Journal of Global Change Data & Discovery2023.7(3):307-313

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Citation:Qu, Y.Dataset of Economic Resilience and Industrial Evolution Path of Cities in the Yangtze River Delta (2002–2016)[J]. Journal of Global Change Data & Discovery,2023.7(3):307-313 .DOI: 10.3974/geodp.2023.03.09 .

Dataset of Economic Resilience and Industrial Evolution Path of Cities in the Yangtze River Delta (2002-2016)

Qu, Y.1,2

1. Zhanjiang University of Science and Technology, Zhanjiang 524094, China;

2. Institute of Marine Sustainable Development, Liaoning Normal University, Dalian 116029, China

 

Abstract: According to the data about GDP and export products and using 26 cities in the Yangtze River Delta region as the research area, the author constructed a dataset of economic resilience and industrial evolution path of cities in the Yangtze River Delta region (2002?C2016) to reveal related temporal and spatial evolution characteristics. This dataset includes economic resilience, the number of new industries of 2-digit HS codes and dependence degree of urban industrial evolution path in the Yangtze River Delta from 2002 to 2016. The dataset is archived in the .xlsx format with a size of 117 KB.

Keywords: economic resilience; industrial evolution; path dependence; Yangtze River Delta

DOI: https://doi.org/10.3974/geodp.2023.03.09

CSTR: https://cstr.escience.org.cn/CSTR:20146.14.2023.03.09

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.2023.10.06.V1 or https://cstr.escience.org.cn/CSTR:20146.11.2023.10.06.V1.

1 Introduction

With the development of globalization, the Black Swan and Gray Rhino events have attracted more and more attention from scholars and policy makers. Especially since the global financial crisis from 2007 to 2008, continuously improved awareness of economic risk prevention has made economic resilience one of the hot spots in the research field of economic development[1]. In the framework of evolutionary economic geography, economic resilience is an important concept that facilitates our deep insights into the resistance, recovery, relocation and renewal capabilities of economic systems in the face of risks[2].

Existing studies mostly focus on the measurement, temporal and spatial distribution and influencing factors of economic resilience. Industrial structure is considered to be one of the most important influencing factors[3]. Industrial evolution is closely related to local resource endowment and development history, and the change in industrial structure can be analyzed from the characteristics of industrial evolution path. Industrial evolution path dependence refers to a strong correlation between old and new industries in a region. The original technology, equipment and labor force provide facilitate the generation and development of the new industry, but local development is also potentially restricted by rigid institution and other issues caused by too intimate relations[4]. Therefore, exploring industrial evolution path dependence and its impact on economic resilience is of great value for supporting existing local industries, guiding the development of new industries, improving urban economic resilience, and promoting high-quality economic development.

There are few quantitative studies due to the lack of methods to calculate the industrial evolution path dependence. The Yangtze River Delta, one of the most developed regions in China, is typical and representative in the research on economic resilience and industrial evolution path.

Therefore, the author establishes the dataset of economic resilience and industrial evolution path of cities in the Yangtze River Delta (2002?C2016) using national economic accounting data and export product data, which provides data support for investigating urban industrial evolution path and its impact on economic resilience of cities in the Yangtze River Delta.

2 Metadata of the Dataset

Table 1 summarizes the metadata of the Dataset of economic resilience and industrial evolution path of cities in the Yangtze River Delta (2002-2016)[5], with dataset full and short names, authors, year, data format, data size, data files, data publisher, and data sharing policy included.

3 Methods

3.1 Data Sources

According to the Yangtze River Delta urban agglomeration development plan approved by National Development and Reform Commission of China in 2016, the Yangtze River Delta urban agglomeration includes a total of 26 cities, including a municipality directly under the central government and some cities in three provinces, with a total area of about 210,000 km2. We constructed the dataset based on export product data of countries around the world and cities in the Yangtze River Delta, as well as GDP data of China and cities in the Yangtze River Delta from 2002 to 2016. The export product data of countries around the world was derived from the UN Comtrade Database[7], the export product data of cities in the Yangtze River Delta from the China Customs Enterprise Database[8], Gross domestic product (GDP) data of China and cities in the Yangtze River Delta from the National Data of National Bureau of Statistics[9] and the China Urban Statistical Yearbook[10].

3.2 Research Method

3.2.1 Economic Resilience

According to the method proposed by Martin[11], the economic resilience of cities was calculated based on the GDP data of China and cities in the Yangtze River Delta considering

 

Table 1  Metadata summary of the Dataset of economic resilience and industrial evolution path of Cities in the Yangtze River Delta (2002?C2016)

Items

Description

Dataset full name

Dataset of economic resilience and industrial evolution path of cities in the Yangtze River Delta (2002-2016)

Dataset short name

Res_EvolPath_YangtzeRiverDelta

Author

Qu, Y. HOH-8736-2023, Zhanjiang University of Science and Technology, Institute of Marine Sustainable Development, Liaoning Normal University, quyi1412@163.com

Geographical region

Yangtze River Delta in China

Year

20022016

Data format

.xlsx

 

 

Data size

117 KB

 

 

Data files

(1) Economic resilience of cities in the Yangtze River Delta from 2002 to 2016

(2) The number of new industries of 2-digit HS codes of cities in the Yangtze River Delta from 2002 to 2016

(3) Industrial evolution path dependence of cities in the Yangtze River Delta from 2002 to 2016

Foundations

National Natural Science Foundation of China (41976207); Ministry of Education of P. R. China (22JJD790029); Educational Department of Liaoning Province (LJKQZ2021090); Zhanjiang University of Science and Technology (PPJHYLZY-202205)

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

 

(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 [6]

Communication and searchable system

DOI, CSTR, Crossref, DCI, CSCD, CNKI, SciEngine, WDS/ISC, GEOSS

 

short-term shock and ??slow burn??. The equation is expressed as:

                                                                                               (1)

where, Resc,t and ??Ec,t are the economic resilience and the change in GDP of city c from t-year to (t+T)-year respectively. is GDP of city c in t-year. is GDP??s gradient of whole country from t-year to (t+T)-year. According to the theory of industry life cycle, an industry needs to experience a period of development from being initially established to being relatively competitive. Referring to the research of relevant scholars[12?C14], we define T=4 in the dataset construction.

3.2.2 Industrial Evolution Path

We employed the export product data of countries around the world and the export product data of cities in the Yangtze River Delta for calculation. Figure 1 shows the developing process of industrial evolution path data. The steps are presented as follows:

(1) We identified the new and old industries according to the revealed comparative advantage index (RCA) of the export products of cities in the Yangtze River Delta.

(2) We calculated the RCA of all countries around the world and constructed the matrix of industry correlation between any industry i and j.

(3) We calculated the maximum proximity of new industry and old industry in each city.

(4) Monte Carlo method was used to calculate the distribution of the counterfactual maximum proximity in each city.

(5) We determined whether the new industry is path-dependent one by one, and then calculated the path dependence of each city.

The specific calculation formulas above can be found in the reference[1].

 

Figure 1  Technology roadmap of dataset development

 

4 Data Results and Validation

4.1 Data Composition

The dataset consists of three sections, namely economic resilience, the number of new industries of 2-digit HS codes and industrial evolution path dependence of cities in the Yangtze River Delta from 2002 to 2016.

4.2 Data Products

(1) Figure 2 shows the economic resilience of cities in Yangtze River Delta from 2002 to 2016, with an overall trend of ??decline firstly, then rise, and finally decline again??. Shanghai??s economic resilience was less than 0 except for the periods of 2002?C2006 and 2012?C2016. The economic resilience of cities in Jiangsu province (Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yangzhou, Zhenjiang, Taizhou (Jiangsu), Yancheng) was mostly larger than 0, with relatively consistent variation trend. The economic resilience of cities in Zhejiang province (Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, Zhoushan, Taizhou (Zhejiang) and Jinhua) was less than 0 in the research period, showing a relatively obvious ??depression??. There was little difference among cities except Zhoushan. The economic resilience of cities in Anhui province (Hefei, Chuzhou, Ma??anshan, Wuhu, Xuancheng, Tongling, Chizhou and Anqing) was generally higher than that of other provinces. However, the change amplitude between different cities in the same statistical year and between different statistical years of the same city was larger than that of other provinces.

(2) Table 2 summarizes the number of new industries in the Yangtze River Delta in the research period. The number of new industries in Shanghai, Nanjing, Wuxi, Changzhou, Suzhou and Taizhou (Jiangsu) declined steadily, and that in cities in Zhejiang province fluctuated and declined except Zhoushan. Cities in Anhui province respectively experienced a significant increase and decrease in the number of new industries in the periods of 2007?C2011 and 2012?C2016 except Hefei and Tongling.

Figure 2  Economic resilience of cities in the Yangtze River Delta (20022016)

 

Table 1  The list of new industries of cities in the Yangtze River Delta (2002‒2016)

 City

2002
2006

2003
2007

2004
2008

2005
2009

2006
2010

2007
2011

2008
2012

2009
2013

2010
2014

2011
2015

2012
2016

Shanghai

52

50

43

34

31

23

18

25

18

15

14

Nanjing

50

53

58

44

39

38

46

58

36

39

23

Wuxi

62

62

57

40

48

36

38

33

25

19

18

Changzhou

78

70

62

40

41

29

26

33

27

30

22

Suzhou

30

35

40

27

30

35

25

26

21

14

13

Nantong

65

57

47

38

39

37

46

69

69

105

75

Yangzhou

56

74

67

52

36

41

67

50

32

29

27

Zhenjiang

54

67

56

49

39

56

101

64

37

30

22

Taizhou (Jiangsu)

59

63

47

40

45

33

39

46

36

50

43

Yancheng

66

68

68

70

58

44

59

66

48

99

68

Hangzhou

55

52

48

45

39

33

22

26

28

29

39

Ningbo

46

55

53

35

32

23

24

19

29

20

13

Jiaxing

75

69

69

56

36

38

43

37

36

27

20

Huzhou

58

50

61

36

38

37

27

26

24

27

29

Shaoxing

67

55

58

32

27

25

25

25

17

18

31

Zhoushan

21

16

13

8

9

5

3

4

5

44

134

Taizhou (Zhejiang)

33

39

40

41

24

20

18

16

10

8

13

Jinhua

76

59

46

35

28

27

45

50

54

60

20

Hefei

71

64

68

81

58

51

81

65

27

29

22

Chuzhou

52

50

46

33

33

40

121

165

152

130

61

Ma??anshan

23

21

29

63

62

81

111

176

93

114

82

Wuhu

47

42

43

47

37

50

75

109

135

153

52

Xuancheng

73

40

55

48

50

80

128

156

149

128

54

Tongling

13

30

34

25

34

17

53

19

9

21

99

Chizhou

21

34

36

38

39

35

92

99

43

28

26

Anqing

53

49

61

49

59

38

100

152

169

203

95

(3) Figure 3 shows the path dependence of cities in Yangtze River Delta from 2002 to 2016. The path dependence value of almost all cities was higher than 0.5, indicating that the industrial evolution of these cities was mainly characterized by path dependence. At the same time, the average path dependence of each research period fluctuated and declined, and the advantages from developing new industries by relying on local old ones gradually weakened.

 

Figure 3  Industrial evolution path dependence of cities in the Yangtze River Delta (2002‒2016)

5 Discussion and Conclusion

Urban economic resilience mirrors the comprehensive ability of urban economic system to cope with risks, and the evolution path of urban industry is considered to be one of the factors affecting economic resilience. The constructed dataset provides a data reference for analyzing the spatio-temporal distribution of economic resilience and the characteristics of industrial evolution path of cities in the Yangtze River Delta region, and exploring the impact of industrial evolution path dependence on economic resilience.

Firstly, the economic resilience of cities in the Yangtze River Delta exhibits a variation trend of ??decline firstly, then rise, and finally decline again?? on the whole. Secondly, the number of new industries in Shanghai, as well as most of the cities in Jiangsu and Zhejiang provinces shows a downward trend, and that in Anhui province fluctuates greatly. Finally, the industrial evolution of cities in the Yangtze River Delta region is path-dependent, whose advantage, however, tends to weaken.

The constructed dataset reflects the city-level economic resilience and industrial evolution in the Yangtze River Delta region, which provides data support for understanding the evolution characteristics of economic resilience in various cities, helps to scientifically predict the development trend of new industries and reasonably guide the optimization of industrial structure. Based on this dataset, future studies are advised to continue to excavate data such as culture and social relations by using new technologies and new means, so as to conduct a more in-depth and comprehensive exploration of factors affecting economic resilience.

 

Conflicts of Interest

The authors declare no conflicts of interest.

 

References

[1]      Li, B., Qu, Y. Influence of industrial evolution path dependence and path creation on regional economic resilience: Taking the coastal areas of China as an example [J]. Acta Geographica Sinica, 2023, 78(4): 824?C839.

[2]      Martin, R. Regional economic resilience, hysteresis and recessionary shocks [J]. Journal of Economic Geography, 2012, 12(1): 1?C32.

[3]      Sun, J. W., Sun, X. Y. Research progress of regional economic resilience and exploration of its application in China [J]. Economic Geography, 2017, 37(10): 1?C9.

[4]      Boschma, R. Towards an evolutionary perspective on regional resilience [J]. Regional Studies, 2015, 49(5): 733?C751.

[5]      Qu, Y. Dataset of economic resilience and industrial evolution path of cities in the Yangtze River Delta (2002-2016) [J/DB/OL]. Digital Journal of Global Change Data Repository, 2023. https://doi.org/10.3974/geodb.2023.10.06.V1. https://cstr.escience.org.cn/CSTR:20146.11.2023.10.06.V1.

[6]      GCdataPR Editorial Office. GCdataPR data sharing policy [OL]. https://doi.org/10.3974/dp.policy.2014.05 (Updated 2017).

[7]      UN Comtrade Database [DB/OL]. https://comtradeplus.un.org/.

[8]      EPS Data [DB/OL]. https://www.epsnet.com.cn/.

[9]      National Data of National Bureau of Statistics [DB/OL]. https://data.stats.gov.cn/.

[10]   National Bureau of Statistics of China. China Statistical Yearbook: 2003?C2017 [M]. Beijing: Chian Statistics Press, 2003?C2017.

[11]   Martin, R., Sunley, P., Gardiner, B., et al. How regions react to recessions: Resilience and the role of economic structure [J]. Regional Studies, 2016, 50(4): 561?C585.

[12]   Boschma, R., Minondo, A., Navarro, M. The emergence of new industries at the regional level in Spain: a proximity approach based on product relatedness [J]. Economic Geography, 2013, 89: 29?C51.

[13]   Zhu, S. J., He, C. F., Zhou, Y. How to jump further and catch up? Path-breaking in an uneven industry space [J]. Journal of Economic Geography, 2017, 17(3): 521?C545.

[14]   Li, W., He, C. F. Firm ownership structure and regional industrial development paths in China [J]. Geographical Research, 2021, 40(5): 1295?C1319.

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