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 Agricultural Science; Vol. 7, No. 2; 2025 
ISSN 2690-5396   E-ISSN 2690-4799 

https://doi.org/10.30560/as.v7n2p36 

36                             Published by IDEAS SPREAD 
 

Analysis of the Impact of New Digital Infrastructure Construction on 
the Urban-Rural Income Gap: A Mediation Perspective Based on 

Agricultural Industrial Agglomeration 

Yanxing Li1 

1 School of Economics and Management, Chongqing Normal University, Chongqing, China 

Correspondence: Yanxing Li, School of Economics and Management, Chongqing Normal University, Chongqing, 
China. 

 

Received: November 20, 2025   Accepted: December 14, 2025   Online Published: December 17, 2025 

 

Abstract 

Narrowing the urban-rural income gap is an essential requirement and a crucial focus for achieving common 
prosperity. Based on panel data samples from 30 Chinese provinces (excluding Hong Kong, Macao, Taiwan, and 
Tibet) from 2014 to 2023, this study empirically examines the impact effects, mechanisms, and heterogeneity of 
new digital infrastructure on the urban-rural income gap using a two-way fixed effects model. The findings indicate 
that, overall, the construction of new digital infrastructure widens the urban-rural income gap, a result which holds 
under a series of robustness checks. Examining sub-samples by region across the country reveals that the widening 
effect of new digital infrastructure on the urban-rural income gap is concentrated in the less developed central and 
western regions, with no significant impact observed in economically developed regions. Further analysis 
demonstrates that new digital infrastructure indirectly narrows the urban-rural income gap through the mediating 
mechanism of agricultural industrial agglomeration. 

Keywords: new digital infrastructure, urban-rural income gap, mediation effect, agricultural industrial 
agglomeration 

1. Introduction 

Common prosperity is an inherent requirement of the socialist system and a key component of Chinese 
modernization. Achieving common prosperity is both a long-term vision and a protracted development process. 
The report of the 20th National Congress of the Communist Party of China explicitly states that China must 
resolutely prevent polarization, promote common prosperity, and achieve social harmony and stability. Jiang 
Yongmu et al. (2021)[1] As China's economy transitions from a phase of high-speed growth to one of high-quality 
development, entering the new century has seen rapid economic growth, steady social progress, and gradual 
improvement in people's living standards. However, income disparities between regions, urban and rural areas, 
and different social groups have also widened. Li Shi et al. (2022)[2] The persistent urban-rural income gap has 
become a key constraining factor in achieving the goal of common prosperity. Entering the second decade of the 
21st century, with the deepening development of digitalization, informatization, and intelligence, new digital 
infrastructure has become a focal point for governments and enterprises worldwide. This includes 5G networks, 
the Internet of Things, artificial intelligence, big data centers, and industrial internet. Governments have introduced 
various policies to promote digital infrastructure development. For example, the Chinese government proposed 
the "new infrastructure" policy, identifying new infrastructure such as 5G, AI, and industrial internet as key 
development directions; the U.S. government has also increased investment in data centers and cloud computing 
infrastructure. In 2019, the General Office of the CPC Central Committee and the General Office of the State 
Council issued the "Digital Village Development Strategy Outline," identifying accelerating digital infrastructure 
construction in rural areas as a key task, particularly emphasizing significantly improving rural network and 
information facilities and deepening the Network Poverty Alleviation Action. In 2023, five departments including 
the Cyberspace Administration of China, the Ministry of Agriculture and Rural Affairs, the National Development 
and Reform Commission, the Ministry of Industry and Information Technology, and the National Rural 
Revitalization Administration jointly issued the "Key Tasks for Digital Village Development in 2023," proposing 
to continuously enhance the supply capacity of communication infrastructure in rural and remote areas and 
gradually extend 5G network and gigabit optical network coverage to villages. Digital infrastructure construction 
is a crucial measure in implementing the national digital village development strategy. This raises the questions: 



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Will digital infrastructure construction significantly promote the narrowing of the urban-rural income gap? What 
are the underlying mechanisms? And how can the positive role of digital infrastructure construction in promoting 
common prosperity between urban and rural areas be better leveraged? 

Literature closely related to this paper falls into two main categories: First, examinations of the economic effects 
of digital infrastructure. Some scholars believe digital infrastructure has a positive impact on income development. 
For instance, Xia Jiechang et al. (2023)[3] argue that as the foundation for digital economic development, digital 
infrastructure construction can promote resource sharing and information flow, enhance social productivity by 
improving production efficiency, drive economic transformation, and thereby provide a material and technological 
foundation for achieving common prosperity. He Xiaoyu et al. (2023)[4] contend that new digital infrastructure 
can significantly promote the quality of regional economic growth, with resource allocation effects, technological 
innovation effects, and industrial upgrading effects being important pathways through which new digital 
infrastructure influences the enhancement of economic growth quality. Second, discussions on the impact of the 
digital economy on the urban-rural income gap. Zhao Kangjie (2025)[5] argues that due to the objective existence 
of the "digital divide," the income-increasing effect for urban residents is higher than for rural residents, and new 
digital infrastructure exacerbates urban-rural income inequality. In recent years, an increasing number of scholars 
have begun studying the non-linear impact of the digital economy on the urban-rural income gap. Bai Xu (2023)[6] 
posits that the digital economy can narrow the urban-rural income distribution gap through the urban-rural 
integration effect of reduced communication costs and the rural revitalization effect of agricultural technological 
progress, but it can widen the gap through the employment substitution effect of industrial structure upgrading. 
The impact of digital economy development on the urban-rural income distribution gap shows a trend of first 
strengthening and then inhibiting. Li Xiumin et al. (2023)[7] argue that the impact of new digital infrastructure on 
economic growth exhibits an "inverted U-shaped" pattern, and new digital infrastructure has a significant spatial 
spillover effect on economic growth. Additionally, Luo Mingzhong et al. (2023)[8] argue that rural digitalization 
has a single human capital threshold effect on the income gap and ecological environment; the higher the level of 
human capital in the rural labor force, the greater its effect. 

Currently, in recent years, research on the digital economy and the urban-rural income gap has continuously 
emerged, yielding many valuable theoretical and practical results. However, most scholars focus their research 
perspective on the overall level of the digital economy, lacking studies that take new digital infrastructure as the 
entry point. Therefore, exploring the relationship between new digital infrastructure and the urban-rural income 
gap holds significant theoretical and practical value. By studying the correlation between digital infrastructure and 
the urban-rural income gap, it can provide scientific and forward-looking theoretical support and decision-making 
basis for local governments to enhance the level of digital infrastructure construction and optimize the urban-rural 
income distribution pattern. Hence, this research is significant for promoting digital economic development and 
narrowing the urban-rural income gap. 

2. Theoretical Analysis and Research Hypotheses 

2.1 The Impact Mechanism of New Digital Infrastructure on the Urban-Rural Income Gap 

Xu Mengmeng et al. (2024)[9] identify issues such as inadequate relevant laws and regulations, weak digital 
infrastructure, a relative lack of agricultural technical talent, and the "digital divide" further exacerbating the risk 
of income imbalance in rural digital development. While promoting social progress, the construction of new digital 
infrastructure may also exacerbate the urban-rural income gap through various channels. From a technical 
perspective, these high-tech facilities often require high operational skills and professional knowledge. Rural areas, 
due to relatively scarce educational resources, generally have lower digital literacy among residents, making it 
difficult for them to fully master and utilize these new technologies, placing them at a more disadvantageous 
position in the digital economy era. From an economic structure perspective, the construction and operation of 
new digital infrastructure require substantial capital investment. This capital-intensive nature naturally 
concentrates resources in economically developed urban areas, while rural areas, due to funding shortages, find it 
harder to obtain equal development opportunities. The unequal distribution of data factors is also an important 
reason. In the digital economy era, data has become a key production factor, but rural residents are often merely 
data providers rather than beneficiaries, with most of the value generated from data flowing to urban enterprises 
that control the technology and platforms. This imbalance in data factor distribution further widens the urban-rural 
income gap. Furthermore, the agglomeration effect of new digital infrastructure is evident, with high-quality 
resources such as talent, capital, and technology accelerating their concentration in cities, creating a "Matthew 
effect," while rural areas face the dilemma of resource outflow. From an institutional environment perspective, the 
current digital skills training system tailored for rural areas is still incomplete, making it difficult for farmers to 
quickly adapt to digital transformation. Simultaneously, digital financial services and business models suitable for 



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rural characteristics still need development, all of which constrain rural residents' ability to benefit equally from 
new digital infrastructure. These factors collectively mean that new digital infrastructure, in the absence of 
effective intervention measures, may become a new factor widening rather than narrowing the urban-rural gap. 

Therefore, this paper proposes the theoretical hypothesis: H1: New digital infrastructure exacerbates the urban-
rural income gap. 

2.2 Regional Heterogeneity in the Impact of New Digital Infrastructure Construction on the Urban-Rural Income 
Gap 

In economically developed regions, the impact of new digital infrastructure on exacerbating the urban-rural income 
gap is not significant, primarily because these regions already possess a relatively complete infrastructure network, 
higher levels of human capital, and mature industrial systems, enabling them to effectively absorb and transform 
the development opportunities brought by digital technology. Urban and rural residents in developed regions 
generally possess strong digital literacy and skill adaptability. Various market actors can access digital resources 
relatively equally, while the government also possesses stronger fiscal capacity and policy execution power to 
promote the balanced distribution of digital dividends. Therefore, the popularization of digital infrastructure 
actually helps promote urban-rural integrated development. Currently, China's digital village construction faces 
many shortcomings, such as a lack of overall planning, the urban-rural digital divide, and regional development 
imbalances. The difficulty in bridging the urban-rural digital divide has become a major obstacle in digital village 
construction. Zhao Hui et al. (2025)[10] and Zhao Ruonan et al. (2024)[11] argue that the urban-rural digital divide 
brought about by informatization development is also one reason for the widening income gap between urban and 
rural residents. The digital divide hinders the development of Chinese-style modernization by reducing market 
vitality, widening the urban-rural income gap, and slowing down industrial structure upgrading. Therefore, in 
economically less developed regions, due to constraints such as weak foundational conditions, human capital 
shortages, and insufficient marketization, the introduction of new digital infrastructure is more likely to produce a 
differentiation effect: urban elite groups, leveraging their educational background and social resources, can quickly 
master digital technology and benefit from it, while rural residents, due to insufficient digital skills and poor 
financial accessibility, find it difficult to effectively participate in the digital economy, leading to a further 
widening of the "digital divide." Simultaneously, governments in less developed regions often lack sufficient fiscal 
investment and institutional innovation capacity to guide the balanced allocation of digital resources, causing 
digital technology to tend to agglomerate in urban areas, ultimately forming a "the strong get stronger" 
development pattern, significantly exacerbating the urban-rural income gap. 

Therefore, this paper proposes theoretical hypothesis H2: In economically developed regions, new digital 
infrastructure has no significant impact on exacerbating the urban-rural income gap. Conversely, in economically 
less developed regions, new digital infrastructure has a significant impact on exacerbating the urban-rural income 
gap. 

2.3 The Transmission Mechanism of New Digital Infrastructure's Impact on the Urban-Rural Income Gap 

Agricultural industrial agglomeration is the process where production factors, business entities, and related 
institutions concentrate in specific geographical areas and form a synergistic network. It represents the meso-level 
form of the evolution of rural industries towards modernization and organization. New digital infrastructure 
effectively catalyzes agricultural industrial agglomeration by optimizing factor allocation and strengthening 
industrial synergy. The scale economies, knowledge spillovers, and industrial integration effects brought about by 
agglomeration constitute another key structural mediating pathway influencing the urban-rural income gap. 

Therefore, this paper proposes theoretical hypothesis H3: New digital infrastructure can indirectly narrow the 
urban-rural income gap by promoting agricultural industrial agglomeration, meaning agricultural industrial 
agglomeration plays a partial mediating role. 

3. Research Design and Data Description 

3.1 Baseline Regression Model 

Based on the previous theoretical analysis, to examine the impact of new digital infrastructure on the urban-rural 
income gap, the following baseline regression model is constructed: 

𝑇ℎ𝑒𝑖𝑙 , 𝛼 𝛼 𝑖𝑛𝑓𝑟𝑎 , 𝛼 𝑙𝑛𝐺𝑑𝑝 , 𝛼 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 , 𝛾 𝜌  ,      （1） 

 

 

 



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3.2 Variable Measurement and Description 

3.2.1 Dependent Variable 

The dependent variable in this paper is the urban-rural income gap (Theil). Existing analyses mostly use three 
methods to measure the urban-rural income gap: the ratio of urban to rural residents' disposable income per capita, 
the Gini coefficient, and the Theil index. Following the approach of Xu Wenzhuan (2023)[12]. The ratio of urban 
to rural residents' disposable income per capita does not include the proportion of urban and rural populations and 
is a static indicator that cannot reflect population mobility between urban and rural areas. The Gini coefficient 
measures overall income inequality and is more sensitive to income changes in the middle class. However, the 
main body of the urban-rural income gap lies at the two ends, and the Gini coefficient cannot reflect the urban-
rural income gap situation. This paper uses the Theil index to represent the urban-rural income gap. This index 
can more scientifically reflect the level of income disparity, considers the factor of population change and 
differences between and within groups, and more comprehensively captures the diversity and complexity of the 
urban-rural income gap. Additionally, this paper uses the ratio of urban to rural residents' disposable income per 
capita (Urir) as a substitute indicator for the dependent variable to conduct robustness checks. The specific 
measurement method for the Theil index is as follows: 

𝑇ℎ𝑒𝑖𝑙 , ∑ ln2
1                       （2） 

3.2.2 Explanatory Variable 

There is limited literature involving the measurement of digital infrastructure construction, with only a few 
individual studies including new digital infrastructure among multiple dimensions for evaluating the digital 
economy. This paper draws on the measurement method of Wu Xianfu (2021)[13] to measure new digital 
infrastructure, selecting 3 first-level indicators and 18 second-level indicators to reflect the development level of 
new digital infrastructure. As shown in Table 1, information infrastructure mainly reflects China's information 
infrastructure from five dimensions: optical cable line length per square kilometer, internet ports per capita, mobile 
phone base stations per square kilometer, mobile internet penetration rate, proportion of internet access users, and 
domain names per capita. Integrated infrastructure mainly reflects China's integrated infrastructure from eight 
aspects: length of public tram operation per capita, railway operating mileage per capita, road length per capita, 
expressway mileage per capita, proportion of e-commerce enterprises, proportion of information industry 
personnel, e-commerce sales per capita, and software business revenue per capita. There are fewer indicators for 
measuring innovation infrastructure, mainly reflecting it from four aspects: proportion of R&D personnel, 
proportion of science and technology expenditure, R&D funding intensity, and patent applications per capita. 

 

Table 1. New Digital Infrastructure Indicator System 

 First-level Second-level Indicator Attributes 

New Digital 
Infrastructure 
Construction 

 

Information 
Infrastructure 

 

Optical cable line length per 
sq. km 

+ 

Internet ports per capita + 
Mobile phone base stations 

per sq. km 
+ 

Mobile internet penetration 
rate 

+ 

Proportion of internet access 
users 

+ 

Domain names per capita + 

Integrated 
Infrastructure 

 

Length of public tram 
operation per capita 

+ 

Railway operating mileage 
per capita 

+ 

Road length per capita + 
Expressway mileage per 

capita 
+ 

Proportion of e-commerce 
enterprises 

+ 



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Proportion of information 
industry personnel 

+ 

E-commerce sales per capita + 
Software business revenue 

per capita 
+ 

Innovation 
Infrastructure 

 

Proportion of R&D personnel + 
Proportion of science and 
technology expenditure 

+ 

R&D funding intensity + 
Patent applications per capita + 

 

3.2.3 Control Variables 

To objectively and comprehensively measure the impact of the explanatory variable on the dependent variable, 
this paper refers to studies on the urban-rural income gap by Xie Xuemei (2021)[14], Chen Wen et al. (2021)[15], 
and Li Xiaozhong (2021)[16], selecting marketization level (Market), economic development level (pgdp), 
urbanization rate (urb), industrial structure (stru), human capital level (edu), openness to the outside world (fdi), 
etc., as control variables.  

3.3 Sample Selection and Data Sources 

The sample data for this study are panel data from 30 Chinese provinces/municipalities/autonomous regions 
(excluding Hong Kong, Macao, Taiwan, and Tibet) for the period 2014-2023. Data for the various variables 
involved in this study mainly come from sources such as the China Statistical Yearbook, the Ministry of Commerce 
Statistical Database, and the China Macroeconomic Database. 

4. Research Results and Analysis 

4.1 Descriptive Statistics 

The descriptive statistical results for all variables are shown in Table 2. From Table 3, it can be seen 
that the mean of the urban-rural income gap (Theil) is 0.083, with a standard deviation of 0.038, 
indicating that areas with large urban-rural income gaps are relatively few. The maximum value is 0.21, 
and the minimum value is 0.017, indicating significant differences in urban-rural income inequality 
across different regions of China. The table shows that each variable has a relatively large range of 
variation, which can better reflect developmental differences among regions, indicating that the basic 
data for studying the relationship between new digital infrastructure and the urban-rural income gap are 
reasonable. 

 

Table 2. Descriptive Statistics 

 Variable Symbol  Sample Size  Mean  S.D Min Max 

 theil 300 0.083 0.038 0.017 0.21 

 urir 300 2.524 0.446 1.682 3.808 

 infra 300 0.147 0.113 0.019 0.61 

 market 300 8.351 1.882 3.58 12.864 

 pgdp 300 10.946 0.432 10.003 12.155 

 urb 300 0.614 0.114 0.379 0.896 

 stru 300 1.417 0.757 0.665 5.283 

 edu 300 0.022 0.008 0.006 0.114 

 fdi 300 0.259 0.257 0.008 1.257 

 

4.2 Baseline Regression Analysis 

Before conducting the baseline regression, F-tests and Hausman tests were first performed on the regression model. 
The models rejected the null hypothesis, and a two-way fixed effects model was used for regression analysis. This 



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means the model controls for both time-fixed effects and individual-fixed effects simultaneously to eliminate 
potential individual heterogeneity that does not vary over time and time trends that do not vary across individuals. 
The results are shown in Table 4. Column (1) in the table shows the regression results for the core explanatory 
variable without including any control variables. Its impact coefficient is 0.092 and significant at the 1% level, 
indicating that new digital infrastructure has a positive impact on the urban-rural income gap. Specifically, a one-
unit increase in the new digital economy index leads to a corresponding increase of 0.092 units in the degree of 
the urban-rural income gap. Columns (2) to (7) show the regression results after gradually adding control variables 
for marketization level (market), economic development level (pgdp), urbanization rate (urb), industrial structure 
(stru), human capital level (edu), and openness (fdi). The results show that after adding control variables, the 
coefficient of the core explanatory variable ranges between 0.036 and 0.103, still significantly positive at the 1% 
level. The magnitude of the regression coefficients after adding control variables shows little difference from the 
single-variable analysis results, so the empirical conclusion of the baseline regression is robust. This indicates a 
significant positive correlation between new digital infrastructure and the urban-rural income gap, i.e., the 
construction of new digital infrastructure widens the urban-rural income gap. This conclusion aligns with 
hypothesis H1. For the control variables, economic development level and urbanization degree are both 
significantly negative at the 1% level, indicating that economic development and urbanization construction are 
conducive to narrowing the urban-rural income gap, which also proves the importance of economic development 
and urbanization construction. Conversely, industrial structure is significantly positive at the 1% level, proving 
that industrial structure upgrading widens the urban-rural income gap. The adjustment of industrial structure 
increases the demand for labor skills, making rural residents lacking skills unable to qualify for jobs, leading to 
reduced income. Additionally, marketization level, human capital level, and openness have no significant impact 
on the urban-rural income gap. 

 

Table 3. Baseline Regression Results 

 (1) (2) (3) (4) (5) (6) (7) 

 theil theil theil theil theil theil theil 

infra 0.092*** 0.099*** 0.103*** 0.047*** 0.041*** 0.041*** 0.036*** 

 (0.014) (0.014) (0.012) (0.012) (0.012) (0.012) (0.013) 

market  -0.004*** -0.003*** 0.001 0.001 0.001 0.001 

  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) 

pgdp   -0.067*** -0.042*** -0.036*** -0.035*** -0.036*** 

   (0.009) (0.008) (0.008) (0.008) (0.008) 

urb    -0.297*** -0.280*** -0.281*** -0.260*** 

    (0.032) (0.032) (0.032) (0.036) 

stru     0.008*** 0.008*** 0.009*** 

     (0.003) (0.003) (0.003) 

edu      -0.040 -0.052 

      (0.059) (0.060) 

fdi       -0.011 

       (0.008) 

_cons 0.099*** 0.127*** 0.832*** 0.709*** 0.622*** 0.619*** 0.616*** 

 (0.002) (0.007) (0.090) (0.080) (0.085) (0.085) (0.085) 

N 300 300 300 300 300 300 300 

R2 0.809 0.819 0.854 0.890 0.893 0.894 0.894 

Year YES YES YES YES YES YES YES 

ID YES YES YES YES YES YES YES 

Standard errors in parentheses 

* p < 0.1, ** p < 0.05, *** p < 0.01 



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4.3 Robustness Checks 

To verify the validity of the previous regression results, the following three methods were used for robustness 
checks: 

1. Winsorization. To make the data smoother, this paper performed winsorization at the 1% and 99% percentiles 
for the data of the explanatory variable, dependent variable, and control variables. The regression results are shown 
in Column (1) of Table 4. Digital infrastructure still has a significant positive impact on the urban-rural income 
gap at the 1% level, with an impact coefficient of 0.035. 

2. Lagged Explanatory Variable. Since digital infrastructure construction takes time, the construction effectiveness 
of digital infrastructure in the previous year may affect the current year's urban-rural income gap. Therefore, this 
paper lagged the new digital infrastructure construction variable by one period for regression. The results are 
shown in Table 4 Column (2). The results show that new digital infrastructure still has a positive impact on the 
urban-rural income gap. 

3. Replacing the Dependent Variable. Referring to existing literature, this paper uses the ratio of urban to rural 
residents' disposable income per capita (urir) to measure the urban-rural income gap. The results are shown in 
Column (3) of Table 4. Digital infrastructure still has a positive impact on the urban-rural income gap, consistent 
with the baseline regression results of this study. 

 

Table 4. Robustness Check Results 

Variable (1) (2) (3) 
 Winsorization Lagged Explanatory Var.  Replace Dep. Var. 
infra 0.035***  0.664*** 
 
linfra             
  

(0.012) 0.027** 
(2.370) 

(0.133) 

market 0.001* -0.001 0.003 
 (0.001) (-0.790) (1.016) 
pgdp -0.031*** -0.027*** -0.044* 
 (0.008) (-6.173) (-1.920) 
urb -0.245*** -0.211*** -0.240** 
 (0.034) (-8.161) (-2.162) 
stru 0.010*** 0.012*** 0.014 
 (0.003) (6.079) (1.402) 
edu -0.101 -0.144*** -1.349 
 (0.109) (-3.074) (-0.996) 
fdi -0.011 -0.005 0.045 
 (0.008) (-0.781) (1.526) 
Observations 300 270 300 
Adjusted R-squared 0.981 0.881 0.546 
Year YES YES YES 
ID YES YES YES 

Standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

 

4.4 Heterogeneity 

4.4.1 Heterogeneity Analysis 

Considering the differences in resource endowments and economic development stages across regions in 
China[^1], and the significant gaps in new digital infrastructure, this study conducts a classified analysis of regional 
heterogeneity. First, this study conducts separate regressions for the eastern, central, and western regions. The 
regression results are shown in Columns (1) and (2) of Table 5. The results show that in both the eastern and 
central-western regions, the regression coefficient for digital infrastructure construction is significant, meaning the 
effect of digital infrastructure construction widening the urban-rural income gap holds in the eastern and central-
western regions. Therefore, since coastal areas have a better foundation in digital infrastructure construction 



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compared to non-coastal areas, this study further conducts separate regression tests for economically developed 
and less developed regions. The regression results are shown in Columns (3) and (4) of Table 5. The results 
indicate: In economically developed regions, the regression coefficient for new digital infrastructure construction 
is not significant. From the above sub-regional test results, it can be seen that the impact effect of new digital 
infrastructure construction widening the urban-rural income gap is mainly manifested in economically less 
developed regions. The dividend effects brought by digital infrastructure construction include the following 
aspects: First, digital infrastructure construction is conducive to the development of agricultural industries, 
promoting agricultural products from villages to cities and increasing farmers' income levels; second, digital 
infrastructure construction facilitates the return of talent to rural areas, providing intellectual support for 
agricultural industry development; third, digital infrastructure construction provides new platforms for industrial 
development, such as e-commerce and live streaming, expanding sales channels for agricultural products, all of 
which become new platforms for increasing farmers' income. Regional development imbalance is a basic national 
condition in China. Due to the existence of the digital divide, economically less developed regions have 
deficiencies in accessing and utilizing digital infrastructure, meaning the positive effect of digital infrastructure 
construction on narrowing the urban-rural income gap has not yet manifested. Therefore, in economically less 
developed regions, digital infrastructure construction widens the urban-rural income gap. This conclusion aligns 
with hypothesis H2. 

 

Table 5. Heterogeneity Analysis Regression Results 

 (1) (2) (3) (4) 
 Eastern Central-Western Developed Regions Less Developed 

Regions 
infra 0.047*** 0.084** 0.014 0.180*** 
 (3.372) (2.608) (1.196) (4.761) 
market -0.000 0.003 -0.000 0.003** 
 (-0.229) (1.643) (-0.410) (2.084) 
pgdp -0.046*** -0.004 -0.027** -0.039*** 
 (-4.026) (-0.315) (-2.576) (-3.444) 
urb -0.216*** -0.331*** -0.234*** -0.383*** 
 (-5.898) (-3.368) (-5.978) (-4.420) 
stru 0.001 0.013** 0.014*** 0.009** 
 (0.211) (2.255) (3.968) (2.007) 
edu -0.012 -0.038 -0.025 -0.189 
 (-0.291) (-0.240) (-0.536) (-1.073) 
fdi -0.016* -0.076** -0.000 -0.078*** 
 (-1.838) (-2.244) (-0.056) (-3.616) 
_cons 0.725*** 0.285** 0.509*** 0.696*** 
 (5.782) (2.153) (4.486) (6.076) 
 Year Yes Yes Yes Yes 
ID Yes Yes Yes Yes 
N 100 80 120 180 
R2 0.906 0.932 0.906 0.915 
F 44.459 48.053 55.097 97.660 

***p<0.01", "**p<0.05", "*p<0.10 

 

4.5 Mediation Effect Test 

Based on the above analysis of policy effects, to further study the mechanism through which new digital 
infrastructure affects the urban-rural income gap, this paper selects agricultural industrial agglomeration as the 
mediating variable and uses Jiang Ting's method for testing. The mediation effect test results are shown in Table 
6. In Column (1) of Table 6, after adding the mediating variable, the test result coefficient is -0.151 and passes the 
test at the 1% significance level. This shows that agricultural industrial agglomeration plays a mediating role in 
the relationship between new digital infrastructure construction and the urban-rural income gap, i.e., new digital 
infrastructure construction narrows the urban-rural income gap by increasing agricultural industrial agglomeration 



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effects, proving the existence of hypothesis H3. The regression results in Column (2) of Table 6 are basically 
consistent with the baseline regression results. 

 

Table 6. Mediation Effect of Agricultural Industrial Agglomeration 

 (1) (2) 

 gig theil 

infra -0.931*** 0.054 

 (-4.416) (0.590) 

market 0.009 -0.005 

 (0.614) (-0.886) 

pgdp -0.231** -0.230*** 

 (-2.507) (-5.935) 

urb 0.921* -1.531*** 

 (1.782) (-7.083) 

stru 0.248*** 0.153*** 

 (6.352) (8.745) 

edu 15.827*** -11.300*** 

 (3.924) (-6.545) 

fdi -0.189 -0.032 

 (-1.400) (-0.562) 

gig  -0.151*** 

  (-5.883) 

_cons 2.642*** 4.532*** 

 (3.442) (13.887) 

N 300 300 

R2 0.320 0.922 

F 17.614 384.937 

***p<0.01", "**p<0.05", "*p<0.10 

 

5. Conclusions 

To achieve common prosperity, it is essential to address the problem of unbalanced and inadequate development 
to ensure that all people share the fruits of development equally. In the process of high-quality development, it is 
necessary to continuously increase per capita income, narrow the urban-rural income gap, promote common urban 
and rural development, and thereby achieve common prosperity. From the perspective of digital infrastructure 
construction, this study selects panel data from 30 Chinese provinces from 2014 to 2023 and uses a two-way fixed 
effects model to examine the impact effects, mechanisms, and heterogeneity of new digital infrastructure 
construction on the urban-rural income gap. The main conclusions are as follows: Digital infrastructure 
construction widens the urban-rural income gap, a result which holds under a series of robustness checks. In-depth 
analysis of the mechanism reveals that digital infrastructure helps increase urban residents' disposable income per 
capita, but its promoting effect on increasing rural residents' net income per capita has not yet manifested. 
Examining sub-samples by region across the country finds that the effect of digital infrastructure construction 
widening the urban-rural income gap is more significant in the western regions and economically less developed 
regions. Tests on mediating variables also found that, by increasing agricultural industrial agglomeration, digital 
infrastructure construction narrows the urban-rural income gap. 

The research results of this paper provide theoretical support for narrowing the urban-rural income gap and 
achieving common prosperity. To this end, this paper proposes the following policy recommendations: (1) 



as.ideasspread.org Agricultural Science Vol. 7, No. 2; 2025 

 45       Published by IDEAS SPREAD 
 

Accelerate the construction of digital infrastructure and build a sound digital infrastructure system. On the one 
hand, promote the construction of 5G networks, industrial internet, big data centers, power stations, etc.; on the 
other hand, use new-generation information technology to digitally and intelligently upgrade traditional 
infrastructure, thereby providing solid support for digital economic development. (2) Digital infrastructure 
construction in rural areas lags behind, and per capita income is low, leading to a continuously widening urban-
rural income gap. Therefore, it is necessary to scientifically plan and comprehensively coordinate digital 
infrastructure construction, focusing on strengthening digital infrastructure construction in rural areas, western 
regions, and less developed regions, providing sufficient funding and technical support, promoting the deep 
integration of digital technology and the rural economy, bridging the digital divide, thereby increasing per capita 
income in rural areas and achieving common prosperity. (3) Increase the proportion of education expenditure in 
fiscal expenditure, strengthen human capital accumulation and investment, improve the quality of education 
development, optimize the allocation of educational resources, and increase support for human capital investment 
in backward and rural areas. Simultaneously, optimize the allocation of human capital and improve the efficiency 
of human capital allocation across industries and sectors. Encourage specialized digital talent to flow to rural and 
backward areas, transmit advanced digital technology application experience from developed regions, and improve 
the digital skills of residents in rural areas. (4) Accelerate the development of rural productive service industries 
such as rural finance, enhance the ability of rural areas to utilize new-generation information technology, realize 
the value of the network economy, thereby narrowing the urban-rural income gap and achieving common 
prosperity. 

References 

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Xie Xuemei, & Zhu Qiwei. (2021). How does corporate green innovation practice solve the "harmonious 
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Xu Mengmeng, Li Bingchao, Li Jiayao, et al. (2023). The mechanism, current situation, and countermeasures of 
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convergence of urban and rural residents' income gap. Urban Problems, (01), 70-84. 
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Copyrights 

Copyright for this article is retained by the author(s), with first publication rights granted to the journal. 

This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution 
license (http://creativecommons.org/licenses/by/4.0/). 

 

 

 
















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    /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
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    /UKR <FEFF04120438043a043e0440043804410442043e043204430439044204350020044604560020043f043004400430043c043504420440043800200434043b044f0020044104420432043e04400435043d043d044f00200434043e043a0443043c0435043d044204560432002000410064006f006200650020005000440046002c0020044f043a04560020043d04300439043a04400430044904350020043f045604340445043e0434044f0442044c00200434043b044f0020043204380441043e043a043e044f043a04560441043d043e0433043e0020043f0435044004350434043404400443043a043e0432043e0433043e0020043404400443043a0443002e00200020042104420432043e04400435043d045600200434043e043a0443043c0435043d0442043800200050004400460020043c043e0436043d04300020043204560434043a0440043804420438002004430020004100630072006f006200610074002004420430002000410064006f00620065002000520065006100640065007200200035002e0030002004300431043e0020043f04560437043d04560448043e04570020043204350440044104560457002e>
    /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing.  Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
  >>
  /Namespace [
    (Adobe)
    (Common)
    (1.0)
  ]
  /OtherNamespaces [
    <<
      /AsReaderSpreads false
      /CropImagesToFrames true
      /ErrorControl /WarnAndContinue
      /FlattenerIgnoreSpreadOverrides false
      /IncludeGuidesGrids false
      /IncludeNonPrinting false
      /IncludeSlug false
      /Namespace [
        (Adobe)
        (InDesign)
        (4.0)
      ]
      /OmitPlacedBitmaps false
      /OmitPlacedEPS false
      /OmitPlacedPDF false
      /SimulateOverprint /Legacy
    >>
    <<
      /AddBleedMarks false
      /AddColorBars false
      /AddCropMarks false
      /AddPageInfo false
      /AddRegMarks false
      /ConvertColors /ConvertToCMYK
      /DestinationProfileName ()
      /DestinationProfileSelector /DocumentCMYK
      /Downsample16BitImages true
      /FlattenerPreset <<
        /PresetSelector /MediumResolution
      >>
      /FormElements false
      /GenerateStructure false
      /IncludeBookmarks false
      /IncludeHyperlinks false
      /IncludeInteractive false
      /IncludeLayers false
      /IncludeProfiles false
      /MultimediaHandling /UseObjectSettings
      /Namespace [
        (Adobe)
        (CreativeSuite)
        (2.0)
      ]
      /PDFXOutputIntentProfileSelector /DocumentCMYK
      /PreserveEditing true
      /UntaggedCMYKHandling /LeaveUntagged
      /UntaggedRGBHandling /UseDocumentProfile
      /UseDocumentBleed false
    >>
  ]
>> setdistillerparams
<<
  /HWResolution [2400 2400]
  /PageSize [612.000 792.000]
>> setpagedevice

