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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: [email protected]
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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