REGIONAL PRODUCTIVE FORCES DISTRIBUTION – A CASE STUDY ON CHINA’S JIANGSU PROVINCE Yiping Liu, Nanjing University of Aeronautics and Astronautics, China Jhony Choon Yeong Ng, Nanjing University of Aeronautics and Astronautics, China Yuling Han, Nanjing University of Aeronautics and Astronautics, China

This article is published in the Journal of International Finance and Economics, Volume 16, Issue 2, pp. 37-48. http://dx.doi.org/10.18374/JIFE-16-2.3

ABSTRACT Regional productive forces distribution reflects a region’s standard of economic development and its economic potential. In this research, we proposed an analytical framework for the investigation of a region’s productive forces distribution, and we applied it in our study on the Jiangsu Province of China. The results of our case study show that the distributions of productive forces of cities from the Southern Jiangsu region, Middle Jiangsu region, and Northern Jiangsu region are network distribution, point-axis distribution, and extreme point distribution respectively. Keywords: Regional Productive Forces Connection; Gravity Model 1. INTRODUCTION

Distribution; Productive Forces;

Economic

Productive forces distribution refers to the process of allocating factors of production to different regions and industries. The soundness of a region’s distribution affects its economic structure, regional competitiveness, and its capacity for sustainable development. As time passes, interventions must be made to the distribution to ensure that it can adapt to the objective needs of the region’s economy. Thus, the construction of an analytical framework that scholars and practitioners can use to determine the soundness of a region’s productive forces distribution, and to base their interventional recommendations on, has important theoretical and empirical implications. We aimed to propose such a framework in this paper. Toward this end, we conducted the current case study on the Jiangsu Province of China. Jiangsu Province was chosen for this research because of its uniqueness and representativeness. Jiangsu Province is divided by the local Chinese into three sub-groups: Northern Jiangsu, Middle Jiangsu and Southern Jiangsu. In terms of standard of economic development, Northern Jiangsu is at the early stage of industrialization, Middle Jiangsu is at the middle stage of industrialization, and Southern Jiangsu is at the mature stage of industrialization. A framework that is tested on such unique data is arguable cogently to have greater validity, generalizability and practicality, and hence has greater significance to the Asian productive forces distribution literature. 2. LITERATURE REVIEW Scholars from both the West and the East have conducted many studies on regional productive forces distribution. Classical theories include the centre-periphery theory, growth 1

pole theory, and point-axis theory (Liu, Fang, & Shi, 2009). Chinese scholars tend to focus their research efforts on macro distributional issues such as regional specialization and general development, regional division and co-operation, and regional economic segregation. They are interested in how the state can influence and control the distribution and combination of a region’s productive forces to ensure that the region’s production systems can operate efficiently, and that the region’s resources are optimally allocated. For example, Zhuo, Chen and Sun (2008) used spatial balance theory to study the distribution of Wuxi city’s productive forces. The authors studied how the city had distributed its productive forces amongst its manufacturing industry, modern service industry, agriculture industry, and industrial park. The study provided the basis for the optimization of the city’s productive forces distribution, and it also provided the directions for the city’s economic upgrades. Based on the growth-pole theory and growth-axis theory, Dadao Lu proposed the theoretical model of point-axis system spatial structural evolution (Gu, Du, & Jin, 2013; Jiang, Liu, & Liu, 2013; Lu, 1995). He believed that the majority of the society’s economic essentials are concentrated on several nodes. An axis is formed between the nodes when they are connected by linear infrastructures. The axis has a strong attraction towards its adjacent regions, and the nodes will extend its influence along the axis. In general, the Chinese literature on regional productive forces distribution is mainly qualitative, and there is a lack of systematic quantitative research (Liu, Fang, Shi, & Guo, 2008; Shi, 2013; Wang & Zhang, 2014; Yan, 2013). Hence, in this paper, we tested our proposed framework using the data from the Jiangsu Province of China. 3. METHODOLOGY The major factors that can influence a region’s productive forces distribution include: regional economic scale, standard of productive forces, and the intensity of economic connection between cities. By determining the standard of development of a region’s productive forces, and by estimating the economic connection intensities of cities, we can segregate cities into different classes. Then, the nature of a region’s productive forces distribution can be determined based on the proportion of cities from different classes that a region has. We propose that a region’s productive forces distribution can be analyzed using the following procedures: 3.1 Step 1: Determination of Productive Forces Standard The standard of a city’s productive forces indicates a city’s economic development and economic capacity. It is influenced by a city’s gross domestic product (GDP), technological standard, and population. We use q to represent city, and K(q) to present the productive forces standard of city q, as represented in equation (1).

kiq represents the i factor that influences

the productive forces standard of city q.

K (q)  f (k1q , k2q ,

, kiq ,

, kmq )

（1）

3.2 Step 2: Classification of Productive Forces Standard We divide cities into different classes based on the standard of their productive forces. K(q)

2

represents the standard of productive forces of city q, where K min( o )  K (q )  K max( o ) , and q

productive forces standard is represented by

q

T (o) . Based on the value of T (o) , we can

determine the level of a city’s productive forces. 3.3 Step 3: Estimation of Economic Connection Intensity Two cities with strong economies are likely to have more economic interactions and stronger economic connections if they are physically closer and have greater differences in their capital flows. Borrowing from the theory of gravity, we propose that the intensity of the economic connection between city q and p can be represented by equation (2).

Rpq  k pq

G p Gq S vpq

(u p a p  uq aq )

（2）

where

k pq : contact coefficient between city q and p; G p , Gq : GDP of city q and p;

u p , uq : development coefficient of city q and p; a p , aq : production yield of city q and p;

S pq : distance between city q and p; v : distance index. 3.4 Step 4: Classification of Economic Connection Intensity We segregate cities into different groups according to their city connection intensity. We let

R pq to represent the intensity of the economic connection between city p and q, where

Rmin( o )  Rpq  Rmax( o ) , and L(o) represents economic connection intensity. We determine the intensity of economic connection between cities using L(o) . 3.5 Step 5: Determination of Regional Productive Forces Distribution The nature of a region’s productive forces distribution is determined at this final stage based on the proportion of cities from different classes that each region has. We classify a region that is mainly made up of cities with strong economic connection intensity as one that has “network distribution”. We classify a region that is mainly made up of cities with weak economic connection intensity as one that has “extreme point distribution”. We classify a region that is in between the other two distribution patterns as one that has “point-axis distribution”. 4.0 ANALYTICAL STRATEGY 4.1 Estimation of Productive Forces Standard We use population scale, economic function, technological function and economic capacity as indicators of city productive forces. Assuming that there are n cities, and the productive forces 3

standard of city q is

K (q) , we measure city productive forces by using equation (3): K (q) 

k Aq  k Bq  kCq  k Dq 4

（3）

Whereby,

k Aq 

Aq n

1 Aq  n q 1

represents the population of city q (indicator of urbanization), and

Aq

represents the number of city residents working in nonagricultural sectors;

k Bq 

Bq 1 n q B n q 1

represents economic function (indicator of economic strength and

standard of economic progress), and

kCq 

Cq 1 n q C n q 1

Bq

represents regional GDP;

represents technological function (indicator of a city’s capacity to absorb

foreign technology, resources, and information), and

C q represents the number of

middle-class and above technological talents;

k Dq 

Dq 1 n q D n q 1

represents economic development (indicator of a city’s economic

developmental capacity), and

Dq represents the amount of city q’s fixed capital investment.

4.2 Classification of City Productivity We classify cities into tier-1 high productive forces city, tier-2 high productive forces city, and low productive forces city based on each city’s standard of productive forces K(q) (see Table 1). TABLE 1: CLASSIFICATION OF PRODUCTIVE FORCES Classification High Productive Forces

Rank

Criterion

Tier-1

K ( q)  2

Tier-2

1  K ( q)  2

K (q)  1

Low Productive Forces 4.3 Estimation of Economic Connection Intensity

Assuming city p and q, the intensity of economic connection between two cities can be estimated using equation (2). In (2),

k pq represents the coefficient of the economic connection

between city p and q. It represents the factors that influence the two cities' interactions (e.g., 4

living environment, geographical condition and cultural similarity). We set the connection between two cities with high productive forces;

k pq = 1 to indicate

k pq = 0.8 to indicate the

connection between a high productive forces city and a low productive forces city; and k pq = 0.6 to indicate the connection between two cities with low productive forces.

G p and Gq represents the GDPs of city p and q. According to the agglomeration economy theory, if a region’s economy is more developed and has higher GDP, it will have greater attraction toward other cities, and it will have more significant agglomeration effect.

u p , uq represent development coefficient. It is influenced by a city’s political environment. A city that has more open trading policies will have a more favorable trading environment and hence higher corresponding productive forces city,

u p , uq values. We set u p , uq = 1 to indicate tier-1 high

u p , uq = 0.8 to indicate tier-2 high productive forces city, and u p , uq

= 0.6 to indicate low productive forces city.

a p , aq represent the production yields of city p and q. We measure a p , aq by using the corresponding city’s integrated industrial economic benefit index.

(u p a p  uq aq ) represents the yield of two cities’ flow of productive forces. The flow of two cites’ productive forces is a directional vector, which flows from a city with lesser gains from productive forces to a city with greater gains from productive forces.

S pq represents the distance between city p and q. We measure S pq by calculating the economic distance between two cities using Gao and Luo’s (2006) calculation method. Shorter distance between two cities indicates closer connection. V represents distance index. It indicates the influence of a city’s transportation infrastructure and geographical limitations on a city’s interactions with the other cities. Taking into consideration the conditions of our sample cities’ transportation infrastructure and geographical limitations, we set v = 1. 4.4 Classification of Economic Connection Intensity We determine the parameters of economic connection intensity,

Rmin( o ) , Rmax( o ) , based on the

conditions of the economic development and the economic environment of a region. We divide the intensities of economic connection between cities into three categories (strong, moderately strong and weak) by using three conditions:

R pq  Rmax( o ) , Rmin( o )  Rpq  Rmax( o ) and 5

Rmin( o )  R pq . 4.5 Determination of Regional Productivity Pattern Assuming that:

n

U p   H pq p 1

n1 represents the number of city when U p  0 , n2 represents the number of city when

U p  1 or 2, and n3 represents the number of city when U p  3 . 4.5.1 Pattern 1: Extreme Point Regional Productive Forces Distribution When n1

 n , the economic connections of all cities are either moderately strong or weak.

Such cities tend to develop independently, and they tend to have low quality interactions and have limited capacity for economic radiation. We termed such regional distribution pattern “extreme point productive forces distribution”. 4.5.2 Pattern 2: Network Regional Productive Forces Distribution When

n2  n3 and n3 / n  1/ 3 , a region will have a greater portion of cities with strong

economic connections, more efficient flow of productive forces, and more significant economic diffusion effect. We termed such regional distribution pattern “network productive forces distribution”. 4.5.3 Pattern 3: Point-Axis Regional Productive Forces Distribution When

n2  n3 and n2 / n  1/ 3 , cities of a region will tend to have weak economic

connections. The distributional pattern of the region’s cities lies between the extreme pole productive forces distribution and network productive forces distribution, and we termed it “point-axis productive forces distribution”. 5.0 Jiangsu Province’s Productive Forces Distribution 5.1 Southern Jiangsu Productive Forces Distribution 5.1.1 Standards of Southern Jiangsu Productive Forces and Connectivity Cities belonging to the Southern Jiangsu region include: Nanjing, Zhenjiang, Changzhou, Wuxi,Suzhou, Jiangyin, Yixing, Liyang, Jintan, Changshu, Zhangjiagang, Kunshan, Wujiang, Taicang, Danyang, Yangzhong and Jurong. Based on the data reported in the Jiangsu Province’s 2012 annual report and the respective cities’ 2012 annual report, we calculated the standard of productive forces for each Southern Jiangsu city with equation (3), and we divided

6

them into different categories (see Table 2). With equation (2), we calculated the economic connection intensity of Southern Jiangsu cities (see Table 3). Taking Rmin( o )

 100, Rmax( o )  1000 , we divided the cities’ economic connection

into three categories (strong, moderately strong and weak).

TABLE 2: CLASSIFICATION OF SOUTHERN JIANGSU PRODUCTIVE FORCES STANDARD Classification High Productive Forces

Cities

Tier 1（ K (q)  2 ）

Nanjing, Wuxi, Suzhou

Tier 2（ 1  K (q)  2 ）

Changzhou, Kunshan Jiangyin, Yixing, Liyang, Jintan, Changshu,

Low Productive Forces K (q)  1 ）

Zhangjiagang, Wujiang, Taicang, Danyang, Jurong, Zhenjiang

5.1.2 Southern Jiangsu Productive Forces Distribution 5 out of the 16 cities of Southern Jiangsu were highly productive, and they were located around Suzhou city, Changzhou city and Wuxi city. These cities had close economic connection, efficient flow of factors of production, relatively better development environment, and significant economic diffusion effect. Amongst the cities with weak productive forces, most of them did not have strong connection intensity with the other cities, and they had weaker economies. Cities that were located to the south of Nanjing and Shanghai, such as Liyang and Jintan, had very weak economic connection intensity with the other cities, and some of them were still developing their economies independently despite their weak economic infrastructures. Given that

n1 : n2 : n3  6 : 4 : 6, n2  n3 , n3 / n  6 / 16  1/ 3 , the distribution pattern of

Southern Jiangsu cities had characteristics of network distribution. In sum, the regional distribution pattern of the Southern Jiangsu cities belonged to the early stage of the network productive forces distribution (see Figure 1).

7

FIGURE

1:

ILLUSTRATION

OF

SOUTHERN

JIANGSU

PRODUCTIVE

FORCES

DISTRIBUTION

8

TABLE 3: SOUTHERN JIANGSU CITIES' ECONOMIC CONNECTION INTENSITY City Nanjing Wuxi Jiangying

Nanjing

Wu-

Jin-

Chang

Zhangjia

yang

tan

-shu

-gang

1

3

3

1

3

2

1

3

3

1

3

1

3

3

0

2

3

0

Yixing

Changzhou

1

2

3

0

1 0

xi 0

Li-

Jiangying

Yixing Changzhou Liyang Jintan Suzhou Changshu Zhangjiaga ng Kunshan Wujiang Taicang Zhenjiang Danyang

Zhen

Dan-

Ju-

-jiang

yang

rong

3

1

2

2

2

2

2

2

3

3

3

3

2

3

3

3

3

3

3

3

3

3

2

2

2

3

3

1

2

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

0

1

1

1

1

2

1

2

3

0

1

2

3

3

3

3

3

0

3

3

3

3

3

3

0

3

2

3

3

3

0

2

3

3

3

0

3

3

3

0

2

2

0

3

Suzhou

Kunshan

Wujiang

Taicang

3

3

3

2

2

2

1

3

2

3

2

3

2

3

1

0

2 0

Jurong

0

*1 = strong; 2 = moderately strong; 3 = weak

9

5.2 Northern Jiangsu Productive Forces Distribution Pattern 5.2.1 Standards of Northern Jiangsu Productive Forces and Connectivity Cities belonging to the Northern Jiangsu region include: Xuzhou, Lianyungang, Huai'an, Yancheng, Xinyi, Pizhou, Dongtai, Dafeng and Suqian. Repeating the procedures that we had used in the last section, we obtained the productive forces and connectivity standards of Northern Jiangsu cities (see Table 4 and Table 5). TABLE 4: CLASSIFICATION OF NORTHERN JIANGSU PRODUCTIVE FORCES STANDARD Classification High Productive Forces

Cities

Tier 1（ K (q)  2 ）

Xuzhou

Tier 2（ 1  K (q)  2 ）

Lianyungang, Huai'an, Yancheng

Low Productive Forces（ K (q)  1 ）

Xinyi, Pizhou, Dongtai, Dafeng, Suqian

TABLE 5: NORTHERN JIANGSU CITIES' ECONOMIC CONNECTION INTENSITY City Xuzhou Xinqi

Xuzhou 0

Xinqi

Pizhou

Lianyungang

Huaian

Yanch-

Dongtai

Dafeng

Suqian

eng 3

3

2

3

2

3

3

3

0

3

3

2

3

3

3

3

0

3

3

3

3

3

3

0

2

2

3

3

3

0

2

3

3

3

0

3

3

3

0

3

3

0

3

Pizhou Lianyunga ng Huaian Yancheng Dongtai Dafeng Suqian

0

*1 = strong; 2 = moderately strong; 3 = weak 5.2.2 Northern Jiangsu Productive Forces Distribution 4 out of the 9 cities were highly productive, and they relied mainly on the Jiangsu portion of the Long-Hai railway and Ning-Lian highway to keep in close contact. As these cities only occupied 12.8% of the region’s landscape, it had limited economic radiation capacity. Cities with low productive forces had poor economy and poor economic infrastructure. They had low quality economic interactions with its neighboring cities as it did not have the necessary factors of production and the necessary infrastructures, and they were relatively economically isolated. The economic connection intensity between cities with high productive forces and cities with low productive forces, and the economic connection intensity between cities with low productive forces were weak. Given that n1 : n2 : n3

 9 : 0 : 0, n1  n , the productive forces distribution of cities from the

Northern Jiangsu region had characteristics of extreme point distribution. Hence, the regional distribution pattern of the Northern Jiangsu cities belonged to the mature stage of the extreme pole productive forces distribution. 10

5.3 Middle Jiangsu Productive Forces Distribution 5.3.1 Standards of Middle Jiangsu Productive Forces and Connectivity Cities belonging to the Middle Jiangsu region include: Nantong, Yangzhou, Taizhou Qidong, Rugao, Haimen, Yizheng, Gaoyou, Jiangdu, Xinghua, Jingjiang, Taixing, and Jiangyan. Repeating the procedures that we had used in the last section, we obtained the productive forces and connectivity standards of Middle Jiangsu cities (see Table 6 and Table 7). TABLE 6: CLASSIFICATION OF MIDDLE JIANGSU PRODUCTIVE FORCES STANDARD Classification High Productive Forces

Cities

Tier 1（ K (q)  2 ）

Nantong, Yangzhou

Tier 2（ 1  K (q)  2 ）

Taizhou

Low Productive Forces（ K (q)  1 ）

Qidong, Rugao, Haimen, Yizheng, Gaoyou, Jiangdu, Xinghua, Jingjiang, Taixing, Jiangyan

5.3.2 Middle Jiangsu Productivity Distribution Pattern Only 3 out of the 14 cities in the Middle Jiangsu region were highly productive, and they were mainly located along the Long River. Cities in the region had great differences in term of their standards of productive forces. Between cities that were strong in serving its main economic functions and cities that were weak in serving its main economic functions, only Tongzhou city and Nantong city had strong economic connection. The economic radiation capacities of the cities that were strong in serving its main economic functions were weak. Cities that had weak productive forces were not very different in term of their capacity to serve their main economic functions, and they had weak economic connections. In general, the region’s economic connectivity was weak. Given that n1 : n2 : n3

 6 : 6 :1, n2  n3 , n2 / n  6 / 13  1/ 3 , the productive forces distribution

of Middle Jiangsu cities had characteristics of axis distribution. The productive forces distribution of the region lacks a gradual laddering effect. Cities with high productive forces had polarizing effects on the cities with weak productive forces. Hence, the distribution pattern of cities from the Middle Jiangsu region belonged to the early stage of point-axis distribution. 6.0 CONCLUSION Based on our analyzes on the productive forces distributions of the Southern Jiangsu region, Middle Jiangsu region and Northern Jiangsu region, we found that: (1) Cities on the Hu-Ning developmental axis were approaching their mature and steady growth stage. Cities on the developmental axis situated along the Long River were still at its infant stage of development. As governmental strategic development policies are implemented sequentially, the economic conditions of the region will improve. With the attentions and helps from the government, the economies of cities located along the East Long-Hai developmental axis has started to grow slowly. On the other hand, cities situated along the coastal areas were still at their infant stage of development.

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TABLE 7: MIDDLE JIANGSU CITIES' ECONOMIC CONNECTION INTENSITY City

Nantong

Nantong Qidong Rugao Haimeng

0

Qidong

Rugao

Haimeng

Yang-

Yi-

Gao-

zhou

zheng

you

Jiangdu

Taizhou

Xing-

Jing-

hua

jiang

Taixing

Jiangyan

1

2

2

1

3

3

3

1

3

1

3

3

0

3

1

3

3

3

3

3

3

3

3

3

0

3

3

3

3

3

3

3

3

3

3

0

3

3

3

3

3

3

3

3

3

0

3

3

2

1

3

3

3

3

0

3

3

3

3

3

3

3

0

3

3

3

3

3

3

0

3

3

3

3

3

0

3

3

3

2

0

3

3

3

0

1

3

0

3

Yangzho u Yizheng Gaoyou Jiangdu Taizhou Xinghua Jingjiang Taixing Jiangyan

0

*1 = strong; 2 = moderately strong; 3 = weak

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(2) The regional distribution pattern of cities in the Southern Jiangsu region was found to be at the mature stage of network productive forces distribution. Cities in the region had strong economic connections with each other due to the transportation conveniences brought by the Hu-Ning railway and highway. Cities in the Southern Jiangsu region also had a strong economic radiation effect on cities located to the north of the region, bringing benefits to cities from the Middle Jiangsu region and Northern Jiangsu region. However, cities in this region had weak economic connections with cities located toward the south of the region. (3) In the Middle Jiangsu region, cities to the north of the Long River had weak economic connections. Cities located on the Long River developmental axis should become the focus of future developments. The subsequent maturity of the Long River development belt is likely to bring changes to the current conditions of the Middle Jiangsu and Southern Jiangsu laddered development, thus slowly blurring the economic delineations between the two regions. By leveraging on the influence of the Hu-Ning developmental axis and Long River developmental axis, Southern Jiangsu region has the potential to transform its regional distribution pattern into the network productive forces distribution. (4) In the Northern Jiangsu region, the East Long-Hai railway will become the new developmental axis of the Jiangsu Province’s economy. East Long-Hai concentrated industrial zone, East Long-Hai urban developmental axis, regional modern logistic network, and green ecological passage are likely to be formed along the region between Xuzhou and Lianyungang. The productive forces distribution \ of the Northern Jiangsu region is then likely to be upgraded, and is likely to transform from the current extreme point productive forces distribution to the point-axis productive forces distribution. 7.0 REFERENCES Gao, R. and Luo, S., "Some Theoretic Questions about Metropolitan Regions and the Development of Metropolitan Regions in China", Modern Urban Research, Number 8, Pages 4-11, 2006. Gu, P.. Du, J. and Jin, S., "An Empirical Research on Urban-Rural Coordinated Development in Jiangsu Province from 2002 to 2011", East China Economic Management, Volume 27, Number 12, Pages 30-33, 2013. Jiang, T., Liu, G. and Liu, C, "The Research on the Spatial Distribution of the Level of Comprehensive Economic and Social Development", East China Economic Management, Volume 27, Number 12, Pages 21-25, 2013. Liu, S., Fang, Z. and Shi H., Theory of Technology Transfer and Applications, CRC Press, 2009. Liu, S., Fang, Z., Shi, H. and Guo, B., "The Research on the Formation of Technology Productivity and Flow Effect", Science Press, 2008. Lu, D. Regional development and spatial structure, Science Press, 1995. Shi, G., "The Essence of Urbanization is the New Adjustment of Economy and Productivity Layout Pattern", Review of Economic Research, Volume 5, Pages 64-65, 2013. Wang, D. and Zhang, Y., "Features of Node Localization Lifestyle-Based on Analysis about Relations Function between Productivity and Lifestyle", Journal of Harbin Institute of Technology,

Volume 16, Number 1, Pages 26-33, 2014.

Yan, W., "Shenzhou International: Transnational Productivity Layout", China Textile & Apparel, Volume 7, Pages 48-49, 2013.

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Zhuo, Z., Chen, W. and Sun, W., "Study on framework of productive forces based on the theory of spatial balance – With Wuxi as a case", Areal Research and Development, Volume 27, Number 1, Pages 19-27, 2008. AUTHOR PROFILES Professor Yiping Liu is the Dean of Entrepreneurship Education at the Nanjing University of Aeronautics and Astronautics. His research interests include: entrepreneurship, econometrics, and performance management. Dr Jhony Choon Yeong Ng is an Assistant Professor of Organizational Behavior at the Nanjing University of Aeronautics and Astronautics. His research interests include: performance management, goal orientation theory, and the dark sides of organizations. Ms Yuling Han is a student at the Nanjing University of Aeronautics and Astronautics. Her research interests include: entrepreneurship and econometrics.

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