The Decline of National Innovation and Its Implication

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The Decline of National Innovation and Its Implication
Jingbo Yao
Room 1432, Xinzhi Building, No. 28, Fucheng Road, Haidian District, Beijing
angela@cas-harbour.org
Keywords: Innovation decline, Institutional environment, Human capital improvement, GII index
Abstract. Based on the Annual Global Innovation Index (GII) reports, this paper found that many
countries in the world have experienced a decline in national innovation capacity in different years,
which has restricted the development of the country to some extent. Presently, the global economic
growth is sluggish, the development uncertainty is huge, and the understanding of innovation
decline is of great significance to the development of a country. This paper made a definition of
innovation decline and analyzed its influencing factors. Finally, the result of this paper showed that
the improvement of infrastructure quality will promote the occurrence of innovation decline.
However, the institutional environment and R&D expenditure of government cannot affect
innovation decline significantly.
1. Introduction and literature review
Because of the pioneering introduction of economic innovation theory put forward by
Schumpeter[1], people have paid more and more attention to the innovation researches, such as the
connotation and extension of innovation, the role and importance of innovation, the influencing
factors of innovation, the reasons for lacking of innovation and the countermeasures. Moreover, the
main body of innovation is not limited to individuals, but also the institution, organization, industry,
region, or even the country. Freeman[2] pioneered the concept of the National Innovation System
(NIS) by studying the miracle of Japan's economic growth and the important role of the government.
Lundvall[3], Nelson[4], and the OECD[5] also studied this issue deeply and emphasized the role of
the government, who was the system supplier and market corrector in the overall national
innovation system for national innovation. Furthermore, under the premise of recognizing the
market as the main body of innovation, Howells[6], Curtis[7], and Block[8] believed that
innovation was not only a matter of the market and the government should participate in national
innovation actively and selectively. Smith[9] even believed that government was the core of
national innovation. Additionally, Fromhold-Eisebith[10] highlighted the function of the innovation
systems approach for policy conceptualization. Based on the experience of the European Union and
the United States, Kravchenko[11] analyzed the problems of assessing and measuring national
innovation systems. At present, the combination of technological innovation and institutional
innovation, management innovation, business innovation and cultural innovation are tight
increasingly, and the national development mode is gradually shifting from factor-driven to
innovation-driven. This process is reshaping the world's competitive landscape and changing the
power of country. The innovation drive has become a core strategy for more and more countries to
seek competitive advantages, such as, "America's Innovation Strategy: Ensuring Our Economic
Growth and Prosperity" and "National Strategic Plan for Advanced Manufacturing" (America), "
Basic Plan of Science and Technology" (Japan), "Germany 2020 High Technology Strategy"
(Germany), "Chinese Manufacturing in 2025" (China). On one hand, the importance of innovation
is unquestionable so that it has become the focus of many countries. The one who can lead
innovation can also lead the global development process. On the other hand, the decline in the
growth rate of total factor growth (TFP), which is significantly positively correlated with national
innovation capabilities, explains 85% economic slowdown in more than 130 countries around the
world [12]. At the same time, according to the annual national innovation index’s scores and
rankings released by Cornell University, the World Intellectual Property Organization (WIPO) and
other institutions, the country's absolute innovation ability and relative ranking is neither static, nor
International Conference on Education Science and Economic Development (ICESED 2019)
Copyright © 2020 The Authors. Published by Atlantis Press SARL.
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continuously improving. Whether it is in developed countries or developing countries, the
phenomenon of decline in innovation capacity, has generally occurred, and the emergence of
innovation decline is not a small probability event (more details are shown in section 3). Because of
this, the decline of innovation has become one of the major risks of the national development.
Presently, the global economic growth is sluggish, the development uncertainty is huge. The
understanding of innovation decline and the role of the government are of great significance to the
development of the country. At present, there are few scholars who regard "innovation decline" as a
direct research object. Therefore, this paper used the global innovation index to define "innovation
decline" and tried to use the panel Logit model to explore the influencing factors of the "innovation
decline" and the government's role in preventing this risk.
2. Identification of innovation decline
2.1 Definition of innovation decline
Referring to Barry Eichengreen, Donghyun Park, Kwanho Shin [12] for the definition of the
dummy variable "economic slowdown", and according to the value of the global innovation index
and its ranking, "absolute innovation decline" and "relative innovation decline" can be defined.
Among them, "absolute innovation decline" refers to the decline of the innovation output index, and
"relative innovation decline" refers to the decline in the ranking of the innovation output index.
Specifically, if a country's innovation output index declines in a certain year, its corresponding
"absolute innovation decline" is assigned a value of "1". Otherwise, it is assigned a value of "0".
Similarly, if a country's rank of the innovation output index declines in a certain year, its
corresponding "relative innovation decline" is assigned a value of "1", and vice versa is assigned a
value of "0". By doing the above operations for all the individuals in the sample, two dummy
variables were obtained, which were called "innovation decline".
However, there are some problems with "innovation decline" defined by the above method.
There, the decline of the innovation output index was taken as an example. For a country, if the
value of the innovation output index of a certain year is less than that of last year, but the difference
is very small, it is not appropriate to define the year as "innovation decline". For example, the
scores of Algeria were 16.74 and 16.68 in 2014 and 2015 respectively. The difference between the
two was only 0.06. It is not appropriate to identify Algeria in 2015 as "innovation decline". In
addition, due to small changes year by year in the annual output of the Innovation Output Index
(refer to the GII report appendix for detail), the smaller differences might not be due to the
differences in real innovation capabilities rather than the statistical methods or statistical errors. To
alleviate this problem, it can be judged as "innovation decline" only when the innovation index falls
by 5% and above (or 10% and above). In terms of "relative innovation decline", it can be judged as
"innovation decline" to alleviate this problem only when the ranking declines by 3 and above (or 5
and above). Additionally, the one-year ranking decline sometimes might be accidental, but the
probability of accidental decline for two consecutive years will be reduced greatly. Therefore, it can
be judged as "relative innovation decline" when the rank of innovation output index has fallen by
three or five units in two consecutive years.The above definitions are summarized as follows:
1. Absolute innovation decline – based on the innovation output index score
(a) Score（T）-Score（T-1）<=0；
(b) (Score（T）-Score（T-1）)/ Score（T-1）>=-0.05；
(c) (Score（T）-Score（T-1）)/ Score（T-1）>=-0.10；
2. Relative innovation decline - based on the rank of innovation output index
(a) Rank（T）- Rank（T-1）>=1；
(b) Rank（T）- Rank（T-1）>=3；
(c) Rank（T）- Rank（T-1）>=5；
(d) Rank（T）- Rank（T-1）>=1 and Rank（T-1）- Rank（T-2）>=1；
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(e) Rank（T）- Rank（T-1）>=3 and Rank（T-1）- Rank（T-2）>=3；
(f ) Rank（T）- Rank（T-1）>=5 and Rank（T-1）- Rank（T-2）>=5；
2.2 Performance of innovation decline
According to the definition of "innovation decline" in 2.1, Table 1 can be obtained according to the
GII index and its ranking data. According to the TFP data in Penn World Table 9.0, Table 2 can be
obtained. From Table 1, Table 2, we can get the following facts. (1) "Innovation decline" is not a
small probability phenomenon: From the perspective of "absolute innovation decline", in the period
of 2012-2017, the number of innovation decline defined by falling, falling more than 5%, and
falling more than 10% are respectively 323, 186, 98 and the corresponding decline frequency are
respectively 0.55, 0.31, 0.17; from the perspective of "relative innovation decline", in the period of
2012-2017, the number of rank declining, declining 3 units or more, and declining 5 units or more
are 291, 206, and 167, respectively, and the corresponding decline frequencies are 0.49, 0.35, and
0.28, respectively. What is more, the number of rank declining, declining 3 units or more, and
declining 5 units or more for two consecutive years are respectively 119. 57, 38, and the
corresponding sliding frequencies are 0.20, 0.10, 0.06. (2) "Innovation decline" is widespread, but
to a different extent: in 2012-2017, almost all countries suffered from "innovation decline", but
countries with different income levels suffered from different "innovation decline". The difference
is that from the perspective of "absolute innovation decline", the decline rate of low-income
countries, lower-middle countries, upper-middle countries, and high-income countries is 0.51, 0.34,
0.32, and 0.24, respectively. In countries with higher per capita income, the lower the probability of
decline in their ability to innovate, the fact that the scores fall by 10% or more also supports the
above judgment; From the perspective of "relative innovation decline", taking de1_3 as an example,
the decline frequencies of low-income countries, lower-middle countries, upper-middle countries,
and high-income countries were 0.46, 0.34, 0.40, and 0.29, respectively (see table 2). It seems that
the probability of a substantial decline in the ranking of innovation in high-income countries is
smaller, and the decline in other ranking methods also supports this judgment. (3) The absence of
"absolute innovation decline" does not mean that "relative innovation decline" will not happen, and
vice versa is not true: in the United States, for example, in 2014 and 2015, the GII output scores of
US were 52.27 and 52.89 respectively, but its ranking slipped from 7 to 9. What is more, the scores
of the United States in 2016 and 2017 were 54.08 and 53.93 respectively, but the ranking rose from
7 to 5. (4) The score of innovation ability is positively correlated with TFP and GDP per capita:
After eliminating the petroleum country (economic structures of those countries are quite unique),
the author drew the scatter plot of the innovation output score and TFP (see figure 1) and finds that
the innovation output score has strong positive correlations with TFP. Countries with stronger
innovation capabilities, in general, have higher total factor productivity, which also implies the
negative impact of "innovation decline" on TFP. This finding is in line with the findings of many
other scholars. Based on the research of Solow [13] and Barro [14], TFP was considered to
represent the generalized technology. The TFP growth rate was considered as a generalized
technological advancement, and this generalized technique was considered to include three factors:
narrow technical level, resource allocation efficiency, scale effect [15]. Furthermore, Zhang Lijun
[16] believed that innovation capability was an important part of TFP and verified that regional
innovation capability has a significant positive impact on TFP growth rate through production
function method. In addition, as can be seen from figure 2, samples with bigger innovation scores
than average have higher TFP, while TFPs with lower innovation rankings are also slightly lower
than those without decline. It can also be seen from figures 3 and 4 that both TFP and innovation
output scores are significantly positively correlated with per capita income. At the same time, the
TFP of the sample with a decline in innovation ranking is also slightly lower than the sample with
no decline. Finally, it can also be seen from figures 3 and 4 that both TFP and innovation output
scores are significantly positively correlated with income per capita.
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Table 1 Innovation decline statistics I
sde1
sde1_5
sde1_10
de1
de1_3
de1_5
de2
de2_3
de2_5
observations
low-income
40
35
19
30
31
26
10
11
8
68
lower-middle
income
72
42
26
66
42
36
30
9
4
124
upper-middle
income
87
50
30
76
62
54
34
20
15
154
high-income
124
59
23
119
71
51
45
17
11
245
sum
323
186
98
291
206
167
119
57
38
591
decline
frequency
0.55
0.31
0.17
0.49
0.35
0.28
0.2
0.1
0.06
Note: sde1, sde1_5, and sde1_10 respectively represent the decline in innovation output, decline
5% and above, and decline 10% or more. de1, de1_3, and de1_5 respectively represent the rank of
innovation output declines, declines at least 3 units, and declines at least 5 units. de2, de2_3, and
de_5 respectively represent the rank of innovation output declines, declines at least 3 units, and
declines at least 5 units for two consecutive years. A country in a year is regarded as an observation
here.
Table 2 Innovation decline statistics II
sde1
sde1_5
sde1_10
de1
de1_3
de1_5
de2
de2_3
de2_5
observations
low-income
0.59
0.51
0.28
0.44
0.46
0.38
0.2
0.16
0.12
68
lower-middle
income
0.58
0.34
0.21
0.53
0.34
0.29
0.2
0.07
0.03
124
upper-middle
income
0.56
0.32
0.19
0.49
0.4
0.35
0.2
0.13
0.1
154
high-income
0.51
0.24
0.09
0.49
0.29
0.21
0.2
0.07
0.04
245
sum
0.55
0.31
0.17
0.49
0.35
0.28
0.2
0.1
0.06
591
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Fig. 1. Innovation and TFP I
Fig. 2. Innovation and TFP II
Note: "1" means yes, "0" means no.
Fig. 3. Income and innovation score
Note: LI、LM、UM、HI respectively represent low-income countries, lower-middle income
countries, upper-middle income countries, and high-income countries.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
10 20 30 40 50 60 70
TFP
Score
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Fig. 4. Income and TFP
Note: LI、LM、UM、HI respectively represent low-income countries, lower-middle income
countries, upper-middle income countries, and high-income countries.
3. Factors behind the innovation decline
3.1 About the composition of the innovation index
In order to more fully and comprehensively describe the country's ability to innovate, the GII report
does not adopt a single indicator, but constructs a comprehensive index of innovation that is
weighted in some way by the innovation input index and the innovation output index. Among them,
there are five pillars in the input index of innovation capability, namely Institution, Human capital
and research, Infrastructure, Market sophistication, Business sophistication. More information can
be seen in figure 5.
Fig. 5. GII indicator framework
In conclusion, the innovation input index comprehensively measures the investment of country's
innovation in a certain year (including institutional environment, market organization, human
capital, physical capital and other dimensions), while the innovation output index measures the
innovative outcomes of a country's innovation in a given year. In this paper, the author used the
innovation output as the measure of the country's ability to innovate. Undoubtedly, the output of
innovation results will be affected by inputs inevitably , which include the role of the government
and market. The government's role in innovation can be broadly divided into three categories: the
first is the provision of public goods such as infrastructure; the second is to create an innovative
institutional environment, and the third is government innovation-related expenditures (such as
R&D expenditures). In the next section, the panel Logit model will be used to explore the role of
these three factors.
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3.2 Empirical model and variable description statistics
Since the dependent variable is a "0-1" dummy variable, it is not suitable to use the linear regression
model. Therefore, the binary regression model is selected. Since the selected data is panel data and
the Probit model does not support it, the panel Logit model is used. The specific structure is as
follows:
(1)
(2)
P( P(
P (
F ( (3)
Where is the unobservable latent variable, measures the individual effect，F is the
cumulative distribution function of . If obeys the logical distribution, the above regression
model is the Logit model.
P( (4)
(5)
In this article, means innovation decline and "1" indicates occurrence. In part 2.1,
"innovation decline" is divided into "absolute innovation decline" and "relative innovation decline",
but in the next section "innovation decline" only refers to "absolute innovation weakness".
stands for the various factors that affect innovation decline , see Table 3. is an unobservable,
non-observable individual effect, that is, a unique factor that affects a country's "innovation decline".
Since the GII report has a large difference in calculation methods around 2011, only the data of the
Innovation Index of 2012-2017 is used here, and the description of the variables is shown in Table 3.
Among them, sd1_5 and sd1_10 represent the innovation decline calculated on the basis of 5% and
10% decreasing of the scores; inf, ins, hcr, msof and bsof are the five pillars of the GII index,
representing infrastructure, institutional environment, human capital and R&D, market
sophistication, business sophistication; ps, qe are political stability, government efficiency,
measuring the national political environment; rq, rl are the quality of supervision and legal rules,
measuring the state's regulatory environment; esb, ept, and eri measure the ease to start a business,
ease to pay tax, and ease of bankruptcy insolvency, measuring the business environment of the
country. rd and acrd respectively are accumulate R&D expenditures per capita and government
R&D expenditures, measuring the government's R&D expenditure stock. The above data are from
the GII reports or the World Bank WDI database.
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Table 3 Variable description statistics
Variable
Mean
Std. Dev.
Min
Max
Observations
sde1_5
0.315
0.465
0.000
1.000
N = 448
sde1_10
0.170
0.376
0.000
1.000
N = 448
inf
40.334
13.213
11.400
69.800
N = 448
ins
63.984
16.533
25.400
95.400
N = 448
hcr
35.162
15.062
0.700
68.300
N = 448
msof
48.798
12.449
19.000
87.100
N = 448
bsof
36.333
11.251
12.600
76.900
N = 448
ps
-0.011
0.851
-2.700
1.490
N = 450
ge
0.332
0.908
-1.280
2.250
N = 449
rq
0.402
0.931
-1.290
8.000
N = 449
rl
0.209
0.983
-1.290
2.090
N = 450
esb
67.325
34.516
0.070
100.000
N = 450
ept
55.666
31.095
0.010
97.190
N = 449
eri
41.444
28.552
0.120
98.310
N = 450
rd
927.075
959.345
34.323
6047.472
N = 208
acrd
50.372
123.768
0.184
803.147
N = 208
3.3 Empirical results
3.3.1 Infrastructure
In order to measure the impact of general infrastructure on "innovation decline", the author used the
infrastructure index in the GII input index to measure the quality of the infrastructure, which is
represented by "inf" in the model. The regression results are shown in Table 4, and column (1) is
the base regression, which measures the impact of infrastructure on innovation decline without
considering other factors. Columns (2) through (4) measure the impact of infrastructure on
innovation decline under other factors. In order to further increase the robustness of the results, this
paper reports in the column (5) the regression results of the 10% based innovation decline. The
results of columns (1) through (5) show that inf is statistically significant and its coefficient is
positive, which means that the higher the score of the infrastructure, the greater the probability of
innovation decline, while other influencing factors remain the same. This is easy to understand,
although in terms of the function of the infrastructure, its improvement can provide a good material
basis for innovation. However, from the perspective of its implementation process, infrastructure
investment may squeeze private investment by squeezing out limited credits, raising market interest
rates, increasing government debt, and distorting economic structure. Because private investment is
the mainstay of innovation, this process will inhibit market innovation and the development of
national innovation. Especially when the supply of infrastructure exceeds normal levels, the
continued increase in infrastructure investment may have a greater inhibitory effect on innovation.
Table 4 Infrastructure
(1)
(2)
(3)
(4)
(5)
inf1_5
inf2_5
inf3_5
inf4_5
inf_10
main
inf
0.134***
0.131***
0.133***
0.135***
0.152***
(0.0312)
(0.0314)
(0.0322)
(0.0327)
(0.0426)
ins
0.0333
-0.0368
-0.0374
(0.0453)
(0.0539)
(0.0651)
hcr
-0.0355
-0.0141
-0.0873*
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(0.0350)
(0.0369)
(0.0520)
msof
0.0460*
0.0498*
0.0419
(0.0259)
(0.0281)
(0.0332)
bsof
-0.0643**
-0.0707**
-0.0655
(0.0317)
(0.0342)
(0.0407)
N
363
363
363
363
244
R2
Standard errors in parentheses
* p < 0.1, ** p < 0.05, *** p < 0.01
Note: "_5" means decreasing 5% or above, "_10" means decreasing 10% or above.
3.3.2 Institutional environment
In order to study the impact of different institutional environments on "innovation decline", the
author selected a series of indicators to measure the institutional environment. It can be seen from
column (4) and column (5) of Table 4 that the influence of institutional environment ins on
innovation decline is not statistically significant under other factors. Of course, the selection of
composite index may have an impact on the results. In order to more closely measure the impact of
different institutional environments, it is necessary to use subdivided indicators instead of
composite indices. In order to measure the political environment, the author selected political
stability and safety and government effectiveness. In order to measure the regulatory environment,
the author selected regulatory quality and rule of law; In order to measure the business environment,
the author chose the cost to start a business, the cost of resolving insolvency, and the time to prepare
and pay taxes. In the regression models, the above variables are represented by "ps", "ge", "rq", "rl",
"csb", "ric", "tppt", respectively. It can be seen from Table 5 that whether or not to consider the
influence of other factors, the political stability and security level of the political environment and
the impact of government efficiency on the lack of innovation are not statistically significant; as can
be seen from Table 6, although the regulatory quality and rule of law have negative coefficients,
which means the improvement of regulatory quality tends to reduce the probability of "innovation
decline", they are not statistically significant. In addition, as can be seen from Table 7, the
indicators for measuring the business environment, whether it is the cost of starting a business, the
cost of resolving the bankruptcy problem or the time of tax payment is not statistically significant.
All in all, this shows that the institutional environment itself does not affect the occurrence of
innovation decline.
Table 5 Political environment
(1)
(2)
(3)
(4)
(5)
(6)
ps1_5
ps2_5
ps3_5
ge_5
ps_10
ge_10
main
ps
-0.0756
0.0120
0.195
0.291
(0.348)
(0.372)
(0.383)
(0.457)
inf
0.133***
0.134***
0.137***
0.151***
0.149***
(0.0315)
(0.0323)
(0.0326)
(0.0421)
(0.0423)
hcr
-0.0407
-0.0120
-0.0156
-0.0851*
-0.0845
(0.0342)
(0.0369)
(0.0370)
(0.0516)
(0.0517)
msof
0.0450*
0.0510*
0.0413
0.0353
(0.0268)
(0.0277)
(0.0329)
(0.0332)
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bsof
-0.0648**
-0.0624*
-0.0620
-0.0579
(0.0322)
(0.0320)
(0.0388)
(0.0386)
ge
0.741
-0.211
(0.605)
(0.874)
N
363
363
363
362
244
243
R2
Standard errors in parentheses
* p < 0.1, ** p < 0.05, *** p < 0.01
Note: "_5" means decreasing 5% or above, "_10" means decreasing 10% or above.
Table 6 Regulatory environment
(1)
(2)
(3)
(4)
(5)
(6)
rq1_5
rq2_5
rq3_5
rq_10
rl_5
rl_10
main
rq
-0.404
-0.370
-0.431
-1.072
(0.473)
(0.463)
(0.489)
(0.824)
inf
0.132***
0.132***
0.152***
0.126***
0.150***
(0.0314)
(0.0321)
(0.0420)
(0.0324)
(0.0420)
hcr
-0.0399
-0.0109
-0.0805
-0.00865
-0.0842
(0.0343)
(0.0369)
(0.0517)
(0.0370)
(0.0518)
msof
0.0428
0.0377
0.0521*
0.0415
(0.0267)
(0.0326)
(0.0280)
(0.0340)
bsof
-0.0659**
-0.0622
-0.0617*
-0.0591
(0.0322)
(0.0393)
(0.0318)
(0.0384)
rl
1.550
0.643
(1.417)
(1.729)
N
362
362
362
243
363
244
R2
Standard errors in parentheses
* p < 0.1, ** p < 0.05, *** p < 0.01
Note: "_5" means decreasing 5% or above, "_10" means decreasing 10% or above.
Table 7 Business environment
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
csb1_5
csb2_5
csb3_5
tppt1_5
tppt2_5
ric1_5
ric_10
ric2_5
main
csb
-0.0347**
-0.0165
-0.0183
(0.0164)
(0.0162)
(0.0168)
tppt
0.00135
0.00202
(0.00233)
(0.00235)
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ric
-0.137
-27.75
-0.188
(0.359)
(3221.1)
(0.390)
inf
0.124***
0.122***
0.145***
0.140***
0.141***
0.170***
0.134***
(0.0328)
(0.0338)
(0.0335)
(0.0336)
(0.0322)
(0.0446)
(0.0323)
hcr
-0.0422
-0.0132
-0.0211
-0.0112
-0.0211
-0.101*
-0.0117
(0.0344)
(0.0370)
(0.0358)
(0.0369)
(0.0358)
(0.0538)
(0.0369)
msof
0.0409
0.0543**
0.0453*
0.0531**
0.0389
0.0441*
(0.0271)
(0.0260)
(0.0268)
(0.0259)
(0.0323)
(0.0267)
bsof
-0.0678**
-0.0669**
-0.0639**
(0.0326)
(0.0325)
(0.0321)
N
363
363
363
363
363
363
244
363
R2
Standard errors in parentheses
* p < 0.1, ** p < 0.05, *** p < 0.01
Note: "_5" means decreasing 5% or above, "_10" means decreasing 10% or above.
3.3.3 Government research and development expenditure
In theory, government expenditures such as R&D spending have a certain impact on national
innovation capabilities. For example, CY Lee [17] believes that public R&D has four potential
channels for enterprise innovation: technological capability enhancement effect, demand creation
effect, and R&D cost reduction effect, and project overlap effects. He also said that due to the
existence of multiple channels, it is difficult to assess the overall effect of public R&D. In addition,
Isabel Busom [18], PA David, BH Hall and AA Toole [19], AM Domínguez [20], E Taymaz and Y
Ucdogruk[21] also studied the impact of government R&D expenditure on corporate innovation.
But none of them reached a consistent conclusion. Here, the author examines the impact of the
government's cumulative R&D expenditure per capita and the government's total accumulated R&D
expenditure on "innovation decline" when other factors keep constant. As the results shown in table
8, the coefficients of R&D expenditure are not statistically significant, which means there is
insufficient evidence to prove that government R&D expenditure can affect the probability of
"innovation decline".
Table 8 Government R&D expenditures
(1)
(2)
(3)
(4)
(5)
(6)
rd1_5
rd2_5
rd3_5
rd_10
acrd_5
acrd_10
main
rd
0.000863
-0.00247
-0.00354
-0.00348
(0.00308)
(0.00353)
(0.00469)
(0.00702)
ins
0.171*
0.0161
-0.0806
-0.0320
-0.108
(0.0959)
(0.124)
(0.148)
(0.126)
(0.154)
inf
0.0475
0.0449
0.0305
0.00314
0.00906
(0.0647)
(0.0693)
(0.0984)
(0.0678)
(0.0986)
msof
0.170*
0.182
0.190**
0.219*
(0.0901)
(0.136)
(0.0894)
(0.133)
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bsof
-0.0841
-0.0364
-0.0539
0.00460
(0.0823)
(0.136)
(0.0769)
(0.126)
acrd
0.0653
0.0307
(0.0652)
(0.0565)
N
127
127
127
60
127
60
R2
Standard errors in parentheses
* p < 0.1, ** p < 0.05, *** p < 0.01
Note: "_5" means decreasing 5% or above, "_10" means decreasing 10% or above.
In this part, the author studied the role of the government affecting "innovation decline" from
three aspects. The results show that the higher the quality of infrastructure, the higher the
probability of innovation decline. When other conditions remain unchanged, the institutional
environment, whether as a whole or a separate sub-item, has no statistically significant impact on
the decline of innovation. The government's R&D expenditures, whether per capita or total, will not
affect the occurrence of innovation decline.
4. Conclusion and policy implication
This paper defined the "innovation decline" through the global innovation index jointly released by
Cornell University, the European Business School, and the World Intellectual Property
Organization (WIPO). Moreover, this paper described the innovation decline and attempted to
explore the factors of "innovation decline" by using the Logit model. It turns out that the lack of
innovation is a common phenomenon on the global scale. Almost all countries, regardless of their
level of economic development and social systems, have experienced the innovation decline.
Additionally, this phenomenon is not a small probability event. Many countries have experienced
varying degrees of innovation decline, which is undoubtedly a huge risk for national development.
Through further in-depth study of the factors affecting innovation decline, this paper also found
that,, the improvement of infrastructure quality would promote the occurrence of innovation decline,
which was contrary to our intuition. The reason behind this might be the squeezing-out effect of
public investment on private investment and other innovative activities. At the same time, although
the institutional environment and government R&D expenditure are beneficial to the improvement
of national innovation, there is insufficient evidence to prove that these two factors can affect
innovation decline.
Based on the conclusions above, this paper has the following policy implications: the
government must be highly cautious about increasing the infrastructure investment, especially when
the country's infrastructure exceeds normal levels. The reason for this is that excessive
infrastructure investment might squeeze out market innovation and promote innovation decline.
However, there are still some shortcomings in this article. Due to the huge differences in the
connotation and extension of national innovation capabilities and the complexity of its measurement,
it is difficult for the author to obtain completely reliable numerical values to describe the country's
absolute innovation ability. Although the GII index is a good measurement of innovation capacity,
it is not equal to the real innovation ability, and its method of index construction is still
controversial. Although this paper uses a variety of methods to identify the innovation to alleviate
the problem of the data itself, it cannot be eradicated. Therefore, it is necessary to use more and
more scientific data to measure the country's innovation ability to verify the robustness of the
results.
Advances in Economics, Business and Management Research, volume 116
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