Dataset of Regional Economic Resilience and Industrial
Evolution Path in China??s Coastal Areas (2002‒2017)
Qu, Y.1,2,3 Wu, F. Y.2,3 Li, B.2,3*
1. Zhanjiang University of Science and Technology,
Zhanjiang 524094, China;
2. Key Research Base of Humanities and Social Sciences of
the Ministry Education, Center for Studies of Marine Economy and Sustainable
Development, Liaoning Normal University, Dalian 116029, China;
3. University Collaborative Innovation Center of Marine
Economy High-Quality Development of Liaoning Province, Dalian 116029, China
Abstract: China??s
coastal areas include Liaoning, Hebei, Tianjin, Shandong, Jiangsu, Shanghai,
Zhejiang, Fujian, Taiwan, Guangdong, Hong Kong, Macao, Guangxi and Hainan.
Based on export product data and national economic accounting data of China??s
coastal areas (data on Hong Kong, Macao, and Taiwan are temporarily missing),
the author calculated the dataset of regional economic resilience and the
industrial evolution path by applying the calculating method of regional
economic resilience proposed by Martin et
al. (2016) and the method of identifying new industrial path dependence and
path creation proposed by Coniglio et al.
(2018). This dataset includes regional economic resilience, new industries of
2-digit Harmonized System (HS) codes, industrial evolution path dependance,
industrial evolution path creation, other variables affecting the regional
economic resilience in China??s coastal areas from 2002 to 2017 and economic
resilience and its influencing factors of core cities in the Yangtze River
Delta from 2002 to 2016. The dataset is archived in .xlsx format with data size
of 124 KB.
Keywords: industrial evolution; path dependence; path
creation; economic resilience; China??s coastal areas
DOI: https://doi.org/10.3974/geodp.2023.01.06
CSTR: https://cstr.escience.org.cn/CSTR:20146.14.2023.01.06
Dataset Availability Statement:
The dataset
supporting this paper was published and is accessible through the Digital Journal of
Global Change Data Repository at: https://doi.org/10.3974/geodb.2023.03.09.V1 or
https://cstr.escience.org.cn/CSTR:20146.11.2023.03.09.V1.
1
Introduction
The
continuous development of economic globalization has brought countries and regions
closer together through global production networks. However, regional economic
systems are simultaneously exposed to increasingly uncertain shocks and disturbances[1?C3]. Especially since the 2007‒2008
global financial crisis, the different characteristics of regional economic
systems in response to various shocks and the analysis of the reasons have gradually
attracted scholars?? attention. As a result, regional economic resilience has
become a hot topic in regional research[4?C6].
The development
of a regional economy is a process of new industries emerging and old industries
withdrawing[7]. New industries in a region
reflect the direction and characteristics of the evolution path of the regional
industrial structure. The evolution path of a regional industry can be divided
into two main types: path dependence and path creation, which are classified
according to the proximity between the new industry and the original
industries. Path dependence means that the new industry is closely related to
the original industries in terms of technology and knowledge, while path creation
denotes that there is less connection between the new industry and the original
industries, thus enabling innovation in production and technology.
China??s coastal
areas have a good industrial foundation and are characterized by rapid economic
development[8]. It is worthwhile analyzing the characteristics of economic resilience
and industrial evolution and study the influence of the industrial evolution
path on economic resilience[9,10]. However, how to measure the
evolutionary path and calculate the degree of path dependence and break
creation by quantitative means has long puzzled scholars. The model constructed
by Coniglio and related studies carried out by Li compensate for the above
deficiencies[11,12]. We use national economic accounting and export product data to
obtain the regional economic resilience and industrial evolution path dataset
of China??s coastal areas from 2002 to 2017.
2 Metadata of the Dataset
Table
1 summarizes the metadata of the Dataset of regional economic resilience and industrial
evolution path in China??s coastal areas (2002?C2017)[13].
It includes the dataset full and short names, authors, year, data format, data
size, data files, data publisher, and data sharing policy, etc.
Table 1 Metadata summary of the Dataset of regional economic
resilience and industrial evolution path in China??s coastal areas (2002?C2017)
|
Items
|
Description
|
|
Dataset full name
|
Dataset of
regional economic resilience and industrial evolution path in China??s coastal
areas (2002?C2017)
|
|
Dataset short
name
|
Res_EvolPath_CoastalChina
|
|
Authors
|
Qu, Y. HOH-8736-2023, Zhanjiang University of Science and Technology,
Key Research Base of Humanities and Social Sciences of the Ministry
Education, Center for Studies of Marine Economy and Sustainable Development,
Liaoning Normal University, University Collaborative Innovation Center of
Marine Economy High-Quality Development of Liaoning Province,
quyi1412@163.com
Wu, F. Y.
HOH-8937-2023, Key Research Base of Humanities and Social Sciences of the
Ministry Education, Center for Studies of Marine Economy and Sustainable
Development, Liaoning Normal University, University Collaborative Innovation
Center of Marine Economy High-Quality Development of Liaoning Province,
531583887@qq.com
|
|
|
Li, B.
HPD-0607-2023, Key Research Base of Humanities and Social Sciences of the
Ministry Education, Center for Studies of Marine Economy and Sustainable
Development, Liaoning Normal University, University Collaborative Innovation
Center of Marine Economy High-Quality Development of Liaoning Province, libo_ok@126.com
|
|
Geographical region
|
Coastal areas of
China (except Hong Kong, Macao and Taiwan)
|
|
Year
|
2002‒2017
|
|
Data format
|
.xlsx
|
|
|
|
Data size
|
124 KB
|
|
|
(To be
continued on the next page)
(Continued)
|
Items
|
Description
|
|
Data files
|
(1) Regional
economic resilience in China??s coastal areas from 2002 to 2017
(2) New
industries of 2-digit HS codes in China??s coastal areas from 2002 to 2017
(3) Industrial
evolution path dependance in China??s coastal areas from 2002 to 2017
(4) Industrial
evolution path creation in China??s coastal areas from 2002 to 2017
(5) Other
variables affecting the regional economic resilience in China??s coastal areas
from 2002 to 2017
(6) Economic
resilience and influencing factors of core cities in Yangtze River Delta from
2002 to 2016
|
|
Foundations
|
National Natural
Science Foundation of China (41976207, 42076222)
|
|
Data publisher
|
Global Change Research Data Publishing & Repository,
http://www.geodoi.ac.cn
|
|
Address
|
No. 11A, Datun
Road, Chaoyang District, Beijing 100101, China
|
|
Data sharing
policy
|
Data from the Global
Change Research Data Publishing & Repository includes metadata, datasets (in the Digital Journal of Global Change Data Repository),
and publications (in the Journal of Global Change Data & Discovery). Data sharing policy includes: (1) Data are openly available
and can be free downloaded via the Internet; (2) End users are encouraged to
use Data subject to citation; (3) Users, who are by definition
also value-added service providers, are welcome to redistribute Data
subject to written permission from the GCdataPR Editorial Office and the
issuance of a Data redistribution license; and (4) If Data are used to
compile new datasets, the ??ten per cent principal?? should be followed such
that Data records utilized should not surpass 10% of the new
dataset contents, while sources should be clearly noted in suitable places in
the new dataset [14]
|
|
Communication and
searchable system
|
DOI, CSTR, Crossref, DCI, CSCD, CNKI,
SciEngine, WDS/ISC, GEOSS
|
3 Methods
3.1 Data Sources
|
Figure 1 Flowchart of dataset deveplopment
|
China??s coastal areas include Liaoning, Hebei,
Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Taiwan, Guangdong,
Hong Kong, Macao, Guangxi, and Hainan, but data on Hong Kong, Macao, and
Taiwan are temporarily missing. The dataset is developes based on the export
data and national account data from 2002 to 2017[15?C17]. The export
data include countries?? and regions?? data in the UN Comtrade Database and China??s coastal areas?? data in the DRCNet. The
national account data include gross domestic product (GDP) data for China??s
coastal areas. Figure 1 shows the building process of this dataset.
3.2 Data Collection or Processing
3.2.1 Regional Economic Resilience
There
are two main methods for quantifying regional economic resilience. The first is
to use a traditional indicator system. This method considers the availability
and representativeness of relevant indicators and constructs an indicator
system to calculate the value of economic resilience through subjective or
objective weighting methods.
The second,
proposed by Martin[18], compares the actual change in a
region within a specific economic cycle with the expected change under the
national average level to reflect the economic resilience of each region within
a specific economic cycle. This method can avoid subjective consciousness of indicator
selection, unreasonable and imperfect indicator system construction, and other
problems. Moreover, it is a results-oriented measurement method. Therefore, we
selected the second method to measure the regional economic resilience of
China??s coastal areas:
(1)
where,
is the economic
resilience of region k from t to t+T; 
is the change in
annual GDP of region k from t to t+T; 
is the GDP of the
region in year t and 
is the national
rate of change in GDP from t to t+T year. In this paper, T=4.
3.2.2 New Industries
This
study identifies new and old industries based on the Revealed Comparative Advantage
Index (RCA) of regional export products according to Coniglio??s criteria for
the division of old and new industries[11]. For the export industry
of region k in year t,
(2)
where
is the RCA of product i in year t.
3.2.3 Evolution Path of New Industries
According
to Hidalgo??s concept of industrial proximity[19], Coniglio proposed
a method for identifying the evolutionary path of new industries based on
proximity[11]. The process is as follows:
First, we construct
a correlation matrix and compute its proximity. For industry i in country
c,
(3)
The proximity of
industry i and industry j:
(4)
Second, we compute maximum proximities between new and old industries; the equation is as
follows:
(5)
where 
is the matrix of maximum proximities between new industry and old industry in region k
from t to t+T. We use
and
to denote old industries at t and new industries at t+T,
respectively.
Finally, we
identify the evolutionary path of the new industries. We use the Monte Carlo
method to randomly select industries with RCA less than 1 in regions k
and t with the same number of . Then we calculated the average of their maximum proximities. We repeat this process 2,000 times
to obtain the counterfactual distribution of average proximities. A new
industry is considered path-dependent if its maximum proximity is more than the 95% confidence interval of the
counterfactual distribution. Alternatively, a new industry is considered
path-creative if its maximum proximity is less than the 5%
confidence interval.
3.2.4 Path Dependence and Path Creation
According
to the method proposed by Li and He[12], the industrial evolution
path dependence in a certain region and specific period of time refers to the
ratio of the number of path-dependent new industries to the number of all new
industries. The same is true for industrial evolution path creation.
4 Data Results and Validation
4.1 Data Composition
The dataset
includes regional economic resilience, new product distribution, industrial evolution
path dependence, path creation, and other variables affecting regional economic
resilience in China??s coastal areas from 2002 to 2017 and its influencing factors
of core cities in the Yangtze River Delta from 2002 to 2016. The data size is
124 KB.
4.2 Data Products
(1)
The regional economic resilience curve of China??s coastal areas shows a trend
of opening?Cclosing?Copening (Figure 2). At the initial stage of the study
period, the resilience of some regions had obvious differences. Over time, the
inter-regional differences gradually weakened and reached a minimum in
2005‒2009. However, the resilience curves of different regions then entered a
state of scattered change. We divide them into five types according to the
change characteristics of the curve. 1) The resilience curves of Shanghai and
Guangdong are U-shaped, reaching the lowest values of ‒0.324 and ‒0.195 in
2009‒2013 and 2008‒2012, respectively. 2) Fujian, Guangxi, and Hainan belong to
the rising fluctuation category. 3) The economic resilience of Jiangsu and
Zhejiang has a W-shape. 4) Liaoning and Shandong also had a similar trend of
change, experiencing the process of first rising, then falling, then rising and
falling again. 5) For Hebei and Tianjin, the resilience curves showed a
downward trend, followed by an upward trend and finally a downward trend.
Figure 2 Map of regional economic resilience in
China??s coastal areas from 2002 to 2017
(2) Table 2
shows the number of new industries in China??s coastal areas from 2002 to 2017.
We find that the number of new industries presents a
declining trend followed by a rising trend. Most regions had the lowest number
of new industries around 2011‒2015, after which the numbers increased. However,
for the majority of regions, the pace of new industry development has still not
recovered to pre-crisis levels, and there are fewer new industries now than
there were before the 2007‒2008 global financial crisis. We obtain the number
of new industries from the 2-digit HS and draw the heat maps of new industries
of each region in the periods of 2002‒2006, 2007‒2011, 2008‒2012, and 2013‒2017
(Figure 3). There were a large number of new products in the period 2002?C‒2006,
which are mainly distributed in Chapters 72, 73, 76, 84, and 85. Shandong and
Shanghai have more new industries in Chapters 28 and 29, respectively. The
number of new products in almost all regions decreased to varying degrees
during 2007‒2011 and 2008‒2012. The number of new products increased in more
than half of the regions in 2013‒2017, with Guangxi experiencing the most obvious
growth.
Table
2 The number
of new industries in China??s coastal areas from 2002 to 2017
|
Year
|
Liaoning
|
Hebei
|
Tianjin
|
Shandong
|
Jiangsu
|
Shanghai
|
Zhejiang
|
Fujian
|
Guangdong
|
Guangxi
|
Hainan
|
|
2002??2006
|
52
|
59
|
42
|
70
|
25
|
51
|
42
|
27
|
13
|
55
|
49
|
|
2003??2007
|
54
|
55
|
45
|
61
|
43
|
45
|
39
|
36
|
14
|
47
|
33
|
|
2004??2008
|
47
|
57
|
59
|
53
|
50
|
37
|
34
|
45
|
17
|
52
|
41
|
|
2005??2009
|
46
|
62
|
48
|
44
|
39
|
28
|
31
|
35
|
14
|
74
|
21
|
|
2006??2010
|
29
|
42
|
49
|
30
|
30
|
21
|
27
|
33
|
20
|
61
|
20
|
|
2007??2011
|
26
|
35
|
33
|
31
|
27
|
20
|
16
|
26
|
13
|
59
|
23
|
|
2008??2012
|
46
|
38
|
23
|
22
|
21
|
15
|
14
|
30
|
13
|
43
|
17
|
|
2009??2013
|
49
|
30
|
23
|
27
|
23
|
19
|
13
|
18
|
17
|
39
|
19
|
|
2010??2014
|
20
|
30
|
25
|
21
|
18
|
16
|
12
|
23
|
26
|
38
|
18
|
|
2011??2015
|
19
|
25
|
21
|
30
|
14
|
11
|
14
|
20
|
18
|
50
|
12
|
|
2012??2016
|
19
|
24
|
27
|
23
|
12
|
20
|
14
|
17
|
26
|
51
|
15
|
|
2013??2017
|
21
|
31
|
47
|
37
|
15
|
47
|
22
|
31
|
25
|
141
|
28
|
Figure 3 Heat maps of new industries in
China??s coastal areas from 2002 to 2017
(3) Tables 3 and
4 represent the path dependence and path creation in China??s coastal areas,
respectively. We find that the path dependence and path creation of each region
always fluctuate, and the overall dependence is higher than the creation in the
same period. After dividing Chinas coastal areas into three large regional
ranges, we find no similar characteristics within the Bohai Rim, Yangtze River
Delta, and pan-Pearl River Delta coastal areas.
Table
3 The path
dependence in China??s coastal areas from 2002 to 2017
|
Year
|
Liaoning
|
Hebei
|
Tianjin
|
Shandong
|
Jiangsu
|
Shanghai
|
Zhejiang
|
Fujian
|
Guangdong
|
Guangxi
|
Hainan
|
|
2002‒2006
|
0.596
|
0.593
|
0.500
|
0.543
|
0.720
|
0.608
|
0.643
|
0.704
|
0.615
|
0.600
|
0.531
|
|
2003‒2007
|
0.556
|
0.655
|
0.733
|
0.557
|
0.744
|
0.689
|
0.615
|
0.583
|
0.714
|
0.447
|
0.515
|
|
2004‒2008
|
0.468
|
0.561
|
0.542
|
0.623
|
0.620
|
0.649
|
0.676
|
0.667
|
0.647
|
0.462
|
0.537
|
|
2005‒2009
|
0.500
|
0.645
|
0.646
|
0.455
|
0.744
|
0.679
|
0.710
|
0.686
|
0.429
|
0.473
|
0.429
|
|
2006‒2010
|
0.655
|
0.571
|
0.551
|
0.533
|
0.600
|
0.476
|
0.741
|
0.667
|
0.600
|
0.492
|
0.750
|
|
2007‒2011
|
0.538
|
0.600
|
0.545
|
0.581
|
0.704
|
0.500
|
0.375
|
0.654
|
0.615
|
0.559
|
0.652
|
|
2008‒2012
|
0.500
|
0.658
|
0.652
|
0.545
|
0.667
|
0.467
|
0.571
|
0.633
|
0.846
|
0.651
|
0.824
|
|
2009‒2013
|
0.633
|
0.633
|
0.696
|
0.519
|
0.652
|
0.737
|
0.462
|
0.444
|
0.588
|
0.538
|
0.632
|
|
2010‒2014
|
0.750
|
0.600
|
0.680
|
0.524
|
0.556
|
0.625
|
0.750
|
0.348
|
0.538
|
0.342
|
0.667
|
|
2011‒2015
|
0.632
|
0.680
|
0.571
|
0.633
|
0.214
|
0.727
|
0.714
|
0.750
|
0.444
|
0.660
|
0.583
|
|
2012‒2016
|
0.632
|
0.542
|
0.593
|
0.652
|
0.500
|
0.350
|
0.500
|
0.706
|
0.654
|
0.686
|
0.733
|
|
2013‒2017
|
0.238
|
0.581
|
0.638
|
0.324
|
0.467
|
0.638
|
0.545
|
0.613
|
0.360
|
0.667
|
0.464
|
Table 4 The path creation in China??s coastal
areas from 2002 to 2017
|
Year
|
Liaoning
|
Hebei
|
Tianjin
|
Shandong
|
Jiangsu
|
Shanghai
|
Zhejiang
|
Fujian
|
Guangdong
|
Guangxi
|
Hainan
|
|
2002‒2006
|
0.404
|
0.390
|
0.500
|
0.443
|
0.280
|
0.392
|
0.357
|
0.296
|
0.385
|
0.400
|
0.469
|
|
2003‒2007
|
0.444
|
0.345
|
0.267
|
0.443
|
0.256
|
0.311
|
0.333
|
0.389
|
0.286
|
0.553
|
0.455
|
|
2004‒2008
|
0.532
|
0.439
|
0.458
|
0.377
|
0.380
|
0.351
|
0.324
|
0.333
|
0.353
|
0.538
|
0.463
|
|
2005‒2009
|
0.500
|
0.355
|
0.354
|
0.523
|
0.256
|
0.321
|
0.290
|
0.314
|
0.571
|
0.527
|
0.524
|
|
2006‒2010
|
0.345
|
0.429
|
0.449
|
0.467
|
0.400
|
0.476
|
0.259
|
0.303
|
0.350
|
0.508
|
0.250
|
|
2007‒2011
|
0.462
|
0.371
|
0.455
|
0.419
|
0.296
|
0.500
|
0.625
|
0.346
|
0.385
|
0.441
|
0.348
|
|
2008‒2012
|
0.500
|
0.342
|
0.348
|
0.409
|
0.333
|
0.533
|
0.429
|
0.367
|
0.154
|
0.349
|
0.118
|
|
2009‒2013
|
0.367
|
0.367
|
0.304
|
0.407
|
0.304
|
0.263
|
0.538
|
0.556
|
0.412
|
0.462
|
0.368
|
|
2010‒2014
|
0.250
|
0.400
|
0.320
|
0.429
|
0.389
|
0.375
|
0.250
|
0.652
|
0.462
|
0.658
|
0.333
|
|
2011‒2015
|
0.368
|
0.320
|
0.429
|
0.367
|
0.786
|
0.273
|
0.286
|
0.250
|
0.556
|
0.340
|
0.417
|
|
2012‒2016
|
0.368
|
0.458
|
0.370
|
0.348
|
0.500
|
0.650
|
0.500
|
0.294
|
0.308
|
0.314
|
0.267
|
|
2013‒2017
|
0.762
|
0.419
|
0.362
|
0.649
|
0.533
|
0.362
|
0.455
|
0.387
|
0.640
|
0.333
|
0.536
|
5 Discussion and Conclusion
From the
perspective of evolutionary economic geography, path dependence and path creation
are considered important factors affecting the performance and response of regional
economies when facing shocks.
Compared with previous studies that
analyzed the structure of all incumbent industries, discussing the
characteristics of the industrial evolution path from the perspective of new
industries enables us to grasp the characteristics of dynamic evolution and its
development trend more accurately. However, few studies have qualitatively
measured the degree of path dependence and path creation of industrial
evolution. In this study, we take China??s coastal areas as an example and
compute the proximity between industries by using export product data from
around the world, which eliminates the limitation of the traditional
calculation method using the input?Coutput method. We obtain the dataset of
regional economic resilience and industrial evolution path of China??s coastal
areas based on export product data and GDP data. The conclusions are as
follows. (1) The regional economic resilience of China??s coastal areas showed
obvious differences in some areas at the initial stage. The inter-regional
differences gradually weaken in a short period of time but then enter a longer
period of scattered change. (2) The number of new industries first declined and
then increased. (3) Path dependence was higher than path creation in the same
period. This dataset provides data to support the study of economic resilience
and industrial evolution in China??s coastal areas.
Author Contributions
Li,
B. designed the algorithms of dataset. Qu, Y. and Wu, F. Y. contributed to the
data processing and analysis. All authors wrote the data paper.
Conflicts of Interest
The authors
declare no conflicts of interest.
References
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Guan, H. M., Yang, Q. S., Hao
F. L., et al. Economic resilience characteristics of Shenyang city based
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