Download An Empirical Study on the Relationship between Input of

An Empirical Study on the Relationship between Input of
R&D and Patent in China
Ge Zhongquan Zhang Bin
School of Management, University of Electronic Science and Technology of China Chengdu 610054
Jin Tao
China University Of Mining and Technology, Beijing 100081
Abstract This paper empirically researches the relationship between Input of R&D and Patent in China.
The results show that the national outputs has positive effect on R&D inputs, but the degree of such effects
are different among different industries. Further, we investigate the outcomes of Chinese R&D inputs. The
results show that R&D inputs enhance patent application and patent authorization, and the positive
relationship exists between the quantity of patent application and authorizations and R&D inputs. Finally
some policy implications are given based on the empirical results.
Key Words GDP, Input of R&D; patent application, patent authorization, regression
1 Introduction
With the great development of science and technology, Intellectual property becomes more and more
important role in a great new era of knowledge economy, and national competition of integrative power
almost will embody competition of quantity of patent in 21st century. Therefore, research on the patent
development becomes very necessary in China.
Recently, in the field of quantity economy about researches on patent, achievements have been made
among overseas scholars, such as Schmookler (1966), Scherer (1965), Boundetal(1984), Hall,
Griliches,Hausman (1986), Schankerman and Pakes(1984)[1] etc. From the point of view of manufacturers,
studied on the correlation between the corporation input of R&D and quantity of corporation patent
application and patent authorization, an exploitable research work has been made by domestic scholars, .Li
Zhengwei & Wu Xiaobo(2002) analysed the reason that the input of R&D in proportion to GDP was
lower[2] , Chen Zhen & Li liya(2003) did comparative research on the input of R&D to typical classical
countries [3], Wang Bin hui & KeZhongyi(2003) made an analysis of marginal output of the input of R&D
to GDP in Guangdong province [4], YaoJianwen(2002) studied on countermeasures based on the actuality
about the input-output of R&D [5], Yaoyang and Zhangqi(2001) studied affection of investment of
R&D [6] ,etc.
Generally, the research on patent overseas focuses on quantity economy concerning manufacturers,
the research on patent domestically is mostly qualitative analysis, and quantitative analysis research on
patent is quite few.
In this paper, with an empirical method in macro scope, regression will be studied not between GDP
and R&D input, but between R&D input and quantity of patent application and authorization from1987 to
2003, to hope to offer some reference to patent policy in China.
2 Hypotheses and data
The research hypotheses are as follows:
(1) The most directly reason affecting the input of R&D is GDP in China;
(2) The input of R&D is the main factor that affects thquantity of patent application and authorization.
The data used in this paper come from China statistical yearbook on science and technology
1987-2003 and Statistical Yearbook of China 1987-2003 , the data collected input of R&D, output
of R&D(the quantity of patent application and authorization),GDP, increased value of agriculture,
increased value of the second industry, increased value of the third industry were given in table1 and table
2.
（
）
（
）
3 The model
According to the method of Damodar N.Gujarati[7] and Yuan Yintang[8] , the empirical model is
assumed to be a unitary linear regression model, this model described is as follows
：
（i =1，2，…，n）
Thereinto, the estimated values of parameter β 、 β are as follows：
β =( Y － β X )
β ＝ ∑ ( X − X )( Y − Y ) / ∑ ( X − X )
Thereinto，the samples mean X 、 Y are as follows:
X ＝∑ X / n
∧
（1）
∧
Yi = β 1 + β 2 X i
∧
∧
2
1
2
1
∧
i
2
（2）
（3）
∧
∧
2
i
i
（4）
（5）
i
Y = ∑ Yi / n
And, apply F-test to test significance of the regression equation, apply t-test to test significance of
coefficient of regression, apply R
2
to do test goodness of fittest of relativity curve.
、
Table 1 R&D input GDP and increased value of different industries
increased
increased
increased
R&D input
value of
value of the
GDP(billio
value of the
agriculture
billion
year
second
third industry
n yuan)
billion
industry
yuan
(billion yuan)
(billion yuan)
yuan
1987
74
11962.5
3204.3
5251.6
3506.6
（
（
）
）
1988
89.5
14928.3
3831
6587.2
4510.1
1989
112.31
16909.2
4228
7278
5403.2
1990
125.43
18547.9
5017
7717.4
5813.5
1991
150.79
21617.8
5288.6
9102.2
7227
1992
209.84
26638.1
5800
11699.5
9138.6
1993
256.19
34634.4
6882.1
16428.5
11323.8
1994
309.8
46759.4
9457.2
22372.2
14930
1995
348.69
58478.1
11993
28537.9
17947.2
1996
404.48
67884.6
13844.2
33612.9
20427.5
1997
509.16
74462.6
14211.2
37222.7
23028.7
1998
551.12
78345.2
14552.4
38619.3
25173.5
1999
678.91
82067.5
14472
40557.8
27037.7
2000
895.7
89442.2
14628.2
44935.3
29878.7
2001
1042.5
95933.3
14609.9
49069.1
32254.3
2002
1287.6
102398
14883
52982
34533
2003
1520.1
116694
17247
61778
37669
4 The results
4.1 The relationship between GDP and R&D input
4.1.1 Influence of GDP on R&D input
（ ）（ ）
In Table 1, if Yi is defined as period i’s inputs of R&D (1987-2003), Xi is defined as period i’s GDP
(1987-2003). With equation 1 - 5 and the data in Table 1, we get
(6)
Y = 0.11886 X − 165.7318
（
，
）
（
，
，
The absolute value of t-Statistics is 10.31376 N=17 α=0.05 , it is greater than t0 t0=1.753, N=17
α=0.05
so the coefficient of regression (6) is significant at the level of 0.05. The absolute value of
F-Statistics is 106.3736 N=17 α=0.05
it is bigger than f0 (f0=4.54, N=17 α=0.05) so the regression
），
（
，
），
，
2
equation (6) itself is significant at the level of 0.05. Further we get R =0.8764, that is, the fitting is
relatively better. [8]
From the significant regression (6), it is clear that the influence of GDP on is positive. This implies
that an increase of GDP results in an increase of R&D input. This conclusion just exploits the general
relationship between national output and R&D input, but it does not tell us what happen in specific
industries. The following subsection will study the industrial characteristics more carefully.
4.1.2 Industrial characteristics of the relationship between output of different industries and R&D input
In this subsection we take the agricultural, the second and the third industry as examples to show how
different industrial output influences on the input of R&D.
First, we consider the agriculture industry. Table 1 gives the data of the agricultural increased value
from 1987 to 2003. Following the same process, we get the regressing equation as follows
(7)
Y = 0.0754 X ′ − 268.5452
Where, Y is the inputs of R&D and X ′ is the agricultural increased value.
（
，
）
The t-Statistics is 6.001 N=17 α=0.05 and significant at the level of 0.05, the F-Statistics is 36.0111
（N=17，α=0.05）and significant at the level of 0.05, and
R 2 =0.7059. These results show the positive
relationship between the agricultural increased value and R&D input. The positive tendency directly says
that the input of R&D increases (decreases) as the agricultural increased value increases (decreases).
Second, the regression relationship of the second industry is
(8)
Y = 0.0226 X ′′ − 125.0761
Where, Y is the inputs of R&D and X ′′ is the increased value of the second industry.
The t-Statistics is 11.3571
（
，
）
（N=17，α=0.05）and significant at the level of 0.05, the F-Statistics is
2
128.9826 N=17 α=0.05 and significant at the level of 0.05, and R =0.8958. These results also show the
positive relationship between the output (defined as the increased value) and R&D input in the second
industry. The positive relationship gives us the fact that the increased value has a positive effect on the
input of R&D in the second industry.
Finally, in the third industry, the regression result is
(9)
Y = 0.0364 X ′′′ − 159.4605
Where, Y is the inputs of R&D and X ′′′ is the increased value of the third industry.
The t-Statistics is 11.5132
（
，
）
（N=17，α=0.05）and significant at the level of 0.05, the F-Statistics is
2
132.5542 N=17 α=0.05 and significant at the level of 0.05, and R =0.8983. The values of t-Statistics,
2
F-Statistics and R show the same tendency of the influence the output (defined as the increased value)
on R&D input as those in the agricultural and the second industry.
Note that the coefficient in a regression represents the respective degree of the influence of the
independent variable on the dependent variable. The regression coefficients are 0.075403, 0.0226 and
0.0364 in the agricultural, the second and the third industry respectively. Comparing these coefficients, it is
easy to get the different degree of the influence of national output on R&D inputs in different industries:
the influence degree is greatest in the agricultural industry and is the lowest in the third industry.
4.2 The outcomes of the input of R&D
In the former section, the relationship between national output and R&D input is analyzed. Now we
are likely to study the outcomes of R&D inputs in China. Because a patent often implicitly entitles its
owner to use it monopolistically, the owner may achieve a large mount of economic profits. To some extent,
this means that the quantity of paten application and authorization can be used as the measure of the
outcome of R&D input (this is for convenience of research in this paper). Table 2 gives the data from 1987
to 2003, which is necessary in our analysis.
Table2 quantity of patent application and patent authorization
quantity of patent
quantity of patent
Year
authorization piece
application piece
1987
26077
6811
（
（
）
1988
28787
11500
1989
32905
17129
1990
41469
22588
1991
50040
24616
1992
67000
31000
1993
77000
62000
1994
78000
43000
1995
83000
45000
1996
103000
44000
1997
114000
51000
1998
121989
67889
1999
134240
100154
2000
170690
105344
2001
203582
114252
2002
252632
132401
2003
308000
182000
）
With the data in Table 1 and Table 2, we first study the effect of R&D input on the quantity of patent
application because patent applications sever as a transformation between efforts (R&D inputs) and the
outcomes (patent authorizations). Applying equation (1)-(5), we get the regression relationship between
R&D input and the quantity of patent application:
(10)
y = 182.9598Y + 19126.77
where Y is the inputs of R&D and y is the quantity of patent application.
The t-Statistics is 42.6891 N=17 α=0.05 and significant at the level of 0.05, the F-Statistics is
（
，
）
（
，
）
2
1822.357 N=17 α=0.05 and significant at the level of 0.05, and R =0.9918. These implies that the
larger the inputs of R&D, the more the patent applications. This coincides with our economic intuitions:
inputs drive always outputs.
We now investigate the final outcome (patent authorization) of R&D input. Using the data of R&D
inputs and the data of the quantity of patent authorization, we have the following regression result
(11)
y ′ = 107.8992Y + 8023.890
where, Y is the inputs of R&D and y′ is the quantity of patent authorization.
The t-Statistics is 17.7058 N=17 α=0.05 and significant at the level of 0.05, the F-Statistics is
，
（
，
）
2
313.4959 (N=17 α=0.05) and significant at the level of 0.05, and R =0.9543. These statistics show that
we must spend more on R&D inputs if we want to gain more paten authorizations. More precisely, if we
hope to get one more patent authorization, we must spend about 108 billion Yuan RMB on R&D
expenditure.
5 conclusions
According to the above regressions, we can conclude that, in China, the national outputs has positive
effect on R&D inputs, but the degree of such effects are different among different industries. Further, we
investigate the outcomes of Chinese R&D inputs. The results show that R&D inputs enhance patent
application and the positive relationship exists between the quantity of patent authorizations and R&D
inputs. However, the results also show that a patent authorization seems to marginally be costly: an
additional patent authorization needs an extra R&D expenditure of 108 billion Yuan RMB.
Although these conclusions are consistent with our economics intuitions and standard economic
theories, they give us many implications for patent-related policy making: (1) In order to protect
intellectual ownership through patent authorization, governments should stimulate patent creators to lay a
strong emphasis on patent application; (2) governments should continue improving the environment of
technological innovation so as to deduce the cost of creations; (3) governments should attach much
importance on the training of technological creators (the source of creation) and patent processors who can
skillfully finish all steps from application to authorization (realization of patent authorization).
References
，
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[3] Chen Zhen, Li Lifen, Pei Fangfang.The compare research of R&D devotion for typical country.
Exploitation and Innovation, 2003(5), p4~6
[4] Wang Binhui, Ke Zhongyi. An analysis of magrginal to GDP rate of R&D input in Guangdong province.
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