The Study on Relationship between Innovation and Productivity

The Study on Relationship between Innovation and Productivity
WU Kai, MI Zhongchun
School of Management, University of Science & Technology of China, P. R. China, 230026
Abstract This paper empirically examines the relationship between innovation and productivity,
measures their determinants at the region level, and represents the regional independent innovation
capability. The Simultaneous Equations Models (SEM) is based on a knowledge production function.
The empirical part is based on the data of large and medium-sized industrial enterprises of 30 provinces
of China. The model is estimated using two econometric methods. Some interesting findings emerge.
The model is robust and can be used to estimate the relationship between innovation and productivity,
measure their determinants at the region and country level.
Key words Productivity, R&D, Innovation Output, Knowledge Production Function, SEM
1 Introduction
Innovations are at the heart of economic growth, and independent innovation is crucial for
changing the mode of economic growth. There has been a large volume of work in examining the link
between the innovation and productivity, mainly use Cobb–Douglas production function. Such as
Griliches (1986), Jaffe (1984, 1986), Guellec (2004), and in China He Wei (2003), Cai Hong (2004) etc.
they examined the impact of R&D (Research and Development) investment on productivity.
A serious limitation of above method is that it only measures the relationship between R&D input
and output. It neglects a link labeled by Pakes and Griliches (1984) as the ‘knowledge production
function’, i.e. production of commercially valuable knowledge or innovation output. Crépon, Duguet
and Mairesse (1998) launched an four-equation model (CDM), which consistently estimate the causal
effect of innovation investment on innovation output and the causal effect of innovation output on
productivity accounts for both selectivity and simultaneity issues. In their model it is explicitly taken
into account that it is not innovation input, but rather innovation output that increases productivity. Their
empirical analysis is based on French firm level data. CDM model offers a better understanding of the
relationship of innovation and productivity from a new angle.
There is no literature investigates the relationship between innovation and productivity with China
regional data based on CDM model, in doing so, this paper uses a framework of Simultaneous Equations
Models (SEM) according to the major idea of CDM model, to estimate above relationship and their own
determinants, and to describes the profile of independent innovation capability. The study employs
macro data of 30 provinces of China, so the problem doesn’t exist which is described in CDM model
that whether firms decide to perform R&D investment. Then the CDM model is reduced to two
equations including: knowledge production function and productivity function, the former links the
innovation inputs and innovation output, the latter links innovation output and productivity. In addition,
many researches define innovation output as the patents and papers. This paper will employs a set of
variable rather new, innovation output is defined as value of new product, and the expenditure on import
of technology is regarded as a determinant of innovation output. The results support the hypothesis that
innovation output is a significant determinant of productivity. Also some interesting findings emerge,
R&D investment is short-sight, and the ability of transforming imported technology into market value is
so weak, elasticity of productivity with respect to capital intensity is far higher than to labor etc. The
results in a certain extend reflect the status of innovation in China, and the model can be used for
estimating the relationship between innovation and productivity, measured their own determinants at the
region or country level.
The rest of this paper is organized as follows. Section 2 presents the theoretical framework
foundations, section 3 presents the model used in this paper, section 4 provides the description of data
and variables, empirical results are given in section 5, section 6 summarizes the main work and draws
conclusions.
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2 The Theoretical Foundations
The framework used here is based on a Cobb-Douglas production function explaining variation in
region output by a number of standard input variables and a R&D investment variable. It is also widely
used in many studies. The relation is written as:
q=α+βx+γk+ε
(1)
Where the low-case letters denote log of variables expressed in per employee terms. q is output, x
is a set of standard input variables such as human capital, physical capital, k is R&D investment. β is the
elasticity of output with respect to a set of inputs, γ is elasticity of output with respect to R&D, α is a
constant of efficiency, ε is a random error term.
A serious limitation of above method is that it only measures the relationship between R&D input
and output. It neglects a link labeled by Pakes and Griliches (1984) as the ‘knowledge production
function’, i.e. production of commercially valuable knowledge or innovation output. Crépon, Duguet
and Mairesse (1998) launched an four-equation model (CDM), which consistently estimate the causal
effect of innovation investment on innovation output and the causal effect of innovation output on
productivity accounts for both selectivity and simultaneity issues.
When only the innovation sample is used, a selection bias may arise. And when several links is
considered in a simultaneous framework, one possible problem is that some explanatory variables often
are determined jointly with the dependent variable, i.e. they are not exogenously given and there will be
simultaneity. CDM model eliminate the selectivity and simultaneity biases efficiently, their main idea is:
R&D decision → R&D intensity → innovation output → productivity, is written as:
g* = x0b0 + u0
(2)
k* = x1b1 + u1
(3)
t* = ak k* + x2b2 + u2
(4)
q = at t* + x3b3 + u3
(5)
Where g* express investment decision, k* is a latent innovation input, t* is expected innovation
output, q is productivity, x are explanatory variables such as employment, physical capital etc.
Equation (4) is knowledge production function expressing the link of innovation input and
innovation output, and (5) linking innovation output and productivity, k* is endogenous in equation (4),
and t* is endogenous in equation (5), To overcome the problem of endogenous explanatory variables
and derive consistent estimators, CDM use a reduced form of the model, Lööf (2002) and Heshmati
(2006) consider an instrumental variables approach, found that the innovation output elasticity increase
significantly than that of using OLS.
3 A Framework of Simultaneous Equations Models (SEM)
Considering that it not innovation input, but rather innovation output that increases productivity
(Crépon, Duguet and Mairesse, 1998), a SEM is applied to estimate region innovation and productivity
refers to CDM model. Firms perform R&D investment to improve process and introduce new products,
but this virtually increases the productivity. To describing this process in two stages: 1, from R&D
investment to innovation output; 2, innovation and other inputs increase the productivity. Figure 1
indicates the framework:
R&D
Knowledge Capital
Innovations/Patents
Technology Import
Productivity
Capital Intensity / Labor Quality
Figure 1: The Framework of Simultaneous Equations Models
Knowledge capital includes new product and patent, i.e. innovation output. Capital intensity can be
defined as total capital per employee, and labor quality can be defined as the ratio of scientists and
engineers. Productivity can be defined in several alternative ways, such as: gross value per employee,
value added per employee, sales per employee.
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Wang Chunfa argued that the independent innovation capability is mainly an institutional one,
unequal to technology capability. Relatively, technology capability indicates the ability holding the
technology direction and seeking break through, most like solving the technologic puzzle, and
independent innovation capability indicates the ability transforming technology knowledge into
commercial products and make money, mainly represents the overall competitive strength in market.
This process also can be seen in Figure 1.
To describe the two transform processes using two equations: knowledge production function and
productivity function, the former links the innovation inputs and innovation output, the latter links
innovation output and productivity, they are written as:
t* = α0 + β0 xm + γ0 k + ε0
(6)
q = α1 + β1 xn + γ1 t + ε1
(7)
where t* is expected innovation output, q is productivity, xm , xn are explanatory variables, γ0 is the
elasticity of innovation with respect to R&D investment, γ1 is the elasticity of productivity with respect
to innovation output. ε0, ε1 are random error terms. t* is dependent variable in equation (6), at the same
time it is endogenous in equation (7).
Note that this paper researches on regional level, the problem doesn’t exist that whether firms
decide to perform R&D investment, which is described in equations (2) and (3) of CDM model, the
sample used here including all enterprises, i.e. regional data contains both the innovation performed
firms and not performed ones, so there is no selectivity bias. Hence equations (2), (3) do not appear in
this study.
4 Data and Variables
In order to describe the general characteristics of innovation and productivity in China, This paper
applies the data of large and medium-sized industrial enterprises at the province level in empirical study.
Since above 40% value added in whole industry is created by large and medium-sized industrial
enterprises, they play an important role in whole industry and even in the whole national economy, so
it’s valuable to research on it. Empirical estimates is based on the data of large and medium-sized
industrial enterprises of 30 provinces (except XiZang for data missing) of China from 2000 to 2005
from China Statistical Yearbook on Science and Technology and China Statistical Yearbook 2001 – 2006.
All variable are expressed in logarithms, details in table 1:
Table 1 the Definitions of Variable
k
r
b
q
l
c
e
R&D
Personnel Expenditure Gross Value
Value of new
Total Scientists &
Definitions
expenditure for S&T on Import of of Industrial Employee
Capital/ l Engineers/ r
product / l
/r
Activities Technology/ r Output/ l
Variables
t
Most study define innovation output as patent, also some use new products numbers (Yan Bing,
2005). In Annual Report of Regional Innovation Capability of China (2003) the Research Group on
Development and Strategy of Science and Technology of China point out that, innovation output is
measured as value of new products is better than patent, for new products are defined relating to
drawback, they can reflect the market value of new technology. Lööf (2002) use sales of new products
per employee as innovation output. In this paper innovation output is define as value of new products
per employee, denoting innovation output intensity and ability. And gross value of industrial output per
employee express the productivity q. R&D investment is intramural expenditure on S&T activities. The
ratio of scientists and engineers expresses the skill composition of labor of a region. The data is from
China Statistical Yearbook on Science and Technology. Total capital include fixed capitals and the
floating capitals, data is from China Statistical Yearbook.
5 Empirical Results
Econometric software Eviews 5.0 is employed to estimate the dataset of large and medium-sized
industrial enterprises of 30 provinces of China, using Ordinary Least Squares (OLS) regression and the
Two-Stage Least Squares (TSLS) regression, and the OLS results are given in table 2:
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k R&D intensity
r Personnel for S&T
Knowledge b Import of Technology
Production R-squared
Function
Adjusted R-squared
Durbin-Watson stat
F-statistic
t Innovation output
l Employee
c Capital intensity
Productivity e Skill composition
Function
R-squared
Adjusted R-squared
Durbin-Watson stat
F-statistic
Table 2 OLS Result of the Data
2001
2002
2003
1.575***
1.806***
1.846***
0.206*
0.326***
0.199*
0.028
-0.1
-0.23
0.576
0.62
0.521
0.528
0.576
0.466
1.505
1.462
1.32
11.793
14.123
9.44
0.219***
0.178***
0.192***
0.045
0.073**
0.088*
0.774***
0.596***
0.69***
0.565*
0.445**
0.603**
0.785
0.785
0.69
0.75
0.751
0.640
1.841
2.065
2.147
22.798
22.87
13.94
2004
0.883***
-0.02
0.463***
0.6
0.553
1.54
12.983
0.198***
0.084**
0.706***
0.395*
0.77
0.734
2.037
21.039
2005
1.165***
0.152*
0.263***
0.65
0.609
1.70
16.086
0.178***
0.073**
0.596***
0.445**
0.785
0.751
2.065
22.87
Significant at the 1% (***), 5% (**) and 10% (*) levels of significance.
R-squared shows the fitness is rather good. Durbin-Watson stat indicates that there is no obvious
serial correlation. F-statistic is high enough so that can reject the hypothesis that all coefficients are zero.
Most elasticity coefficients are at the 1% level of significance. The correlation matrix of variables shows
that the correlation between input variables is very low, while the standard error also is very tiny, so
multicollinearity is not a serious problem. Table 3 gives a comparison of OLS and TSLS results, the
elasticity of innovation output with respect to productivity doesn’t vary significantly between the two
methods, so the simultaneity bias can be neglected, and OLS can give a consistent result.
Table 3 Elasticity of innovation output with respect to productivity in the OLS and TSLS Methods
2001
2002
2003
2004
2005
OLS
0.219***
0.178***
0.192***
0.198***
0.178***
Innovation
output
TSLS
0.242***
0.197***
0.186***
0.186***
0.186***
Significant at the 1% (***) level of significance.
Instruments for 2SLS: k, l, c, e, b.
In table 2, the elasticity of R&D investment with respect to innovation output is high also unstable,
when increase 1% in R&D, will bring an increase more than 1% in innovation output. The elasticity of
R&D labor with respect to innovation output is rather low and not significant. This indicates that the
innovation activities are more dependent on massive input of capital, and the efficiency of labor is low.
The ability is rather low and not significant in transforming the imported technology into value of new
products. The elasticity of innovation output with respect to productivity is significant and stable, about
0.2, it prove that innovation output is a significant determinant of productivity. elasticity of labor with
respect to productivity is so low and not significant, may caused by inconsistent statistical criterion, this
seems to be plausible in China that many firms are overstaffed and cause low efficiency. The elasticity
of capital intensity with respect to productivity is highest than others, it denotes that the increase of
productivity mainly depends on the abundant capital. At last the ratio of scientists and engineers has a
significant contribution on productivity. The elasticity of variables keep stable from 2000 to 2005, with
little fluctuate which denotes that the model in this paper is robust and able to reflect the profile of
overall characters of innovation and relationship of China.
When lagged variables are used in knowledge production function, it shows that no lag is the best,
and lag for one period is better, lagging for more periods are bad. It shows that the short-sight of R&D
investment is so notable. As can be seen from table 2 the elasticity of R&D with respect to innovation
output range from 0.883 to 1.846, the numbers are too big and unstable, it further indicate that the
short-term effect of R&D is too remarkable, and have little effect in long run, firms tend to pursue the
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immediate benefit, lack long-term investment, this will do harm to long-term stable sustainable
development of industry economy, He Wei (2003) get similar conclusion. This mostly because firms
tend to imitate, import and reform technology and have no strong intensive innovation consciousness,
more appreciate immediate benefits. Expenditure for technology development accounts for about 30
percent of R&D expenditure, the great part of money is spent in technology import and reform. Hence
the long term effect is so weak.
All evidences indicate that capacity is weak in independent R&D, as well as the ability of
absorbing imported technology and in transforming into market value. But where is the position of our
own talent? The increase of productivity depends on a mass of input of capital and the consumption of
sources. In transforming the extensive economic mode into an intensive one, independent innovation
plays a crucial role. For long-term stable sustainable development of economy, many things should be
done, such as the government conduct and create an innovation circumstance and system, firms intensify
the consciousness of innovation, strengthen the protection of intellectual property, intensify
development of a well-trained workforce etc.
6 Conclusion
It is a complex dynamic process from technological input to output, and there are many factors
influence the increase of productivity. This paper attempts to seize the main relationship of the
innovation activities according its nature process, and to reflect the causal effect of this process through
a model of SEM. Then empirically estimates the relationship of innovation and productivity at a region
level of China, and gives a description of the independent innovation capacity. The SEM model includes
two equations: knowledge production function and productivity function, the former links the
innovation inputs and innovation output, the latter links innovation output and productivity, consistently
estimate the causal effect of innovation investment on innovation output and the causal effect of
innovation output on productivity. A set of representative variables are employed and measured in a
rather new way. The model is estimated by OLS and TSLS methods, some interesting results come out.
The elasticity of R&D investment with respect to innovation output is high but unstable, which is far
higher that R&D labor. The contribution of imported technology is unstable. The elasticity of capital
intensity with respect to productivity is highest than others. The elasticity of innovation output with
respect to productivity is significant and stable, which prove the hypothesis that innovation output is a
significant determinant of productivity. All these reflect the capital-intensive economic growth mode and
weak capacity of independent innovation. The model is robust and can be used to measure the
relationship between innovation and productivity, measure their determinants at the region or country
level.
Further work should be done to consider more factors, such as the impact of R&D on the lagged
productivity, and the impact of productivity on R&D in the coming years, etc. Also the long time series
data should be estimated.
References
[1] Crépon, B., E. Duguet and J. Mairesse. Research, Innovation and Productivity: An Econometric
Analysis at the Firm Level. Economics of Innovation and New Technology 7, 1998: 115-158.
[2] Pakes, A. & Griliches, Z. Patents and the R&D at Firm Level: A First Look, In Z. Griliches, R&D,
Patents and Productivity. Chicago: University of Chicago Press, 1984: 390-409.
[3] He Wei. The Impact of R&D Expenditure on Output of Large and Medium-sized Industrial
Enterprises in China. Economic Science, 2003, 3: 5-11 (in Chinese).
[4] Cai Hong, Gao Jie, Xu Xiaowen. The Demonstration Research of Economic Impact of R&D
Investment. Studies in Science of Science, 2004, 2: 53-58 (in Chinese).
[5] Wang Chunfa. Several Views of Independent Innovation Capability. Theoretical Horizon, 2007, 1:
48-50 (in Chinese).
The Author can be contacted from Email: [email protected]
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