark 1997) have such low expectations about obtaining any type of job that they are very happy at work when they do have a job. In our context, disabled workers perhaps obtain a different return in terms of satisfaction from their jobs with respect to non-disabled workers. However, such a comparison should be made with caution because differences might also come from the differences in the characteristics of both types of workers, i.e., international evidence shows that disabled workers are mainly concentrated in low paying jobs, and they have on average much lower educational levels. Are job satisfaction differentials related to different expectations about jobs or are they related to the different characteristics of disabled and non-disabled workers? On the other hand, previous literature has shown a close link between health status and satisfaction, which would be the opposite with respect to the interpretation related to lower expectations about jobs. In other words, worse health (here, disability) decreases satisfaction. Then, it is important to check whether the effect of disability is merely an aspect of the documented influence of health on satisfaction or not. Therefore, we will check two opposite hypotheses: whether disability increases job satisfaction through decreasing expectations about jobs or behaves as a health-like variable reducing how people enjoy some aspect of their lives (here jobs). Consistent answers to these questions can only be provided using micro-data from a survey with enough sample size to estimate different job satisfaction equations for the disabled and non-disabled working population. In addition, we decompose the job satisfaction differential between both groups of workers using the OaxacaBlinder methodology, widely applied to analyze wage differentials, but a novelty in this literature about disability and satisfaction. Then, we will separate the effects of different returns in satisfaction for disabled and non-disabled workers with respect to the effects of different characteristics of both groups of workers. We use Spanish data from the European Community Household Panel (ECHP) from 1995 to 2001. The variables on disability included in the ECHP do not strictly follow the international standard proposed by the World Health Organization (WHO), but they are based on the definition of disability as limitations in daily activities which traditionally are the most important aspect of the classifications proposed by the WHO concerning disability. Our econometric estimations are based on random-effects linear models on job satisfaction using the Spanish ECHP, although the results are very robust to different estimation models (including pooling estimations). The results obtained show that disabled workers are more likely to report higher levels of job satisfaction due to the fact that they have lower expectations. Disabled individuals enjoy higher

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53

returns from job characteristics such as hourly wages, job tenure and job matching than do non-disabled ones. The remainder of the paper is as follows. In “Review of literature”, we present a review of recent literature concerning job satisfaction and disability, proposing the two hypotheses to be tested in the empirical analysis. In “Econometric model, data and variables”, we explain the econometric model, the database and the main variables included in our analysis. In “Empirical analysis”, we present and discuss our results. “Conclusion” summarizes our main results and presents a short reflexion on the policy implications of our results.

2 Review of literature Although the analysis of job satisfaction of workers has been widely investigated by psychologists and sociologists (e.g. Argyle 1989; Hodson 1985), the interest of economists is relatively recent. Following the seminal papers by Hamermesh (1977) and Freeman (1978), there are an important number of works analysing the relationship between the levels of job satisfaction and job quits (McEvoy and Cascio 1985; Akerlof et al. 1988; Clark et al. 1998), gender (Clark et al. 1996; Clark 1997; Sloane and William 2000), income and wage growth (Clark 1999; Clark and Oswald 1996; Jones and Sloane 2003; Diaz-Serrano and Cabral 2005), trade union (Borjas (1979); Miller (1990); Meng (1990); Bender and Sloane (1998); Renaud (2002)), establishment size and work environment (Idson 1990; Gazioglu and Tansel 2002), absenteeism and productivity (Mangione and Quinn 1975; Clegg 1983), and international comparisons of job satisfaction (Ahn and García 2004), among others. Job satisfaction differences by gender are relevant for our objective because this literature explains these differences in terms of expectations about jobs. Following Clark (1997), women have such low expectations about what they can obtain from work that they have higher satisfaction in almost any type of job with respect to men. The rationale for such low expectations is the disadvantaged position of women in the labour market.1 As women expect little from jobs, they value very much what they do obtain.2 If we assume that disability can be considered as a personal condition attached to a poor position in the labour market, we obtain a first hypothesis about a positive influence of disability on job satisfaction: Hypothesis 1: Disabled workers are more likely to enjoy higher job satisfaction due to the fact that they have lower expectations about jobs. 1 The term “disadvantaged group in the labour market” is frequently used, but to our knowledge there is not any standardized definition. At any rate, we understand that markedly low participation, lower wages, wage discrimination, occupational segregation, etc., are characteristics common to disadvantaged groups in the labour market. Obviously, women suffer all these labour market problems as well as do people with disabilities. 2 Nevertheless, some authors have found opposite results that are opposite to those obtained by Clark for some countries. For example, Kraiser (2002) finds that the satisfaction level is lower for women than for men in The Netherlands and Portugal, and Cabral (2005) does not find significant differences in job satisfaction by gender in Portugal.

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A positive relationship between job satisfaction and health status has empirically been demonstrated in other studies such as, for example, Clark et al. (1996), Clark and Oswald (1996), Kraiser (2002), and Ahn and García (2004). These last authors not only find a positive influence but they also obtain that health is the single most important determinant of overall job satisfaction. It is important to note that disability and health are not strictly coincident. For example, a very important disability such as total blindness is not necessarily related to very poor health status. Nevertheless, some disabilities can potentially be closely linked to lower levels of health status if they proceed from some illnesses causing, at the same time, not only limitations and disabilities but poor health too. We will go back to this issue in the descriptive analysis section. Therefore, we should consider that disability could behave as a health-like variable, negatively affecting people’s welfare (and job satisfaction as an aspect of their lives). From this perspective, we state the second hypothesis to be tested: Hypothesis 2: Disabled workers are more likely to enjoy lower job satisfaction due to the fact that they have poor health status. Therefore, we have two confronting hypotheses and we need an empirical analysis to test which of the two hypotheses is supported by the data. There is previous literature about disability and job satisfaction, but it is mainly descriptive. In this sense, we have Burke (1999), McAfee and McNaughton (1997a,b), Renaud (2002), Rose (2003) and Uppal (2005). Burke (1999) analyses the influence of disability status and work experiences on satisfaction for women in Ontario, Canada. The descriptive analysis carried out reveals that working women with disabilities report lower levels of job satisfaction, poorer psychological health, higher job insecurity and lower levels of income. However, and as the author himself points out, his results must be considered with care, because the sample might not be representative of the working women in Ontario. In addition, McAfee and McNaughton (1997a,b), using a reduced sample of individuals working in certain occupations for a set of US states, found that workers with disabilities reported moderate levels of overall job satisfaction, strong dissatisfaction with pay and promotions, and high satisfaction with co-workers and supervision. Renaud (2002) investigates the levels of job satisfaction reported by union and nonunions workers in 1989 in Canada. This author includes in the estimated job satisfaction model a dummy variable related to the existence of disability and obtains that workers with disabilities are less satisfied as compared to workers without disabilities. This result for the disability dummy variable contrasts with the positive coefficient obtained by Rose (2003) as he analyses the levels of job satisfaction in occupations in the UK. Recently, Uppal (2005) analysed levels of job satisfaction of disabled individuals and the effects of certain workplace characteristics on them for the year 1991 in Canada. He estimates an ordered probit model for the whole sample (disabled and non-disabled) and his results show how those individuals with all types of disabilities, except speech, have lower levels of job satisfaction as compared to those who are able-bodied. However, when introducing the workplace characteristics into the model, the negative effect for individuals with a mobility disability disappears, whereas for other types of disabilities the magnitude decreases. He concludes that the absence of

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55

assistive technology or job accommodations at the workplace may be the cause of the unexplained differences in the levels of job satisfaction between workers with and without disabilities. Nevertheless, the work of Uppal (2005) presents some methodological limitations, which we try to overcome in this research. Uppal (2005) estimates a unique job satisfaction model for the whole sample due to the small number of available observations for the disabled sample (only 443). As a novelty with respect to Uppal (2005), we use panel data to deal with unobserved heterogeneity and present different estimations on job satisfaction for each group (disabled vs. non-disabled). Then, we compare the estimated coefficients and decompose the job satisfaction differentials into characteristics and returns using the traditional Oaxaca-Blinder’s decomposition (1973), because disabled and non-disabled individuals might obtain different returns in terms of satisfaction, and personal and job characteristics are clearly different in both groups of workers. 3 Econometric model, data and variables In order to estimate the determinants of the job satisfaction of non-disabled (N) and disabled individuals (D) we apply, as does most of the previous empirical evidence on job satisfaction, the theoretical framework developed by Clark and Oswald (1996), which is based on the definition of the individual’s utility from working as follows: U j = u j (y, h, i, w) j = N , D

(1)

where y is income, h is hours of work and i and w are a set of variables measuring the individual and job characteristics, respectively. The estimation of this utility function has been habitually carried out using satisfaction (in our case job satisfaction) as a proxy, through the utilization of different models. As usual in satisfaction literature, our dependent variable ranges from 1 (completely dissatisfied) to 6 (completely satisfied).3 This variable can be considered either strictly categorical or continuous. In the first case, the model to be estimated is an ordered probit model, and, in the second case, a typical OLS regression will be suitable. In the previous literature, both options have been widely used. On one hand, psychologists have usually considered these answers as cardinal (Schwartz 1995; Ng 1997). On the other hand, some economists assume the ordinality of the answers because individuals can recognise and predict the satisfaction levels of others and translate verbal labels, such as “very good” and “very bad”, into roughly the same numerical values (Van Praag 1991; Ferrer-i-Carbonell and Frijters 2004). Nevertheless, Ferrer-i-Carbonell and Frijters (2004) conclude that assuming cardinality or ordinality has little impact on estimation results. Here, we will assume cardinality, because the results are rather similar and their interpretation is simpler. In addition, as we have a panel, we have estimated a random effects model on job satisfaction.4 3 For example, see Clark and Oswald (1996), Clark (1997), Gardner and Oswald (2001) and Kraiser (2002). 4 The estimations of the ordered probit models with random effects are available upon request.

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However, a sample selectivity problem may appear when we estimate job satisfaction equations if the sample is not random. In this case, the estimations will be inconsistent and biased. In our analysis, it is possible to suppose that if dissatisfied disabled individuals can leave the labour market more easily than equally dissatisfied non-disabled individuals, the remaining disabled workers will have higher average levels of job satisfaction because the sample is biased. Thus, if dissatisfied disabled individuals are less likely to be in employment than dissatisfied non-disabled individuals, the observed distribution of the job satisfaction will be biased. The underlying assumption is that the potential satisfaction is related to the probability of being an employee (Clark 1997). As we have panel data, there is another eventual selection problem related to non-random sample attrition. If disability is linked to a higher (or lower) propensity to remain in the sample, the estimated coefficients will be biased (see Baltagi and Song, 2006, for a survey of this type of bias). To deal with the selectivity problem, some authors have applied the well known methodology proposed by Heckman (1979) such as, for example, Clark (1997) and Sanz de Galdeano (2002). However, as we have panel data we have to apply a different methodology. Here, we will follow the proposal of Verbeek and Nijman (1992) for random effects models.5 These authors propose an additional variable test to check for the presence of selection bias in linear regressions with random effects. These are: first, the number of waves the ith individual participates in the panel; second, a binary variable taking the value 1 if and only if the ith individual is observed over the entire sample and zero otherwise, and, third, a binary variable indicating whether the individual was observed in the previous period or not. As Baltagi and Song (2006) remark, considering these variables in the regression is a way to check as to whether the pattern of missing observations in the panel affects the underlying regression. The main advantage of this additional variable test is that it does not require estimation of the model under selectivity or a specification of the response mechanism (Verbeek and Nijman 1992). As in our case selectivity bias is rejected by the additional variable test, we proceed to estimate a GLS random-effects model. With the estimated coefficients of this model we apply the conventional Oaxaca-Blinder’s decomposition method (1973) to decompose the job satisfaction differential between non-disabled and disabled individuals as follows:6     (2) L¯ N − L¯ D = X¯ N − X¯ D βˆ N + X¯ D βˆ N − βˆ D where the left-hand side measures the mean job satisfaction difference between both groups, the first term of the right-hand side represents the part of the difference attributed to differences in observed characteristics (endowments), and the second term shows the part of the difference that is due to the differences in the obtained rewards for those characteristics (which is commonly considered as a measure of the discrimination). Moreover, the endowment and discrimination components have been decomposed into 5 Wooldridge (1995) has proposed a different approach for fixed-effects models. 6 As selectivity is rejected in our estimations, we do not include the selection term (usually called λ) in the

Oaxaca-Blinder decomposition.

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subcomponents in order to estimate the contribution of each explanatory variable to the mean job satisfaction differential between non-disabled and disabled workers. As noted, the data used in this work have been taken from the European Community Household Panel (ECHP) covering the period 1995–2001 for the Spanish case. This annual longitudinal survey designed by EUROSTAT contains not only information at the household level but data on the individuals’ characteristics such as gender, marital status, age, educational level, health, and so on, as well as questions related to their labour status, earnings, working hours, firm size, type of contract, occupation, activity sector, etc.7 The questions about disability are the following: Q158: Do you have any chronic physical or mental health problem, illness or disability? I f Y es→Q159 Q159: Are you hampered in your daily activities by this chronic or mental health problem, illness or disability? Yes, severely/Yes, to some extent/No Those who answer “Yes” (severely or to some extent) can be defined as people with disabilities (either mental or physical). Of course, this is a self-evaluation and it does not refer to an “objective” definition of disability. Nevertheless, it provides a useful approach to measuring the self-perceived disability. The initial filtering question was added in the second wave (1995). In order to avoid any problems related to this change in the questionnaire, we will only use data from 1995 onwards.8 The ECHP definition of disability is not exactly correspondent with the international definition provided by the World Health Organization (WHO), and, therefore, there is a lack of comparability with international surveys on disability. However, the questions of the ECHP contain the main objective of the WHO definition which relates disability to limitations on daily activities. Therefore, the figures obtained from the ECHP give an approximation of the phenomenon of disability, and though not strictly comparable with other data sources designed to follow the international definitions of disability, they are closer to them than any sort of administrative data (which usually focuses strictly on disability with respect to work). Finally, the questionnaire allows us to define two subtypes of disability: those answering “Yes, severely” can be considered as people with severe disabilities, and those answering “Yes, to some extent” can be considered as people with moderate disabilities. At any rate, with only one exception in “Descriptive results”, we will use only one category which consists of the aggregation of both subtypes of disability. The main reason is that the subtypes do not correspond to any standard subgroups of the WHO definition of disability. The main effect of this aggregation is that we will have a disabled population with greater heterogeneity than in other definitions (especially with respect to the administrative definitions). Previous literature on disability in Spain has shown the accuracy of the disability definition provided by the ECHP. Malo (2003) shows that for working-age people the prevalence rate of disability using the 1999 Survey on Disabilities, Impairments 7 See Peracchi (2002) for a review of the main characteristics of the ECHP and a discussion of the non-response and attrition problems. 8 In addition, we exclude the first wave (launched in 1994) because the type of contract was not included

in the first wave, and, potentially, this variable might be important in our job satisfaction equations.

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and Health Status (SDIHS) launched by the Spanish Statistical Office was 5%, while using the ECHP for the same year the prevalence rate went up to 10 percent (and the prevalence rate of severe disabilities was 3.5 percent). This author also shows that the disabled people in the ECHP are similar in age and gender distribution to those in the SDIHS, but with a higher employment rate. In fact, “ECHP-disabled” have a similar employment rate to the data provided by the ad hoc module on disability of the Labour Force Survey (LFS) launched in 2002 (where the prevalence rate of disability for Spain was 8.7 percent). It is likely that the reason for these differences is the time span considered in the definitions of disability. The SDIHS stated that 1 year was necessary in order to consider a limitation in daily activities as a disability (according to WHO recommendations), while the LFS stated 6 months was, and the ECHP included only a generic reference to a “chronic” situation (see above). Therefore, the ECHP (as well as the LFS) tends to classify more people as disabled and will include more working individuals. The questionnaire of the ECHP provides answers about the individuals’ levels of satisfaction with regards to some aspects of their lives, among them their levels of job satisfaction. The question we use in this work is the following: How satisfied are you with your work or main activity? The respondents can choose among six possible responses, ranging from “completely dissatisfied” (= 1) to “completely satisfied” (= 6). Thus, the job satisfaction level is based on the individual’s own evaluation. The samples have been obtained by using the data from the ECHP from 1995 to 2001. It consists of working-age workers (aged 16–64) who are wage and salary earners and work at least 15 or more hours a week during the reference week of the interview.9 The reason for excluding those individuals in a paid employment working less than 15 h a week from the sample is because we do not have information for this group of workers on some variables that cannot be omitted in the following estimation process (for example, firm size, public sector or job responsibility). Final sample sizes in the non-disabled and disabled individuals’ job satisfaction equations are 23,375 and 1,018 observations, respectively. Although the problem of non-response in the satisfaction questions is very minimal in the ECHP, the relatively small number of observations available in the estimation of the disabled individuals’ job satisfaction equation does not allow different estimations by gender. The explanatory variables included in the job satisfaction equations are those traditionally used in the existing literature (for example, Clark and Oswald 1996; Clark et al. 1996; Clark 1997; Sloane and William 2000; European Commission 2002; Diaz-Serrano and Cabral 2005). We include individuals’ characteristics such as gender, age, marital status, educational level and health status. With regards to the job characteristics, we include in the equations variables such as hourly wages, job tenure, hours of work, occupation, industry, region of residence, private versus public sector, job responsibility, type of contract, non-wages subsidies, job matching, overeducation, number of periods previously unemployed in the last 5 years, and year of the interview.

9 Apart from self-employed workers, we exclude from the final sample apprentices and workers with

training contracts.

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Table 1 Mean job satisfaction and percentage of highly satisfied (reporting a job satisfaction of 5 or 6 on the 1–6 scale) for non-disabled (N ) and disabled (D) individuals in Spain All N Mean value % Highly satisfied

Male D

t-test a

N

Female D

t-test a

N

t-test a

D

4.24

3.88

7.17

4.25

3.87

5.78

4.23

3.89

4.26

49.40

40.39

5.03

49.50

40.49

3.80

49.22

39.66

3.36

Source: European Community Household Panel. Period 1995–2001. All means and percentages have been calculated by using weighted data a t-statistic for the equality of means or proportions by disability status

4 Empirical analysis 4.1 Descriptive results Table 1 shows the mean job satisfaction levels for non-disabled and disabled individuals during the period 1995–2001, and, to shed further light on the distribution around the mean value of non-disabled and disabled individuals’ job satisfaction levels, the percentage of highly satisfied individuals (those individuals reporting a job satisfaction of 5 or 6). First, the mean job satisfaction is higher for non-disabled individuals than for disabled ones (4.24 compared to 3.88) and the differential is significant at the 5% level. This job satisfaction differential is also found for males and females, registering a significant difference of 0.38 and 0.34 percentage points, respectively. Second, the percentage of non-disabled individuals who declare themselves highly satisfied in all the satisfaction measures is greater than for individuals with disabilities for the whole sample. This difference is almost 9 percentage points in favour of the former. We obtain similar results for the male and female samples. As we explained in “Review of literature”, disability and health are potentially correlated, although it is critical to the interest of our second hypothesis that both variables do not correlate perfectly. Appendix Table 4 shows the relationship between disability and health status, and for a more careful analysis we present individuals with disabilities disaggregated into people with moderate disabilities and with severe disabilities (following the ECHP question on disability described in “Econometric model, data and variables”). First of all, we can see that for those with very good health and with good health, almost all of them are people without disabilities (99.43 and 98.73% respectively). However, looking at the distribution by disability, 95.92% of the total sample corresponds to people without disabilities. Therefore, the huge percentages of non-disabled people in very good and good health are very closely related to the sample distribution among disabled and non-disabled persons. Considering column percentages, we can appreciate that people with disabilities have higher percentages for the lower statuses of health, especially for people with severe disabilities. Therefore, disability and bad health are related, especially for the case of severe disability, but we believe that this table shows that they do not share exactly the same information. Intuitively, we can take again the example of blindness. When it is generated by a chronic illness (such as diabetes) it is probably linked to poor health status, but when

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blindness is related to a congenital problem of the eyes, this disability and the health status of the individual will probably be orthogonal. Finally, Table 4 shows the mean job satisfaction considering health status and disability. We can see that job satisfaction increases with health for all groups, with the exception of people with severe disabilities. This exception is related to the lower job satisfaction for people with severe disabilities and very good health. As this cell represents very few individuals, probably the low figure is not representative.

4.2 Econometric estimations Following the methodology presented in “Econometric model, data and variables”, Table 2 shows the estimation results. However, before proceeding with the remarks on the results we wish to comment on some previous and additional estimations. All of them are not included in the article, but they are available upon request. First of all, we have checked whether to estimate pooling all waves or to use the panel nature of the database. The results obtained from the Lagrange-Multiplier test for random effects confirmed that considering the panel dimension was a better option for non-disabled and disabled samples.10 As we explained before, a long standing discussion is whether job satisfaction is an ordinal or a cardinal variable. To check this point, we have estimated random-effects ordered probits for both samples and with the same variables as in GLS random-effects models shown in Table 3, and the results were very similar. As the interpretation of coefficients is straightforward in a linear model, we have preferred the GLS estimations. This similarity in the results can be found in other studies (e.g. Sloane and William 2000; Ferrer-i-Carbonell and Frijters 2004) and reduces to some extent the econometric problem of choosing among different possible estimation methods. In “Econometric model, data and variables”, we have explained that selectivity bias was a potential problem in our estimations, and we have applied the additional variables test proposed by Verbeek and Nijman (1992) for random-effects linear models. This test rejected the relevance of a selectivity bias. Appendix Table 5 shows the coefficients of the additional variables of this test and they are not statistically significant at conventional levels in both samples (for disabled and non-disabled individuals). Therefore, the main estimations of this article presented in Table 2 are not affected by a relevant selectivity bias. Finally, although all models have been estimated for disabled and non-disabled people samples, we have estimated a simpler random-effects model where disability was included as a dummy. As expected, the coefficient of this dummy is negative and it shows that being disabled decreases job satisfaction in 0.225. Uppal (2005) estimates

10 In a previous version of this article we estimated all models pooling the database. The statistical significance and the sign of the coefficients were usually similar for all variables in both samples (disabled and non-disabled people). Following the traditional methodology by Heckman (1979) we checked for selectivity in these pooled-data models and Heckman’s lambda was not significant for both samples. Of course, these estimations are also available upon request.

123

864.168

1,557.99

0.649

Age squared

Married/cohabiting

0.468

Primary or less (reference)

117.653

Job tenure

Job tenure squared

0.060

0.110

15–29

30–35

Hours of work

6.829

7.939

Log (real hourly net wages)

Job characteristics

0.127

0.012

Bad or very bad

0.629

Good

Fair

0.233

Very good (reference)

Health status

0.325

0.207

University

Secondary

146.100

7.391

0.498

10.850

0.350

37.951

Female

Educational level

z

7.97

−4.51 −2.91

−0.171

1.49

−0.086

0.001

14.44 −1.99

−7.07

−0.506 0.397

−13.74

−0.371

−0.012

−11.89





−0.214





−7.21 −4.15

−0.214

0.70

−0.104

0.017

0.001

1.95 −7.80

0.050 −0.054

0.105

0.083

130.431

8.508

6.663

0.330

0.437

0.205

0.028

0.697

0.158

0.144

0.724

2, 063.13

43.972

0.369

Mean

Coef.

Mean

SD

All disabled

All non-disabled

Age

Individual characteristics

Variables

Table 2 Job satisfaction: random effects estimates by disability status

149.308

7.623

0.465

974.492

11.392

SD

0.68

0.047

−0.137

0.001

0.022

0.548

−0.538

−0.301

−0.126





−0.328

−0.157

0.29

−0.78

−0.32

0.72

3.95

−1.93

−1.10

−0.45





−2.40

−0.83

2.63 −0.51

0.001

−2.83

z

−0.061

−0.089

0.077

Coef.

Job satisfaction and disability 61

123

123

0.485

0.258

41+

0.229

0.159

0.086

0.079

0.072

0.055

Trans./finance/real estate

Public Administration

Education

Health services

Other industries

0.123

0.048

Retail and trade

0.106

Construction

Hotel and restaurants

0.162

0.237

0.265

0.338

0.425

0.304

0.132

0.061

0.137

0.033

Agri/fores/fish (reference)

Manufacturing



4.21

4.98

6.04

4.58

2.33

0.89

3.98

2.85

2.54



−6.19

−0.276

0.144

Elementary occupations

Industry

−3.93

−0.184

0.102

Operators/assemblers

−2.70

−0.116

0.208

Skilled agri. and fishery

−3.18 −1.42

0.143

−0.129

0.113

Clerks

Service and sales

−0.061

−0.78

−0.028

0.172

0.116



−0.61

Manag./profe. (reference)



−0.012



−2.81



−0.088

Associate professionals

Occupation

0.087

36–39

0.097

0.053

0.078

0.092

0.088

0.058

0.121

0.134

0.203

0.076

0.268

0.092

0.261

0.151

0.080

0.059

0.088

0.272

0.467

0.073

Mean

z

Coef.

Mean

SD

All disabled

All non-disabled

40 (reference)

Variables

Table 2 continued

SD

0.089

0.011

0.400

0.38

0.04

1.37

0.41

−1.56

−0.369 0.115

0.15 −0.30

0.035 −0.082

−1.52 −1.39

−0.318

−0.67

0.34

1.08

0.45

−0.13

1.80



0.32



−0.87

z

−0.306

−0.177

0.097

0.291

0.123

−0.036

0.486



0.034



−0.163

Coef.

62 R. Pagán, M. Á. Malo

0.124

0.133

0.226

0.165

0.059

0.748

Madrid

Centre

East

South

Canary Islands

Private sector

0.123

0.170

Northwest (reference)



0.678

0.275

0.025

0.022

Open-ended (reference)

Fixed-term

No contract

Others

−6.53 −1.62

−0.360 −0.084

− −7.20

−0.171







0.739

Type of contract

None (reference)

0.181

6.49 4.40

0.097

0.080

Supervisor

0.221

3.18 −1.55

0.166

−1.38

−0.055 −0.050

0.44

0.016

0.01

−8.18

−0.354 0.000

−0.41

− −0.016

Intermediate

Job responsibility

z

0.028

0.051

0.273

0.647

0.800

0.159

0.041

0.772

0.058

0.233

0.271

0.099

0.078

0.130

0.132

Mean

Coef.

Mean

SD

All disabled

All non-disabled

Northeast

Region

Variables

Table 2 continued

SD



−0.161

0.174

0.086





−0.62

0.76

0.63





1.98 −0.07

0.446 −0.009

−1.07

−0.89

−0.200 −0.182

−2.64

−1.05 −0.54

−0.087 −0.440

−2.28

−0.484

−1.55

z

−0.203

−0.286



Coef.

Job satisfaction and disability 63

123

123 z

12.59

No. observations

Source: European Community Household Panel. Period 1995–2001

23,375

0.130

2001

Constant

0.139

0.134

1999

2000

0.143

0.138

1997

1998

0.163

0.152

1995 (reference)

1996

Years

5.47 −3.43

0.089 −0.023

0.559

0.769

Non-wages subsidies

Previously unemployed −

13.07

−4.85

−0.131 2.933

−3.51 −3.59

−0.091 −0.095

−7.08 −3.16

−0.174 −0.080

−2.90

−0.069



−8.72



0.575

− −0.140

0.479

None (reference)

Overqualified

1.97

0.091

0.052

0.430

Good

0.252

1,018

0.111

0.126

0.138

0.170

0.133

0.151

0.172

1.118

0.490

0.446

0.633

0.085

0.282

Mean

Coef.

Mean

SD

All disabled

All non-disabled

Fair

Job matching

Variables

Table 2 continued

SD

3.279

−0.303

−0.372

−0.285

−0.389

−0.268

−0.311



−0.033

0.051

−0.216



2.88

−1.85

−2.37

−1.88

−2.77

−1.83

−2.23



−1.53

0.53

−2.42



0.08 −1.47

0.009

z

−0.226

Coef.

64 R. Pagán, M. Á. Malo

Job satisfaction and disability

65

Table 3 Decomposition of the overall job satisfaction difference between non-disabled and disabled individuals Observed job satisfaction difference (D) Characteristics (C) Returns (R) Contribution of each component Characteristics (C/D) Returns (R/D) Total (C/D) + (R/D) Decomposition into subcomponents

0.345 0.271 0.074 0.786 0.214 1.000 Characteristics Ci

Returnsb

Ci /D(%)

Ri

Ri /D(%)

−0.001

−0.28

−0.010

−2.89

0.328

95.16

1.514

439.45

Age squared

−0.342

−99.34

−0.547

−158.64

Married/cohabiting

−0.001

−0.37

0.056

16.36

Educational levela

−0.044

−12.66

−0.029

−8.28

0.186

54.00

−0.007

−1.91 −291.56

Female Age

Health statusa Log (real hourly net wages)

0.066

19.15

−1.004

Hours of worka

0.003

0.72

0.004

1.04

Job tenure

0.007

1.90

−0.282

−81.90 32.52

−0.005

−1.52

0.112

Occupationa

0.033

9.59

0.001

0.30

Industrya

0.004

1.04

0.037

10.79

Job tenure squared

Regiona

−0.014

−4.03

−0.004

−1.19

Private sector

0.001

0.35

0.101

29.43

Job responsibilitya

0.011

3.12

0.047

13.71

Type of contracta

0.010

2.77

0.083

24.18 −23.61

Job matching a Overqualified

0.038

10.91

−0.081

−0.018

−5.24

0.034

9.79

0.006

1.78

0.019

5.39

Non-wages subsidies Previously unemployed Yearsa

0.008

2.35

0.011

3.08

−0.003

−0.80

−0.002

−0.53

Constant





0.020

5.90

Total

0.271

78.58

0.074

21.42

Source: Authors’ calculations from the estimation results shown in Table 2 a Mean value of all coefficients of the dummies created for that variable b Decomposition of “returns” using the methodology proposed by Gardeazabal and Ugidos (2004)

an ordered probit model introducing different types of disability as dummies. This author finds that with the exception of the speech impaired, individuals with disabilities are likely to be less satisfied with their jobs as compared to the non-disabled.11 11 And mobility disability becomes non-significant when a detailed variables set of workplace characte-

ristics is introduced in the model (Uppal 2005).

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4.2.1 Results The main estimation results are shown in Table 2. A first glance is provided by the mean values of the variables used in the estimation process (see also Table 2) in order to understand the determinants of the levels of overall job satisfaction for non-disabled and disabled individuals. As many other studies have pointed out, the disabled individuals working in a paid employment are older and, therefore, have more experience and tenure as compared to non-disabled individuals. Along these lines, Malo (2003) shows with a national survey on disability for Spain that 61 per cent of people with disabilities of working age in Spain are between 50 and 64. One of the most important characteristics of disabled individuals is their lower educational level. For example, only 14.4% of the disabled individuals have a university educational level,12 whereas for the non-disabled individuals this percentage rises to 32.5%. With regards to the job characteristics, it is noteworthy that disabled individuals receive lower hourly wages than non-disabled workers, which is related to wage discrimination, but also to the lower productivity of workers with disabilities (Malo and Pagán 2007), as Table 2 confirms, through their relatively higher participation in low-skilled occupations in jobs with a lower level of responsibility, and with less non-wages subsidies. In addition, they suffer from poorer job matching, although they are less overqualified,13 confirming previous results obtained by Blázquez and Malo (2005) with the ECHP. Finally, workers with disabilities have spent more time in unemployment in the previous five years than non-disabled individuals, confirming that workers with disabilities have a weaker attachment to the labour market, which is coherent with previous empirical literature (Blázquez and Malo 2005; Malo 2004). As we had expected, healthier workers have higher job satisfaction levels. However, only the coefficient of the “bad or very bad” variable is significant for the disabled sample. The reason for including the individual’s health status in the job satisfaction equations is to control for productivity differences, regardless of the disability status of the individual worker (Johnson and Lambrinos 1985; Baldwin and Johnson 1992, 1994). As for gender differences, non-disabled females report higher overall job satisfaction levels than do males after introducing other control variables into the regressions. This result is in line with the work of Clark (1997), but only occurs for the non-disabled

12 Considering the whole population of people with disabilities in working age in Spain, only 3.6% of them

have a university degree (Malo (2003)). The higher percentage for our sample of workers with disabilities means that a university degree is a powerful instrument for obtaining a job for people with disabilities. In addition, international evidence shows that people with disabilities obtain high returns from higher education (Dean and Dolan 1992; Hendricks et al. 1997). 13 The variable “job matching” included in our job satisfaction equations measures the degree to which a job is related or not to the education or training of the worker. To create this variable we use two questions; “Have you had formal training or education that has given you skills needed for your present type of work?” and “How much has this training or education contributed to your present work?”. The variable “overqualified” has been constructed from the following question: “Do you feel that you have skills or qualifications to do a more demanding job than the one you have now?”.

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individual sample. For the disabled individuals’ sample the coefficient of the “female” variable is far from being significant at conventional levels. Following Clark (1997), identical males and females with the same jobs and expectations would report identical job satisfaction, but females’ expectations are lower than males’. This author argues that lower expectations of females likely result from the weaker position in the labour market that females have held in the past. However, this gender differential disappears for young workers, more highly educated workers, professionals and those in maledominated workplaces because these groups are all likely to have higher expectations about what their jobs should entail.14 According to these arguments, if there is not a job satisfaction differential by gender for the disabled individuals, we should consider that female disabled workers have the same overall job expectations as male disabled workers (but lower than non-disabled workers’). For both samples, there exists a well-defined U-shaped relationship between age and job satisfaction. However, the magnitude of the age effect on the job satisfaction is greater for the disabled individuals. An additional year of age reduces the job satisfaction more in the disabled sample than in the non-disabled one.15 This U-shaped relationship is also found by Clark et al. (1996), Clark (1997), and Gardner and Oswald (2001), among others. We do not find a significant effect of being married or cohabiting on job satisfaction, either for the non-disabled or the disabled individuals. The opposite finding is quite common in previous literature (for example, Clark 1997; European Commission 2002; Kraiser 2002) and is due to the fact that married workers are happier at their jobs. With respect to the educational level of the individual, in general we obtain a negative relationship between education and job satisfaction. However, just for the sample of non-disabled workers, those with university educational level are less satisfied with their jobs compared to individuals with lower levels of education. This finding for nondisabled people coincides with previous literature (for example, Clark and Oswald (1996); Clark (1997); Gardner and Oswald (2001)). According to Clark (1997), education brings rewards but also raises expectations, leading to greater disappointment and dissatisfaction. The lack of significance of this variable for people with disabilities shows that they are as happy at work as disabled workers with lower levels of education. This result is surprising, and even more so when we see at the same time that the negative effect of the secondary level is three times higher than for non-disabled workers. As mere conjecture, we can say that people with disabilities with a university degree are probably quite different than the rest of people with disabilities who usually only reach the lower educational levels.

14 Recently, Bender et al. (2005) suggest that much of the satisfaction difference associated with gender

segregation in the workplaces results from the exclusion of determinants of satisfaction that measure the flexibility between work and home. These determinants seem to be of greater value for females and when they are taken into account, the gender composition of the workplace plays no role in determining the females’ job satisfaction. 15 Blanchflower and Oswald (2007) analyse overall life satisfaction and discuss the U-shaped age effect as some kind of cohort effect. In our case, it is possible to think that this cohort effect may be much more present for disabled workers than for non-disables ones.

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With respect to job characteristics, the hourly wage (in logarithm) is one of the most important factors determining individuals’ overall job satisfaction. Job satisfaction is positively related to hourly wages in both samples. However, the magnitude of the effect of doubling wage is larger for the disabled workers than for the disabled ones. This result is relevant because it implies that employers may use the hourly wage as a more effective and powerful tool within the firm in order to increase the level of job satisfaction of their disabled workers. As for the effect of job tenure on job satisfaction, the estimation results are different as we compare both types of workers. For nondisabled individuals, only the coefficient of job tenure is significant and negative. Similar results (but completed with a U-shaped pattern) are obtained by Gardner and Oswald (2001) and the European Commission (2002). On the other hand, job tenure does not affect the job satisfaction of the disabled workers. Non-disabled individuals working less than 40 h a week are less satisfied with their jobs. This pattern in the non-disabled sample is an unexpected result, contrary to most of the empirical evidence on job satisfaction. However, other studies such as, for example, Leontaridi and Sloane (2001) for the case of British higher paid males, Cabral (2005) for Portugal, and Pouliakas and Theodossiou (2003) and Diaz-Serrano and Cabral (2005) for a set of European countries (among them, Spain) have obtained identical results. In contrast, none of the coefficients of the hours of work in the disabled job satisfaction equation are statistically different from the reference category (40 h a week), in spite of having a distribution which is very similar to non-disabled workers. An explanation is that having a job is more valuable for disabled individuals than working hours per week, which becomes irrelevant. In many cases, getting a job for certain disabled individuals (for example, individuals with mental or intellectual limitations) is a huge personal challenge whereby employment is synonymous with success and a source of higher levels of personal satisfaction.16 By occupation, non-disabled workers in low occupations (especially in operators/assemblers and elementary occupations) report lower levels of job satisfaction. However, for disabled workers no significant difference has been found by occupation. Those non-disabled individuals who work in education and health services declare the highest levels of job satisfaction. Once more, we do not find differences by industry for the disabled sample. Furthermore, we detect regional differences in the levels of job satisfaction of disabled and non-disabled workers. For example, the lowest level of job satisfaction is found in Madrid for both samples. In addition, those disabled workers living in the south suffer significant satisfaction reductions. Concerning responsibility in the job, those non-disabled and disabled workers in supervisory positions have higher job satisfaction levels than other workers. However, the magnitude of the coefficient of this dummy variable (supervisor) is substantially

16 For example, see Livermore et al. (2000) for an exhaustive analysis of the determinants of the labour participation of disabled individuals (for example, the existence of other non-wages incomes, transfer and benefit receptors, wage levels, health care services, higher transportation costs, adaptability and accommodation adjustments in the workplace, recruitment costs, public policies, match between demand and supply, influence of the family and friends, among others).

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larger in the disabled sample than in the non-disabled one (and akin to the effect of doubling hourly wages on the job satisfaction). This finding has to be related to the low sample size of disabled individuals working in supervisory positions (only 4.1%). With respect to the effect of the type of contract on job satisfaction, the results reveal that for people with disabilities none of the estimated coefficients for this variable are significant at the 5% level. However, this does not happen for the non-disabled workers, wherein those with a permanent contract are more satisfied with their jobs than those with a temporal contract or without a contract. The inexistence of differences in job satisfaction by the type of contract for the disabled workers can be again justified by the fact that for these individuals, having a job, independently of the type of contract or the number of hours of work, becomes an important achievement and, therefore, an unambiguous increase in job satisfaction. Thus, the most important thing for disabled individuals is to be employed in the labour market and “escape” from inactivity or unemployment. The existence of a good match between workers’ formation and their jobs has a positive effect on their levels of job satisfaction. However, this only occurs for the non-disabled worker sample. This result is consistent with those previously obtained by Battu et al. (1997); Johnson and Johnson (2002) and Cabral (2005). The lack of significance for the disabled sample can be linked with the empirical analysis of mismatch and disability carried out by Blázquez and Malo (2005). At a descriptive level, these authors show that people with disabilities have a lower proportion of over-education or over-qualification but a higher proportion of mismatch in a broad sense. However, they do not find a significant influence of disability on the probability of being mismatched.17 With respect to the non-wages subsidies provided by the employers (i.e. provision of child and health care, education and training, leisure and subsidised housing), they have only a significant positive impact on nondisabled workers job satisfaction levels. An inverse relationship exists between the number of unemployment spells in the last five years and the job satisfaction level, but only for non-disabled individuals. The coefficients of the year dummies indicate a sustained decline in job satisfaction, which has been steeper for disabled workers.

4.2.2 The Oaxaca-Blinder decomposition of job satisfaction differences Using the estimation results shown in Table 2, we calculate the job satisfaction difference between non-disabled and disabled individuals by applying the well known Oaxaca-Blinder’s methodology. We present these calculations in Table 3. The observed mean job satisfaction difference between both groups is 0.345. The differences in characteristics are 78.6 percent of the total observed job satisfaction differential, whereas differences in returns given for those characteristics represent 21.4 percent of

17 Nevertheless, Blázquez and Malo (2005) also find that when people with disabilities are mismatched they leave their mismatch with a lower probability, unless they move to non-employment or marginal employment.

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such differential. Namely, the magnitude of the differences in returns is quite small compared to that for the differences in characteristics. At first sight, this result supports the second hypothesis, because disabled people enjoy a lower level of job satisfaction than non-disabled people. Then, disability would behave as a health-like variable. However, the characteristics component includes not only job characteristics but individuals’ characteristics too. What is the influence of job characteristics on job satisfaction? Looking at Table 3 we can observe the decomposition of each component into subcomponents.18 With respect to the differences in characteristics, the difference in age is the subcomponent with the greatest contribution to the observed job satisfaction difference (95.16%), followed by health status (54%) and hourly wages (19.15%), while the educational level is the most important variable reducing the job satisfaction differential for people with disabilities related to characteristics (−12.66%). With respect to the differences in returns, the returns to age are substantially higher for non-disabled workers relative to disabled workers. We have to keep in mind that disabled workers are older than non-disabled workers and that the age effect on job satisfaction is stronger for the former. This age effect is so strong that the sign of the difference in returns changes from negative to positive as age and its squared are included (from −0.893 to 0.074). That is, the returns to age are compensating the higher returns in terms of job satisfaction obtained by disabled workers from other individual and job characteristics. In this sense, the differences in the returns to hourly wages considerably reduce the job satisfaction differential (−291.56%). The differences in job tenure also contribute drastically to reducing the observed differential (−81.9%). Stability and job security become one of the most important factors determining the job satisfaction of disabled individuals. Educational level decreases the difference in returns with respect to non-disabled people (−8.28%), but note that over qualification compensates such an effect (9.79%). Therefore, an eventual policy measure promoting a higher return of the educational level for people with disabilities should be complemented by specialized intermediation services in order not to increase over qualification of this group. The differences in the rest of subcomponents increase or decrease the observed job satisfaction differential, but these contributions are much less relevant. Therefore, the results show that people with disabilities enjoy higher returns in terms of job satisfaction from their job characteristics (especially from hourly wages, job tenure and job matching), confirming that disability status has different effects than health status. This result supports hypothesis 1. In other words, people with disabilities have lower expectations with respect to what they can obtain from jobs and, therefore, ceteris paribus they are happier at job than non-disadvantaged groups (as Clark 1997 has previously shown for women with respect to men). As we are interested in how people with disabilities obtain satisfaction from their jobs, the most important

18 To decompose the unexplained component (returns) we have used the methodology proposed by Gardeazabal and Ugidos (2004), and based on the applications of restrictions on the estimated coefficients for each set of dummy variables. Thus, we can identify the real contribution of all dummy variables to the differences in returns, even those used as a reference group.

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results are those related exclusively to job characteristics and not to characteristics as a whole. Hence, we find support in asserting that job satisfaction of people with disabilities from job characteristics is greater because of their lower expectations about jobs. 5 Conclusions In this article, we have analysed the relationship between job satisfaction and disability for the Spanish case, using 1995–2001 waves of the ECHP. Considering previous literature, we have proposed two different hypotheses about the influence of disability on job satisfaction: a positive effect explained by lower expectations with respect to what they can obtain in the labour market with respect to non-disabled workers; and a negative one in line with results linking worse health status to lower job satisfaction levels. At a descriptive level, we have found a job satisfaction differential against workers with disabilities. In order to control for other variables, we have estimated the determinants of these differentials, as Uppal (2005), but using a random-effects linear model. The main novelty of this research consists in the decomposition of job satisfaction differentials into their subcomponents using the Oaxaca-Blinder methodology. The Oaxaca-Blinder decomposition shows that differences in characteristics increase the observed job satisfaction differential, which explains the majority of the observed job satisfaction difference (78.6%), whereas the differences in returns given for those characteristics explain the rest of the observed difference (21.4%). The decomposition of the return component into subcomponents shows that people with disabilities have greater returns in terms of job satisfaction from their job characteristics, confirming the hypothesis related to the lower expectations explanation proposed by Clark (1997) in the case of women. In addition, our results also mean that disability should be included in estimations of job satisfaction determinants as a variable other than health, because disability does not behave as a health-like variable. Finally, our results have some relevant policy implications for improving the job quality of people with disabilities. The importance of the characteristics term stresses the relevance of promoting the application of the principle of equal opportunities, closely linked to the employment strategy currently pursued by the European Commission, in order to eliminate the prejudices, fears, and particularly the lack of information of employers regarding the disabled population. Policies such as increasing educational levels of the disabled would be part of the strategy for improving job quality of these workers, while being complemented by additional measures so as to not increase mismatch. In short, labour market policy for workers with disabilities should focus on the characteristics of those jobs held by these workers. Acknowledgments Previous versions of this article were presented at the “IX Encuentro de Economía Aplicada”, 2006, in Jaén and at the “IV Jornadas Científicas de Investigación sobre Personas con Discapacidad”, 2006, in Salamanca. We thank the conference participants, two anonymous referees and the editor for useful comments. The usual disclaimer applies.

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Appendix

Table 4 Two-way table of frequencies of the variables “Health status” and “disability” and mean job satisfaction Health status

All non-disabled

All disabled Moderate

Total Severe

Very good % row

99.43

% column

24.15

Mean job satisfaction

0.54

4.456

0.04

3.85

1.07

4.600

2.898

100 23.3 4.455

Good % row

98.73

1.16

0.11

% column

62.47

21.72

7.6

Mean job satisfaction

4.211

4.338

4.134

100 60.69 4.213

Fair % row

86.51

12.21

1.28

% column

12.41

51.73

21.06

Mean job satisfaction

3.954

3.863

3.794

100 13.75 3.938

Bad or very bad 32.07

37.74

30.19

% column

% row

0.97

22.7

70.27

Mean job satisfaction

3.741

Total

95.92 100

Mean job satisfaction

3.679

3.471

3.25

0.84

100

4.242

4.247

100 3.878

100 2.07 3.636 100 100 4.227

Source: European Community Household Panel. Period 1995–2001. Wage and salary earners (aged 16–64). Weighted data

Table 5 Job satisfaction: random effects estimates for testing the selectivity bias hypothesis Variables

All non-disabled

All disabled

Coef.

z

Coef.

z

Testing selectivity bias Number waves

0.010

0.91

−0.073

All waves

0.043

1.13

0.249

−1.41 1.44

Last wave

−0.081

−1.67

−0.157

−0.54

Constant

2.921

12.48

3.696

3.12

No observations

23,375

1,018

These results have been obtained by including in the job satisfaction regressions shown in Table 2 three new additional variables following Verbeek and Nijman (1992). Although we only report the coefficients of these three variables, the full regression is available upon request. Source: European Community Household Panel. Period 1995–2001

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References Ahn N, García J (2004) Job Satisfaction in Europe. FEDEA working paper 2004–2016 Akerlof G, Rose A, Yellen J (1988) Job switching and job satisfaction in the US labor market. Brookings Pap Econ Act 2:495–582 Argyle M (1989) The psychology of happiness. Routledge, London Baldwin M, Johnson W (1992) Estimating the employment effects of wage discrimination. Rev Econ Stat 74(3):446–455 Baldwin M, Johnson W (1994) Labor market discrimination against men with disabilities. J Hum Resour 29(31):865–887 Baltagi B, Song S (2006) Unbalanced panel data: a survey. Stat Pap 47:493–523 Battu H, Belfield C, Sloane P (1997) Overeducation among graduates: a cohort view. University of Aberdeen, Mimeo Bender K, Sloane P (1998) Job satisfaction, trade unions and exit-voice revisited. Ind Labor Relat Rev 51:222–240 Bender K, Donohue S, Heywood J (2005) Job satisfaction and gender segregation. Oxf Econ Pap 57:479– 496 Blanchflower D, Oswald A (2007) Is well-being U-shaped over the life cycle? NBER Working Paper 12935 Blázquez M, Malo M (2005) Educational mismatch and labour mobility of people with disabilities. Rev Econ Lab 2(1):31–55 Blinder A (1973) Wage discrimination: reduced form and structural estimates. J Hum Resour 8(4):436–455 Borjas G (1979) Job satisfaction, wages, and unions. J Hum Resour 14(1):21–40 Burke R (1999) Disability and women’s work experiences: an explanatory study. Int J Sociol Soc Policy 19(2):21–33 Cabral J (2005) Skill mismatches and job satisfaction. Econ Lett 89:39–47 Clark A (1996) Job satisfaction in Britain. Br J Ind Relat 34(2):189–217 Clark A (1997) Job satisfaction and gender: why are women so happy at work? Lab Econ 4(4):341–372 Clark A (1999) Are wages habit-forming? Evidence from micro data. J Econ Behav Organ 39:179–200 Clark A, Oswald A (1996) Satisfaction and comparison income. J Public Econ 61(3):359–381 Clark A, Oswald A, Warr P (1996) Is job satisfaction U-shaped in age? J Occup Organ Psychol 69: 57–81 Clark A, Georgellis Y, Sanfey P (1998) Job satisfaction, wage changes, and quits: evidence from Germany. Res Labor Econ 17:95–121 Clegg A (1983) Psychology of employee lateness, absence and turnover: A methodological critique and an empirical study. J Appl Psychol 68:88–101 Dean D, Dolan R (1992) Efficacy of higher education for persons with work disabilities. Econ Educ Rev 11(1):51–60 Diaz-Serrano L, Cabral J (2005) Low pay, higher pay and job satisfaction within the European Union: empirical evidence from fourteen countries. IZA Discussion Paper, no 1558 European Commission (2002) Employment in Europe 2002: Recent trend and prospects, Chap. 3 Ferrer-i-Carbonell A, Frijters P (2004) How important is methodology for the estimates of the determinants of happiness?. Econ J 114(July):641–659 Freeman R (1978) Job satisfaction as an economic variable. Am Econ Rev 68:135–141 Gardeazabal J, Ugidos A (2004) More on identification in detailed wage decompositions. Rev Econ Stat 86(4):1034–1036 Gardner J, Oswald J (2001) What has happened to the quality of workers’ lives in Britain? University of Warwick, Mimeo Gazioglu S, Tansel A (2002) Job Satisfaction, Work Environment and Relations with Managers in Britain. Mimeo, Department of Economics, Middle East Technical University, Ankara Hamermesh D (1977) Economics aspects of job satisfaction. In: Ashenfelter, Oates (eds) Essays in labor market analysis. Wiley, New York Heckman J (1979) Sample selection bias as an specification error. Econometrica 47:153–161 Hendricks W, Schiro-Geist C, Broadbent E (1997) Long-term disabilities and college education. Ind Relat 36(1):46–60 Hodson R (1985) Workers comparisons and job satisfaction. Soc Sci Q 66:266–280 Johnson G, Johnson W (2002) Perceived over-qualification and dimensions of job satisfaction: a longitudinal analysis. J Psychol 134:537–555

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74

R. Pagán, M. Á. Malo

Johnson W, Lambrinos J (1985) Wage discrimination against handicapped men and women. J Hum Resour 20(2):264–277 Jones R, Sloane P (2003) Low pay, higher pay and job satisfaction in Wales. University of Wales Swansea, WELMERC Discussion Papers, no 5 Idson T (1990) Establishment size, job satisfaction and the structure of work. Appl Econ 22:1007–1018 Kraiser L (2002) Job satisfaction: A comparison of standard, non-standard and self-employment patterns across Europe with a special note to the gender/job satisfaction paradox. EPAG Working Paper, University of Essex, no 27 Leontaridi R, Sloane P (2001) Measuring the quality of jobs. European Low-Wage Employment Research Network (LOWER) Working Paper, no 7 Livermore G, Stapleton D, Nowak M, Wittenburg D, Eiseman E (2000) The economics of policies and programs affecting the employment of people with disabilities. Cornell University, mimeo. Accessible on the web direction: http://www.ilr.cornell.edu/rrtc McEvoy G, Cascio W (1985) Strategies for reducing employee turnover. A meta-analysis. J Appl Psychol 70:342–353 Mangione T, Quinn R (1975) Job satisfaction, counterproductive behaviour and drug use at work. J Appl Psychol 60:114–116 Malo M (2003) Las personas con discapacidad en el mercado de trabajo español. Revista del Ministerio de Trabajo y Asuntos Sociales. Economía y Sociología 46:99–126 Malo M (2004) Cómo afectan las discapacidades a la probabilidad de ser activo en España?. Cuadernos de Economía 27(74):75–108 Malo M, Pagán R (2007) Existe la doble discriminación salarial por sexo y discapacidad en España? Un análisis empírico con datos del panel de hogares. Moneda y Crédito, 225:7–42 McAfee J, McNaughton D (1997a) Transitional outcomes: job satisfaction of workers with disabilities – part one: general job satisfaction. J Vocat Rehabil 8:135–142 McAfee J, McNaughton D (1997b) Transitional outcomes: job satisfaction of workers with disabilities— part two: satisfaction with promotions, pay, co-workers, supervision, and work conditions. J Vocat Rehabil 8:243–251 Meng R (1990) The relationship between unions and job satisfaction. Appl Econ 22(12):1635–1648 Miller P (1990) Trade unions and job satisfaction. Aust Econ Pap 29(55):226–228 Ng Y (1997) A case of happiness, cardinalism, and interpersonal comparability. Econ J 107(445):1848– 1858 Oaxaca R (1973) Male-female wage differentials in urban labor markets. Int Econ Rev 4(3):693–709 Peracchi F (2002) The European community household panel: a review. Empir Econ 27:63–90 Pouliakas K, Theodossiou I (2003) Socio-economic differences in the perceived quality of high and low-paid jobs in Europe. Mimeo Renaud S (2002) Rethinking the union membership/job satisfaction relationship: some empirical evidence in Canada. Int J Manpower 23(2):137–150 Rose M (2003) Good deal, bad deal? job satisfaction in occupations. Work Employ Soc 17(3):503–530 Sanz de Galdeano A (2002) Gender differences in job satisfaction and labour market participation: UK evidence from propensity score estimates. European University Institute, Mimeo Schwartz N (1995) What respondents learn from questionnaires: the survey interview and the logic of conversation. Int Stat Rev 63:153–177 Sloane P, William H (2000) Job satisfaction, comparison earnings and gender. Labour 14(3):473–501 Uppal S (2005) Disability, workplace characteristics and job satisfaction. Int J Manpower 26(4):336–349 Van Praag B (1991) Ordinal and cardinal utility: an integration of the two dimensions of the welfare concept. J Econom 50:69–89 Verbeek M, Nijman T (1992) Testing for selectivity bias in panel data models. Int Econom Rev 33(3):681– 703 Wooldridge J (1995) Selection corrections for panel data models under conditional mean independence assumptions. J Econom 68:115–132

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