Education occupation mismatch in vietnam determinants and effects on earnings

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

ERASMUS UNVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS

VIETNAM – THE NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

EDUCATION-OCCUPATION MISMATCH IN VIETNAM:
DETERMINANTS AND EFFECTS ON EARNINGS

BY

PHAN THI THANH THAO

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, August 2016


UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

ERASMUS UNVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

EDUCATION-OCCUPATION MISMATCH IN VIETNAM:
DETERMINANTS AND EFFECTS ON EARNINGS

A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

PHAN THI THANH THAO

Academic Supervisor:
Dr. TRUONG DANG THUY

HO CHI MINH CITY, August 2016


CERTIFICATION
“I certify that the substance of this thesis has not already been submitted for any degree
and have not been currently submitted for any other degree.
I certify that to the best of my knowledge and help received in preparing this thesis and
all sources used have been acknowledged in this thesis.”

PHAN THI THANH THAO


ACKNOWLEDGEMENTS
The process of writing a thesis is a collaborative experience involving the support and

helps from many people. I want to express my gratitude to those who give me the
tremendous support to complete this thesis.
I am deeply indebted to my parents for their invaluable supports and constant reminders.
The sentence I hear every day is “lose weight and finish your thesis, daughter”. I really
appreciate for their efforts in reminding a very lazy girl like me. And their boundless
love are motivation for my endeavor in building up my life more interesting and
valuable.
I wish to express my heartfelt gratitude to my supervisor Dr. Truong Dang Thuy for his
valuable suggestions during the time I write this thesis. He has also encouraged and
reminded me to pursue this topic from the initial ideas to the final completion. I am really
thankful him for his guidance and patience.
Finally, after finishing this thesis, I realize that each success is a process of continuous
effort. And more difficulties you overcome, more values you get for your life.

Phan Thi Thanh Thao
August, 2016


ABSTRACT
We examine the education-occupation mismatch in horizontal and vertical respects; and
their impacts on earnings of Vietnamese workers. We start by clarifying definitions and
causal reasons of mismatch between education and occupation: in major and level.
Analyzing survey data from 267 workers, we find that the mismatch between schooling
major and working field which is caused by unavailability of job in the schooling field
(demand-related horizontal mismatch) has a negative effect on earnings. And the
mismatch between schooling major and working field caused by remaining reasons
(supply-related horizontal mismatch mismatch) has no statistically significant impact on
earnings. Interestingly, a horizontal mismatch because of supply-related reasons for
workers who learned science major has a positive effect on earnings. Furthermore, when
examining the effect of vertical mismatch, a negative effect of under-education on wage

is found whereas over-educated years have no significant effect on wage.
From policy perspective, we recommend that people should avoid major mismatch for
best earnings. However, in case individuals learn science and work in mismatched career
voluntarily, their earnings will be better than ones in adequate career. Moreover, students
should avoid over-education to reduce the waste of resources unless they want to study
more for their own preferences.


Contents
INTRODUCTION ............................................................................................................ 1
1.1.

Problem statement ............................................................................................ 1

1.2.

Research objectives........................................................................................... 3

1.3.

Main research questions .................................................................................. 4

1.4.

Organization of the study ................................................................................ 4

LITERATURE REVIEW................................................................................................. 5
2.1. Mismatch in major between career and schooling (horizontal educationoccupation mismatch) ................................................................................................. 5
2.1.1.

Definition .................................................................................................... 5

2.1.2.

Determinants of horizontal education-occupation mismatch ................ 6

2.2. Over-education and under-education (Vertical education-occupation
mismatch) .................................................................................................................... 8
2.2.1. Definition ........................................................................................................ 8
2.2.2. Determinants of vertical education-occupation mismatch ...................... 11
2.3.

Effect of education-occupation mismatch on earnings ............................... 14

2.3.1.

Mincer’s earnings model ......................................................................... 14

2.3.2.

Wage effect of horizontal education-occupation mismatch ................. 17

2.3.3.

Wage effect of vertical education-occupation mismatch...................... 18

METHODOLOGY AND DATA ................................................................................... 20
3.1.

Empirical models ............................................................................................ 20

3.1.1.

Horizontal mismatch and earnings ........................................................ 20

3.1.2.

Vertical mismatched and earnings ......................................................... 25

3.2.

Data source ...................................................................................................... 28

RESULTS ...................................................................................................................... 29
4.1.

Descriptive statistics ....................................................................................... 29

4.1.1.

Horizontal education-occupation mismatch.......................................... 29

4.1.2.

Vertical education-occupation mismatch .............................................. 40


4.2.

Regression results ........................................................................................... 48

4.2.1.

Horizontal education-occupation mismatch.......................................... 48

4.2.1.1.

Determinants of horizontal education-occupation mismatch ............. 48

4.2.1.2.

Effect of horizontal education-occupation mismatch on earnings ..... 53

4.2.2.

Vertical education-occupation mismatch .............................................. 59

4.2.2.1.

Determinants of vertical education-occupation mismatch ................. 59

4.2.2.2.

Effects of vertical education-occupation mismatch on earnings ........ 63

CONCLUSION AND POLICY IMPLICATION .......................................................... 71
5.1.

Conclusions...................................................................................................... 71

5.2.

Policy implications .......................................................................................... 73

5.3.

Limitations ...................................................................................................... 74

REFERENCES ............................................................................................................... 76
APPENDIX .................................................................................................................... 81


LIST OF TABLES
Table 4. 1: Descriptive statistics of continuous variables .............................................. 29
Table 4. 2: Age among horizontal mismatched groups ................................................. 30
Table 4. 3: Schooling years among horizontal mismatched groups .............................. 30
Table 4. 4: Experience in current firm among horizontal mismatched groups .............. 31
Table 4. 5: Experience in current working field among horizontal mismatched groups
........................................................................................................................................ 31
Table 4. 6: Reasons for mismatch among horizontal mismatched groups .................... 32
Table 4. 7: Education level among horizontal mismatched groups ............................... 33
Table 4. 8: Schooling major group and horizontal mismatched groups ........................ 33
Table 4. 9: Gender and horizontal mismatched groups ................................................. 34
Table 4. 10: Marital status among horizontal mismatched groups ................................ 34
Table 4. 11: Number of children and horizontal mismatched groups ............................ 35
Table 4. 12: Mobility status among horizontal mismatched groups .............................. 35
Table 4. 13: Long-term health status among horizontal mismatched groups ................ 36
Table 4. 14: Firm type and horizontal mismatched groups............................................ 37
Table 4. 15: Working place among horizontal mismatched groups .............................. 37
Table 4. 16: Immigration status among horizontal mismatched groups ........................ 38

Table 4. 17: Earnings level among horizontal mismatched groups ............................... 39
Table 4. 18: Fulltime/part-time job and horizontal mismatched groups ........................ 39
Table 4. 19: Age of vertical mismatched groups ........................................................... 40
Table 4. 20: Schooling years among vertical mismatched groups ................................. 41
Table 4. 21: Experience in current firm among vertical mismatched groups ................ 41
Table 4. 22: Experience in current field among vertical mismatched groups ............... 42
Table 4. 23: Gender and vertical mismatched groups .................................................... 42
Table 4. 24: Education level of vertical mismatched groups ......................................... 43
Table 4. 25: Schooling major group among vertical mismatched groups ..................... 44
Table 4. 26: Marital status among vertical mismatched groups .................................... 44
Table 4. 27: Number of children among vertical mismatched groups ........................... 45
Table 4. 28: Firm type and vertical mismatched groups ................................................ 46
Table 4. 29: Fulltime/part-time job among vertical mismatched groups ....................... 46
Table 4. 30: Mobility status among vertical mismatched groups .................................. 47
Table 4. 31: Long-term health status among vertical mismatched groups .................... 47
Table 4. 32: Working place among vertical mismatched groups ................................... 48
Table 4. 33: Immigration status among vertical mismatched groups ............................ 48


Table 4. 34: Determinants of horizontal mismatched education: Ordinal logistic
regression ....................................................................................................................... 50
Table 4. 35: Marginal effect of determinants of horizontal mismatched education ...... 51
Table 4. 36: Effects of horizontal mismatched education on earnings .......................... 55
Table 4. 37: The earnings effects of mismatch by schooling majors ............................. 58
Table 4. 38: Determinants of over-educated years ........................................................ 61
Table 4. 39: Effects of vertical mismatched education on earnings (Duncan and
Hoffman model) ............................................................................................................. 64
Table 4. 40: Effects of vertical mismatched education on earnings (Verdugo and
Verdugo model) ............................................................................................................. 69

LIST OF GRAPHS
Graph 4. 1: Distribution of over-education (Duncan and Hoffman model) .................. 60
Graph 4. 2: Distribution of under-education (Duncan and Hoffman model) ................ 60
Graph 4. 3: Effects of vertical mismatched education on earnings. .............................. 67
Graph 4. 4: Effects of vertical mismatched education on earnings for male and female.
........................................................................................................................................ 68


LIST OF APPENDICES
APPENDIX 1: t-test for determinants of horizontal mismatched education ................. 81
APPENDIX 2: Chi-squared test for determinants of horizontal mismatched education
........................................................................................................................................ 82
APPENDIX 3: t-test for determinants of vertical mismatched education ..................... 83
APPENDIX 4: Chi-squared test for determinants of vertical mismatched education ... 84
APPENDIX 5: Questionaire .......................................................................................... 85


CHAPTER 1

INTRODUCTION
1.1.

Problem statement

The last fifteen years have a rapidly increase in the number of students with high
education level in Vietnam. It can be demonstrated through the increase in proportion of
college and university graduates over population which nearly doubled from over 25
graduates/10,000 people in 2005 to nearly 49 graduates/10,000 people in 2014 (GSO
Vietnam, 2016). This great change is also found in rapid increase of master graduates

which was only 0.66 graduates/10,000 people in 2005 and raised five times to nearly 3.5
graduates/10,000 people in 2014 (GSO Vietnam, 2016). One of the reasons explaining
this dramatically increase in number of high educated graduate may be Spence’s (1973)
job-screening model, which says that in an imperfect information labor market,
employers use education as a signal to recognize individuals with higher ability and
productivity. As a result, employees tend to overly invest in education for better job
opportunity and future wage.
This large increase in highly educated labor force causes an unbalance in labor market
where supply excess demand. This disequilibrium in labor market makes high educated
workers accept unskilled job or a mismatched job to avoid to be unemployed. In an
interview set up by Hiep Pham (2013), Le Duy Luong – the human resource director of
a Japanese electronics company in Hoa Cam Industrial Zone – said that hundreds of bluecollar worker in his company had university degrees. Furthermore, Hiep Pham (2013)
also noticed that over-education is rising in Vietnam. The job employees are working
does not require as much knowledge as they learned in school and it seems a waste when
they are over-educated (a vertical education-occupation mismatch).
Another result of supply excess in labor market is that employees have to work in an
unrelated job to his schooling major (horizontal education-occupation mismatch). At the

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time when individual chose university major, he expected that he could work in the field
of that schooling major in the future. However, there are many indicators affecting his
decision in choosing the studied major: expected wage, changes in labor market
equilibrium, non-price orientations, and the probability of graduation of that major. And
it seems many young people do not know clearly what they want, what they can and
what they should. So that this is also a reason for the mismatch between career they are
working and the university major they learned.
These education-occupation mismatches are not only a waste in money and human
capital but also a reflection of labor market failure. There are many studies about

mismatch in education grade and in schooling major including Tsang and Levin (1985),
Sicherman (1990), Bauer (2002), Björklund and Kjellström (2002), Büchel and Mertens
(2004), Robst (2007), Dolton and Silles (2008), Nordin, Persson and Rooth (2010).
Bender and Heywood (2011). However this research issue is quite new in Vietnam.
As mentioned above, individuals have tendency to learn more because they believe in a
higher future earnings. But is it true that wage will change with the change of education
level? When considering the effect of over-education and under-education on earnings,
it is found that employees with over-education earn less than ones with adequate
education level (Kiker et al., 1997; Dolton, 2008). However, how much over-education
or under-education affect earnings? It can be 35-40 percent declining in earnings for
over-educated person as Dolton (2008) found from data of one large civil university in
the UK. Furthermore, Kiker et al. (1997) examined a sample of 50,000 Portuguese
individuals and found that over-educated workers earn approximately 8 percent less than
similar workers with the same education level who are working in an adequate job.
On the other hand, some studies found that the effect of an additional year of overschooling is positive. Duncan and Hoffman (1981) also revealed that return to an
additional year of over education can be positive for US workforce. Nevertheless, they

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also found that this estimated return to an additional year of over-education is only a half
of return to an additional year of required education.
According to Kiker et al. (1997), workers with less education than requirement for the
job earn 16.3% more than those with the same education level who are working in an
adequate job. Bauer (2002) used a large panel data set of Germany in period 1984-1998
to examine workers with similar job but different education levels, and he concluded that
under-educated employees bear a penalty for an additional year of deficit education is 611 percent. The same result is also found by Duncan and Hoffman (1981) with 4.2%
decrease in earnings for an additional year of deficit education.
Furthermore, using data from National Survey of American College Graduates in 1993,
he found that workers with mismatched between schooling major and career bear a

decrease in earnings 10-12% depending on mismatched type (Robst, 2007). Another
evidence comes from study of Nordin et al. (2010) for Swedish people from 28-36 years
old which indicates an earnings penalty of 12-20% for mismatched workers.
Although the field is widely investigated, there are very few studies about this issue in
Vietnam. This study will give a basic overview about horizontal and vertical mismatched
education and their impacts on earnings. Particularly, this study examines the
determinants of education mismatch and its impacts on wage, using data from a survey
of 267 respondents.
1.2.

Research objectives

My main research objectives are two-fold. Firstly, I examines determinants which affect
probability of education-occupation mismatch both in two respects: horizontal and
vertical. These determinants include demographic factors, job characteristics and
schooling majors. Secondly, the effects of over-education/under-education (vertical
mismatch) and mismatched in major (horizontal mismatch) on earnings are examined in
more details.

3


Based on the research results, some policies are suggested to solve existing over/under
education and mismatched between schooling major and career.
1.3.

Main research questions

There are three main questions which need to be clarified in this research:
 Firstly, which determinants have statistically significant impact on probability of

education-occupation mismatch?
 Secondly, how do over-education and under-education affect earnings?
 Thirdly, how does mismatched between schooling major and career affect
earnings?
1.4.

Organization of the study

This thesis consists of five chapters. After this introduction chapter, the remaining of this
thesis is arranged as follow. Chapter 2 is the theoretical framework to have a basic
knowledge in this topic. It discusses about over/under education and mismatched major
in details: definition, determinants, impact on earnings and several model which were
applied by previous researches. Chapter 3 is research methodology. This chapter presents
methods I apply to estimate the determinants of vertical and horizontal mismatch, and
the effect of mismatched education on earnings. Chapter 4 is the result which indicates
descriptive statistics and regression results. Chapter 5 concludes the study with policy
implications and limitations.

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CHAPTER 2

LITERATURE REVIEW
2.1.

Mismatch in major between career and schooling (horizontal educationoccupation mismatch)

2.1.1. Definition
When choosing a career, individual tries to maximize his satisfaction based on the

compromise between his opportunities and limitations of environment. The trade-off in
this case causes a great number of people work in unrelated job with their schooling
major.
According to Robst (2007), there are two main reason categories which explain why an
individual choose an unrelated career: supply-related reasons and demand-related
reasons. Supply-related reasons which are defined as voluntary by workers, including
pay-promotion opportunities, change in job interests, working environments and
conditions, working location and family and social related reasons. Demand-related
reason, which can be regarded involuntary, is the unavailability of job in the schooling
field. While supply-related reasons do not suggest labor market failure, demand-related
reasons indicate the inefficiency in market.
Supply related factors are suggested base on the change in individual’s preferences,
constrains or changes in information about career characteristics. Two subcategories of
supply related indicators are classified: career oriented and amenity. Pay and promotion
opportunities and change in career interest are considered as job oriented factors. A job
with high salary and chances to higher position in career is attractive and hard to be
refused although it is unrelated to their learned knowledge and skills from school. For
example, a person with a bachelor in sales works as a marketing manager because this
job gives him higher wage and better position. According to Nordin et al. (2010), primary
investment decision is taken based on expectation about future earnings and occupational

5


characteristics which can be changed after working in matched jobs. Besides, changes in
other occupation’s information can attract workers to these jobs. These reasons indicate
that workers are interested in their existing job characteristics and their mismatch is
completely active.
The other subcategory is amenity which includes working condition, job location and
family-related reasons. As indicated by Sicherman (1990), women’s utility is more

influenced by non-market conditions than men’s. The non-market conditions include
working hours, household duties, illness in the family. For example, a married woman
in a small labor market has less chance to find a related job because of disadvantages in
geographic and time. These reasons constrain workers from finding an interesting job.
Sicherman (1990) indicated that men change job more often because of career oriented
reasons and better opportunities while women change job because of amenity (Robst,
2008).
In demand factors, the inability to find a related job is considered as an excess supply
problem in labor market. It means that there are more graduates than jobs in field they
want to work in. Another problem in demand side reasons is that there are jobs in market
but labors cannot get such position caused by incomplete information in job search.
Furthermore, low ability and other non-educational individual characteristics are also
reasons for demand-related horizontal mismatch. In a research of Robst (2007), unlike
his expectation, the result indicates that there are more men who accept to work in
unrelated job because of these reasons than women. These reasons indicate an
inefficiency of labor market.
2.1.2. Determinants of horizontal education-occupation mismatch
There are not much literatures discussing about the mismatch based on major of
schooling. Following Robst (2007), Nordin et al. (2010), Bender and Heywood (2011)
career mismatch can be classified into three categories: related career, partly related
career and completely unrelated career. Zhu (2014) suggested three main determinants

6


which affect mismatch probability: demographic variables, job characteristics and major
they learned
First of all, with demographic variables, Robst (2007) used age, married status, the
highest degree, race, disabled. His result indicated that the likelihood of mismatch
increases with age, disability and probability to be horizontal mismatched is higher for

single individuals than married ones. Opposite expectation comes from Madamba and
De Jong (1997), job mismatch is expected to be more common among younger than
older workers. The explanation is that when a worker older, he has more time to find the
suitable job to his major. Furthermore, following Robst (2007), workers with high-level
degree such as Master, Professional, and Doctor have less likelihood of being
mismatched than workers with Bachelor degree only.
Secondly, there are three main subcategories are considered in job characteristics,
including working sector, career stage and working city. These determinants is clarified
by Bender and Heywood (2011) when using a micro-panel data set of US workers who
receive PhD degree in science or engineering. The first indicator which is mentioned is
working sector. The employees who work in government or business sector face more
tendency to have mismatched career compared to academic sector. The career stage is
also considered with three stages: early stage (less or equal 10 years since degree),
middle stage (11-24 years since degree) and late stage (25 or more years since degree).
The results indicated that early in their career is more likely to be matched between
education and occupation compared with later stage. Furthermore, these mismatch may
be not the result of inefficiency in labor market.
Another determinant is working city presented by Zhu (2014) including: type of province
where the employees are working, the same between work and home province, the same
between work and college-located place. The explanation is suggested by Abel (2012)
that there is a causal relationship between job matching and agglomeration which uses
population size or employment density as its proxies. Abel and Deitz (2015) clarified

7


that in a big city with more concentrated labor market, the cost of searching job is lower
and career opportunity range is wider, workers have more chances to have a related job
with their schooling major, so they are more likely to match their human capital to job.
The research from Abel and Deitz (2015) indicated that the college-educated individuals

work in more agglomerated metropolitan areas have higher proportion of working in
career related to their schooling major. More precisely, career matching increase by
about 0.15 percentage point as metropolitan area population increase by one million
people. The employment density is also researched with conclusion of an increase by
100 workers per square mile causes by about 0.25 percentage point in probability of
working in a related job.
Thirdly, the learned major in school is expected to have a significant impact on the
probability of having mismatched job. The probability of mismatch job of the major with
general knowledge and skills is expected lower than the major with specific knowledge
and skills. Workers who learned in general majors have more choices for their career
because there are many jobs can be suitable with their major. Following Robst (2008),
there are some majors which focus on occupation requiring specific skills (such as
architecture, doctor) so that there is less chance for them to have a related job and less
probability for completely match career in these majors. On the other hand, the
occupation general skills and knowledges which focus on general human capital increase
the transferable in career and workers can work in many related field with their schooling
major.
2.2.

Over-education

and

under-education

(Vertical

education-occupation

mismatch)

2.2.1. Definition
According to Dolton and Silles (2008), if a person has education level higher than
required by his job, that worker is considered to be over-educated. Similarly, a worker
is considered to be under-educated if his education level is lower than his position’s

8


requirement. More academic definitions are noticed by Rumberger (1981) in two ways.
Firstly, over-education is considered as a decrease in the economic position of whitecollar workers relative to historically higher levels, particularly in monetary aspect.
Secondly, over-education can be defined as unrealized expectations related to benefit of
education. This definition is considered with a conception that every students have their
own expectation about future job but this expectation may not be come true after
graduation. However, both two definitions from Rumberger (1981) are quite weak
because they ignored some important components such as: non-monetary aspect of
schooling, change over time of expectation and difficulties in measuring individual’s
expectation. Thusly, the first definition from Dolton and Silles (2008) seems the best one
which notices that productivity and earnings associated with job characteristics, not
individual performance.
Above definition of over/under-education is identified through a comparison between
attained education and required education. Thus the question need to be clarified is what
required education is and how to measure it. Hartog (2000), Dolton and Silles (2008)
summarized three ways to measure required education in their own researches. The first
is an analyst of skills or knowledge requirements for each occupation. The second is
worker’s self-assessment in survey which reflect own thinking about his educational
requirement. The third using mean or mode of education level across a range of
occupations as the basic to classify over-education. In this method, average or modal
value of education for the occupational group are considered as the bases in there a
worker is considered over-educated if he has education level varying by one or two
standard deviations from the bases.

The existence of over-education can be explained by neoclassical economic theory
where enterprises make input decisions and production with given technology and
relative prices. With the assumption of zero-information cost, the labor market will
respond promptly to a change in relative labor supply and reach new equilibrium price

9


of labor. In other words, an increase in graduate supply will reduce relative wage and
enterprises can adjust production structure to take advantage of cheaper and more
abundant skilled labor force. Nevertheless, in worker’s aspect, they will redesign their
investment plan and expectation if they recognize that additional investment in education
has a smaller rate of return than their belief or alternative investment. After adjustments
of enterprises and labor, the skills of employees will be fully utilized in the long run. In
other words, over/under education only exist in short term when there is a temporary
disequilibrium between supply and demand in labor market.
However, how long is the long run for this adjustment of labor market? According to
Tsang and Levin (1985), in spite of lower wage, individuals continue to invest in
education if they think their private rate of return of this investment stay high enough.
This can happen if wages of lower education levels are turning down and increasing
speed of unemployment of these levels are equal or higher than that of the higher level.
Another potential is that if workers have higher education, they have higher probability
to be in the upper tail of earnings distribution. The last potential is expressed by worker’s
expectation that they think falling in rate of return to higher-education as a temporary
trend and it will be better in long-run. In conclusion, the long-run equilibrium of labor
market only can be reached in a distant future.
Furthermore, the existence of over-education can be also explained as a symptom of
human capital deficits. A worker can use his over-schooling to substitute or compensate
for deficiencies in other fields of human capital which are not only simply knowledge in
school but also work experience and on-the-job training (Sloane et al., 1996). As

mentioned in occupational mobility theory, if these deficiencies can be corrected by
experience and on-the-job training, over-education will be eliminated day by day.
Nevertheless, over-education is a long-term problem which is correlated to differences
in permanent ability across graduates.

10


According to job-competition model of Thurow (1975), imaging there are two queues:
one for jobs and one for candidates. Each job in the job queue has specific requirements,
productivity characteristics and payment range. In candidate queue, each person has his
own position which depends on his education and experience. The higher position in
queue, the higher probability to have expected job in job queue. So that, individual tries
to increase his education level to have a better position in candidate queue with desire to
have a job which even be underemployed his knowledge and skills.
There is another explanation for the existence of over-education in labor market such as
the study of Jovanovic (1979). This study indicated that the imperfect in information of
labor market which causes mismatching between employer and employee about
worker’s productivity is the root of this problem. A worker can temporarily accept a job
which requires less knowledge and skills in order to express his truly productivity.
Another one came from Frank (1978) and McGoldrick and Robst (1996) that
geographical differences cause over-education. Employees who are restricted in a
specific labor market have higher risk to work in an over-educated job than workers in a
large labor market. The last one mentioned here came from Robst (1995) which
mentioned quality of studying. He argued that if a person attends a low-quality university
get less knowledge and skills than person in a high-quality university. So that, overeducation does not mean over-qualified for the job, an over-education can be necessary
to meet the requirement of job.
2.2.2. Determinants of vertical education-occupation mismatch
Dolton and Silles (2008) examined the determinants of probability of over/undereducation, including: studying major, class of degree (denotes how well a student passes
the final exam: at the first, second or third time), studying qualifications (denotes what

education level a person are being: post-graduate, degree, sub-degree, no qualifications)
and professional qualifications (including academic, professional or vocational).
Furthermore, he noticed some determinants related to the existing job, including:

11


working sector, occupation (denotes manager, professional, associate professional), firm
size, employment characteristics (self-employed, part-time and full-time), training,
experience for current job and squared experience. Moreover, macroeconomic variables
are found to have significant impact: unemployment, national statistics of graduates
(include university participation rate and graduate unemployment rate), labor market
mobility (job-motivated change). And the last group is personal characteristics (includes
gender, age, partner and child).
Dolton and Silles (2001) indicated that studying major have an important impact on the
likelihood of mismatched education. There is a theory that graduates in less vocationallyoriented qualification faculties have more tendency to work in an over-educated job. For
instance, Dolton and Silles (2001) found that graduates in faculty of education have more
probability of working in adequate job with their educated level than people in other
faculties. He also pointed out a higher likelihood of being over-educated of graduates in
arts, humanities and languages. Furthermore, class of degree reflects many unmeasured
characteristics of workers, especially ability. Most employers have the tendency to hire
workers with high ability which is considered as higher class of degree to reduce on-thejob training cost.
Following Dolton and Silles (2008), employees who are working in a small enterprise
with less than 25 workers have more probability in over-educated job. In his own
research in 2001, he argued that there is a more professional recruitment system and
more jobs in large firms than in small firms. Therefore, probability of mismatched
between education level and job’s requirements is reduced. Furthermore, working sector
is also thought as a significant determinant which affects mismatched probability
through differences in nature of competition. For instance, employees in public sectors
may have more risk to be over-educated because of low competitive working

environment.

12


Beside job related indicators, human related characteristics also have a remarkable part
in job-match probability. Firstly, different types of family engagement have different
effect on over/under education or in choosing a related job. For instance, men may have
more responsibility for financial support in family so they can choose a high-earnings
job which does not make full use of their skills and abilities (underemployed job). On
the other hand, women seem to have more responsibility for taking care of their family’s
members so they have tendency to choose an underemployed job with flexible working
time. Moreover, Dolton and Silles (2001) indicated that graduates who face family
engagement at an early age may have larger influence on mismatched likelihood than
individual with this engagement at older age. The reason can come from the trade-off
between high search cost and current consumption demand so fresh graduates have to
find any job despite of mismatched education level. Secondly, impact of marital status
on the probability of over-education can be explained through the effect of his partner’s
job. For example, a worker can be limited in job-searching range because of his partner’s
work place. Thirdly, health status has a mixed impact on mismatched career. On the one
hand, disable graduates can face many difficulties in searching an adequate job because
of limitation in moving or passing working stress. On the other hand, they can find a
related job easier than a graduates with good health because of subsidize policies of
government. For instance, article no.35 (Vietnamese disability law, 2010) encourages
enterprise hiring disable workers whereas these firms can have priority such as
exempting enterprise income tax, borrowing capital with low interest rate, lessening land
rental cost, and many other policies following the proportion of disable workers in firm.
Finally, debt problem in studying process may make graduates find an immediate career
to finance these loans although this career is not fit to his education level.
Another determinant is the imperfect information whereas candidates cannot know

exactly the actual required education level of job they are applying for. The only thing
they know is required level to entry job which is estimated by company. This difference

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in requirement of job causes many workers realize that they are over/under educated in
existing job.
2.3.

Effect of education-occupation mismatch on earnings

2.3.1. Mincer’s earnings model
Mincer (1958) applied the concept of compensating differences as the main idea to
clarify why individuals with distinct schooling levels received distinct earnings. In this
research, he assumed there are the identical abilities and opportunities among
individuals, so that difference in required occupation’s level hence of in their training
has different compensation. And size of this compensation differential is calculated by
subtracting cost of different human capital investment from present value of earnings
flow. Mincer (1958) argued that human capital investment expenses depend on the
length of schooling years in two aspects: the deferral of earnings in training period and
educational services/equipment cost (include tuition fee and books cost). In this research,
educational services/equipment cost is assumed to be zero to simplify the calculation.
Furthermore, individual’s wage is assumed to be unchanged during working life. With
above assumptions, the present value of life-earnings is equated:
𝑙

Vn = an ∫𝑛(𝑒 −𝑟𝑡 )𝑑𝑡 =

𝑎𝑛

𝑟

(𝑒 −𝑟𝑛 − 𝑒 −𝑟𝑙 )

Where:
Vn is present value of earnings flow with s years of schooling
an is annual earnings of individual with s years of schooling
r is externally interest rate at which future earnings are discounted
l is length of working life plus length of schooling for all persons, it means length of
working life of persons without education
By comparing present value of life-time earnings, Mincer (1958) found that differences
in annual earnings is the result of differences in the length of training (schooling years).
Equating the present values of two individuals with different schooling years Vn = Vn-d,

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