The relationship between higher education and
economic growth in China
Jilei Cai
(10435018)
Bachelor Thesis Economics and Business
Specialization: Economics and Finance
Faculty of Economics and Business
University of Amsterdam
Supervisor: Ieva Rozentale
Abstract:
Previous literatures have researched the relationship between higher education and
economic growth, but there are only a few papers about the China. To analysis the
correlation between higher education and economic growth in China, this paper uses
conintegration test, Granger causality test and variance decomposition based on
vector autoregressive model to estimate a modified augmented neoclassical model for
time series data over the 1978-2012 period in China. We find that both higher
education and physical capital investment have positive and statistically significant
effect on the growth of GDP per capita. We also find that higher education not only
promotes national income directly but also boosts national income indirectly through
enhancing the productivity of labor. The policy implication of our results will also be
discussed in this paper.
1
Table of Contents
1. Introduction .............................................................................. 3
2. Literature Review ..................................................................... 6
3. Methods and data ................................................................... 10
3.1. Econometric Model .......................................................... 10
3.2. Sources and Data.............................................................. 13
4. Econometric Analysis ............................................................. 16
4.1. Stationarity test................................................................. 16
4.2. VAR model and Cointegration test .................................... 17
4.3. Granger causality test ...................................................... 21
4.4. Variance Decomposition ................................................... 22
5. Conclusions and Recommendations ...................................... 26
Bibliography ................................................................................. 28
2
1. Introduction
Schultz (1961) theorized that the capability of a nation to use physical capital
effectively is a function of its level of human capital and that if its level of human
capital does not increase along with its level of physical capital, then human capital
will limit this nation’s economic growth. Both macroeconomic literature (Pereria &
Aubyu, 2009) and microeconomic literature (Psacharopoulos, 1995) propose a
positive correlation between the human capital and education in the economic growth
progression. These opinions are accordance with our definition of human capital —
education influences the economic growth through improving the productivity of
labor. Thus, it is both the quantity of labor and education level embodied in labor
influence the level of economic growth.
It is admitted that primary education accounts for the largest part of public
expenditure in most developing countries. This situation also happens in China, the
Chinese population has an almost universal primary level education because primary
education is totally free. However, the neglect of higher education will lead to the low
quality of labor force and limit the development of national income (World Bank,
2000). Moreover, McMahon (1998) concludes that additional development in primary
education does not provide a good return when universal primary education has been
achieved. Chinese national income, on the other hand, always keeps at a high growth
rate. This causes the author’s interest to analyze whether higher education is the factor
that drives economic growth in China.
The purpose of this paper is to investigate relationship between higher education
and economic growth in China in the period from 1978 to 2012. We do so by using
the Vector Auto Regressive (VAR) model to estimate an expanded Solow growth
model with higher enrolment rates as the proxy of human capital.
Before 1978, China, who’s Real GDP per capita was only 10% of Real GDP per
capita of Brazil and 2.5% of Real GDP per capita of the United States, was one of the
poorest countries in the world. Since 1978, China moved from centrally planned
3
economy to market-based economy and achieved a Real GDP per capita growth rate
of nearly 8% every year (World Bank, 2002). With this open economy, the GDP of
China reached 58786 hundred million in 2010, only lags behind the GDP of the
United States. Further, with the development of information and communication
technology in recent decades, the development strategy of China has dramatically
changed from traditional natural resource-oriented production to knowledge-based
production (Ministry of Education of the PRC, 1993). Similar to the case of many
other emerging economies, increasing knowledge and skills of workforce is an
important strategy to raise the competitiveness of China in the world market.
Intellectual human capital is becoming more and more important in this era. Qazi et al.
(2013) proposed that ‘Higher education provides intellectual human capital such as
critical thinkers, researchers, scholars, innovators and responsible citizens to societies’
(p. 2). Thus, since the number of students attended higher education in China is far
lower than other countries have the same level of national income; higher education
institutions in China are guided by the Chinese government to expand the enrolment
rate of higher education in 1999, the growth rate of higher education enrolment rate
increased from 8.5% to about 20% every year. This situation shows the importance of
higher education to economic growth has been appreciated among policy makers in
China in last 15 years.
This paper attempts to contribute to the literature on the topic since very few
empirical studies have been conducted on the correlation between higher education
and economic growth in China. Such investigation not only adds to the empirical
research on the topic, but it also has policy implications. High-income countries
benefit more from higher education (Petrakis and Stamatakis, 2002). If higher
education is as important as we referred above, the high increase rate of higher
education enrolment rate can explain the GDP rapid growth in China from 1999 to
2010. Thus, this research will provide information for the Chinese governments to
decide whether policy efforts should focus on increasing the higher education levels.
The rest of this paper is organized as follows: Section II provides a brief review of
4
previous papers that are relevant to this paper; Section III explores the theoretical
foundations for expanded Solow growth model we use and discusses the data sources;
Section IV presents and analyzes the statistical results while Section V concludes the
paper.
5
2. Literature Review
An interest in the causal relationship between education and economic growth
continues to draw much attention from scholars. It is widely considered that education
levels can influence the economic growth directly (Schlottmann, 2010). We therefore
discuss some studies that emphasize the importance of education in the progress of
economic growth.
The Solow growth model, which was the central framework to interpret economic
growth, only took exogenous factors (technology and population) as the factors that
boost the income growth. Despite the pervasive use of the Solow growth model,
Schultz (1961), a famous American economist, noticed that there is a difference
between the growth rate of output and the growth rate of physical capital and labor
input, proposing that the major explanation for this difference is probably the level of
human capital. Then, in 1980s and 1990s, Lucas (1988) introduced the endogenous
growth theory and Mankiw, Romer and Weil (1992) proposed the expanded
neoclassical growth model to investigate the contribution of human capital to the
economic growth. In the literatures of expanding Solow growth model, the human
capital is seen as the asset input and hence it predicts that ‘countries with faster
growth rate of education will have faster transition growth rates and higher incomes’
(Gyimah-Brempong, Paddison & Mitiku, 2006, p. 8). Mankiw et al. (1992) added a
new variable (human capital accumulation) to the Solow growth model and found that
the education has positive and strongly significant effect on the economic growth rate
in a sample of 88 countries. In the literatures of endogenous growth model, education
is regarded as a process that increases the production technology and enhances the
knowledge of workers so that they can adapt foreign technology more easily. (Romer,
1990; Barro, 1997). Further, Lucas (1988) and Romer (1986) suggested that education
could restrain the reduction of diminishing returns of physical capital inputs to
economic growth through both internal effect and spill-out effect. According to the
theory of Lucas, ‘the internal effect of education is the effect of individual human
capital on productivity and the spill-out effect of education is its ability to increase the
6
efficiency of socio-economic growth activities’ (Geng, Li & Cai, 2009, p. 1). Hence, it
is clear that in both endogenous and expanded Solow growth model, education raises
the growth rate of income.
All the papers reviewed above were based on the endogenous growth model and
expanded Solow growth model, however, there are papers using different models to
research the correlation between human capital and economic growth. The quality of
human capital, such as the knowledge, skills and competencies, increased by
attending education contributes a lot to the improvement of a country’s income (Barro,
1999; Burja & Burja, 2013). Benhabib and Spirgel (1994) began their study based on
a Cobb-Douglass production function rather than a growth equation. Their main result
is that education levels do not raise the growth rate of income directly; instead, it
raises the income indirectly through facilitating the adoption of advanced technology
and promoting the productivity of physical capital. In addition, Barro and Lee (1994)
were the first to use average years of schooling among adults whose age 25 and older
to represent the stock of human capital. They found that male secondary schooling is
positively related to economic growth while female secondary schooling is negatively
related to economic growth. The results of Barro and Lee are supported by the study
of Sala-i-Martin (1997).
Previous findings verify the positive relationship between education and economic
growth, there are also various studies highlight the effect of higher education on
national income. Higher education can affect national income positively through
developing domestic technology or through adapting foreign technology to local
conditions. Greiner and Semmler (2002) find that positive externalities of physical
capital inputs exist only when educated human capital is available. Furthermore, Hall
and Jones (1999) note that promotion of technology is not able to be dependent on
primary and secondary education, but on higher education. Higher education is also
able to increase the quality of other inputs. For example, physical exercise, which is
an important part of higher education, improves health of human capital that is crucial
determinant of economic growth (Nelson and Phelps, 1966). Kimenyi (2011) also
7
argued that growth of national income has positive effect on higher education in
Africa. Moreover, the impact of higher education on national income comes about
more through its social role than through its technical role (Gradstein & Justman,
2002). Higher education, Gyimah-Brempong et al. (2006) argue, ‘reduces the cost of
enforcing desirable social norms, lessens the potential for ethnic conflicts in ethnically
diverse societies, as well as decrease transaction costs by shrinking social distance
between individuals in a society’ (p. 8). Furthermore, Agiomirgianaskis, Asteriou and
Monasitiriotis (2002) and Voon (2002) find tertiary education has a stronger growth
impact than primary education and secondary education in Hong Kong.
Gyimah-Brempong, Paddison and Mitiku (2006) suggested that higher education
workforces have a large effect on the income growth in a sample of African countries
from 1960 to 2000.
Chinese scholars also have done some researches on the relationship between
education and economic growth in China. The contribution rate of education to
growth rate of the average annual GDP in China increased from 8.84% to 12.66%
from 1982 to 2000 (Cui, 2001). The finding of Cui confirms the positive effect of
education on economic growth in China. In addition, Cai (1999) used the Feder model
to estimate the indirect effect and direct effect of education on economic growth. Cai
argued that indirect effects of education, through enhanced health, utilization of
physical capital investment and reduction crime rate, have more significant influences
on national income than direct effects of education. Yu et al. (2014) proposed that
education and economic growth can promote each other in China, meaning that
increase of GDP can reciprocally promote the education through allocating more
social sources to invest. Chen and Feng (2000) also confirmed that human capital
(higher education enrollment rate) is the endogenous growth factor in promoting
national income.
Based on the papers discussed above, the role of higher education seems to be a
considerable growth factor in China. We might also hypothesis that higher education
can promote national income indirectly through enhancing the efficiency of exploiting
8
physical capital.
9
3. Methods and Data
This section explains the research method and data collected. Section 3.1 gives a
detailed explanation of the variant augmented neoclassical model used. Section 3.2
explains how we choose and collect the data we need and gives a simple analysis of
these data.
3.1. Econometric Model
The approach we use to investigate the correlation between the higher education and
economic growth in China is to estimate an augmented neoclassical model proposed
by Mankiw, Romer and Weil (1992) by using the Vector Auto Regressive (VAR)
model for the years 1978-2012.
The neoclassical model, originally proposed by Solow (1956), assumed an
aggregate production function and took the rates of saving and population growth as
exogenous variables determined the level of income per capita. Schultz (1961),
however, argued that the estimation of the returns of investment in education to
economic growth should take both physical capital input and human capital input into
account since these two inputs are complementary. Additionally, Mankiw et al.’s
(1992) work tested Solow model and argued that effects of saving and population
growth on income growth are too large in the Solow model. Mankiw et al. (1992)
proposed that human capital accumulation was correlated with saving rates and
population growth; this confirmed that estimated coefficients on saving and
population growth was biased by ignoring human capital in the model. Thus, Mankiw
et al. augmented the Solow model by adding a proxy for human capital as an
additional explanatory variable. Specifically, a Cobb-Douglas production function of
Mankiw et al. model is assumed in the following form:
Y t = K t αH t
β
A t L t
1−α−β
(1)
where Y is aggregate output; K is physical capital; H is the stock of human capital; A
10
represents the level of technology; L is labor; and t is time. Mankiw et al. assume that
L and A grow at constant and exogenous rates n and g, respectively. The exponents α
and β measure the elasticity of output to the physical capital and human capital inputs.
Assuming decreasing returns to physical and human capital, that is α + β < 1, the
steady-state level of income per capita can be derived from the equation (1) as the
following form:
Y
α+β
α
β
ln L = lnA + gt − 1−α−β ln⁡
(n + g + δ) + 1−α−β ln⁡
(sκ ) + 1−α−β ln⁡
(sh )
(2)
where sk is the fraction of income invested in physical capital; sh is the fraction of
income invested in human capital; n, g and δ are all exogenously determined growth
rate of population, technology and depreciation rate of capital, respectively. In the
neoclassical model, Solow (1956) argued that A term reports not only technology but
country-specific resource endowments, climate, institutions, and so on. He assumed
that lnA = a + ε, where a is constant and ε is country-specific. Thus, Mankiw et al.
(1992) assumed that the error term (ε) is independent of the explanatory variables,
which allowing estimation to continue by utilizing ordinary least squares (OLS). In
this situation, the problem of endogeneity will not happen as the independent
variables are not correlated with the error term.
Following the study of Mankiw et al. (1992), we assume the growth equation like
the equation (2). Nevertheless, this paper makes one modification to the augmented
neoclassical model: we use the higher education enrolment rate as a measure of
human capital as the higher education human capital is the major interest in this thesis.
Based on the foregoing, this paper postulates the variant of the augmented
neoclassical growth equation. Formally, the equation is written as:
lnq t = a0 + a1 lnk t + a2 ln⁡
(n + g + δ) + a3 lnht
(3)
where q t is the GDP per capita; k t is the gross capital formation as a fraction of
GDP; n, g and δ are growth rate of population, technology and depreciation rate of
capital, respectively; ht is the higher education enrolment rates. All the variables are
11
turned to their natural logarithms form for analysis.
The estimation of the higher education enrolment rate is calculated by using the
following equation (National Bureau of Statistics of China, 2012):
Et
GHERt = P t × 100
(4)
where GHERt = Gross Higher Enrolment Ration in year t; E t = Enrolment for
higher level of education in year t (age 18-23); P t = Population in age-group which
officially corresponds to higher level of education in year t (age 18-23). There is no
possibility that people older than 23 are enrolled in higher education as people over 23
are not allowed to take the Gaokao which is the higher education entrance
examination.
To investigate the relationship between higher education and economic growth,
empirical literatures were conducted by using different econometric analysis methods
to estimate the augmented neoclassical growth equation. First, we use two unit root
tests to identify the integrated order of the variables. If all variables are integrated of
order zero in levels, all variables are stationary. Then, we can use OLS directly to do
regression to estimate our growth equation. However, if some variables are stationary
only in their first difference, using OLS directly to do the regression will cause the
problem of spurious regression, meaning that growth equation estimated through OLS
cannot truly reflect the relationship between independent variable and explanatory
variables. In this situation, we should build Vector Auto Regressive (VAR) model and
examine whether non-stationary variables in levels have cointegration relationship
(long-run stable relationship) between higher education and economic growth through
using Johansen test based on the VAR model we built. The presence of cointegration
relationships among the non-stationary variables in levels implies that Granger
causality also exists. Last, we employ the variance decomposition to further examine
the results of cointegration test and Granger causality test. This paper gets all
econometrical results through using econometrical software (Eviews 8).
12
3.2. Sources and Data
The dependent variable in our model is the GDP per capita (lnq t ). The explanatory
variables are investment in physical capital, higher education enrolment rate, the sum
of annual growth rate of labor, depreciation, and technology. Following earlier
researchers, we measure physical capital investment as the gross capital
investment/GDP ratio (lnk t ) and measure the growth rate of population as the rate of
labor force growth (lnnt ) in a period. Following Mankiw et al. (1992), we measure
the sum of growth rate of depreciation and technology remains constant, presuming
that g + δ = 0.05. The proxy of human capital is the key issue in our model. Different
proxies have been used to measure the impact of education on national income
(Tsamadias & Pegkas, 2012). For example, some researchers, such as Barro (1999)
and Perakis & Stamatakis (2002), use enrolment ratios while others use education
expenditure/GDP ratio (McMahon, 1987; Appiah and McMahon, 2002). Pegkas and
Tsamadias (2012) argued that the school enrolment rate is just the quantitative
measurement of human capital, which means the quality of human capital is not
considered. This paper, therefore, uses the higher education enrolment rate as the
proxy of human capital. The reason for choosing the year from 1978 to 2012 is that
the China experienced ten-year Chinese Cultural Revolution before 1978. During
1967 to 1977, all higher education institutions were not allowed to recruit the students
(Barnouin & Yu, 1994).
The time series data were collected over the 1978-2012 period. The data of the
GDP per capita, Gross capital formation/GDP ratio, the annual growth rate of GDP
per capita, and the annual growth rate of Gross capital formation/GDP ratio were
obtained form World Bank’s World Development Indicators (Washington, DC: World
Bank, 2013) and were for the years 1978 to 2012. The data of the annual growth rate
of population of labor force, the annual higher education enrolment rate and the
annual growth rate of higher education enrolment rate were obtained from China
Statistical Yearbook, China Education Finance Statistical Yearbook and China
Population Statistics Yearbook 1978-2009 (National Bureau of Statistics of China,
13
1978-2009).
After a first data analysis, we notice that there has been a significant increase in
GDP per capita as well as a great increase in higher education enrolment rate during
1978-2012 (Figure 1).
7000
35
6000
30
5000
25
4000
20
3000
15
2000
10
1000
5
0
0
GDP per capita($)
Higher education enrolment rate
Figure 1. GDP per capita and Higher education enrolment rate (1978-2012).
Table 1. Statistical description of the sample data
Variables
Mean value
Minimum value
Maximum value
GDP
1254
154.97 (year 1978)
6091 (year 2012)
K
39.52%
33.33% (year 1981) 49% (year 2012)
L
63486.8
40152 (year 1978)
76704 (year 2012)
H
10.57%
1.55% (year 1978)
30% (year 2012)
GDP growth rate
0.11
-0.108 (year 1987)
0.2878 (year 2008)
K growth rate
0.018
-0.051 (year 1994)
0.187 (year 1993)
L growth rate
0.02
0.0032 (year 2008)
0.1703 (year 1990)
H growth rate
0.096
-0.093 (year 1982)
0.335 (year 1979)
More specifically, during 1978-2012, GDP per capita of China experienced an
14
average annual increase of 11% and the highest growth rate of GDP per capita existed
in year 2008 and the lowest growth rate appeared in year 1987. During the examined
period, the enrolment of higher education has shown a radical increase, with 1.55% in
1978 to 30% in 2012, exhibiting an average annual growth rate of 9.6% (Table 1).
Moreover, the maximum value of GDP per capita, Gross capital investment/GDP ratio,
population of labor force, and higher education enrolment rate all appear in year 2012
while the minimum value of these indicators all exist in the late 1970s and early
1980s.
15
4. Econometric Analysis
This section will provide the results and discussion of econometric tests
4.1. Stationarity test
Firstly, in order to check the stationary of the variables (GDP per capita, physical
capital investment, population of labor force and higher education enrolment rate), we
employ two different unit root tests. The stationarity of the data set is assessed using
Augmented Dickey and Fuller (ADF) (1979) and Phillips and Perron (PP) (1988). In
this step, these two tests are applied to test the existence of unit roots for each variable
in levels and in first differences. The variables are clearly identified by including
intercept and including intercept and trend. Schwarz (1978) determined the criteria for
the optimal lag length of the ADF unit root test. The PP statistics are acquired by the
Bartlett Kernel and the automatic bandwidth parameter approach (Newey and West,
1994). Moreover, for the ADF and PP tests, the null hypothesis predicts that the
variable tested has unit root that means the variable is non-stationary.
Table 2. Results of unit root tests
ADF test
Variables
With
(in levels & first intercept
PP test
With intercept With intercept With intercept
in and trend in in equation
and trend in
differences)
equation
equation
ln𝐪𝐭
1.879797
-0.347772
2.683128
-0.480893
Δ ln𝐪𝐭
-3.490470**
-4.46163***
-3.577764***
-4.345298***
ln𝐤 𝐭
-0.579244
-2.288689
-0.579244
-2.288689
Δ ln𝐤 𝐭
-4.823228*** -4.829060***
-4.783779***
-4.755812***
ln(𝐧 + 𝐠 + 𝛅)𝐭
-6.006345*** -8.322542***
-5.817465***
-9.122103***
Δ ln(𝐧 + 𝐠 + 𝛅)𝐭
-9.796345*** -9.628440***
-15.46653***
-15.36163***
ln𝐡𝐭
0.164256
-0.485902
-1.974240
Δ ln𝐡𝐭
-3.820118*** -3.753158***
-3.990215***
-3.925223***
-3.896973**
16
equation
Note: ***, ** represents the rejection of the null hypothesis (the variable tested is non-stationary) of ADF test and
PP test at 1% and 5% level of significance respectively. The hypothesis is tested by F-test. If the calculated
F-statistics is lower than the lower bounds critical value given by Mackinnon (1996), the null hypothesis is
rejected.
The results in Table 2 show that GDP per capita (lnq t ), higher education (lnht ) and
physical capital investments (lnk t ) of ADF test has unit roots in their levels but they
do not have unit costs in their first differences at the 1% level of significance,
meaning that these three variables are stationary in their first differences instead of in
their levels.. The labor growth rate is integrated of order zero both in its levels and in
its first differences. Based on these results, applying OLS to do the regression directly
will cause the problem of spurious regression as only labor growth rate is stationary in
levels. Hence, we have to employ VAR model to examine the relationship between
higher education and economic growth.
4.2. VAR model and Cointegration test
To estimate the relationship between complete endogenous variables, VAR model
regress the lagged value of explained variables on the dependent variable in the form
of simultaneous equations (Sims, 1980). VAR model, therefore, is always been
utilized to explore the dynamic relationship between explained variables and
dependent variable of a growth equation (Anwar et al. 2011). However, in order to
examine the reliability of VAR model we built, a cointegration test is required.
Herrerias (2010) argued that the first requirement to use VAR model is that
explained variables should all be stationary in first difference and the second
requirement is that degree of freedom of lag length should maintain reasonable logical.
Stationary tests show that GDP per capita, physical capital investment and higher
education are stationary in first differences despite the unit root tests show that they
follow an I(1) process in levels, which means the first requirement of building VAR
model is fulfilled. However, the variable (n+g+δ) is exogenous in our model now as it
17
is stationary in both levels and first difference. Thus, in order to apply VAR model
directly to determine the relationship among three variables used in our model, we
should determine the reasonable logical lag length. Seven versions of the system were
estimated for identifying the optimal lag length: a one, a two, a three, a four, a five, a
six and a seven-lag version. The result of lag length determined by likelihood ratio
statistics, Akaike information criterion (AIC), Schwarz criterion and Hannan-Quinn
information criteria is shown in Table 3. Among these criterions, AIC is the most
common one to be used to define optimal lag length (Pegkas & Tsamadias, 2014;
Soytas & Sari, 2009). Table 3 shows that the AIC identified seven lags as optimal lag
length.
Table 3. Vector autoregressive lag order selection criteria
Lag
LogL
LR
FPE
AIC
SC
HQ
0
4.409496
NA
0.000182
-0.100678
0.042058
-0.057042
1
116.5017
192.1580
1.16e-07
-7.464405
-6.893461*
-7.289862
2
128.6342
18.19874
9.50e-08*
-7.688155
-6.689001
-7.382704
3
134.2825
7.262146
1.29e-07*
-7.448750
-6.021388
-7.012391
4
142.5257
8.832026
1.55e-07
-7.394695
-5.539124
-6.827428
5
146.2252
3.170964
2.88e-07
-7.016085
-4.732305
-6.317911
6
152.6239
4.113435
5.27e-07
-6.830276
-4.118288
-6.001194
7
193.6410
17.57877*
1.14e-07
-9.117214*
-5.977017
-8.157224*
Note: LR, likelihood ratio; FPE, final prediction error; AIC, Akaike information criterion; SC, Schwarz
criterion; HQ, Hunnan-Quinn information criteria; NA, not applicable. The * indicates the lag length
determined by each criteria.
After defining the optimal lag length, it is possible to build VAR model and check
whether GDP per capita, physical capital investment and higher education are
cointegrated through cointegration test. Only when these three variables are
cointegrated, the VAR model we built to estimate the augmented neoclassical model
18
will include economical meaning. We use the reduced rank procedure developed by
Johansen (1988) to conduct the cointegration test. The Johansen multivariate
cointegration approach is utilized to regress the variables at their original levels to
examine the long-run relationship between lnq t , lnk t , and lnht . The estimation
procedure of cointegration test assumes only an intercept in the VAR estimation
(Johansen and Jeselius, 1990). Furthermore, two statistics, the Trace and maximum
Eigenvalue test, are recommended to check the long-run relationship between
variables (Yu, Zhao, Xu & Wang, 2014). The result of cointegration test is shown in
Table 4.
Table 4. Johansen and Juselius cointegration test GDP per capita, physical capital
investments, and higher education enrolment rates: sample 1978-2012
Series: ln𝐪𝐭 ln𝐤 𝐭 ln𝐡𝐭
Hypothesised Eigenvalue
Trace
5% critical Max-eigen
5% critical
No. of CE(s)
Statistics
value
statistic
value
None*
0.884693
78.96479
29.79707
60.48448
21.13162
At most 1*
0.469630
18.48030
15.49471
17.75707
14.26460
At most 2
0.025499
0.723235
3.841466
0.723235
3.841466
Note: No. of CE(s) indicates the number of cointegrating relationship. *Trace and Max-Eigen tests
indicate 2 cointegrating equation at the 5% level.
Lags interval: 1 to 6
Table 5. Normalized cointegrating coefficients (standard error in parentheses)
LNQ
LNK
LNH
1.000000
-4.698494
-0.642227
(0.73083)
(0.04297)
The null hypothesis in the cointegration test is that there is no cointegration vector,
meaning no long-run relationship. The null hypothesis of at most one co-integrated
19
vector in the Trace test could be rejected at 5% whereas the null hypothesis of at most
two cointegration relationships could not be rejected at 5%, which implies that there
are two cointegration relationships. The finding of the Trace test was further
supported by the results of the maximum Eigenvalue test, in which only the null
hypothesis that there are at most two cointegrating vector could be rejected at 5%.
Thus, the results (Table 4) lead to the conclusion that the GDP per capita, physical
capital investments, and higher education enrolment rates are cointegrated and these
variables have a long-run relationship during the period examined. The existence of
cointegration among GDP per capita, physical investment and higher education
confirms that the VAR model we estimated has economical meaning. In addition,
Table 5 shows that lnq t and lnht has opposite sign of their cointegrating
coefficients. As both of the LNQ and LNH are at the same side of the equation, the
opposite sign of their cointegrating coefficients means that GDP per capita and higher
education are positively related.
One estimated cointegration relationship estimated by VAR model is presented in
the following equation (standard error in parentheses):
Lnq t = 0.408959lnk t + 0.138002lnht - 1.183283
(0.52207)
(0.30094)
(-0.27540)
From the above estimated equation, it is concluded that physical capital investment
and higher education have a significant positive long-run effect on economic growth
since the coefficients of these two variables are statistically significant at 1% level.
The elasticity of GDP per capita regarding higher education, which uses enrolment
rate as proxy, is 0.138. This means that a 1% increase in higher education enrolment
rates will foster economic growth by about 0.138%. The role of higher education in
determining and explaining economic growth seems to be very important. The
findings of this paper of the positive long-run relationship between higher education
and economic growth are consistent with most of the previous literatures discussed
above.
20
4.3. Granger causality test
This step is to clarify the relation by using Granger causality test, which is based on
the VAR model, introduced by Wiener (1956) to judge whether there exists mutual
interactions between variables. As this paper has proved that there exists long-term
positive correlation between GDP per capita, physical capital investments, and higher
education, there must be Granger causality in at least one direction. The Granger
causality means if prediction accuracy of current value of y is significantly increased
by including information about past value of x, x is said to Granger-cause y (Granger,
1969; Granger 1980). In this paper, if GDP per capita is Granger caused by higher
education, it means higher education has positive effect on economic growth. The
Granger Causality implemented here employed the seven-lag for all variables. The
result is shown in Table 6.
Table 6. Granger causality test
Null hypothesis
Lag length
F-Statistics
Probability
Conclusion
LNK does not
7
1.29877
0.3242
Accepted
7
2.53127
0.0704
Refused
7
0.66777
0.6960
Accepted
7
0.50897
0.8122
Accepted
7
1.77724
0.1758
Accepted
7
0.30828
0.9375
Accepted
Granger cause LNH
LNH does not
Granger cause LNK
LNQ does not
Granger cause LNH
LNH does not
Granger cause LNQ
LNQ does not
Granger cause LNK
LNK does not
Granger cause LNQ
Table 6 indicates that physical capital investment and higher education are not
21
Granger causes of economic growth while economic growth does not Granger cause
physical capital investment and higher education at the seventh lag order. Furthermore,
higher education is the Granger cause of physical capital investment even if physical
capital investment is not the Granger cause of higher education. This result is
consistent with the paper mentioned above that higher education can improve the
productivity of labor to use physical capital investment. However, the Granger
causality test does not show any Granger causal interaction between economic growth
and higher education, meaning that higher education and economic growth do not
have any relationship. On the other hand, the result of cointegration test shows that
higher education is important in economic growth. In conclusion, as there is a
difference between result of cointegration test and result of Granger causality test, we
employ variance decomposition under VAR system in next section to further gauge
the relationship between economic growth and higher education.
4.4. Variance Decomposition
Variance decomposition divides fluctuation of each variable into proportions to
attribute shocks to individual variables through different time period to analyze the
relative importance between independent variables (Masih and Masih, 1997).
Variance decompositions are also constructed from VAR model and can directly
address the contribution of independent variables to forecasting dependent variable
(Litterman and Weiss, 1985). In many papers, it is argued that variance decomposition
is the most plausible way to identify causal relationship between considered variables
based on economic theory (Lorde et al., 2010; Hye, 2012; Raza and Jawaid, 2013).
The results of variance decomposition of lnq t , lnk t and lnht are shown in Table 7,
Table 8 and Table 9.
Table 7. Variance decomposition of lnq t
𝐥𝐧𝐪𝐭
Periods
S.E.
lnq t
lnk t
22
lnht
1
0.100445
71.46655
3.750836
24.78216
2
0.163219
61.14688
5.545117
33.30800
3
0.194368
51.29020
6.618379
42.09142
4
0.227889
44.09545
8.948426
46.95612
5
0.264557
40.18416
10.37454
49.44130
6
0.281447
38.15113
11.12389
50.72497
7
0.295453
37.38681
10.79793
51.81526
8
0.308318
35.08524
10.18365
54.73111
9
0.318273
33.76510
9.558862
56.67603
10
0.332877
32.21747
8.738523
59.04401
Table 8. Variance decomposition of lnk t
𝐥𝐧𝐤 𝐭
Periods
S.E.
lnq t
lnk t
lnht
1
0.046735
0.000000
35.07359
64.92641
2
0.061708
19.12031
27.14098
53.73871
3
0.062750
20.26702
27.52908
52.20390
4
0.064223
19.54868
26.28247
54.16986
5
0.072038
15.84163
22.20441
61.95395
6
0.073226
15.38951
21.62711
62.98338
7
0.079517
18.45460
18.40667
63.13873
8
0.083792
17.92530
17.63932
64.43538
9
0.084899
19.92560
17.29310
62.78130
10
0.086729
22.29611
17.52944
60.17444
Table 9. Variance decomposition of lnht
𝐥𝐧𝐡𝐭
Periods
S.E.
lnq t
lnk t
lnht
1
0.060286
0.000000
0.000000
100.0000
23
2
0.121528
4.526725
1.883613
93.58966
3
0.172423
7.606181
1.072267
91.32155
4
0.213534
6.914769
1.275989
91.80924
5
0.252009
8.110373
1.887734
90.00189
6
0.293540
8.483441
2.088816
89.42774
7
0.315127
7.384770
2.055981
90.55925
8
0.341041
9.234812
1.757013
89.00818
9
0.372147
10.31930
1.505058
88.17565
10
0.407493
11.59256
1.276800
87.13064
When we use the Eviews to do the variance decomposition, the results will be
divided into ten periods automatically. This division of time period can help us to
better analyze the furcating effect between different variables. Results of Table 7
show that in the first period 71.47% of the variance of economic growth is explained
by its own innovations while higher education only accounts for about 24.78%. In the
second period, 61.15% explained by own innovation and 33.31% by higher education,
which increased almost 10 percent from period 1. Then, the impact of higher
education on economic growth begins to experience a stable increase from 42.09% in
the third period to 59.04% in the tenth period whereas the change of GDP explained
by own innovation decreased from 51.29% to 32.22% over the same period. Thus,
higher education has a long-run positive effect on economic growth. Furthermore, the
impact of physical capital investment made up only 3.75% in the first period. Despite
the shock of physical capital investment rose to a higher value (8.95%) in the fourth
period, it fluctuated at an average level of 10% within a limited range. We can
conclude that physical capital investment has a slight shock on economic growth over
ten periods. Higher education, however, had much more significant influence on
economic growth. All these confirmed that the higher education is a major impetus to
accelerate economic growth in China.
Based on the variance decomposition of lnk t in Table 8, the variance of physical
24
capital investment is predominantly explained by higher education, maintaining about
60% during ten periods. So these results reaffirm that higher education had a
long-term positive effect on physical capital investments.
According to the variance decomposition of lnht in Table 9, higher education
suffers a shock from its own innovation by 100, 93.6, 89.43 and 87.13% in period 1, 2,
6 and 10, respectively. Higher education suffers a shock from economic growth by 0,
4.53, 8.48 and 11.59% in period 1, 2, 6 and 10, respectively. These findings suggest
that economic growth has an unapparent effect on higher education, meaning that the
bidirectional causal relationship between higher education and economic growth does
not exist in China.
25
5. Conclusions and Recommendations
This paper provides an investigation of the relationship between higher education and
economic growth in China by using the time series data from 1978 to 2012. Firstly,
this paper presents a review of empirical literatures that have identified the
relationship between economic growth and higher education. Empirical studies show
that higher education can directly promote economic as well as indirectly stimulate
the economic through increasing the utilization of physical capital investment.
Secondly, in order to examine the relationship between higher education and
economic growth in China during the period 1978-2012, this paper uses the VAR
model to estimate a variant augmented neoclassical growth equation and employs the
higher education enrolment rate as the proxy for human capital. The empirical
econometric analysis shows that GDP per capita, physical capital investments and
higher education are cointegrated when GDP per capita is the dependent variable. The
Johansen test identifies that the higher education has long-term positive effect on
higher education in China, 1% increase in higher education enrolment rates will foster
economic growth by about 0.138%. The Granger causality test, however, shows that
there is no Granger causal relationship between higher education and economic
growth, meaning that higher education does not have any effect on economic growth.
But there is a directional Granger causality from higher education to physical capital
investment, which confirms the evidence that higher education enhance the utility of
human capital to utilize the physical capital investments in China. Finally, we employ
the variance decomposition to further gauge the correlation between higher education
and economic growth. The variance decomposition supports the result of
cointegration test that higher education is a major impetus for economic growth and
the result of Granger causality that higher education is important for utilization of
physical investment. On the other hand, the results of variance decomposition show
that the effect of economic growth on higher education is very subtle in China. Our
main conclusion is that the role of higher education on promoting economic growth
seems to be very crucial in China during the period 1978-2012.
26
We note that our findings of the positive effect of higher education on economic
growth is likely to be biased because the growth rate of labor, which is stationary in
levels, is excluded from our econometric analysis. In order to better estimate
relationship between higher education and economic growth, it is recommended that
other variables such as higher education investment should be included in the growth
equation. Our future research will consider investigating the dynamic correlation
between higher education investment and higher education.
Implications for policy can also be drawn from our results. As the insufficient
higher education investment and emigration of higher education human capital, the
shortage of higher education has become a challenge for the economic growth of
China. Hence, it is recommended that Chinese policy makers should pay attention to
higher education human resources and make policies increase the higher education
investment to build and attract outstanding university faculty. Our results recommend
Chinese higher education institutions to take actions to prevent this massive ‘brain
strain’. In addition, they should make a great effort to attract these emigrant experts
back home to help them to build excellent higher education institutions. In this way,
the enrolment rates of higher education will increase, meaning that economic growth
will be further boosted. To sum up, in the development of national income of China,
the value of higher education should be recognized.
27
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