Open Journal of Social Sciences, 2015, 3, 171-196
Published Online November 2015 in SciRes. http://www.scirp.org/journal/jss
http://dx.doi.org/10.4236/jss.2015.311023
How to cite this paper: Yu, Y.C. (2015) Tax Contribution and Income Gap between Urban and Rural Areas in China. Open
Journal of Social Sciences, 3, 171-196. http://dx.doi.org/10.4236/jss.2015.311023
Tax Contribution and Income Gap between
Urban and Rural Areas in China
Yichao Yu
School of Economics, Jinan University, Guang zhou, China
Received 20 October 2015; accepted 14 November 2015; published 17 November 2015
Copyright © 2015 by author and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Abstract
This article analyses the relationship of tax contribution and income gap between urban and rural
areas. First of all, we comb their relationship from theoretical knowledge. Secondly, we use 2000-
2014 panel data of 29 provinces and cities in our country (except Tibet) to establish the fixed ef-
fects model for analysis. Results show that the improvement of tax contribution will expand the
income gap between urban and rural areas. This is due to that turnover tax contribution is the
most important part in the tax contribution. From the structural analysis, improvement of turno-
ver tax and income tax contribution are not conducive to narrow the income gap between urban
and rural areas. The improvement of property tax contribution is conducive to narrow the income
gap between urban and rural areas. Finally, from the empirical results, we can give the policy
suggestion of structural tax cuts and others.
Keywords
Income Gap between Urban and Rural Areas, Tax Contribution, Fixed Effects Model, Structural Tax
Cuts
1. Introduction
Since 1978, our country’s economy maintained a high speed development, the per capita GDP rose from 381
yuan in 1978 to 46,531 yuan in 2014, growth in 122 times. At the same time, income of urban and rural resi-
dents had greatly improved. Urban per capita disposable income in 1978 was 343 yuan. The per capita disposa-
ble income reached 28,844 yuan in 2014. Rural resident per capita net income of 1978 was 134 yuan, and it
reached 10 489 yuan in 2014. However, with the rapid growth of economy and increase of residents’ income, th e
problem of income distribution was increasingly prominent in our country, specially the income gap between
urban and rural areas. Urban and rural income ratio was 2.75 times in 2014. The income gap between urban and

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172
rural areas had become one of the important problems that we need to resolve now. Tax is an important means
of government to adjust the income distribution. By studying the relationship of tax and the income gap between
urban and rural areas in China, it helps us to put forward policy suggestions with narrowing the income gap be-
tween urban and rural areas.
2. Literature Review
The earliest domestic scholars studied Chinese income distribution problem in the late 1980s, such as Zong-
sheng Chen, Wang-Dao Chen . They analyzed income gap between urban and rural areas in China from different
angle, such as present situation, evolution characteristic and reason of income gap. To the 90s of 20th century,
scholars began to study the relationship between taxation and income distribution, such as Guoqing Wang
(1995), Lu Renf a (1996), Xujian Guo (1998) and others. In recent years, tax refund and weakening of the func-
tion of the tax fair had become the focus of at tention. D omesti c li t e r ature coul d be di vided into 3 cate go ries:
The first kind is about the position of the relationship between the tax revenue and the income distribution.
Tax was an important tool to adjust the in come distr ibution. We could use the progres s ive tax, tax in c entives, tax
cuts and other ways to make a fairer income distribution [1]. Pei-Yong Gao thought tax was an important means
for government to adjust the income gap between residents. The government should combine tax adjustment
work and the construction of the tax system, so that tax system could really play a role in adjusting income gap
between residents [2]. In order to improve our tax system and realize the function of adjusting income gap, we
need to find the cause of the income gap. If it is not reasonable in the tax system, we need to reform and pay
more attention to system construction [3].
The second kind is about empirical research on taxation and income distribution. Hua Liu used data of the
world bank to analyze the relationship between turnover tax and income gap. Results show that turnover tax was
not conducive to narrow the income gap [4]. Zi Yin Shi used intermediate progressive index to analyze income
redistribution effect of personal income tax, the average tax rate played a key role on income redistribution ef-
fect [5].
The third kind is about tax refund and weakening of the function of tax fair. Existing research literature had
confirmed that tax adjustment function of the income gap was very weak in China, and there was a phenomenon
of “reverse adjustment”. Xi-Min Yue used the cash flow statement and household survey data to measure tax
burden level of each family. Results show that Chinese tax structure was regressive. Compared with urban areas,
tax burden of rural areas were more regress ive [6]. Ying Wan stu died distribution o f turnover ta x burden. Tur n-
over tax was regressive. Value added tax and business tax was regressive, but consumption tax was not ob-
viously regressive [7]. Qiong-Zhi Liu argue that Chinese personal income tax was regressive. It was not nar-
rowing the income gap in redistribution phase [8].
Domestic scholars began late to res earch the relationship of tax and income distributio n. Generally speaking,
domestic research literature based on the existing tax theory abroad. They studied preference in the principle,
system design and policy orientation. They used more qualitative analysis but less quantitative analysis. This
was due to personal data was difficult to obtain. This paper, through the measurement model, analyzes the rela-
tionship between tax contribution and the income gap by using qualitative analysis and quantitative analysis.
3. Definition of Tax Contribution
Contribution rate refers to the ratio of effective or useful results to the number of occupied (consumed). It is an
index that can be used to analyze the economic benefit. Specifically, contribution rate is the ratio of the amount
of contribution to the amount of input, the ratio of the amount of output to the amount of consumption, the ratio
of the amount of income to the amount of occupancy. According to definition of the contribution rate, we can
define tax contribution as taxpayers have the ability to create tax in the case of occupying social resources. Tax
contribution is a relative index. We need to consider the amount of social resources that are occupied by the
taxpayer when we calculate tax contribution. The amount of social resources occupied by the taxpayer can be
expressed by the gross domestic product. Tax contribution is the ratio of the total tax revenue to GDP. That is,
tax contribution = the tax revenue/GDP. Tax contribution can be divided into turnover tax contribution, income
tax contribution and property tax contribution. Turnover tax contribution is the ratio of turnover tax revenue to
gross domestic pr oduct. Turno ver tax include value added tax, business tax, consumption tax and tariffs; Income
tax contribution is the ratio of income tax revenue to gross domestic product. Income tax includes en terpri se in-

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173
come tax and personal income tax; property tax con tribution is the ratio of property tax revenue to gross domes-
tic product. Property tax includes real estate tax, land value-added tax, urban land use tax, travel tax and deed
tax. Turnover tax contribution (income tax contribution or property tax contribution) = turnover tax revenue
(income tax revenue or property tax revenue)/GDP.
4. Theoretical Analysis
Analysis from the perspective of tax structure, turnover tax is easy to tax shifting. The added-value tax and
business tax covers most of goods, including daily necessities. The elasticity of demand of daily necessities is
small. The enterprises are easy to shift the tax burden on consumers by improving the commodity prices. Low-
income residents who bear the tax burden is higher than the high-income residents. It is not conducive to narrow
the income gap. Consumption tax is mainly aimed at high income residents. It helps to adjust the income gap
between residents. High income residents are mainly concentrated in cities. So it can adjust income gap between
urban and rural areas. Therefore, it is not conducive to narrow the income gap between urban and rural areas by
improving value added tax contribution and business tax contribution. It is conducive to narrow the income gap
between urban and rural areas by improving consumption tax contribution. Value added tax and business tax are
the most important part in the turnover tax. So it is not conducive to narrow the income gap between urban and
rural areas by improving turnover tax contribution. Yi Liu analyzes turnover tax burden in different income
groups. The empirical results show that turnover tax burden in different income groups is close to the proportion.
So increase of turnover tax contribution is not conducive to narrowing the income gap [9]. Shaorong Li et al.
(2006) according to the empirical test, it is concluded that increase of turnover tax contribution will expand the
income gap between capital owners and labor owners. High income groups have more capital income, low in-
come groups have less capital income. Labor income is the most important part of low income groups [10]. So
turnover tax contribution will improve the income gap of residents. Ying Wan has the same point [7].
Whether the enterprise income tax can be used to adjust the income gap between urban and rural areas is con-
troversial for the moment. Enterprise income tax affects the net profit of enterprises, reduces profit rate of capi-
tal. The capital of urban residents is high, the capital of rural residents is low. So it is beneficial to narrow the
gap between urban and rural areas by improving enterprise income tax contribution. However, the private enter-
prise’s life consumption is also easy to be included in the cost, resulting in the reduction of taxable income,
which is not conducive to make income distribution fair. Personal income tax is recognized as one of the most
effective means of adjusting income gap. Income distribution effects of personal income tax mainly come from
progressive. Tax rate increases with increase of income level. It will help to reduce the income gap between high
income and low income groups and achieve the purpose of adjusting income gap between urban and rural areas.
But there are many defects in the current personal income tax system in China. For example, the tax rate of ur-
ban residents is far lower than the nominal tax, personal income tax of rural residents is lack, the structure of the
individual income tax is unreasonable, the private economy of tax contribution is too low, tax evasion is serious,
etc. It is not conducive to narrow the income gap between urban and rural areas by improving personal income
tax contribution. Qiongzhi Liu found that the individual income tax has a certain degree of retirement. At the re-
distribution stage, improvement of tax contribution has not narrowed the degree of income gap [10]. Hua-Sheng
Ouyang u se micro data to study the personal income tax. The results show that individual income tax exists the
phenomenon of “reverse adjustment”. The growth of individual income tax contribution is not conducive to
narrow residents’ income gap [11].
Object of property tax is the taxpayer’s owned or controlled property. Generally speaking, the higher income
of residents is, the more the property is. Property income of urban residents is much higher than that of rural
residents in China. Property tax can effectively adjust the income gap between urban and rural areas. Increase of
property tax contribution will also help to narrow the income gap between urban and rural areas.
Structure of tax system is overall arrangement of various taxes of a country. And the main tax category plays
a decisive role in overall function of tax system. Turnover tax is the main tax category of our country. Therefore,
improvement of tax contribution will expand the income gap between urban and rural areas. Property tax, con-
sumption tax and personal income tax play an important role for adjusting the residents income gap, but there
are some important tax that are missing in the property tax. Consumption tax and personal income tax system is
not reasonable. That is the reason why tax contribution cannot adjust income gap (Jian-Dong Chen, Xia Zhu
soldier, 20 11) [12].
Analysis from the perspective of the resident department revenue, the national income is allocated among the

Y. C. Yu
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government, enterprises and residents. In primary distribution stage, the added value of residents in departments
as a starting point, residents get labor remuneration by providing labor and get interest, rent, bonuses and other
income by providing property. Labor remuneration is a major source of income of residents department, propor-
tion of property income is very small. In the process of getting income, the residents department need pay pro-
duction tax to government departments. Production tax is given priority to with turnover tax. Primary distribu-
tion stage emphasizes on efficiency principle. Production tax is not conducive to narrow income gap between
residents. So the higher tax contribution, the greater income gap between residents. In the stage of redistribution，
government departments use the income tax, transfer payments and other ways to adjust income share of the
residents and the business sector. It could optimize the distribution pattern of national income. In terms of de-
partment of residents, government departments will receive income tax and social insurance from residents. At
the same time, the government departments will transfer a portion of income to residents. Then it becomes the
final formation of disposable inco me of residents. Redistribution pays great attention to fairn ess principle. Gov-
ernment department tax is conducive to narrow income gap between residents. So improvement of tax contribu-
tion is beneficial to narrow income gap between the residents.
5. Empirical Analysis
On the basis of above theoretical analysis, we use 2000-2014 panel data of 29 provinces an d cities in our coun-
try (except Tibet) to establish eco nometric model for analysis.
5.1. Setting of Econometric Model and Variable Selection
In general, panel data has the characteristics of the time series, and it also can reflect the characteristics of the
cross-section data. Panel data is widely used in econometric research. On the basis of our research, we set the
following model:
Model 1:
2
01 2
.
ititit it
INEPGDP PGDP αγ γε
=++ +
Model 2:
2
0121 2345
.
itititititititit it
INEPGDPPGDPCSTR URFIOPEN αγγβ βγγγε
=++++++ ++
Model 3:
2
01212345
.
itititititititit it
INEPGDP PGDPLCSCCCFIOPEN αγγβββγ γε =+++ +++++
Among them, α said intercept, β and γ said explanation variable coefficient, εsaid random perturbation terms;
the subscript i said 29 provinces and cities (i = 1, 2, ···, 29). The subsc ript t said ti me (t = 2000 , 2001, ···, 2014);
INE said income gap between urban and rural areas，it is explained variable. Explanatory variable can be divided
into core explanatory variable and other control variables, the specific variable settings and interpretation in
Table 1.
Explained variable is income gap between urban and rural areas (INE). We use the ratio of urban to rural in-
come to reflect. Core explanatory variable is tax contribu tion that is divided into two categor ie s : on e is reflecting
Table 1. Explained variable, core explanatory variable and other control variables.
The variable name Symbol Interpretation
Explained variable
The income gap between urban
and rural areas
INE
Urban per capita disposable income divided
by the per capita net income of rural residents
Core explanatory variable
Tax
contribution C Tax revenue divided by GDP
Turnover tax contribution
LC Turnover tax r evenue divided by GDP
Income tax contribution
SC Income tax revenue divided by GDP
Property tax contribution
CC Pr operty tax revenue divided by GDP
Other control variables
economic development
PGDP GDP per capita
urbanization
UR Urban population divided by the total population
Fiscal spending
FI The governme nt fiscal spending divided by GDP
Economic openness
OPEN Import and export amount divided by GDP
The data source: China statistical yearbook, The Chinese tax yearbook.

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175
the tax revenue scale (C). The other one is reflecting structure of tax revenue, including turnover tax contribu-
tion (LC), income tax contribution (SC) and property tax contribution (CC). Turnover tax contribution is equal
to the ratio of turnover tax revenue to GDP. Income tax contribution is equal to the ratio of income tax revenue
to GDP. Property tax contribution is equal to the ratio of income tax revenue to GDP. In order to make econo-
metric model results more convincing, we introduce other control variables to the model. Other control variables
include economic development (PGDP), urbanization (UR), fiscal expenditure (FI) and economic openness
(OPEN). Economic development is equal to GDP per capita. Urbanization is equal to urban population divided
by the total population. Fiscal expenditure is equal to government fiscal spending divided by GDP. Economic
openness is equal to import and export amount divided by GDP.
5.2. Data Source
The data of urban per capita disposable income, rural per capita net income, GDP, proportion of urban popula-
tion, total population, total import and export amount, and government fiscal spending are from 2000-2014 Chi-
na statistical yearboo k [13], part of data are from the statistical yearbook of provin ces and cities. The data of to-
tal tax revenues, turnover tax, income tax and property tax are from 2000-2014 Chinese tax yearbook [14]. Ta bles
2-10 show the value of each variable.
Table 2. The value of the income gap between urban and rural areas. Unit: yuan.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing
2.25
2.30
2.31
2.48
2.53
2.40
2.41
2.33
Tianjin
2.25
2.27
2.18
2.26
2.28
2.27
2.29
2.33
Hebei
2.28
2.30
2.49
2.54
2.51
2.62
2.71
2.72
Shanxi 2.48 2.76 2.90 3.05 3.05 3.08 3.15 3.15
Inner Mongolia
2.52
2.81
2.90
3.09
3.12
3.06
3.10
3.13
Liaoning 2.27 2.27 2.37 2.47 2.42 2.47 2.54 2.58
Jilin
2.38
2.45
2.72
2.77
2.61
2.66
2.68
2.69
Heilongjiang 2.29 2.38 2.54 2.66 2.49 2.57 2.58 2.48
Shanghai
2.09
2.19
2.13
2.23
2.36
2.26
2.26
2.33
Jiangsu 1.89 1.95 2.05 2.18 2.20 2.33 2.42 2.50
Zhejiang
2.18
2.28
2.37
2.45
2.45
2.45
2.49
2.49
Anhui 2.74 2.81 2.85 3.19 3.01 3.21 3.29 3.23
Fujian 2.30 2.46 2.60 2.68 2.73 2.77 2.84 2.84
Jiangxi 2.39 2.47 2.75 2.81 2.71 2.75 2.76 2.83
Shandong 2.44 2.53 2.58 2.67 2.69 2.73 2.79 2.86
Henan 2.40 2.51 2.82 3.10 3.02 3.02 3.01 2.98
Hubei 2.44 2.49 2.78 2.85 2.78 2.83 2.87 2.87
Hunan 2.83 2.95 2.90 3.03 3.04 3.05 3.10 3.15
Guangdong
2.67
2.76
2.85
3.05
3.12
3.15
3.15
3.15
Guangxi 3.13 3.43 3.63 3.72 3.77 3.72 3.57 3.78
Hainan
2.46
2.62
2.82
2.80
2.75
2.70
2.89
2.90
Sichuan 3.10 3.20 3.14 3.16 3.06 2.99 3.11 3.13
Guizhou
3.73
3.86
3.99
4.20
4.25
4.34
4.59
4.50
Yunnan 4.28 4.43 4.50 4.50 4.76 4.54 4.47 4.36
Shaanxi
3.55
3.68
3.97
4.06
4.01
4.03
4.10
4.07
Gansu 3.44 3.57 3.87 3.98 3.98 4.08 4.18 4.30
Qinghai
3.47
3.76
3.70
3.76
3.74
3.75
3.82
3.83
Ningxia
2.85
3.04
3.16
3.20
3.11
3.23
3.32
3.41
Xinjiang 3.49 3.74 3.70 3.41 3.34 3.22 3.24 3.24

Y. C. Yu
176
Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 2.32 2.29 2.19 2.23 2.21 2.20 2.18
Tianjin 2.46 2.46 2.41 2.18 2.11 2.04 2.06
Hebei 2.80 2.86 2.73 2.57 2.54 2.48 2.44
Shanxi 3.20 3.30 3.30 3.24 3.21 3.14 3.04
Inner Mongolia 3.10 3.21 3.20 3.07 3.04 2.97 2.98
Liaoning 2.58 2.65 2.56 2.47 2.47 2.43 2.51
Jilin 2.60 2.66 2.47 2.37 2.35 2.32 2.22
Heilongjiang 2.39 2.41 2.23 2.07 2.06 2.03 2.02
Shanghai 2.33 2.31 2.28 2.26 2.26 2.24 2.22
Jiangsu 2.54 2.57 2.52 2.44 2.43 2.39 2.33
Zhejiang 2.45 2.46 2.42 2.37 2.37 2.35 2.31
Anhui 3.09 3.13 2.99 2.99 2.94 2.85 2.81
Fujian 2.90 2.93 2.93 2.84 2.81 2.76 2.75
Jiangxi 2.74 2.76 2.67 2.54 2.54 2.49 2.43
Shandong 2.89 2.91 2.85 2.73 2.73 2.66 2.64
Henan 2.97 2.99 2.88 2.76 2.72 2.64 2.61
Hubei 2.82 2.85 2.75 2.66 2.65 2.58 2.55
Hunan 3.06 3.07 2.95 2.87 2.87 2.80 2.77
Guangdong 3.08 3.12 3.03 2.87 2.87 2.84 2.83
Guangxi 3.83 3.88 3.76 3.60 3.54 3.43 3.41
Hainan 2.87 2.90 2.95 2.85 2.82 2.75 2.73
Sichuan 3.07 3.10 3.04 2.92 2.90 2.83 2.83
Guizhou 4.20 4.28 4.07 3.98 3.93 3.80 3.73
Yunnan 4.27 4.28 4.06 3.93 3.89 3.78 3.75
Shaanxi 4.10 4.11 3.82 3.63 3.60 3.52 3.50
Gansu 4.03 4.00 3.85 3.83 3.81 3.71 3.70
Qinghai 3.80 3.79 3.59 3.39 3.27 3.15 3.13
Ningxia 3.51 3.46 3.28 3.25 3.21 3.15 3.12
Xinjiang 3.26 3.16 2.94 2.85 2.80 2.72 2.71
Table 11 is descriptive statistics of each variable.
5.3. Estimation Method and Measurement Test Results
We use the rev ie w s 6.0 software and choose LLC test, ADF-F test and PP-F test for unit root test of the va-
riables. Due to space limitations, we do not list each variable inspection process. Results of unit root test show
that all variables are significant. We could suggest that each variable is zero order single whole and don’t need
to do cointegration test.
Panel data model can generally be divided into three categories: mixed regression model, fixed effects regres-
sion model and random effects regression model. In order to determine what kind of panel data model to use.
We use the likelihood ratio test and Ha usman test to determine model type. Lik elihood ratio test is used to select
the fixed effects regression model or mixed regression model. The null hypothesis choose mixed regression

Y. C. Yu
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Table 3. The value of tax contribution.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing 0.36 0.43 0.42 0.42 0.44 0.37 0.42 0.46
Tianjin 0.20 0.23 0.24 0.25 0.27 0.28 0.30 0.32
Hebei 0.07 0.08 0.08 0.08 0.08 0.10 0.10 0.11
Shanxi 0.11 0.13 0.13 0.14 0.16 0.17 0.18 0.19
Inner Mongolia 0.09 0.09 0.10 0.10 0.11 0.12 0.12 0.13
Liaoning 0.11 0.14 0.14 0.14 0.15 0.16 0.16 0.16
Jilin 0.10 0.11 0.12 0.12 0.12 0.11 0.11 0.11
Heilongjiang 0.11 0.12 0.12 0.11 0.12 0.13 0.14 0.13
Shanghai 0.31 0.33 0.34 0.39 0.42 0.38 0.40 0.53
Jiangsu 0.09 0.12 0.13 0.14 0.15 0.15 0.15 0.17
Zhejiang 0.12 0.16 0.16 0.17 0.19 0.19 0.19 0.20
Anhui 0.07 0.08 0.08 0.09 0.09 0.10 0.11 0.11
Fujian 0.09 0.11 0.11 0.08 0.12 0.13 0.14 0.14
Jiangxi 0.07 0.08 0.07 0.08 0.08 0.08 0.09 0.10
Shandong 0.09 0.11 0.10 0.10 0.10 0.11 0.12 0.12
Henan 0.07 0.07 0.07 0.07 0.07 0.08 0.08 0.09
Hubei 0.07 0.07 0.08 0.08 0.08 0.11 0.11 0.11
Hunan 0.07 0.08 0.08 0.08 0.09 0.09 0.10 0.10
Guangdong 0.19 0.21 0.22 0.22 0.22 0.19 0.19 0.22
Guangxi 0.09 0.10 0.10 0.10 0.10 0.10 0.10 0.10
Hainan 0.08 0.10 0.10 0.11 0.12 0.13 0.14 0.17
Sichuan 0.08 0.09 0.09 0.09 0.09 0.10 0.11 0.12
Guizhou 0.13 0.13 0.14 0.14 0.16 0.16 0.17 0.17
Yunnan 0.21 0.20 0.20 0.20 0.20 0.20 0.21 0.22
Shaanxi 0.11 0.12 0.12 0.12 0.13 0.13 0.14 0.15
Gansu 0.10 0.11 0.12 0.12 0.12 0.12 0.12 0.13
Qinghai 0.09 0.10 0.10 0.10 0.10 0.11 0.12 0.13
Ningxia 0.11 0.13 0.12 0.12 0.14 0.13 0.13 0.14
Xinjiang 0.11 0.12 0.12 0.12 0.14 0.15 0.16 0.17

Y. C. Yu
178
Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 0.50 0.51 0.44 0.48 0.51 0.53 0.58
Tianjin 0.32 0.27 0.30 0.30 0.29 0.28 0.33
Hebei 0.11 0.11 0.12 0.12 0.13 0.13 0.18
Shanxi 0.21 0.19 0.18 0.18 0.19 0.18 0.23
Inner Mongolia 0.13 0.12 0.13 0.14 0.14 0.14 0.19
Liaoning 0.17 0.17 0.18 0.18 0.19 0.18 0.23
Jilin 0.12 0.12 0.12 0.13 0.14 0.15 0.20
Heilongjiang 0.13 0.13 0.13 0.14 0.15 0.15 0.20
Shanghai 0.48 0.44 0.47 0.50 0.52 0.51 0.56
Jiangsu 0.17 0.17 0.17 0.18 0.19 0.19 0.24
Zhejiang 0.21 0.20 0.06 0.21 0.22 0.22 0.27
Anhui 0.12 0.12 0.13 0.14 0.15 0.15 0.20
Fujian 0.15 0.14 0.15 0.15 0.16 0.17 0.22
Jiangxi 0.10 0.11 0.12 0.12 0.14 0.14 0.19
Shandong 0.12 0.12 0.10 0.14 0.15 0.14 0.19
Henan 0.08 0.08 0.08 0.09 0.10 0.10 0.15
Hubei 0.11 0.11 0.11 0.11 0.13 0.13 0.18
Hunan 0.10 0.09 0.09 0.10 0.11 0.11 0.16
Guangdong 0.22 0.21 0.22 0.22 0.23 0.23 0.28
Guangxi 0.10 0.10 0.11 0.12 0.13 0.13 0.18
Hainan 0.19 0.21 0.23 0.25 0.25 0.24 0.29
Sichuan 0.12 0.12 0.13 0.13 0.14 0.15 0.20
Guizhou 0.17 0.17 0.18 0.18 0.19 0.19 0.24
Yunnan 0.22 0.22 0.23 0.22 0.23 0.23 0.28
Shaanxi 0.15 0.15 0.16 0.17 0.17 0.16 0.21
Gansu 0.12 0.15 0.15 0.15 0.16 0.15 0.20
Qinghai 0.14 0.15 0.15 0.15 0.16 0.16 0.21
Ningxia 0.15 0.14 0.15 0.15 0.17 0.18 0.23
Xinjiang 0.18 0.19 0.20 0.22 0.22 0.21 0.26

Y. C. Yu
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Table 4. The value of turnover tax contribution.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing 0.19 0.22 0.22 0.22 0.23 0.16 0.18 0.18
Tianjin 0.16 0.17 0.18 0.19 0.20 0.20 0.21 0.22
Hebei 0.05 0.05 0.05 0.05 0.06 0.07 0.07 0.07
Shanxi 0.08 0.09 0.10 0.10 0.12 0.12 0.12 0.12
Inner Mongolia 0.07 0.07 0.07 0.08 0.09 0.09 0.09 0.09
Liaoning 0.06 0.06 0.07 0.07 0.07 0.07 0.07 0.07
Jilin 0.07 0.08 0.09 0.09 0.09 0.08 0.08 0.08
Heilongjiang 0.08 0.08 0.08 0.07 0.08 0.08 0.09 0.08
Shanghai 0.19 0.22 0.24 0.28 0.29 0.26 0.27 0.28
Jiangsu 0.07 0.09 0.09 0.10 0.11 0.11 0.11 0.11
Zhejiang 0.06 0.07 0.08 0.08 0.08 0.08 0.08 0.09
Anhui 0.05 0.06 0.06 0.06 0.06 0.07 0.08 0.08
Fujian 0.04 0.05 0.05 0.06 0.06 0.06 0.06 0.06
Jiangxi 0.05 0.05 0.05 0.06 0.06 0.06 0.06 0.07
Shandong 0.05 0.05 0.05 0.05 0.05 0.05 0.06 0.06
Henan 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
Hubei 0.05 0.05 0.05 0.06 0.06 0.07 0.07 0.07
Hunan 0.05 0.06 0.06 0.06 0.07 0.07 0.07 0.07
Guangdong 0.09 0.11 0.11 0.11 0.12 0.10 0.10 0.10
Guangxi 0.06 0.07 0.07 0.07 0.07 0.07 0.07 0.07
Hainan 0.05 0.07 0.07 0.08 0.09 0.09 0.10 0.13
Sichuan 0.06 0.06 0.07 0.07 0.07 0.07 0.07 0.08
Guizhou 0.10 0.10 0.11 0.11 0.11 0.11 0.12 0.11
Yunnan 0.16 0.15 0.15 0.15 0.15 0.15 0.15 0.15
Shaanxi 0.08 0.09 0.09 0.09 0.10 0.09 0.10 0.10
Gansu 0.08 0.08 0.09 0.09 0.10 0.09 0.09 0.10
Qinghai 0.07 0.07 0.08 0.08 0.08 0.08 0.09 0.10
Ningxia 0.08 0.09 0.09 0.09 0.10 0.10 0.10 0.10
Xinjiang 0.07 0.08 0.09 0.09 0.10 0.11 0.11 0.12

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Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 0.18 0.17 0.17 0.17 0.17 0.17 0.19
Tianjin 0.22 0.20 0.22 0.21 0.20 0.19 0.21
Hebei 0.07 0.08 0.08 0.08 0.08 0.08 0.10
Shanxi 0.14 0.12 0.12 0.12 0.11 0.10 0.12
Inner Mongolia 0.09 0.08 0.08 0.08 0.08 0.07 0.09
Liaoning 0.07 0.07 0.07 0.07 0.07 0.06 0.08
Jilin 0.08 0.08 0.08 0.09 0.08 0.08 0.10
Heilongjiang 0.08 0.08 0.08 0.09 0.09 0.08 0.10
Shanghai 0.27 0.28 0.30 0.32 0.32 0.31 0.33
Jiangsu 0.11 0.11 0.11 0.11 0.11 0.11 0.13
Zhejiang 0.09 0.09 0.09 0.09 0.09 0.09 0.11
Anhui 0.08 0.08 0.09 0.09 0.08 0.08 0.10
Fujian 0.06 0.06 0.07 0.07 0.07 0.07 0.09
Jiangxi 0.07 0.07 0.08 0.08 0.08 0.08 0.10
Shandong 0.06 0.06 0.07 0.07 0.07 0.07 0.09
Henan 0.05 0.05 0.05 0.05 0.05 0.05 0.07
Hubei 0.07 0.07 0.07 0.07 0.07 0.07 0.09
Hunan 0.07 0.07 0.07 0.07 0.07 0.07 0.09
Guangdong 0.10 0.10 0.10 0.10 0.11 0.10 0.12
Guangxi 0.07 0.07 0.08 0.08 0.08 0.08 0.10
Hainan 0.14 0.15 0.16 0.16 0.15 0.13 0.15
Sichuan 0.07 0.07 0.08 0.08 0.08 0.08 0.10
Guizhou 0.11 0.11 0.11 0.11 0.11 0.11 0.13
Yunnan 0.15 0.15 0.16 0.15 0.15 0.14 0.16
Shaanxi 0.10 0.10 0.11 0.11 0.10 0.10 0.12
Gansu 0.09 0.11 0.11 0.11 0.11 0.10 0.12
Qinghai 0.09 0.10 0.10 0.10 0.10 0.10 0.12
Ningxia 0.10 0.10 0.10 0.09 0.11 0.11 0.13
Xinjiang 0.12 0.13 0.14 0.14 0.14 0.13 0.15

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Table 5. The value of income tax contribution.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing 0.14 0.18 0.18 0.18 0.19 0.19 0.23 0.25
Tianjin 0.04 0.06 0.05 0.05 0.06 0.07 0.08 0.08
Hebei 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Shanxi 0.02 0.02 0.02 0.02 0.03 0.03 0.04 0.05
Inner Mongolia 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.02
Liaoning 0.02 0.03 0.02 0.02 0.03 0.03 0.03 0.03
Jilin 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Heilongjiang 0.03 0.03 0.03 0.03 0.03 0.04 0.04 0.04
Shanghai 0.06 0.07 0.08 0.07 0.10 0.10 0.10 0.13
Jiangsu 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.04
Zhejiang 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.05
Anhui 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Fujian 0.02 0.03 0.02 0.03 0.03 0.03 0.03 0.03
Jiangxi 0.01 0.02 0.01 0.01 0.02 0.02 0.02 0.02
Shandong 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.02
Henan 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Hubei 0.01 0.02 0.02 0.02 0.02 0.03 0.03 0.03
Hunan 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02
Guangdong 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.05
Guangxi 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.02
Hainan 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03
Sichuan 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.03
Guizhou 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.04
Yunnan 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.04
Shaanxi 0.02 0.03 0.02 0.02 0.02 0.02 0.03 0.03
Gansu 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Qinghai 0.02 0.02 0.01 0.01 0.01 0.02 0.02 0.02
Ningxia 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.02
Xinjiang 0.02 0.03 0.02 0.02 0.02 0.03 0.03 0.03

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Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 0.30 0.30 0.24 0.27 0.29 0.31 0.32
Tianjin 0.08 0.06 0.06 0.07 0.06 0.06 0.07
Hebei 0.03 0.02 0.02 0.03 0.03 0.02 0.03
Shanxi 0.04 0.05 0.04 0.05 0.05 0.05 0.05
Inner Mongolia 0.03 0.03 0.03 0.04 0.04 0.03 0.03
Liaoning 0.03 0.02 0.02 0.02 0.02 0.02 0.02
Jilin 0.03 0.03 0.03 0.03 0.03 0.03 0.04
Heilongjiang 0.04 0.03 0.03 0.04 0.04 0.04 0.05
Shanghai 0.14 0.12 0.13 0.14 0.14 0.14 0.15
Jiangsu 0.04 0.04 0.04 0.05 0.04 0.04 0.05
Zhejiang 0.04 0.03 0.04 0.04 0.04 0.04 0.04
Anhui 0.03 0.02 0.03 0.03 0.03 0.03 0.04
Fujian 0.03 0.03 0.03 0.03 0.03 0.03 0.04
Jiangxi 0.02 0.02 0.02 0.03 0.03 0.03 0.03
Shandong 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Henan 0.02 0.02 0.02 0.02 0.02 0.02 0.03
Hubei 0.03 0.03 0.02 0.03 0.03 0.03 0.03
Hunan 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Guangdong 0.04 0.03 0.04 0.04 0.03 0.04 0.04
Guangxi 0.02 0.02 0.02 0.02 0.02 0.02 0.03
Hainan 0.04 0.04 0.04 0.05 0.05 0.05 0.06
Sichuan 0.03 0.03 0.03 0.03 0.03 0.03 0.04
Guizhou 0.04 0.04 0.04 0.04 0.04 0.04 0.05
Yunnan 0.04 0.04 0.04 0.04 0.04 0.04 0.04
Shaanxi 0.04 0.03 0.03 0.04 0.04 0.04 0.04
Gansu 0.02 0.02 0.02 0.02 0.02 0.02 0.03
Qinghai 0.03 0.03 0.03 0.03 0.03 0.03 0.04
Ningxia 0.02 0.03 0.03 0.04 0.03 0.03 0.04
Xinjiang 0.04 0.03 0.04 0.05 0.05 0.05 0.05

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Table 6. The value of property tax contribution.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing 0.010 0.009 0.010 0.011 0.009 0.006 0.007 0.011
Tianjin 0.004 0.003 0.004 0.004 0.003 0.003 0.004 0.005
Hebei 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.003
Shanxi 0.004 0.004 0.004 0.003 0.003 0.003 0.003 0.003
Inner Mongolia 0.006 0.005 0.005 0.005 0.005 0.004 0.005 0.006
Liaoning 0.004 0.004 0.004 0.004 0.004 0.004 0.005 0.008
Jilin 0.003 0.003 0.003 0.004 0.004 0.003 0.003 0.004
Heilongjiang 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003
Shanghai 0.004 0.004 0.004 0.004 0.005 0.007 0.008 0.008
Jiangsu 0.002 0.002 0.002 0.003 0.003 0.004 0.004 0.006
Zhejiang 0.002 0.002 0.002 0.002 0.003 0.004 0.004 0.005
Anhui 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.004
Fujian 0.002 0.002 0.002 0.003 0.003 0.003 0.004 0.004
Jiangxi 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.004
Shandong 0.003 0.003 0.003 0.004 0.003 0.004 0.004 0.005
Henan 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.003
Hubei 0.002 0.002 0.003 0.003 0.002 0.003 0.003 0.003
Hunan 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002
Guangdong 0.003 0.003 0.004 0.004 0.004 0.004 0.004 0.004
Guangxi 0.003 0.003 0.004 0.004 0.004 0.004 0.004 0.004
Hainan 0.006 0.006 0.006 0.005 0.006 0.006 0.007 0.008
Sichuan 0.002 0.002 0.003 0.003 0.003 0.003 0.004 0.005
Guizhou 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.005
Yunnan 0.005 0.004 0.005 0.005 0.004 0.004 0.004 0.004
Shaanxi 0.004 0.004 0.004 0.004 0.004 0.003 0.003 0.004
Gansu 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.004
Qinghai 0.003 0.002 0.002 0.002 0.002 0.002 0.002 0.002
Ningxia 0.005 0.004 0.004 0.004 0.004 0.003 0.004 0.005
Xinjiang 0.003 0.003 0.003 0.003 0.003 0.003 0.004 0.004

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Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 0.012 0.013 0.013 0.016 0.016 0.018 0.019
Tianjin 0.006 0.007 0.007 0.009 0.010 0.011 0.012
Hebei 0.004 0.005 0.003 0.006 0.007 0.008 0.009
Shanxi 0.005 0.006 0.005 0.005 0.006 0.008 0.009
Inner Mongolia 0.008 0.007 0.003 0.008 0.009 0.011 0.012
Liaoning 0.009 0.010 0.005 0.013 0.016 0.016 0.017
Jilin 0.006 0.006 0.003 0.007 0.008 0.008 0.009
Heilongjiang 0.005 0.006 0.006 0.008 0.009 0.009 0.010
Shanghai 0.010 0.010 0.011 0.015 0.018 0.016 0.017
Jiangsu 0.008 0.008 0.007 0.011 0.012 0.013 0.014
Zhejiang 0.007 0.008 0.008 0.009 0.010 0.010 0.011
Anhui 0.006 0.006 0.007 0.008 0.010 0.011 0.012
Fujian 0.006 0.006 0.007 0.008 0.008 0.011 0.012
Jiangxi 0.005 0.005 0.006 0.006 0.008 0.010 0.011
Shandong 0.006 0.006 0.004 0.007 0.009 0.010 0.011
Henan 0.004 0.005 0.005 0.006 0.007 0.007 0.008
Hubei 0.004 0.005 0.004 0.006 0.007 0.009 0.010
Hunan 0.003 0.003 0.004 0.005 0.006 0.006 0.007
Guangdong 0.007 0.007 0.008 0.009 0.011 0.011 0.012
Guangxi 0.005 0.005 0.005 0.006 0.007 0.008 0.009
Hainan 0.011 0.014 0.017 0.022 0.023 0.026 0.027
Sichuan 0.007 0.006 0.007 0.009 0.010 0.011 0.012
Guizhou 0.006 0.006 0.007 0.007 0.007 0.009 0.010
Yunnan 0.006 0.006 0.007 0.008 0.009 0.009 0.010
Shaanxi 0.005 0.005 0.005 0.006 0.007 0.007 0.008
Gansu 0.005 0.004 0.004 0.006 0.006 0.007 0.008
Qinghai 0.003 0.004 0.003 0.003 0.004 0.005 0.006
Ningxia 0.007 0.005 0.006 0.006 0.008 0.009 0.010
Xinjiang 0.004 0.007 0.006 0.006 0.007 0.008 0.009

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Table 7. The value of economic development. Unit: yuan.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing 24,127 26,980 30,730 34,777 40,916 45,993 50,467 60,096
Tianjin 17353 19,141 21,387 25,544 30,575 35,783 41,163 47,970
Hebei 7663 8362 9115 10251 12,487 14,814 16,962 20,033
Shanxi 5722 6226 7082 8641 10,741 12,647 14,497 16,945
Inner Mongolia 6502 7210 8146 10,015 12,728 16,371 20,693 26,521
Liaoning 11,226 12,070 13,000 14,270 15,835 19,074 21,914 26,057
Jilin 7351 7893 8714 9854 11,537 13,350 15,720 19,383
Heilongjiang 8294 8900 9541 10,638 12,449 14,467 16,268 18,580
Shanghai 30,047 31,799 35,445 38,486 46,338 52,535 58,837 62,041
Jiangsu 11,773 12,925 14,397 16,830 20,031 24,953 28,943 34,294
Zhejiang 13,461 14,713 16,978 20,149 24,784 27,703 31,874 37,411
Anhui 4961 5313 5817 6375 7768 8810 10,055 12,045
Fujian 11,194 11,691 12,739 14,125 16,235 18,646 21,471 25,908
Jiangxi 4851 5221 5829 6678 8189 9440 11,145 13,322
Shandong 9555 10,195 11,340 13,268 16,413 20,096 23,794 27,807
Henan 5499 5959 6487 7376 9201 11,347 13,313 16,060
Hubei 6293 6867 7437 8378 9898 11,554 13,360 16,386
Hunan 5425 6120 6734 7589 9165 10,562 12,139 14,869
Guangdong 12,736 13,852 15,365 17,798 20,876 24,647 28,747 33,890
Guangxi 4652 4668 5099 5969 7461 8788 10,296 12,555
Hainan 6894 7315 8041 8592 9812 11,165 12,810 14,923
Sichuan 4956 5376 5890 6623 7895 9060 10,613 12,963
Guizhou 2759 3000 3257 3701 4317 5119 6305 7878
Yunnan 4770 5015 5366 5870 7012 7835 8970 10,609
Shaanxi 4549 5511 6161 7057 8638 9899 12,840 15,546
Gansu 4129 4386 4768 5429 6566 7477 8757 10,346
Qinghai 5138 5774 6478 7346 8693 10,045 11,889 14,507
Ningxia 5376 6039 6647 7734 9199 10,349 12,099 15,142
Xinjiang 7470 7945 8457 9828 11,541 13,184 15,000 16,999

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Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 64,491 66,940 73,856 81,658 87,475 93,213 95,344
Tianjin 58,656 62,574 72,994 85,213 93,173 99,607 101,390
Hebei 22,986 24,581 28,668 33,969 36,584 38,716 42,650
Shanxi 21,506 21,522 26,283 31,357 33,628 34,813 39,221
Inner Mongolia 34,869 39,735 47,347 57,974 63,886 67,498 70,692
Liaoning 31,739 35,149 42,355 50,760 56,649 61,686 64,166
Jilin 23,521 26,595 31,599 38,460 43,415 47,191 50,303
Heilongjiang 21,740 22,447 27,076 32,819 35,711 37,509 41,610
Shanghai 66,932 69,164 76,074 82,560 85,373 90,092 92,733
Jiangsu 40,014 44,253 52,840 62,290 68,347 74,607 76,477
Zhejiang 41,405 43,842 51,711 59,249 63,374 68,462 70,918
Anhui 14,448 16,408 20,888 25,659 28,792 31,684 35,238
Fujian 29,755 33,437 40,025 47,377 52,763 57,856 60,310
Jiangxi 15,900 17,335 21,253 26,150 28,800 31,771 35,286
Shandong 32,936 35,894 41,106 47,335 51,768 56,323 59,046
Henan 19,181 20,597 24,446 28,661 31,499 34,174 37,837
Hubei 19,858 22,677 27,906 34,197 38,572 42,613 45,592
Hunan 18,147 20,428 24,719 29,880 33,480 36,763 40,122
Guangdong 37,638 39,436 44,736 50,807 54,095 58,540 61,318
Guangxi 14,652 16,045 20,219 25,326 27,952 30,588 34,270
Hainan 17,691 19,254 23,831 28,898 32,377 35,317 38,847
Sichuan 15,495 17,339 21,182 26,133 29,608 32,454 36,031
Guizhou 9855 10,971 13,119 16,413 19,710 22,922 26,316
Yunnan 12,570 13,539 15,752 19,265 22,195 25,083 28,639
Shaanxi 19,700 21,947 27,133 33,464 38,564 42,692 45,628
Gansu 12,421 13,269 16,113 19,595 21,978 24,296 28,137
Qinghai 18,421 19,454 24,115 29,522 33,181 36,510 39,846
Ningxia 19,609 21,777 26,860 33,043 36,394 39,420 42,907
Xinjiang 19,797 19,942 25,034 30,087 33,796 37,847 40,822

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Table 8. The value of urbanization.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing 0.79 0.78 0.79 0.79 0.80 0.84 0.84 0.85
Tianjin 0.52 0.53 0.54 0.54 0.54 0.75 0.76 0.76
Hebei 0.26 0.29 0.31 0.34 0.36 0.38 0.39 0.40
Shanxi 0.36 0.35 0.38 0.39 0.40 0.42 0.43 0.44
Inner Mongolia 0.43 0.43 0.44 0.45 0.46 0.47 0.49 0.50
Liaoning 0.45 0.46 0.46 0.47 0.47 0.59 0.59 0.59
Jilin 0.50 0.50 0.51 0.52 0.52 0.53 0.53 0.53
Heilongjiang 0.52 0.52 0.53 0.53 0.53 0.53 0.54 0.54
Shanghai 0.92 0.91 0.92 0.91 0.90 0.89 0.89 0.89
Jiangsu 0.42 0.43 0.45 0.47 0.48 0.51 0.52 0.53
Zhejiang 0.49 0.51 0.51 0.52 0.53 0.56 0.57 0.57
Anhui 0.30 0.31 0.32 0.33 0.35 0.36 0.37 0.39
Fujian 0.42 0.42 0.44 0.45 0.46 0.49 0.50 0.51
Jiangxi 0.28 0.30 0.32 0.34 0.36 0.37 0.39 0.40
Shandong 0.27 0.28 0.29 0.31 0.32 0.45 0.46 0.47
Henan 0.23 0.24 0.26 0.27 0.29 0.31 0.32 0.34
Hubei 0.40 0.41 0.41 0.42 0.43 0.43 0.44 0.44
Hunan 0.30 0.31 0.32 0.33 0.35 0.37 0.39 0.40
Guangdong 0.55 0.56 0.58 0.59 0.60 0.61 0.63 0.63
Guangxi 0.27 0.27 0.29 0.29 0.32 0.34 0.35 0.36
Hainan 0.40 0.41 0.42 0.43 0.44 0.45 0.46 0.47
Sichuan 0.27 0.28 0.29 0.30 0.32 0.33 0.34 0.36
Guizhou 0.24 0.24 0.24 0.25 0.26 0.27 0.27 0.28
Yunnan 0.24 0.25 0.26 0.27 0.28 0.30 0.31 0.32
Shaanxi 0.32 0.34 0.35 0.36 0.37 0.37 0.39 0.41
Gansu 0.24 0.25 0.26 0.27 0.29 0.30 0.31 0.32
Qinghai 0.35 0.36 0.38 0.38 0.38 0.39 0.39 0.40
Ningxia 0.33 0.33 0.34 0.37 0.41 0.42 0.43 0.44
Xinjiang 0.34 0.34 0.34 0.34 0.35 0.37 0.38 0.39

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Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 0.85 0.85 0.86 0.86 0.86 0.86 0.91
Tianjin 0.77 0.78 0.80 0.81 0.82 0.82 0.87
Hebei 0.42 0.44 0.44 0.46 0.47 0.48 0.53
Shanxi 0.45 0.46 0.48 0.50 0.51 0.53 0.58
Inner Mongolia 0.52 0.53 0.56 0.57 0.58 0.59 0.64
Liaoning 0.60 0.60 0.62 0.64 0.66 0.66 0.71
Jilin 0.53 0.53 0.53 0.53 0.54 0.54 0.59
Heilongjiang 0.55 0.56 0.56 0.57 0.57 0.57 0.62
Shanghai 0.89 0.89 0.89 0.89 0.89 0.90 0.95
Jiangsu 0.54 0.56 0.61 0.62 0.63 0.64 0.69
Zhejiang 0.58 0.58 0.62 0.62 0.63 0.64 0.69
Anhui 0.41 0.42 0.43 0.45 0.47 0.48 0.53
Fujian 0.53 0.55 0.57 0.58 0.60 0.61 0.66
Jiangxi 0.41 0.43 0.44 0.46 0.48 0.49 0.54
Shandong 0.48 0.48 0.50 0.51 0.52 0.54 0.59
Henan 0.36 0.38 0.39 0.41 0.42 0.44 0.49
Hubei 0.45 0.46 0.50 0.52 0.54 0.55 0.60
Hunan 0.42 0.43 0.43 0.45 0.47 0.48 0.53
Guangdong 0.63 0.63 0.66 0.67 0.67 0.68 0.73
Guangxi 0.38 0.39 0.40 0.42 0.44 0.45 0.50
Hainan 0.48 0.49 0.50 0.51 0.52 0.53 0.58
Sichuan 0.37 0.39 0.40 0.42 0.44 0.45 0.50
Guizhou 0.29 0.30 0.34 0.35 0.36 0.38 0.43
Yunnan 0.33 0.34 0.35 0.37 0.39 0.40 0.45
Shaanxi 0.42 0.44 0.46 0.47 0.50 0.51 0.56
Gansu 0.34 0.35 0.36 0.37 0.39 0.40 0.45
Qinghai 0.41 0.42 0.45 0.46 0.47 0.49 0.54
Ningxia 0.45 0.46 0.48 0.50 0.51 0.52 0.57
Xinjiang 0.40 0.40 0.43 0.44 0.44 0.44 0.49

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Table 9. The value of fiscal spending.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing 0.20 0.23 0.22 0.23 0.25 0.25 0.19 0.21
Tianjin 0.13 0.14 0.14 0.15 0.15 0.15 0.15 0.16
Hebei 0.09 0.10 0.10 0.11 0.11 0.11 0.12 0.13
Shanxi 0.15 0.18 0.19 0.21 0.21 0.22 0.22 0.22
Inner Mongolia 0.19 0.23 0.25 0.25 0.26 0.25 0.21 0.22
Liaoning 0.12 0.14 0.14 0.15 0.16 0.18 0.18 0.19
Jilin 0.16 0.18 0.18 0.18 0.20 0.21 0.20 0.21
Heilongjiang 0.13 0.15 0.15 0.15 0.16 0.15 0.18 0.19
Shanghai 0.15 0.16 0.17 0.20 0.22 0.22 0.20 0.21
Jiangsu 0.08 0.09 0.09 0.10 0.11 0.11 0.11 0.12
Zhejiang 0.08 0.10 0.11 0.12 0.11 0.11 0.11 0.11
Anhui 0.11 0.13 0.14 0.14 0.15 0.15 0.17 0.20
Fujian 0.09 0.10 0.09 0.10 0.10 0.10 0.11 0.12
Jiangxi 0.12 0.14 0.16 0.16 0.16 0.16 0.17 0.19
Shandong 0.08 0.09 0.09 0.10 0.10 0.09 0.10 0.10
Henan 0.10 0.10 0.11 0.12 0.12 0.13 0.14 0.15
Hubei 0.10 0.11 0.11 0.11 0.12 0.12 0.16 0.17
Hunan 0.10 0.12 0.13 0.14 0.16 0.16 0.16 0.18
Guangdong 0.13 0.14 0.14 0.14 0.14 0.14 0.11 0.12
Guangxi 0.13 0.17 0.19 0.18 0.19 0.18 0.18 0.20
Hainan 0.14 0.15 0.17 0.18 0.19 0.20 0.20 0.24
Sichuan 0.12 0.15 0.16 0.16 0.17 0.17 0.19 0.21
Guizhou 0.22 0.28 0.29 0.28 0.31 0.33 0.31 0.35
Yunnan 0.22 0.25 0.25 0.26 0.27 0.26 0.26 0.29
Shaanxi 0.18 0.21 0.22 0.20 0.22 0.22 0.22 0.23
Gansu 0.20 0.24 0.26 0.26 0.27 0.28 0.27 0.30
Qinghai 0.29 0.38 0.39 0.36 0.35 0.36 0.40 0.44
Ningxia 0.25 0.35 0.38 0.32 0.32 0.35 0.32 0.34
Xinjiang 0.16 0.19 0.24 0.23 0.22 0.24 0.26 0.26

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Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 0.21 0.22 0.22 0.23 0.23 0.23 0.24
Tianjin 0.17 0.18 0.18 0.19 0.19 0.20 0.21
Hebei 0.14 0.15 0.16 0.17 0.17 0.17 0.18
Shanxi 0.23 0.23 0.26 0.26 0.25 0.25 0.26
Inner Mongolia 0.24 0.25 0.23 0.26 0.24 0.23 0.24
Liaoning 0.20 0.20 0.21 0.21 0.21 0.21 0.22
Jilin 0.22 0.23 0.25 0.25 0.23 0.23 0.24
Heilongjiang 0.22 0.23 0.26 0.27 0.25 0.25 0.26
Shanghai 0.21 0.22 0.22 0.23 0.22 0.22 0.23
Jiangsu 0.13 0.13 0.14 0.15 0.14 0.14 0.15
Zhejiang 0.12 0.12 0.14 0.14 0.13 0.14 0.15
Anhui 0.22 0.24 0.26 0.27 0.26 0.25 0.26
Fujian 0.12 0.13 0.14 0.15 0.15 0.16 0.17
Jiangxi 0.22 0.24 0.25 0.27 0.26 0.27 0.28
Shandong 0.10 0.11 0.12 0.13 0.13 0.13 0.14
Henan 0.15 0.16 0.18 0.18 0.19 0.19 0.20
Hubei 0.18 0.18 0.19 0.20 0.19 0.20 0.21
Hunan 0.19 0.20 0.21 0.22 0.21 0.21 0.22
Guangdong 0.12 0.12 0.14 0.15 0.14 0.15 0.16
Guangxi 0.22 0.23 0.26 0.27 0.25 0.25 0.26
Hainan 0.29 0.33 0.35 0.38 0.36 0.35 0.36
Sichuan 0.27 0.28 0.29 0.29 0.27 0.26 0.27
Guizhou 0.38 0.41 0.42 0.49 0.48 0.45 0.46
Yunnan 0.31 0.34 0.37 0.41 0.40 0.40 0.41
Shaanxi 0.26 0.27 0.27 0.29 0.27 0.25 0.26
Gansu 0.36 0.39 0.43 0.43 0.41 0.41 0.42
Qinghai 0.46 0.51 0.69 0.72 0.69 0.65 0.66
Ningxia 0.37 0.39 0.41 0.42 0.41 0.39 0.40
Xinjiang 0.30 0.32 0.40 0.42 0.41 0.41 0.42

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Table 10. The value of economic openness.
2000 2001 2002 2003 2004 2005 2006 2007
Beijing 1.89 1.72 1.53 1.77 2.14 2.40 0.82 0.79
Tianjin 0.98 0.92 1.03 1.18 1.42 1.49 1.45 1.32
Hebei 0.09 0.09 0.10 0.12 0.16 0.15 0.19 0.23
Shanxi 0.10 0.10 0.11 0.13 0.18 0.15 0.18 0.25
Inner Mongolia 0.17 0.12 0.13 0.13 0.14 0.15 0.13 0.14
Liaoning 0.38 0.35 0.36 0.42 0.47 0.49 0.52 0.54
Jilin 0.13 0.15 0.15 0.23 0.22 0.18 0.19 0.20
Heilongjiang 0.09 0.09 0.10 0.11 0.13 0.15 0.20 0.23
Shanghai 1.12 1.11 1.21 1.72 2.12 2.05 1.93 2.01
Jiangsu 0.49 0.50 0.61 0.88 1.14 1.21 1.30 1.31
Zhejiang 0.43 0.45 0.51 0.65 0.75 0.78 0.95 0.96
Anhui 0.10 0.10 0.11 0.14 0.15 0.16 0.18 0.20
Fujian 0.49 0.48 0.55 0.62 0.75 0.74 0.79 0.75
Jiangxi 0.07 0.06 0.06 0.09 0.10 0.10 0.14 0.17
Shandong 0.27 0.28 0.30 0.35 0.40 0.41 0.48 0.48
Henan 0.04 0.04 0.05 0.06 0.08 0.07 0.08 0.09
Hubei 0.07 0.07 0.07 0.09 0.10 0.12 0.15 0.15
Hunan 0.06 0.06 0.06 0.07 0.10 0.09 0.10 0.10
Guangdong 1.66 1.51 1.72 2.00 2.17 2.19 1.93 1.90
Guangxi 0.09 0.07 0.09 0.11 0.13 0.13 0.15 0.16
Hainan 0.23 0.28 0.28 0.32 0.42 0.27 0.30 0.52
Sichuan 0.07 0.07 0.08 0.10 0.12 0.11 0.12 0.13
Guizhou 0.06 0.05 0.05 0.07 0.09 0.07 0.09 0.11
Yunnan 0.08 0.08 0.09 0.10 0.13 0.13 0.15 0.17
Shaanxi 0.12 0.10 0.10 0.11 0.13 0.13 0.15 0.14
Gansu 0.05 0.07 0.07 0.09 0.11 0.14 0.18 0.20
Qinghai 0.06 0.06 0.05 0.08 0.12 0.07 0.14 0.08
Ningxia 0.15 0.17 0.12 0.16 0.20 0.17 0.21 0.21
Xinjiang 0.16 0.11 0.15 0.25 0.25 0.30 0.31 0.39

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Continued
2008 2009 2010 2011 2012 2013 2014
Beijing 2.02 1.40 1.68 1.78 1.58 1.49 1.54
Tianjin 1.11 0.69 0.74 0.72 0.65 0.62 0.67
Hebei 0.19 0.12 0.17 0.17 0.13 0.13 0.18
Shanxi 0.17 0.08 0.12 0.10 0.08 0.08 0.13
Inner Mongolia 0.10 0.06 0.06 0.07 0.05 0.05 0.10
Liaoning 0.46 0.32 0.36 0.34 0.30 0.29 0.34
Jilin 0.18 0.12 0.16 0.16 0.15 0.13 0.18
Heilongjiang 0.23 0.13 0.20 0.24 0.19 0.18 0.23
Shanghai 1.84 1.38 1.66 1.65 1.44 1.35 1.40
Jiangsu 1.06 0.76 0.92 0.84 0.70 0.63 0.68
Zhejiang 0.78 0.60 0.75 0.72 0.61 0.60 0.65
Anhui 0.19 0.12 0.16 0.16 0.16 0.16 0.21
Fujian 0.64 0.50 0.60 0.63 0.56 0.53 0.58
Jiangxi 0.17 0.13 0.19 0.22 0.18 0.18 0.23
Shandong 0.42 0.31 0.38 0.39 0.34 0.33 0.38
Henan 0.08 0.05 0.06 0.09 0.12 0.13 0.18
Hubei 0.16 0.10 0.14 0.14 0.10 0.10 0.15
Hunan 0.09 0.06 0.08 0.08 0.07 0.07 0.12
Guangdong 1.53 1.17 1.35 1.28 1.17 1.18 1.23
Guangxi 0.15 0.14 0.15 0.16 0.16 0.16 0.21
Hainan 0.26 0.23 0.35 0.40 0.36 0.32 0.37
Sichuan 0.15 0.12 0.15 0.20 0.23 0.23 0.28
Guizhou 0.09 0.05 0.05 0.07 0.07 0.07 0.12
Yunnan 0.14 0.10 0.15 0.14 0.15 0.15 0.20
Shaanxi 0.11 0.08 0.10 0.09 0.07 0.09 0.14
Gansu 0.16 0.08 0.15 0.14 0.11 0.11 0.16
Qinghai 0.06 0.04 0.05 0.04 0.04 0.05 0.10
Ningxia 0.15 0.07 0.10 0.09 0.07 0.09 0.14
Xinjiang 0.44 0.23 0.27 0.27 0.24 0.23 0.28

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Table 11. Variable descriptive statistical results.
Variable Mean Median Max imu m M i n imu m Std. Dev. Observations
INE 2.9805 2.8500 4.7600 1.8900 0.5999 435
C 0.1618 0.1342 0.5316 0.0612 0.0907 435
LC 0.0996 0.0842 0.3236 0.0442 0.0497 435
SC 0.0381 0.0267 0.3137 0.0078 0.0427 435
CC 0.0055 0.0044 0.0256 0.0016 0.0034 435
PGDP 2.4027 1.8501 9.9607 0.2759 1.8937 435
UR 0.4723 0.4466 0.9188 0.2320 0.1542 435
FI 0.2148 0.1979 0.7164 0.0768 0.0994 435
OPEN 0.3995 0.1583 2.3998 0.0413 0.5055 435
The data source: China statistical yearbook, The Chinese tax yearbook.
model. When test results reject the null hypothesis, we choose the fixed effects regression model. Hausman test
is used to select fixed effects regression model or random effects regression models. The null hypothesis choo se
random effects regression model. When test results reject the null hypothesis, we choose the fixed effects re-
gression model.
We can see from Table 12, Likelihood ratio test and Hausman test results show that the P value of the model
1 - 3 is far less than 1% under 1% significance level. We should reject the null hypothesis and choose fixed effects
regression model. The results of Table 12 show re gress ion r es ults of m odel 1 - 3 by usi ng fixe d effe cts model.
As the res ults of Table 13 show, all variables get through t test. Mo st variables reject the null h ypothesis un-
der 1% significance level in model 1 - 3. Part of variables rejects the null hypothesis under 5% or 10% signific-
ance level. R-squared and Adjusted R-squared of model 1 - 3 reach more than 85%. It shows that the whole
model has strong linear degree. F statistics of model 1 - 3 are significant under 1% significance level. At the
same time, each variable symbol of model 1 - 3 is consistent with normal expectations. We can undertake eco-
nomic analysis.
Model 1 is mainly used to demonstrate the relationship of Chinese actual situation and the “inverted U” hy-
pothesis. According to the empirical results, the coefficient of per capita GDP is positive and the coefficient of
per capita GDP square is negative. The test have good results. It shows that the relationship of income gap be-
tween urban and rural areas and economic development presents the Kuznets effect. With development of
economy, income gap between urban and rural areas deteriorate in the beginning, and then improve.
In model 2, coefficient of tax con tribution (C) is 3.38.Th e test has passed. It show that a positive relationship
between income gap and tax contribution. If tax contribution increases 1%, income gap between urban and rural
areas will expand 3.38%. General theory is that the relationship of tax contribution and income gap presents an
opposite relationship, we can use tax to adjust residents’ income gap, namely “high contribution -high fair, low
contribution -low fair”. Tax contribution is higher, it means national government has a stronger ability to con-
centrate financial resources and use resources. Then national government can provide more public goods to im-
prove people’s life and increase degree of economic and social justice. And low tax contribution is not benefi-
cial to economic and social justice. Actual situation of other countries also can confirm this point of view. But
the situation is different in our country. Since 2000, tax contribution of our country has been growing all the
time, growth from 12.8% in 2000 to 19.6% in 2014. However, income gap between urban and rural areas have
no decline, urban and rural income ratio rose from 2.76 in 2000 to 3.33 in 2009, down slightly after 2009, but
still maintaining high proportio n. At present, our country exist the objective fa ct of “high contribution-low fair”.
This is due to turnover tax contribution is the most important part in the tax contribution. In 2014, our country’s
tax contribution is 19.6%. Turnover tax contribution is 10.1%. Income tax contribution is just 5.0%. Turnover
tax contribution is more than 50% of total tax contribution, nearly twice as much as income tax contribution.
High turnover tax contribution is not conducive to narrow income gap between urban and rural areas. In the
“high contribution -high fair” country, income tax contribution is the most important part in the tax contribution.
high tax contribution is conducive to narrow the income gap between urban and rural areas.

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Table 12. Likelihood ratio test and Hausman test.
Likelihood ratio test Hausman test
Model 1 105.3096*** 56.1250***
Model 2 63.8690*** 192.1945***
Model 3 63.8993*** 186.2075***
Note: * * *, * * and *, is respe ctively at 1%, 5% and 10% significance levels.
Table 13. Regression results analysis .
Variable Model 1 Model 2 Model 3
Constant 2.9763*** (282.9252) 1.9086*** (14.1188) 2.4290*** (19.1040)
LOG(PGDP) 0.1284*** (6.3165) 0.1780*** (4.9300) 0.1601*** (4.3665)
(LOG(PGDP))2 −0.0754*** (−6.1903) −0.1532*** (−11.1603) −0.1051*** (−6.5018)
C 3.3822*** (10.3095)
LC 5.0003*** (9.1589)
SC 1.6448*** (3.3063)
CC −10.4574** (−2.3374)
UR 1.1518*** (5.3147) 1.1392*** (4.7438)
FI −2.4103*** (−8.9478) −2.4027*** (−8.5981)
OPEN 0.0688* (1.6928) 0.0781** (1.9769)
Observation 435 435 435
R-squared 0.9149 0.9329 0.9367
Adjusted R-squared 0.9081 0.9266 0.9305
F-statistic 134.4331 147.0548 151.6095
Prob (F-statistic) 0.0000 0.0000 0.0000
Note: * * *, * * and *, is respe ctively at 1%, 5% and 10% significance levels.
Model 3 shows turnover tax contribution (LC), income tax contribution (SC) and property tax contribution
(CC) on the influence of income gap between urban and rural areas. Coefficients of turnover tax contribution
and income tax contribution are positive. They are 5.00 and 1.64 respectively. Coefficient of property tax con-
tribution is negative, it is 10.46. This means that improvement of turnover tax contribution and income tax con-
tribution will expand income gap between urban and rural areas; Improvement of property tax contribution will
narrow the income gap between urban and rural areas. Coefficient of property tax contribution is very big. It in-
dicates that property tax contribution has an obvious effect of narrowing income gap. Yet property tax income of
our country is very small at present. Property tax can’t play a role. The reason why income tax contribution ex-
pand income gap between urban and rural areas is that income tax system of our country is not perfect, such as
adopting classified collection of individual income tax system, unreasonable expense deduction, unscientific tax
rate structure, lack of tax collectio n and administration, etc.
Other control variables in model 2 and 3. Coefficient of urbanization (CZ) is positive. It will expand income
gap between urban and rural areas and has a significant effect. There are two reasons: one is rich rural reside nts
may transform into urban residents; the other one is economic results that create by rural migrant farmers mainly
stay in the city, and urban residents enjoy most part of them. It leads to expand income gap between the urban
and rural areas. Fiscal expenditure (FI) is helpful to narrow the income gap between urban and rural areas. This
is because since 2002, Chinese fiscal spending pay more attention to the people’s livelihood. The government

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increase spending on basic education, health care, social security, etc. Fiscal expenditure on rural residents is
also increasing year by year. The government is gradually changing the fiscal policy of “urban bias”. We can see
from the empirical results, fiscal expenditure is indeed narrowing income gap between urban and rural areas.
Economic openness (OPEN) has the function of expanding income gap between urban and rural areas. With
Chinese economic developing, foreign trade focuses more on production technology and capital intensive prod-
ucts. Production technology requirement of these high-tech products is high. But the salary is also well. High
technical level of workers is mainly concentrated in cities. Improvement of economic openness contributes to
expand income gap bet we en urba n a n d rural areas.
6. Conclusions and Policies
We can draw the conclusion from above theoretical and empirical analysis: improvement of tax contribution is
not conducive to narrow the income gap between urban and rural areas in our country. Improvement of turnover
tax contribution and income tax contribution will expand income gap between urban and rural areas. Improve-
ment of the property tax contribution will narrow income gap between urban and rural areas. Fiscal spending’
increase will narrow the income gap between urban and rural areas. The urbanization’ and economic openness’
increase will expand the income gap between urban and rural areas. Based on the above analysis, we can put
forward the following Suggestions:
We need to deepen our country’s curren t structural tax cuts. “Structural tax cuts” means in order to achieve a
specific goal, according to specific groups and specific tax, it reduce tax burden level. Since 2008, the structural
tax cuts had played a role in adjusting income gap between urban and rural areas, but its role is limited. It failed
to reduce macro tax burden and change tax structure. So we should continue to deepen reform of structural tax
cuts. First of all, we should vigorously promote to replace the business tax with the value-added tax, reduce
proportion of turnover tax, inhibit further increase of macro tax burden, and improve income level of resident
department. The second, we should adjust current consumption tax system, expand consumption tax items, and
adjust consumption tax rate. The third, we should improve personal income tax system, establish taxation pat-
tern in accordance with Chinese national conditions, improve the system of expense deduction, and formulate
scientific and reasonable tax rate. The fourth, we should improve the system of property tax, reform property tax
system, and impose inheritance tax and gift tax.
It is very important for us to vigorously promote fiscal policies about people’s livelihood. Compared with ur-
ban fiscal expenditure, financial expenditure in rural areas is still limited. Therefore, it is necessary to further in-
crease proportion of fiscal expenditure and optimize structure of expenditure for supporting agriculture. In addi-
tion, it is also important to increase the rural education investment. We should ensure that rural education and
urban education are equality. In order to realize urban and rural residents without differentiation, we need to im-
prove the rural social security sys tem.
In the process of urbanization, we should reform the household registration system, establish unified house-
hold registration system, and establish a social security network across the country. It is beneficial for urban and
rural labor to realize barrier-free flow. At the same time, it also could realize urban and rural public service
equalization.
In terms of economic openness, we should maintain a certain proportion of labor-intensive products. It is
beneficial to ensure income level of rural unskilled labor. On the other hand, we should actively improve the
skill levels of rural labor force to match production of capital-intensive products. That is a good way to ensure
improvement of income level of rural residents, then narrow income gap between urban and rural areas.
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