The Different Impact of Innovative Human Capital and R&D Subsidy on the
Technological Progress: an Empirical Study Based on Zhejiang
Yi- bing Tang, Li-jun Meng, Cheng-xia Hu
School of Economics and Management, China Jiliang University, Hangzhou, China
(tangyangtony@163.com, snowwhite@zju.edu.cn, hcx115@163.com)
Abstract -How to promote the construction of innovative
province: to improve the supply of innovation human capital
or R&D incentives? Based on the time-series data in
Zhejiang province during the period 2003–2009, the
empirical analysis showed that supply of innovative human
capital have a significant promoting effect on technological
innovation and technological advance, while the effect of
R&D encouraging policy is much less than that of supplying
innovative human capital. The implication of policy is
obvious: the policy of improvement in the supply of
innovative human capital should be preferred to promote
R&D-based economic growth, and the policy of stimulating
the demand of R&D should be conduct on the condition that
the supply of innovative human capital is elastic.
Keywords -Innovative human capital, science and
technology policy, technological advance
I. INTRODUCTION
R&D subsidy policies that inspire R&D needs have a
long-term growth effect, which is an important conclusion
based on endogenous growth researches. The endogenous
growth researches confirmed that the technological
progress could promote economic growth, and
technological advance depended on firm’s R&D
investment. R&D subsidies would encourage firm to
invest more R&D resources in the horizontal and vertical
innovation, therefore promote economic growth.
Romer(1990), Aghion and Howitt(1992) have found that
R&D subsidy encourage firm’s R&D investment, thus
improve long-term economic growth rate[1][2]. The policy
that indirectly stimulates the firm’s R&D needs could
also promote the long-term economic growth, RiveraBatiz and Romer(1991) holded that reduction of tariffs
could lead to the improvement of world economic
growth[3]. Based on these theory results, every country
can encourage R&D investment through R&D subsidies
and tax incentives.
However Jones (1995) questioned the above results
and suggestions [4]. He pointed out that the endogenous
growth model has the feature of “economic of scale”, that
is to say, the more R&D resources are invested, the faster
the economic growth is, but this is unrealistic. Since 1950,
the amount of scientists and engineers involved in R&D
of France, Germany, Japan and the United States is
increasing rapidly, but the growth rate of per-capital
income does not show upward trend. R&D subsidies had
expanded the relative size of the R&D department, but it
did not affect the long-term growth rate.
Segerstrom(1998) obtained the same conclusion based on
the analysis of vertical innovation model, while the
analysis in the framework of horizontal and vertical towdimensional innovation, R&D subsidies even reduced
long-term growth rate[5]. Segerstrom(2000) and
Young(1998) found that R&D subsidies had no long-term
growth effects based on horizontal and vertical innovating
model analysis[6][7]. Howitt (1999) showed that the effect
of R&D subsidies on long-term economic growth is not
clear [8] .
If the R&D subsidies cannot promote economic
growth, then what kind of policy can promote effectively
R&D-driven economic growth?
On one hand, R&D subsidy policy impacts R&Ddriven economic growth, but in order to analyze the effect
of this policy on economic growth, we need to know how
R&D subsidies impacts technological progress. Generally,
technological advance has two main patterns, namely,
innovation and imitation, all of these patterns promote
economic growth together. In some countries, such as
Japan, imitation has made extremely important
contributions to economic growth (Rosenberg and
Steinmueller, 1988) [9]. Innovation and imitation promote
mutually and restrain mutually in the process of
technological advance; On the other hand, innovation
develops new technologies, and increases the amount of
the technologies available to imitate; while imitation also
contribute to the promotion and application of innovative
technologies. On the other hand, the technology
breakthrough made by innovation reduces income of
imitation investment previously. At the same time,
imitation has also stimulated demand of innovative
investment through enhancing market competition. The
relationship between innovation and imitation and the
policy effects on encouraging technological progress
(such as innovation subsidies, imitation subsidies and
intellectual property protection) have been the hot issues
in endogenous growth literature recently. Whether using
partial equilibrium analysis method or general equilibrium
analysis method, innovation subsidies improved the
strength of innovation and reduce imitation strength. But
by using a dynamic general equilibrium model,
Segerstrom (1991) obtained diametrically opposite
conclusion [10]. Davidson and Segerstrom’s (1992)
analysis also showed that strengthening the degree of
patent protection reduced innovation intensity in the
steady-state equilibrium and improved imitation strength
[11]
. This conclusion has been questioned by Cheng and
Tao (1999), they believed that the hypothesis of lineartechnology production function led to this counterintuitive conclusion [12]. Since there are reciprocal
relationship of R&D subsidies on innovation and
imitation, the relationship between R&D subsidy policy
and innovation and imitation may be an important factor
that leads to R&D-driven economic growth.
On the other hand, the supply of R&D resources is
an important factor, which also affects the R&D-driven
economic growth. Generally, it is widely recognized that
human capital is a basic input element of R&D. More
importantly, R&D requires specific innovative skills, and
these innovative skills embodies in scientists and
engineers, who have a significant positive relationship
with R&D output. The success of R&D depends on the
innovative skills of scientists and engineers rather than
R&D investment (Leiponen, 2005) [13]. The lack of
innovation skills is a major obstacle to innovation
(Mohnen and Röller, 2005) [14]. Colombo and Grilli(2005)
examined the impact of specific human capital and
general education of entrepreneurs on the growth of hightech industries, and found that the average education years
of entrepreneurs has no significant impact on firm growth,
while scientists and engineers education have an
significant positive effect on firm growth[15]. Since the
specific innovative skills embodied in scientists and
engineers are main factors that determine the success of
R&D, thus improving the efficiency of innovative skills is
expected to promote R&D-driven economic growth.
II. DATA AND SELECTION STATIONARITY TEST
A. Data Selection and Processing
The data used in this study came from the Zhejiang
Statistical Data Compilation of 60 Years, Zhejiang
Statistical Yearbook and Zhejiang Science and
Technology Statistical Yearbook from 2003-2009. the
following time series are mainly used in our paper: (1) the
amount of patent application quantity (CHXING), on
behalf of technology innovation; (2) the number of
scientists and engineers (INCAP), a measure of the core
R&D resources of Zhejiang province; (3) science and
technology financial funds (YFJL), on behalf of the R&D
input indexes of Zhejiang province; (4) Malmquist
technological progress index (TECHPRO). Malmquist
productivity index is calculated by using the data
envelopment analysis(DEA), the decomposition indicators
of technological progress index measured on behalf of the
technological progress, technical boundaries of the period
move from t to t+1.using on-front2.0 software to calculate
the Malmquist technical progress index.
B.Testing of Time Series Stationarity
Before the test, we use the ADF unit root algorithem
to test the stationarity of each variable. Test results are
shown in table I.
In table I, it is shown that LINCAP, LYFJL,
LCHXING, TECHPRO sequences are all non-smooth,
and their first-order difference sequence are stationary, so
all variables are I(1) sequence.
TABLE I
UNIT ROOT TEST RESULTS
Variable
Testing form
ADF Testing
ADF Statistics
Critical Value
(C,T,K）
LINCAP
-2.15
（C,T,1）
-3.23（10%）
LINCAP
-4.73
（C,N,1）
-3.72（1%）
LYFJL
-0.54
（C,T,1）
-4.36（5%）
LYFJL
-3.41
（C,N,0）
-2.98（5%）
LCHXING
-3.28
（C,T,2）
-3.60（5%）
LCHXING
-5.39
（C,N,0）
-3.71（1%）
TECHPRO
0.79
（N,N,2）
-1.61（10%）
-5.07
TECHPRO （N,N,1）
-2.66（1%）
Notes:(C,T,K) denotes constant term in the equation of the unit root
test, time trend and the lag order respectively; N does not include C or T;
K value of the minimum criteria based on AIC and SC select;  denotes
the difference operator.
III. EMPRICIAL TEST RESULTS
Cointegration test uses the way of EG two-step, the
two cointegration equation to be tested can be written as:
LCHXINGt=b0+b1LINCAPt +b2LYFJLt+μt （1）
TECHPROt= c0+c1LINCAPt +c2LYFJLt+μt （2）
According to the cointegration test of the trace
statistic test methods, cointegration tests begin from the
null hypothesis that cointegration relationship does not
exist (table 2). For (1), beginning from the null
hypothesis, r=0, track statistics value is 43.22005, it is
greater than the 5% significance level critical, 35.19275,
That should be rejected the null hypothesis H0: r=0,
accept the alternative hypothesis, H1: r≫ 1. In the next
test, null hypothesis, r ≪ 1, is accepted on the 5%
significance level, which shows that there is a
cointegration relationship between variables at the 5%
significance level. According to the same method, the two
cointegration relationships of (2) are determined.
For the first test, (3) can be written as:
LCHXINGt=6.10LINCAPt-2.99LYFJLt-7.42 (3)
（0.96088） （1.8606）
[-6.35366] [1.8125]
Similarly, for the second test, (4) can be written as:
TECHPROt=0.87LINCAPt+0.37LYFJLt-1.80 (4)
（0.24112） （0.23686） （0.06078）
[-2.9444]
[-1.5404]
[2.95415]
TABLE Ⅱ
COINTEGRATION TEST RESULTS
Null
Alternative Track
Charac- 5%
Lag
Hypothesis Hypothesis Statistics teristic
Critical interval
Value
Value
value
Test1 r=0*
43.2
0.61
35.19
[1,1]
r≫1
18.5
0.41
20.26
r≪1
r≫2
r=3
4.71
0.17
9.16
r≪2
Test2 r=0*
98.6
0.87
54.08
[1,2]
r1
50.0
0.66
35.19
r≪1*
r≫2
24.3
0.55
28.26
r2
r≫3
Notes: * denotes rejection of the null hypothesis at the 5% significance
level; critical value is given by the software Eviews5.0.
Each cointegration equation shows that the increase
in the number of scientists and engineers can significantly
promote technological innovation and technological
progress in the long time, Scientists and engineers is the
main driving force of technological innovation and
technological progress. The number of Scientists and
engineers increases one percentage point will promote
technological innovation by 6.105 percentage points, and
promote technological progress by 0.873 percentage
points. The national financial funding of simulating R&D
needs has no significant to technical innovation and
technological progress. Test results of Granger causal
relationship listed in table Ⅲ. We can see the short-term
causal relationship between variable from it.
Table Ⅲ shows that the null hypothesis 1, 2, 3 be
rejected, suggesting that the increase in the number of
scientists and engineers is due to innovation and
technological progress. According to the empirical
analysis of Zhejiang, we can get the following conclusion:
The effect of Zhejiang’s science and technology policy
simulating R&D needs is far less than the effect of
innovation manpower supply. That is, the national
financial funding of simulating R&D needs has no
significant to technical innovation and technological
progress.
Serial
1
2
3
4
5
TABLE Ⅲ
GRANGER CAUSAL RELATIONSHIP TEST RESULTS
The Null
F
P
X2
P
Conclusion
Hypothesis
10.47 0.008
20.94 0.00 Rejection
2= 3=0
 
 4=  5=0
 2=  3=0
 1=  2= 
 4=  5=0
3=0
0.39
0.69
0.79
0.67
Acceptation
10.47
0.008
20.94
0.00
Rejection
31.70
0.0002
95.10
0.00
Rejection
0.85
0.47
1.69
0.43
Acceptation
Notes: the parameter constraints test using by Wald test.
IV. CONCLUSION
In this paper, based on an Empirical Study of
Zhejiang, the following conclusion is made: increasing
supply of innovation manpower has distinct effects on
technological innovation and technical advance. While
the effect of science and technology policy of stimulating
R&D needs is much smaller than that of increase in the
supply of core R&D resources. The guiding significance
of the theory results on economic development in
Zhejiang province is mainly embodied in: the policy
priority of promoting technical progress should focus on
enhancing the supply of R&D resources, especially
improving the quality of scientists and engineers training.
In the process of “constructing the innovative province”,
if Zhejiang province increases innovation demand without
considering the supply of corresponding innovative
human capital provided by higher education system, the
policy effect might make income equality increasing , but
the growth effect will not obvious. In the past ten years,
Zhejiang province’s R&D expenditure of stimulating
R&D demand was growing rapidly, but the science and
technology policy of stimulating R&D did not
significantly improve the efficiency of independent R&D.
Therefore, in order to promote the construction of the
innovative province, science and technology policy
should focus on the training of innovation talents,
increasing the government fiscal expenditure of
innovative human’s cultivation, and improving the
training efficiency and quality of innovative human.
ACKNOWLEDGMENT
This work was supported by Zhejiang Soft Science
Research Program Grant Funded by Science and
Technology Department
of Zhejiang Province
(2010C35019) (2011C35029) and Key Universities
Research Institute of Humanities and Social Sciences in
Zhejiang province-Standardization and Intellectual
Property Management.
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