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Spatial Pattern and Influencing Factors of County Consumption Level

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Spatial Pattern and Influencing Factors of County Consumption Level Vol. 11(2), pp. 24-33, February, 2018 DOI: 10.5897/JGRP2017.0677 Article Number: 104F56E56235 ISSN 2070-1845 Journal of Geography and Regional Planning Copyright © 2018 Author(s) retain the copyright of this article http://www.academicjournals.org/JGRP Full Length Research Paper Spatial pattern and influencing factors of county consumption level Li Jiulin1,2*, Chu Jinlong1,2, Chen Xiaohua1,2, Gu Kangkang1,2 and Wang Yongzheng1,2 1School of Architecture and Urban Planning, Anhui Jianzhu University, Anhui, China. 2Research Center of Urbanization Development in Anhui Province, Hefei 230022, Anhui, China. Received 27 November, 2017; Accepted 14 February, 2018 The aim of this paper is to improve the level of consumption in the region and achieve sustained and steady regional economic growth. According to global autocorrelation calculation, the difference in county consumption level of the Wanjiang City Cluster since 2004 generally takes on an increasing trend, while the consumption level presents unbalanced development. Evolution of spatial distribution pattern indicates that high level consumption areas are mainly distributed along Yangtze River and these areas are constantly expanding, while low level consumption areas are situated in central Anhui and southwest mountain areas of Anhui. The differentiation pattern of consumption level gets constantly increasing and tends to remain stable. Quantile regression approach was used in different quantiles to depict effects of influencing factors on consumption level. In all quantiles, per capita GDP, per capita financial revenue, and average wage exert a positive effect on county consumption level, while the per capita industrial added value and net income of farmers produce a negative effect on county consumption level. Based on the overall grasp of the influencing factors of the regional consumption level and the quantile regression analysis, in order to improve the consumption level in the region and reduce the difference in regional consumption, policy makers should make every effort to improve the basic conditions for regional economic development and implement it in a targeted manner when formulating the regional consumption policy Countermeasures to Improve Regional Consumption Level. Key words: County consumption level, evolution of spatial pattern, exploratory spatial data analysis (ESDA), quantile regression, Wanjiang City Cluster. INTRODUCTION With development of social economy, economic contacts growth, resident consumption is an essential factor between regions become increasingly close and keeping regional economic development and bringing resources flow more frequently. Regional economic about regional differences, and regional difference in connection not only intensifies or abates spatial consumption level is also a concentrated expression and difference, but also promotes dynamic changes to miniature of imbalance in regional economic development regional spatial pattern; as leading force of economic (Haider et al, 2016; Zhang and University, 2017; Ma et *Corresponding author. E-mail: [email protected]. Author agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Jiulin et al. 25 al., 2016; Wei et al, 2016; Liu et al, 2015; Zhang and province of China (Figure 1). It is an important part of the Pan-Delta Tian, 2005; Mei-Juan et al, 2014). Economic Area Cooperation and has an important strategic position Currently, studies about the consumption level mainly in the transfer of industries in the central and western regions. Wanjiang City to undertake industrial transfer demonstration area is focus on economics and geography, including located in Anhui Province, China, including Hefei, Wuhu, Ma On consumption characteristics, spatial difference of Shan, Tongling, Anqing, Chizhou, Chaohu, Chuzhou, Xuancheng 9 consumption, and influencing factors. Wang and Zhang throughout the city, And Jin'an District and Shucheng County of (2012) and Li and Huang (2007) studied rural Lu'an City, a total of 59 counties (cities, districts), the land area of 2 consumption structure and characteristics of 76,000 km and a population 30.58 million people. undergraduate consumption. Lin (2003) explored the relation between changes in consumption demands and Global spatial correlation regional economic development and the relation between consumption and industrial structure. Jiang and Luo Spatial autocorrelation (2008) compared differences in resident consumption Generally, there exists certain degree of spatial interaction in structure between Jiangsu, Shanxi and Sichuan behavior between regions. In order to analyze potential spatial provinces, and gave pertinent recommendations for interaction in urbanization development of counties from the raising the consumption level. Jiao (2013) analyzed perspective of public goods, this paper employed exploratory distribution and spatial correlation of urban consumption spatial data analysis (ESDA) technique to calculate and evaluate space in China, and found that urban spatial consumption whether the county consumption level of the Wanjiang City Cluster shows spatial autocorrelation (dependence) in geographical space. market structure is mainly manifested as separation effect rather than agglomeration effect. Guan et al. (2012) (i) Firstly, this study tested the global spatial correlation using the explored changes to regional pattern of China’s social Moran’s I. consumption level, and concluded that the consumption n m n n level is mainly under the influence of per capita GDP, W (Y Y)(Y Y) / S 2 W traffic accessibility, percentage of output value of tertiary ij i j ij Moran’s I = i1 j1 i1 j1 (1) industry, and per capita income of urban and rural residents. The above studies were related to the low level where, of consumption in the region involving small-scale n n 2 1 county-level units, and less involved in the spatial S (X Y) / n i Y (Yi ) analysis of the interaction and interaction between i1 , n i1 (2) regional consumption levels and the detection of genetic mechanism. Yi denotes the observed value of i-th region, Yj signifies the The level of regional consumption not only has the observed value of the j-th region, n is the number of regions, and historical evolution of time, but also shows the Wij refers to binary contiguous spatial weight matrix (Teng and Shi, 2013). remarkable regional differentiation in space. As one of the Based on the above method, with the aid of GeoDa1.4.0 indicators of macroeconomic analysis, the spatial pattern software, the paper calculated the global Moran’s I for different and evolution pattern of regional consumption level can period of county consumption level of the Wanjiang City Cluster, as be used to study the development direction and shown in Figure 2. equilibrium of national or regional consumption levels. (ii) After global autocorrelation analysis, it was necessary to use This study can reflect not only the general characteristics local spatial correlation indicators to analyze possible spatial of economic spatial differentiation but also the correlation with local significance. Generally, Moran scatter plot is significance of understanding the spatial pattern of employed to make estimation (Wang et al, 2011). In Moran scatter regional economic differences. plots, counties in the Wanjiang City Cluster are located separately Wanjiang City Cluster refers to the City Belt along the in four quadrants which correspond to different types of spatial correlation: in the first quadrant, high consumption level counties Yangtze River in Anhui Province. On the basis of the are surrounded by other high consumption level counties (high- study on mutual spatial influence and interaction of the high, HH); in the second quadrant, low consumption level counties county consumption level of the Wanjiang City Cluster in are surrounded by high consumption level counties (low-high, LH); 2004-2016, this study used quantile regression approach in the third quadrant, low consumption level counties are to explain the effect of influencing factors of regional surrounded by other low consumption level counties (low-low, LL); consumption level, in the hope of raising the consumption in the fourth quadrant, high consumption level counties are surrounded by low consumption level counties (high-low, HL). The level of backward areas and realizing stable and first and third quadrants show positive spatial autocorrelation, while sustainable growth of regional economy. the second and fourth quadrants reflect negative spatial autocorrelation. If the county consumption level indices are uniformly distributed in four quadrants, there exists no spatial MATERIALS AND METHODS autocorrelation between counties. The calculation formula: Study area Z I E (I)/ VAR (I) The Wanjiang city belt is an important development area for the d n n (3) implementation of the strategy of promoting the rise of the central 26 J. Geogr. Reg. Plann. Figure 1. Map of study area. where En (I) 1/(n 1) Q( x) infy: F (y x) , = , Y (0,1) (5) VAR (I) (n2w nw 3w2)/ w2(n1)(n2)(n3) E2(I) n 1 2 0 0 n This study can estimate the quantile regression coefficient using the (4) weighted least absolute deviation (WLAD): Quantile regression method arg min ' ' ' () (yi xi()) arg min[ ' Yi Xi ' (1)Yi Xi ] yi xi yi xi RP RP The quantile regression method, introduced by Koenker and (6) Bassett (1978), is used to estimate linear relationship between regression variable X and explained
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