Study on the Influence of Social Factors on Economic Income Under
the Theory of Regional Coordination
Qiuyue Chen and Yuchen Hu
College of Economics Sichuan Agricultural University Chengdu, China
Keywords: Tibet-Related Areas, Regional Coordination, Social Factors, Economic Income, Resource Allocation.
Abstract: In the context of regional coordination, how to reasonably allocate the resources needed for the
development of social aspects and economic aspects, such as education, communication, and transportation,
has become an important link in achieving the goal of rural revitalization. Based on research data from
Tibet-related areas in four provinces and Lhasa City in Tibet Autonomous Region, this paper empirically
investigates the influence of social factors on economic income in ethnic areas using least squares
regression and propensity matching score model (PSM). The results show that education and
communication levels have a significant positive effect on farm household income in both Tibet and Tibet-
related areas of the four provinces. Through Pearson correlation matrix analysis between Tibetan
autonomous prefectures and representative cities in Tibet, it is found that there is a synergistic correlation
between school-age children's schooling rate, road transportation development, and farm household income
per capita in the two regions, which can promote joint development through a coordinated approach. Based
on this, the article proposes to increase the investment and support for education in ethnic regions, further
improve the construction of telecommunication and communication systems in ethnic regions, and establish
a comprehensive and diversified regional synergy system.
1 INTRODUCTION
In recent years, the government has been paying
more and more attention to the economic and social
development of ethnic regions and has taken a series
of policy actions to provide support and guarantee
for the economic development and livelihood
improvement of ethnic regions in China. With the
in-depth implementation of various policies and
guidelines dedicated to improving social
development, ethnic areas have been improving in
terms of education capability, communication level
and transportation development, and the overall
social development is getting better (Atasoy, 2013).
In the period of transition from precise poverty
alleviation to rural revitalization, the level of farm
household income is still one of the indicators that
needs to be focused on, which reflects the
development of economic level in ethnic areas. At
this stage, the influence of social factors on economy
and the reasonable allocation of resources between
the two have become one of the key concerns in
ethnic areas. Based on this, an in-depth study of the
influence of social factors on farm household
income in ethnic regions is of great significance for
regional development, and the performance of such
influence under the theory of regional coordination
is also a focus of our attention.
2 THEORY AND MECHANISM
ANALYSIS
Firstly, social factors in ethnic areas can promote
farmers' income increase by improving population
quality, innovating production methods, enhancing
production skills and enriching life experiences.
Second, social factors in ethnic areas can promote
farmers' income increase by accelerating information
transfer and cultural exchange and further enriching
the ways of income generation (Fleisher, 2009).
Lack of knowledge and poor communication are
important reasons limiting the development of ethnic
areas. High-efficiency communication and
developed transportation facilities will speed up the
exchange and dissemination of knowledge and
technology, which will help increase the economic
Chen, Q. and Hu, Y.
Study on the Influence of Social Factors on Economic Income Under the Theory of Regional Coordination.
DOI: 10.5220/0011730800003607
In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 93-98
ISBN: 978-989-758-620-0
Copyright
c
2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
93
income of households. Finally, social factors in
ethnic areas can promote the increase of household
economic income of farmers through inter-regional
coordination and mutual synergy (Tokila, 2011). At
the theoretical level, social factors in ethnic areas
affect farm household income from the above three
aspects, but the effect of their influence still needs to
be tested empirically. In this paper, we will take the
coordinated development of Tibet and Tibet-related
regions in four provinces as the goal, analyze the
influence of social factors in ethnic regions on
farmers' household economic income through
multiple regression and PSM models, explore the
impact-related points that can be coordinated
between Tibet and Tibet-related regions in four
provinces, and further explore the path of
coordinated and coordinated development of Tibetan
society and economy in Tibet-related regions in four
provinces (Chen, 2008).
3 DATA FOUNDATION AND
MODEL BUILDING
3.1 Data Sources
The data used in this paper come from micro
household data from field research in Tibetan-
related areas in four provinces and Tibet, as well as
macro data from the Sichuan Statistical Yearbook
2020, Qinghai Statistical Yearbook 2020, Gansu
Statistical Yearbook 2020, Yunnan Statistical
Yearbook 2020, and the 2020 National Economic
and Social Development Statistical Bulletin of
representative cities and Tibetan autonomous
prefectures. Among them, the research data
specifically include Hongyuan County, Ganzi
County, Ruoerge County, Markang County, Dafu
County, and Danba County in Tibet-related areas of
Sichuan; Diebe County, Zhuoni County, and Xiahe
County in Tibetan areas of Gansu; Deqin County,
Shangri-La County, and Weixi County in Tibetan
areas of Yunnan; GuiDe County and Duran County
in Tibetan areas of Qinghai and Lhasa City in Tibet
Autonomous Region. A total of 480 questionnaires
were distributed in the survey in Tibet-related areas
of the four provinces, with 454 valid questionnaires
and an actual recovery rate of 94.58%; a total of 780
questionnaires were distributed in the survey in
Lhasa, with 745 valid questionnaires and an actual
recovery rate of 95.5% (Jiang, 2012).
3.2 Explanatory Variables
3.2.1 For Author/S of Only One Affiliation
(Heading 3): To Change the Default,
Adjust the Template as Follows
In this paper, the economic income of farm
households was selected as the explanatory variable,
and the raw data were standardized in order to
eliminate the effects of differences in the average
income levels of different villages and the
measurement unit scale. The formula is:
𝑎 =
𝑎
−𝑎
𝑠
(1)
where, i denotes the states, a denotes the indicator to
be standardized, denotes the mean of this indicator
in Tibetan areas or Tibet-related areas in four
provinces, and s denotes the standard deviation.
3.2.2 Core Explanatory Variables
In this paper, social factors related to farm
households were selected as the core explanatory
variables, firstly, telecommunication network
situation included whether telecommunication was
connected (1=connected, 0=not connected) and
source of electricity (1=powered by national grid,
0=self-generated); education situation was selected
as the core explanatory variable for education level
(1=uneducated, 2=not attended school but could
read and write, 3=graduated from elementary school,
4=graduated from junior high school, 5=general
high school, 6= secondary school, vocational high
school, 7=college undergraduate, 8=university
undergraduate, 9=graduate and above), and
transportation status was selected as the core
explanatory variables (1=yes, 0=no).
3.2.3 Control variables
In this paper, health status (1=very bad, 2=bad,
3=fair, 4=good, 5=very good), ethnicity (1=Tibetan,
2=other), number of laborers, number of household
yaks, employment status, and agricultural insurance
coverage (1=insured, 2=uninsured) were selected as
control variables.
3.3 Model Establishment
In order to avoid the problem of multicollinearity
and eliminate the effects caused by differences in
magnitudes, a multiple linear regression model (1)
was established after standardizing some of the
variables. Further, an OLS+ robust standard error
model (2) was established to deal with the
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heteroskedasticity of the model and make the model
more robust. In addition, this paper also matches
each household with no telecommunication network
or no education with a propensity score for a
household with telecommunication network or
education, so that these two households are not
identical only at the level of whether they have
telecommunication network or education, and other
variables are basically the same.
For the entire group of farmers, this net effect is
called the average treatment effect (ATT) and is
expressed as:
ATT=E(Y
1
|D=1)-E(Y
0
|D=1)=E(Y
1
-Y
0
|D=1) (2)
where is the household economic income of the
farmer with a better telecommunication network and
is the household economic income of the farmer
with a worse telecommunication network.
4 EMPIRICAL RESULTS AND
ANALYSIS
4.1 Empirical Results and Analysis
In this paper, the effect of social development
factors on household economic income was tested
with farm household economic income as the
dependent variable and household social
characteristics factors as the independent variables,
and the results are shown in Table 1. From model (1)
and model (2), it can be seen that social
characteristics such as telecommunication network
status and education level significantly affect
household economic income.
Comparing the empirical results of the four
Tibetan provinces with those of Tibet, it is easy to
find that, in terms of telecommunication, the
economic income of farm households with
telecommunication networks in villages in the four
Tibetan provinces is significantly higher than those
without telecommunication networks, while the
higher the economic income of farm households
with electricity sources in Tibet tends to be from the
national grid, indicating that when farm households
have telecommunication networks in their villages,
their households can receive faster and more timely
Table 1: Empirical results.
Projects
Tibet-related areas in four
p
rovinces
Tibet
Variables Model (1) Model (2) Model (1) Model (2)
Standardized household economic income
Telecom Network
Situation
0.337 0.337** 0.396 0.396***
(0.307) (0.152) (0.468) (0.105)
Education level
0.178*** 0.178*** 0.144*** 0.144***
(0.054) (0.060) (0.045) (0.052)
Road condition
0.431 0.431* 0.246 0.246
(0.420) (0.220) (0.321) (0.323)
Health status
0.200*** 0.200*** -0.042 -0.042
(0.058) (0.049) (0.086) (0.087)
Ethnicity
0.104
(0.438)
0.104
(0.175)
-1.393*
(0.742)
-1.393
(1.463)
Labor force and
Employment
0.324***
(0.059)
0.324***
(0.066)
0.055*
(0.029)
0.055
(0.034)
Total number of yaks
0.007*** 0.007*** 0.015*** 0.015***
(0.003) (0.002) (0.003) (0.004)
Agricultural Insurance
-0.304*** -0.304** -0.246** -0.246**
(0.117) (0.129) (0.120) (0.117)
Study on the Influence of Social Factors on Economic Income Under the Theory of Regional Coordination
95
In particular, the national power supply is more
efficient than the farmers' own power generation, so
it is conducive to the production and living of the
farmers' households, thus promoting the increase of
their economic income. Therefore, Tibet and the
Tibet-related regions in the four provinces can
promote the overall economic development of the
region by improving the level of telecommunication
(especially the national electricity supply and the
popularity of intelligent communication). As for
education, both regions show a significant positive
effect of education level on household economic
income, indicating that both regions can further
promote coordinated regional economic
development by improving education services,
enriching farmers and herdsmen's knowledge, and
innovating production methods and efficiency while
improving population quality. In terms of road
construction, the road construction in the four Tibet-
related provinces positively affects the economic
income of farm households at a statistical level of
10%. On the one hand, the development of
transportation will speed up the exchange and
synergy among regions and drive the overall
economic development of western ethnic regions,
thus promoting the improvement of household
economic income; on the other hand, the
construction of roads will affect the labor force
transfer to a certain extent, and the development of
transportation will drive the outflow of labor, and its
joint offset with the promotion effect brought by
regional linkage shows positive influence, which
will promote the house-hold economic income of
farmers in a comprehensive way (Liu, 2021). This
effect is not significant in Tibetan region, so Tibet
can learn from the highlights of road construction in
Tibet-related areas of four provinces to further
improve road transportation service capacity, thus
further increasing the economic income of local
farm households.
4.2 Robustness Test
In order to ensure the scientificity of the research
results, this paper adopts the method of replacing
variables for robustness testing. Jing You et al.
(2020) had used the indicator of years of education
to measure the educational attainment of the
research subjects in their study, so this paper will
replace the educational attainment with years of
education, i.e., the original categorical variables will
be replaced by the specific length of years of
education. After the OLS+ robustness standard error
model regression, it was found that the significance
levels and coefficient signs of the core explanatory
variables remained basically unchanged, and social
factors such as telecommunication network status and
education level remained significant, indicating that
the results were more robust, as shown in Table 2.
Table 2: Robustness test results.
Tibet-related areas
in four provinces
Tibet
Family economic income
Telecommunica
tions Network
Status
0.324** 0.399***
(0.133) (0.105)
Years of
education
0.040** 0.050***
(0.020) (0.018)
Road condition
0.469** 0.265
(0.224) (0.319)
Health status
0.209*** 0.042
(0.048) (0.088)
Ethnicity
0.139 -1.379
(0.157) (1.468)
Labor force and
employment
0.326*** 0.052
(
0.066
)
(
0.034
)
Total number of
yaks
0.007*** 0.015***
(0.002) (0.004)
Agricultural
Insurance
-0.284** -0.247**
(0.130) (0.117)
4.3 Propensity Score Matching Model
(PSM) Analysis of The Effect of
Social Factors on Economic Income
The results of the propensity score matching model
(PSM) analysis on social factors for income increase
in Tibet and Tibet-related areas in four provinces
using stata15.0 software can be obtained as shown in
Table 3. In terms of telecommunication network, the
average treatment effect ATT measured by the one-
to-one matching method, caliper matching method,
and kernel matching method in the four Tibetan
provinces and Tibet are all significantly positive at
the 1% level, which indicates that the development
of communication business can significantly
increase the economic income of farm households.
In terms of education level, the average treatment
effect ATT measured by the nuclear matching
method in the four Tibetan provinces is significantly
positive at the 10% level, and the results measured
by the three methods in Tibet are significantly
positive at the 5% level or more, which indicates
that the development of education in the two regions
can increase the economic income of farm
households to a certain extent (Sun, 2016). Taking
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96
Table 3: PSM Propensity Score Matching Table.
Variables Area
Before and after
matchin
g
unmatched matched ATT T
Telecommu
nications
Network
Tibet-related
areas in four
provinces
One to One Matching 0.527 0.056 0.0432 3.75
***
Caliper Matching 0.527 0.110 0.0226 2.96
***
Nuclear Matching 0.527 0.097 0.0643 3.66
***
Tibet
One to One Matching 0.039 0.267 0.0107 3.48
***
Caliper Matching 0.039 0.014 0.0107 3.25
***
Nuclear Matching 0.039 0.008 0.0107 3.54
***
Education
level
Tibet-related
areas in four
provinces
One to One Matching 0.050 0.020 0.1172 -0.27
Caliper Matching 0.050 0.002 0.1259 1.04
Nuclear Matching 0.050 0.006 0.1172 1.64
*
Tibet
One to One Matching 0.032 0.008 0.2125 2.29
**
Caliper Matching 0.032 0.002 0.2192 2.59
***
Nuclear Matching 0.032 0.001 0.2125 2.51
**
Roads
Tibet-related
areas in four
provinces
One to One Matching 0.119 0.075 0.1333 -0.61
Caliper Matching
0.119
0.036 0.2343 -0.85
Nuclear Matching
0.119
0.058 0.2165 -1.09
Tibet
One to One Matching 0.057 0.020 -0.0383 -0.22
Caliper Matching 0.057 0.003 -0.0383 -1.06
Nuclear Matching 0.057 0.018 -0.0383 -0.71
the caliper matching method to test the development
of telecommunication networks in Tibetan-related
areas in four provinces as an example, the calculated
mean treatment effect ATT= 0.0226 and is
significantly positive at the 1% level of significance,
indicating that the economic income of farm
households receiving education in Tibetan-related
areas in four provinces is 4.3% (i.e., 0.0226/ 0.527)
higher than that of farm households not receiving
education.
4.4 Correlation Analysis under
Regional Synergy Theory
In order to study the regional synergistic correlation
between Tibet-related areas in four provinces and
Tibet, this paper adopts the Pearson correlation
coefficient to measure the correlation between
representative indicators of social and economic
development in the two regions, which is calculated
as follow:
r=
N
∑
x
i
y
i
-
∑
x
i
∑
y
i
N
∑
x
i
2
-(
∑
x
i
)
2
N
∑
y
i
2
-(
∑
y
i
)
2
(3)
The correlation analysis shows that there is a
strong positive correlation between the per capita
disposable income of farming households in the four
Tibetan-related provinces and the Tibet Autonomous
Region and the schooling rate of school-age
children, and there is a strong positive correlation
between the road density in the four Tibetan-related
provinces and the per capita disposable income of
farming households in the Tibet Autonomous
Region, so there is a certain connection between the
economic and educational development of the two
regions, especially in the stage of precise poverty
alleviation. The concept of "helping the poor to help
the wise" makes education in the two regions
popular and synergistic, and in the future, we can
combine more ethnic cultural characteristics to
promote the synergistic development of education
and culture in the two regions. At the same time, the
analysis of the matrix results also reveals that
although there is a positive correlation between the
road density in the Tibet-related areas of the four
provinces and the per capita disposable income of
farm households in the Tibet Autonomous Region,
the value of the correlation coefficient is not high
enough, and the positive role that the synergy effect
should play is not fully reflected, and the
transportation development in the Tibet-related areas
will, to a certain extent, drive the economic
development of the surrounding areas and even the
Tibetan region, thus increasing the per capita income
Study on the Influence of Social Factors on Economic Income Under the Theory of Regional Coordination
97
of farm households in Tibet (Su, 2016). In the
future, the two regions can strengthen the synergistic
linkage in transportation construction, and drive the
synergistic economic progress of the two regions
through the transportation development of Tibet-
related areas.
5 CONCLUSION
Focusing on the development of ethnic regions, this
study explores the influence of social factors on the
economic income of farm households from an
empirical perspective, based on micro research and
macro yearbook data. The results of the study show
that. (1) telecommunication network situation and
education level have a significant positive impact on
the economic income of farm households, and the
results of PSM propensity score matching are higher
in Tibet than in Tibet-related areas in four provinces.
(2) The effect of road transportation situation on
household economic income is not significant, and
the results obtained in Tibetan-related areas in the
four provinces are not significant. (3) There is a
strong positive correlation between school-age
children's enrollment rates in both regions, and there
is a strong positive correlation between road density
in Tibet-related regions and per capita disposable
income of farm households in the Tibet Autonomous
Region, and the two regions can strengthen
synergistic links in education and transportation in
the future. Based on the above analysis, this paper
proposes the following recommendations: First, the
state should continue to increase the investment and
support for education in rural areas, especially ethnic
areas. Second, local governments should further
improve the construction of telecommunication and
communication systems in ethnic areas to enhance
the ability of farmers and herdsmen to increase their
income. Third, grassroots management organizations
should establish a comprehensive and diversified
regional synergy system and strengthen the
coordination and cooperation of Tibetan-related
areas in the four provinces.
ACKNOWLEDGMENT
This paper is supported by the Sichuan University
Student Innovation Training Program project
"Research on the path of credit support for yak
industry from the perspective of supply and demand
- Hongyuan County as an example" (Project No.:
202010626099).
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