Research on Dynamic Change of China's Regional Innovation Efficiency—Based on Malmquist Index

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International Journal of Advances in Management Science (IJ-AMS) Volume 3 Issue 4, November 2014 DOI: 10.14355/ijams.2014.0304.06

Research on Dynamic Change of China's Regional Innovation Efficiency—Based on Malmquist Index Gao Hua School of Economic and Management, Wuhan University, Wuhan 430072, Hubei, China Xth6800@163.com Abstract In constructing the panel data of 30 provinces from 1998 to 2010, this paper constructs provincial innovation activities in the production frontier based on Malmquist Index and DEA Model, decomposing the innovation efficiency growth into technological progress and technical efficiency change. The paper also investigates regional innovation sources of growth and differentiation. The results show that: (1) The innovation TFP growth not only comes from technological progress, but also from technical efficiency, and the contribution of technical efficiency is slightly greater than that of technological progress from 1998 to 2010. Technology efficiency growth is mainly caused by the growth of pure technical efficiency, in spite of the decline of scale efficiency.

(2) The innovation TFP growth is relatively stable after 2000, except for a decline during 2003-2005. The technology efficiency change reached the top in 2002 and had grown steadily after that. The technology progress change always grows steadily except for a decline in 2002. (3) The TFP growth annually of the East, Middle and West is 1.019, 1.008 and 1.015 respectively. There exists gap between Eastern and Mid-Western, and the gap between the Middle and Eastern is bigger than the Western and Eastern. The TFP growth of eastern region mainly comes from technological progress, while the TFP growth of central and western mainly comes from technical efficiency.

Keywords Regional Innovation Efficiency; Malmquist Index; Technological Efficiency; Technological Progress

Introduction There is a growing awareness among regional authorities that the economic growth and competitiveness of their regions depend largely on the capacity of innovation. The concept of ‘regional innovation system’ had been suggested by some scholars (Freeman, 1987; Lundvall, 1992). With the focus on regional innovation, evaluation and measurement of relevant regional innovation has

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become a hot spot. Many researchers have noticed that the innovation systems in developing economies and transitional economies have quite different systematic characteristics from those in developed countries (Hu and Mathews, 2005; Gu and Lundvall, 2006; Liu and White, 2001). As a developing country, China should not only pay attention to total input and output of innovation resource, but also concentrate on innovation efficiency during building innovation-style country. As we know, regional innovation efficiency is the key to regional innovation capability.

Liu Shunzhong(2002) has evaluated the Innovating Performance of Regional Innovation System.

Zhoulijuan(2013) has measured China’ s regional innovation efficiency by collecting provincial panel data of regional innovation inputs and outputs in 1998 － 2010. Li Jing (2008) has made an empirical analysis of China’s regional innovation efficiency based on the panel data of China’s 29 provinces during 1998-2005.

Although these researches have studied regional innovation efficiency, but the dynamic research about regional innovation efficiency is few, not less than research on the dynamic changes of innovation efficiency at level of provinces. Regional innovation studies show that innovation activities are not even distributed spatially. Therefore, it is necessary to analyze dynamic changes of innovation efficiency at a regional level. This paper employs DEA-Malmquist Productivity Index (distance function approach) to measure the productivity changes over time and get insight sources of its changes. That is, we introduce Malmquist TFP index analysis on innovation efficiency of 30 provinces from 1998-2010 for a better grasp of regional innovation efficiency changes and trends, and then develop a feasible policy for regional innovation capability improvement.

By comparing innovation performance change, this

International Journal of Advances in Management Science (IJ-AMS) Volume 3 Issue 4, November 2014

paper draws upon and contributes to two streams of research: the regional innovation system and innovation efficiency change at the regional level.

Furthermore, by focusing on the empirical analysis of innovation efficiency change, this paper helps to bridge the gap between theoretical work and quantitative analysis, and addresses the researchers’ appeals for increasing the rigor and specificity of the innovation system approach. This paper is structured as follows: Section 2 introduces the research models.

Section 3 discusses the construction of data. In Section 4, findings and results are discussed in detail. Finally, Section 5 forms a conclusion.

Models The Malmquist Productivity Index based on the DEA (Data Envelop Analysis) and developed by the Fare et al (1992, 1994). The DEA was a linear programming tool available to DMU (Decision Making Units) to evaluate the performance based on the multiple outputs and inputs and the methodology was originated by Charnes et al (1978) based on the frontier line which was developed by Farell (1957).

There are a number of captivating features to use DEA Malmquist Productivity Index approach. It is a nonparametric approach which means it doesn’t require any functional form and it also shows the best practicing frontier.

Secondly, its nature is non-statistical which suggests that the result from DEA doesn’t produce any standard errors. On account of Malmquist Index, it is based on simple calculation as showed by Fare (1995). The index can be related to the superlative Tornqvist and Fisher Ideal quantity indexes, under certain conditions as showed by Caves (1982) and Fare & Grosskopf(1992).

Fare et al (1992) developed the DEA-based Malmquist productivity index as the geometric mean of two Malmquist productivity indexes of Caves et al (1982).

Therefore, Malmquist Index can be decomposed into two components, the efficiency change and technical change and the values of these components can be the evidences of the productivity change sources. In our methodology, it is to assume that there is k=1, …. K number of province which is using n=1….N number of inputs at each time period t=1….T and yield m=1, ….M number of outputs.

Following the Fare et al (1994), the output-oriented Malmquist productivity index between the period t and t+1 can be defined as,

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1

t D to ( xt , y t ) 2 D ( xt +1, yt +1) D o ( xt + 1, y t + 1) t ,t +1 ×[ × ] M o ( xt +1, yt +1, xt , yt ) = t D o ( xt , y t ) D to+ 1( xt + 1, y t + 1) D to+ 1( xt , y t ) t +1 o

(1) Where D to( x t +1, y t ) shows the distance from the period t+1 observations to the period t.

The first component TEC o = D to+1( x t +1, y t +1) / D to( x t , y t ) measures the relative technical efficiency changes at the period t and t+1. The component shows the score of changes in efficiency over the time which reflects catching up effect of DMU to the frontier. The second 1

D t0( x t + 1, y t + 1) D t0( x t , y t ) 2 component = × ] FS o [ D t0+ 1( x t + 1, y t + 1) D t0+ 1( x t , y t ) measures the shift in technology (frontier) between the time period t and t+1. If the score of FS o is greater than unity suggests the positive shift in the frontier, less than unity represents the negative shift or technical deterioration and equal to unity indicates that there is no change in the technology frontier .

Thus TFP change is decomposed into two components: efficiency change TEC o and technical change FS o .

Efficiency change measures whether production is getting closer to or farther away from the frontier.

Technical change captures the shift in technology between the two periods. It indicates whether or not technical progress occurred at the input–output combination of a particular industry. A value <1 indicates technical progress or efficiency improvement correspondingly. A value of >1 indicates deterioration in performance.

The improved productivity performance could be evidence if the score of Malmquist index represents greater than unity, if it comes less than unity which indicates productivity regress. The Malmquist productivity index is estimated by using nonparametric linear programming techniques. The efficiency change can be further decomposed into the scale change and pure efficiency change under the variable returns to scale; therefore it requires calculation of two more additional linear programming (Fare et al 1994).

Data and Variable Data The paper takes the sample of 1998–2010 panel data of 30 provinces from Statistical Yearbook of China, Science &Technology Statistical Yearbook of China.

China exhibits regional imbalance in terms of economy

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and technique. To analyze the imbalance, China’s provinces are divided into three regions according to their position and economic situation. Due to data incomplete, some provinces as Xizang are deleted from the sample. So in this paper, the Eastern region consists of Beijing, Tianjin, Shanghai, Hebei, Liaoning, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan. The Central region includes Jilin, Heilongjiang, Sanxi, Anhui, Jiangxi, Henan, Hubei, Hunan. The Western region consists of Guangxi, Neimenggu, Chongqing, Sichuan, Guizhou, Yunnan, Shanxi, Qinghai, Ningxia, Gansu, Xianjiang.

Variables 1)

Innovation Output

Regional innovation output should include two parts: R&D output and achievement transformation output. Due to its availability and reliability, patent data remains one of the most popular sources from which various indicators can be constructed.

Domestic patent data has been systematically collected by the State Intellectual Property Office (SIPO) of China for over 20 years and constitutes a rich dataset. As we know, domestic patents in China are classified into three categories: inventions, utility models and designs. These three patent types are very different in terms of economic value, technological importance and resource commitment. Inventions represent the most technologically sophisticated innovation output and are “new-to the-world.” So we employ domestic invention patents granted as a proxy for the level of R&D output. In the stage of achievements commercialization, we employ technology market turnover as a proxy for the level of commercial innovation output. All expenditure indexes were adjusted by comparable price index in 1998 in order to remove the inflation impact.

2)

Innovation Input

The innovation input consists of financial and human capital. We select R&D personnel full-time equivalent and R&D capital stock as innovation input variables.

3)

Constructing R&D Capital Stock

Constructing and measuring R&D stock were techniques that were first discussed in Griliches (1979). A perpetual inventory method was suggested in constructing R&D capital stock.

Following the method of Griliches (1979), the R&D

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capital of the i th province in the year of t ( K it ) is constructed in the following manner by using the perpetual inventory method as

K it= E it −1 + (1 − δ ) K it −1

(2)

where δ is the depreciation rate of R&D capital, and E it is R&D expenditure of the i province in year t . R&D expenditure should be adjusted by R&D price index in 1998 in order to remove the inflation impact. As Zhu(2003) provided method for reference, we construct R&D price index as , R&D price index =0.55×consumer price index+0.45×fixed assets invest price index. Just as suggested by Hollanders and ter Weel(2002), Hall and Mairesse (1995), and Hu and Jefferson (2004), the initial R&D capital stock is constructed by assuming a constant growth rate of R&D expenditure. That is, the initial R&D capital stock is defined as

K i0 =

E i1 δ +g

(3)

where g is the annual growth rate of R&D expenditure. It should be pointed out that the choice of depreciation rate for R&D capital makes little difference (Hall and Mairesse, 1995). For example, Hollanders (2002) used an annual depreciation rate of 15% for the R&D stock, and found that taking other reasonable rates of depreciation did not change the estimation results substantially. In addition, Hasan (2002) found that experimentation with lower depreciation and longer impact lags for both in-house R&D and imported technologies yielded similar results to those based on a 15% rate of depreciation.

An important aspect to keep in mind when analyzing the transformation of innovation inputs into innovation outputs is the existence of time lags.

According to the extant study, there is no generally accepted length of time lags for R&D to output (Wang and Huang, 2007). In China, it usually takes about 2 years for an invention patent application to be approved (Liang, 2005). So in this paper, a 2-year time lag is used for innovation output.

Results Country Level According to the model and the data, using Deap 2.0 software, the mean of China’s innovation Malmquist

International Journal of Advances in Management Science (IJ-AMS) Volume 3 Issue 4, November 2014

Index and decomposition of 30 provinces were estimated from 1998 to 2010, results are shown as follow.

As Table 1 shows, the annual innovation TFP growth is 2 percent since 1998, of which technological progress increased by 0.5 percent annually, technical efficiency increased by 0.9 percent annually. We can find that the innovation TFP growth during the period results from technological progress partly on one hand, and from technical efficiency partly on the other hand, and the contribution of technical efficiency is slightly greater than that of technological progress. Then, the technical efficiency is decomposed into pure technical efficiency and scale efficiency. We find that innovation scale efficiency decreased by 1 percent, while pure technical efficiency increased by 1.4% annually. The results show that technology efficiency growth is mainly caused by the growth of pure technical efficiency, in spite of the decline of scale efficiency.

TABLE 1 CHINA'S INNOVATION MALMQUIST INDEX AND DECOMPOSITION

TE 0.957 0.747 1.012 1.358 1.028 0.955 1.031 0.941 0.953 1.085 1.07 1.079 1.009

1998-1999 1999-2000 2000-2001 2001-2002 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 mean

TP 0.884 1.106 1.157 0.771 0.865 1.038 0.91 1.099 1.129 1.019 1.152 1.023 1.005

PTE 0.846 0.826 1.171 1.046 0.889 0.992 0.938 1.034 1.076 1.106 1.233 1.103 1.014

SE 0.953 0.921 0.997 1.017 0.958 0.974 0.983 0.945 0.974 1.052 1.058 1.059 0.99

TFP 1.004 0.811 1.016 1.335 1.073 0.981 1.049 0.996 0.979 1.031 1.012 1.019 1.02

Innovation Malmquist Index and decomposition of China 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0

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Effch Techch TFPch

FIG. 1 INNOVATION MALMQUIST INDEX AND DECOMPOSITION OF CHINA

As Figure 1 shows that the innovation TFP growth is relatively stable after 2000, except for a decline during 2003-2005. The change of technology efficiency gets the top in 2002 and grows steadily after that. The change of technology progress always grows steadily except for a decline in 2002. Interestingly, the growth and

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decline of technical efficiency and technological progress always appears during the period. It is seldom to see that technical efficiency and technological progress increase or decrease simultaneously.

As can be seen from Table 2, the TFP growth of Shaanxi, Sichuan, Beijing rank the highest in 30 provinces, while the TFP growth of Ningxia, Inner Mongolia, Xinjiang rank the lowest. The technical efficiency change of Shaanxi, Sichuan and Gansu is the highest, while Hainan, Ningxia, Hebei is the lowest.

The technical progress change of Beijing, Chongqing, Guangdong is the highest, while Xinjiang, Inner Mongolia, Guangxi is the lowest.

TABLE 2

MALMQUIST INDEX AND DECOMPOSITION OF CHINA'S PROVINCIAL INNOVATION FROM 1998 TO 2010

BeiJing Tianjin Hebei Sanxi Neimenggu Liaoning Jilin Heilj Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shanxi Gansu Qinghai Ningxia Xinjiang mean

TE 1.054 0.995 0.963 0.996 0.978 0.997 1.01 1.033 1.032 1.042 0.991 1.064 0.966 0.976 0.993 1.003 1.044 0.983 1.064 0.965 0.916 1.007 1.099 1.034 1 1.107 1.076 1 0.933 0.991 1.009

TP 1.061 1.002 1.005 1.011 0.932 1.018 1.009 1.015 1.034 1.009 1.019 0.999 1.001 0.995 1.012 1.006 1.025 1.001 1.041 0.969 1.007 1.042 1.018 0.987 1.004 1.015 1.011 1.014 0.972 0.925 1.005

PTE 1.118 0.997 0.968 1.007 0.912 1.015 1.019 1.049 1.067 1.052 1.009 1.062 0.967 0.972 1.005 1.009 1.07 0.985 1.107 0.935 0.922 1.048 1.119 1.02 1.004 1.123 1.088 1.014 0.907 0.917 1.014

SE 1 0.981 0.935 0.958 0.982 0.953 0.982 0.997 0.993 0.988 0.991 1.034 0.966 0.977 0.947 0.974 1.012 0.984 1.02 0.967 1 0.949 1.031 1.028 1 1.041 1.045 1 0.968 1.004 0.99

TFP 1.054 1.015 1.03 1.04 0.995 1.046 1.028 1.036 1.039 1.055 1 1.028 1 1 1.048 1.03 1.032 0.999 1.043 0.998 0.916 1.061 1.066 1.006 1 1.063 1.03 1 0.964 0.987 1.02

Regional Level Table 3 lists the three regional innovations TFP and their decomposition values. The results show that the growth of TFP annually of the East, Middle and West is 1.019, 1.0083 and 1.0147 respectively. The TFP growth annually of the East above than the Middle and West is 0.0107 and 0.0043 respectively. There exists 131

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2)

Middle Region

Among the middle 9 provinces, in addition to declined innovation TFP of Hunan, Jiangxi, Inner Mongolia, the TFP of other 6 provinces is growing.

The first three fastest growing provinces is Hubei (7%), Anhui (6.2%), Heilongjiang (4.9%). The first three fastest growing provinces of technical efficiency is Anhui (6.4%), Hubei (4.4%), Heilongjiang (3.3%). Except for decreasing technical efficiency of Shanxi, Hunan, Jiangxi, Inner Mongolia, the technical efficiency of other 5 provinces is growing.

Except for decreasing

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Among the eastern 11 provinces, except for declined innovation TFP of Tianjin, Hebei, Fujian, Hainan, the TFP of other 7 provinces is growing.

The first three fastest growing provinces is Beijing(11.8%), Guangdong(10.7%) and Shanghai(6.7%). The first three fastest growing provinces of technical efficiency is Guangdong, Beijing, Jiangsu. Except for growing technical efficiency of Guangdong, Beijing, Jiangsu and Shanghai, the technical efficiency of other 7 provinces is decreasing.

The technological advances in eastern provinces are all growing, including the first three fastest growing provinces Beijing(6.1%), Guangdong(4.1%), Shanghai (3.4%).

Thus, the TFP growth in eastern region is mainly contributed by technological progress. As for Jiangsu and Guangdong, the contribution of technical efficiency is bigger than technical progress on TFP growth.

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Effch Techch TFPch

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TFP 1.019 1.0083 1.0147 1.0140

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SE 1.0216 1.0208 1.0169 1.0200

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PTE 0.9791 0.9887 1.0028 0.9900

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TP 1.0188 0.9989 0.9952 1.0050

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Eastern Middle Western Total

TE 1.0003 1.0092 1.0197 1.0090

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EASTERN, MIDDLE AND WESTERN INNOVATION FROM 1998 TO 2010

Innovation Malmquist Index and decomposition in Eastern region 1.2 1 0.8 0.6 0.4 0.2 0 g

MALMQUIST INDEX AND DECOMPOSITION OF CHINA'S

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TABEL 3

technical progress of Anhui, Jiangxi, Inner Mongolia, the technical progress of other 6 provinces is growing. The first three fastest growing provinces is Hubei (2.5%), Heilongjiang (1.5%), Shanxi (1.1%). Among six TFP growth provinces, the contribution of technical efficiency above technological progress is Anhui, Hubei, Heilongjiang, Jilin; the contribution of technical progress above technical efficiency is Shanxi, Henan.

in

gap between Eastern and Mid-Western, and the gap between the Middle and Eastern is bigger than the Western and Eastern. The TFP growth of eastern region mainly comes from technological progress, while the TFP growth of central and western mainly comes from technical efficiency. Although the technological progress of Middle is slightly higher than the Western, however, the technical efficiency of Western is much bigger than the Middle, which leading to the TFP growth of Western is greater than the Middle finally.

an

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FIG. 3 INNOVATION MALMQUIST INDEX AND DECOMPOSITION IN MIDDLE REGION

3)

Western Region

Among the western 10 provinces, in addition to declined innovation TFP of Guangxi, Ningxia, Xinjiang, the TFP of other 7 provinces is growing.

The first three fastest growing provinces is Shanxi (12.3%), Sichuan (11.9%), Gansu (8.8%). The first three fastest growing provinces of technical efficiency is Shanxi (10.7%), Sichuan (9.9%), Gansu (7.6%). Except for decreasing technical efficiency of Guangxi, Ningxia, Xinjiang, the technical efficiency of other 7 provinces is growing.

Except for decreasing technical progress of Guizhou, Guangxi, Ningxia, Xinjiang, the technical progress of other 6 provinces is growing. The first three fastest growing provinces is Chongqing (4.2%), Sichuan (1.8%), Shaanxi (1.5%). Among 7 TFP growth provinces, the contribution of technical efficiency above technological progress is Sichuan, Guizhou,

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Shanxi, Gansu; the contribution of technical progress above technical efficiency is Chongqing, Yunnan and Qinghai.

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and Eastern is bigger than the Western and Eastern.

The TFP growth of eastern region mainly comes from technological progress, while the TFP growth of central and western comes from technical efficiency.

Innovation Malmquist Index and decomposition in Western region

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1.2 1 0.8

Effch Techch TFPch

0.6 0.4 0.2 0

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FIG. 4 INNOVATION MALMQUIST INDEX AND DECOMPOSITION IN WESTERN REGION

Conclusions Based on the panel data of 30 provinces from 1998 to 2010, this paper constructs provincial innovation activities in the production frontier based on Malmquist Index and DEA Model, decomposing the innovation efficiency growth into technological progress and technical efficiency change. The paper also investigates regional innovation sources of growth and differentiation. The results show that: (1) The innovation TFP growth not only come from technological progress, but also from technical efficiency. Technology efficiency growth is mainly caused by the growth of pure technical efficiency. (2) The innovation TFP growth is relatively stable after 2000, except for a decline during 2003-2005. The technology efficiency change reached the top in 2002 and had grown steadily after that. The technology progress change always grows steadily except for a decline in 2002. (3)There exists gap between Eastern and Mid-Western, and the gap between the Middle

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