Conclusions
Qualification mismatch has become a topical issue in labor market policy in recent years,
as there is more and more evidence showing negative aspects of mismatch on both
individuals and the society. This paper investigates the effect of qualification mismatch
on earnings in Vietnam’s labor market. The data are taken from the Labor Force Survey
2015, and the estimations are performed with linear regression models.
First, it has been found that contrary to the qualification mismatch incidence in
developed countries, under-education is a more severe problem in Vietnam than
over-education. This finding is similar to those in other low-income countries due to the
low attained education level of the whole population and an unequal access to formal
schooling of people in rural and remote areas. This is a serious problem which calls for
further research because under-education results in low productivity growth and low
capacity for economic development (ILO, 2013).
Second, over-educated workers tend to have less working experience, while under-educated
tend to have more working experience, which is in line with the prediction of the human
capital theory.
Third, the returns to each year of over-education and under-education are lower than the
returns to each year of required education even when we control for years of working
experience. This shows that experience is not a perfect substitute for formal qualification,
and over- or under-education is not simply a statistical artefact but has a relation to
under-(over-)utilization of human capital. The situation is, however, better for females and
people in urban areas in the sense that they have higher returns to over-education years and
lower penalty for under-education years.
11 trang |
Chia sẻ: hachi492 | Ngày: 15/01/2022 | Lượt xem: 253 | Lượt tải: 0
Bạn đang xem nội dung tài liệu Qualification mismatch in the labor market and the impact on earnings: Evidence from Viet Nam, để tải tài liệu về máy bạn click vào nút DOWNLOAD ở trên
Qualification mismatch in the
labor market and the impact on
earnings: evidence from Vietnam
Huy Le Quang
Department of Education and Employment Over the Life Course,
Institute for Employment Research, Nürnberg, Germany, and
Binh Tran-Nam
School of Taxation and Business Law,
University of New South Wales, Sydney, Australia and
School of Business and Management,
RMIT University Vietnam, Ho Chi Minh City, Vietnam
Abstract
Purpose – The purpose of this paper is to investigate the incidence and earning effects of the vertical
mismatch between attained and required educational qualifications in a developing country’s labor market.
Design/methodology/approach – Following Duncan and Hoffman (1981), this paper uses the augmented
Mincerian wage equation to decompose the actual years of education of a person into years of over-education,
years of required education and years of under-education. These years of education are then fitted in an
ordinary least squares model to measure the earning effects of an employee when his/her attained educational
qualifications are higher or lower than the required educational level in his/her job.
Findings – Unlike studies in developed countries, this paper finds that Vietnam has a higher incidence of
under-education than over-education due to a large proportion of the population in rural and remote areas not
having access to formal education. Further, qualification mismatch has an asymmetric effect on earnings in
the sense that the wage rate is flexible downward but rigid upward. In particular, years of schooling that
are in excess or in deficit of the required level for the job are not compensated with higher earnings.
This paper concludes that although qualification mismatch incidence in Vietnam is different from that in
developed countries, mismatched workers also suffer from significant wage penalty.
Originality/value – This paper makes a significant contribution by providing the first evidence from a
developing country to the vertical mismatch literature which has already been overwhelmed with studies
from advanced economies.
Keywords Earnings, Vietnam, Labour market, Education, Qualification mismatch
Paper type Research paper
1. Introduction
Studies on school-to-work transition have increasingly focused on the mismatch
between required and attained qualifications of workers (Assirelli, 2015). This mismatch
can have certain negative ramifications on individuals such as wage penalty (Allen and
van der Velden, 2001), job dissatisfaction (Tsang and Levin, 1985), depreciation of skills (Büchel
and Mertens, 2004), and lower access to further training and education (Quintini, 2011).
The literature on qualification mismatch often reports findings from Western developed
countries where labor market conditions could be very different from those in a developing
country (Bedir, 2014; Chua and Chun, 2016). This paper aims to close the gap in the
Journal of Economics and
Development
Vol. 21 No. 2, 2019
pp. 223-233
Emerald Publishing Limited
e-ISSN: 2632-5330
p-ISSN: 1859-0020
DOI 10.1108/JED-09-2019-0032
Received 21 April 2019
Revised 16 August 2019
Accepted 16 September 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1859-0020.htm
JEL Classification — I24, I26, J24, J31
© Huy Le Quang and Binh Tran-Nam. Published in Journal of Economics and Development. Published
by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY
4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both
commercial and non-commercial purposes), subject to full attribution to the original publication and
authors. The full terms of this licence may be seen at
223
Qualification
mismatch in
the labor
market
literature by investigating the incidence of vertical qualification mismatch in Vietnam’s
labor market where jobs do not commensurate with people’s educational level. In addition,
we analyze the earning effects of this mismatch using the linear regression technique. The
paper, to the best of our knowledge, is the first paper investigating the qualification
mismatch incidence and effects on earnings in Vietnam.
Vietnam is an interesting case study because of several reasons. First, as a transition
economy, Vietnam has undergone massive structural changes in all sectors from a
central-command to a market-based economy since Doi Moi[1] policy in 1986. The strong
economic growth, the radical shift in economic structure and the emergence of new service
sectors (such as finance, banking, communication) in the economy, on the one hand, have
created new opportunities in the labor market. On the other hand, they have posed
challenges to workers for they require different skill and education profiles (Tran, 2018).
Second, as a country under the strong Confucianism influence, education plays a highly
important role in the society, evidenced by increasing investment in education or social
stratification based on formal qualifications. However, there exists a paradox that the more
educated, the higher the unemployment rate. For instance, the unemployment rate of people
with some forms of post-school training is around 3.5–5.6 percent, while this rate for
people without post-school training is limited to roughly 1.3–1.8 percent (General Statistics
Office, 2014). This official statistics coupled with overwhelming information on newspapers
about the difficulties of university graduates in finding jobs create an illusion that Vietnam
is already at the saturation stage of tertiary education graduates.
Using the Labor Force Survey in 2015 in Vietnam, we show a rather surprising result that
unlike developed countries, Vietnam has a higher incidence of under-education than over-
education, possibly due to the fact that a large proportion of population in rural areas not
having access to formal education. The over-education incidence is around 11.2 percent, while
under-education incidence is 56.1 percent. Further, in a related study, Tran (2018) shows a
non-negligible drop-out because students are not excited about study contents and
pedagogical methodology. This calls for the Government intervention to lift the financial
burden of young people so that they can stay longer in education, but, more importantly, to
radically change the educational system to be more in line with students’ needs and interests.
Using augmented Mincerian wage equation, we find that returns to each year of over-
education and under-education are significantly lower than those to each year of required
education. In terms of policy recommendation, it is appropriate to improve matching processes
in the labor market by more transparent information dissemination, higher incentives to raise
intensity of job search and on-the-job training to update skills and education profiles of workers.
The remainder of the paper is organized as follows: Section 2 briefly describes the
context of education and labor market in Vietnam and reviews literature in the field. Section
3 presents data and methodology in which we formulate our estimation models. Section 4
reports and analyzes empirical results of qualification mismatch incidence and impacts on
earnings. Finally, Section 5 gives concluding remarks and proposes policy
recommendations to improve the labor market success of mismatched workers in Vietnam.
2. Background and literature review
From the supply side, since the 1990s, Vietnam has implemented a number of higher
education reforms, particularly, through wage policies and investment to offer a wider range
of university programs (MOET, 2015; Nguyen and Vu, 2015). From 2000 to 2012, total
expenditure on education as a percentage of GDP increased substantially from 3.57 to
5.73 percent (MOET, 2015). As a result, total tertiary enrollment rate in Vietnam more than
tripled from 732,187 students in 2000 to 2.25m students in 2013 (The United Nations
Educational, Scientific and Cultural Organization, 2013). However, training programs are
often criticized to be inflexible, and focusing on theory rather than practice (Hayden, 2005).
224
JED
21,2
Further, under the influence of Confucianism, the society pays more attention to formal
qualifications and little to employability and productive skills; hence, there is a significant
discrepancy between what students learn at school and what they can do at work (MOET,
2015). Eventually, the low productive skills of graduates lead to a mismatch between formal
qualifications and occupations (Chua and Chun, 2016).
From the demand side, the International Labor Organization (ILO) finds that there is a
clear bias toward lower quality job in terms of monetary rewards, stability and security due
to limited number of “good” jobs in low-income countries (ILO, 2013). We, therefore, expect
to find a high incidence of qualification mismatch, given the current situation of demand and
supply in the labor market in Vietnam.
Theoretically speaking, every occupation has a required level of education (Er) for
optimal job performance (Hartog, 2000; Mendes de Oliveira et al., 2000; McGuinness, 2006;
Chiswick and Miller, 2009). Individuals whose education level is higher than this required
level are classified as over-educated (Eo), and those whose education level is lower than this
required level are classified as under-educated (Eu). Collectively, they form the
over-education-required education- under-education (ORU) status (Beckhusen et al., 2013).
In the case of over-education, a worker’s human capital is not fully utilized, leading to labor
productivity below the potential level. Given that firms pay no wage premium for human
capital that does not enhance labor productivity, over-educated workers would receive lower
wages than workers with the same qualifications but well-matched to their jobs (Allen and
van der Velden, 2001; Black, 2013). In the same manner, under-educated workers are also
generally found to earn lower wages than their well-matched colleagues (Verhaest and
Omey, 2012). However, this result is rather trivial and not surprising because compared to
adequately educated workers, under-educated workers invest less in their education, and
thus receive less in earnings. Therefore, in this paper, we focus more on the effect of
over-education on earnings.
Becker (1964) argues that human capital does not only consist of schooling years, but also
labor market experience, on-the-job training, innate ability and social network (family or peer
influence). Therefore, individuals, deemed to be over-educated, tend to lack labor market
experience or necessary job training. On the contrary, under-educated individuals tend to
possess more experience to justify their lack of formal schooling (Becker, 1964; Nielsen, 2011).
In other words, perceived mismatch may partly be a statistical artefact that reflects
unobserved labor market sorting due to individual heterogeneities (ability/motivation) within
educational categories (McGuinness and Pouliakas, 2016; Rohrbach-Schmidt and Tiemann,
2016). The excess (deficit) education, therefore, does not genuinely relate to under
(over)-utilization of skills, but to compensate for the deficiencies (surplus) in human capital
(Black, 2013). Consequently, only years of actual education matter to earnings. In that sense,
the returns to each year of required, over- and under-education should be equal. In other
words, there is no reward or penalty of being over- or under-educated.
Empirical evidence from developed countries shows that the over-education rate
among native-born citizen ranges from under 20 percent in Luxemburg and Poland to over
50 percent in Southern Europe, while under-education is negligible (Organization for
Economic Co-operation and Development, 2015). Recent empirical studies in developing
countries also present a significant portion of an over-educated workforce: approximately
38.6 percent in developing Asia, 30.1 percent in Peru but only 10.80 percent in Egypt
(Chua and Chun, 2016; ILO, 2013; Bedir, 2014). Further, according to the ILO (2013),
under-education is also substantial in low-income countries, for instance, 56.4 percent in
Cambodia, 45.7 percent in Liberia, 43 percent in Jordan, but only 21.8 percent in Sri Lanka
(ILO, 2013; Chua and Chun, 2016).
In this paper, we investigate the qualification mismatch in Vietnam to form a better
comparison with other countries.
225
Qualification
mismatch in
the labor
market
3. Methodology and data
3.1 Methodology
Measuring qualification mismatch incidence and its impacts on earnings. First, as the Labor
Force Survey does not provide the exact number of years a person spends on schooling,
we need to convert his/her final qualification into years of education. In other words, to
measure the actual years of education (Ei), we use the theoretical years of schooling
defined in the International Standard Classification of Education – 2011 (ISCED – 11) for
an individual who has achieved a given level of qualification. For example, a person who
has Bang tot nghiep pho thong trung hoc (Baccalaureate or ISCED – 11 Level 3–4) will be
assigned 12 years of education. Second, to identify required level of schooling (Eri ),
we use the objective method based on the International Standard Classification of
Occupations – 2008 (ISCO – 08). In particular, the ISCO – 08 defines four skill levels
required for the job, from elementary (1) to highly skilled (4) level. These skills levels are
then mapped to the corresponding required level of education in the ISCED – 11 to
determine the required years of schooling for a job. For instance, a person, working as a
manager with a skill level of 4, will be required to have at least 16–18 years of education
(the exact number of years depends on the sub-category of this management position).
The advantages of using objective method as opposed to subjective method are that it is
based on the technology of the job and overcomes the measurement errors often found in
survey data (Hartog, 2000; Leuven and Oosterbeek, 2011).
Following Duncan and Hoffman (1981), we decompose actual years of education using
the following definition:
Ei Eri þEoiEui ;
where:
Eoi
EiEri ; if Ei4Eri
0; otherwise
(
; and Eui
EriEi; if EioEri
0; otherwise
(
:
The augmented Mincerian earnings function can then be written as:
LnY i ¼ b0þb1Eri þb2Eoi þb3Eui þXigþei; (1)
where LnYi is the natural logarithm of hourly wages; β1, β2, β3 are the returns to years of
required education, over-education and under-education, respectively; Xi is a vector control
for individual characteristics; and εi is a classical, idiosyncratic error.
The ordinary least square (OLS) estimate of βs will be unbiased and consistent estimates
of the causal effect of education on earnings, provided that there is no correlation between εi
and Ei (Cov(εi, Ei) ¼ 0). However, because of the non-random assignment of individuals to
completed schooling levels and to job requirements, this assumption may not hold, which
may lead to biased estimates (Leuven and Oosterbeek, 2011). In particular, this is a classical
example of omitted variable bias, in which the non-observable personal characteristics of a
person such as cognitive and non-cognitive skills might have a positive correlation with the
decision to invest in education. This investment in education, in turn, determines the wages
that he/she can earn later in life. In this case, the coefficient estimates of Ei does not simply
give the direct effect of schooling years on wages, but rather its sum with the indirect effects
of other unobservable characteristics (such as cognitive and non-cognitive skills and
motivation level) on wages. Consequently, the coefficient estimates of schooling years on
earnings are often biased upward.
226
JED
21,2
In order to minimize the unobserved individual heterogeneity, which results in biased
estimates, the model controls for a detailed set of individual characteristics: age cohorts,
labor market experience, gender, marital status and region of settlement. This practice,
unfortunately, does not rule out the possibility that the model still contains some
unobservable variables. However, this would not harm our specific analysis since we do not
focus on the returns to education (over-education and under-education) per se, but on
whether over- and under-educated workers have lower returns to education than a
well-matched worker having the same characteristics. Therefore, under our assumption that
the biases of the coefficient estimates of Eri ; E
o
i and E
u
i go in the same direction (i.e. upward
bias), we can still make the intended comparison.
3.2 Data
The paper uses the Labor Force Survey conducted by the Vietnam Ministry of Planning
and Investment under the technical assistance of the ILO in 2015. This is a representative
annual household survey aimed at collecting fundamental information about the labor
market such as employment activities, education, income and job search behaviors. This
cross-sectional dataset consists of 205,714 people. Our target sample is people in the
working age (from 15 to 64 years old) and working full-time in the formal sectors of
the economy (excluding the military sector) as the information of people working in the
informal sectors is usually unreliable (Black, 2013; Bedir, 2014). We do not include people
working in the informal sectors in our analysis because people working in these sectors
often face with low incomes, limited opportunities for skill development and precarious
working conditions (ILO, 2013). Thus, this group of workers often has different motives to
participate in the labor market than the workers in the formal sectors. Including workers
in the informal sectors in our models will potentially lead to selection biases. Further, we
want to maintain only a sample of workers in the formal sectors to be comparable with
other previous studies in this field.
Following the approach of Bedir (2014), the informal sector consists of people working
for companies without business registration, in subsistent farming, or whose wages fall
below the minimum wage in 2015 (2.150m Vietnamese Dong/month). After excluding people
outside our target sample, the effective sample size is 29,635.
4. Findings and discussion
4.1 Qualification mismatch incidence
A summary of key variables in our sample is presented in Table I. On average, we find that
years of acquired education of Vietnamese workers are slightly lower than years of required
education (roughly two years in deficit). This can be explained by low proportion of
individuals having tertiary degrees (approximately 35 percent, compared to over 60 percent
in developed countries) (ILO, 2013; Chua and Chun, 2016). The table also shows a
statistically significant difference in tertiary educational attainment between two genders
(males: 30.8 percent, female: 37.6 percent)[2].
In Table I, we report the incidence of qualification mismatch. The incidence of
under-education seems to be more substantial (56.1 percent) than over-education
(only 11.2 percent). In particular, we find that qualification mismatch is a bigger issue for
males than for females. However, the unemployment rate of female workers (2.3 percent) is
twice as high as that of males (1.1 percent), even though both rates are rather small.
This is an interesting finding because despite the higher education reform which
increases the number of tertiary education students, over-education seems not to be a big
problem in Vietnam’s labor market, unlike a common claim that Vietnam is at the stage of
“too many masters, and too few laborers” (Tran, 2018). The qualification mismatch with
227
Qualification
mismatch in
the labor
market
more pronounced under-education rate could be explained by relatively low educational
attainment. But, more importantly, labor markets in developing countries are often
characterized by a large share of informal sector employment. This sector generally does not
require high levels of qualifications, which makes over-education incidence more severe
among self-employed workers (Chua and Chun, 2016). Since we already omitted
observations in the informal sector of the economy, over-education incidence could be
underestimated in our effective sample.
In Table II, we compare the distribution of mismatch across years of working experience,
educational attainment, regions of settlement and occupational groups. There is evidence
that over-education rate decreases with years of experience (from 22.8 to only 5.7 percent),
and under-education rate increases with years of experience (from 39 percent up to
74.5 percent). These results are expected in the standard human capital theory as working
experience can be treated as a substitute to formal qualifications (Becker, 1964; Nielsen,
2011; Black, 2013). The higher over-education rate for younger workers could also arise from
greater difficulties in signaling their abilities or lack of information and experience with job
search (Chua and Chun, 2016). And the higher under-education rate of people approaching
retirement could be consistent with rising educational levels and changing job expectations
in developing countries (Chua and Chun, 2016).
Disaggregation by regions of settlement does not show significant difference in the
distribution of mismatch. However, the situation in rural areas seems to be better,
evidenced by higher well-matched rate and lower under-education rate. In Vietnam’s
context, we would argue that this result is due to the fact that people move from rural to
Total Male Female
Years of acquired education 12.03 (4.05) 11.83 (4.05) 12.27 (4.03)
Years of required education 14.14 (2.48) 14.05 (2.54) 14.25 (2.39)
Education level ( figures in percentage)
Primary or less (o ¼ ISCED 1) 0.137 0.140 0.133
Lower secondary (ISCED 2) 0.193 0.207 0.176
Upper secondary (ISCED 3–4) 0.308 0.321 0.294
Undergraduate (ISECD 5–6) 0.339 0.308 0.376
Postgraduate (ISCED 7–8) 0.023 0.024 0.021
Total 1.000 1.000 1.000
% mismatch incidence using difference between years of education acquired and required
Well-matched 0.311 0.288 0.337
Over-educated 0.112 0.116 0.109
Under-educated 0.561 0.585 0.531
Unemployed 0.016 0.011 0.023
Total 1.000 1.000 1.000
Mean age 36.09 36.80 35.27
% female in population 0.464
% share of employment by occupation
High-skill white collar 0.398 0.361 0.442
Low-skill white collar 0.266 0.234 0.303
Crafts and related trade worker 0.271 0.332 0.201
Elementary and skilled agricultural worker 0.065 0.073 0.054
Total 1.000 1.000 1.000
Log (ln) hourly wage 3.23 (0.52) 3.28 (0.53) 3.17 (0.49)
Observations (n) 29,635 15,885 13,750
Note: Figures are unweighted, and standard errors are in parentheses
Source: Authors’ calculation
Table I.
Summary statistics –
Labor Force Survey
(15–64 years old)
228
JED
21,2
urban areas to find employment, which, thus, raises the incidence of under-education in
the city.
Statistics of qualification mismatch across occupational groups reveal the highest
under-education rate in Crafts and related trade workers with 91.7 percent, as this category
generally does not require high formal qualifications to perform the job.
4.2 Impacts of qualification mismatch on earnings
Table III summarizes our estimates of impacts on earnings of qualification mismatch using
OLS under the ORU framework. The first column reports the raw estimates in a model with
no control variables. In the second column, we add a full list of control variables. In columns
3 and 4, we compare the results between males and females. In columns 5 and 6, we compare
the results between rural and urban areas.
First, we find that in the model with all control variables, the return to years of required
education is approximately 7.8 percent, the return to years of over-education is 6.3 percent and
to years of under-education is −4.1 percent, ceteris paribus. These results are higher than
findings in the mismatch literature. For example, Groot and van den Brink (2000), in a
meta-analysis of 50 estimates on the returns to different educational components in developed
countries, show that the unweighted averages of the return to required education of 5.6 percent,
to over-education of 3 percent and to under-education of −1.5 percent, respectively.
Second, the returns to qualification mismatch also show heterogeneous impacts with
respect to gender and region of settlement. In particular, females have higher returns to both
required and over-education, and lower under-education wage penalty compared to males.
This is similar to the findings of Moock et al. (2003) that the returns to higher education are
higher for females than for males in Vietnam. In addition, the returns to years of required
education in urban areas is larger than in rural areas (8.7 and 7.3 percent, respectively).
However, years of under-education is penalized more strongly in urban than in rural areas,
Qualification – job match
Well-matched Over-educated Under-educated Total
Working experience
Less than 5 years 0.382 0.228 0.390 1.000
From 6 to 10 years 0.368 0.148 0.484 1.000
From 11 to 20 years 0.356 0.106 0.538 1.000
From 21 to 30 years 0.269 0.071 0.660 1.000
Above 30 years 0.198 0.057 0.745 1.000
Education level
Primary or less (o ¼ ISCED 1) 0.144 0.000 0.856 1.000
Lower secondary (ISCED 2) 0.000 0.098 0.902 1.000
Upper secondary (ISCED 3–4) 0.123 0.037 0.840 1.000
Undergraduate (ISECD 5–6) 0.761 0.180 0.059 1.000
Postgraduate (ISCED 7–8) 0.000 1.000 0.000 1.000
Regions
Urban 0.274 0.113 0.613 1.000
Rural 0.347 0.115 0.538 1.000
Occupational groups
High-skill white collar 0.647 0.076 0.277 1.000
Low-skill white collar 0.107 0.149 0.744 1.000
Crafts and related trade worker 0.034 0.049 0.917 1.000
Elementary and skilled agricultural worker 0.306 0.484 0.210 1.000
Source: Authors’ calculation
Table II.
Distribution of
education mismatch
by sector
229
Qualification
mismatch in
the labor
market
Fu
ll
sa
m
pl
e
Fu
ll
sa
m
pl
e
M
al
e
Fe
m
al
e
U
rb
an
R
ur
al
(1
)
(2
)
(3
)
(4
)
(5
)
(6
)
Y
ea
rs
of
re
qu
ir
ed
ed
uc
at
io
n
0.
06
9*
**
(0
.0
01
)
0.
07
8*
**
(0
.0
02
)
0.
07
6*
**
(0
.0
02
)
0.
08
1*
**
(0
.0
02
)
0.
08
7*
**
(0
.0
03
)
0.
07
3*
**
(0
.0
02
)
Y
ea
rs
of
ov
er
-e
du
ca
tio
n
0.
04
8*
**
(0
.0
03
)
0.
06
3*
**
(0
.0
03
)
0.
05
8*
**
(0
.0
04
)
0.
06
8*
**
(0
.0
04
)
0.
07
9*
**
(0
.0
05
)
0.
05
3*
**
(0
.0
04
)
Y
ea
rs
of
un
de
r-
ed
uc
at
io
n
−
0.
02
6*
**
(0
.0
01
)
−
0.
04
1*
**
(0
.0
01
)
−
0.
04
3*
**
(0
.0
02
)
−
0.
03
9*
**
(0
.0
02
)
−
0.
04
2*
**
(0
.0
02
)
−
0.
03
9*
**
(0
.0
02
)
A
ge
co
ho
rt
s
15
–
25
R
ef
R
ef
R
ef
R
ef
R
ef
26
–
35
0.
01
8
(0
.0
11
)
−
0.
00
3
(0
.0
16
)
0.
03
5*
*
(0
.0
16
)
0.
02
7
(0
.0
18
)
0.
01
2
(0
.0
15
)
36
–
45
0.
02
8
(0
.0
19
)
−
0.
00
4
(0
.0
27
)
0.
05
8*
*
(0
.0
27
)
0.
02
0
(0
.0
30
)
0.
03
3
(0
.0
25
)
46
–
55
0.
02
7
(0
.0
27
)
−
0.
02
7
(0
.0
39
)
0.
08
7*
*
(0
.0
38
)
−
0.
02
3
(0
.0
44
)
0.
05
7*
(0
.0
35
)
56
–
64
−
0.
00
4
(0
.0
37
)
−
0.
01
8
(0
.0
51
)
0.
00
4
(0
.0
55
)
−
0.
10
7*
(0
.0
59
)
0.
06
7
(0
.0
47
)
Y
ea
rs
of
ex
pe
ri
en
ce
0.
02
7*
**
(0
.0
01
)
0.
03
0*
**
(0
.0
02
)
0.
02
3*
**
(0
.0
02
)
0.
02
5*
**
(0
.0
02
)
0.
02
8*
**
(0
.0
02
)
Y
ea
rs
of
ex
pe
ri
en
ce
-s
qu
ar
ed
−
0.
00
4*
**
(0
.0
00
)
−
0.
00
4*
**
(0
.0
00
)
−
0.
00
3*
**
(0
.0
00
)
−
0.
00
3*
**
(0
.0
00
)
−
0.
00
4*
**
(0
.0
00
)
G
en
de
r
M
al
e
R
ef
R
ef
R
ef
Fe
m
al
e
−
0.
10
4*
**
(0
.0
05
)
−
0.
13
3*
**
(0
.0
08
)
−
0.
08
4*
**
(0
.0
07
)
M
ar
ita
ls
ta
tu
s
Si
ng
le
R
ef
R
ef
R
ef
R
ef
R
ef
M
ar
ri
ed
0.
05
3*
**
(0
.0
07
)
0.
08
8*
**
(0
.0
11
)
0.
03
1*
**
(0
.0
09
)
0.
02
5*
*
(0
.0
11
)
0.
07
6*
**
(0
.0
09
)
R
eg
io
n
U
rb
an
R
ef
R
ef
R
ef
R
ur
al
−
0.
07
9*
**
(0
.0
05
)
−
0.
09
7*
**
(0
.0
07
)
−
0.
06
2*
**
(0
.0
07
)
Co
ns
ta
nt
2.
33
4*
**
(0
.0
20
)
1.
95
8*
**
(0
.0
25
)
1.
98
5*
**
(0
.0
34
)
1.
81
6*
**
(0
.0
36
)
1.
89
3*
**
(0
.0
43
)
1.
90
1*
**
(0
.0
30
)
N
um
be
r
of
ob
se
rv
at
io
ns
29
,6
35
29
,6
35
15
,8
85
13
,7
50
12
,5
86
17
,0
49
R
2
0.
13
2
0.
24
8
0.
24
5
0.
23
6
0.
22
2
0.
27
2
N
ot
es
:O
R
U
M
od
el
.D
ep
en
de
nt
va
ri
ab
le
:n
at
ur
al
lo
ga
ri
th
m
of
ho
ur
ly
w
ag
es
.S
am
pl
e
of
w
or
ke
rs
fr
om
15
to
64
ye
ar
s
ol
d,
w
or
ki
ng
fu
ll-
tim
e
in
th
e
fo
rm
al
se
ct
or
s
of
th
e
ec
on
om
y
(e
xc
lu
di
ng
th
e
m
ili
ta
ry
se
ct
or
).
W
e
us
e
ag
e
co
ho
rt
du
m
m
ie
s
in
st
ea
d
of
ag
e
to
av
oi
d
m
ul
tic
ol
lin
ea
ri
ty
w
ith
ye
ar
s
of
ex
pe
ri
en
ce
s.
H
et
er
os
ke
da
st
ic
ity
ro
bu
st
st
an
da
rd
er
ro
rs
in
pa
re
nt
he
se
s.
*,
**
,*
**
Si
gn
ifi
ca
nt
at
10
,5
an
d
1
pe
rc
en
t
le
ve
ls
,r
es
pe
ct
iv
el
y
S
ou
rc
e:
A
ut
ho
rs
’
ca
lc
ul
at
io
n
Table III.
Qualification
mismatch and impacts
on earnings – OLS
230
JED
21,2
probably due to the fact that there are more under-educated people in urban than in
rural areas.
Other control variables also show expected directions and are statistically significant at
the 1 percent level. As shown by adjusted R-squared, the models explain roughly
24–27 percent of the variations in individuals ‘earnings. This explanatory power may seem
relatively low, but it is quite common in empirical research on wages (Black, 2013).
In short, unlike the conventional Mincerian model of the returns on education, where the
relationship between education and earnings is taken for granted as linear and strictly
increasing (i.e. strictly monotonic), the relationship between investment in education and
return to education under the ORU model is likely not monotonic because years of
over-education and under-education result in lower returns than years of required
education. In addition, as we noted earlier, these estimates could still be biased due to
confounding factors which are unobserved but affect both education and wages such as
individual abilities and motivation.
5. Conclusions
Qualification mismatch has become a topical issue in labor market policy in recent years,
as there is more and more evidence showing negative aspects of mismatch on both
individuals and the society. This paper investigates the effect of qualification mismatch
on earnings in Vietnam’s labor market. The data are taken from the Labor Force Survey
2015, and the estimations are performed with linear regression models.
First, it has been found that contrary to the qualification mismatch incidence in
developed countries, under-education is a more severe problem in Vietnam than
over-education. This finding is similar to those in other low-income countries due to the
low attained education level of the whole population and an unequal access to formal
schooling of people in rural and remote areas. This is a serious problem which calls for
further research because under-education results in low productivity growth and low
capacity for economic development (ILO, 2013).
Second, over-educated workers tend to have less working experience, while under-educated
tend to have more working experience, which is in line with the prediction of the human
capital theory.
Third, the returns to each year of over-education and under-education are lower than the
returns to each year of required education even when we control for years of working
experience. This shows that experience is not a perfect substitute for formal qualification,
and over- or under-education is not simply a statistical artefact but has a relation to
under-(over-)utilization of human capital. The situation is, however, better for females and
people in urban areas in the sense that they have higher returns to over-education years and
lower penalty for under-education years.
As the data do not allow us to investigate the horizontal mismatch incidence where
people work in a profession different than the one they have been trained for, we can only
focus on the vertical mismatch where education is quantitatively higher or lower than the
required level of education in the job that they hold. We acknowledge that this could be a
drawback of this research because we do not have information about workers’ fields of
study; however, this paper still makes a significant contribution by providing first
evidence from a developing country to the vertical mismatch literature which has
already been overwhelmed with studies from advanced economies. In terms of policy
responses, it seems appropriate to introduce measures that improve matching process in
the labor market through more transparent information dissemination, higher incentives
to raise the intensity of job search and on-the-job trainings should be provided to raise
the productivity as well as the earnings level of workers, especially in the case of
under-education.
231
Qualification
mismatch in
the labor
market
Acknowledgments
The authors would like to thank all conference participants at the 5th International
Conference on Vietnam Studies in 2016 in Vietnam and anonymous reviewers for helpful
comments and suggestions. Huy Le-Quang also gratefully acknowledges financial support
from the Graduate School (GradAB) at the Institute for Employment Research and
Friedrich-Alexander University in Erlangen – Nuremberg. All remaining errors are the
authors’ own. The views, opinions, findings and conclusions expressed in this paper are
strictly those of the authors.
Notes
1. Doi Moi means reform or renovation.
2. Pearson χ2 test of difference in proportions ¼ 243.2, indicating a statistically significant difference
in tertiary educational attainment between males and females at the 1 percent level.
References
Allen, J. and van der Velden, R.K.W. (2001), “Education mismatches versus skill mismatches: effects
on wages, job satisfaction, and on-the-job search”, Oxford Economic Papers, Vol. 53 No. 3,
pp. 434-452.
Assirelli, G. (2015), “Credential and skill mismatches among tertiary graduates: the effect of labor
market institutions on the differences between fields of study in 18 countries”, European
Societies, Vol. 17 No. 4, pp. 535-568.
Becker, G. (1964), Human Capital, Columbia University Press, New York, NY.
Beckhusen, J., Florax, R.J.G.M., Poot, J. and Waldorf, B.S. (2013), “Attracting global talent and then
what? Overeducated immigrants in the United States”, Journal of Regional Science, Vol. 53 No. 5,
pp. 834-854.
Bedir, N. (2014), “The impact of overeducation and undereducation on earnings: Egypt in a
post-revolutionary era”, master thesis, Lund University, Lund.
Black, D.J. (2013), “The utilization of human capital from education in Australian labour markets:
over-education?”, PhD thesis, Melbourne Institute of Applied Economic and Social Research,
The University of Melbourne, Melbourne.
Büchel, F. and Mertens, A. (2004), “Overeducation, under-education, and the theory of career mobility”,
Applied Economics, Vol. 36 No. 8, pp. 803-816.
Chiswick, B.P. and Miller, P.W. (2009), “The international transferability of immigrants’ human
capital”, Economics of Education Review, Vol. 28 No. 2, pp. 162-169.
Chua, K. and Chun, N. (2016), “In search of a better match: qualification mismatches in developing
Asia”, ADB Economics Working Paper Series No. 476, Manila.
Duncan, G.J. and Hoffman, S. (1981), “The incidence and wage effects of over-education”, Economics of
Education Review, Vol. 1 No. 1, pp. 75-86.
General Statistics Office (2014), Statistical Yearbook of Vietnam – 2014, Statistical Publishing House,
Hanoi.
Groot, M. and van den Brink, H.M. (2000), “Overeducation in the labour market: a meta-analysis”,
Economics of Education Review, Vol. 19 No. 2, pp. 149-158.
Hartog, J. (2000), “Over-education and earnings: where are we, where should we go?”, Economics of
Education Review, Vol. 19 No. 2, pp. 131-147.
Hayden, M. (2005), The Legislative and Regulatory Environment of Higher Education in Vietnam,
The World Bank, Washington, DC.
ILO (2013), Global Employment Trends for Youth 2013: A Generation At Risk, ILO, Geneva.
232
JED
21,2
Leuven, E. and Oosterbeek, H. (2011), “Overeducation and mismatch in the labor market”, Handbook of
the Economics of Education, Vol. 4, Elsevier, pp. 283-326.
McGuinness, S. (2006), “Overeducation in the labour market”, Journal of Economic Surveys, Vol. 20
No. 3, pp. 387-418.
McGuinness, S. and Pouliakas, K. (2016), “Deconstructing theories of overeducation in Europe: a wage
decomposition approach”, in Polachek, S.W., Pouliakas, K., Russo, G. and Tatsiramos, K. (Eds),
Skill Mismatch in Labor Markets (Research in Labor Economics, Volume 45), Emerald
Publishing Limited, Bingley, pp. 81-127.
Mendes de Oliveira, M., Santos, M.C. and Kiker, B.F. (2000), “The role of human capital and technological
change in overeducation”, Economics of Education Review, Vol. 19 No. 2, pp. 199-206.
MOET (2015), Vietnam National Education for All – 2015 Review, MOET, Hanoi.
Moock, P.R., Patrinos, H.A. and Venkataraman, M. (2003), “Education and earnings in a transition
economy: the case of Vietnam”, Economics of Education Review, Vol. 22 No. 5, pp. 503-510.
Nguyen, V.N. and Vu, N.T. (2015), “Higher education reforms in Vietnam: current situations, challenges
and solutions”, VNU Journal of Science, Vol. 31 No. 4, pp. 85-97.
Nielsen, C.P. (2011), “Immigrant over-education: evidence from Denmark”, Journal of Population
Economics, Vol. 24 No. 2, pp. 449-520.
Organization for Economic Co-operation and Development (2015), Indicators of Immigrant Integration
2015: Settling In, OECD Publishing, Paris.
Quintini, G. (2011), “Over-qualified or under-skilled: a review of existing literature”, OECD Social,
Employment and Migration Working Paper No. 121, Paris.
Rohrbach-Schmidt, D. and Tiemann, M. (2016), “Educational (mis)match and skill utilization in
Germany: assessing the role of worker and job characteristics”, Journal for Labour Market
Research, Vol. 49 No. 2, p. 99, doi: 10.1007/s12651-016-0198-9.
Tran, T.T. (2018), “Youth transition to employment in Vietnam: a vulnerable path”, Journal of
Education and Work, Vol. 31 No. 1, pp. 59-71.
Tsang, M. and Levin, H. (1985), “The economics of overeducation”, Economics of Education Review,
Vol. 4 No. 2, pp. 93-104.
United Nations Educational, Scientific and Cultural Organization (2013), Higher Education in Vietnam,
Institute for Statistics, UNESCO, New York, NY.
Verhaest, D. and Omey, E. (2012), “Overeducation, undereducation and earnings: further evidence on
the importance of ability and measurement error bias”, Journal of Labor Research, Vol. 33 No. 1,
pp. 76-90.
Corresponding author
Huy Le Quang can be contacted at: huy.le@gmx.de
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
233
Qualification
mismatch in
the labor
market
Các file đính kèm theo tài liệu này:
- qualification_mismatch_in_the_labor_market_and_the_impact_on.pdf