Descriptive Study of 2009-2013 China Area per Capita GDP
Renhao Jin, Fang Yan, Jie Zhu
School of Information, Beijing Wuzi University, Beijing, China
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To cite this article:
Renhao Jin, Fang Yan, Jie Zhu. Descriptive Study of 2009-2013 China Area per Capita GDP.Journal of World Economic Research.Vol.4, No. 5, 2015, pp. 109-114. doi: 10.11648/j.jwer.20150405.11
Abstract: This paper studied area level per capita GDP data from 2009 to 2013 in China. The bar chart, bubble chart and map chart are used to display a growth trend on area per capita GDP. It is pointed out that areas with higher Per Capita GDP have relative lower growth rate on Per Capita GDP. Moran's I coefficients and Geary's C coefficients are used to measure the Spatial autocorrelation in the Per capita GDP data. The results of Moran's I coefficient and Geary's c coefficients test showed that global spatial autocorrelation are accepted, while local spatial autocorrelation are rejected.
Keywords: China GDP, Area per Capita GDP, Spatial Analysis
1. Introduction
Gross domestic product (GDP) is defined by the Organization for Economic Co-operation and Development (OECD) as "an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs)." The more familiar use of GDP estimates is to calculate the growth of the economy from year to year (and recently from quarter to quarter). The pattern of GDP growth is held to indicate the success or failure of economic policy and to determine whether an economy is 'in recession'.
Recently years, China’s total GDP has ranked in the second in the world, and some economists predict that China’s GDP will catch up United States’ in 15 years. China economic has attracted many researchers in the world. However, China Per Capita GDP are still relative lower than that of western countries. This paper does a description study on China Per Capita GDP. The data used in this paper are obtained from China Statistical Yearbook, published by National Bureau of Statistics of China.
There are 28 provinces and 4 municipalities directly under the Central Government of China mainland. Beijing, Shanghai, Tianjin, Chongqing are the four municipalities, and Beijing and Shanghai are two very important cities in China and the World.Beijing is located in the north of China, which is the center of North China and capital of China. Shanghai is in the east of China, and the areas around Shanghai is the richest part in China.
As shown in Figure 1, from 2005 to 2014, China GDP is always in rising status, although it increasing rate is decreasing. However, in 2014, its increasing rate is still about 7.2%, which is relative high figure comparing 2.4% of United Status and negative increasing rate in some Europe countries. In 2005, China GDP is only about 17237.8 Billion (China Yuan), but in 2014, it is almost 4 times more. With this high speed development, in 2007 and 2010, China catch up with Germany and Japan respectively. At the same time China’s population keeps relative stable, so the rising rate of Per Capita GDP is similar to that of total GDP.
2. The Area per Capita GDP
The area level per capita data are listed in the table 1, and it contains 5 years data from 2009 to 2013. Similar to China GDP overall increasing trend, Per Capita GDP in all areas is increasing. As shown in Figure 2, there are 8, 4, and 1 areas in the first chart (marked as 12000) in year 2009, 2010, 2011 respectively, but in year 2012 and 2013 no area is in that chart. At the same time, the charts with larger marked values all have experienced increasing process but with different extent. Just as reported by articles in Medias, Chinese citizens are becoming richer and richer. With this phenomena, Chinese people spend more and more money for shopping in the world.
No. | Areas | Per Capita GDP (China Yuan) | ||||
2009 | 2010 | 2011 | 2012 | 2013 | ||
1 | Beijing | 66940 | 73856 | 81658 | 87475 | 93213 |
2 | Tianjin | 62574 | 72994 | 85213 | 93173 | 99607 |
3 | Hebei | 24581 | 28668 | 33969 | 36584 | 38716 |
4 | Shanxi | 21522 | 26283 | 31357 | 33628 | 34813 |
5 | Neimenggu | 39735 | 47347 | 57974 | 63886 | 67498 |
6 | Liaoning | 35149 | 42355 | 50760 | 56649 | 61686 |
7 | Jilin | 26595 | 31599 | 38460 | 43415 | 47191 |
8 | Heilongjiang | 22447 | 27076 | 32819 | 35711 | 37509 |
9 | Shanghai | 69164 | 76074 | 82560 | 85373 | 90092 |
10 | Jiangsu | 44253 | 52840 | 62290 | 68347 | 74607 |
11 | Zhejiang | 43842 | 51711 | 59249 | 63374 | 68462 |
12 | Anhui | 16408 | 20888 | 25659 | 28792 | 31684 |
13 | Fujian | 33437 | 40025 | 47377 | 52763 | 57856 |
14 | Jiangxi | 17335 | 21253 | 26150 | 28800 | 31771 |
15 | Shandong | 35894 | 41106 | 47335 | 51768 | 56323 |
16 | Henan | 20597 | 24446 | 28661 | 31499 | 34174 |
17 | Hubei | 22677 | 27906 | 34197 | 38572 | 42613 |
18 | Hunan | 20428 | 24719 | 29880 | 33480 | 36763 |
19 | Guangdong | 39436 | 44736 | 50807 | 54095 | 58540 |
20 | Guangxi | 16045 | 20219 | 25326 | 27952 | 30588 |
21 | Hainan | 19254 | 23831 | 28898 | 32377 | 35317 |
22 | Chongqing | 22920 | 27596 | 34500 | 38914 | 42795 |
23 | Sichuan | 17339 | 21182 | 26133 | 29608 | 32454 |
24 | Guizhou | 10971 | 13119 | 16413 | 19710 | 22922 |
25 | Yunnan | 13539 | 15752 | 19265 | 22195 | 25083 |
26 | Xizang | 15295 | 17319 | 20077 | 22936 | 26068 |
27 | Shanxi | 21947 | 27133 | 33464 | 38564 | 42692 |
28 | Gansu | 13269 | 16113 | 19595 | 21978 | 24296 |
29 | Qinghai | 19454 | 24115 | 29522 | 33181 | 36510 |
30 | Ningxia | 21777 | 26860 | 33043 | 36394 | 39420 |
31 | Xinjiang | 19942 | 25034 | 30087 | 33796 | 37181 |
The Bubble Chart of area Per Capita GDP is shown in Figure 3.The size of bubble is proportional to the value scale of Per Capita GDP (Unit: China Yuan) at the corresponding area and year. It can be seen from the plot that the rank of Per Capita GDP of each area keeps stable, although different areas have different growth rate. For the areas like Beijing and Shanghai, the bubbles from 2009 to 2013 have little changes in the size, which means its growth rate is at a small scale. However, for the areas like Jiangsu and Heilongjiang, bubbles have a significant changes in size, and it is because these areas have a high growth rate.
The average Per Capita GDP map is shown in Figure 5. The average Per Capita GDPare calculated by the mean value of Per Capita GDP value from 2009 to 2012. In this figure, all areas are marked by bubbles. The size of bubble is proportional to the value scale of average value of Per Capita GDP at the corresponding area. It can be seen that the areas in the east China along the coast line and Beijing have relative large bubbles. This phenomena is opposite to that is shown in Figure 4 as these areas have relative small bubbles. However, it is very reasonable that the higher Per Capita GDP the more difficult to increase it.
3. The Spatial Autocorrelation of Area per Capita GDP
In this section, the spatial autocorrelation among the Per Capita GDP data is investigated. The average Per Capita GDP for all areas is the target objective variable. It can be seen from the Figure 5 that large bubbles are distributed along the coastline in the east of China. The Moran’s I coefficient and Geary's Ccoefficient are used to measure the spatial autocorrelation.
The Moran’s I coefficient are defined as
,
and the Geary's c coefficient are calculated as
.
In statistics, Moran's I is a measure of spatial autocorrelation developed by Patrick A.P. Moran. Like autocorrelation, spatial autocorrelation means that adjacent observations of the same phenomenon are correlated. Negative (positive) values indicate negative (positive) spatial autocorrelation. Moran's IValues range from − 1 (indicating perfect dispersion) to + 1 (perfect correlation). A zero values indicates a random spatial pattern. For statistical hypothesis testing, Moran's I values can be transformed to Z-scores in which values greater than 1.96 or smaller than − 1.96 indicate spatial autocorrelation that is significant at the 5% level. The Z-scores transformation can be easily written as
.
Geary's C is also a measure of spatial autocorrelation or an attempt to determine if adjacent observations of the same phenomenon are correlated. Geary's C is inversely related to Moran's I, but it is not identical. Moran's I is a measure of global spatial autocorrelation, while Geary's C is more sensitive to local spatial autocorrelation.Geary's C is also known as Geary's contiguity ratio or simply Geary's ratio.The value of Geary's C lies between 0 and 2. 1 means no spatial autocorrelation. Values lower than 1 demonstrate increasing positive spatial autocorrelation, whilst values higher than 1 illustrate increasing negative spatial autocorrelation.
The Moran's I coefficient and Geary's c coefficient are calculated and shown in table 2. It can be seen that average area Per Capita GDP in China are displayed positive autocorrelation both from Moran's I coefficient and Geary's c coefficient, which means the per capita GDP data in the nearby areas are tended to be similar. However, at the same time it is also noticed that only weak positive autocorrelation are found in the data, as the Moran's I coefficient are close to 0, and Geary's c coefficient are close to 1.The p-value of the spatial autocorrelation test are 0.0078 and 0.1311 for Moran's I coefficient and Geary's c coefficients respectively, thus spatial autocorrelations of area Per Capita GDP value are accepted by Moran's I coefficient but rejected by Geary's c coefficients test. Recalling that Moran's I is a measure of global spatial autocorrelation, while Geary's C is more sensitive to local spatial autocorrelation, it can be seen from Figure 5 that spatial autocorrelation can be found overall but cannot be found in many local areas, for example, the parts away from coastline.
Spatial autocorrelation coefficients | |||
Assumption | Coefficient | Observed | Pr > |Z| |
Randomization | Moran's I | 0.196 | 0.0078 |
Randomization | Geary's C | 0.773 | 0.1311 |
4. Conclusion
This paper firstly make a descriptive study on area level per capita GDP data in China. A growth trend on per capita GDP can be found in all areas in China. However, the disequilibrium is also noticed in different areas. It is also pointed out that areas with higher Per Capita GDP have relative lower growth rate on Per Capita GDP. Then, Moran's I coefficients and Geary's C coefficients are used to measure the Spatial autocorrelation in the Per capita GDP data. Weak positive autocorrelation are found both from Moran's I and Geary's c coefficients. Moreover, from the results of Moran's I coefficient and Geary's c coefficients test, global spatial autocorrelation are accepted, while local spatial autocorrelation are rejected.
Acknowledgements
This paper is funded by the project of National NaturalScience Fund, Logistics distribution of artificial orderpicking random process model analysis and research (Projectnumber: 71371033); and funded by intelligent logisticssystem Beijing Key Laboratory (No.BZ0211); and funded by scientific-research bases---Science & TechnologyInnovation Platform---Modern logistics information andcontrol technology research (Project number:PXM2015_014214_000001); University Cultivation FundProject of 2014-Research on Congestion Model andalgorithm of picking system in distribution center(0541502703).
References