Educational Mismatch, Skill Mismatch and Job Satisfaction

Lu Yin*

Department of Economics, University of Sheffield, UK

June, 2015

Abstract

Most studies on overeducation focus on the relationship between overeducation and wages. The

exploration of the overeducation-job satisfaction relationship remains largely uninvestigated due to

the data limitations. However, the Chinese General Social Survey (CGSS) 2008, provides a range of

job satisfaction measures, which enables us to explore the determinants of overall job satisfaction

and specific aspects of job satisfaction, and especially investigate detailed links among educational

and skill mismatch and job satisfaction in China. This study reports that overeducated people are

more satisfied with workload, working conditions and facilities, their relationship with colleagues

and housing benefits. When educational mismatch and skill mismatch are included simultaneously

into the analysis of job satisfaction, skill mismatch demonstrates stronger negative effects on overall

job satisfaction and all facets of job satisfaction than educational mismatch. Empirical results

indicate that overeducation may not result in negative effects on productivity as a priori expectations

and skill mismatch is a better indicator to explain job satisfaction, which can urge the firms and

policy makers to put more emphasis on improving the match between job content and individuals’

skill levels. Moreover, the analysis of job satisfaction should focus on a relative perspective.

Key words: Educational mismatch; skill mismatch; job satisfaction.

JEL classification: I20; J24; J28.

*Corresponding author email: lyin1@sheffield.ac.uk

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1 Introduction The economics of overeducation has attracted much attention in recent years. If one’s acquired years

of schooling are higher than required educational level of his job, he is estimated as “overeducated”.

In contrast, workers are considered to be undereducated if their actual educational levels are lower

than required education of their jobs. The possible reasons for the existence of overeducation in the

literature are increased supply of graduates (Freeman (1976)), imperfect information and deliberate

search (Hartog (2000)), and compensation for other skills and abilities (Dolton and Vignoles (2000)).

Most studies on overeducation focus on the relationship between overeducation and wages.

Empirical evidence has indicated that overeducated individuals earn less than people who have a

similar educational level but are correctly educated. However, employees who have higher

educational level than the required educational level of their jobs have higher wages than those who

work in the same job but are estimated as matched groups. Moreover, both returns to required

education and surplus education are positive, but returns to required education are higher than returns

to surplus education (Alba-Ramirez (1993); Duncan and Hoffman (1982); Hartog (1985); Rumberger

(1987)). In addition, overeducation is also a serious concern for organisations, because overeducation

is linked with low job satisfaction, high mobility, high turnover rate and poor health status (Fleming

and Kler (2008)). As a result, production costs of companies may increase due to the reduced work

effort of overeducated employees, which implies that there should be a negative relationship between

overeducation and productivity. That is to say, overeducated people may behave in counterproductive

ways (Tsang and Levin (1985)). Indeed, many researchers have found that overeducated people show

more job dissatisfaction (Kalleberg and Sørensen (1973)), experience higher rates of absenteeism and

are more likely to switch jobs (Sheppard and Herrick (1972); Vroom (1964)). To some extent,

considering the potential costs of overeducation, firms may avoid employing overeducated

candidates (Büchel (2002)).

Throughout the literature, the overall analysis of job satisfaction in China is very limited due to the

absence of data on job satisfaction. Moreover, comparing with other fields of overeducation research,

there is no study to explore the relationship between educational mismatch and job satisfaction in

China currently. However, a new dataset, the Chinese General Social Survey (2008), provides a

range of job satisfaction measures, which enables us to explore the determinants of overall and

specific aspects of job satisfaction and investigate detailed links between overeducation and

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undereducation and job satisfaction in China. For example, not only do we focus upon overall job

satisfaction, we also investigate satisfaction with: salary, welfare, workload, working conditions and

facilities, the relationship with colleagues, the relationship with boss, commuting distance to job

location and housing benefits. This is a contribution to the overeducation literature in China. In

addition, the existing literature regarding overeducation states that overeducated individuals have

wage penalties and low job satisfaction without controlling for skill mismatch due to the data

constrains of skill mismatch. However, in this study, the availability of data on skill mismatch allows

us to take both educational mismatch and skill mismatch into consideration simultaneously to

explore the corresponding effects on job satisfaction.

This study seeks to answer the following questions: first, what are the determinants of overall job

satisfaction and whether relative deprivation variables are important in determing job satisfaction.

Second, whether overeducation reduces overall job satisfaction and whether this negative

relationship can be applied to different aspects of job satisfaction. Third, does skill mismatch or

educational mismatch play an important role in explaining individuals’ job satisfaction differentials?

The remainder of the chapter is constructed as follows. Section 2 provides a detailed summary of the

literature related to this topic. In section 3 and section 4, I will explicitly introduce the data and

econometric methods used in the analysis. Section 5 explores the determinants of overall job

satisfaction in the Chinese labour market and also investigates the relationship between overall job

satisfaction and eight aspects of job satisfaction and educational mismatch. Finally, the section 6

presents a discussion and conclusion.

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2 Literature review

2.1 The concept of job satisfaction

One of the most interesting notions in social science is job satisfaction, which has been investigated

in several disciplines such as psychology (Argyle (1987); Locke (1976)), sociology (Hodson (1985);

Kalleberg and Loscocco (1983)) and management (Hunt and Saul (1975). Job satisfaction can also be

called well-being at work (Blanchflower and Oswald (1999)). Locke (1976) offers a very

comprehensive and systematic review of the literature on job satisfaction from the aspect of

psychology. It discusses explicitly the concept, causes and effects of job satisfaction. Moreover, it

also covers the measurement and research methods in the study of job satisfaction. Argyle (1987)

treats job satisfaction as one of the three most important predictors of overall well-being, the

remaining two being marriage and family satisfaction. Lévy-Garboua and Montmarquette (2004)

argues that job satisfaction shows people’s attitudes toward their job experience and also can be

treated as an indicator to examine whether employees would choose the same job again if

opportunity available. Hamermesh (1999) argues that job satisfaction reflects the employees’ whole

judgment about job characteristics and can be used as an index to make comparisons with other

potential job market’s opportunities. Job satisfaction also reflects the extent people favour their work

(Millan et al. (2013)). Moreover, job satisfaction allows economists to have a better understanding of

the fundamental concept of aggregate well-being than job earnings, which is a one-sided criterion to

judge well-being (Argyle (2013); Heywood et al. (2009)).

However, job satisfaction is a concept that has been rarely considered in economics. Although job

satisfaction data is easy to collect in surveys, the process of deciding job satisfaction varies with

individuals. That is to say, people may have different interpretations of scales of job satisfaction

answers. Many economists argue that job satisfaction reflects people’s subjective judgment, which

may generate meaningless figure in the economic analysis (Hamermesh (1999)). However,

psychologists and sociologists have used job satisfaction data for many years and the validity of data

has been tested thoroughly, which indicates that useful information is indeed contained in the

questions on job satisfaction (Blanchflower and Oswald (1999)). The first economic paper on job

satisfaction is Hamermesh (1977), which employs job satisfaction data to test people’s occupational

choices. Freeman (1978) argues that subjective variables like job satisfaction indeed convey useful

and important information for us to understand and predict people’s occupational choice and

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behaviours and should not be ignored. However, job satisfaction may lead to complexities due to

their dependency on psychological states (Freeman (1978)).

Recently, many economists treat self-reported job satisfaction as a useful tool to explore labour

market behaviour, such as productivity, quits and absenteeism (Gazioglu and Tansel, 2006; Hulin et

al. (1985) and Johns and Xie (1998) ). Clark et al. (1998) found that people who have lower job

satisfaction are more likely to have higher absenteeism and a higher possibility of quitting. Indeed, it

is easy to understand that dissatisfied workers try to change jobs or workplaces in order to get job

satisfaction (Kickul et al. (2004)). In addition, high job satisfaction is related to positive performance

within a firm (Freeman et al. (2008); Ostroff (1992)). Moreover, Seo et al. (2004) found that job

satisfaction has a positive link with employees’ perception of their quality of life. The above

evidence provides feasible reasons why job satisfaction should be concerned in the economics.

2.2 Relative deprivation and expectations

The term “relative deprivation” was firstly created by Stouffer and his colleagues in their research

about the U.S. soldiers (Stouffer et al. (1949) ). They argued that relative deprivation can reflect the

inequality of subjective feelings of dissatisfaction among groups. However, they didn’t propose a

explicit definition of relative deprivation. Merton and Lazersfeld (1950) argue that the concept of

relative deprivation may act as a median to explain the relationship between an independent variable,

such as gender and educational level, and a dependent variable, such as satisfaction with job or life

satisfaction. The first formal statement of relative deprivation is from Davis (1959), who stated that

any social group may be divided into two groups, namely non-deprived individuals and deprived

persons. Relative deprivation will occur if a deprived person compares himself with a non-deprived

individual (Davis (1959)). Crosby (1976) expands the above point of view to explore the role of

relative deprivation in the study of satisfaction with pay level. She suggests that there are six

preconditions for individuals to experience relative deprivation: (1) they want X; (2) they deserve to

get X; (3) they know that other people have X; (4) they evaluate that possessing X is feasible; (5)

they refuse to admit that personality is a cause of their current failure to possess X; (6) they expect

more than they have now based on previous experience. Feldman et al. (1997) argue that the extent

of relative deprivation is decided by the distance between what an individual expects and what they

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have now. Moreover, the more one expects from future outcomes, the greater the sense of relative

deprivation the individual will have now.

The social comparison process is a very important aspect in the relative deprivation theory (Johnson

and Johnson (2000)). Walker and Smith (2002) argue that some existing objective phenomenon, such

as, working women are less happy than their male colleagues (Crosby (1982)), is determined by how

individuals make comparisons. Choosing a different comparison group may result in different

responses (Kulik and Ambrose (1992)). For example, individuals make self-assessments of their

current status against a comparison group regarding some relevant dimensions, such as skill level,

educational level and so on (Adams (1965)). Therefore, the subjective evaluation about job

satisfaction may be a result of a comparison between respondents and their comparison others.

It is well recognised that individuals normally compare themselves with the comparison groups in

terms of income, consumption, status or utility. However, empirically, relative deprivation is hard to

measure because of data unavailability as the comparison process made by people is unknown (Frey

and Stutzer (2002)). Most economists focus on an income dimension to measure relative deprivation.

Cappelli and Sherer (1988) employ the gap between individual wage and the average wages in their

occupation as the measurement of relative deprivation. Clark and Oswald (1996) estimate predicted

earning y* of each person through conventional earning equation and then relative deprivation is

determined by the gap between y* and absolute wage y. Moreover, Gao and Smyth (2010) estimate

the income of the reference group by obtaining the income from a “typical” employee with given

characteristics. In addition, dummy variables representing over-paid and under-paid are employed in

the study of Sloane and Williams (1996) to measure relative deprivation. Some scholars measure

relative deprivation from the aspect of inequality on social status. Walker and Smith (2002)

summarise that interpersonal or intergroup comparison in terms of social status is a source of relative

deprivation. Hu (2013) uses self-reported questions in the Chinese General Social Survey (CGSS)

2006 to indicate relative deprivation on social status1. Moreover, a strand of literature has confirmed

that relative deprivation on social status is a very important factor to explore subjective well-being

((Veenstra (2005) and Zhang et al. (2011)).

1 In Hu (2013)’s study, relative deprivation on social status is expressed with the question: “In your opinion, which strata

does your financial situation belong to?” The answer choices are as follows: 1: upper level; 2: mid-upper level; 3: middle

level; 4: mid-lower level and 5: lower level.

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Overeducation may be a resource of relative deprivation, which could have negative effects on job

satisfaction (Johnson and Johnson (2000)). Johnson and Johnson (2000) is the first study to use

relative deprivation theory to explain the relationship between overeducation and job satisfaction.

According to Martin and Shehan (1989), education is an important determinant to decide

expectations employees bring to the workplace. A well-educated individual may have high

expectations towards their job after several years of study. They may expect more from many aspects

of a qualified job, such as fully skill utilization, good salary and high prestige, than less

well-educated employees. When they acquire a job that is lower than their educational level, they

may incur skill mismatch (Sattinger (1993)), decreased salary ((Verdugo and Verdugo (1989);

Alba-Ramirez (1993)), and thus relative deprivation may happen. Overeducated people have two

kinds of comparison groups to choose. One is those individuals who have the same educational level

as overeducated people but are correctly educated. They may feel deprived in comparison with other

colleagues in the same company with same level of educational qualifications. While the other group

of people are those who work in the same job with overeducated people but are correctly educated,

namely their peers (Peiró et al. (2010)).

Although relative deprivation theory is widely recognized in psychology, some economists argue that

lack of empirical evidence is an important reason why relative deprivation theory is not in the centre

of economic research (Clark and Oswald (1996)).

2.3 The measurement of job satisfaction

There are two ways to explore job satisfaction data. The first is the One-dimension method, also can

be called the global measure of job satisfaction, which is commonly used in studies by economists. It

needs respondents to take a whole assessment of their job (Nielsen and Smyth (2008)). Most

researchers adopt direct verbal self-reporting methods to measure overall job satisfaction. Hoppock

(1935) is the first to use this method. There are generally two kinds of questions asked in the surveys

to respondents. The format of answers is measured in ordered scales rather than Likert scales in the

first category. For example, in the British Household Panel Survey (BHPS), individuals are asked

“All things considered, how satisfied or dissatisfied are you with your present job satisfaction using

the same 1-7 scale? The answers are given a number from 1 to 7, where a value 1 representing “not

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satisfied at all” and a value 7 corresponded to “completely satisfied”2. In addition, the data on job

satisfaction in the German Socioeconomic Panel (GSOEP) are scaled broader than those in BHPS,

where respondents are given answers ranging from 0 (very dissatisfied) to 10 (very satisfied)3.

Another kind of question asks respondents to reply to the job satisfaction question with yes, no or

unknown. For example, Johnson and Johnson (2000) employ job satisfaction questions from a

two-wave panel study of members of a Midwestern American Postal Workers Union local, where

individuals were asked to reply with yes, no or cannot decide.

A job is consisted of complex tasks, roles, responsibilities and rewards (Locke (1976)). If we want to

get a thorough understanding of the job itself, we need to analysis its constituent elements. The other

method is called the multidimensional method, which asks respondents to report job satisfaction for

some particular aspects of their job. For example, Clark (1996) employs the British Household Panel

Survey (BHPS) to explore job satisfaction data from seven aspects: promotion prospects, total pay,

relations with supervisors, job security, ability to work on their own initiative, the actual work itself,

and hours of work. In addition, the Triple Audit Opinion Survey (TAOS) in United States ask

respondents to rate the degree of satisfaction from 25 aspects of the job (Lee and Wilbur (1985)).

Both methods have advantages and drawbacks. The global measure enables respondents to judge job

satisfaction on all kinds of job characteristics. Clark (1998) argued that overall job satisfaction data

indeed make a good summary of the information respondents want to convey from their jobs.

However, the one-dimension job satisfaction approach is often criticised in that it does not provide

detailed information regarding satisfaction from different job dimensions. Moreover, empirical

analysis indicates that the whole job satisfaction approach is not equivalent to the multidimensional

job satisfaction measurement (Scarpello and Campbell (1983)). It is widely recognised that

employees may have different attitudes towards different aspects of the job, for example, employees

may be satisfied with the salary of their jobs, but dissatisfied with the relationship with colleagues

(Nielsen and Smyth (2008)). Therefore, the multidimensional method can give us a clear picture of

job satisfaction and provides useful information to managers to identify advantages and weakness to

improve performance. However, the job owns a number of aspects and due to the design limitation,

some useful information will be missed when transforming facet-specific job satisfaction to overall

2 The detailed job satisfaction question of BHPS comes from Clark (1996).

3 The detailed job satisfaction question of GSOEP comes from Hamermesh (1999).

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job satisfaction (Kalleberg and Vaisey (2005)).

Generally, the principal to choose the measurement approach is based upon the application and also

data availability. In terms of application, policy makers may be interested in overall job satisfaction

data because they can manipulate labour behaviour in the whole market and observe changes of job

satisfaction over time (Scarpello and Campbell (1983)). However, for enterprises, they place more

attention on multidimensional job satisfaction data, which can be used to explore why employees

quit their jobs or to improve job satisfaction of employees. However, various aspects of job

satisfaction data would make analysis more difficult than just the one-component analysis. Some

researchers employ factor analysis to reduce the number of job satisfaction variables (Gordon and

Denisi (1995); Brown and McIntosh (1998)).

2.4 Determinants of job satisfaction

Employers always expect their employees to have high satisfaction with their job, because job

satisfaction is a very important index to indicate employees’ labour market behaviour, such as

productivity, quits and absenteeism. Thus, it is very important to explore the determinants of job

satisfaction. Job satisfaction also can be called life satisfaction at work ((Rode (2004) ). According to

Clark and Oswald (1996), an individual’s life utility function is defined as follows:

V=v (μ, ü) (1)

where v represents for a function of an individual’s life utility and μ is utility from work and ü is

utility from other aspects of people’s life. The utility from working is measured in the following

form:

μ=μ(y, i, j,) (2)

where y is individual’s wage, i and j are individual and job characteristics respectively. Equation (2)

is a standard economic model to explore determinants of job satisfaction. However, once considering

relative deprivation, working utility function can be described as

μ=μ(y, i, j,y*) (3)

where y* represents for relative deprivation variables. According to the literature, the explorations by

economists of the determinants of job satisfaction have revealed many consistent and robust findings.

However, the above empirical analysis of job satisfaction is based on a hypothesis that wages are

exogenous in the regression.

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2.4.1 Pay

Most individuals spend a quarter of their life time in paid work. When you ask people reasons why

they choose to work, the majority of them would prefer money as their answers (Jurgensen (1978)).

Therefore, pay is a very important factor for people to measure their current job itself and related

characteristics, such as quit, absenteeism and job satisfaction. It is easy to understand that those who

are paid more should report higher job satisfaction. However, theoretically, pay can have a positive

or negative relationship with job satisfaction. On the one hand, according to Heneman and Judge

(2000), pay has a positive impact on satisfaction with pay and pay satisfaction is one of the most

important determinants of overall job satisfaction. Hulin and Smith (1965) employ a linear model to

predict that pay will result in higher job satisfaction keeping all else equal. Therefore, it is rational to

expect that higher pay will lead to higher job satisfaction, ceteris paribus. However,

self-determination theory states that extrinsic rewards, such as pay, will sometimes undermine

employees’ autonomy when employees have different viewpoints with employers and thus reduce

people’s motivation and degree of satisfaction (Deci and Ryan (2000)). In addition, people who chase

financial success may undermine well-being to some extent, because such controlled orientation

always lasts a long time and people need to sacrifice other pursuits of life in the process of chasing

financial success (Kasser and Ryan (1993)).

In addition, there is an argument that whether absolute or relative earnings are relevant in the

relationship between pay and job satisfaction (Sloane and Williams (2000)). According to Rees

(1993), he suggests that a worker’s utility is decided by individual’s own wage and working hours

without comparing with others based on the neo classical wage theory. Gazioglu and Tansel (2006)

found that the relationship between absolute pay and job satisfaction is significantly positive.

However, many scholars suggested that relative earnings play an important role to determine job

satisfaction ((Clark and Oswald (1996); Meng (1990); Sloane and Williams (2000); Watson et al.

(1996)). Nguyen et al. (2003) put forward four ways to measure relative earnings or income: (1) the

earning gap between individuals and those people who made the same investment at the same time as

them; (2) employees may compare themselves with internal reference group to create expectations4;

(3) individuals in a given job at the time t may make a comparison with people who have same job at

4 McBride (2001) suggests that parents and other relatives can be employed as internal reference groups.

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time t1 (t1< t); (4) the difference between expected income made by the individual and the actual

outcome earnings.

2.4.2 Gender

According to the literature, the most persistent finding is the relationship between gender and job

satisfaction (Brown and McIntosh (1998)). After controlling for a large set of individual and job

characteristics, women are found to be happier than men (Clark (1996); Clark (1997); Sloane and

Williams (2000)). This result is not only examined in Europe and USA, but also explored in other

countries, for example, Canada ((Murray and Atkinson (1981)), China ((Loscocco and Bose (1998)),

Singapore (Goh et al. (1991). There are mainly three plausible explanations for this result. The first

one is that men and women do different types of work according to their personal characteristics and

qualifications (Clark (1996)). The second reason is that men and women value different aspects of

their job when they evaluate job satisfaction. For example, men treat earnings as the most important

factor while women consider their relationship with co-workers and supervisors more important than

men do (Konrad et al. (2000)). However, Clark (1997) argues that those individuals who treat

earnings as the most important determinant of job satisfaction report lower job satisfaction. The third

reason is called the participation effect (Clark (1996)). Dissatisfied women workers may find it easier

to leave the labour market than men and more satisfied women stay in the labour market, which may

create a selection problem (Clark (1996)). Despite the above reasons, Clark (1997) uses wave 1 of

the British Household Panel Survey (BHPS) to explore the relationship between gender and job

satisfaction, which reports that women report higher job satisfaction than men after controlling for a

large number of individual and job characteristics. Through empirical analysis, he claims that the

main reason to explain higher job satisfaction amongst women is that they have lower expectations.

Not because men and women do different jobs or by sample selection, but rather that, women’s jobs

are worse than those in the past and they expect less from their current job (Clark (1997)). In addition,

Bender et al. (2005) argued that some unmeasured characteristics that women value may exist in the

given job if women report higher job satisfaction.

2.4.3 Age

According to life cycle and career stage models, employees in different stages of their career may

have different attitudes towards their job and thus different levels of job satisfaction (Lee and Wilbur

(1985)). Therefore, age is a very important determinant of job satisfaction. There are basically three

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kinds of views in the literature regarding age and job satisfaction. The first one is that the

relationship between age and job satisfaction is U-shaped (Clark et al. (1996); Clark (1996) and

Clark and Oswald (1996)). People report lower job satisfaction when young and then this increases

with age and decreases in old age. Clark et al. (1998) found that job satisfaction is higher for the

youngest and oldest workers. In addition, Warr (1992) provides strong evidence to show that the

relationship between age and job satisfaction is U-shaped. The second finding is that job satisfaction

increases with age, namely a positive relationship (Hulin and Smith (1965); Lee and Wilbur (1985);

Martin and Shehan (1989)). The third one is that there is a negative relationship (Mora et al. (2007).

However, Mora et al. (2007) only focus on young European higher education graduates who have

higher average educational levels, which are more likely to report lower job satisfaction than the full

sample.

2.4.4 Education

Education plays a very important role in the study of labour market behaviour. Sufficient evidence

indicates that individuals with higher levels of education earn more, are not likely to experience

unemployment and can find better jobs than lower educated people (Card (1999)). The motivation

for acquiring high educational attainment is to a do satisfying job (Glenn and Weaver (1982)).

Moreover, according to signaling theory, individuals who have a higher educational level will be

more productive (Riley (1979)). Based on the above findings, better educated people may have an

advantage to find jobs with more intrinsic and extrinsic rewards and at the same time, both rewards

may result in higher job satisfaction. Thus the predicted relationship between education and job

satisfaction is positive. Martin and Shehan (1989) and Cheng et al. (2013) all find a positive

relationship between education and job satisfaction. Blanchflower and Oswald (1999) also find that

education has a positive impact on reporting high job satisfaction. However, when controlling for

income, the coefficient on years of education changes from being significantly positive to

insignificantly negative, which is similar to the findings from Clark and Oswald (1996). Other

studies suggest that the correlation between education and job satisfaction is negative. For example,

Clark et al. (1996) argued that the higher the level of education, the lower the reported satisfaction

level. The reason is that higher educated people have higher expectations for their jobs than lower

educated people. Bender et al. (2005), Clark (1996), Brown and McIntosh (1998) and Gazioglu and

Tansel (2006) all find a negative relationship exists between education and job satisfaction.

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Nonetheless, Glenn and Weaver (1982) argued that education can have either a positive or a negative

effect on job satisfaction. That is to say, some specific individuals may have a strong positive effect

offsetting those who have a negative effect and thus leading to a positive effect in the aggregate data

and vice versa. Therefore, Glenn and Weaver (1982) suggest that separate analysis for different

groups of people is necessary. In light of above findings, the relationship between education and job

satisfaction is a matter of empirical investigation.

2.4.5 Health

It is not surprising that low job satisfaction would result in mental and physical health problem.

Faragher et al. (2005) employed a large-scale meta-analysis of almost 500 studies to explore the

relationship between job satisfaction and both mental and physical health status. The results show

that the correlations between job satisfaction and mental and physical health status are both positive,

especially significant for the mental health5. Bender et al. (2005), Gazioglu and Tansel (2006) and

Clark (1996) all report that good health leads to high job satisfaction.

2.4.6 Marital status

Clark (1996) suggests that married employees report higher job satisfaction. The reason behind this

is that married people are generally happier than single individuals. Clark (1997) reported that

marriage is a significant determinant of overall job satisfaction for women but not for men. However,

results from Brown and McIntosh (1998) and Gazioglu and Tansel (2006) indicate that married

people are less satisfied than single individuals. Single people can be considered as an independent

individual and they have the freedom to make their own job choices without considering others, for

example spouse’s job and location (Clark (1996)).

2.4.7 Establishment size

Employees in larger firms are generally found to be less satisfied than those in smaller

establishments (Idson (1990)). Clark (1996) and Gazioglu and Tansel (2006) also found similar

results. Martin and Shehan (1989) only find a significantly negative relationship between

5 The detailed results in terms of the relationship between mental health and job satisfaction are as follows: burnout

(r=0.409), lowered self-esteem (r=0.351), anxiety (r=0.354), and depression (r=0.366).

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establishment size and job satisfaction on men. Clark (1996) argues that small establishments attract

employees by providing attractive intrinsic rewards, which increases job satisfaction for those who

value such job attributes.

2.4.8 Public sector

Bogg and Cooper (1995) suggest that workers in the public sector experience higher levels of job

satisfaction and lower mental stress than people in the private sector. Moreover, the public sector

always provides stable employment, good promotion opportunities and attractive compensation,

which may lead to high job satisfaction from employees (DeSantis and Durst (1996)). Markovits et al.

(2007) stated that when taking loyalty into consideration, public sector employees report both higher

extrinsic and intrinsic job satisfaction in Greece.

Apart from above determinants of job satisfaction, other significant determinants of job satisfaction

have been observed including union membership (Borjas (1979); Miller (1990)), working hours

(Clark and Oswald (1996); Bartel (1981)) and training opportunities (Gazioglu and Tansel (2006);

Hamermesh (1977)).

2.5 Job satisfaction in China

Spector (1997) argued that different countries and cultures may exhibit different patterns of the

determinants of job satisfaction. Most existing studies of job satisfaction in China focus on some

specific sectors, firms of a particular ownership type or some particular group (Nielsen and Smyth

(2008)), unlike the overall analysis of job satisfaction in other countries (Green and Tsitsianis (2005)),

which has tended to focus on representative sample of the underlying population.

In terms of specific sectors, for example, Sargent and Hannum (2005) explore the factors which

influence teacher satisfaction at the community, school and individual levels. Lu et al. (2007) report

that more than half of surveyed nurses were satisfied with their current jobs, which is in contrast to

popular beliefs. Wang et al. (2013) mainly focus attention on migrant workers in China employing a

migrant survey from Guiyang city. They found that the new generations of migrants have higher job

satisfaction than the old generation, which is in contrast to the expected result. Moreover, working

conditions play an important role in determining job satisfaction among new generations of migrants

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other than personal characteristics, such as income, age and gender. Apart from this, differences in

family characteristics also contribute to the job satisfaction differential between the two generations

of migrant workers.

Studies of job satisfaction confined to specific ownership types include Leung et al. (1996) and Scott

et al. (2003). Leung et al. (1996) focus on joint venture hotels in China to explore the relationship

between justice and job satisfaction6. It is interesting that procedural justice and performance-cased

distributive justice can influence job satisfaction. However, interactional justice has no effect on job

satisfaction. Moreover, Chinese employees working with a management group composed by

overseas Chinese report the highest educational level, which reflects the justice differences derived

from the cultural origin7. Scott et al. (2003) explore job satisfaction in U.S. invested enterprise in

China. The empirical evidence indicated that Chinese employees had higher job satisfaction, a lower

possibility to change jobs and have a better relationship with their peers.

Cheng et al. (2013) and Nielsen and Smyth (2008) provide a broader scope of data than the previous

literature to explore job satisfaction. Cheng et al. (2013) explore the determinants of job satisfaction

of urban locals, first and new generation migrants in urban China using the Chinese General Social

Survey 2008 (CHNS), which includes data across 29 provinces and municipalities in China. Nielsen

and Smyth (2008) employ data from the China Mainland Marketing Research Company (CMMRC),

which covers data across 32 Chinese cities.

2.6 Educational mismatch, skill mismatch and job satisfaction

It is well recognised that a worker’s productivity and skill level is determined by one’s abilities,

attitudes and knowledge (Badillo-Amador and Vila (2013)). When a worker enters into the labour

market, there is a possibility that he may find a job that is not equivalent with the skill requirement of

the job, being either lower or higher, due to the imperfectly competitive labour market, which can be

called job-worker skill mismatch (Sutherland (2012). Job-worker skill mismatch has very important

implications for both employees and employers as the quality of the job-worker match can determine

6 There are three kinds of justice, namely procedural justice, performance-based distributive justice and interactional

justice. 7 This result is compared with workers working with Japanese and employees supervised by a management group from

Western countries.

15

productivity and wages to some extent (Badillo-Amador and Vila (2013)). There are three kinds of

skill mismatch against perfect skill match, namely overskill, underskill and domain mismatch

(Sutherland (2012))8. When the skill level the individual holds is higher than the job required, this

condition is defined as “overskill” and when the skill level is lower than the job required, this can be

described as “underskill”. Battu et al. (2000) argue that the measurement and extent of mismatch are

decided by the way mismatch being defined.

Most of the literature employs education or qualifications to identify job-worker mismatch because

of the convenience and feasibility to collect data (Chevalier (2003)) and thus educational mismatch is

deemed as a good proxy of skill mismatch. Moreover, the assignment theory implies that educational

mismatch and skill mismatch are closely related (Sattinger (1993)). In assignment theory, candidates

are allocated from the top to the bottom of job complexity based on their skills. That is to say, the

most skilled individuals are assigned to the most difficult and advanced jobs and meanwhile, the

least skilled person is allocated to the simplest one (Allen and Van der Velden (2001). Workers report

their educational level as indicators of skill level. However, the heterogeneous theory (Green and

McIntosh (2007)) suggests that these two concept are weakly correlated. Even if two workers have

the same educational qualifications, their skills and abilities are heterogeneous. Accordingly, those

who are overeducated earn less than people who have the same level of qualifications but are

correctly educated is because they have low level of skill (Green and McIntosh (2007)). Moreover,

Sánchez-Sánchez and McGuinness (2013) argue that when the job entry requirement is not equal to

the actual skills needed in the job and educational attainment is a poor signal of human capital,

overeducation is not appropriate to represent skill mismatch status. In the literature, several

researchers have confirmed that educational mismatch does not imply skill mismatch ((Allen and De

Weert (2007); Di Pietro and Urwin (2006)); Halaby (1994)).

Compared with other aspects of overeducation, the issue of job satisfaction still remains largely

unexplored due to the absence of data of job satisfaction (Fleming and Kler (2008)). To date, the

overeducation-job satisfaction relationship attracts the interest of economists mainly from two points

of view: (1) the impact of job satisfaction on productivity (Verhaest and Omey (2006)) and (2)

expectations (Fleming and Kler (2008)). The first literature mentioning the relationship between

8 Because of the lack of domain information in our dataset, we only focus on the discussion of overskill and underskill

here.

16

overeducation and job satisfaction is from Vroom (1964). He found that overeducated employees

showed high job discontent. Tsang and Levin (1985) constructed a production model for a firm to

demonstrate how overeducation can have adverse effects on individual productivity. In this model,

job satisfaction is treated as a proxy of employees’ work effort adding into the value-added

production. They found that employees who have surplus education may induce reduced work effort.

As a result, this would not only incur additional production costs but also decrease potential profits

of firms. Tsang (1987) adopted quantitative analysis to explore the impact of overeducation on job

satisfaction. This study gathered data from 22 U.S. Bell companies in the telephone and telegraph

industry from 1981 to 1982 to make an application of Tsang and Levin (1985) model. Two kinds of

data were employed in this study: firm-level production data and individual-level data. He adopted

three functions, a job-satisfaction function used in the individual-level data, a Cobb-Douglas

production function for the firm level data and a job satisfaction index function9, to make analysis of

the impacts of overeducation on firm productivity. After controlling for worker’s sex, race, age,

education and the level of overeducation, individual data was used in the job-satisfaction function. In

this step, job satisfaction is considered to be an indicator of employer work effort. As expected, the

negative coefficient indicates that overeducated employees show lower job satisfaction. Moreover,

the application of the Cobb-Douglas production function and job satisfaction index function also

confirm that workers who have a higher educational level than jobs require have lower levels of

output.

Based on the literature, education is a very important variable to form people’s expectations from the

workplace, because it can increase individuals’ job expectations and aspirations (Glenn and Weaver

(1982) ). After a number of years of study, an individual expects to acquire a satisfying job, high

earnings and significant social status. Moreover, more educated people may set higher requirements

for their jobs than their lower educated counterparts (Tsang and Levin (1985)). However, if this

expectation is not fulfilled, individuals will report low job satisfaction in their jobs. When individuals

acquire a job below their educational level, they may confront reduced salary, less challenging tasks

and restricted autonomy (Peiró et al. (2010)). That is to say, their expectations about their jobs are

unfulfilled if they are overeducated, which may lead in lower job satisfaction (Fleming and Kler

(2008)). A bulk of literature has focus on this point of view to explain the negative relationship

9 The dependent variable in the job index function defined as the average job satisfaction level of a given occupation

within a specific firm.

17

between job satisfaction and overeducation (Peiró et al. (2010); Hersch (1991); Fleming and Kler

(2008) and Zakariya and Battu (2013)).

In order to better explore the relationship between overeducation and job satisfaction, some papers

exploring the relationship between educational mismatch and job satisfaction in longitudinal analysis

also get expected results10

. Vieira (2005) employ six waves of the European Community Household

Panel (ECHP) for Portugal to explore the effects of overeducation on job satisfaction directly. After

controlling for unobserved heterogeneity, results indicate that overall job satisfaction indeed has a

negative relationship with overeducation. Moreover, connections between job satisfaction with pay,

job satisfaction with the type of work and overeducation are also negative. Similarly, Johnson and

Johnson (2000) found that overeducation can adversely affect job satisfaction in a longitudinal

analysis.

While many existing papers have already presented a convincing link between overeducation and job

satisfaction, the relationship between these two becomes complex when skill mismatch is taken into

consideration. Badillo-Amador and Vila (2013) find that skill mismatch and educational mismatch

have different influences on different aspects of job satisfaction. Overeducated people have lower

overall job satisfaction and lower job satisfaction with the type of job; while skill mismatched people

have lower job satisfaction with pay. Moreover, skill mismatch play a more important role to explain

the differences of job satisfaction between individuals than educational mismatch. In addition, Allen

and De Weert (2007) suggest that both educational mismatch and skill mismatch can influence job

satisfaction by an equal weight. Moreover, Allen and Van der Velden (2001), Green and Zhu (2010)

and Sánchez-Sánchez and McGuinness (2013) all suggest that the relationship between skill

mismatch and job satisfaction is significantly negative while the result of overeducation is

insignificant.

Another important issue has been concerned with whether overeducated people are dissatisfied with

every aspect of job. Zakariya and Battu (2013) suggest that overeducation reduces employees’ job

satisfaction across four dimensions of job (high-self-satisfaction, valuable experience, type of work

and learning opportunities) employing the 2007 Graduate Tracer Study (GTS-07) in Malaysia.

10

Kalleberg (1977) argued that the determinant of job satisfaction may change over time and thus longitudinal data is an

ideal choice to explore the determinant of job satisfaction.

18

Johnson and Johnson (2000) indicate that overeducated people are dissatisfied with pay and

promotion. However, in terms of the work and the relationship with supervisor, there is no evidence

implying that overeducated people report low job satisfaction.

19

3 Data

This study employs the Chinese General Social Survey (CGSS) 2008 to undertake the empirical

analysis. The CGSS is the first continuous national social survey project in China, starting from 2003,

which is conducted jointly by Renmin University and Hong Kong University of Science and

Technology. It adopts the style of face to face interviews and the respondents in households and

communities are randomly selected. To maintain the representativeness of registered households, the

CGSS uses the fifth population census in sampling (Cheng et al. (2013)).

The data of CGSS 2008 cover 29 provinces and municipalities with 6000 observations altogether in

mainland China. In this survey, the response rate, the missing value rate and logic error rate are

54.32%, 3.11% and 5.18% respectively. Based on above figures, the CGSS 2008 is a very

high-quality and valuable dataset in China now (Cheng et al. (2013)).

The CGSS 2008 provides a range of questions about job satisfaction, which are of interest in this

study. Firstly, respondents are asked to rate satisfaction levels with their salary, welfare, workload,

working conditions and facilities, the relationship with their colleagues, the relationship with their

boss, commuting distances to job location and housing benefits, which are eight specific aspects of

the job. The last question is to ask individuals to rate their overall job satisfaction when all things are

considered. Satisfaction is an ordinal variable measuring the respondent’s perception of job

satisfaction in six scales: 1=very satisfied, 2=quite satisfied, 3=average, 4=quite dissatisfied, 5=very

dissatisfied, 6=hard to say. Additionally, the CGSS 2008 also includes a wide-ranging set of

socioeconomic factors. Observations used in this study are restricted to the age between 18 and 60.

This is because the mandatory retirement age in China is 60. Those who are students and have zero

or unknown income are omitted from the analysis. After deleting missing values of all the

dimensions of job satisfaction, observations answering “hard to say” about job satisfaction and

control variables, 2430 valid observations remain. Table 1 presents the summary statistics of all the

dependent and independent variables in this study. Controlled variables are spilt into three categories:

personal characteristics, employment characteristics and relative deprivation.

20

Table 1 Summary statistics

Variables Obs Mean Std. Dev. Min Max

Dependent variables Job satisfaction with

Salary 2430 3.226 0.940 1 5

Welfare 2430 3.109 0.998 1 5

Workload 2430 3.165 0.921 1 5 Working conditions and facilities 2430 3.301 0.861 1 5

The relationship with colleagues 2430 3.909 0.702 1 5

The relationship with boss 2430 3.607 0.767 1 5 Commuting distance to job location 2430 3.553 0.908 1 5

Housing benefits 2430 2.789 1.048 1 5

Overall job satisfaction 2430 3.379 0.777 1 5

Personal characteristics

Educational levels 2430 2.738 1.191 1 6

Required educational level 2430 2.965 0.945 2 5 Years of schooling 2430 10.681 3.195 6 24

Overeducated 2430 0.225 0.417 0 1

Undereducated 2430 0.398 0.489 0 1

Skill mismatched 2430 0.159 0.366 0 1

Age 2430 39.369 11.050 18 60

Age2 2430 1671.973 892.4514 324 3600 male 2430 0.555 0.497 0 1

Nationality 2430 0.928 0.258 0 1

Political affiliation 2430 0.153 0.360 0 1 In a healthy health status 2430 0.933 0.250 0 1

Married 2430 0.812 0.391 0 1

Urban 2430 0.838 0.368 0 1

Relative deprivation

Salary matched with expectation 2430 0.179 0.383 0 1 Lower class 2430 0.467 0.499 0 1

Middle class 2430 0.503 0.500 0 1

Upper class 2430 0.030 0.171 0 1

Employment characteristics

Hourly wage (Yuan) 2430 9.408 16.127 0.052 520.833 State owned 2430 0.421 0.494 0 1

Full-time 2430 0.864 0.343 0 1

Small 2430 0.305 0.460 0 1 Medium 2430 0.341 0.474 0 1

Large 2430 0.355 0.479 0 1

3.1 Personal characteristics

In CGSS 2008, participants are asked to report both their educational qualifications and years of

schooling. Based on the Chinese education system, educational levels are combined into six levels:

1-primary or less, 2. junior high school, 3. Senior high school, 4. College level, 5. University, 6.

Master’s or higher11

. In addition, age is measured in years. According to Clark et al. (1996), it is

possible to have a U-shaped relationship between age and job satisfaction, therefore, age square is

also included in the analysis. The mean age of respondents in the sample is 39 years old. Male is

used as a dummy variable to represent for gender, in which female is the omitted group. From Table 1,

we can see that 55.5% are male and 44.5% are female. The nationality variable is a dummy variable

where nationality is equal to 1 if individual’s nationality is Han12

. The Political variable is also a

11

See Table A 1. 12

There are 56 nationalities in China and Han is the largest group accounting for 91.96 percent of China’s population

according to 1990 census (Gladney (1994)).

21

dummy variable where the respondent’s political affiliation is communists party member, Political is

equal to 113

. In addition, individuals are asked “what do you think about your health status?”

Answers are measured on a 5-point scale ranging from 1(very unhealthy) to 5 (very healthy). In this

study, health variable is measured as a dummy variable where if respondents report their health status

as very healthy, healthy and on average, health variable is equal to 1. Marital status is spilt into two

categories: married and single. As can be seen from Table 1, 81.2% are married. In addition, Urban is

a dummy variable indicating people’s household registration area and 83.8% of respondents come

from urban area. In addition, we can see from Table 1 that 22.5% of workers in the Chinese labour

market are estimated to be overeducated and 39.8% of the labour force is treated as undereducated.

The remaining 37.7% of workers have corrected education required for their jobs. According to the

literature of overeducation using the mode index in China, the incidence of overeducation is between

20% and 30%14

.

3.2 Employment characteristics

Five employment variables available in the survey are included in the analysis since they are related

to the analysis of job satisfaction. The hourly earnings is used to indicate pay in the analysis. In terms

of firm size, there are three categories in the study: small (if participant’s firm employees are

between 1 to 25 workers), medium (if participant’s firm employees are between 26 and 250

employees) and large (if respondent’s firm employs are more than 250 workers). The ‘small group’ is

the benchmark group. Moreover, work unit type is measured as a dichotomous variable whereby

“public sector is 1” and “non-public is 0”. In addition, the CGSS 2008 has three classifications for

work type: 1. full time, 2. part-time and 3. temporary work. One dummy variable is included to

indicate if an individual has a full time job or non-full time job (part-time job and temporary work).

3.3 Relative deprivation

Due to the advantage of rich data in CGSS 2008, two variables can be employed to measure relative

deprivation. Those two variables are called economic relative deprivation and social status relative

13

Categories of political affiliation in the survey are as follows: 1. Communist party member; 2. Democratic parties; 3.

League member; 4. General public. 14

Ren and Miller (2010) found that the incidence of overeducation in the rural area is 27.3% and 27% of workers are

undereducated. In addition, Mayston and Yang (2008) report that about 27% of graduates have surplus education.

22

deprivation respectively15

. According to Nguyen et al. (2003), employees always compare their

income with a comparison group or a deserved level based on their expectancies to create relative

income. Clark and Oswald (1996) argue that if individuals’ income is lower than their expected level

or income from referent groups, relative deprivation will occur. Based on above definition of relative

deprivation, the economic relative deprivation can be measured from the following question in the

survey: what do you think about your earnings? Answers are recoded into two levels: 1. equal to

what I deserved; 0. not equal to what I deserved16

. Then, a dummy variable is constructed to indicate

economic relative deprivations, which can also be treated as the relative income variable to some

extent. Furthermore, relative deprivation on social status is measured through the question: which

social status do you think you are?” Responses are recoded into three levels: 1. lower; 2. middle and

3. upper. Individuals who treat themselves as upper class is the base group.

3.4 The measurement of educational mismatch and skill mismatch

One important issue in this study is the measurement of educational mismatch and skill mismatch.

Due to data constrains, the mode method proposed by Kiker et al. (1997) is the only available

method to choose to measure educational mismatch17

. The mode method defines that workers are

considered to be adequately educated if their actual education level is equal to the mode level of

education within their occupations. Overeducated (undereducated) workers can be defined if their

actual education attainment is higher (less) than the mode level of education18

. According to the

Chinese Dictionary of Occupation classification, occupations are classified into eight categories,

which is shown in Table 219

.

Unlike educational mismatch, skill mismatch can be measured in a more direct way by asking

workers whether they have the required skills to perform job tasks. Based on the literature, there are

three measures of skill mismatch, namely skill deficit, skill surplus and required skill (Desjardins and

Rubenson (2011)). In this study, the skill mismatch variable is constructed from the question in the

survey, “did you meet the standard of employer regarding skills and experiences when you acquiring

15

Hu (2013) also used two variables to measure relative deprivation employing CGSS 2006. 16

Crosby (1982) argues that relative deprivation may happen if one doesn’t have the items he or she deserves. 17

Job analysis (JA) and worker’s self-assessment (WA) are another two methods to measure educational mismatch.

Details can be found in Rumberger (1987) and Sicherman (1991). 18

Details about the highest educational level are in Table A1. 19

The detailed incidence of overeducation and undereducation can be seen from Table 1.

23

this job?” Answers to this question are as follows: 1. matched 2. over, 3. under and 4. unknown.

However, due to few observations in some answer groups20

, it is not possible to construct indicators

of overskilling and skill deficit as Allen and Van der Velden (2001) and Green and McIntosh (2007).

Instead, a 0/1 dummy variable (mismatch=1) is created to indicate skill mismatch, which provides a

direct measure of skill mismatch. The cross-tabulated distribution of educational mismatch and skill

mismatch are shown in Table 3.

Table 2 Occupation classifications

Occupation Freq Percent

Principals in governments, Parties, enterprises and institutions 194 7.98

Professional and technicians 348 14.32

Clerk and administration personnel 274 11.28

Commercial personnel 84 3.46

Service personnel 429 17.65

Production, transport equipment operators and related personnel 371 15.27

Police and soldier 320 13.17

Other practitioner (difficult to classify) 410 16.87

Total 2430 100.00

Table 3 The relationship between educational mismatch and a measure of skill mismatch

Educational mismatch Skill matched Skill mismatched Total

Undereducated 819 (84.78%) 147 (15.22%) 966

Adequately educated 770 (83.88%) 148 (16.12%) 918

Overeducated 455 (83.33%) 91 (16.67%) 546

Total 2044 (84.12%) 386 (15.88%) 2430

As can be seen from Table 3, 84.12 per cent of employees reported that their skills and experiences

are matched with the requirement and only about 15.88 per cent of employees have the problem of

skill mismatch. Based on the above figures, skill mismatch is not a very significant problem

compared to educational mismatch in this study. When taking educational mismatch into

20

There are only 74 observations in the answer “under” in the sample.

24

consideration, 83.88 per cent of employees who are estimated as adequately educated report that

skills are matched with requirements. Similarly, about 83.33 per cent of respondents who have

surplus education have matched skills and experiences. This percentage is quite high compared with

results from Di Pietro and Urwin (2006)21

. However, this finding may explain why overeducated

people earn less than the people who have similar educational level but are correctly educated, is

because the former have suitable skills for lower level of jobs, which is evidence of “heterogeneous

skill within qualification levels ((Green and McIntosh (2007)). That is to say, those overeducated

people who have matched skills are at the bottom of skill distribution of people who have similar

educational level with overeducated people. If considering skills and abilities, those individuals are

suitable for lower level jobs in which have lower educational requirement, even though they have

excessive educational attainment than the current jobs’ requirement. A possible explanation for this

in China is the higher education expansion which occurred after 1999. After expanding the

enrollment rate, many universities and colleges have more students than before so that the

distribution of ability of students has been expanded. Low ability is potentially one of reasons

causing people to be overeducated (Hartog (2000)).

In addition, we find that 16.12 per cent of employees who have accurate educational level for the job

still report that their skills and experiences are not matched with the employment requirement.

Furthermore, after using a correlation test, the correlation coefficient between educational mismatch

and skill mismatch is -0.0051 (p=0.8032), which confirmed that even two workers have the same

educational qualifications, their skills and abilities are heterogeneous22

. Based on above results,

adequate evidence for the heterogeneous skill theory is found and the assignment theory seems not

appropriate in Chinese labour market.

3.5 Satisfaction with various aspects of job

Table 4 presents patterns of eight aspects of job satisfaction and overall job satisfaction in the full

sample. As can be seen, the most frequent response for satisfaction with salary; the relationship with

colleagues; the relationship with boss and commuting distances to job location, is all “quite satisfied”.

In terms of satisfaction with welfare, workload, working conditions and facilities and housing

21

Di Pietro and Urwin (2006) found that about 19.64 per cent of graduates who are estimated as overeducated reporting

that they use “quite a lot” or “a lot” of knowledge and skills in the current jobs. 22

A dummy variable is generated to indicate educational mismatch.

25

benefits, the mode responses are all “average”. Especially, Chinese workers seem more satisfied with

the relationship with colleagues and boss than other aspects of job. Conversely, around 40 per cent of

respondents report that they are “very dissatisfied” or “quite dissatisfied” with housing benefits,

which is the highest figure reporting dissatisfaction among all the aspects of job and overall job

satisfaction. Although the percentage of reporting “quite satisfied” or “very satisfied” are more than

forty percent, more than one fifth of respondents still “very dissatisfied” or “quite dissatisfied” with

their salary, welfare and workload. However, in terms of overall job satisfaction, nearly half of

individuals are “quite satisfied” or “very satisfied” with their job. In all, Chinese workers seem quite

satisfied with their jobs except for the satisfaction with housing benefits.

26

Table 4 Satisfaction with various aspects of job

Satisfaction with Very

dissatisfied

Quite

dissatisfied Average Quite satisfied Very satisfied Total

1 2 3 4 5

Salary 4.61%

(112)

17.00%

(413)

34.20%

(831)

39.59%

(962)

4.61%

(112)

100%

(2430)

Welfare 7.20%

(175)

18.07%

(439)

36.54%

(888)

32.96%

(801)

5.23%

(127)

100%

(2430)

Workload 4.53%

(110)

17.41%

(423)

39.59%

(962)

33.95%

(825)

4.53%

(110)

100%

(2430)

Working conditions and

facilities

3.13%

(76)

12.26%

(298)

40.82%

(992)

38.93%

(946)

4.86%

(118)

100%

(2430)

The relationship with colleagues 0.45%

(11)

2.14%

(52)

20.41%

(496)

60.00%

(1458)

17.00%

(413)

100%

(2430)

The relationship with boss 0.91%

(22)

4.73%

(115)

37.16%

(903)

47.20%

(1147)

10.00%

(243)

100%

(2430)

Commuting distances to job location

2.35% (57)

10.29% (250)

28.56% (694)

47.28% (1149)

11.52% (280)

100% (2430)

Housing benefits 12.96%

(315)

25.72%

(625)

33.83%

(822)

24.40%

(593)

3.09%

(75)

100%

(2430)

Overall job satisfaction 1.85%

(45)

9.34%

(227)

41.40%

(1006)

43.87%

(1066)

3.54%

(86)

100%

(2430)

Table 5 presents the mean satisfaction level of aspects of job satisfaction and overall job satisfaction

for different demographic groups. We also use a t-test or one way ANOVA to examine the differences

between the means among different groups.

As can be seen from Table 5, there seems to be little difference between male and female reporting

satisfaction level. Only in the aspects of the working conditions and facilities, males are less satisfied

than females at the 5 per cent level. In terms of overall satisfaction level, although the figures of

satisfaction level are different, the t-test tells us that the attitudes of male and female are the same.

Clark et al. (1996) present that there is a U-shaped relationship between age and overall job

satisfaction and satisfaction with work itself in UK. In Table 5, we observe that the U-shaped

relationship is only between age and overall job satisfaction and the following aspects of the job:

salary, welfare, working conditions and facilities and housing benefits. In addition, the group of age

31 to 45 years old always reports the lowest level of job satisfaction among the three groups. The

younger (18 to 30 years old) and older group (46 to 60 years old) report higher job satisfaction level.

27

In terms of household registration area, there is a significant difference of overall job satisfaction and

almost all aspects of job satisfaction. Only in the aspect of the relationship with colleagues do urban

people and rural people report similar level of job satisfaction. Two possible reasons may explain this.

The first one is the household inequality between urban and rural areas. Since the economic reform

in 1978, there is a favorable economic and social change in Urban China lifting 400 million Chinese

out of poverty (Ravallion and Chen (2007)). It is reported that the mean per capita income in urban

China is more than three times higher than that in rural area (Sicular et al. (2007)). According to

Easterlin (1995), individual’s personal well-being is determined by a person’s expectations and social

comparisons. The rural people’s reference group is urban people, which may lead to lower job

satisfaction. The other important reason is the household registration system (Hukou), which

automatically allocate all Chinese into two groups: agricultural hukou and non-agricultural hukou23

.

The hukou system indicates different occupational status and conditions. In other words, hukou

system splits the urban labour market into two segmentations. Workers from a rural area always

undertake low status and routine jobs, while higher-ranked job are always undertaken by urban

people who holding non-agricultural hukou, which are impossible for rural people to take24

. Based on

the above two reasons, it is easy to understand why rural people report lower overall job satisfaction

and several aspects of job satisfaction than urban people.

In addition, the cross-tabulation in Table 5 indicates that there is a significantly positive linear

relationship between absolute income (hourly income) and overall job satisfaction and also other

aspects of job satisfaction25

. That is to say, higher income is linked with higher job satisfaction.

Table 6 describes the mean satisfaction level in three different groups: overeducated people,

undereducated people and correctly educated people. One point needs to be mentioned here.

Contrary to the literature, the mean satisfaction level of all job satisfaction dimensions and overall

job satisfaction of overeducated people are higher than those for correctly educated individuals.

Undereducated groups report the lowest job satisfaction levels in all dimensions in this study. In

addition, based on the one way ANOVA in this study, the three groups show no difference for the

23

The urban people all hold non-agricultural hukou and the rural people all hold agricultural hukou. 24

Low status and routine jobs include jobs from construction, domestic services, and some self-employed service and

high-ranked jobs are jobs from state enterprises (Meng and Zhang (2001)). 25

This is the reason why hourly income, not log hourly income, is included in the regression as an independent variable.

However, in the study of Gazioglu and Tansel (2006), log weekly income is adopted as a control variable as results in

their study show a nonlinear relationship between absolute income and job satisfaction.

28

mean satisfaction level only in job satisfaction with pay and job satisfaction with the distances to job

locations. However, Zakariya and Battu (2013) noted that well-matched workers have higher mean

job satisfaction levels than overeducated workers in Malaysia. Tsang (1987) and Allen and Van der

Velden (2001) reported that overeducated people are less satisfied with correctly educated people

with similar educational level. Meanwhile, Hersch (1991) and Groot and Maassen Van Den Brink

(2000) found that overeducated people reported lower job satisfaction level than their colleagues who

have adequate education require for the job. Therefore, according to the existing literature,

overeducated people should report lower satisfaction level than correctly educated workers. However,

based on Table 6, at least in the Chinese labour market, this is contrary to our expectations.

29

Table 5 Mean satisfaction levels of different demographic groups

Mean satisfaction with

Male

Female

18<=Age<=30

31<Age<=45

45<Age<=60

Primary school or

less

Junior high

school

Senior high

school

College level

University Master’

s or higher

Hourly wage <=5

(yuan)

5<Hourly wage<=10

(yuan)

10<Hourly wage<=20

(yuan)

Hourly wage >20

(yuan)

Urban registration

Rural registration

Overall job 3.364 3.397 3.410 3.337 3.414 3.2 3.326 3.390 3.545 3.576 3.368 3.194 3.410 3.578 3.654 3.401 3.265

T test/F value

1.0453 2.84* 10.65*** 41.64*** -3.1918***

Salary 3.217 3.238 3.257 3.172 3.279 3.086 3.192 3.262 3.333 3.307 3 3.023 3.219 3.478 3.580 3.247 3.117

T test/ F value

0.5447 3.26** 3.44*** 40.21*** -2.5102**

Welfare 3.088 3.137 3.163 3.030 3.182 2.741 3.012 3.203 3.321 3.416 3.053 2.802 3.158 3.454 3.550 3.178 2.753

T test/ F value

1.2091 6.31*** 20.80*** 72.92*** -7.8258***

Workload 3.147 3.189 3.202 3.124 3.196 2.889 3.092 3.235 3.349 3.355 3.211 2.959 3.189 3.422 3.433 3.204 2.964

T test/ F value

1.1078 1.97 13.23*** 37.86*** -4.7463***

Working conditions and facilities

3.269 3.341 3.383 3.248 3.311 2.989 3.229 3.336 6.529 3.589 3.474 3.095 3.337 3.510 3.632 3.345 3.074

T test/ F value

2.0435** 4.87*** 21.54*** 42.71*** -5.7593***

The relationship with colleagues

3.911 3.908 3.917 3.891 3.931 3.832 3.846 3.912 4.038 4.039 4.052 3.835 3.899 3.988 4.095 3.918 3.868

T test/ F value

-0.1187 0.76 5.98*** 11.45*** -1.2893

The relationship with boss

3.617 3.593 3.601 3.604 3.615 3.438 3.530 3.638 3.756 3.818 3.526 3.488 3.595 3.765 3.814 3.625 3.511

T test/ F value

-0.7625 0.07 11.47*** 21.26*** -2.6901***

Commuting distance to job location

3.540 3.570 3.542 3.529 3.599 3.476 3.512 3.553 3.599 3.723 3.842 3.495 3.508 3.665 3.706 3.560 3.522

T test/ F value

0.8143 1.38 3.01** 6.67*** -0.7599

Housing benefits

2.772 2.811 2.764 2.755 2.861 2.616 2.764 2.809 2.962 2.853 2.737 2.627 2.730 3.054 3.100 2.809 2.690

T test/ F value

0.9334 2.52* 4.05*** 26.88*** -2.0610**

30

Table 6 Mean satisfaction level among over/under/correctly educated groups

Mean satisfaction with Overeducated Undereducated Correctly

educated

One way

ANOVA

Salary 3.269 3.181 3.247 1.91

Welfare 3.256 2.975 3.163 16.21***

Workload 3.299 3.081 3.175 9.91***

Working conditions and

facilities 3.438 3.215 3.310 11.84***

The relationship with

colleagues 3.967 3.908 3.877 2.83*

The relationship with

boss 3.668 3.597 3.580 2.43*

Commuting distances to

job location 3.619 3.518 3.552 2.18

Housing benefits 2.954 2.731 2.752 8.85***

Overall job satisfaction 3.460 3.332 3.380 4.70***

31

4 Methodology

4.1 Alternative models

First, we use the following specification to explore the determinants of overall job

satisfaction of all workers in China.

Yi = α+β1Xi +μi (4)

where Yi is the overall job satisfaction. Xi is a vector of controlled variables that may affect

job satisfaction, which contains personal characteristics (age, age2, years of schooling, male,

nationality, political affiliation and household registration), employment characteristics (log

hourly income, firm size, work type) and relative deprivation variables (relative deprivation

on social status and economic relative deprivation). μi is error term with a normal distribution.

The central topic of this study is to explore the relationship between overeducation and

overall job satisfaction and dimensions of job satisfaction. Models often used in the

overeducation literature include detailed educational level and two dummy variables

indicating overeducation and undereducation (Verdugo and Verdugo (1989)). In this study, we

also adopt two dummy variables to indicate overeducation and undereducation. However, we

make two explicit comparisons among individuals. The first one is that, by controlling for

actual educational attainment, overeducated people are compared with individuals who have

similar educational level but are correctly educated. The second one is to make a comparison

between overeducated people and persons who are correctly educated in the same job with

overeducated people. The two models are described as follows:

Yi= α1 + β1Xi + λ1Yedui + ρ1overi + γ1underi +ε1 (5)

Yi= α2 + β2Xi + λ2Yredui+ ρ2overi +γ2underi +ε2 (6)

Where the dependent variable (Yi) is overall job satisfaction and alternatively the other eight

facets of job satisfaction, Yedui = actual years of schooling, Yredui = the required level of

education, overi is the dummy variable of overeducation, underi is the dummy variable of

undereducation, and Xi is a vector of control variables including personal characteristics (age,

age2, male, nationality, political affiliation and household registration), employment

characteristics (log hourly income, firm size, work type) and relative deprivation variables

32

(relative deprivation on social status and economic relative deprivation). εi is an error term

with a normal distribution. In equation (5), mismatched people are compared with

well-matched workers who have similar education. In equation (6), mismatched groups are

compared to people who do similar jobs with required education.

Job satisfaction is always treated as a proxy of utility (Clark (1997)). If utility is decided by

the absolute characteristic of the job, we hypothesized that overeducated people are less

satisfied with their jobs in comparison with well-matched workers who have similar

educational levels. Meanwhile, undereducated people are more satisfied with their jobs in

comparison with adequately educated individuals with similar educational level. If utility is a

relative concept, comparing with correctly educated workers in the same job, we expect that

overeducated people are less satisfied with their jobs and undereducated people are more

satisfied with their jobs compared to people who do similar jobs with required education.

Allen and Van der Velden (2001) note that skill mismatch is more suitable to explore the job

satisfaction than educational mismatch. In order to verify any effects of those two match on

job satisfaction and facets of job satisfaction, skill mismatch is also added into equations (4),

(5) and (6), which are specified as follows:

Yi= α0+β1Xi +μ0mismatchi+μi (7)

Yi= α1+β1Xi+λ1Yedui+ρ1overi+γ1underi+μ1mismatchi+ε1 (8)

Yi= α2+ β2Xi +λ2Yredui+ ρ2overi +γ2underi+μ2mismatchi+ε2 (9)

Where mismatch is a dummy variable to indicate skill mismatch and the same independent

variables have been used as equation (4), (5) and (6). μi, ε1 and ε2 are error terms with a

normal distribution. These three models will provide evidence to show which kind of

mismatch is a better indicator of subjective well-being at work.

33

4.2 Estimation methodology

4.2.1 Ordered probit model

In the CGSS 2008, job satisfaction variables are on a scale from 1 (very dissatisfied) to 5

(very satisfied). Therefore, an ordered probit model technique will be employed here26

. The

ordered probit model can be obtained by a latent variable approach27

, which is specified as:

𝑌𝑖∗= ∑ 𝛽𝑋𝑖

′𝑘𝑖=1 + 𝜇𝑖 (10)

𝑌𝑖 = m if 𝛼𝑚−1< 𝑌𝑖∗ <𝛼𝑚 (11)

𝛼𝑚−1, 𝛼𝑚 are a set of unknown threshold coefficients or cutpoints to be estimated. 𝛽 is a

vector of estimated parameters and 𝜇𝑖 is an error term which is normally distributed with

mean zero and variance one28

. 𝑌𝑖∗ is a latent and unobserved variable which can be treated as

an utility value.

Under the latent variable model, the probability that observation i will be treated as m is as

follows:

Pr (𝑌𝑖=m |𝑋𝑖) = Pr (αm-1 < 𝑌𝑖∗

< αm ) (12)

𝑌𝑖∗ can be written as a cumulative probility model

29. The while form of the cumulative

probabilities under the latent variable model can be written as follows:

Pr (𝑌𝑖=m |𝑋𝑖) = {

𝛷(𝛼1 − 𝛽𝑋𝑖′) 𝑚 = 1

𝛷(𝛼𝑚 − 𝛽𝑋𝑖′) − 𝛷(𝛼𝑚−1 − 𝛽𝑋𝑖

′) 1 < 𝑚 ≤ 𝑀 − 1

1 − 𝛷(𝛼𝑀−1 − 𝛽𝑋𝑖′) 𝑚 = 𝑀

(13)

where 𝛷(∗) is the cumulative distribution function. That is to say, the possibility of

individual i making a choice of m depends on the product 𝑌𝑖∗ falling between cutpoints (m-1)

and m (Baum (2006)).

4.2.2 Marginal effects

In the latent variable approach, the effect of one independent variable on the dependent

variable is expressed as changes in when there is one unit change in. However, is a latent and

26

Since the job satisfaction data is ordered, Ordinary Least Squares is not an appropriate estimation tool to

make analysis here (Brown and McIntosh (1998)). 27

A latent variable is assumed to represent the ordered response. 28

𝛽 represents for the similar effect of 𝑋𝑖 on 𝑌𝑖 and 𝑋𝑖 on 𝑌𝑖∗.

29 Maximum likelihood estimation (MLE) is used to estimate the cumulative probability model.

34

unobserved variable, so the alternative choice is to calculate the marginal effects of the

independent variables in the conditional distributions (Baum (2006))30

. Thus, the marginal

effects of can be calculated in the following way ((Powers and Xie (2008)):

𝜕(𝑌𝑖=m |𝑋𝑖)

𝜕𝑋𝑖𝑘={

−𝛷((𝛼1 − 𝛽𝑋𝑖′)𝛽𝑘 𝑚 = 1

[𝛷(𝛼𝑚−1 − 𝛽𝑋𝑖′) − 𝛷(𝛼𝑚 − 𝛽𝑋𝑖

′)]𝛽𝑘 1 < 𝑚 ≤ 𝑀 − 1

𝛷(𝛼𝑀−1 − 𝛽𝑋𝑖′)𝛽𝑘 𝑚 = 𝑀

(14)

5 Empirical results

5.1 Determinants of overall job satisfaction

In this section, the determinants of overall job satisfaction will be discussed. Because of the

small number of observations in some answer categories, it is necessary to combine them

from 5 categories to 3 categories, namely 1. Dissatisfied, 2. Average and 3. Satisfied31

. Table

7 presents results of marginal effects of reporting each answer category of overall job

satisfaction32

.

Age

We observe from Table 7 that there is a non-linear relationship between age and overall job

satisfaction. The negative coefficient of age and positive coefficient of age square indicate

that there is a U-shaped relationship between age and job satisfaction, which is consistent

with results observed in Table 5. Younger workers are new to the labour market and perhaps

they don’t have a definite judgment about their work. As long as they get an expected job33

,

they feel satisfied about their current employment. As they gain more work experience in the

labour market, they know how to set standard to judge their jobs and working conditions.

Therefore, job satisfaction decreases around middle ages. There are two explanations to

30

In the ordered logit model, the odds-ratios is the alternative choice. 31

The answer category “very dissatisfied” and “quite dissatisfied” are combined as “dissatisfied”. The answer

category “Quite satisfied” and “Very satisfied” are combined as “Satisfied”. 32

In Table 7, Specification 2 excludes the effects of log hourly wage on overall job satisfaction. 33

Expected job here can be defined as an graduate job. According to Chevalier (2003), jobs can be classified

into three types: graduate jobs, non-graduate jobs with immediate skill levels and non-graduate jobs with low

skill level.

35

explain the higher job satisfaction among old cohorts. One is the “aging effect”, which states

that higher job satisfaction reported by old people is only because they have better jobs than

young people (Mottaz (1987)). This explanation is similar to the self-selection effect, which

is proposed by (Gazioglu and Tansel (2006)). They argue that older people are experienced

workers and they know how to find a suitable and good job for themselves and thus obtain a

higher job satisfaction. The other different explanation is from expectation theory. Clark

(1997) argues that older people have lower expectations about their jobs because they face

limited choices in the labour market because of old age, which may result in higher job

satisfaction among old age cohorts. The U-shaped finding in this study is consistent with

existing literature of Brown and McIntosh (1998), Clark et al. (1996) and Clark and Oswald

(1996). However, Nielsen and Smyth (2008) suggest that age has a positive effect on job

satisfaction among urban workers in China34

.

Gender

According to Table 7, males have lower job satisfaction than women, but this result is not

significant. The finding that women have higher job satisfaction than men has been

confirmed by many researchers and in many countries, such as in USA and in UK (Bender et

al. (2005)). Although women have lower wages than men, they always compare their

circumstances with those women who stay at home or who hold low-paid jobs. Thus, they are

more satisfied with their jobs than men (Crosby (1982)). This explanation can be applied to

western countries. However in China, as the labour force participation rate of women

increases and meanwhile the labour force participation rate is higher than many countries

(Bauer et al. (1992)), women’ earnings have become an important part of the family’s income.

The reduced gender inequality means the structure of women’ expectation may be closer to

men’s. That is to say, there should be no difference of job satisfaction between men and

women, which is reflected by the insignificant result in this study35

. However, Loscocco and

Bose (1998) and Nielsen and Smyth (2008) all suggest that women have lower job

satisfaction than men in China.

34

Nielsen and Smyth (2008) employed data from an annual survey of about 10000 urban residents conducted

by the China Mainland Marketing Research Company (CMRC). 35

Stacey (1983) argue that participation rate doesn’t ensure the equality between men and women.

36

Education

The result of Table 7 indicates that there is a strong positive relationship between years of

schooling and overall job satisfaction. As indicated in the Table 5, the highest overall job

satisfaction is reported by the group with the University level and the lowest level of overall

job satisfaction is reported by the group with primary school and less degree. Blanchflower

and Oswald (1999) and Clark and Oswald (1996) all found a positive relationship between

education and job satisfaction. However in the above studies, after controlling for income,

this relationship becomes negative and insignificant. In specification 1 in Table 7, we can see

that even after controlling for hourly wage, education still has a positive impact on overall job

satisfaction. Individuals who have a higher educational level are more likely to find a better

job with better pay, higher stability and higher prestige than those with lower levels of

education ((Blanchflower and Oswald (1995) and Fabra and Camisón (2009)). That is to say,

the relationship between education and job satisfaction is largely attributed to the causality

between education and occupational status and earnings (Glenn and Weaver (1982). This

result supports the signaling theory to some extent. Moreover, Cheng et al. (2013) also find

positive effect of education on job satisfaction in China using CGSS 2008. However, Knight

et al. (2009) found that the positive relationship between education and well-being disappears

when taking community variables into consideration in rural China36

. Moreover, according to

the expectation theory, the effect of education on job satisfaction should be negative.

However, Clark and Oswald (1996) argue that the expectations of groups with different

educational level are various, the relationship between education and overall job satisfaction

is ambiguous.

Wages

As can be seen from Table 7, if the hourly wage rate increases by 1 per cent, the probability

of being “satisfied” with the job increases by 0.2 per cent, ceteris paribus. The coefficient of

reporting “satisfied” estimated from Table 7 is significantly positive, which indicates that

higher pay will result in higher job satisfaction. However, this marginal effect is small

compared with other variables, for example marriage or relative deprivation variables. This

small marginal effects is consistent with Cheng et al. (2013), Gao and Smyth (2010) and

36

Twenty-one province dummies are treated as community variables.

37

Knight et al. (2009), which all focus on the study of job satisfaction in the Chinese labour

market37

. Based on above result, the hourly wage is an important determinant of overall job

satisfaction in China, but the marginal effect is small. Moreover, it is also consistent with the

growing concern in the subjective well-being literature that absolute income is weakly

associated with subjective-well-being (Taylor et al. (2003); Clark and Oswald (1996) and

Clark et al. (2008)).

Marriage

According to Table 7, people who are married have higher overall job satisfaction38

. Mixed

results of the relationship between marriage and job satisfaction are presented in the literature

(Gazioglu and Tansel (2006). However, Clark (1996) notes that married people are happier

than single individuals in Britain. In addition, Cheng et al. (2013) report that single

individuals have lower job satisfaction compared with married people. A possible reason for

this finding is that potentially married families have two incomes, so they may not have such

an economic burden as much as single individuals. Therefore, they can choose to stay at jobs

with high job satisfaction or find a job with high job satisfaction. However, single individuals

have to stay in or take jobs with low job satisfaction due to income constraint (Gao and

Smyth (2010)).

Establishment size

Table 7 indicates that employees in small firm size report higher level of overall job

satisfaction than those in medium and large size of firms. Clark et al. (2009), Clark and

Oswald (1996) and Idson (1990) all find similar results. Idson (1990) argues that different

working environment flexibility leads to different job satisfaction. There might be greater

workplace rigidity, a more regimented work structure in larger firms, which may reduce

employees’ freedom to design the way to do work and working hours. However, in small

firms, working schedule and working style are potentially more flexible. Although wages are

high in large firms that can increase job satisfaction, after controlling for the nature of

working environment, the effects of establishment size on job satisfaction is largely reduced 37

Cheng et al. (2013) found the marginal effects of hourly income is only 0.3 per cent. 38

This result is only significant at the 10 per cent level.

38

(Idson (1990)).

Relative deprivation

As can be seen from Table 7, economic relative deprivation variable and social status relative

deprivation variable all have significant results. Individuals in the low level of social status

have the highest possibility (11.5 per cent) to report dissatisfaction and this is followed by the

middle level groups. Meanwhile, employees who have matched wages are 17.7 per cent more

likely to report satisfaction of job than individuals who experience unmatched wages.

Moreover, the economic relative deprivation variable in this study also can be treated as an

indicator of relative income. Compared with hourly wage variable, this relative income

variable has a larger effect on reporting “satisfied”, which supports the concern in the job

satisfaction literature that job satisfaction is more affected by relative terms than absolute

terms ((Clark and Oswald (1996); Gao and Smyth (2010) and Sloane and Williams (1996)).

The significant results of relative deprivation variables in this study show that relative

deprivation plays a very important role in determining job satisfaction. People judge

subjective well-being based on the referent other or expectations. In the Chinese culture,

networks are a good reflection of self-worth (Zhao (2001)). If you are in a low social status,

which means you may face limited networks, mobility and opportunities, you may be less

likely to find a satisfied and high level of job. Thus, downward mobility and unsatisfied

self-worth will create a feeling of deprivation. In addition, one’s expected wage is formed

from two sources. One is the reflexivity of self-worth and the other is your reference group’s

wages. Therefore, unmatched expected wages will make you feel deprived and lower job

satisfaction. Results here indicate that Chinese society is influenced by the relative

comparisons. As with the increasing inequality between rich and poor and dramatic recent

economic changes in China, relative deprivation may be a potential threat to subjective

well-being at work. Hu (2013), Liu and Shuzhuo (2011) and Wang and VanderWeele (2011)

all find similar results.

Skill mismatch

Table 7 shows that employees who are skill mismatched are around 7 percentage units less

likely to report “satisfied” of the overall job (Specification 2). Even after controlling for the

hourly wage, this effect seems remains (Specification 1). That is to say, skill mismatch is

39

negatively relevant to explain individuals’differential job satisfaction39

. Accoding to Allen

and Van der Velden (2001), skill underutilization and skill deficit all have negative effects on

job satisfaction, which is similar to our study. Moreover, Badillo-Amador and Vila (2013)

suggest that all kinds of skill mismatch can strongly reduce employees’ overall job

satisfaction40

. Based on above investigation, skill mismatch is not a desirable result for both

employees and employers. However, Sánchez-Sánchez and McGuinness (2013) find that

overskilled workers are 27.7 percentage units less likely to report “satisfied” in th current

employment and underskilled workers are 5.8 percentage units more likely to report

“satisfied”.

In addition, results from Table 7 indicate that full-time workers are more likely to report

higher job satisfaction than part-time workers, which is similar to the result from Shockey

and Mueller (1994). Full-time workers have high pay, good working conditions, various tasks

and autonomy. Therefore they have higher satisfaction and commitment to the employer

(Shockey and Mueller (1994)). Eberhardt and Shani (1984) report lower job satisfaction in

part-time workers is due to different orientations and expectations when they enter the job.

However, Logan et al. (1973) suggests that job satisfaction of full-time employees and

part-time workers are alike. In addition, workers in public sector have higher job satisfaction,

which is shown in Table 7. Based on the results from Table 7, effects of other factors on job

satisfaction, such as nationality, Political affiliation, household area and healthy status are

insignificant in Chinese labour market.

5.2 Overall job satisfaction and educational mismatch41

As can be seen from Table 8, when educational mismatch variables are only included in the

model (Specification 1), overeducated workers are more likely to report “satisfied” than their

colleagues who are correctly educated, but this effect is not significant. Undereducated

39

Due to the data limitations, we can not classify skill mismatch specifically as over skill underutilization and

skill shortage in this study. 40

Skill mismatch in this study is derived from self-assessment question from survey if one has inadequate skills

to perform current employment (skill deficit) and current personal capacity allows for a more demanding job

(overskilled). 41

From section 5.2 to section 5.9, Specification 1 is the model without controlling for skill mismatch variable.

Specification 2 is the model without controlling for both skill mismatch and the wage effects. Specification 3

concludes all the variables. Specification 4 is the model without controlling for the wage effects. The analysis in

this study is mainly based on the results of Specification 3.

40

workers have 3.9 percent less likely to report “satisfied” than their colleagues who are

correctly educated. Undereducated workers have lower job satisfaction than workers who are

in the same job but are correctly educated. In addition, required educational level is positively

related to job satisfaction, which is consistent with Tsang et al. (1991). The reason behind this

may be that more educated people can find better jobs that are interesting, challenging and

satisfying. In the literature, Büchel (2002) also reports that the effect of overeducated people

compared to adequately educated people who are in the same job on job satisfaction is

positive but insignificant. Moreover, Green and Zhu (2010) suggest that overeducation itself

can not reduce overall job satisfaction. However, Hersch (1991) and Verhaest and Omey

(2006) all suggest that overeducated employees are less satisfied than those who are correctly

educated but work at the same job level. In terms of undereducation, only Verhaest and Omey

(2006) found significantly negative effects using Job Analysis (JA) method to define

educational mismatch. When both educational mismatch and skill mismatch were included in

the model (Specification 3), we can see that people with skill mismatch are more likely to

report lower job satisfaction by around 7 per cent ceteris paribus. Moreover, the effects of

educational mismatch don’t change too much after controlling for skill mismatch. The effects

from skill mismatch are stronger than the effects from educational mismatch. In addition, the

gross effects (Specification 4) from educational mismatch on job satisfaction are higher than

the net effect (Specification 3) from educational mismatch on job satisfaction after taking

hourly wage into consideration42

. More importantly, the above results show that skill

mismatch seem to be a threat to workers’ job satisfaction rather than educational mismatch

and should be treated as a policy concern.

5.3 Job satisfaction with pay and educational mismatch

Because overeducated people earn higher wages than adequately educated worker in the same

job (Hartog (2000)), we hypothesized that overeducated people have higher job satisfaction

with pay than correctly educated people with required educational level. Table 9 Specification

1 reports marginal effects of reporting answer categories of job satisfaction with pay without

taking consideration of skill mismatch. Contrary to our expectations, we find that the

relationship between overeducation and job satisfaction with pay is negative and

42

The gross effect is defined as the relationship between educational mismatch and job satisfaction without

taking wages into consideration and the net effect has controlled for hourly wages.

41

insignificant43

. Such a result indicates that overeducation has no effect on pay satisfaction

although overeducated people have higher wages, which is consistent with Badillo-Amador

and Vila (2013)44

. When a skill mismatch dummy variable is added into analysis, we can see

that employees who have mismatched skills are 4.5 percentage points less likely to report

“satisfied” in terms of job satisfaction with pay than skill matched workers after controlling

for hourly wages (specification 3). Although skill mismatched people have a lower wage

penalty than overeducated people (McGuinness and Sloane (2011)), it is the skill match itself

decreasing job satisfaction with pay. In addition, we can see that relative deprivation

variables (economic relative deprivation and social status relative deprivation) are also

important indicators of job satisfaction with pay.

5.4 Job satisfaction with welfare and educational mismatch45

Table 10 reports marginal effects of reporting answer categories of job satisfaction with

welfare. As can be seen from Table 10 Specification 1, the insignificant coefficient of

overeducation suggests that overeducation does not influence job satisfaction with welfare.

However, undereducated workers are 6.8 per cent less likely to report “satisfied” of job

satisfaction with welfare than correctly educated people working in the same job.

Undereducation appears to reduce job satisfaction with welfare. When skill mismatch is

brought into consideration, skill mismatch also has negative effects on job satisfaction with

welfare. However, the effect of undereducation is stronger than the effect of skill mismatch

on job satisfaction on welfare, which suggests that undereducation is more relevant in

explaining job satisfaction with welfare than skill mismatch.

5.5 Job satisfaction with workload and educational mismatch

As expected, overeducated people are more satisfied with workload than their colleagues 43

The coefficient of undereducation in Table 9 Specification 1 is also insignificant in this study. 44

Fleming and Kler (2008) and Johnson and Johnson (2000) all found that there is a negative relationship

between overeducation and job satisfaction with pay. 45

Since the economic reform in 1978, although there is a massive increase in workers’wages and non-wage

benefits, the structure of workers’compensation is relatively static (Xiao (1991)). Worker’s compensation is

consisted of four parts in China: wages, subsidised housing, medical care and retirement benefits ((Xiao (1991)).

According to the above definition, in this study, the job satisfaction with welfare is related to medical care and

retirement benefits. In terms of retirement benefits, employers would pay employees 70% or more of his wages

as a pension in China and this ratio varies with different types of firms and contract type ((Xiao (1991)). Moreover, in state-owned firms, Children’s schooling, transportation are provided by employers as additional

welfare.

42

with the required educational level for the job and undereducated people are less likely to

report “satisfied” of their workload than employees who are correctly educated in the same

job. Mavromaras et al. (2010) argue that overeducated people are more productive and have

better adaptability. When choosing a lower level of job than their own educational level, they

will finish quicker and be more efficient than correctly educated colleagues. Thus, they may

feel less pressured and relaxed when they are at work and thus have a higher job satisfaction.

Undereducated workers take tasks higher than their skill level and need more time to finish

than adequately educated workers in the same job, which may make them feel deprived and

increase frustration. Table 11 reports marginal effects of reporting answer categories of job

satisfaction with workload. However, skill mismatched individuals have to learn skills needed

for jobs and try their best to finish tasks and thus are less likely to report satisfaction with

their workload, which is also confirmed in this study.

5.6 Job satisfaction with working conditions and facilities and

educational mismatch

As with Table 12 specification 1, overeducated workers are 5.5 percentage units more likely

to be satisfied with working conditions and facilities than their colleagues with required

educational level and undereducated people are 3.8 percentage units less likely to report

“satisfied” of their working conditions and facilities than employees who are correctly

educated in the same job after controlling for the hourly wage. Moreover, when including

skill mismatch into the analysis, the above relationship remains. However, in terms of skill

mismatch, individuals who are skill mismatched are 5.3 percentage units less likely to report

“satisfied”with working conditions and facilities, which suggests a negative relationship

between the job satisfaction with working conditions and facilities and skill mismatch.

5.7 Job satisfaction with the relationship with colleagues and boss and

educational mismatch

Interpersonal relationship (the relationship with colleagues and boss) is described as Guanxi

in Chinese culture, which stems from Confucianism. Guanxi is a very important factor to

build personal relationships and business conduct in Chinese society (Xin and Pearce (1996)).

In addition, it is well recognised that Guanxi (relationship with boss, colleagues and friends)

is a very important determinant when choosing a job in Chinese culture (Park and Luo (2001).

43

If taking educational mismatch into consideration, as can be seen from Table 13 Specification

1, overeducated people are around 4 percentage units more likely to be satisfied with the

relationship with colleagues. However, after controlling for hourly wages, the net effect of

overeducation on the job satisfaction with the relationship with boss is insignificant. In

addition, the relationship between undereducation and job satisfaction with the relationship

with colleagues and boss are both insignificant.

In terms of skill mismatch, it seems that individuals who witness skill mismatch problem are

less likely to report “satisfied” with the relationship with boss, which is converse to the

situations of overeducated people.

5.8 Job satisfaction with commuting distance to job location and

educational mismatch

As with results for satisfaction with commuting distance to job location in Table 15,

overeducated workers and undereducated workers have the similar attitudes towards this kind

of satisfaction with their colleagues working in the same job but are correctly educated. This

result is neither consistent with our expectations nor inconsistent as there is no literature to

explore this issue. This study suggests that there is no relationship between the two variables.

However, skill mismatched workers are less likely to report satisfaction with commuting

distance to job location as other aspects of job satisfaction.

5.9 Job satisfaction with housing benefits46

and educational mismatch

As can be seen from Table 16 specification 1, overeducated workers are 6 percent more likely

to report satisfaction with the housing benefits than individuals who are in the same job but

are correctly educated. The relationship between undereducation and job satisfaction with

housing benefits is insignificant. However, skill mismatched individuals are less likely to

report satisfaction with housing benefits, which indicating a inverse relationship between

these two variables.

In addition, we also explore the comparison between overeducated people and individuals

46

Housing benefits include monetarisation of housing subsidies and rent subsidies. The subsidy level is varied

among provinces and cities (Lee (2000)).

44

with similar level of education but are adequately educated47

. However, the relationship

between overeducation and overall job satisfaction and facets of job satisfaction are

insignificant except for job satisfaction with the relationship with boss and satisfaction with

housing benefits.

47

Results are all attached in the Appendix B.

45

6 Discussion and conclusion Throughout the literature on overeducation in China, most studies on overeducation focus on

the relationship between overeducation and wages. The overall analysis of job satisfaction in

China is very limited due to the absence of data of job satisfaction. However, the Chinese

General Social Survey (2008), provides a range of job satisfaction measures, which enables

us to explore the determinants of overall and specific aspects of job satisfaction and

especially investigate detailed links between overeducation and undereducation and job

satisfaction in China. For example, not only do we focus upon overall job satisfaction, we

also investigate satisfaction with: salary, welfare, workload, working conditions and facilities,

the relationship with colleagues, the relationship with boss, commuting distance to job

location and housing benefits. This is a contribution to the overeducation literature in China.

First, investigating the determinants of overall job satisfaction indicates the following results.

There is a U-shaped relationship between age and overall job satisfaction, which is consistent

with previous empirical results. Males have lower job satisfaction than women, but this result

is not significant. Reduced gender inequality in China may be part of reason to explain this.

In addition, there is a strong positive relationship between years of schooling and overall job

satisfaction even after controlling for hourly wages. Higher pay will result in higher job

satisfaction. That is to say, the wage is an important determinant of overall job satisfaction in

China. Married workers and employees in small firm size report higher level of overall job

satisfaction. Moreover, the significant results of relative deprivation variables in this study

show that relative deprivation plays an important part in determining job satisfaction. With

the ever increasing social and economic inequality and the dramatic economic changes in

China, relative deprivation may be a potential threat to subjective well-being at work.

In terms of educational mismatch, according to Table 6, overeducated people report the

highest job satisfaction level among the three groups. Overeducation may not result in

negative effects on productivity as a priori expectations. Although overeducated people’s pay

is higher than correctly educated people who have the required education for the job,

overeducated people are found to be less likely to report “satisfied” of pay satisfaction.

However, this effect is insignificant and the relationship between overeducation and welfare

is also insignificant. In addition, results indicate that overeducated people are more satisfied

46

with workload, working conditions and facilities, the relationship with colleagues and

housing benefits. We can infer that pay may not be the main concern for overeducated people.

Instead, there may be some compensating benefits of job offered to overeducated people to

achieve maximum utility of employment (McGuinness and Sloane (2011)). That is to say, in

this study, workload, working conditions and facilities, the relationship with colleagues and

housing benefits may be compensating advantages of the job that make overeducated people

choose the job lower than their educational attainment to achieve a balanced tradeoff between

work and life and thus report high job satisfaction.

Another reason which can be inferred from this study is that overeducated people choose to

stay in a job beneath their level of education may due to their own preference of some

characteristics of job, which also can be called voluntary overeducation (Chevalier (2003);

Mavromaras et al. (2010) and Verhaest and Omey (2009)). For example, overeducated people

prefer to have low workload and low mental pressure so that they choose to stay at a job

lower than their educational level. In this study, the results show that overeducated people

may prefer to stay in a job with good working conditions and facilities and good relationship

with colleagues, although the job is lower than their educational level. It is well recognised

that Guanxi (relationship with boss, colleagues and friends) is a very important determinant

when choosing a job in Chinese culture (Park and Luo (2001)). Sousa-Poza and Sousa-Poza

(2000) note that the relationship with colleagues has a positive relationship with job

satisfaction. Therefore, overeducated people may report higher job satisfaction.

In addition, when educational mismatch and skill mismatch are included simultaneously into

the analysis of job satisfaction, we can see that skill mismatch has consistently stronger

negative effect on job satisfaction and all facet of job satisfaction than educational mismatch.

It seems that skill mismatch may undermine job satisfaction within institutions, which should

be the focus of policy rather than overeducation in the Chinese labour market.

In addition, from the insignificant results of the comparison between overeducated people

and adequately educated people who have similar education regarding job satisfaction, we

can infer that overeducated and undereducated people in the Chinese labour market compare

47

themselves with their colleagues with required education in the same job. According to the

relative deprivation theory, the most commonly used referent other is a given individual in

the same job or organization (Johnson and Johnson (2000)). Moreover, the results of this

study also support the point of view from Clark and Oswald (1996) that utility is determined

by the relative characteristics of jobs rather than absolute characteristics of jobs.

However, this study adopts cross sectional data to make empirical analysis. It is unclear

whether unobserved heterogeneity and the endogenous of wages will bias the results, which

will be main concerns in future studies.

48

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Table 7 marginal effects of reporting each answer category of overall job satisfaction

Variables Specification 1 Specification 2

1 2 3 1 2 3

Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Skill mismatched 0.033*** 0.036*** -0.069*** 0.033*** 0.036*** -0.069***

(0.0116) (0.0125) (0.0239) (0.0116) (0.0125) (0.0239)

Salary matched with expectation -0.085*** -0.092*** 0.177*** -0.087*** -0.094*** 0.180***

(0.0121) (0.0124) (0.0235) (0.0121) (0.0124) (0.0235)

age 0.009*** 0.009*** -0.018*** 0.008*** 0.009*** -0.017***

(0.0031) (0.0034) (0.0065) (0.0031) (0.0034) (0.0065)

age2 -0.000*** -0.000*** 0.000*** -0.000*** -0.000*** 0.000***

(0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001)

Actual years of schooling -0.007*** -0.007*** 0.014*** -0.008*** -0.008*** 0.016***

(0.0017) (0.0019) (0.0036) (0.0017) (0.0018) (0.0035)

Hourly wage -0.001*** -0.001*** 0.002***

(0.0004) (0.0004) (0.0008)

Male 0.012 0.013 -0.024 0.009 0.010 -0.019

(0.0088) (0.0094) (0.0182) (0.0087) (0.0094) (0.0181)

Nationality 0.009 0.009 -0.018 0.008 0.009 -0.017

(0.0165) (0.0178) (0.0343) (0.0165) (0.0178) (0.0344)

Political -0.015 -0.016 0.031 -0.016 -0.017 0.033

(0.0134) (0.0144) (0.0277) (0.0134) (0.0144) (0.0277)

Married -0.024* -0.026* 0.049* -0.023* -0.025* 0.048*

(0.0128) (0.0138) (0.0266) (0.0128) (0.0138) (0.0266)

Urban -0.010 -0.011 0.021 -0.014 -0.015 0.029

(0.0126) (0.0136) (0.0261) (0.0125) (0.0135) (0.0260)

Full time -0.031** -0.033** 0.065** -0.030** -0.033** 0.063**

54

(0.0131) (0.0141) (0.0271) (0.0131) (0.0141) (0.0271)

State -0.037*** -0.040*** 0.077*** -0.036*** -0.038*** 0.074***

(0.0106) (0.0113) (0.0217) (0.0106) (0.0113) (0.0217)

Healthy -0.006 -0.007 0.013 -0.007 -0.008 0.015

(0.0170) (0.0183) (0.0353) (0.0170) (0.0184) (0.0354)

Lower 0.115*** 0.124*** -0.240*** 0.129*** 0.139*** -0.268***

(0.0287) (0.0304) (0.0583) (0.0284) (0.0300) (0.0575)

Middle 0.063** 0.068** -0.131** 0.074*** 0.080*** -0.154***

(0.0282) (0.0301) (0.0581) (0.0280) (0.0299) (0.0577)

Medium 0.023** 0.025** -0.048** 0.022** 0.024** -0.046**

(0.0112) (0.0120) (0.0231) (0.0112) (0.0120) (0.0231)

Large 0.023* 0.024* -0.047* 0.020* 0.022* -0.042*

(0.0119) (0.0128) (0.0246) (0.0119) (0.0128) (0.0246)

Number of observations 2430 2430 2430 2430 2430 2430

LR chi2 243.72 235.93

Prob > chi2 0.0000 0.0000

Pseudo R2 0.0520 0.0504

* p<0.10, ** p<0.05, *** p<0.010

55

Table 8 The relationship between overeducation and job satisfaction Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required educational level -0.015*** -0.017*** 0.032*** -0.019*** -0.020*** 0.039*** -0.015*** -0.016*** 0.032*** -0.019*** -0.020*** 0.039***

(0.0055) (0.0059) (0.0113) (0.0054) (0.0058) (0.0111) (0.0055) (0.0059) (0.0113) (0.0054) (0.0058) (0.0110)

Overeducated -0.015 -0.016 0.030 -0.019 -0.020 0.039 -0.015 -0.016 0.031 -0.019 -0.021 0.040

(0.0121) (0.0130) (0.0251) (0.0120) (0.0129) (0.0249) (0.0121) (0.0130) (0.0250) (0.0120) (0.0129) (0.0249)

Undereducated 0.019* 0.020* -0.039* 0.021** 0.023** -0.044** 0.019* 0.020* -0.039* 0.022** 0.023** -0.045**

(0.0103) (0.0111) (0.0214) (0.0103) (0.0111) (0.0214) (0.0103) (0.0111) (0.0214) (0.0103) (0.0111) (0.0213)

Skill mismatched 0.032*** 0.035*** -0.067*** 0.032*** 0.035*** -0.067***

(0.0116) (0.0125) (0.0239) (0.0116) (0.0125) (0.0240)

Salary matched with expectation

-0.088*** -0.094*** 0.182*** -0.090*** -0.096*** 0.186*** -0.086*** -0.093*** 0.178*** -0.088*** -0.095*** 0.182***

(0.0122) (0.0124) (0.0236) (0.0122) (0.0125) (0.0235) (0.0121) (0.0124) (0.0236) (0.0121) (0.0124) (0.0235)

age 0.009*** 0.009*** -0.018*** 0.008*** 0.009*** -0.017*** 0.009*** 0.010*** -0.019*** 0.009*** 0.009*** -0.018***

(0.0032) (0.0034) (0.0065) (0.0032) (0.0034) (0.0065) (0.0032) (0.0034) (0.0065) (0.0032) (0.0034) (0.0065)

age2 -0.000*** -0.000*** 0.000*** -0.000*** -0.000*** 0.000*** -0.000*** -0.000*** 0.000*** -0.000*** -0.000*** 0.000***

(0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001)

Hourly wage -0.001*** -0.001*** 0.002*** -0.001*** -0.001*** 0.002***

(0.0004) (0.0004) (0.0008) (0.0004) (0.0004) (0.0008)

male 0.010 0.011 -0.022 0.007 0.008 -0.015 0.010 0.011 -0.021 0.007 0.008 -0.015

(0.0089) (0.0096) (0.0185) (0.0088) (0.0095) (0.0184) (0.0089) (0.0096) (0.0184) (0.0088) (0.0095) (0.0183)

Nationality 0.010 0.011 -0.021 0.009 0.010 -0.019 0.008 0.008 -0.016 0.007 0.007 -0.014

(0.0166) (0.0178) (0.0344) (0.0166) (0.0179) (0.0344) (0.0165) (0.0178) (0.0344) (0.0166) (0.0179) (0.0344)

Political -0.017 -0.019 0.036 -0.019 -0.020 0.039 -0.017 -0.018 0.035 -0.018 -0.020 0.038

(0.0134) (0.0144) (0.0278) (0.0134) (0.0144) (0.0278) (0.0134) (0.0144) (0.0278) (0.0134) (0.0144) (0.0278)

Married -0.021* -0.023* 0.045* -0.020 -0.022 0.042 -0.022* -0.024* 0.046* -0.021* -0.023* 0.044*

(0.0129) (0.0139) (0.0267) (0.0129) (0.0139) (0.0267) (0.0129) (0.0138) (0.0266) (0.0129) (0.0139) (0.0267)

56

Urban -0.012 -0.013 0.025 -0.017 -0.018 0.035 -0.013 -0.014 0.028 -0.018 -0.019 0.037

(0.0126) (0.0135) (0.0261) (0.0125) (0.0135) (0.0259) (0.0126) (0.0135) (0.0261) (0.0125) (0.0135) (0.0259)

fulltime -0.031** -0.033** 0.064** -0.030** -0.032** 0.063** -0.033** -0.035** 0.068** -0.032** -0.035** 0.067**

(0.0131) (0.0141) (0.0271) (0.0131) (0.0141) (0.0271) (0.0131) (0.0141) (0.0271) (0.0131) (0.0141) (0.0271)

State -0.040*** -0.043*** 0.084*** -0.039*** -0.042*** 0.080*** -0.039*** -0.042*** 0.081*** -0.037*** -0.040*** 0.077***

(0.0106) (0.0113) (0.0217) (0.0106) (0.0113) (0.0217) (0.0106) (0.0113) (0.0217) (0.0106) (0.0113) (0.0217)

healthy -0.005 -0.006 0.011 -0.006 -0.007 0.013 -0.005 -0.005 0.010 -0.006 -0.006 0.012

(0.0171) (0.0184) (0.0355) (0.0171) (0.0184) (0.0355) (0.0170) (0.0184) (0.0354) (0.0171) (0.0184) (0.0355)

lower 0.115*** 0.124*** -0.238*** 0.129*** 0.139*** -0.268*** 0.113*** 0.122*** -0.235*** 0.128*** 0.138*** -0.265***

(0.0288) (0.0305) (0.0585) (0.0286) (0.0301) (0.0577) (0.0287) (0.0304) (0.0584) (0.0285) (0.0301) (0.0576)

middle 0.061** 0.066** -0.127** 0.073*** 0.078*** -0.151*** 0.060** 0.065** -0.126** 0.072** 0.077*** -0.149***

(0.0283) (0.0302) (0.0583) (0.0282) (0.0301) (0.0579) (0.0282) (0.0302) (0.0582) (0.0281) (0.0300) (0.0578)

medium 0.022** 0.024** -0.046** 0.021* 0.023* -0.044* 0.022** 0.024** -0.046** 0.021* 0.023* -0.044*

(0.0112) (0.0120) (0.0232) (0.0112) (0.0121) (0.0232) (0.0112) (0.0120) (0.0231) (0.0112) (0.0121) (0.0232)

large 0.022* 0.024* -0.046* 0.019 0.021 -0.040 0.022* 0.023* -0.045* 0.019 0.021 -0.040

(0.0119) (0.0128) (0.0247) (0.0119) (0.0128) (0.0247) (0.0119) (0.0128) (0.0246) (0.0119) (0.0128) (0.0246)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 231.30 222.34 239.03 229.97

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0494 0.0475 0.0510 0.0491

* p<0.10, ** p<0.05, *** p<0.010

57

Table 9 The relationship between overeducation and job satisfaction with pay

Variables Specification 1 Specification 2 Specification3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required educational

level -0.003 -0.001 0.004 -0.009 -0.003 0.013 -0.003 -0.001 0.003 -0.009 -0.003 0.013

(0.0084) (0.0028) (0.0112) (0.0082) (0.0028) (0.0110) (0.0084) (0.0028) (0.0112) (0.0082) (0.0028) (0.0110)

Overeducated 0.014 0.005 -0.019 0.006 0.002 -0.008 0.014 0.005 -0.019 0.006 0.002 -0.008

(0.0185) (0.0062) (0.0247) (0.0183) (0.0062) (0.0246) (0.0185) (0.0062) (0.0247) (0.0183) (0.0062) (0.0246)

Undereducated 0.011 0.004 -0.014 0.016 0.006 -0.022 0.011 0.004 -0.015 0.017 0.006 -0.022

(0.0159) (0.0054) (0.0213) (0.0159) (0.0054) (0.0213) (0.0159) (0.0054) (0.0213) (0.0159) (0.0054) (0.0213)

Skill mismatch 0.034* 0.011* -0.045* 0.034* 0.011* -0.045*

(0.0178) (0.0061) (0.0238) (0.0179) (0.0061) (0.0239)

Salary matched with

expectation -0.144*** -0.049*** 0.193*** -0.147*** -0.050*** 0.197*** -0.142*** -0.048*** 0.190*** -0.146*** -0.049*** 0.195***

(0.0178) (0.0065) (0.0230) (0.0178) (0.0065) (0.0230) (0.0178) (0.0065) (0.0230) (0.0178) (0.0065) (0.0230)

age 0.007 0.002 -0.009 0.006 0.002 -0.008 0.007 0.002 -0.010 0.006 0.002 -0.009

(0.0048) (0.0016) (0.0064) (0.0048) (0.0016) (0.0065) (0.0048) (0.0016) (0.0064) (0.0048) (0.0016) (0.0065)

age2 -0.000* -0.000* 0.000* -0.000 -0.000 0.000 -0.000* -0.000* 0.000* -0.000 -0.000 0.000

(0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001)

Hourly wage -0.002*** -0.001*** 0.003*** -0.002*** -0.001*** 0.003***

(0.0006) (0.0002) (0.0008) (0.0006) (0.0002) (0.0008)

male 0.006 0.002 -0.008 -0.000 -0.000 0.000 0.006 0.002 -0.008 -0.000 -0.000 0.000

(0.0136) (0.0046) (0.0182) (0.0136) (0.0046) (0.0182) (0.0136) (0.0046) (0.0182) (0.0136) (0.0046) (0.0182)

Nationality -0.028 -0.009 0.037 -0.030 -0.010 0.040 -0.030 -0.010 0.040 -0.032 -0.011 0.042

(0.0253) (0.0086) (0.0338) (0.0253) (0.0086) (0.0339) (0.0253) (0.0086) (0.0338) (0.0253) (0.0086) (0.0339)

Political 0.001 0.000 -0.002 -0.002 -0.001 0.002 0.002 0.001 -0.002 -0.001 -0.000 0.002

(0.0203) (0.0069) (0.0272) (0.0204) (0.0069) (0.0273) (0.0203) (0.0069) (0.0272) (0.0203) (0.0069) (0.0272)

Married -0.008 -0.003 0.011 -0.006 -0.002 0.008 -0.009 -0.003 0.012 -0.007 -0.002 0.009

(0.0198) (0.0067) (0.0265) (0.0198) (0.0067) (0.0266) (0.0198) (0.0067) (0.0265) (0.0198) (0.0067) (0.0266)

58

Urban -0.027 -0.009 0.036 -0.037* -0.012* 0.049* -0.029 -0.010 0.038 -0.038** -0.013** 0.051**

(0.0194) (0.0066) (0.0259) (0.0193) (0.0066) (0.0258) (0.0194) (0.0066) (0.0259) (0.0193) (0.0066) (0.0258)

fulltime -0.046** -0.015** 0.061** -0.045** -0.015** 0.060** -0.048** -0.016** 0.064** -0.047** -0.016** 0.063**

(0.0203) (0.0069) (0.0272) (0.0203) (0.0070) (0.0272) (0.0203) (0.0070) (0.0272) (0.0203) (0.0070) (0.0272)

State -0.014 -0.005 0.019 -0.011 -0.004 0.014 -0.013 -0.004 0.017 -0.009 -0.003 0.012

(0.0161) (0.0054) (0.0215) (0.0161) (0.0055) (0.0216) (0.0161) (0.0054) (0.0215) (0.0161) (0.0055) (0.0216)

healthy -0.061** -0.021** 0.082** -0.063** -0.021** 0.085** -0.060** -0.020** 0.081** -0.063** -0.021** 0.084**

(0.0264) (0.0090) (0.0352) (0.0265) (0.0091) (0.0354) (0.0264) (0.0090) (0.0352) (0.0264) (0.0091) (0.0354)

lower 0.189*** 0.064*** -0.253*** 0.219*** 0.074*** -0.293*** 0.188*** 0.064*** -0.252*** 0.218*** 0.074*** -0.292***

(0.0434) (0.0149) (0.0573) (0.0430) (0.0150) (0.0567) (0.0433) (0.0149) (0.0574) (0.0430) (0.0150) (0.0567)

middle 0.112*** 0.038*** -0.149*** 0.135*** 0.046*** -0.181*** 0.111*** 0.038*** -0.149*** 0.135*** 0.046*** -0.181***

(0.0429) (0.0145) (0.0571) (0.0427) (0.0146) (0.0568) (0.0429) (0.0145) (0.0571) (0.0427) (0.0146) (0.0568)

medium 0.019 0.006 -0.025 0.017 0.006 -0.022 0.019 0.006 -0.025 0.017 0.006 -0.022

(0.0173) (0.0058) (0.0231) (0.0173) (0.0059) (0.0231) (0.0173) (0.0058) (0.0231) (0.0173) (0.0059) (0.0231)

large 0.029 0.010 -0.038 0.023 0.008 -0.031 0.028 0.010 -0.038 0.023 0.008 -0.031

(0.0183) (0.0062) (0.0245) (0.0183) (0.0062) (0.0245) (0.0183) (0.0062) (0.0245) (0.0183) (0.0062) (0.0245)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 211.14 168.78 188.11 172.30

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0410 0.0328 0.0366 0.0335

* p<0.10, ** p<0.05, *** p<0.010

59

Table 10 The relationship between overeducation and job satisfaction with welfare

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required

educational level -0.023*** -0.004** 0.028*** -0.031*** -0.006*** 0.037*** -0.023*** -0.004** 0.028*** -0.031*** -0.006*** 0.036***

(0.0088) (0.0018) (0.0105) (0.0086) (0.0018) (0.0103) (0.0088) (0.0018) (0.0105) (0.0086) (0.0018) (0.0103)

Overeducated -0.002 -0.000 0.002 -0.011 -0.002 0.013 -0.002 -0.000 0.002 -0.011 -0.002 0.013

(0.0195) (0.0037) (0.0232) (0.0194) (0.0038) (0.0231) (0.0195) (0.0037) (0.0232) (0.0194) (0.0038) (0.0231)

Undereducated 0.057*** 0.011*** -0.068*** 0.063*** 0.012*** -0.075*** 0.057*** 0.011*** -0.068*** 0.063*** 0.012*** -0.075***

(0.0168) (0.0035) (0.0200) (0.0168) (0.0036) (0.0200) (0.0168) (0.0035) (0.0200) (0.0167) (0.0036) (0.0200)

Skill mismatched 0.038** 0.007* -0.045** 0.038** 0.007* -0.045**

(0.0188) (0.0037) (0.0224) (0.0188) (0.0038) (0.0225)

Salary matched

with expectation -0.114*** -0.022*** 0.135*** -0.117*** -0.023*** 0.140*** -0.112*** -0.021*** 0.133*** -0.116*** -0.022*** 0.138***

(0.0183) (0.0042) (0.0216) (0.0183) (0.0043) (0.0216) (0.0183) (0.0042) (0.0216) (0.0183) (0.0043) (0.0216)

age 0.006 0.001 -0.007 0.005 0.001 -0.005 0.006 0.001 -0.007 0.005 0.001 -0.006

(0.0051) (0.0010) (0.0061) (0.0051) (0.0010) (0.0061) (0.0051) (0.0010) (0.0061) (0.0051) (0.0010) (0.0061)

age2 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000

(0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001)

Hourly wage -0.002*** -0.000*** 0.003*** -0.002*** -0.000*** 0.003***

(0.0006) (0.0001) (0.0007) (0.0006) (0.0001) (0.0007)

male 0.013 0.002 -0.015 0.006 0.001 -0.007 0.012 0.002 -0.015 0.005 0.001 -0.007

(0.0145) (0.0028) (0.0172) (0.0144) (0.0028) (0.0172) (0.0144) (0.0028) (0.0172) (0.0143) (0.0028) (0.0171)

Nationality -0.063** -0.012** 0.075** -0.064** -0.012** 0.077** -0.065** -0.012** 0.077** -0.067** -0.013** 0.079**

(0.0271) (0.0054) (0.0323) (0.0272) (0.0055) (0.0325) (0.0271) (0.0054) (0.0323) (0.0272) (0.0055) (0.0325)

Political 0.003 0.001 -0.004 0.000 0.000 -0.000 0.004 0.001 -0.004 0.001 0.000 -0.001

(0.0215) (0.0041) (0.0256) (0.0215) (0.0042) (0.0257) (0.0215) (0.0041) (0.0256) (0.0215) (0.0042) (0.0257)

Married -0.003 -0.001 0.003 -0.000 -0.000 0.001 -0.004 -0.001 0.005 -0.001 -0.000 0.002

(0.0210) (0.0040) (0.0250) (0.0210) (0.0041) (0.0251) (0.0210) (0.0040) (0.0250) (0.0210) (0.0041) (0.0251)

60

Urban -0.070*** -0.013*** 0.083*** -0.080*** -0.016*** 0.096*** -0.072*** -0.014*** 0.085*** -0.082*** -0.016*** 0.098***

(0.0204) (0.0043) (0.0244) (0.0203) (0.0044) (0.0243) (0.0204) (0.0043) (0.0244) (0.0203) (0.0044) (0.0243)

fulltime -0.066*** -0.013*** 0.078*** -0.065*** -0.013*** 0.077*** -0.068*** -0.013*** 0.081*** -0.067*** -0.013*** 0.080***

(0.0216) (0.0045) (0.0257) (0.0216) (0.0045) (0.0258) (0.0216) (0.0045) (0.0257) (0.0216) (0.0045) (0.0258)

State -0.054*** -0.010*** 0.064*** -0.050*** -0.010*** 0.060*** -0.053*** -0.010*** 0.063*** -0.049*** -0.009*** 0.058***

(0.0170) (0.0035) (0.0202) (0.0170) (0.0035) (0.0202) (0.0170) (0.0034) (0.0202) (0.0170) (0.0035) (0.0203)

Healthy -0.022 -0.004 0.026 -0.024 -0.005 0.029 -0.021 -0.004 0.025 -0.024 -0.005 0.028

(0.0282) (0.0054) (0.0336) (0.0282) (0.0055) (0.0337) (0.0282) (0.0054) (0.0335) (0.0282) (0.0055) (0.0337)

Lower 0.177*** 0.034*** -0.211*** 0.207*** 0.040*** -0.247*** 0.176*** 0.034*** -0.210*** 0.206*** 0.040*** -0.246***

(0.0442) (0.0091) (0.0523) (0.0436) (0.0094) (0.0516) (0.0441) (0.0091) (0.0523) (0.0436) (0.0094) (0.0516)

Middle 0.105** 0.020** -0.125** 0.128*** 0.025*** -0.153*** 0.105** 0.020** -0.125** 0.128*** 0.025*** -0.153***

(0.0437) (0.0086) (0.0519) (0.0434) (0.0087) (0.0516) (0.0437) (0.0086) (0.0519) (0.0434) (0.0087) (0.0516)

Medium -0.006 -0.001 0.008 -0.008 -0.002 0.010 -0.006 -0.001 0.008 -0.008 -0.002 0.010

(0.0181) (0.0035) (0.0216) (0.0181) (0.0035) (0.0217) (0.0181) (0.0035) (0.0216) (0.0181) (0.0035) (0.0217)

Large -0.034* -0.006* 0.040* -0.039** -0.008** 0.047** -0.034* -0.006* 0.040* -0.039** -0.008** 0.047**

(0.0193) (0.0038) (0.0230) (0.0193) (0.0038) (0.0230) (0.0193) (0.0038) (0.0229) (0.0192) (0.0038) (0.0230)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 264.16 247.84 268.21 251.81

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0502 0.0471 0.0510 0.0478

* p<0.10, ** p<0.05, *** p<0.010

61

Table 11The relationship between overeducation and job satisfaction with workload

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required educational

level -0.029*** -0.009*** 0.038*** -0.029*** -0.009*** 0.038*** -0.029*** -0.008*** 0.038*** -0.031*** -0.009*** 0.040***

(0.0082) (0.0025) (0.0106) (0.0082) (0.0025) (0.0106) (0.0082) (0.0025) (0.0106) (0.0081) (0.0025) (0.0104)

Overeducated -0.039** -0.011** 0.050** -0.039** -0.011** 0.050** -0.039** -0.011** 0.051** -0.041** -0.012** 0.053**

(0.0183) (0.0054) (0.0236) (0.0183) (0.0054) (0.0236) (0.0183) (0.0054) (0.0236) (0.0183) (0.0054) (0.0235)

Undereducated 0.048*** 0.014*** -0.062*** 0.048*** 0.014*** -0.062*** 0.049*** 0.014*** -0.063*** 0.050*** 0.014*** -0.064***

(0.0158) (0.0047) (0.0204) (0.0158) (0.0047) (0.0204) (0.0158) (0.0047) (0.0203) (0.0158) (0.0047) (0.0203)

Skill mismatched 0.036** 0.010* -0.046** 0.036** 0.010* -0.046**

(0.0179) (0.0053) (0.0231) (0.0179) (0.0053) (0.0231)

Salary matched with

expectation -0.115*** -0.033*** 0.149*** -0.115*** -0.033*** 0.149*** -0.114*** -0.033*** 0.146*** -0.114*** -0.033*** 0.147***

(0.0174) (0.0056) (0.0220) (0.0174) (0.0056) (0.0220) (0.0174) (0.0056) (0.0220) (0.0174) (0.0056) (0.0220)

age 0.003 0.001 -0.004 0.003 0.001 -0.004 0.003 0.001 -0.004 0.003 0.001 -0.004

(0.0048) (0.0014) (0.0062) (0.0048) (0.0014) (0.0062) (0.0048) (0.0014) (0.0062) (0.0048) (0.0014) (0.0062)

age2 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000

(0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001)

Hourly wage -0.001 -0.000 0.001 -0.001 -0.000 0.001 -0.001 -0.000 0.001

(0.0004) (0.0001) (0.0005) (0.0004) (0.0001) (0.0005) (0.0004) (0.0001) (0.0005)

male 0.010 0.003 -0.013 0.010 0.003 -0.013 0.010 0.003 -0.012 0.008 0.002 -0.010

(0.0136) (0.0039) (0.0175) (0.0136) (0.0039) (0.0175) (0.0136) (0.0039) (0.0175) (0.0135) (0.0039) (0.0174)

Nationality -0.032 -0.009 0.041 -0.032 -0.009 0.041 -0.034 -0.010 0.044 -0.035 -0.010 0.045

(0.0257) (0.0075) (0.0331) (0.0257) (0.0075) (0.0331) (0.0257) (0.0075) (0.0331) (0.0257) (0.0075) (0.0331)

Political 0.026 0.007 -0.033 0.026 0.007 -0.033 0.026 0.007 -0.033 0.025 0.007 -0.032

(0.0201) (0.0059) (0.0259) (0.0201) (0.0059) (0.0259) (0.0201) (0.0059) (0.0259) (0.0201) (0.0059) (0.0259)

Married 0.022 0.006 -0.029 0.022 0.006 -0.029 0.021 0.006 -0.027 0.022 0.006 -0.028

62

(0.0199) (0.0058) (0.0257) (0.0199) (0.0058) (0.0257) (0.0199) (0.0058) (0.0256) (0.0199) (0.0058) (0.0256)

Urban -0.038** -0.011* 0.049** -0.038** -0.011* 0.049** -0.040** -0.012** 0.052** -0.042** -0.012** 0.055**

(0.0193) (0.0057) (0.0248) (0.0193) (0.0057) (0.0248) (0.0193) (0.0057) (0.0248) (0.0192) (0.0057) (0.0247)

fulltime -0.031 -0.009 0.040 -0.031 -0.009 0.040 -0.033 -0.010 0.042 -0.032 -0.009 0.041

(0.0203) (0.0059) (0.0261) (0.0203) (0.0059) (0.0261) (0.0203) (0.0059) (0.0261) (0.0202) (0.0059) (0.0261)

State -0.047*** -0.014*** 0.061*** -0.047*** -0.014*** 0.061*** -0.046*** -0.013*** 0.059*** -0.045*** -0.013*** 0.058***

(0.0161) (0.0048) (0.0207) (0.0161) (0.0048) (0.0207) (0.0161) (0.0048) (0.0207) (0.0161) (0.0048) (0.0207)

healthy -0.038 -0.011 0.049 -0.038 -0.011 0.049 -0.037 -0.011 0.048 -0.038 -0.011 0.049

(0.0264) (0.0077) (0.0340) (0.0264) (0.0077) (0.0340) (0.0264) (0.0077) (0.0340) (0.0264) (0.0077) (0.0340)

lower 0.116*** 0.034*** -0.149*** 0.116*** 0.034*** -0.149*** 0.115*** 0.033*** -0.148*** 0.124*** 0.036*** -0.160***

(0.0407) (0.0121) (0.0522) (0.0407) (0.0121) (0.0522) (0.0407) (0.0120) (0.0522) (0.0400) (0.0119) (0.0514)

middle 0.055 0.016 -0.071 0.055 0.016 -0.071 0.055 0.016 -0.071 0.063 0.018 -0.081

(0.0402) (0.0117) (0.0518) (0.0402) (0.0117) (0.0518) (0.0402) (0.0117) (0.0518) (0.0397) (0.0116) (0.0512)

medium 0.011 0.003 -0.015 0.011 0.003 -0.015 0.011 0.003 -0.015 0.011 0.003 -0.014

(0.0171) (0.0050) (0.0221) (0.0171) (0.0050) (0.0221) (0.0171) (0.0050) (0.0221) (0.0171) (0.0050) (0.0221)

large 0.029 0.008 -0.038 0.029 0.008 -0.038 0.029 0.008 -0.038 0.028 0.008 -0.036

(0.0183) (0.0053) (0.0235) (0.0183) (0.0053) (0.0235) (0.0183) (0.0053) (0.0235) (0.0182) (0.0053) (0.0235)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 169.91 168.45 173.85 172.39

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0328 0.0325 0.0335 0.0332

* p<0.10, ** p<0.05, *** p<0.010

63

Table 12 The relationship between overeducation and job satisfaction with working conditions and facilities

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required educational level -0.030*** -0.020*** 0.049*** -0.034*** -0.022*** 0.057*** -0.030*** -0.020*** 0.049*** -0.034*** -0.022*** 0.056***

(0.0068) (0.0044) (0.0110) (0.0067) (0.0044) (0.0108) (0.0068) (0.0044) (0.0110) (0.0066) (0.0044) (0.0108)

Overeducated -0.033** -0.022** 0.055** -0.038*** -0.025*** 0.064*** -0.034** -0.022** 0.056** -0.039*** -0.025*** 0.064***

(0.0149) (0.0098) (0.0246) (0.0148) (0.0098) (0.0244) (0.0149) (0.0098) (0.0245) (0.0148) (0.0098) (0.0244)

Undereducated 0.023* 0.015* -0.038* 0.026** 0.017** -0.044** 0.023* 0.015* -0.038* 0.027** 0.018** -0.044**

(0.0127) (0.0084) (0.0211) (0.0127) (0.0084) (0.0210) (0.0127) (0.0084) (0.0210) (0.0127) (0.0084) (0.0210)

Skill mismatched 0.032** 0.021** -0.053** 0.032** 0.021** -0.053**

(0.0144) (0.0095) (0.0237) (0.0144) (0.0095) (0.0238)

Salary matched with

expectation -0.065*** -0.043*** 0.108*** -0.068*** -0.045*** 0.113*** -0.064*** -0.042*** 0.106*** -0.066*** -0.044*** 0.110***

(0.0142) (0.0093) (0.0231) (0.0141) (0.0093) (0.0230) (0.0141) (0.0093) (0.0231) (0.0141) (0.0093) (0.0231)

age 0.006* 0.004 -0.011* 0.006 0.004 -0.010 0.007* 0.004* -0.011* 0.006 0.004 -0.010

(0.0039) (0.0025) (0.0064) (0.0039) (0.0026) (0.0064) (0.0039) (0.0025) (0.0064) (0.0039) (0.0026) (0.0064)

age2 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000

(0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001)

Hourly wage -0.001*** -0.001*** 0.002*** -0.001*** -0.001*** 0.002***

(0.0005) (0.0003) (0.0008) (0.0005) (0.0003) (0.0008)

male 0.017 0.011 -0.028 0.013 0.009 -0.021 0.017 0.011 -0.028 0.013 0.008 -0.021

(0.0110) (0.0072) (0.0182) (0.0109) (0.0072) (0.0181) (0.0110) (0.0072) (0.0182) (0.0109) (0.0072) (0.0181)

Nationality -0.026 -0.017 0.043 -0.027 -0.018 0.045 -0.028 -0.018 0.047 -0.029 -0.019 0.048

(0.0204) (0.0134) (0.0338) (0.0204) (0.0135) (0.0338) (0.0204) (0.0134) (0.0338) (0.0204) (0.0135) (0.0338)

Political -0.009 -0.006 0.015 -0.011 -0.007 0.018 -0.009 -0.006 0.014 -0.010 -0.007 0.017

(0.0163) (0.0107) (0.0270) (0.0163) (0.0107) (0.0270) (0.0163) (0.0107) (0.0270) (0.0163) (0.0107) (0.0270)

Married -0.005 -0.003 0.008 -0.003 -0.002 0.006 -0.006 -0.004 0.010 -0.004 -0.003 0.007

(0.0160) (0.0105) (0.0265) (0.0160) (0.0105) (0.0265) (0.0160) (0.0105) (0.0264) (0.0160) (0.0105) (0.0265)

64

Urban -0.029* -0.019* 0.049* -0.036** -0.024** 0.060** -0.031** -0.020** 0.051** -0.037** -0.025** 0.062**

(0.0155) (0.0102) (0.0256) (0.0154) (0.0102) (0.0254) (0.0155) (0.0102) (0.0256) (0.0154) (0.0102) (0.0254)

fulltime -0.035** -0.023** 0.057** -0.034** -0.022** 0.056** -0.036** -0.024** 0.060** -0.036** -0.024** 0.059**

(0.0162) (0.0107) (0.0268) (0.0162) (0.0107) (0.0268) (0.0162) (0.0107) (0.0268) (0.0162) (0.0107) (0.0268)

State -0.025* -0.016* 0.041* -0.023* -0.015* 0.037* -0.024* -0.016* 0.039* -0.021 -0.014 0.035

(0.0130) (0.0085) (0.0215) (0.0130) (0.0086) (0.0215) (0.0130) (0.0085) (0.0215) (0.0130) (0.0085) (0.0215)

healthy 0.002 0.001 -0.003 -0.000 -0.000 0.000 0.002 0.001 -0.003 0.000 0.000 -0.001

(0.0211) (0.0139) (0.0350) (0.0212) (0.0139) (0.0351) (0.0211) (0.0139) (0.0350) (0.0211) (0.0139) (0.0351)

lower 0.084** 0.055** -0.140** 0.103*** 0.068*** -0.171*** 0.083** 0.055** -0.138** 0.102*** 0.067*** -0.169***

(0.0333) (0.0218) (0.0548) (0.0329) (0.0217) (0.0541) (0.0332) (0.0218) (0.0548) (0.0328) (0.0216) (0.0540)

middle 0.033 0.021 -0.054 0.047 0.031 -0.078 0.032 0.021 -0.053 0.047 0.031 -0.077

(0.0328) (0.0216) (0.0544) (0.0326) (0.0215) (0.0540) (0.0328) (0.0215) (0.0543) (0.0326) (0.0214) (0.0539)

medium -0.011 -0.007 0.018 -0.012 -0.008 0.020 -0.011 -0.007 0.018 -0.012 -0.008 0.020

(0.0137) (0.0090) (0.0227) (0.0137) (0.0091) (0.0228) (0.0137) (0.0090) (0.0227) (0.0137) (0.0090) (0.0227)

large -0.037** -0.025** 0.062** -0.041*** -0.027*** 0.067*** -0.038** -0.025** 0.062*** -0.041*** -0.027*** 0.068***

(0.0147) (0.0097) (0.0242) (0.0147) (0.0097) (0.0242) (0.0147) (0.0097) (0.0242) (0.0147) (0.0097) (0.0242)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 205.80 195.55 210.71 200.46

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0417 0.0396 0.0427 0.0406

* p<0.10, ** p<0.05, *** p<0.010

65

Table 13 The relationship between overeducation and job satisfaction with the relationship with colleagues

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required educational level -0.006*** -0.024*** 0.030*** -0.006*** -0.026*** 0.033*** -0.006*** -0.024*** 0.030*** -0.006*** -0.026*** 0.033***

(0.0022) (0.0086) (0.0107) (0.0022) (0.0084) (0.0104) (0.0022) (0.0086) (0.0107) (0.0022) (0.0084) (0.0104)

Overeducated -0.008 -0.031* 0.039* -0.008* -0.033* 0.041* -0.008* -0.031* 0.039* -0.008* -0.033* 0.041*

(0.0047) (0.0187) (0.0233) (0.0047) (0.0186) (0.0232) (0.0047) (0.0187) (0.0232) (0.0047) (0.0186) (0.0231)

Undereducated -0.000 -0.001 0.001 0.000 0.001 -0.001 -0.000 -0.000 0.000 0.000 0.001 -0.002

(0.0039) (0.0157) (0.0196) (0.0039) (0.0157) (0.0195) (0.0039) (0.0157) (0.0196) (0.0038) (0.0157) (0.0195)

Skill mismatched 0.006 0.026 -0.032 0.006 0.026 -0.032

(0.0044) (0.0176) (0.0219) (0.0044) (0.0176) (0.0219)

Salary matched with

expectation -0.014*** -0.057*** 0.071*** -0.014*** -0.058*** 0.073*** -0.014*** -0.056*** 0.070*** -0.014*** -0.057*** 0.071***

(0.0047) (0.0181) (0.0226) (0.0047) (0.0181) (0.0226) (0.0047) (0.0182) (0.0226) (0.0047) (0.0181) (0.0226)

age 0.000 0.000 -0.000 -0.000 -0.000 0.000 0.000 0.000 -0.001 0.000 0.000 -0.000

(0.0012) (0.0048) (0.0060) (0.0012) (0.0048) (0.0060) (0.0012) (0.0048) (0.0060) (0.0012) (0.0048) (0.0060)

age2 0.000 0.000 -0.000 0.000 0.000 -0.000 -0.000 -0.000 0.000 0.000 0.000 -0.000

(0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001)

Hourly wage -0.000 -0.001 0.001 -0.000 -0.001 0.001

(0.0001) (0.0006) (0.0007) (0.0001) (0.0006) (0.0007)

male 0.000 0.001 -0.002 -0.000 -0.000 0.000 0.000 0.001 -0.001 -0.000 -0.001 0.001

(0.0034) (0.0136) (0.0170) (0.0033) (0.0135) (0.0169) (0.0033) (0.0136) (0.0170) (0.0033) (0.0135) (0.0169)

Nationality 0.009 0.036 -0.044 0.009 0.035 -0.044 0.008 0.034 -0.042 0.008 0.034 -0.042

(0.0065) (0.0262) (0.0326) (0.0065) (0.0262) (0.0326) (0.0065) (0.0262) (0.0326) (0.0065) (0.0262) (0.0326)

Political -0.016*** -0.067*** 0.083*** -0.017*** -0.067*** 0.084*** -0.016*** -0.067*** 0.083*** -0.017*** -0.068*** 0.084***

(0.0057) (0.0219) (0.0273) (0.0057) (0.0219) (0.0273) (0.0057) (0.0219) (0.0273) (0.0057) (0.0219) (0.0273)

Married 0.005 0.021 -0.026 0.005 0.022 -0.027 0.005 0.020 -0.025 0.005 0.021 -0.026

(0.0050) (0.0200) (0.0250) (0.0050) (0.0200) (0.0250) (0.0049) (0.0200) (0.0250) (0.0049) (0.0200) (0.0249)

66

Urban 0.004 0.017 -0.021 0.004 0.015 -0.018 0.004 0.016 -0.019 0.003 0.013 -0.017

(0.0047) (0.0192) (0.0239) (0.0047) (0.0191) (0.0237) (0.0047) (0.0192) (0.0239) (0.0047) (0.0191) (0.0237)

fulltime -0.009* -0.038* 0.047* -0.009* -0.038* 0.047* -0.010** -0.039** 0.049** -0.010** -0.039** 0.049**

(0.0049) (0.0194) (0.0241) (0.0049) (0.0194) (0.0241) (0.0049) (0.0194) (0.0241) (0.0049) (0.0194) (0.0241)

State -0.005 -0.019 0.024 -0.005 -0.018 0.023 -0.004 -0.018 0.022 -0.004 -0.017 0.021

(0.0041) (0.0165) (0.0205) (0.0041) (0.0165) (0.0205) (0.0041) (0.0165) (0.0205) (0.0041) (0.0165) (0.0205)

healthy 0.002 0.008 -0.010 0.002 0.007 -0.009 0.002 0.008 -0.010 0.002 0.008 -0.009

(0.0064) (0.0262) (0.0327) (0.0065) (0.0263) (0.0327) (0.0064) (0.0262) (0.0326) (0.0064) (0.0262) (0.0327)

lower 0.001 0.005 -0.006 0.003 0.014 -0.017 0.001 0.004 -0.005 0.003 0.013 -0.016

(0.0099) (0.0402) (0.0501) (0.0097) (0.0395) (0.0492) (0.0099) (0.0402) (0.0501) (0.0097) (0.0395) (0.0492)

middle -0.006 -0.025 0.031 -0.004 -0.018 0.023 -0.006 -0.026 0.032 -0.005 -0.019 0.023

(0.0098) (0.0397) (0.0495) (0.0097) (0.0393) (0.0489) (0.0098) (0.0397) (0.0495) (0.0097) (0.0393) (0.0489)

medium -0.012*** -0.047*** 0.058*** -0.012*** -0.048*** 0.059*** -0.012*** -0.047*** 0.059*** -0.012*** -0.048*** 0.060***

(0.0043) (0.0169) (0.0210) (0.0043) (0.0169) (0.0210) (0.0043) (0.0169) (0.0210) (0.0043) (0.0169) (0.0210)

large -0.014*** -0.056*** 0.070*** -0.014*** -0.057*** 0.071*** -0.014*** -0.056*** 0.070*** -0.014*** -0.057*** 0.072***

(0.0047) (0.0182) (0.0226) (0.0047) (0.0181) (0.0226) (0.0047) (0.0182) (0.0226) (0.0047) (0.0181) (0.0225)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 93.57 92.42 95.67 94.53

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0310 0.0307 0.0317 0.0314

* p<0.10, ** p<0.05, *** p<0.010

67

Table 14 The relationship between overeducation and job satisfaction with the relationship with boss

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required educational

level -0.023*** -0.055*** 0.078*** -0.024*** -0.059*** 0.083*** -0.023*** -0.055*** 0.078*** -0.024*** -0.059*** 0.083***

(0.0038) (0.0084) (0.0118) (0.0038) (0.0082) (0.0115) (0.0038) (0.0083) (0.0117) (0.0038) (0.0082) (0.0115)

Overeducated -0.009 -0.022 0.031 -0.011 -0.027 0.038 -0.009 -0.023 0.032 -0.011 -0.027 0.039

(0.0076) (0.0183) (0.0258) (0.0075) (0.0181) (0.0256) (0.0075) (0.0182) (0.0257) (0.0075) (0.0181) (0.0255)

Undereducated 0.006 0.014 -0.020 0.007 0.017 -0.024 0.006 0.015 -0.022 0.008 0.018 -0.026

(0.0065) (0.0157) (0.0222) (0.0065) (0.0156) (0.0221) (0.0064) (0.0156) (0.0221) (0.0064) (0.0156) (0.0220)

Skill mismatched 0.029*** 0.071*** -0.101*** 0.029*** 0.071*** -0.101***

(0.0073) (0.0172) (0.0242) (0.0073) (0.0172) (0.0242)

Salary matched with

expectation -0.039*** -0.094*** 0.132*** -0.039*** -0.096*** 0.135*** -0.037*** -0.090*** 0.127*** -0.038*** -0.092*** 0.130***

(0.0077) (0.0175) (0.0247) (0.0078) (0.0175) (0.0247) (0.0077) (0.0175) (0.0246) (0.0077) (0.0175) (0.0246)

age 0.002 0.004 -0.006 0.002 0.004 -0.006 0.002 0.005 -0.007 0.002 0.005 -0.007

(0.0020) (0.0048) (0.0067) (0.0020) (0.0048) (0.0067) (0.0020) (0.0048) (0.0067) (0.0020) (0.0048) (0.0067)

age2 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000

(0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001)

Hourly wage -0.001** -0.001** 0.002** -0.001** -0.001** 0.002**

(0.0003) (0.0006) (0.0009) (0.0003) (0.0006) (0.0009)

male -0.008 -0.018 0.026 -0.009 -0.022 0.031 -0.008 -0.019 0.027 -0.009* -0.022* 0.032*

(0.0056) (0.0135) (0.0190) (0.0056) (0.0134) (0.0189) (0.0055) (0.0134) (0.0189) (0.0055) (0.0133) (0.0188)

Nationality -0.007 -0.016 0.023 -0.007 -0.017 0.024 -0.009 -0.021 0.030 -0.009 -0.022 0.031

(0.0102) (0.0248) (0.0351) (0.0103) (0.0249) (0.0351) (0.0102) (0.0248) (0.0349) (0.0102) (0.0248) (0.0350)

Political -0.023*** -0.056*** 0.079*** -0.024*** -0.058*** 0.081*** -0.023*** -0.055*** 0.078*** -0.024*** -0.057*** 0.081***

(0.0088) (0.0209) (0.0295) (0.0088) (0.0209) (0.0295) (0.0088) (0.0209) (0.0295) (0.0088) (0.0209) (0.0295)

Married -0.007 -0.017 0.024 -0.007 -0.016 0.022 -0.008 -0.019 0.027 -0.008 -0.018 0.026

(0.0080) (0.0194) (0.0275) (0.0080) (0.0194) (0.0275) (0.0080) (0.0194) (0.0273) (0.0080) (0.0194) (0.0273)

68

Urban -0.005 -0.012 0.017 -0.007 -0.017 0.025 -0.006 -0.015 0.021 -0.008 -0.020 0.029

(0.0078) (0.0190) (0.0268) (0.0078) (0.0189) (0.0266) (0.0078) (0.0190) (0.0268) (0.0078) (0.0188) (0.0266)

fulltime -0.014* -0.034* 0.047* -0.014* -0.033* 0.047* -0.015* -0.038* 0.053* -0.015* -0.037* 0.053*

(0.0081) (0.0197) (0.0277) (0.0081) (0.0196) (0.0277) (0.0081) (0.0196) (0.0277) (0.0081) (0.0196) (0.0276)

State -0.019*** -0.046*** 0.065*** -0.018*** -0.044*** 0.062*** -0.017*** -0.043*** 0.060*** -0.017** -0.041** 0.057**

(0.0067) (0.0160) (0.0226) (0.0067) (0.0160) (0.0226) (0.0067) (0.0160) (0.0225) (0.0067) (0.0160) (0.0225)

healthy 0.009 0.023 -0.032 0.008 0.020 -0.029 0.010 0.025 -0.035 0.009 0.022 -0.031

(0.0108) (0.0262) (0.0370) (0.0108) (0.0262) (0.0370) (0.0108) (0.0261) (0.0369) (0.0108) (0.0261) (0.0368)

lower 0.036** 0.088** -0.124** 0.043** 0.105** -0.148** 0.035** 0.085** -0.119** 0.042** 0.102** -0.143**

(0.0177) (0.0425) (0.0600) (0.0176) (0.0420) (0.0592) (0.0176) (0.0424) (0.0598) (0.0175) (0.0418) (0.0590)

middle 0.024 0.059 -0.083 0.030* 0.072* -0.102* 0.024 0.057 -0.081 0.029* 0.071* -0.100*

(0.0175) (0.0422) (0.0595) (0.0174) (0.0418) (0.0591) (0.0173) (0.0420) (0.0593) (0.0173) (0.0417) (0.0588)

medium 0.010 0.023 -0.033 0.009 0.022 -0.031 0.009 0.023 -0.033 0.009 0.022 -0.031

(0.0070) (0.0170) (0.0240) (0.0070) (0.0170) (0.0240) (0.0070) (0.0170) (0.0239) (0.0070) (0.0170) (0.0239)

large 0.013* 0.032* -0.045* 0.012 0.029 -0.041 0.013* 0.032* -0.045* 0.012 0.029 -0.041

(0.0075) (0.0181) (0.0255) (0.0075) (0.0180) (0.0255) (0.0075) (0.0180) (0.0254) (0.0074) (0.0180) (0.0254)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 183.73 178.63 200.67 195.49

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0445 0.0433 0.0486 0.0474

* p<0.10, ** p<0.05, *** p<0.010

69

Table 15 The relationship between overeducation and job satisfaction with commuting distance to job location

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required educational

level -0.010 -0.008 0.018 -0.013** -0.011** 0.024** -0.009 -0.008 0.018 -0.013** -0.011** 0.024**

(0.0063) (0.0055) (0.0119) (0.0062) (0.0054) (0.0116) (0.0063) (0.0055) (0.0119) (0.0062) (0.0054) (0.0116)

Overeducated -0.006 -0.006 0.012 -0.010 -0.009 0.019 -0.006 -0.006 0.012 -0.010 -0.009 0.019

(0.0139) (0.0121) (0.0261) (0.0138) (0.0121) (0.0259) (0.0139) (0.0121) (0.0260) (0.0138) (0.0121) (0.0259)

Undereducated 0.008 0.007 -0.014 0.010 0.009 -0.019 0.008 0.007 -0.015 0.011 0.009 -0.020

(0.0119) (0.0104) (0.0224) (0.0119) (0.0104) (0.0223) (0.0119) (0.0104) (0.0223) (0.0119) (0.0104) (0.0223)

Skill mismatched 0.032** 0.028** -0.060** 0.032** 0.028** -0.060**

(0.0133) (0.0116) (0.0247) (0.0133) (0.0116) (0.0247)

Salary matched with

expectation -0.033** -0.029** 0.063** -0.035*** -0.031*** 0.066*** -0.032** -0.028** 0.059** -0.034** -0.029** 0.063**

(0.0133) (0.0115) (0.0247) (0.0133) (0.0115) (0.0247) (0.0133) (0.0115) (0.0247) (0.0133) (0.0115) (0.0247)

age 0.006 0.005 -0.010 0.005 0.005 -0.010 0.006 0.005 -0.011 0.005 0.005 -0.010

(0.0037) (0.0032) (0.0068) (0.0037) (0.0032) (0.0068) (0.0037) (0.0032) (0.0068) (0.0037) (0.0032) (0.0068)

age2 -0.000* -0.000* 0.000* -0.000 -0.000 0.000 -0.000* -0.000* 0.000* -0.000* -0.000* 0.000*

(0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001)

Hourly wage -0.001** -0.001** 0.002** -0.001** -0.001** 0.002**

(0.0005) (0.0004) (0.0009) (0.0005) (0.0004) (0.0009)

male 0.016 0.014 -0.031 0.013 0.012 -0.025 0.016 0.014 -0.030 0.013 0.011 -0.024

(0.0103) (0.0090) (0.0192) (0.0102) (0.0089) (0.0191) (0.0103) (0.0090) (0.0192) (0.0102) (0.0089) (0.0191)

Nationality 0.014 0.012 -0.026 0.013 0.011 -0.025 0.012 0.010 -0.022 0.011 0.010 -0.020

(0.0195) (0.0170) (0.0364) (0.0195) (0.0170) (0.0365) (0.0194) (0.0170) (0.0364) (0.0195) (0.0170) (0.0365)

Political -0.050*** -0.044*** 0.094*** -0.051*** -0.045*** 0.096*** -0.050*** -0.044*** 0.093*** -0.051*** -0.044*** 0.095***

(0.0159) (0.0138) (0.0295) (0.0159) (0.0138) (0.0295) (0.0159) (0.0138) (0.0294) (0.0159) (0.0138) (0.0294)

Married 0.005 0.005 -0.010 0.006 0.006 -0.012 0.004 0.004 -0.008 0.005 0.005 -0.010

(0.0149) (0.0130) (0.0279) (0.0149) (0.0130) (0.0279) (0.0149) (0.0130) (0.0279) (0.0149) (0.0130) (0.0279)

70

Urban 0.012 0.010 -0.022 0.007 0.006 -0.013 0.010 0.009 -0.020 0.006 0.005 -0.011

(0.0147) (0.0128) (0.0275) (0.0146) (0.0127) (0.0273) (0.0147) (0.0128) (0.0275) (0.0146) (0.0127) (0.0273)

fulltime -0.021 -0.018 0.039 -0.020 -0.018 0.038 -0.023 -0.020 0.042 -0.022 -0.019 0.042

(0.0152) (0.0132) (0.0283) (0.0151) (0.0132) (0.0283) (0.0152) (0.0132) (0.0283) (0.0151) (0.0132) (0.0283)

State -0.009 -0.008 0.017 -0.007 -0.006 0.014 -0.007 -0.007 0.014 -0.006 -0.005 0.011

(0.0121) (0.0106) (0.0227) (0.0121) (0.0106) (0.0227) (0.0121) (0.0106) (0.0227) (0.0121) (0.0106) (0.0227)

healthy 0.008 0.007 -0.015 0.007 0.006 -0.013 0.008 0.007 -0.016 0.007 0.006 -0.013

(0.0201) (0.0176) (0.0377) (0.0201) (0.0176) (0.0377) (0.0201) (0.0175) (0.0376) (0.0201) (0.0176) (0.0376)

lower 0.077** 0.067** -0.144** 0.092*** 0.080*** -0.172*** 0.076** 0.067** -0.143** 0.091*** 0.079*** -0.170***

(0.0320) (0.0278) (0.0596) (0.0316) (0.0274) (0.0587) (0.0320) (0.0278) (0.0596) (0.0316) (0.0274) (0.0586)

middle 0.037 0.032 -0.069 0.048 0.042 -0.090 0.037 0.032 -0.069 0.048 0.042 -0.090

(0.0316) (0.0276) (0.0591) (0.0313) (0.0273) (0.0586) (0.0316) (0.0276) (0.0591) (0.0313) (0.0273) (0.0586)

medium 0.018 0.015 -0.033 0.017 0.014 -0.031 0.018 0.015 -0.033 0.017 0.014 -0.031

(0.0129) (0.0112) (0.0240) (0.0129) (0.0112) (0.0241) (0.0128) (0.0112) (0.0240) (0.0128) (0.0112) (0.0240)

large -0.005 -0.004 0.009 -0.007 -0.006 0.013 -0.005 -0.004 0.009 -0.007 -0.006 0.014

(0.0138) (0.0120) (0.0258) (0.0138) (0.0120) (0.0258) (0.0138) (0.0120) (0.0258) (0.0137) (0.0120) (0.0257)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 84.58 78.34 90.44 84.24

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0187 0.0173 0.0200 0.0186

* p<0.10, ** p<0.05, *** p<0.01

71

Table 16 The relationship between overeducation and job satisfaction with housing benefits

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Required

educational level 0.019* -0.002* -0.016* 0.010 -0.001 -0.008 0.019* -0.002* -0.016* 0.010 -0.001 -0.009

(0.0108) (0.0014) (0.0094) (0.0106) (0.0014) (0.0092) (0.0108) (0.0014) (0.0094) (0.0106) (0.0014) (0.0092)

Overeducated -0.064*** 0.008** 0.056*** -0.075*** 0.010*** 0.065*** -0.065*** 0.008** 0.056*** -0.075*** 0.010*** 0.065***

(0.0238) (0.0033) (0.0207) (0.0236) (0.0033) (0.0206) (0.0238) (0.0033) (0.0207) (0.0236) (0.0033) (0.0206)

Undereducated -0.015 0.002 0.013 -0.008 0.001 0.007 -0.015 0.002 0.013 -0.007 0.001 0.007

(0.0208) (0.0027) (0.0181) (0.0208) (0.0027) (0.0181) (0.0208) (0.0027) (0.0181) (0.0207) (0.0027) (0.0181)

Skill mismatched 0.052** -0.007** -0.045** 0.052** -0.007** -0.045**

(0.0235) (0.0032) (0.0205) (0.0236) (0.0032) (0.0206)

Salary matched

with expectation -0.088*** 0.012*** 0.077*** -0.093*** 0.012*** 0.081*** -0.086*** 0.011*** 0.075*** -0.090*** 0.012*** 0.079***

(0.0221) (0.0033) (0.0192) (0.0221) (0.0033) (0.0193) (0.0221) (0.0033) (0.0192) (0.0221) (0.0033) (0.0193)

age 0.011* -0.001* -0.009* 0.010 -0.001 -0.008 0.011* -0.001* -0.010* 0.010 -0.001 -0.009

(0.0063) (0.0008) (0.0055) (0.0063) (0.0008) (0.0055) (0.0063) (0.0008) (0.0055) (0.0063) (0.0008) (0.0055)

age2 -0.000* 0.000* 0.000* -0.000 0.000 0.000 -0.000* 0.000* 0.000* -0.000* 0.000* 0.000*

(0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001)

Hourly wage -0.003*** 0.000*** 0.002*** -0.003*** 0.000*** 0.002***

(0.0007) (0.0001) (0.0006) (0.0007) (0.0001) (0.0006)

male 0.022 -0.003 -0.019 0.013 -0.002 -0.012 0.021 -0.003 -0.019 0.013 -0.002 -0.011

(0.0178) (0.0023) (0.0155) (0.0177) (0.0023) (0.0154) (0.0178) (0.0023) (0.0154) (0.0177) (0.0023) (0.0154)

Nationality -0.049 0.006 0.043 -0.051 0.006 0.044 -0.052 0.007 0.045 -0.054 0.007 0.047

(0.0336) (0.0045) (0.0292) (0.0337) (0.0044) (0.0294) (0.0336) (0.0045) (0.0292) (0.0337) (0.0044) (0.0294)

Political -0.010 0.001 0.009 -0.015 0.002 0.013 -0.010 0.001 0.009 -0.015 0.002 0.013

(0.0261) (0.0034) (0.0227) (0.0261) (0.0034) (0.0228) (0.0261) (0.0034) (0.0227) (0.0261) (0.0034) (0.0228)

Married -0.081*** 0.011*** 0.070*** -0.078*** 0.010*** 0.068*** -0.082*** 0.011*** 0.071*** -0.079*** 0.010*** 0.069***

(0.0260) (0.0037) (0.0226) (0.0260) (0.0036) (0.0227) (0.0260) (0.0037) (0.0226) (0.0260) (0.0036) (0.0227)

72

Urban 0.004 -0.000 -0.003 -0.009 0.001 0.008 0.001 -0.000 -0.001 -0.012 0.002 0.010

(0.0255) (0.0033) (0.0222) (0.0253) (0.0033) (0.0221) (0.0255) (0.0033) (0.0222) (0.0253) (0.0033) (0.0221)

fulltime -0.080*** 0.010*** 0.070*** -0.078*** 0.010*** 0.068*** -0.083*** 0.011*** 0.073*** -0.081*** 0.010*** 0.070***

(0.0268) (0.0037) (0.0234) (0.0268) (0.0037) (0.0234) (0.0268) (0.0038) (0.0234) (0.0268) (0.0037) (0.0234)

State -0.081*** 0.011*** 0.070*** -0.075*** 0.010*** 0.066*** -0.079*** 0.010*** 0.068*** -0.073*** 0.009*** 0.064***

(0.0209) (0.0031) (0.0183) (0.0209) (0.0030) (0.0183) (0.0209) (0.0031) (0.0183) (0.0210) (0.0030) (0.0183)

healthy -0.012 0.002 0.011 -0.015 0.002 0.013 -0.011 0.001 0.010 -0.014 0.002 0.012

(0.0345) (0.0045) (0.0300) (0.0346) (0.0044) (0.0302) (0.0345) (0.0045) (0.0300) (0.0346) (0.0044) (0.0301)

lower 0.165*** -0.021*** -0.144*** 0.204*** -0.026*** -0.178*** 0.163*** -0.021*** -0.142*** 0.202*** -0.026*** -0.176***

(0.0520) (0.0075) (0.0453) (0.0512) (0.0076) (0.0447) (0.0520) (0.0074) (0.0452) (0.0512) (0.0076) (0.0446)

middle 0.087* -0.011 -0.076* 0.118** -0.015** -0.103** 0.087* -0.011 -0.075* 0.117** -0.015** -0.102**

(0.0515) (0.0069) (0.0447) (0.0510) (0.0069) (0.0444) (0.0514) (0.0069) (0.0447) (0.0509) (0.0069) (0.0444)

medium 0.047** -0.006** -0.041** 0.044* -0.006* -0.038* 0.047** -0.006** -0.041** 0.044* -0.006* -0.038*

(0.0224) (0.0030) (0.0195) (0.0224) (0.0030) (0.0196) (0.0224) (0.0030) (0.0195) (0.0224) (0.0030) (0.0196)

large 0.025 -0.003 -0.021 0.017 -0.002 -0.015 0.024 -0.003 -0.021 0.017 -0.002 -0.015

(0.0238) (0.0031) (0.0207) (0.0238) (0.0031) (0.0207) (0.0238) (0.0031) (0.0207) (0.0238) (0.0031) (0.0207)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 147.77 130.88 152.68 135.76

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0279 0.0247 0.0288 0.0257

* p<0.10, ** p<0.05, *** p<0.01

73

Appendix A1

Table A 1 Highest educational level

Highest educational level Freq Percent

Primary school or less 370 15.23

Junior high school 728 29.96

Senior high school 770 31.69

College level 312 12.84

University 231 9.51

Master’s or higher 19 0.78

Total 2430 10.00

74

Table B1 The relationship between overeducation and overall job satisfaction

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of schooling -0.007*** -0.008*** 0.015*** -0.009*** -0.009*** 0.018*** -0.007*** -0.008*** 0.015*** -0.009*** -0.009*** 0.018***

(0.0021) (0.0022) (0.0042) (0.0020) (0.0022) (0.0041) (0.0021) (0.0022) (0.0042) (0.0020) (0.0022) (0.0041)

Overeducated 0.008 0.008 -0.016 0.008 0.009 -0.017 0.008 0.008 -0.016 0.008 0.009 -0.017

(0.0125) (0.0135) (0.0260) (0.0126) (0.0135) (0.0261) (0.0125) (0.0135) (0.0260) (0.0125) (0.0135) (0.0260)

Undereducated -0.002 -0.002 0.005 -0.004 -0.004 0.008 -0.002 -0.002 0.004 -0.003 -0.004 0.007

(0.0106) (0.0113) (0.0219) (0.0106) (0.0114) (0.0219) (0.0105) (0.0113) (0.0219) (0.0105) (0.0113) (0.0219)

Skill mismatched 0.033*** 0.036*** -0.069*** 0.033*** 0.036*** -0.069***

(0.0116) (0.0125) (0.0239) (0.0116) (0.0125) (0.0239)

Salary matched with

expectation -0.087*** -0.094*** 0.181*** -0.089*** -0.096*** 0.184*** -0.085*** -0.092*** 0.177*** -0.087*** -0.094*** 0.180***

(0.0122) (0.0124) (0.0235) (0.0122) (0.0124) (0.0235) (0.0121) (0.0124) (0.0236) (0.0121) (0.0124) (0.0235)

Hourly wage 0.008*** 0.009*** -0.017*** 0.009*** 0.009*** -0.018***

(0.0032) (0.0034) (0.0065) (0.0032) (0.0034) (0.0065)

age -0.000*** -0.000*** 0.000*** 0.008** 0.009** -0.016** -0.000*** -0.000*** 0.000*** 0.008*** 0.009*** -0.017***

(0.0000) (0.0000) (0.0001) (0.0032) (0.0034) (0.0065) (0.0000) (0.0000) (0.0001) (0.0032) (0.0034) (0.0065)

age2 -0.001*** -0.001*** 0.002*** -0.000*** -0.000*** 0.000*** -0.001*** -0.001*** 0.002*** -0.000*** -0.000*** 0.000***

(0.0004) (0.0004) (0.0008) (0.0000) (0.0000) (0.0001) (0.0004) (0.0004) (0.0008) (0.0000) (0.0000) (0.0001)

Male 0.011 0.012 -0.024 0.009 0.009 -0.018 0.011 0.012 -0.023 0.008 0.009 -0.017

(0.0089) (0.0095) (0.0183) (0.0088) (0.0095) (0.0183) (0.0088) (0.0095) (0.0183) (0.0088) (0.0095) (0.0182)

Nationality 0.011 0.012 -0.023 0.010 0.011 -0.022 0.009 0.009 -0.018 0.008 0.009 -0.017

(0.0166) (0.0178) (0.0344) (0.0166) (0.0179) (0.0344) (0.0165) (0.0178) (0.0343) (0.0166) (0.0178) (0.0344)

Political -0.016 -0.017 0.033 -0.017 -0.018 0.035 -0.015 -0.016 0.031 -0.016 -0.018 0.034

(0.0134) (0.0144) (0.0278) (0.0134) (0.0144) (0.0278) (0.0134) (0.0144) (0.0277) (0.0134) (0.0144) (0.0277)

Married -0.022* -0.024* 0.047* -0.022* -0.023* 0.045* -0.023* -0.025* 0.049* -0.023* -0.024* 0.047*

(0.0129) (0.0139) (0.0267) (0.0129) (0.0139) (0.0267) (0.0129) (0.0138) (0.0266) (0.0129) (0.0139) (0.0267)

75

Urban -0.009 -0.010 0.019 -0.013 -0.014 0.027 -0.010 -0.011 0.022 -0.014 -0.015 0.030

(0.0126) (0.0136) (0.0262) (0.0126) (0.0135) (0.0261) (0.0126) (0.0136) (0.0262) (0.0125) (0.0135) (0.0260)

Full time -0.029** -0.031** 0.061** -0.029** -0.031** 0.059** -0.031** -0.033** 0.064** -0.030** -0.033** 0.063**

(0.0131) (0.0141) (0.0271) (0.0131) (0.0141) (0.0271) (0.0131) (0.0141) (0.0271) (0.0131) (0.0141) (0.0271)

State -0.038*** -0.041*** 0.080*** -0.037*** -0.039*** 0.076*** -0.037*** -0.040*** 0.076*** -0.035*** -0.038*** 0.073***

(0.0106) (0.0113) (0.0217) (0.0106) (0.0113) (0.0217) (0.0106) (0.0113) (0.0217) (0.0106) (0.0113) (0.0217)

Healthy -0.007 -0.007 0.014 -0.008 -0.008 0.016 -0.006 -0.006 0.012 -0.007 -0.008 0.015

(0.0170) (0.0183) (0.0354) (0.0171) (0.0184) (0.0354) (0.0170) (0.0183) (0.0353) (0.0170) (0.0184) (0.0354)

Lower 0.118*** 0.127*** -0.244*** 0.132*** 0.142*** -0.273*** 0.116*** 0.125*** -0.241*** 0.130*** 0.140*** -0.270***

(0.0288) (0.0304) (0.0584) (0.0285) (0.0301) (0.0576) (0.0287) (0.0304) (0.0583) (0.0285) (0.0300) (0.0575)

Middle 0.065** 0.070** -0.134** 0.076*** 0.082*** -0.157*** 0.064** 0.069** -0.133** 0.075*** 0.081*** -0.156***

(0.0283) (0.0302) (0.0583) (0.0282) (0.0300) (0.0579) (0.0282) (0.0302) (0.0582) (0.0281) (0.0300) (0.0578)

Medium 0.024** 0.025** -0.049** 0.023** 0.025** -0.048** 0.024** 0.026** -0.049** 0.023** 0.025** -0.048**

(0.0112) (0.0120) (0.0232) (0.0112) (0.0121) (0.0232) (0.0112) (0.0120) (0.0232) (0.0112) (0.0121) (0.0232)

Large 0.023* 0.024* -0.047* 0.020* 0.022* -0.042* 0.022* 0.024* -0.047* 0.020* 0.022* -0.042*

(0.0119) (0.0128) (0.0246) (0.0119) (0.0128) (0.0246) (0.0119) (0.0128) (0.0246) (0.0119) (0.0128) (0.0246)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 235.97 228.32 244.20 236.55

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0504 0.0487 0.0521 0.0505

* p<0.10, ** p<0.05, *** p<0.010

76

Table B2. The relationship between overeducation and job satisfaction with pay

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of

schooling -0.003 -0.001 0.004 -0.006* -0.002* 0.008* -0.003 -0.001 0.004 -0.006* -0.002* 0.008*

(0.0031) (0.0011) (0.0042) (0.0031) (0.0010) (0.0041) (0.0031) (0.0011) (0.0042) (0.0031) (0.0010) (0.0041)

Overeducated 0.022 0.007 -0.029 0.022 0.008 -0.030 0.022 0.007 -0.029 0.022 0.008 -0.030

(0.0191) (0.0065) (0.0255) (0.0191) (0.0065) (0.0256) (0.0191) (0.0065) (0.0255) (0.0191) (0.0065) (0.0256)

Undereducated 0.004 0.001 -0.005 0.001 0.000 -0.002 0.004 0.001 -0.006 0.002 0.001 -0.002

(0.0162) (0.0055) (0.0217) (0.0162) (0.0055) (0.0218) (0.0162) (0.0055) (0.0217) (0.0162) (0.0055) (0.0217)

Skill mismatch 0.034* 0.011* -0.046* 0.034* 0.012* -0.046*

(0.0178) (0.0061) (0.0238) (0.0179) (0.0061) (0.0239)

Salary matched with

expectation

-0.144*** -0.048*** 0.192*** -0.147*** -0.050*** 0.197*** -0.142*** -0.048*** 0.190*** -0.145*** -0.049*** 0.194***

(0.0178) (0.0065) (0.0230) (0.0178) (0.0065) (0.0230) (0.0178) (0.0065) (0.0230) (0.0178) (0.0065) (0.0230)

age 0.007 0.002 -0.009 0.006 0.002 -0.008 0.007 0.002 -0.010 0.006 0.002 -0.008

(0.0048) (0.0016) (0.0065) (0.0048) (0.0016) (0.0065) (0.0048) (0.0016) (0.0064) (0.0048) (0.0016) (0.0065)

age2 -0.000* -0.000* 0.000* -0.000 -0.000 0.000 -0.000* -0.000* 0.000* -0.000 -0.000 0.000

(0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001)

Hourly wage -0.002*** -0.001*** 0.003*** -0.002*** -0.001*** 0.003***

(0.0006) (0.0002) (0.0008) (0.0006) (0.0002) (0.0008)

male 0.006 0.002 -0.008 0.000 0.000 -0.001 0.006 0.002 -0.008 0.000 0.000 -0.000

(0.0136) (0.0046) (0.0182) (0.0135) (0.0046) (0.0181) (0.0136) (0.0046) (0.0181) (0.0135) (0.0046) (0.0181)

Nationality -0.027 -0.009 0.037 -0.029 -0.010 0.039 -0.029 -0.010 0.039 -0.031 -0.010 0.041

(0.0253) (0.0086) (0.0338) (0.0253) (0.0086) (0.0339) (0.0253) (0.0086) (0.0338) (0.0253) (0.0086) (0.0339)

Political 0.004 0.001 -0.005 0.001 0.000 -0.002 0.004 0.001 -0.006 0.002 0.001 -0.002

(0.0203) (0.0068) (0.0271) (0.0203) (0.0069) (0.0272) (0.0203) (0.0068) (0.0271) (0.0203) (0.0069) (0.0271)

Married -0.009 -0.003 0.011 -0.007 -0.002 0.009 -0.010 -0.003 0.013 -0.008 -0.003 0.011

77

(0.0198) (0.0067) (0.0265) (0.0198) (0.0067) (0.0266) (0.0198) (0.0067) (0.0265) (0.0198) (0.0067) (0.0266)

Urban -0.025 -0.008 0.033 -0.033* -0.011* 0.045* -0.026 -0.009 0.035 -0.035* -0.012* 0.047*

(0.0195) (0.0066) (0.0260) (0.0194) (0.0066) (0.0259) (0.0195) (0.0066) (0.0260) (0.0194) (0.0066) (0.0259)

fulltime -0.044** -0.015** 0.059** -0.043** -0.015** 0.058** -0.046** -0.016** 0.062** -0.045** -0.015** 0.060**

(0.0204) (0.0069) (0.0272) (0.0204) (0.0070) (0.0272) (0.0204) (0.0070) (0.0272) (0.0204) (0.0070) (0.0272)

State -0.012 -0.004 0.016 -0.008 -0.003 0.011 -0.010 -0.004 0.014 -0.007 -0.002 0.009

(0.0161) (0.0054) (0.0216) (0.0161) (0.0055) (0.0216) (0.0162) (0.0054) (0.0216) (0.0161) (0.0055) (0.0216)

healthy -0.061** -0.021** 0.082** -0.064** -0.022** 0.086** -0.060** -0.020** 0.081** -0.063** -0.021** 0.084**

(0.0264) (0.0090) (0.0352) (0.0264) (0.0091) (0.0353) (0.0263) (0.0090) (0.0352) (0.0264) (0.0091) (0.0353)

lower 0.190*** 0.064*** -0.254*** 0.220*** 0.074*** -0.294*** 0.189*** 0.064*** -0.253*** 0.219*** 0.074*** -0.293***

(0.0433) (0.0149) (0.0573) (0.0429) (0.0149) (0.0566) (0.0433) (0.0149) (0.0573) (0.0429) (0.0149) (0.0566)

middle 0.113*** 0.038*** -0.152*** 0.137*** 0.046*** -0.184*** 0.113*** 0.038*** -0.151*** 0.137*** 0.046*** -0.183***

(0.0429) (0.0145) (0.0571) (0.0427) (0.0145) (0.0567) (0.0429) (0.0145) (0.0571) (0.0427) (0.0145) (0.0567)

medium 0.020 0.007 -0.027 0.019 0.006 -0.025 0.020 0.007 -0.027 0.019 0.006 -0.025

(0.0173) (0.0058) (0.0231) (0.0173) (0.0059) (0.0232) (0.0173) (0.0058) (0.0231) (0.0173) (0.0059) (0.0232)

large 0.029 0.010 -0.039 0.024 0.008 -0.032 0.029 0.010 -0.039 0.024 0.008 -0.032

(0.0183) (0.0062) (0.0245) (0.0183) (0.0062) (0.0245) (0.0183) (0.0062) (0.0245) (0.0183) (0.0062) (0.0245)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 185.36 170.85 189.00 174.54

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0360 0.0332 0.0367 0.0339

* p<0.10, ** p<0.05, *** p<0.010

78

Table B3 The relationship between overeducation and job satisfaction with welfare

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of schooling

-0.009*** -0.002*** 0.011*** -0.012*** -0.002*** 0.015*** -0.009*** -0.002*** 0.011*** -0.012*** -0.002*** 0.015***

(0.0033) (0.0007) (0.0039) (0.0032) (0.0007) (0.0038) (0.0033) (0.0007) (0.0039) (0.0032) (0.0007) (0.0038)

Overeducated 0.029 0.005 -0.034 0.030 0.006 -0.035 0.029 0.005 -0.034 0.030 0.006 -0.035

(0.0202) (0.0039) (0.0241) (0.0202) (0.0040) (0.0241) (0.0202) (0.0039) (0.0241) (0.0202) (0.0040) (0.0241)

Undereducated 0.028 0.005 -0.033 0.025 0.005 -0.030 0.028* 0.005 -0.034* 0.025 0.005 -0.030

(0.0172) (0.0033) (0.0204) (0.0172) (0.0034) (0.0205) (0.0171) (0.0034) (0.0204) (0.0171) (0.0034) (0.0205)

Skill mismatched 0.039** 0.008** -0.047** 0.040** 0.008** -0.047**

(0.0188) (0.0037) (0.0224) (0.0188) (0.0038) (0.0225)

Salary matched

with expectation -0.113*** -0.022*** 0.135*** -0.116*** -0.023*** 0.139*** -0.111*** -0.021*** 0.132*** -0.114*** -0.022*** 0.137***

(0.0183) (0.0042) (0.0216) (0.0183) (0.0043) (0.0216) (0.0183) (0.0042) (0.0216) (0.0183) (0.0043) (0.0216)

age 0.005 0.001 -0.006 0.004 0.001 -0.005 0.006 0.001 -0.007 0.004 0.001 -0.005

(0.0051) (0.0010) (0.0061) (0.0051) (0.0010) (0.0061) (0.0051) (0.0010) (0.0061) (0.0051) (0.0010) (0.0061)

age2 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000

(0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001)

Hourly wage -0.002*** -0.000*** 0.003*** -0.002*** -0.000*** 0.003***

(0.0006) (0.0001) (0.0007) (0.0006) (0.0001) (0.0007)

male 0.015 0.003 -0.017 0.009 0.002 -0.010 0.014 0.003 -0.017 0.008 0.002 -0.010

(0.0144) (0.0028) (0.0171) (0.0143) (0.0028) (0.0171) (0.0144) (0.0028) (0.0171) (0.0143) (0.0028) (0.0171)

Nationality -0.062** -0.012** 0.074** -0.063** -0.012** 0.075** -0.064** -0.012** 0.077** -0.065** -0.013** 0.078**

(0.0271) (0.0054) (0.0323) (0.0272) (0.0055) (0.0325) (0.0271) (0.0054) (0.0323) (0.0271) (0.0055) (0.0324)

Political 0.003 0.001 -0.004 0.001 0.000 -0.001 0.004 0.001 -0.005 0.001 0.000 -0.002

(0.0215) (0.0041) (0.0256) (0.0215) (0.0042) (0.0256) (0.0215) (0.0041) (0.0256) (0.0214) (0.0042) (0.0256)

Married -0.004 -0.001 0.005 -0.002 -0.000 0.002 -0.005 -0.001 0.006 -0.003 -0.001 0.004

79

(0.0210) (0.0040) (0.0250) (0.0210) (0.0041) (0.0251) (0.0210) (0.0040) (0.0250) (0.0210) (0.0041) (0.0251)

Urban -0.068*** -0.013*** 0.081*** -0.077*** -0.015*** 0.092*** -0.069*** -0.013*** 0.082*** -0.079*** -0.015*** 0.094***

(0.0205) (0.0043) (0.0245) (0.0204) (0.0044) (0.0244) (0.0205) (0.0043) (0.0245) (0.0204) (0.0044) (0.0244)

fulltime -0.065*** -0.012*** 0.077*** -0.063*** -0.012*** 0.076*** -0.067*** -0.013*** 0.080*** -0.065*** -0.013*** 0.078***

(0.0216) (0.0045) (0.0258) (0.0216) (0.0045) (0.0258) (0.0216) (0.0045) (0.0258) (0.0216) (0.0045) (0.0258)

State -0.053*** -0.010*** 0.063*** -0.049*** -0.009*** 0.058*** -0.051*** -0.010*** 0.061*** -0.047*** -0.009*** 0.056***

(0.0170) (0.0035) (0.0203) (0.0170) (0.0035) (0.0203) (0.0171) (0.0034) (0.0203) (0.0170) (0.0035) (0.0203)

Healthy -0.024 -0.005 0.029 -0.027 -0.005 0.033 -0.023 -0.004 0.028 -0.027 -0.005 0.032

(0.0281) (0.0054) (0.0335) (0.0282) (0.0055) (0.0337) (0.0281) (0.0054) (0.0335) (0.0282) (0.0055) (0.0336)

Lower 0.182*** 0.035*** -0.216*** 0.212*** 0.041*** -0.253*** 0.181*** 0.035*** -0.215*** 0.211*** 0.041*** -0.252***

(0.0442) (0.0092) (0.0523) (0.0436) (0.0094) (0.0516) (0.0442) (0.0092) (0.0523) (0.0436) (0.0094) (0.0516)

Middle 0.110** 0.021** -0.131** 0.134*** 0.026*** -0.160*** 0.109** 0.021** -0.130** 0.133*** 0.026*** -0.159***

(0.0438) (0.0086) (0.0520) (0.0435) (0.0088) (0.0516) (0.0438) (0.0086) (0.0520) (0.0435) (0.0088) (0.0516)

Medium -0.005 -0.001 0.006 -0.007 -0.001 0.008 -0.005 -0.001 0.006 -0.007 -0.001 0.008

(0.0182) (0.0035) (0.0216) (0.0182) (0.0035) (0.0217) (0.0181) (0.0035) (0.0216) (0.0182) (0.0035) (0.0217)

Large -0.033* -0.006* 0.039* -0.038** -0.007* 0.045** -0.033* -0.006* 0.039* -0.038** -0.007* 0.045**

(0.0193) (0.0038) (0.0230) (0.0193) (0.0038) (0.0230) (0.0193) (0.0038) (0.0230) (0.0192) (0.0038) (0.0230)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 264.90 249.44 269.29 253.87

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0503 0.0474 0.0512 0.0482

* p<0.10, ** p<0.05, *** p<0.010

80

Table B4 The relationship between overeducation and job satisfaction with workload

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of

schooling -0.011*** -0.003*** 0.014*** -0.012*** -0.003*** 0.015*** -0.011*** -0.003*** 0.014*** -0.012*** -0.003*** 0.015***

(0.0031) (0.0009) (0.0040) (0.0030) (0.0009) (0.0039) (0.0031) (0.0009) (0.0040) (0.0030) (0.0009) (0.0039)

Overeducated -0.002 -0.001 0.002 -0.001 -0.000 0.002 -0.002 -0.001 0.003 -0.002 -0.000 0.002

(0.0191) (0.0055) (0.0246) (0.0191) (0.0055) (0.0246) (0.0191) (0.0055) (0.0246) (0.0191) (0.0055) (0.0246)

Undereducated 0.013 0.004 -0.017 0.013 0.004 -0.017 0.014 0.004 -0.018 0.013 0.004 -0.017

(0.0162) (0.0047) (0.0208) (0.0161) (0.0047) (0.0208) (0.0161) (0.0047) (0.0208) (0.0161) (0.0047) (0.0208)

Skill mismatched 0.037** 0.011** -0.048** 0.037** 0.011** -0.048**

(0.0179) (0.0053) (0.0231) (0.0179) (0.0053) (0.0231)

Salary matched with expectation

-0.114*** -0.033*** 0.147*** -0.115*** -0.033*** 0.148*** -0.113*** -0.033*** 0.145*** -0.113*** -0.033*** 0.146***

(0.0174) (0.0056) (0.0220) (0.0174) (0.0056) (0.0220) (0.0174) (0.0056) (0.0220) (0.0174) (0.0056) (0.0220)

age 0.002 0.001 -0.003 0.002 0.001 -0.003 0.003 0.001 -0.003 0.002 0.001 -0.003

(0.0048) (0.0014) (0.0062) (0.0048) (0.0014) (0.0062) (0.0048) (0.0014) (0.0062) (0.0048) (0.0014) (0.0062)

age2 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000

(0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001)

Hourly wage -0.000 -0.000 0.001 -0.000 -0.000 0.001

(0.0004) (0.0001) (0.0005) (0.0004) (0.0001) (0.0005)

male 0.012 0.004 -0.016 0.011 0.003 -0.014 0.012 0.003 -0.016 0.011 0.003 -0.014

(0.0135) (0.0039) (0.0174) (0.0135) (0.0039) (0.0174) (0.0135) (0.0039) (0.0174) (0.0135) (0.0039) (0.0174)

Nationality -0.031 -0.009 0.040 -0.031 -0.009 0.040 -0.034 -0.010 0.043 -0.034 -0.010 0.044

(0.0257) (0.0075) (0.0331) (0.0257) (0.0075) (0.0331) (0.0257) (0.0075) (0.0331) (0.0257) (0.0075) (0.0331)

Political 0.025 0.007 -0.032 0.024 0.007 -0.031 0.025 0.007 -0.033 0.024 0.007 -0.031

(0.0201) (0.0058) (0.0259) (0.0201) (0.0058) (0.0259) (0.0201) (0.0058) (0.0259) (0.0200) (0.0058) (0.0258)

81

Married 0.021 0.006 -0.027 0.021 0.006 -0.028 0.020 0.006 -0.026 0.020 0.006 -0.026

(0.0199) (0.0058) (0.0257) (0.0199) (0.0058) (0.0257) (0.0199) (0.0058) (0.0256) (0.0199) (0.0058) (0.0256)

Urban -0.036* -0.010* 0.046* -0.038** -0.011* 0.049** -0.037* -0.011* 0.048* -0.040** -0.011** 0.051**

(0.0194) (0.0057) (0.0250) (0.0193) (0.0057) (0.0248) (0.0194) (0.0057) (0.0249) (0.0193) (0.0057) (0.0248)

fulltime -0.030 -0.009 0.039 -0.029 -0.008 0.037 -0.032 -0.009 0.041 -0.031 -0.009 0.040

(0.0203) (0.0059) (0.0261) (0.0203) (0.0059) (0.0261) (0.0203) (0.0059) (0.0261) (0.0203) (0.0059) (0.0261)

State -0.046*** -0.013*** 0.060*** -0.045*** -0.013*** 0.058*** -0.045*** -0.013*** 0.058*** -0.043*** -0.013*** 0.056***

(0.0162) (0.0048) (0.0208) (0.0161) (0.0048) (0.0207) (0.0162) (0.0048) (0.0208) (0.0161) (0.0048) (0.0208)

healthy -0.041 -0.012 0.053 -0.042 -0.012 0.054 -0.040 -0.012 0.052 -0.041 -0.012 0.053

(0.0264) (0.0077) (0.0340) (0.0264) (0.0077) (0.0340) (0.0263) (0.0077) (0.0340) (0.0263) (0.0077) (0.0339)

lower 0.121*** 0.035*** -0.156**

* 0.130*** 0.038*** -0.167*** 0.119*** 0.035*** -0.154*** 0.128*** 0.037*** -0.165***

(0.0407) (0.0121) (0.0522) (0.0400) (0.0119) (0.0513) (0.0407) (0.0121) (0.0522) (0.0400) (0.0119) (0.0513)

middle 0.060 0.017 -0.077 0.067* 0.020* -0.087* 0.060 0.017 -0.077 0.067* 0.019* -0.087*

(0.0403) (0.0117) (0.0518) (0.0398) (0.0116) (0.0512) (0.0402) (0.0117) (0.0518) (0.0397) (0.0116) (0.0512)

medium 0.012 0.004 -0.016 0.012 0.003 -0.015 0.012 0.004 -0.016 0.012 0.003 -0.015

(0.0172) (0.0050) (0.0221) (0.0172) (0.0050) (0.0221) (0.0172) (0.0050) (0.0221) (0.0171) (0.0050) (0.0221)

large 0.030 0.009 -0.039 0.029 0.008 -0.037 0.030 0.009 -0.038 0.028 0.008 -0.037

(0.0183) (0.0053) (0.0235) (0.0182) (0.0053) (0.0235) (0.0183) (0.0053) (0.0235) (0.0182) (0.0053) (0.0235)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 169.99 168.69 174.28 173.01

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0328 0.0325 0.0336 0.0334

* p<0.10, ** p<0.05, *** p<0.010

82

Table B5. The relationship between overeducation and job satisfaction with working conditions and facilities

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of

schooling -0.014*** -0.009*** 0.023*** -0.015*** -0.010*** 0.025*** -0.014*** -0.009*** 0.023*** -0.015*** -0.010*** 0.026***

(0.0026) (0.0017) (0.0041) (0.0025) (0.0016) (0.0040) (0.0026) (0.0017) (0.0041) (0.0025) (0.0016) (0.0040)

Overeducated 0.009 0.006 -0.015 0.010 0.007 -0.017 0.009 0.006 -0.015 0.010 0.007 -0.017

(0.0155) (0.0101) (0.0256) (0.0155) (0.0102) (0.0256) (0.0154) (0.0101) (0.0256) (0.0154) (0.0101) (0.0256)

Undereducated -0.017 -0.011 0.028 -0.019 -0.012 0.031 -0.017 -0.011 0.028 -0.018 -0.012 0.030

(0.0130) (0.0085) (0.0214) (0.0130) (0.0085) (0.0215) (0.0130) (0.0085) (0.0214) (0.0130) (0.0085) (0.0214)

Skill mismatched 0.034** 0.022** -0.056** 0.034** 0.022** -0.057**

(0.0144) (0.0094) (0.0237) (0.0144) (0.0095) (0.0237)

Salary matched with

expectation -0.064*** -0.042*** 0.106*** -0.066*** -0.043*** 0.110*** -0.062*** -0.041*** 0.103*** -0.064*** -0.042*** 0.107***

(0.0141) (0.0093) (0.0230) (0.0141) (0.0093) (0.0230) (0.0141) (0.0093) (0.0230) (0.0141) (0.0093) (0.0230)

age 0.006 0.004 -0.010 0.005 0.003 -0.009 0.006 0.004 -0.010 0.005 0.004 -0.009

(0.0039) (0.0025) (0.0064) (0.0039) (0.0025) (0.0064) (0.0039) (0.0025) (0.0064) (0.0039) (0.0025) (0.0064)

age2 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000

(0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001)

Hourly wage -0.001*** -0.001*** 0.002*** -0.001*** -0.001*** 0.002***

(0.0005) (0.0003) (0.0008) (0.0005) (0.0003) (0.0008)

male 0.019* 0.012* -0.032* 0.016 0.010 -0.026 0.019* 0.012* -0.031* 0.015 0.010 -0.025

(0.0109) (0.0072) (0.0180) (0.0109) (0.0071) (0.0180) (0.0109) (0.0072) (0.0180) (0.0108) (0.0071) (0.0180)

Nationality -0.025 -0.016 0.041 -0.026 -0.017 0.042 -0.027 -0.018 0.045 -0.028 -0.018 0.046

(0.0204) (0.0134) (0.0337) (0.0204) (0.0134) (0.0338) (0.0204) (0.0134) (0.0337) (0.0204) (0.0134) (0.0337)

Political -0.006 -0.004 0.010 -0.008 -0.005 0.013 -0.006 -0.004 0.010 -0.007 -0.005 0.012

83

(0.0163) (0.0107) (0.0269) (0.0163) (0.0107) (0.0269) (0.0163) (0.0106) (0.0269) (0.0162) (0.0107) (0.0269)

Married -0.007 -0.004 0.011 -0.006 -0.004 0.009 -0.008 -0.005 0.013 -0.007 -0.004 0.011

(0.0160) (0.0105) (0.0264) (0.0160) (0.0105) (0.0265) (0.0160) (0.0104) (0.0264) (0.0160) (0.0105) (0.0264)

Urban -0.024 -0.016 0.040 -0.030* -0.019* 0.049* -0.026* -0.017* 0.043* -0.031** -0.020** 0.051**

(0.0155) (0.0102) (0.0257) (0.0154) (0.0102) (0.0256) (0.0155) (0.0102) (0.0257) (0.0154) (0.0102) (0.0255)

fulltime -0.032** -0.021* 0.053** -0.031* -0.020* 0.052* -0.034** -0.022** 0.056** -0.033** -0.022** 0.055**

(0.0162) (0.0107) (0.0268) (0.0162) (0.0107) (0.0268) (0.0162) (0.0107) (0.0268) (0.0162) (0.0107) (0.0268)

State -0.022* -0.014* 0.036* -0.019 -0.013 0.032 -0.020 -0.013 0.033 -0.018 -0.012 0.029

(0.0130) (0.0085) (0.0215) (0.0130) (0.0085) (0.0215) (0.0130) (0.0085) (0.0215) (0.0130) (0.0085) (0.0215)

healthy -0.001 -0.001 0.002 -0.003 -0.002 0.005 -0.001 -0.000 0.001 -0.002 -0.002 0.004

(0.0211) (0.0138) (0.0349) (0.0211) (0.0139) (0.0349) (0.0210) (0.0138) (0.0348) (0.0211) (0.0138) (0.0349)

lower 0.091*** 0.059*** -0.150*** 0.108*** 0.071*** -0.179*** 0.089*** 0.059*** -0.148*** 0.107*** 0.070*** -0.177***

(0.0333) (0.0218) (0.0548) (0.0328) (0.0216) (0.0540) (0.0333) (0.0218) (0.0547) (0.0328) (0.0216) (0.0539)

middle 0.040 0.026 -0.066 0.053 0.035 -0.089 0.039 0.026 -0.065 0.053 0.035 -0.088

(0.0329) (0.0215) (0.0544) (0.0326) (0.0214) (0.0539) (0.0329) (0.0215) (0.0543) (0.0326) (0.0214) (0.0539)

medium -0.008 -0.005 0.013 -0.009 -0.006 0.015 -0.008 -0.005 0.013 -0.009 -0.006 0.015

(0.0137) (0.0090) (0.0227) (0.0137) (0.0090) (0.0228) (0.0137) (0.0090) (0.0227) (0.0137) (0.0090) (0.0227)

large -0.036** -0.023** 0.059** -0.038*** -0.025*** 0.064*** -0.036** -0.024** 0.060** -0.039*** -0.025*** 0.064***

(0.0147) (0.0096) (0.0242) (0.0147) (0.0097) (0.0242) (0.0147) (0.0096) (0.0242) (0.0146) (0.0096) (0.0241)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 215.39 207.25 220.92 212.90

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0436 0.0420 0.0448 0.0431

* p<0.10, ** p<0.05, *** p<0.010

84

Table B6 The relationship between overeducation and job satisfaction with the relationship with colleagues

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of

schooling -0.001* -0.005* 0.007* -0.002** -0.006** 0.008** -0.001* -0.006* 0.007* -0.002** -0.006** 0.008**

(0.0008) (0.0032) (0.0040) (0.0008) (0.0031) (0.0039) (0.0008) (0.0032) (0.0040) (0.0008) (0.0031) (0.0039)

Overeducated -0.002 -0.009 0.011 -0.002 -0.008 0.010 -0.002 -0.009 0.011 -0.002 -0.008 0.010

(0.0048) (0.0196) (0.0245) (0.0048) (0.0196) (0.0245) (0.0048) (0.0196) (0.0245) (0.0048) (0.0196) (0.0244)

Undereducated -0.006 -0.023 0.029 -0.006 -0.024 0.030 -0.006 -0.023 0.028 -0.006 -0.024 0.029

(0.0040) (0.0162) (0.0201) (0.0040) (0.0162) (0.0201) (0.0040) (0.0162) (0.0201) (0.0040) (0.0162) (0.0201)

Skill mismatched 0.007 0.027 -0.033 0.007 0.027 -0.033

(0.0044) (0.0176) (0.0219) (0.0044) (0.0176) (0.0219)

Salary matched with expectation

-0.014*** -0.057*** 0.071*** -0.014*** -0.058*** 0.072*** -0.014*** -0.056*** 0.069*** -0.014*** -0.057*** 0.070***

(0.0047) (0.0182) (0.0226) (0.0047) (0.0181) (0.0226) (0.0047) (0.0182) (0.0227) (0.0047) (0.0182) (0.0226)

age 0.000 0.000 -0.000 -0.000 -0.000 0.000 0.000 0.000 -0.001 0.000 0.000 -0.000

(0.0012) (0.0048) (0.0060) (0.0012) (0.0048) (0.0060) (0.0012) (0.0048) (0.0060) (0.0012) (0.0048) (0.0060)

age2 0.000 0.000 -0.000 0.000 0.000 -0.000 -0.000 -0.000 0.000 0.000 0.000 -0.000

(0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001)

Hourly wage -0.000 -0.001 0.001 -0.000 -0.001 0.001

(0.0001) (0.0006) (0.0007) (0.0001) (0.0006) (0.0007)

male 0.001 0.004 -0.005 0.001 0.003 -0.003 0.001 0.004 -0.005 0.001 0.002 -0.003

(0.0033) (0.0136) (0.0169) (0.0033) (0.0135) (0.0168) (0.0033) (0.0136) (0.0169) (0.0033) (0.0135) (0.0168)

Nationality 0.009 0.035 -0.044 0.009 0.035 -0.044 0.008 0.034 -0.042 0.008 0.033 -0.042

(0.0065) (0.0262) (0.0326) (0.0065) (0.0262) (0.0326) (0.0065) (0.0262) (0.0326) (0.0065) (0.0262) (0.0326)

Political -0.018*** -0.073*** 0.091*** -0.018*** -0.073*** 0.091*** -0.018*** -0.073*** 0.090*** -0.018*** -0.073*** 0.091***

(0.0057) (0.0219) (0.0273) (0.0058) (0.0219) (0.0273) (0.0057) (0.0219) (0.0273) (0.0057) (0.0219) (0.0273)

85

Married 0.005 0.021 -0.026 0.005 0.021 -0.027 0.005 0.020 -0.025 0.005 0.020 -0.025

(0.0050) (0.0201) (0.0250) (0.0050) (0.0201) (0.0250) (0.0050) (0.0201) (0.0250) (0.0050) (0.0201) (0.0250)

Urban 0.004 0.015 -0.019 0.003 0.013 -0.016 0.003 0.014 -0.017 0.003 0.011 -0.014

(0.0048) (0.0193) (0.0240) (0.0047) (0.0192) (0.0239) (0.0047) (0.0193) (0.0240) (0.0047) (0.0192) (0.0239)

fulltime -0.010** -0.040** 0.050** -0.010** -0.039** 0.049** -0.010** -0.041** 0.051** -0.010** -0.041** 0.051**

(0.0049) (0.0194) (0.0242) (0.0049) (0.0194) (0.0241) (0.0049) (0.0194) (0.0242) (0.0049) (0.0194) (0.0242)

State -0.006 -0.022 0.028 -0.005 -0.021 0.026 -0.005 -0.021 0.026 -0.005 -0.020 0.025

(0.0041) (0.0165) (0.0206) (0.0041) (0.0165) (0.0206) (0.0041) (0.0166) (0.0206) (0.0041) (0.0165) (0.0206)

healthy 0.001 0.005 -0.006 0.001 0.004 -0.005 0.001 0.005 -0.006 0.001 0.004 -0.005

(0.0065) (0.0262) (0.0327) (0.0065) (0.0262) (0.0327) (0.0064) (0.0262) (0.0326) (0.0064) (0.0262) (0.0327)

lower 0.002 0.007 -0.008 0.004 0.018 -0.022 0.001 0.006 -0.007 0.004 0.016 -0.020

(0.0099) (0.0403) (0.0502) (0.0097) (0.0395) (0.0492) (0.0099) (0.0403) (0.0501) (0.0097) (0.0395) (0.0492)

middle -0.006 -0.025 0.031 -0.004 -0.016 0.020 -0.006 -0.025 0.031 -0.004 -0.016 0.020

(0.0098) (0.0398) (0.0496) (0.0097) (0.0393) (0.0490) (0.0098) (0.0398) (0.0496) (0.0097) (0.0393) (0.0490)

medium -0.012*** -0.049*** 0.061*** -0.012*** -0.050*** 0.062*** -0.012*** -0.049*** 0.061*** -0.012*** -0.050*** 0.062***

(0.0044) (0.0169) (0.0211) (0.0044) (0.0169) (0.0211) (0.0044) (0.0169) (0.0211) (0.0044) (0.0169) (0.0210)

large -0.014*** -0.056*** 0.070*** -0.014*** -0.058*** 0.072*** -0.014*** -0.057*** 0.070*** -0.014*** -0.058*** 0.072***

(0.0047) (0.0182) (0.0226) (0.0047) (0.0181) (0.0226) (0.0047) (0.0182) (0.0226) (0.0047) (0.0181) (0.0226)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 88.26 86.63 90.50 88.91

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0293 0.0287 0.0300 0.0295

* p<0.10, ** p<0.05, *** p<0.010

86

Table B7. The relationship between overeducation and job satisfaction with the relationship with boss

Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of

schooling -0.007*** -0.017*** 0.024*** -0.008*** -0.019*** 0.026*** -0.007*** -0.017*** 0.024*** -0.008*** -0.019*** 0.027***

(0.0014) (0.0032) (0.0045) (0.0014) (0.0031) (0.0043) (0.0014) (0.0031) (0.0044) (0.0014) (0.0031) (0.0043)

Overeducated 0.016** 0.039** -0.055** 0.016** 0.039** -0.055** 0.016** 0.039** -0.055** 0.016** 0.039** -0.055**

(0.0080) (0.0192) (0.0270) (0.0080) (0.0192) (0.0271) (0.0079) (0.0191) (0.0270) (0.0079) (0.0191) (0.0270)

Undereducated -0.018*** -0.044*** 0.062*** -0.019*** -0.046*** 0.065*** -0.018*** -0.043*** 0.061*** -0.018*** -0.045*** 0.063***

(0.0068) (0.0162) (0.0228) (0.0068) (0.0162) (0.0228) (0.0067) (0.0161) (0.0227) (0.0067) (0.0161) (0.0227)

Skill mismatched 0.030*** 0.074*** -0.105*** 0.030*** 0.074*** -0.105***

(0.0074) (0.0173) (0.0243) (0.0074) (0.0173) (0.0243)

Salary matched with

expectation -0.038*** -0.092*** 0.130*** -0.039*** -0.094*** 0.133*** -0.036*** -0.088*** 0.124*** -0.037*** -0.090*** 0.127***

(0.0077) (0.0176) (0.0248) (0.0077) (0.0176) (0.0247) (0.0077) (0.0175) (0.0247) (0.0077) (0.0175) (0.0247)

age 0.002 0.004 -0.006 0.001 0.003 -0.005 0.002 0.005 -0.007 0.002 0.004 -0.006

(0.0020) (0.0048) (0.0068) (0.0020) (0.0048) (0.0068) (0.0020) (0.0048) (0.0067) (0.0020) (0.0048) (0.0067)

age2 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000 -0.000 -0.000 0.000

(0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001)

Hourly wage -0.001** -0.001** 0.002** -0.001** -0.001** 0.002**

(0.0003) (0.0006) (0.0009) (0.0003) (0.0006) (0.0009)

male -0.005 -0.013 0.018 -0.007 -0.016 0.023 -0.006 -0.014 0.019 -0.007 -0.017 0.024

(0.0055) (0.0135) (0.0190) (0.0055) (0.0134) (0.0189) (0.0055) (0.0134) (0.0189) (0.0055) (0.0134) (0.0188)

Nationality -0.007 -0.016 0.023 -0.007 -0.017 0.024 -0.009 -0.021 0.030 -0.009 -0.022 0.031

(0.0102) (0.0249) (0.0351) (0.0103) (0.0249) (0.0352) (0.0102) (0.0248) (0.0350) (0.0102) (0.0248) (0.0350)

Political -0.025*** -0.062*** 0.087*** -0.026*** -0.064*** 0.090*** -0.025*** -0.061*** 0.086*** -0.026*** -0.063*** 0.089***

87

(0.0088) (0.0209) (0.0296) (0.0089) (0.0209) (0.0296) (0.0088) (0.0209) (0.0295) (0.0088) (0.0209) (0.0295)

Married -0.007 -0.018 0.026 -0.007 -0.017 0.024 -0.009 -0.021 0.029 -0.008 -0.020 0.028

(0.0081) (0.0195) (0.0276) (0.0081) (0.0195) (0.0276) (0.0080) (0.0195) (0.0274) (0.0080) (0.0195) (0.0275)

Urban -0.005 -0.011 0.016 -0.007 -0.017 0.023 -0.006 -0.014 0.020 -0.008 -0.019 0.027

(0.0079) (0.0192) (0.0271) (0.0079) (0.0191) (0.0269) (0.0079) (0.0191) (0.0270) (0.0078) (0.0190) (0.0268)

fulltime -0.014* -0.034* 0.049* -0.014* -0.034* 0.048* -0.016* -0.038* 0.054* -0.016* -0.038* 0.054*

(0.0082) (0.0198) (0.0279) (0.0082) (0.0197) (0.0278) (0.0082) (0.0197) (0.0278) (0.0081) (0.0197) (0.0278)

State -0.020*** -0.048*** 0.068*** -0.019*** -0.046*** 0.064*** -0.018*** -0.044*** 0.062*** -0.017** -0.042*** 0.059***

(0.0068) (0.0161) (0.0227) (0.0068) (0.0161) (0.0227) (0.0067) (0.0161) (0.0227) (0.0067) (0.0161) (0.0227)

healthy 0.007 0.016 -0.022 0.005 0.013 -0.019 0.007 0.018 -0.025 0.006 0.015 -0.021

(0.0108) (0.0262) (0.0370) (0.0108) (0.0262) (0.0370) (0.0107) (0.0261) (0.0368) (0.0107) (0.0261) (0.0368)

lower 0.039** 0.095** -0.134** 0.047*** 0.113*** -0.160*** 0.038** 0.092** -0.129** 0.045** 0.110*** -0.155***

(0.0178) (0.0427) (0.0602) (0.0176) (0.0421) (0.0593) (0.0177) (0.0425) (0.0600) (0.0175) (0.0419) (0.0591)

middle 0.027 0.064 -0.091 0.033* 0.079* -0.112* 0.026 0.063 -0.089 0.032* 0.078* -0.110*

(0.0175) (0.0423) (0.0598) (0.0174) (0.0420) (0.0592) (0.0174) (0.0422) (0.0595) (0.0173) (0.0418) (0.0590)

medium 0.009 0.021 -0.030 0.008 0.020 -0.028 0.009 0.021 -0.030 0.008 0.020 -0.029

(0.0071) (0.0171) (0.0241) (0.0071) (0.0171) (0.0241) (0.0070) (0.0170) (0.0240) (0.0070) (0.0170) (0.0240)

large 0.013* 0.031* -0.044* 0.012 0.029 -0.041 0.013* 0.031* -0.044* 0.012 0.029 -0.040

(0.0075) (0.0181) (0.0256) (0.0075) (0.0181) (0.0256) (0.0075) (0.0181) (0.0255) (0.0075) (0.0180) (0.0254)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 169.40 163.95 187.47 182.03

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0410 0.0397 0.0454 0.0441

* p<0.10, ** p<0.05, *** p<0.010

88

Table B8. The relationship between overeducation and job satisfaction with commuting distance to job location

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of schooling

-0.003 -0.003 0.007 -0.005** -0.004** 0.009** -0.004 -0.003 0.007 -0.005** -0.004** 0.009**

(0.0024) (0.0021) (0.0045) (0.0023) (0.0020) (0.0044) (0.0024) (0.0021) (0.0045) (0.0023) (0.0020) (0.0043)

Overeducated 0.005 0.005 -0.010 0.006 0.005 -0.011 0.005 0.005 -0.010 0.006 0.005 -0.011

(0.0145) (0.0126) (0.0271) (0.0145) (0.0126) (0.0271) (0.0144) (0.0126) (0.0271) (0.0144) (0.0126) (0.0271)

Undereducated -0.003 -0.003 0.007 -0.005 -0.004 0.009 -0.003 -0.003 0.006 -0.004 -0.004 0.008

(0.0122) (0.0107) (0.0229) (0.0122) (0.0107) (0.0229) (0.0122) (0.0106) (0.0228) (0.0122) (0.0107) (0.0228)

Skill

mismatched 0.033** 0.029** -0.061** 0.033** 0.029** -0.062**

(0.0133) (0.0116) (0.0247) (0.0133) (0.0116) (0.0248)

Salary matched

with

expectation

-0.033** -0.029** 0.062** -0.035*** -0.031*** 0.065*** -0.031** -0.027** 0.059** -0.033** -0.029** 0.062**

(0.0133) (0.0115) (0.0247) (0.0133) (0.0115) (0.0247) (0.0133) (0.0115) (0.0247) (0.0133) (0.0115) (0.0247)

age 0.005 0.005 -0.010 0.005 0.004 -0.009 0.006 0.005 -0.011 0.005 0.005 -0.010

(0.0037) (0.0032) (0.0068) (0.0037) (0.0032) (0.0068) (0.0037) (0.0032) (0.0068) (0.0037) (0.0032) (0.0068)

age2 -0.000* -0.000* 0.000* -0.000 -0.000 0.000 -0.000* -0.000* 0.000* -0.000* -0.000* 0.000*

(0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0001)

Hourly wage -0.001** -0.001** 0.002** -0.001** -0.001** 0.002**

(0.0005) (0.0004) (0.0009) (0.0005) (0.0004) (0.0009)

male 0.017* 0.015* -0.032* 0.014 0.013 -0.027 0.017* 0.015* -0.032* 0.014 0.012 -0.027

(0.0102) (0.0089) (0.0191) (0.0102) (0.0089) (0.0190) (0.0102) (0.0089) (0.0191) (0.0102) (0.0089) (0.0190)

Nationality 0.014 0.012 -0.026 0.013 0.012 -0.025 0.012 0.010 -0.022 0.011 0.010 -0.021

(0.0195) (0.0170) (0.0364) (0.0195) (0.0170) (0.0365) (0.0194) (0.0170) (0.0364) (0.0195) (0.0170) (0.0365)

Political -0.050*** -0.044*** 0.094*** -0.051*** -0.045*** 0.096*** -0.050*** -0.044*** 0.094*** -0.051*** -0.045*** 0.095***

(0.0159) (0.0137) (0.0294) (0.0159) (0.0138) (0.0294) (0.0159) (0.0137) (0.0294) (0.0159) (0.0137) (0.0294)

89

Married 0.005 0.004 -0.009 0.006 0.005 -0.011 0.004 0.003 -0.007 0.005 0.004 -0.009

(0.0149) (0.0130) (0.0279) (0.0149) (0.0130) (0.0280) (0.0149) (0.0130) (0.0279) (0.0149) (0.0130) (0.0279)

Urban 0.012 0.011 -0.023 0.008 0.007 -0.015 0.011 0.010 -0.021 0.007 0.006 -0.013

(0.0148) (0.0129) (0.0276) (0.0147) (0.0128) (0.0275) (0.0148) (0.0129) (0.0276) (0.0147) (0.0128) (0.0275)

fulltime -0.020 -0.018 0.038 -0.020 -0.017 0.037 -0.022 -0.020 0.042 -0.022 -0.019 0.041

(0.0152) (0.0132) (0.0284) (0.0152) (0.0132) (0.0284) (0.0152) (0.0132) (0.0284) (0.0152) (0.0133) (0.0284)

State -0.009 -0.008 0.016 -0.007 -0.006 0.013 -0.007 -0.006 0.013 -0.005 -0.005 0.010

(0.0122) (0.0106) (0.0228) (0.0121) (0.0106) (0.0227) (0.0122) (0.0106) (0.0228) (0.0121) (0.0106) (0.0227)

healthy 0.007 0.006 -0.013 0.006 0.005 -0.010 0.008 0.007 -0.014 0.006 0.005 -0.011

(0.0201) (0.0175) (0.0376) (0.0201) (0.0176) (0.0377) (0.0200) (0.0175) (0.0376) (0.0201) (0.0175) (0.0376)

lower 0.078** 0.068** -0.146** 0.093*** 0.081*** -0.174*** 0.077** 0.068** -0.145** 0.092*** 0.080*** -0.172***

(0.0320) (0.0278) (0.0596) (0.0315) (0.0274) (0.0586) (0.0320) (0.0278) (0.0595) (0.0315) (0.0274) (0.0586)

middle 0.038 0.033 -0.071 0.049 0.043 -0.093 0.038 0.033 -0.071 0.049 0.043 -0.093

(0.0316) (0.0276) (0.0591) (0.0313) (0.0273) (0.0585) (0.0316) (0.0276) (0.0591) (0.0313) (0.0273) (0.0585)

medium 0.018 0.016 -0.034 0.017 0.015 -0.032 0.018 0.016 -0.034 0.017 0.015 -0.032

(0.0129) (0.0112) (0.0241) (0.0129) (0.0112) (0.0241) (0.0129) (0.0112) (0.0240) (0.0129) (0.0112) (0.0241)

large -0.004 -0.004 0.008 -0.007 -0.006 0.013 -0.004 -0.004 0.008 -0.007 -0.006 0.013

(0.0138) (0.0120) (0.0258) (0.0138) (0.0120) (0.0258) (0.0138) (0.0120) (0.0258) (0.0137) (0.0120) (0.0258)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 84.41 78.33 90.47 84.49

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0186 0.0173 0.0200 0.0187

* p<0.10, ** p<0.05, *** p<0.010

90

Table B9. The relationship between overeducation and job satisfaction with housing benefits

Variables Specification 1 Specification 2 Specification 3 Specification 4

Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied Dissatisfied Average Satisfied

Actual years of

schooling -0.000 0.000 0.000 -0.003 0.000 0.003 -0.000 0.000 0.000 -0.004 0.000 0.003

(0.0041) (0.0005) (0.0035) (0.0040) (0.0005) (0.0035) (0.0041) (0.0005) (0.0035) (0.0040) (0.0005) (0.0035)

Overeducated -0.073*** 0.009*** 0.064*** -0.072*** 0.009*** 0.062*** -0.073*** 0.010*** 0.064*** -0.072*** 0.009*** 0.063***

(0.0246) (0.0035) (0.0215) (0.0247) (0.0034) (0.0215) (0.0246) (0.0035) (0.0214) (0.0246) (0.0034) (0.0215)

Undereducated -0.005 0.001 0.005 -0.009 0.001 0.008 -0.005 0.001 0.005 -0.009 0.001 0.007

(0.0212) (0.0028) (0.0184) (0.0212) (0.0027) (0.0185) (0.0211) (0.0028) (0.0184) (0.0212) (0.0027) (0.0185)

Skill mismatched 0.052** -0.007** -0.045** 0.052** -0.007** -0.046**

(0.0235) (0.0032) (0.0205) (0.0236) (0.0032) (0.0206)

Salary matched with expectation

-0.088*** 0.011*** 0.077*** -0.092*** 0.012*** 0.080*** -0.086*** 0.011*** 0.074*** -0.089*** 0.011*** 0.078***

(0.0221) (0.0033) (0.0193) (0.0221) (0.0033) (0.0193) (0.0221) (0.0033) (0.0193) (0.0221) (0.0033) (0.0193)

age 0.011* -0.001* -0.009* 0.010 -0.001 -0.008 0.011* -0.001* -0.010* 0.010 -0.001 -0.009

(0.0063) (0.0008) (0.0055) (0.0063) (0.0008) (0.0055) (0.0063) (0.0008) (0.0055) (0.0063) (0.0008) (0.0055)

age2 -0.000* 0.000* 0.000* -0.000* 0.000* 0.000* -0.000* 0.000* 0.000* -0.000* 0.000* 0.000*

(0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001) (0.0001) (0.0000) (0.0001)

Hourly wage -0.003*** 0.000*** 0.002*** -0.003*** 0.000*** 0.002***

(0.0007) (0.0001) (0.0006) (0.0007) (0.0001) (0.0006)

male 0.018 -0.002 -0.016 0.011 -0.001 -0.010 0.018 -0.002 -0.015 0.011 -0.001 -0.009

(0.0177) (0.0023) (0.0154) (0.0176) (0.0023) (0.0154) (0.0177) (0.0023) (0.0154) (0.0176) (0.0023) (0.0154)

Nationality -0.048 0.006 0.042 -0.049 0.006 0.043 -0.051 0.007 0.045 -0.052 0.007 0.046

(0.0336) (0.0044) (0.0293) (0.0337) (0.0044) (0.0294) (0.0336) (0.0045) (0.0293) (0.0337) (0.0044) (0.0294)

Political -0.000 0.000 0.000 -0.005 0.001 0.004 0.000 -0.000 -0.000 -0.004 0.001 0.004

(0.0261) (0.0034) (0.0227) (0.0261) (0.0033) (0.0227) (0.0260) (0.0034) (0.0227) (0.0260) (0.0033) (0.0227)

91

Married -0.082*** 0.011*** 0.071*** -0.079*** 0.010*** 0.069*** -0.083*** 0.011*** 0.073*** -0.081*** 0.010*** 0.071***

(0.0260) (0.0037) (0.0227) (0.0260) (0.0036) (0.0227) (0.0260) (0.0037) (0.0227) (0.0260) (0.0036) (0.0227)

Urban 0.009 -0.001 -0.008 -0.001 0.000 0.001 0.007 -0.001 -0.006 -0.004 0.000 0.003

(0.0256) (0.0033) (0.0223) (0.0255) (0.0033) (0.0223) (0.0256) (0.0033) (0.0223) (0.0255) (0.0033) (0.0223)

fulltime -0.076*** 0.010*** 0.066*** -0.073*** 0.009** 0.064*** -0.079*** 0.010*** 0.069*** -0.076*** 0.010*** 0.066***

(0.0268) (0.0037) (0.0234) (0.0269) (0.0037) (0.0235) (0.0268) (0.0037) (0.0234) (0.0268) (0.0037) (0.0235)

State -0.073*** 0.010*** 0.064*** -0.068*** 0.009*** 0.059*** -0.071*** 0.009*** 0.062*** -0.065*** 0.008*** 0.057***

(0.0210) (0.0030) (0.0183) (0.0210) (0.0030) (0.0183) (0.0210) (0.0030) (0.0183) (0.0210) (0.0029) (0.0184)

healthy -0.009 0.001 0.008 -0.013 0.002 0.011 -0.008 0.001 0.007 -0.011 0.001 0.010

(0.0345) (0.0045) (0.0300) (0.0346) (0.0044) (0.0301) (0.0345) (0.0045) (0.0300) (0.0345) (0.0044) (0.0301)

lower 0.164*** -0.021*** -0.143*** 0.200*** -0.026*** -0.175*** 0.162*** -0.021*** -0.141*** 0.198*** -0.025*** -0.173***

(0.0521) (0.0074) (0.0453) (0.0512) (0.0076) (0.0446) (0.0520) (0.0074) (0.0453) (0.0511) (0.0076) (0.0446)

middle 0.089* -0.012* -0.078* 0.118** -0.015** -0.103** 0.089* -0.012* -0.077* 0.118** -0.015** -0.103**

(0.0515) (0.0069) (0.0448) (0.0510) (0.0070) (0.0444) (0.0515) (0.0069) (0.0448) (0.0510) (0.0069) (0.0444)

medium 0.051** -0.007** -0.045** 0.049** -0.006** -0.043** 0.052** -0.007** -0.045** 0.049** -0.006** -0.043**

(0.0225) (0.0031) (0.0196) (0.0225) (0.0030) (0.0196) (0.0224) (0.0031) (0.0195) (0.0225) (0.0030) (0.0196)

large 0.026 -0.003 -0.022 0.019 -0.002 -0.017 0.026 -0.003 -0.022 0.019 -0.002 -0.017

(0.0238) (0.0031) (0.0207) (0.0238) (0.0031) (0.0208) (0.0238) (0.0031) (0.0207) (0.0238) (0.0031) (0.0207)

Observations 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430 2430

LR chi2 144.79 130.77 149.61 135.67

Prob > chi2 0.0000 0.0000 0.0000 0.0000

Pseudo R2 0.0274 0.0247 0.0283 0.0256

* p<0.10, ** p<0.05, *** p<0.010