# Spatio-Temporal Characteristics of Rural Economic Development in Eastern Coastal China

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methodology

#### 2.1. Study Area and Data

^{2}in area, is located at the forefront of economic reform and has become open to the outside world. During the past three decades, since the adoption of the economic reform and opening up policy in 1978, the industrialization and urbanization in coastal China has been accelerated. The economies of metropolitan areas, such as the “Beijing-Tianjin-Hebei”, “Yangtze River Delta” and “Pearl River Delta” areas, have grown vigorously, and the rapid urbanization and centralization of the population and industries have been the obvious characteristics of the urban-rural integrated development in this study region [16]. As a result, the rural area in this location has changed tremendously, and the obvious enhancement of the rural economic development level is a symbol of these changes. In 2012, the per capita income of the rural region amounted to 1628.1 USD, which was much higher than that of China as a whole (1256.6 USD) during the same period. When compared with 1978, it had increased 18 times. However, the unbalanced development resulted in differences in the regional physical conditions, as well as the socio-economic development levels, and these still exist today in the study area.

#### 2.2. Analytical Framework

#### 2.3. Assessment Measures

_{i}is each individual income in region i, similar to Y

_{j}, n being the number of stylebooks, and u = mean income. The TW index, calculated based on Wolfson index, is used to depict a centralized character [26].

_{i}is the number the population in region i and N is the total number of individuals. y

_{i}is the GDP per capita in region i, and m is the middle GDP per capita of all regions. k is the number of provinces. θ and r are constants, and in this paper, θ = 1, r = 0.5. The TW scores are between 0 and 1. When TW is 1, the region is assumed to be completely polarized. In contrast, if TW is 0, the region is completely non-polarized.

_{ij}is the GDP of province j in region i, Y is the total GDP of the study area ($={\displaystyle \sum _{i}{\displaystyle \sum _{j}{Y}_{ij}}}$), N

_{ij}is the population of province j in region i and N is the total population of all areas ($={\displaystyle \sum _{i}{\displaystyle \sum _{j}{N}_{ij}}}$).

_{i}as follows to measure between-province GDP inequality for region i,

_{i}is the total GDP of region i ($={\displaystyle \sum _{j}{Y}_{ij}}$), N

_{i}is the total population of region i ($={\displaystyle \sum _{j}{N}_{ij}}$) and ${T}_{BR}={\displaystyle \sum _{i}\left(\frac{{Y}_{i}}{Y}\right)}\mathrm{log}\left(\frac{{Y}_{i}/Y}{{N}_{i}/N}\right)$ measures GDP inequality between regions.

_{WR}) and the between-region component (T

_{BR}). The within-region component is a weighted average of the between-province GDP inequalities for each region (T

_{i}).

#### 2.4. Spatial Autocorrelation Model

_{i}is the attribute value of region i and $\overline{r}$ is the mean value of the attributes of all regions. The factor w

_{ij}is a weight that equals one if the distance of the variable r

_{i}and r

_{j}belongs to this interval and zero otherwise. S is the sum of weights for a given interval, and N is the total number of provinces. If I >0, under the given level of significance, it is a positive correlation, and the larger value indicates the greater relevance of the spatial distribution. If there is no spatial autocorrelation, the expected value of I is −1/(n − 1), which can be approximated by 0 if n is large.

_{it}is local Moran’s I statistics of the region i in year t, r

_{it}is the attribute value of the region i in year t, u

_{t}is the mean of all regional attribute values, n is the number of regions and S

^{2}is the sample variance. If I

_{it}> 0, region i has a local positive correlation with other regions, and I

_{it}< 0 shows negative correlation. A multiple-sequences Monte Carlo simulation test is used to test the significant level of LISA [35]. A small p-value (p < 0.05) indicates that the neighbor effect of region i is relatively high, while a large p-value (p > 0.95) indicates that it is relatively low [36].

## 3. Results

#### 3.1. Dynamic Variation of Rural Development

**Figure 3.**The contrast and evolution trends of annual net income in eastern coastal rural areas during the period of 1978 to 2012 (HB, Hebei; BJ, Beijing; TJ, Tianjin; SD, Shandong; JS, Jiangsu; SH, Shanghai; ZJ, Zhejiang; FJ, Fujian; GD, Guangdong; HN, Hainan; the meanings of the abbreviations in other parts of the manuscript are the same). (

**a**) at the national and regional level; (

**b**) at provincial level.

#### 3.2. Spatio-Temporal Differences in the Eastern Coastal Area

#### 3.3. Spatial Autocorrelation Analysis of Rural Development

#### 3.3.1. Analysis of Global Spatial Autocorrelation

Year | 1990 | 1996 | 2000 | 2008 | 2012 |
---|---|---|---|---|---|

Moran’s I | 0.17 | 0.14 | 0.20 | 0.42 | 0.48 |

Z Score | 6.41 | 5.47 | 7.40 | 15.89 | 18.32 |

Variance | 0.0007 | 0.0007 | 0.0007 | 0.0007 | 0.00068 |

p-Value | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 |

#### 3.3.2. Analysis of Local Spatial Autocorrelation

## 4. Discussion

#### 4.1. Resources Endowment

#### 4.2. Economic Location

#### 4.3. Policy Factors

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Agarwal, S.; Rahman, S.; Errington, A. Measuring the determinants of relative economic performance of rural areas. J. Rural Stud.
**2009**, 5, 309–321. [Google Scholar] [CrossRef] - Yilmaz, B.; Daşdemir, I.; Atmis, E.; Lise, W. Factors affecting rural development in turkey: Bartın case study. For. Policy Econ.
**2010**, 12, 239–249. [Google Scholar] [CrossRef] - Long, H.L.; Zou, J.; Liu, Y.S. Differentiation of rural development driven by industrialization and urbanization in eastern coastal China. Habitat Int.
**2009**, 33, 454–462. [Google Scholar] [CrossRef] - Terluin, I.J. Differences in economic development in rural regions of advanced countries: An overview and critical analysis of theories. J. Rural Stud.
**2003**, 19, 327–344. [Google Scholar] [CrossRef] - Hayati, D.; Karbalaee, F. Revising agricultural development by rethinking rural development strategy in Iran. Tech. J. Eng. Appl. Sci.
**2013**, 3, 1411–1417. [Google Scholar] - Sanderson, S. Poverty and conservation: The new century’s “Peasant Question?”. World Dev.
**2005**, 33, 323–332. [Google Scholar] [CrossRef] - Courtney, P.; Hill, G.; Roberts, D.; Deborah, R. The role of natural heritage in rural development: An analysis of economic linkages in Scotland. J. Rural Stud.
**2006**, 22, 469–484. [Google Scholar] [CrossRef] - Lise, W. An Econometric and Game Theoretic Model of Common Pool Resource Management: People’s Participation in Forest Management in India; Nova Science Publishers Inc.: Hauppauge, NY, USA, 2007. [Google Scholar]
- Narain, U.; Gupta, S.; Veld, K.V. Poverty and resource dependence in rural India. Ecol. Econ.
**2008**, 66, 161–176. [Google Scholar] [CrossRef] - Long, H.L.; Zou, J.; Pykett, J. Analysis of rural transformation development in China since the turn of the new millennium. Appl. Geogr.
**2011**, 31, 1094–1105. [Google Scholar] [CrossRef] - Peng, Y.S. What has spilled over from Chinese cities into rural industry? Mod. China
**2007**, 33, 287–319. [Google Scholar] [CrossRef] - Kadri, S. Neighborhood milieu in the cultural economy of city development: Berlin’s Helmholtzplatz and Soldiner in the German “Social City” program. Cities
**2011**, 28, 95–106. [Google Scholar] [CrossRef] - Cheng, Z.M.; Wang, H.N. Do neighborhoods have effects on wages? A study of migrant workers in urban China. Habitat Int.
**2013**, 38, 222–231. [Google Scholar] [CrossRef] - Michele, M.; Annalisa, D.B.; Rocco, R. Economic and environmental sustainability of forestry measures in Apulia Region Rural Development Plan: An application of life cycle approach. Land Use Policy
**2014**, 41, 284–289. [Google Scholar] [CrossRef] - Toivo, M. Needs for rural research in the northern Finland context. J. Rural Stud.
**2010**, 26, 73–80. [Google Scholar] [CrossRef] - Liu, Y.S.; Wang, L.J.; Long, H.L. Spatio-temporal analysis of land-use conversion in the eastern coastal China during 1996–2005. J. Geogr. Sci.
**2008**, 18, 274–282. [Google Scholar] [CrossRef] - National Bureau of Statistics of China. China Statistical Yearbook (1996–2013); China Statistics Press: Beijing, China, 1996–2013. [Google Scholar]
- National Bureau of Statistics of China. China Compendium of Statistics; China Statistics Press: Beijing, China, 2009. [Google Scholar]
- National Bureau of Statistics of China. Comprehensive Statistical Data and Materials on 50 Years of New China; China Statistics Press: Beijing, China, 1999. [Google Scholar]
- National Bureau of Statistics of China. China County Statistical Yearbook (2001–2013); China Statistics Press: Beijing, China, 2001–2013. [Google Scholar]
- National Bureau of Statistics of China. China Rural Statistical Yearbook (1985–2013); China Statistics Press: Beijing, China, 1985–2013. [Google Scholar]
- Ilberya, B.; Watts, D.; Little, J.; Gilga, A.; Simpsond, S. Attitudes of food entrepreneurs towards two grant schemes under the first England Rural Development Programme, 2000–2006. Land Use Policy
**2010**, 27, 683–689. [Google Scholar] [CrossRef] - Vennesland, B. Measuring rural economic development in Norway using data envelopment analysis. For. Policy Econ.
**2006**, 7, 109–119. [Google Scholar] [CrossRef] - Alvaredo, F. A note on the relationship between top income shares and the Gini coefficient. Econ. Lett.
**2011**, 110, 274–277. [Google Scholar] [CrossRef] - Zheng, B.H.; Brian, C. Statistical inference for testing inequality indices with dependent samples. J. Econom.
**2001**, 101, 315–335. [Google Scholar] [CrossRef] - Wang, Y.Q.; Tsui, K.Y. Polarization orderings and new classes of polarization indices. J. Public Econ. Theory.
**2000**, 2, 349–363. [Google Scholar] [CrossRef] - Theil, B.H. The information approach to demand analysis. Econometrica
**1965**, 33, 67–87. [Google Scholar] [CrossRef] - Akita, T. Decomposing regional income inequality in China and Indonesia using two-stage nested Theil decomposition method. Reg. Sci.
**2003**, 37, 55–77. [Google Scholar] [CrossRef] - Bourguignon, B.F. Decomposable income inequality measures. Econometrica
**1979**, 47, 901–920. [Google Scholar] [CrossRef] - Shorrocks, B.A.F. The class of additively decomposable inequality measures. Econometrica
**1980**, 48, 613–625. [Google Scholar] [CrossRef] - Dale, M.R.T.; Fortin, M.J. Spatial autocorrelation and statistical tests in ecology. Ecoscience
**2002**, 9, 162–167. [Google Scholar] - Legendre, P. Spatial autocorrelation: Trouble or new paradigm? Ecology
**1993**, 74, 1659–1673. [Google Scholar] [CrossRef] - O’Sullivan, D.; Unwin, D.J. Geographic Information Analysis; Wiley Press: New York, NY, USA, 2003. [Google Scholar]
- Shortridge, A. Practical limits of Moran’s autocorrelation index for raster class maps. Comput. Environ. Urban Syst.
**2007**, 31, 362–371. [Google Scholar] [CrossRef] - Anselin, L. Local indicators of spatial association-LISA. Geogr. Anal.
**1995**, 27, 93–115. [Google Scholar] [CrossRef] - Qiao, X.N.; Yang, D.G.; Zhang, X.H. Evolution stages of oasis economy and its dependence on natural resources in Tarim River Basin. Chin. Geogr. Sci.
**2009**, 19, 265–273. [Google Scholar] [CrossRef] - Liu, Y.S.; Wang, G.G.; Zhang, F.G. Spatio-temporal dynamic patterns of rural area development in eastern coastal China. Chin. Geogr. Sci.
**2013**, 23, 173–181. [Google Scholar] [CrossRef] - Tan, K. Revitalized small towns in China. Geogr. Rev.
**1986**, 76, 138–148. [Google Scholar] [CrossRef] - Xie, Y.C.; Batty, M.; Zhao, K. Simulating emergent urban form using agent based modeling: Desakota in the Suzhou-Wuxian region in China. Ann. Assoc. Am. Geogr.
**2007**, 97, 477–495. [Google Scholar] [CrossRef] - Long, H.L.; Liu, Y.S.; Li, X.B. Building new countryside in China: A geographical perspective. Land Use Policy
**2010**, 27, 457–470. [Google Scholar] [CrossRef]

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**MDPI and ACS Style**

Wang, G.; Wang, M.; Wang, J.; Yang, C.
Spatio-Temporal Characteristics of Rural Economic Development in Eastern Coastal China. *Sustainability* **2015**, *7*, 1542-1557.
https://doi.org/10.3390/su7021542

**AMA Style**

Wang G, Wang M, Wang J, Yang C.
Spatio-Temporal Characteristics of Rural Economic Development in Eastern Coastal China. *Sustainability*. 2015; 7(2):1542-1557.
https://doi.org/10.3390/su7021542

**Chicago/Turabian Style**

Wang, Guogang, Mingli Wang, Jimin Wang, and Chun Yang.
2015. "Spatio-Temporal Characteristics of Rural Economic Development in Eastern Coastal China" *Sustainability* 7, no. 2: 1542-1557.
https://doi.org/10.3390/su7021542