An Observation on Graduates’ Over-Education and Under-Education of Higher Education

An Observation on Graduates’ Over-Education and Under-Education
of Higher Education
DING Hua, PANG Na
School of Economics and Trade, Henan University of Technology, P. R. China, 450052
[email protected]
Abstract: With the intensive expansion of higher education in China in recent years, the question arises
whether its graduates are being oversupplied to the labor market. This paper employs the first survey
conducted in universities of China covering the questions of individuals’ personal characters, academic
achievement, employment information and self-estimated qualifications required in jobs and intend to
discover the incidence of over-education and under-education, what determines over-education and
under-education and whether the stylized facts on wages hold in China.
Key words: Over-education, Under-education, Higher education, Wages, Logit model
1 Introduction
Since the policy of higher education expansion aiming to slow down the employment pressure was
implemented, there has been a rapid expansion of China’s higher education since 1999 that the
enrolment rate to tertiary education soaring more than five times. The attendants to higher education
expand with the increasing of unemployment rate, but at a higher speed.
The impact of over (under)education on wages. In the last issue, two hypotheses will be examined: The
earnings of workers in occupations that require less schooling than they actually have (i.e. they are
overeducated) are less than the earnings of workers with the same level of education as themselves.
However, those overeducated workers earn more than the workers with similar jobs but not acquiring
more than job needed qualifications. The earnings of workers in occupations that require more schooling
than they actually have(undereducated) are more than the earnings of workers with the same
qualification but in a job just requiring their level of schooling. But, undereducated workers receive
fewer earnings than the workers who obtained job needed qualifications. In this paper, we will mainly
explore three questions: the incidence of over-education and under-education. In particular, we examine
the graduate labour market in China using a recent survey conducted in 2003 from 45 universities in 7
provinces for each level of higher education to represent the whole Chinese graduate labour market.
Through self-reported answers on their actual qualifications and estimated required qualifications on
jobs, we can statistically summarize the percentage of over (under) educated individuals. The above
stylized facts were found by Duncan and Hoffman (1981); Hartog (1986); Rumberger (1987); Hartog
and Oosterbeek (1988); Sicherman (1991); Sloane et al (1999) and Dolton and Vignoles (2000) in UK,
USA and other developed countries.
2 The incidence of over-education and under-education
This paper employs the survey data conducted by a project fund by the Ministry of Education of China
in 2007. The survey was designed to use sample selection method and choose three provinces in each
representative economic development area (the East, the Middle and the West). In each provinces, 6
higher education institutions were chosen, where there are 2 elite universities, 2 common universities
and 2 polytechnic colleges. However due to the inequality of universities in each province and other
practical problems, the real investigated provinces and higher education institutions are as follows:
Beijing (4), Shangdong (6), Guangdong(6), Hunan(6), Shannxi (4), Yunnan (17), Guangxi (1).
Altogether, there are 7 provinces and 45 universities took part in the survey and the total sample number
is 18722. Among these samples, 39.3% percent individuals acquired college or equivalent qualifications
and graduates who obtained bachelor, master, PhD occupies 57%, 3.0% and 0.6% percent respectively.
Male proportion for these four levels of degrees are 52.7%, 65.4%, 59.2% and 73.9% respectively.
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Gender ratio is almost balanced in each level of qualification, except doctoral level. There are around 20
percent graduates are believed they are overeducated in the China’s labour market, among which college
graduate, bachelor, master and doctor takes 12.9%, 21%, 36% and 42% respectively.
3 The impact of over-education and under-education on wages
In order to examine the two stylized facts that the effects of mismatch on individuals’ wages we create a
two-step method. Firstly, examine whether over-education (under-education) may have a negative
(positive) effect on wages for individuals’ acquired the same level of qualifications by regressing log
wages on individuals’ actually obtained education, plus two dummy variables of over-education and
under-education. Secondly, we test whether over-education (under-education) may have a positive
(negative) effect on wages for individuals’ doing the jobs that required the same level of education.
From these two regression results we can clearly know whether the two hypotheses hold in China.
The purpose can be realized by regressing equation(1), where S means actually acquired qualifications,
X represents all the other variables that may affect individuals’ wages including personal characters,
academic achievement and employment information. λ is the Inverse Mills ratio, which intends to
overcome self-selection bias and inefficiency of OLS estimation because of truncated dataset and ε is
the disturbance term. Over and under are two dummy variables, which can explain whether
overeducated (undereducated) graduates may earn less (more) than the graduates with the same
qualification, but exact job required
log E = a1 + a2 S + a3over + a 4under + a5 X + λ + ε
(1)
Table 1 tells us that there are positive and significant returns to bachelors and masters, especially for
masters that may contribute 55% percent increase to wages comparing to bachelor degree (for male 59%
and for female 53%). we regress the log earning equation on the job required qualifications,
overeducated (undereducated) part in each level of qualification and other vectors in equation (2), in
order to evaluate the premium for over-education and penalty for under-education.
log E = a1 + a 2 q r + a3 q o + a 4 q u + a5 X + λ + ε
variables
gender
college
degree
Master
PhD
Over
under
cadre
ethnic
regis
pmember
univrank
univgrade
English
Pquali
Govern
statown
joinvent
East
Middle
law
medicine
science
(2)
Table 1 OLS Log wages’ Regressing on Actually Acquired Qualifications
Combined
Male
Female
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
0.0004
0.013
-----0.183** 0.09
0.009
0.11
-0.443***
0.13
0.124*
0.08
0.075
0.11
0.384***
0.13
0.545*** 0.03
0.588*** 0.04
0.532***
0.05
-0.016
0.07
0.086
0.08
-0.524***
0.15
-0.052*** 0.02
-0.069*** 0.02
-0.015
0.03
0.023*
0.02
0.042**
0.02
-0.015
0.03
0.078*** 0.01
0.069*** 0.02
0.105***
0.02
0.004
0.02
0.027
0.03
-0.015
0.04
-0.044*** 0.01
-0.038*** 0.01
-0.049***
0.01
-0.001
0.01
-0.0001
0.02
0.004
0.02
-0.024*** 0.003
-0.031*** 0.004
-0.005
0.01
0.005
0.01
0.005
0.01
0.012
0.01
-0.022*** 0.01
-0.034*** 0.01
0.007
0.01
-0.021*** 0.01
-0.021*** 0.01
-0.027***
0.01
-0.048*** 0.02
-0.097*** 0.02
0.033
0.03
-0.002
0.02
-0.019
0.02
0.022
0.03
0.147*** 0.02
0.130*** 0.03
0.167***
0.04
0.039
0.03
-0.003
0.03
0.143***
0.05
-0.165*** 0.03
-0.158*** 0.03
-0.159***
0.06
-0.108*** 0.03
-0.099**
0.05
-0.061
0.05
-0.205*** 0.04
-0.166*** 0.06
-0.169***
0.05
-0.095*** 0.02
-0.036
0.04
-0.139***
0.04
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engineering
agriculture
econ
history
education
management
philosophy
constant
Adjusted
R-squared
-0.036*
-0.237***
0.022
-0.162***
0.019
-0.042*
-0.084
7.771***
0.2623
0.02
0.05
0.03
0.05
0.09
0.02
0.08
0.10
-0.016
-0.183***
0.038
-0.178*
0.206
-0.014
-0.110
7.643***
0.2566
0.03
0.06
0.04
0.07
0.17
0.03
0.10
0.13
-0.028
-0.347***
0.033
-0.143**
-0.084
-0.046
-0.058
7.770***
0.3084
0.03
0.10
0.05
0.07
0.11
0.03
0.13
0.15
Note: * means the coefficient is significant at 10 percent interval, ** means the coefficient is significant
at 5 percent interval and *** means the coefficient is significant at 1 percent interval.
We regress the log earning equation on the job required qualifications, overeducated (undereducated)
part in each level of qualification and other vectors in equation (2), in order to evaluate the premium for
over-education and penalty for under-education. Where qr represents the job required qualification, qo
denotes overeducated parts and q means undereducated parts.
log E = a1 + a 2 q r + a3 q o + a 4 q u + a5 X + λ + ε
Table 2 OLS Log Wages’ Regression on Job Required Qualifications
Combined
Male
Female
Coef.
Std. Err.
Coef.
Std. Err.
Coef.
Std. Err.
-0.001
0.01
----Gender
-0.035
0.04
-0.057
0.04
0.013
0.07
College
0.072*** 0.03
0.100*** 0.03
0.051
0.04
Degree
0.455*** 0.03
0.452*** 0.04
0.485*** 0.05
Master
-0.049
0.05
-0.004
0.06
-0.218**
0.11
PhD
0.014
0.07
-0.016
0.08
0.008
0.11
Ovcoll
0.005
0.03
-0.001
0.04
0.025
0.05
Ovdeg
0.506*** 0.05
0.529*** 0.07
0.503***
0.09
Ovmas
0.231**
0.10
0.299*** 0.11
-0.060
0.22
Ovphd
-0.064** 0.03
-0.054
0.04
-0.085**
0.04
Uncoll
-0.113
0.11
0.088
0.14
-0.244
0.18
Unmas
-0.436*** 0.04
-0.435*** 0.04
-0.463*** 0.06
Undeg
-0.025*** 0.003
-0.033*** 0.004
-0.006
0.01
univrank
-0.001
0.01
0.001
0.01
-0.0001
0.01
univgrade
-0.020*** 0.01
-0.033*** 0.01
0.014
0.01
English
-0.022*** 0.01
-0.021*** 0.01
-0.028*** 0.01
Pquali
-0.048*** 0.01
-0.042*** 0.01
-0.057*** 0.01
Regis
0.001
0.02
0.025
0.03
-0.018
0.04
Ethnic
0.071*** 0.01
0.065*** 0.02
0.092***
0.02
Cadre
0.004
0.01
-0.0003
0.02
0.014
0.02
Pmember
-0.056*** 0.02
-0.101*** 0.02
0.024
0.03
Govern
0.005
0.02
-0.011
0.02
0.030
0.03
statown
0.154*** 0.02
0.129*** 0.03
0.180***
0.04
joinvent
0.037
0.03
-0.001
0.03
0.120**
0.06
east
-0.176*** 0.03
-0.164*** 0.03
-0.194***
0.06
middle
7.605*** 0.06
7.688*** 0.07
7.340***
0.12
constant
0.2448
0.2393
0.2871
Adjusted R-squared
variable
Table 2 tells us the wage premium for those who have acquired a higher level of degree then the current
job required is positive and significant, especially for masters and doctors. Similarly, the wage penalty
for the undereducated part of acquired degree or college qualification is huge and essential. Other
affecting variables are robust in both tables. One significant difference between table1 and table 2 is the
reward to all levels of over-education is positive and to under-education is negative for all three groups.
Table1 and table 2 can clearly explain the two hypotheses that we brought forward: (1) the earnings of
workers in occupations that require less schooling than they actually have (overeducated) are less than
the earnings of workers with the same level of education as themselves. (2) the earnings of workers in
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occupations that require more schooling than they actually have (undereducated) are more than the
earnings of workers with the same qualification but in a job just requiring their level of schooling.
4 Conclusion
There are around 20% graduates are overeducated and the percentages of overeducated master and PhD
reach 35.8% and 42.0% respectively. Among all the overeducated graduates, 36% of which are
apparently overeducated. Since they are satisfied with their current jobs and with comparatively low
human capital, they can be separated from mismatch. Considering these two facts, over-education may
be less than 10%, in other words, the expanded graduates were almost absorbed by the graduate labour
market. For the reasons that may determine over-education among all the possible variables, party
member, university grade and English grade are particular important. Overeducated graduates earn less
than the same level of graduates whose job need actually acquired schooling but more than the
employees who doing the same level of jobs but not obtaining more than job need qualifications. The
same argue suits for undereducated graduates. In the context of rewards (loss) to extra (less) education
of job required, the overeducated (undereducated) part comparing to a master (college) or PhD (first)
degree obtain the highest premium (penalty). Though female are more likely to be overeducated, there is
no evidence on gender discrimination on wages. Among all the qualifications, the return to the master is
the highest, attains 59% for men and 53% for women. In addition, university rank, English grade, family
background, registration status, cadre, company ownership and working place all influence individuals’
wages significantly.
References
[1] McGoldrick, K & Robst, J. Gender differences in over-education: a test of the theory of differential
over-qualification. The American Economic Review, 1996,86(2): 280 284
[2] Frenette, M. The overqualified Canadian graduate: the role of the academic program in the incidence,
persistence and economic returns to over-qualification. Economics of Education Review,
2006,23:29 45
[3] Dearden, L., Mclntosh, S., Myck, M. &Vignoles, A. The returns to academic and vocational
qualifications in Britain. The Bulletin of Economic Research,2006,54(3): 249 274
[4] Battu, H., Belfield, C.R. & Sloane P.J. (1999). Over-education among graduates: a cohort view.
Education Economics,1999,7(1), 21 38
[5] Sloane, P. J., Battu, H. & Seaman, P.T. Over-education, under-education and the British labor force.
Applied Economics, 1999,31(11), 143 1453
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