Factor Market Distortions and Rural Information Inequality:
Based on Empirical Analysis
Yongqi Zhang
1
and Jingzhi Cao
2
1
College of Economics, Sichuan Agricultural University, Chengdu, China
2
College of Management, Sichuan Agricultural University, Chengdu, China
Keywords: Factor Market Distortion, Rural Information Inequality, E-Commerce Index, Information Distance.
Abstract: Based on the 2018 China Household Tracking Survey data, the impact of factor market distortions on rural
information inequality is examined in conjunction with macro data at the regional level. The research findings
indicated that factor market distortions significantly increased rural information inequality based on indicators
of the breadth and intensity of Internet use. Heterogeneity analysis shows that labor factor market distortions
have a more significant impact. Introducing the e-commerce index as an indicator of information depth, the
study finds that the contribution of factor market distortions to rural information inequality still holds, and
this positive effect is more pronounced for the pioneer provinces. Heterogeneity analysis shows that focusing
on capital factor market distortions has a stronger debilitating effect on alleviating rural information
inequality. The above findings suggest that, in the context of the national implementation of the digital rural
development strategy to inject new momentum into the successful implementation of the rural revitalisation
strategy, the government should focus on different types of market distortions in addition to correcting the
overall distortions in factor markets. Only by doing so can the “digital divide” in rural China be effectively
broken.
1 INTRODUCTION
According to the 47th survey report released by
China Internet Network Information Center
(CNNIC), as of December 2020, the number of rural
Internet users in China reached 309 million, an
increase of 54.71 million from March 2020; the
Internet penetration rate in rural areas was 55.9%, an
increase of 9.7 percentage points from March 2020,
and the gap between urban and rural Internet
penetration rates shrank to 19.8 percentage points.
The rapid penetration of the Internet has to a certain
extent alleviated the information inequality in rural
areas, but at the same time, problems such as the poor
operation of Internet + finance and stagnant Internet
+ e-commerce projects need to be solved, and the
problem of unbalanced and insufficient development
of science and technology in China is still prominent.
The “ last mile” of network infrastructure in poor
areas has not been completely bridged, and the “
digital divide” still exists (Zhao, Zhou, 2019, Wang,
Zhao, 2020). In 2018, the Opinions of the State
Council of the Central Committee of the Communist
Party of China on the Implementation of Rural
Revitalization Strategy and the Strategic Plan for
Rural Revitalization (2018-2022) stated the
importance of implementing digital rural strategy and
expanding digital agriculture construction. In May
2019, the General Office of the CPC Central
Committee and the General Office of the State
Council issued the Outline of the Digital Countryside
Development Strategy, which clearly indicates that
digital countryside will become a strategic direction
for rural revitalization and accelerate the
development of information technology to achieve
the long-term goal of comprehensively promoting
and facilitating the development of agriculture and
rural modernization. In the 14th Five-Year Plan, the
Party Central Committee clearly expressed the
urgency of developing digital economy, promoting
the deep integration of digital economy and real
economy, scientifically laying out and promoting the
construction of new infrastructure based on
information network and driven by technological
innovation, which is conducive to promoting stable
growth, adjusting structure and benefiting people's
livelihood. However, on a national scale, the level of
702
Zhang, Y. and Cao, J.
Factor Market Distortions and Rural Information Inequality: Based on Empirical Analysis.
DOI: 10.5220/0011232100003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 702-713
ISBN: 978-989-758-593-7
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
development of China's digital economy in
agriculture and rural areas, influenced by the level of
economic development and the level of science and
technology, shows a situation where the east is strong
and the west is weak, the south is strong and the north
is weak, and both suburban and regional development
are unbalanced. The differential development of
digital economy will lead to rural information
inequality, which will have a negative impact on the
rural revitalization strategy and the overall goal of the
14th Five-Year Plan. Therefore, in the era of
technology where new technologies such as cloud
computing and artificial intelligence are constantly
iterating and evolving, it is of great theoretical value
and practical significance to discuss how to alleviate
information inequality in rural areas due to “ tool
exclusion” and “ evaluation exclusion” caused by
network technology. It is of practical significance.
Information inequality refers to the diverse
information gaps between different types of subjects
at the macro and micro levels of communication
technologies and in the actual activities of availability
and use of information resources (Wang, Zhang, Jia,
2019). It has been studied that the factors that
influence information inequality are
multidimensional. It can be encapsulated as natural,
social and individual factors. Overall, geographical
factors (Stornaiuolo, Thomas, 2017, Barnett, et al.,
2017, Park, 2017). among natural factors, economic
factors (Gagné, et al., 2018), resource factors
(Courtois, Verdegem, 2016, Robinson, Wiborg,
Schulz, 2018), and social class factors (Yu, Zhou,
2016, McNicol, Aillerie, 2017, Xu, 2017) among
social factors, and educational factors (Liao, et al.,
2016, Bol, Helberger, Weert, 2018), skill factors
(Chen, Lee, Straubhaar, Spence, 2014, Katz,
Gonzalez, 2016), psychological factors (Rashid 2016,
Potnis 2016), and health factors among individual
factors (Li, Yang, Li, 2016). Combined with previous
studies, it can be seen that domestic and foreign
scholars have a solid foundation for research on
information inequality, but at the same time, there are
three issues that deserve attention: first, most of the
previous studies stay at the theoretical level, using
micro databases, and empirical analysis to explore
information inequality in rural areas is not common
in the research literature. Second, previous scholars
have not explored the extent of the impact of rural
information inequality and the mechanistic paths
using the factor market distortion perspective in the
context of China's economic development,
considering the reality that factor marketization
varies across regions and factor market distortion
(Yu, Wu, 2020, Zhang, Zhou, Li, 2011). Third, most
of the literature focuses on information inequality in
terms of computer and Internet applications, and does
not conduct an in-depth analysis of information
inequality in the context of frontier technology
environment. In order to make up for the
shortcomings of the above studies and further
measure the impact related to factor market inputs on
rural information inequality in the context of the new
era, this study takes the following measures: first, for
sample selection, the latest issue of CFPS 2018 micro
data of farm households combined with the China
Sub-Provincial Market Report Index (2018) and the
China E-Commerce Development Index Report
(2018) are used to establish a new panel data, a
sample of 2508 farm households is selected for
empirical analysis; second, considering the
heterogeneity of factor market distortions, this article
further extends to discuss the extent of the impact of
different types of factor market distortions on rural
information inequality. The rest of the article is
structured as follows: the second part is mechanism
analysis; the third part is data sources and model
design; the fourth part is empirical analysis; and the
fifth part is concluding remarks and
recommendations.
2 MECHANISM ANALYSIS
In neoclassical economics, the production of firms
seeking to maximize profits occurs at a position
where the marginal cost of factors is equal to the
marginal output. However, in the case of distorted
factor market prices, it will lead to deviations
between the actual prices of technology, capital, labor
and other factors of production and equilibrium
prices, making the use and allocation of factors by
enterprises and other market players fail to achieve
the Pareto optimal state, thus leading to efficiency
losses, while the market segmentation blocks the free
flow of factors such as R&D capital, significantly
stalls the update and use of network technology, and
ultimately exacerbates information inequality under
the realistic conditions of inconsistent network
technology market environment. This inequality is
even more serious in rural areas where the network
infrastructure is not perfect.
Factors of production such as technology, capital,
and labor, as the lifeblood of economic development,
play an important role in the allocation of information
resources in rural areas (Liu, Liu, 2020) Technology
market: (1) The disparity in the level of science and
technology in rural areas of China makes the ability
of information dissemination and audience access
Factor Market Distortions and Rural Information Inequality: Based on Empirical Analysis
703
vary among regional farmers. The emergence of a
series of high-tech industries, such as unmanned
supermarkets and cashless cities, has led to the
upgrading of the industrial structure in regions with
these high-tech industries, which in turn has greatly
contributed to enhancing the usefulness of Internet
use among local rural residents. In contrast, the
industrial structure in remote rural areas will be
stagnant due to the distortion of the technology
market, which will eventually lead to the lack of
knowledge and ability to master new technologies
and make farmers in these areas “ disadvantaged”,
and the information gap between them and the “
Internet-connected” groups will continue to widen.
(2) Technology market distortion will also cause
inequality in factor income shares and wages, which
will further weaken the probability and possibility of
accessing network technology in remote rural areas,
including promoting the use of network activities
such as shopping, entrepreneurship and social
networking by farmers, resulting in negative effects
and ultimately reducing the possibility of using
network technology for productive activities by
farmers in remote areas.
Labor factor market: (1) Along with the
accelerated urbanization in China, the problem of
urban-rural dual structured in China has become more
obvious, concentrating on the lack of reform of the
household registration system and the concentration
of too many high-quality resources in big cities, in
addition, the mechanism of labor mobility has not
realized the freedom without security, thus
exacerbating the distortion of the labor market. The
unreasonable spatial distribution of technical talents
as an important part of modern high quality labor
force will lead to inconsistency in the efficiency of
Internet technology in rural areas of various
provinces and cities. (2) Under the dual constraints of
fiscal decentralization and local governments' pursuit
of GDP growth, governments at all levels will shrink
enterprise costs, attract external investment, and
promote economic growth and political performance
by lowering labor prices, etc. Low labor prices will,
on the one hand, lead enterprises to make more use of
tangible factors and form path dependence, making
them pay less attention to the innovative ability of
Internet technology; on the other hand, it will also
inhibit consumers' ability to purchase innovative
products from local enterprises, thus forming a low-
end vicious circle, resulting in the Internet
penetration rate and Internet technology in remote
rural areas not achieving simultaneous improvement,
and ultimately exacerbating information inequality
among rural areas.
Capital factor markets: (1) Theoretically,
distortions in capital factor markets can increase rural
information inequality by increasing the “ credit
constraint effect ” and unproductive rent-seeking
behavior. In the context of China's incomplete
interest rate market reform, local governments tend to
let financial institutions invest their credit funds in
low-risk construction projects that can quickly
achieve economic benefits in order to pursue GDP
growth, while high-tech enterprises based on network
technology generally have long investment cycles
and high investment risks (Guo, Sun, 2019). (2) With
local governments having a say in the pricing and
allocation of capital factors, enterprises and other
market players have sufficient incentives to use
unproductive rent-seeking opportunities to pursue
their own interests, which will result in a waste of
resources in this game process, eventually making the
beneficiary enterprises give up or delay their
intention to introduce new network equipment and
technologies, and discouraging other enterprises from
using new technologies such as the Internet for
production.
Given that the diffusion of network technologies
is selective and innovative, the degree of local
government intervention in the technology, capital,
and labor markets in different regions will be
heterogeneous, resulting in significant differences in
the degree of distortion in the above three factor
markets. The serious segmentation of factor markets
will lead to the situation that factor prices are
undervalued or highly differentiated, and the degree
of resource skewing within regions will also lead to
resource mismatch, and the degree of network
technology development in rural areas will also show
divergence, which will eventually lead to different
barriers and costs of Internet access for farmers in
different regions, thus affecting the reception and
dissemination of information among farmers, and
thus exacerbating rural information inequality.
3 DATA SOURCES AND MODEL
DESIGN
3.1 Data Sources
The data are mainly from the China Household
Tracking Survey (CFPS) 2018 data. CFPS data
covers a wide range of provinces and a large survey
sample, and is considered a national tracking survey
data, which can better reflect the situation of rural
households' internet use in the new era. In order to
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704
avoid outliers and missing values from biasing the
experimental results, this paper treats the data as
follows: (1) delete samples with missing income and
income less than 0; (2) retain the labor force sample.
The age of men is 16-60 years old and the age of
women is 18-55 years old; (3) the missing values of
core variables are removed. After these three steps,
the final research sample of 2508 rural residents was
obtained. In addition, this paper uses the China Sub-
Provincial Market Report Index (2018) to measure
the factor market distortion index, and introduces the
China E-Commerce Development Index Report
(2018) to further investigate the relationship between
factor market distortion and rural information
inequality in depth.
3.2 Variable Selection
Explanatory variable: rural information inequality.
Most previous studies have used “Internet use” as an
indicator to test individual use of the Internet, without
taking into account the deeper information inequality.
Therefore, in this paper, we use the Foster-Greer-
Thorbecke (FGT) index (Foster, Greer & Thorbecke
2010) to establish an information distance indicator
to measure rural information inequality based on
previous studies. The information distance is
measured as the ratio of the difference between the
sample average Internet use and the Internet use of
the farm households to the sample average Internet
use. The specific measurement formula is as follows.
n
0
i1
i
ii
P
i
=
−
=
(1)
Where is the average Internet
i
usage and is the
Internet usage of farm households.
i
i
If the distance
from the average
0
P
Internet usage is closer, it
represents the smaller information inequality gap. In
addition, considering that Internet usage is suitable
for presenting the state of Internet usage breadth, but
not describing the state of Internet usage intensity,
this paper uses “online purchase amount” in CFPS
questionnaire as an indicator of Internet usage
intensity, and further tests the degree of influence of
factor market distortion on rural information
inequality on this basis. Finally, with the continuous
improvement of network technology, the continuous
promotion of innovative business models such as
cross-border e-commerce and rural e-commerce in
each region has a significant impact on the
information flow in rural areas. Therefore, this paper
introduces the e-commerce index in each region and
uses the formula of Equation 1 to create a new
indicator of rural information inequality.
Core explanatory variable: factor market
distortion. Combined with previous studies, there are
two types of measures on factor market distortion
measures: one is the production function method and
the other is the marketization index method (Wang,
Sji, 2015). Comparatively, the marketization index
method is able to demonstrate both the relative
differences in the degree of factor market distortions
across regions and also the changes of regional factor
markets themselves over time (Wu, Tan, Wang,
2020). Therefore, in this paper, referring to the
practice of previous scholars (Lin, Du, 2013), the
degree of factor market development (overall factor
market), the marketization index of technological
achievements (technology factor market), the degree
of marketization of the financial sector (capital factor
market), and the index of human resources supply
conditions (labor factor market) in each province and
city in 2016 are matched with the CFPS database
using the relative difference between the
corresponding factor market index and the maximum
value of the factor market index in the sample to
present the degree of distortion of the corresponding
factor market, on the basis of which
(max ) / max
it it it
f
ac factor factor factor=−
(2)
The formula
it
f
actor
is the corresponding factor
market index in each province, which
max
it
f
actor
is the maximum value of the corresponding factor
market index in each region.
Control variables: this paper follows the
traditional literature and controls for age, age
squared, gender, social trust and other relevant
variables by combining individual characteristics,
family characteristics, and social characteristics (Yu
& Wu 2020). Table I provides a statistical description
of the main variables.
Factor Market Distortions and Rural Information Inequality: Based on Empirical Analysis
705
Table 1: Descriptive statistics.
Variables
Observed
value
Mean Value
Standard
deviation
Minimum value Maximum value
Outcome variable
Information inequality (breadth) 2508 -5.05e-08 1.830 -3.347 1.000
Information inequality (intensity) 2508 -2.09e-07 3.148 -43.40 1.004
Information inequality (depth) 2508 1.82e-07 0.542 -1.389 0.532
Core variables
Factor market distortion 2508 0.304 0.174 -2.68e-08 0.898
Technology market distortions 2508 16.57 3.368 6.10e-07 19.890
Capital market distortions 2508 3.508 1.644 3.81e-07 9.380
Labor market distortions 2508 13.13 2.933 -4.58e-07 20.470
Control variables
Age 2508 38.82 11.13 18 60
Age squared 2508 1631 878.8 324 3600
Gender (male=1) 2508 0.626 0.484 0 1
Years of education 2508 7.579 4.743 0 19
Political capital (party member = 1) 2508 0.067 0.249 0 1
Health status (very healthy=5) 2508 3.253 1.136 1 5
Marital status (married=1) 2508 0.829 0.377 0 1
Social trust (yes=1) 2508 0.533 0.499 0 1
Social network (logarithmic) 2508 7.445 2.247 0 11.290
Farming (yes=1) 2508 0.629 0.483 0 1
Personal income (logarithmic) 2508 10.100 0.946 0 13.420
Outworking (yes=1) 2508 0.701 0.458 0 1
Household size 2508 4.467 2.101 1 15
Household savings (log) 2508 6.967 4.599 0 14.510
Government rating (very good=5) 2508 2.578 1.064 0 5
Eastern region (yes=1) 2508 0.327 0.469 0 1
3.3 Descriptive Analysis
According to Table II, it can be found that, relying on
the 2018 e-commerce development index, the five
provinces of Guangdong, Zhejiang, Beijing,
Shanghai, and Jiangsu seize the leading position and
are regarded as the first echelon of China's e-
commerce development. Shandong, Fujian, Sichuan,
and Anhui provinces gradually highlight the
advantages of e-commerce and are regarded as the
second echelon of e-commerce development in
China. The four provinces of Heilongjiang, Guangxi,
Xinjiang and Gansu have more room for e-commerce
development and can be regarded as the fourth
echelon of China's e-commerce development. The
remaining provinces belong to the middle force of
China's e-commerce development and are regarded as
the third echelon of China's e-commerce
development. The division of the four gradients also
indicates that the scale of e-commerce development
is not consistent across Chinese provinces and cities,
indicating that there are certain gaps in the
development of e-commerce in each province.
Subdividing each province's e-commerce index
into scale, growth, penetration and support indices, it
can be found that the leading five provinces are above
the national average except for the growth index.
Similar to the pioneer provinces, the growth index of
the dominant provinces is also slightly lower than the
national average. The indices of the middle provinces
show the opposite trend with the pioneer provinces
and dominant provinces - the scale index, penetration
index and support index are lower than the national
average, but the growth index is significantly higher
than the national average, indicating that although the
middle provinces do not yet have obvious scale
advantages and superior support environment, they
have all made efforts in e-commerce economy. The
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706
growth index of potential provinces is slightly equal
to the national average, but the rest of the index is
significantly lower than the national average. It
indicates that the e-commerce development
environment of potential provinces still needs further
optimization, including access to corresponding
support in logistics and capital.
Table 2: Results of e-commerce development index measurement by provincial administrative regions in 2018.
Rank Province
E-commerce
development index in
2018
Scale index
Growth
Index
Penetration Index
Support
Index
1 Guangdong 65.60 100 24.65 52.17 68.99
2 Zhejiang 52.62 61.62 11.94 87.29 53.04
3 Beijing 45.84 58.22 21.56 38.54 61.82
4 Shanghai 38.87 50.28 20.22 36.70 44.39
5 Jiangsu 33.05 48.36 14.71 34.35 27.86
6 Shandong 32.58 46.28 35.58 19.01 18.41
7 Fujian 31.44 24.29 33.48 42.82 30.01
8 Sichuan 29.86 24.40 63.30 11.56 15.91
9 Anhui 27.83 21.91 59.21 15.80 11.02
10 Shaanxi 25.73 11.25 68.38 10.02 13.46
11 Hunan 25.66 15.90 63.97 12.17 8.34
12 Henan 25.22 19.95 52.83 10.82 14.34
13 Chongqing 24.67 14.38 64.18 9.68 8.31
14 Hubei 24.64 20.25 46.76 13.08 16.21
15 Jiangxi 23.62 11.28 61.54 13.28 8.15
16 Hebei 22.24 14.61 44.43 15.35 14.75
17 Tianjin 20.26 9.76 44.45 10.78 71.90
18 Tibet 19.81 0 66.01 12.79 3.74
19 Ningxia 19.47 1.05 68.29 5.19 4.99
20 Jilin 19.33 3.40 62.15 2.20 10.82
21 Hainan 18.59 2.37 51.26 16.39 8.35
22 Shanxi 18.48 4.66 57.67 3.03 9.13
23 Guizhou 18.46 7.25 51.29 10.65 4.85
24 Yunnan 18.31 7.38 54.70 7.73 2.52
25 Qinghai 18.08 0.42 60.66 6.91 6.92
26 Liaoning 17.22 8.64 40.51 1.80 18.54
27 Inner Mongolia 17.02 4.80 49.95 6.02 8.23
28 Heilongjiang 16.72 3.05 53.40 1.68 9.83
29 Guangxi 16.26 6.67 46.06 6.66 5.38
Factor Market Distortions and Rural Information Inequality: Based on Empirical Analysis
707
Rank Province
E-commerce
development index in
2018
Scale index
Growth
Index
Penetration Index
Support
Index
30 Xinjiang 15.17 2.54 52.21 0.85 5.29
31 Gansu 12.85 3.02 39.92 6.53 2.53
32 Average 25.66 19.61 47.91 16.83 18.97
Table 3: Baseline regressions of elemental market distortions on rural information inequality.
(1)
Information
inequality (breadth)
(2)
Information
inequality (breadth)
(3)
Information
inequality
(intensity)
(4)
Information
inequality
(intensity)
Factor market
distortion
0.751***
(3.58)
0.615**
(2.30)
1.544***
(4.37)
0.867**
(1.98)
Age
0.133***
(5.17)
0.231***
(5.48)
Age squared
-0.001***
(-3.06)
-0.001***
(-2.80)
Gender
-0.100
(-1.39)
0.298**
(2.57)
Education level
-0.075***
(-10.38)
-0.072***
(-6.09)
Political Capital
-0.471***
(-3.58)
-0.781***
(-3.62)
Health level
0.003
(0.09)
0.038
(0.80)
Marital status
0.122
(1.19)
0.092
(0.55)
Social trust
0.027
(0.20)
0.399*
(1.80)
Social network
-0.041***
(-2.81)
-0.029
(-1.21)
Plantation industry
0.081
(1.14)
0.438***
(3.76)
Personal income
(logarithmic)
-0.183***
(-5.01)
-0.432***
(-7.20)
Outworking
0.170**
(2.37)
0.066
(0.56)
Household size
0.043**
(2.57)
0.014
(0.49)
Household savings
-0.022***
(-3.17)
-0.031***
(-2.67)
Government
evaluation
-0.010
(-0.30)
0.024
(0.44)
Eastern Region
0.041
(0.42)
-0.119
(-0.75)
Observed value 2508 2508 2508 2508
Note: The t-statistic is the data in parentheses; differences in confidence level significance are indicated by ***, **, and *
among the 1%, 5%, and 10% levels, respectively. Same as below.
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
708
3.4 Empirical Model Design
The benchmark regression model set in this paper is
as follows.
iicc
Ine Dist X
α
β
λε
=+ + +
(3)
where
𝑖 represents individuals, 𝐼𝑛𝑒 represents
rural information inequality,
𝑋
represents a set of
variables that affect rural information inequality, and
𝜀
is a random disturbance term.𝛽 represents the
effect of factor market distortion on rural information
inequality, as the coefficient of focus in this paper.
𝛽
Being positive, represents that factor market
distortion can significantly rural expanding
information inequality;
𝛽 being negative, represents
that factor market distortion can significantly reduce
rural information inequality;
𝛽 being insignificant,
represents that factor market distortion has no
significant effect on rural information inequality.
4 EMPIRICAL ANALYSIS
4.1 Impact of Overall Factor Market
Distortion on Rural Information
Inequality
Table III shows the results of the benchmark
regression of factor market distortions on information
inequality. The results of model 1 indicate that the
coefficient of factor market distortion is 0.751, which
is significant at 1%, indicating that the likelihood of
factor market distortion leading to increased
information inequality in rural areas is as high as
75.1% when other variables are not controlled. The
results of Model 2 indicate that factor market
distortions still have a significant positive effect on
information inequality when other control variables
are controlled for, and are significant at the 5%
statistical level. This suggests that factor market
distortions have a significant effect on information
inequality in rural areas. The results of model 3 show
that factor market distortion has a significant positive
effect on information inequality (intensity) and is
significant at the 1% statistical level, indicating that
factor market distortion has a significant inhibitory
effect on the development of online shopping
platforms and the increase of farmers' willingness to
shop online in rural areas. The results of model 4 show
that factor market distortion still has a significant
positive effect on information inequality (intensity)
after controlling for other variables, and the likelihood
that factor market distortion will lead to an increase in
the gap between farmers' willingness to shop online in
each province reaches 86.7%.
In addition to the significant effect of factor
market distortion on information inequality, some of
its remaining characteristic variables also have a
significant effect on information inequality. The
positive coefficient of age and the negative
coefficient of age squared indicate that the effect of
age on information inequality has an inverted U-
shape, i.e., as age increases, information inequality in
rural areas increases and then decreases. It is easy to
understand that the higher the level of education, the
wider the range of exposure to people and the higher
the probability of acquiring frontier information,
thus, the more obvious the effect of mitigating
information inequality. In addition, political capital,
social network, personal income (logarithm), and
household savings also play a role in mitigating
information inequality to some extent. In contrast,
farmers with out-of-home work experience and larger
household size significantly raise information
inequality. Possible explanations for this are that
farmers with out-of-home work experience are more
likely to use the internet for continuous access to
information sources. Farmers with larger household
size have higher household economic pressure, lower
average education level (Li, Yang, 2014), and less
probability of using the Internet to access
information.
Based on the above regression results, it can be
found that there is a significant positive effect of
factor market distortions on rural information
inequality, both from the information breadth
perspective and by relying on the information
intensity perspective test, i.e., factor market market
distortions significantly widen information inequality
in rural areas. Factor market distortion, as a special
product of China's market-oriented reform, makes
factors of production such as capital and labor deviate
from their real prices, causing inefficient use of
resources, industrial structure consolidation and other
corresponding problems, which eventually affects the
utility of Internet use by farmers in each region
through resource mismatch and talent mismatch, and
thus exacerbates information inequality in rural areas.
4.2 Impact of Factor Market
Distortions in Technology, Capital,
and Labor on Rural Information
Inequality
The impact of overall factor market distortions on
rural information inequality was analyzed above. To
further investigate the impact of different factor
market distortions on information inequality, this
Factor Market Distortions and Rural Information Inequality: Based on Empirical Analysis
709
paper subdivides the factor markets. Based on the
Cobb Douglas production function, this paper
introduces three factors, namely, technology, capital,
and labor, and further explores the impact of these
three types of factor distortions on rural information
inequality.
According to the regression results of Model 1
and Model 2 in Table IV, we are able to find that
technology market distortions significantly widen
information inequality, indicating that technology
market distortions are not conducive to alleviating
information inequality. The regression results of
Model 3 and Model 4 show that capital market
distortions increase information inequality. When
there are distortions in the capital factor market,
enterprises will consider the
“opportunity cost” and
prefer to inject capital into economically developed
areas, thus reducing the frequency of Internet
replacement and usage in remote rural areas. The
results of Model 5 and Model 6 suggest that labor
market distortions have a significant contribution to
rural information inequality. The possible
explanation is that with the accelerated urbanization
process, the labor market segmentation problem has
not been properly solved, and the opportunities and
ways of free flow of labor factors are narrowly
restricted, which leads to the lower willingness of
rural residents with higher education and more
knowledge to return to their hometowns for work, and
the resulting
“ motivation effect ” increases rural
information inequality. The resulting
“ motivation
effect
” increases rural information inequality.
According to the regression results of models 1 to 6,
it can be found that the positive effect of labor market
distortion on rural information inequality is the most
significant and far exceeds the regression coefficient
of technology market distortion.
Models 7 and 8 are the results of the full sample
analysis with the inclusion of technology market
distortions, capital market distortions, and labor
market distortions, while other variables are
controlled. According to the regression results of
Model 7 and Model 8, it can be found that the positive
effects of technology market distortion, capital
market distortion, and labor market distortion on rural
information inequality remain significant. This
indicates that among all factor inputs, the continued
attention of policy makers to the changes in labor
market distortions will be more effective in
alleviating rural information inequality. Information
technology capability, which is the ability to use
information resources, is in this paper being
contrasted with the Internet use variable, where the
breadth and intensity of Internet use is objectively
constrained by an individual's knowledge base,
mindset, and learning effects. Individuals who are
socially advantaged can make deeper use of
information technology by possessing leading digital
resources and technological devices, which will
further extend their own advantage, and this labor
distortion will lead to inequality being continuously
reproduced in the digital space (Shi 2014).
4.3 Treatment of Endogeneity
The above empirical analysis of factor market
distortions does not consider endogeneity problems
due to omitted variables and measurement errors,
such as the quality of regional institutions and
relational culture, which both affect rural information
inequality. To mitigate the endogeneity problem
caused by missing variables, this paper draws on
previous scholars to construct as instrumental
variables for the corresponding market distortions,
where denotes the mean value of the corresponding
market distortion. The advantage of this approach
is that the data itself is used to construct the
appropriate instrumental variables (Lewbel 1997).
Table 4: Impact of technology, capital, and labor market distortions on rural information inequality.
(1)
Information
inequality
(breadth)
(2)
Information
inequality
(intensity)
(3)
Information
inequality
(breadth)
(4)
Information
inequality
(intensity)
(5)
Information
inequality
(breadth)
(6)
Information
inequality
(intensity)
(7)
Information
inequality
(breadth)
(8)
Information
inequality
(intensity)
Technology
market
distortions
0.437**
(2.02)
0.834**
(2.25)
0.125**
(2.34)
0.170**
(2.47)
Capital market
distortions
0.545**
(2.36)
1.752***
(4.42)
0.053**
(2.16)
0.597***
(2.62)
Labor market
distortions
0.984***
(4.01)
1.856***
(4.40)
0.759***
(3.02)
1.036**
(2.24)
Control
variables
No No No No No No Yes Yes
Observed value 2508 2508 2508 2508 2508 2508 2508 2508
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
710
Table V summarizes the regression results for the
second stage of the full-sample instrumental variables
model. In the first stage, the regression coefficients of
the instrumental variables are all significant at 1%,
indicating that the selected variables have a strong
correlation. The Wald test of the joint significance
test passes the 1% significance test in Models 1 - 4,
indicating that the test rejects the exogenous
hypothesis that the corresponding market is distorted,
thus supporting the appropriateness of using
instrumental variables to run the regressions. The
results of the second-stage regression in the table
show that factor market distortions and labor market
distortions still have significant effects on rural
information inequality after the endogeneity issue is
taken care of, thus validating the robustness of the
baseline regression analysis of factor market
distortions on rural information inequality.
4.4 Further Discussion
Following the previous formula for measuring
information inequality, this paper again re-measures
the rural information inequality indicators using the
Chinese e-commerce development index. Among
them, Model 1 is to test the overall regression results
of factor market distortion on information inequality,
and Models 2-Model 5 are to categorize 31 provinces
into pioneer, dominant, middle and potential
provinces according to the ranking differences of e-
commerce development index and following the
regional structure. Model 6 is to further consider the
heterogeneity of factor markets and further test the
degree of influence of different types of factor market
distortions on information inequality of e-commerce
in China. The regression results of Model 1 show that
the coefficient of factor market distortion is 2.358,
which is significant at 1%, indicating that there is a
significant positive effect of factor market distortion
on information inequality (e-commerce), i.e., factor
market distortion exacerbates information inequality,
corroborating the baseline regression results of this
paper. The regression results of Model 2 to Model 4
show that the order of the effect of factor market
distortion on information inequality is pioneer
province
→ middle province → potential province
→ dominant province. The reason is that the
application of big data, cloud computing and other
transaction scenarios are more common and frequent
in the pioneer provinces, and the factor market
distortion will lead to inconsistent resource allocation
within the provinces, which in turn will aggravate the
e-commerce development gap among regions, thus
leading to stronger information inequality. According
to the China E-commerce Development Index Report
(2018), the growth index of dominant provinces is
lower than the national average, indicating that their
growth rate is relatively slow, and thus the impact of
factor market distortions on them is relatively weak.
The regression results of model 6 show that for the
information inequality caused by e-commerce
development, we should pay more attention to the
capital market distortion and technology market
distortion, which means that in the future e-commerce
development process, governments at all levels need
to prevent the
“ credit constraint effect ” and
unproductive rent-seeking behaviors, emphasizing the
combination of business ecology and government
governance. This means that in the future
development of e-commerce, governments at all
levels need to prevent the
“credit constraint effect”
and non-productive rent-seeking behavior, emphasize
the organic combination of business ecology and
government governance, grasp the development
direction of e-commerce from the industrial
perspective, and realize the docking of e-commerce
and digital economy, so as to provide important
impetus for the development of digital countryside.
Table 5: Endogeneity tests of overall factor market distortions on rural information inequality.
(1)
Information inequality
(
breadth
)
(2)
Information
ine
q
ualit
y
(
intensit
y)
(3)
Information
ine
q
ualit
y
(
breadth
)
(4)
Information inequality
(
intensit
y)
Factor market
distortion
0.709**
(2.43)
1.635***
(2.91)
Technology
market distortions
0.516*
(1.79)
0.685*
(1.97)
Capital market
distortions
0.532**
(
2.03
)
0.725**
(
2.16
)
Labor market
distortions
0.739**
(
1.98
)
1.06**
(
2.13
)
Control variables Yes Yes Yes Yes
Observed value 2508 2508 2508 2508
Factor Market Distortions and Rural Information Inequality: Based on Empirical Analysis
711
Table 6: Extended analysis of information inequality in rural areas due to distortion of elemental markets.
(1)
Information
inequality
(depth)
(2)
Information
inequality
(depth)
(3)
Information
inequality
(depth)
(4)
Information
inequality
(depth)
(5)
Information
inequality
(depth)
(6)
Information
inequality
(depth)
Factor market
distortion
2.358***
(54.11)
9.273***
(14.25)
0.583***
(15.74)
0.726***
(25.53)
0.671***
(77.71)
Technology
market
distortions
0.515***
(13.30)
Capital market
distortions
1.924***
(30.00)
Labor market
distortions
-0.027
(-0.53)
Control
variables
Yes Yes Yes Yes Yes Yes
Observed value 2508 443 362 1282 440 2508
5 CONCLUDING REMARKS AND
RECOMMENDATIONS
In this paper, a new panel data based on CFPS (2018)
micro data of farm households combined with macro
data of China Sub-Provincial Market Report Index
(2018) and China E-Commerce Development Index
Report (2018) is composed to empirically analyze the
relationship between factor market distortions and
rural information inequality. It is found that: 1) rural
information inequality indicators established using
the breadth and intensity of Internet use have a
significant positive effect of factor market distortion,
i.e., overall factor market distortion significantly
enhances rural information inequality. Subdividing
into factor market types, labor market distortions
have a more significant effect on rural information
inequality. After testing with various regression
analysis methods including instrumental variables, it
is found that the above findings still hold. 2)
Combined with the frontier technology environment,
the positive effect of factor market distortion on rural
information inequality remains significant using the
rural information inequality index established in
depth by the China E-commerce Development Index,
and this positive effect is more pronounced for the
pioneer provinces. Subdividing into factor market
types, the positive influence of capital market
distortion and technology market distortion on rural
information inequality is more significant.
Combining the above findings, this paper draws
the following policy implications: First, the
government needs to accelerate factor market reform
comprehensively, reduce excessive government
intervention in factor markets, and strive to build the
market as the center of resource allocation. Second,
the government needs to implement the reform
measures of
“Increasing internet speeds and reducing
costs
” , and in the process of increasing the rural
network penetration rate, it can also consider using
the Internet channel to break the separation and
segmentation of the labor market, increase the
probability of free flow of labor factors market, and
then use the advantages of technical talents
themselves to alleviate the rural information
inequality. Thirdly, due to the late start of e-
commerce projects, the development of e-commerce
is not consistent in each province. Therefore, policy
makers need to pay attention to the flow of factor
inputs in pioneering regions and promote the degree
of matching between e-commerce and traditional
industries in advantageous regions, in addition, each
province needs to combine its own characteristics and
focus on the negative impact of different types of
factor market distortions to ultimately guarantee the
overall healthy development of e-commerce in China.
The shortcomings of this study are: first, due to
the limitation of survey data availability, the study
does not include macro data such as regional
economic development level, thus not fully reflecting
all the influencing factors limiting information
inequality in rural areas of China. Secondly, this
paper only considers the impact of technology,
technology and information inequality in rural areas.
Second, this paper only considers the degree of
influence of factor market distortions of technology,
labor, and capital on rural information inequality, and
future research will focus on examining the influence
of other factor market distortions, such as land, on
rural information construction, in order to more
comprehensively reflect the association between
factor market distortions and rural information
inequality.
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
712
ACKNOWLEDGEMENTS
This Research was funded by National Social Science
Foundation of China Key Project
“ Long-term
Mechanism of Rural Relative Poverty Control in
China
” (No. 20AZD079).
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