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Internet Development and Urban–Rural Consumption Inequality: Evidence from Chinese Cities

School of Business, Xiangtan University, Xiangtan 411105, China
School of Public Administration, Xiangtan University, Xiangtan 411105, China
Author to whom correspondence should be addressed.
Sustainability 2023, 15(12), 9755;
Submission received: 8 May 2023 / Revised: 15 June 2023 / Accepted: 16 June 2023 / Published: 19 June 2023


The impact of the digital dividends from Internet development on urban and rural residents is influenced by the existing urban–rural dual structure, resulting in heterogeneous and time-varying impacts on urban–rural consumption inequality. This study aims to investigate the nonlinear effect and mechanism of the Internet development on urban–rural consumption inequality in China. Using panel data from 263 prefecture-level cities between 2016 and 2019, we employ a two-way fixed effect model and a threshold model to examine this relationship. The findings of our study are as follows: (a) Internet development exhibits a U-shaped relationship with urban-rural consumption inequality. This U-shaped relationship is statistically consistent and stable in the whole country and in cities outside the five major urban agglomerations. (b) The level of urbanization acts as a threshold for the relationship between Internet development and urban–rural consumption inequality. (c) The influence of Internet development on urban–rural consumption inequality operates through its impact on income inequality and premature industrial structure. To effectively harness the positive impact of Internet development in reducing urban–rural consumption inequality, several key aspects deserve attention: acknowledging regional disparities and leveraging the Internet’s positive impact on urban–rural consumption inequality, considering the joint effects of Internet and urbanization developments, addressing digital divides among vulnerable groups, and promoting effective integration between the Internet and industry, particularly in manufacturing.

1. Introduction

The Sustainable Development Goals were introduced by the United Nations in 2015 with the objective of eradicating poverty and reducing inequality by 2030. In China, a significant developmental disparity exists between urban and rural areas. The Report of the 20th National Congress of the Communist Party of China (CPC) identified the advancement of balanced regional development and urban–rural integration as a crucial step toward facilitating high-quality growth. Although traditional economic models have favored urban areas over rural regions owing to their abundance of resources and higher productivity levels [1], the digital age has presented opportunities for rural communities to leverage technology and drive innovation, transformation, and growth within the digital economy [2]. Examining the impact of Internet development on urban–rural consumption inequality can shed light on the role and potential of the Internet in reducing these economic disparities and achieving sustainable development.
Consumption inequality closely mirrors income inequality, often to a greater extent than indicated by overall reported expenditures [3,4]. Economists typically focus on individuals’ utility function, which pertains to consumption and leisure rather than income [5]. The consumption gap provides a distinct indication of welfare disparities among socioeconomic groups compared with the income gap [6]. It reflects the imbalanced distribution of consumption resources, capabilities, and opportunities, influenced by individual differences in abilities as well as disparities in resources and opportunities [7]. Empirical evidence based on data from the Urban Household Survey (UHS) suggests that consumption inequality in urban China has increased by 67% during the sample period, surpassing the original figure of 36% reported by raw data. The disparity in consumption was more pronounced in the central and western regions, which played a pivotal role in driving up consumption inequality [8]. Expenditure inequality exhibits a greater magnitude in China compared with India, whereas income inequality is less pronounced. These variations can be attributed to differences in population demographics, with China being more urbanized and having smaller household sizes, as well as divergences in conditional income distributions based on the achieved educational level of the household head [9].
Information and communication technologies (ICTs), particularly the Internet, have been considered essential in facilitating market access, reducing transaction costs, and augmenting income for a significant proportion of individuals residing in developing nations since the beginning of the century [10]. Internet accessibility has a positive impact on poverty reduction, with greater benefits seen in the rural sector for reducing extreme income and multidimensional poverty compared with urban areas [11]. Digital inclusive finance, based on the Internet, can effectively narrow the per capita disposable income gap between urban and rural areas [12]. Although the Internet presents a potential solution for mitigating health disparities, ameliorating income inequality, promoting healthier lifestyles, and mitigating depression, the existence of a “digital divide” among rural residents, elderly individuals, and low-income populations highlights the urgent need for redress [13]. The digital divide can be observed across multiple levels, including inter-country, inter-regional, and socio-demographic (such as race, age, income, education, etc.) groups [14,15,16,17]. Researchers have shifted their focus from the first-level digital divide of Internet access to the second-level digital divide, which refers to the “soft” gap caused by differences in communication literacy and levels of Internet use [18]. In other words, uniform availability and proficiency in ICTs are not evenly distributed across different consumer segments [19].
The Internet has facilitated the digital transformation of the economy and has had a significant impact on social development. For example, the digital economy can effectively drive household consumption, with a more pronounced impact on urban residents than rural residents [20]. Digital economic development has also significantly contributed to enhancing the economic structure and material well-being of the elderly population [21]. Certain studies suggest that digital finance has the potential to mitigate consumption inequality by boosting household trickle-down consumption, particularly among low-income groups. Moreover, the level of digital financial development within a county is positively correlated with the amount of trickle-down consumption in Chinese households [22,23]. Recent research has focused on the inverted-U-shaped relationship between the development of the digital economy and the urban–rural consumption gap [24]. However, contrasting findings have been reported in other studies. On the basis of the data from the China Family Panel Studies (CFPS) covering 155 counties between 2010 and 2016, Internet penetration may cause an increase in consumption inequality. Additionally, higher education and a certain threshold of Internet penetration can mitigate the negative effects of the Internet [25].
Previous research has conducted comprehensive investigations into the correlation between Internet-related content and consumption, as well as its inequality. However, most studies focus only on the inequality of food or energy consumption while failing to comprehensively analyze the causes and shifts in the urban–rural consumption gap [26,27,28]. This leaves room for debate. First, the impact of Internet-related content on consumption inequality remains a contentious issue. Second, most studies fail to account for nonlinear relationships. Third, many empirical studies use provincial panels, leading to imprecise findings. Therefore, this study aims to explore the impact of Internet development on urban–rural consumption inequality, as well as the potential influencing mechanisms.
The remainder of the paper is structured as follows. First, drawing on a comprehensive literature review, we propose four research hypotheses and the corresponding theoretical framework to examine the impact of Internet development on urban–rural consumption inequality. Second, we employ panel data of prefecture-level cities or above from 2016 to 2019 to empirically examine the aforementioned hypotheses as well as conduct robustness tests and heterogeneity analyses. Third, we summarize and analyze the findings and providing corresponding recommendations.

2. Theoretical Analysis and Research Hypothesis

The development and application of the Internet have strengthened the interconnectedness between urban and rural areas, resulting in improved factor allocation, upgraded industrial structure, and impacted income distribution. The widespread use of the Internet has the potential to greatly enhance the consumer structure of individuals [29,30]. It is expected that the Internet will serve as a significant impetus in dismantling the urban–rural dual structure and unleashing consumption potential for both urban and rural residents. However, the existence of a “digital divide” among rural residents, elderly individuals, and low-income populations has resulted in new disparities in opportunities and impeded equitable access to the advantages of digital resources [31]. This may potentially exacerbate consumption inequality between urban and rural areas. Owing to the objective fact of uneven development between urban and rural areas and the difference in the ability of urban and rural residents to use the Internet, the above “digital dividend” and “digital divide” may have different manifestations in different stages of development. Therefore, we posit Hypothesis 1:
Hypothesis 1. 
The impact of Internet development on urban–rural consumption inequality exhibits a nonlinear relationship.
China faces the objective reality of imbalanced regional economic development and the urban–rural dual structure [31]. These factors contribute to a significant digital divide between the eastern, middle, and western regions of China as well as between cities and urban and rural areas. Urbanization, as a crucial driver for China’s leapfrog development, is closely linked to the distribution of income between urban and rural areas and consumption inequality. Urban areas, as important spatial carriers for economic activities, also provide essential infrastructure and talent support for the growth of the Internet. In general, a higher level of urbanization corresponds to a smaller digital divide, indicating that the level of urbanization may impact the relationship between Internet access and consumption inequality in urban–rural areas [32]. Therefore, we posit Hypothesis 2:
Hypothesis 2. 
Urbanization has a threshold effect on the impact of Internet development on urban–rural consumption inequality.
A significant urban–rural income disparity will inevitably result in a substantial consumption gap between urban and rural residents [3,4]. The application and development of the Internet deeply affect the income level and distribution of urban and rural residents [33]. As a universal informational medium, the Internet narrows the urban–rural divide by facilitating the exchange and sharing of information, enhancing the mobility of labor resources, and improving resource allocation. This significantly improves the income level of rural residents. The Internet, as a cost–effective and efficient method of information acquisition, reduces the costs associated with information searching, thus enhancing job matching efficiency, promoting employment opportunities and entrepreneurship, facilitating industrial upgrading, and diversifying income sources for residents. The rapid development of the Internet not only provides employment opportunities for farmers and improves their income level, but also accelerates the accumulation and optimization of their human capital [34], ultimately impacting the income gap between urban and rural areas. Based on Keynes’ absolute income hypothesis, consumption is determined by income. The income gap between urban and rural residents has a significant positive effect on the consumption gap. Therefore, we posit Hypothesis 3:
Hypothesis 3. 
Internet development impacts urban–rural consumption inequality through its influence on urban–rural income inequality.
The industrial structure has a significant impact on urban–rural consumption inequality [35,36]. With the advent of the Internet era, it has driven the modernization and development of the service industry, manufacturing industry and agriculture with information technology, gradually linking all aspects of the national economy. The integration of the Internet and traditional industries is a double-edged sword for China’s economy. In the case of inadequate innovation, the rapid development of the service industry over the manufacturing industry may lead to a premature industrial structure, which will have a negative impact on sustained economic growth [37]. This is especially evident when workers move from high-productivity manufacturing departments to low-productivity service industries such as food delivery. Premature industrial structure in the process of industrial evolution means that although the industrial structure is close to that of developed countries, the contradiction between supply and demand or sluggish demand and insufficient effective supply cannot be effectively resolved due to the lack of per capita income and national innovation capacity. Premature industrial structures constrain the development of the manufacturing industry, trigger a retreat in the industrial sector, result in sluggish national economic growth, and increase the risk of falling into the “middle-income trap” [38], further affecting the consumption sector and its urban–rural inequality. Therefore, we posit Hypothesis 4:
Hypothesis 4. 
Internet development impacts urban–rural consumption inequality through its influence on the premature industrial structure.
Figure 1 presents the mechanism diagram, briefly illustrating the main ideas of Hypotheses 1 to 4. A nonlinear relationship and a threshold effect between Internet development and urban–rural consumption inequality can be concluded. The influencing mechanism is that the Internet development affects urban–rural consumption inequality by influencing urban–rural income inequality and premature industrial structure.

3. Materials and Methods

3.1. Variables Selection

The variables in this study mainly include explained variables, core explanatory variables, a threshold variable, mechanism variables, and control variables. The details are as follows:

3.1.1. Explained Variable

Urban–rural consumption inequality can be measured using various methods in academic research [12]. The first method is absolute consumption inequality, which subtracts rural residents’ consumption from urban residents’ consumption. However, the relative degree of consumption inequality is challenging to determine due to the significant consumption disparities between cities. The second approach involves calculating the Gini coefficient based on the Lorenz curve. Although this indicates overall consumption inequality, the contribution of intra-group and inter-group gaps is difficult to determine. The third method is relative consumption inequality, which uses the ratio of urban to rural residents’ consumption. However, this cannot accurately reflect the impact resulting from changes in the proportion of urban and rural populations. The fourth method is the Theil index, which has the significant advantage of decomposability, allowing the urban–rural gap to be separated from the overall gap while maintaining consistency. The Theil index can also reflect changes in urban and rural consumption polarization while considering demographic factors. Higher values indicate higher levels of inequality. In this paper, the Theil index is used to measure the total urban–rural consumption inequality, combining existing popular practices. The formula for the Theil index is as follows:
t h e i l i t = C u , i t C i t ln ( C u , i t C i t / P u , i t P i t ) + C r , i t C i t ln ( C r , i t C i t / P r , i t P i t )
C u , i t , C r , i t , respectively, represent the urban and rural consumption expenditure of year t in city i. P u , i t , P u , i t , respectively, represent the urban and rural permanent population of year t in city i. C it , P i t , respectively, represent the total consumption expenditure and total population in both urban and rural areas of year t in city i.

3.1.2. Core Explanatory Variable

The core explanatory variable in this study is the level of Internet development. The Tencent Research Institute’s China Internet Plus Index is used as a proxy variable to measure Internet development. To account for measurement differences, the variable is logarithmically transformed.

3.1.3. Threshold Variable

We employ the level of urbanization as a threshold variable, which is measured by the proportion of urban permanent residents in the total population of the region.

3.1.4. Mechanism Variables

Referring to the research of Zou et al. [6] and Tian et al. [39], we select the urban–rural income inequality and the industrial structure upgrading as the mechanism variables. The urban–rural income inequality is also expressed by the Theil index. The industrial structure precocity is represented by the ratio of value added by the tertiary industry to that of the secondary industry minus 1.

3.1.5. Control Variables

To mitigate the endogeneity problem arising from missing variables, this study incorporates significant regional economic characteristic variables that impact urban–rural consumption inequality as control variables. Based on previous research [13,25] and considering the theoretical impact of control variables on dependent variables, the following control variables are selected: economic development level (measured by GDP per capita, logarithmically processed), industrial structure (expressed as the proportion of workers in the tertiary industry), fiscal expenditure (expressed as the proportion of general fiscal expenditure budget to GDP), educational expenditure (expressed as the proportion of educational expenditure to GDP), level of economic openness (expressed as foreign–invested enterprises’ industrial output as a percentage of GDP), and unemployment rate (expressed as the proportion of urban registered unemployed people to the total population).

3.2. Data Source and Processing

Data for the core explanatory variables are obtained from the Tencent Research Institute’s “Internet Plus” Index Report for China, which provides a comprehensive reflection of the Internet development status of cities in several dimensions. Data for urban–rural consumption inequality, disposable income inequality, industrial structure, and control variables are collected from the China Statistical Yearbook and EPS data platform for cities at the prefectural level or above. Panel data from 2016 to 2019 are selected and processed as the sample for this research. Samples with missing main variables are removed, and some missing values are filled in. The data are then combined into balanced panel data, resulting in 1323 observations.
Table 1 provides the descriptive statistical results for the main variables. During the sample period, the minimum, maximum, and standard deviation of urban–rural consumption inequality (measured by the Theil index) for each city are 0.001, 0.319, and 0.035, respectively, indicating certain differences in the urban–rural consumption gap among cities. The level of Internet development varies from 0.048 to 3.604, highlighting significant differences in the level of Internet development among cities in China. The multicollinearity test results show that the variance inflation factor (VIF) values of all variables are less than 10, indicating no multicollinearity among the variables.

3.3. Research Methods

3.3.1. Basic Model

In this study, the Hausman test first significantly rejects the original hypothesis, indicating that the fixed effects model should be selected. To assess the joint significance of time dummy variables, we incorporated them into the individual fixed effects model and conducted an F statistic test. The results indicate the necessity of incorporating time fixed effects. Therefore, we employ a panel two-way fixed effect econometric model to examine the impact of Internet development on urban–rural consumption inequality. To investigate the potential nonlinear relationship, we introduce the quadratic term of Internet development into the model. Specifically, we construct the following panel model:
t h e i l i t = α 0 + α 1 n e t i t + α 2 n e t i t 2 + γ Z i t + λ t + u i + ε i t
In model (2), i represents cities at or above the prefecture level, whereas t denotes the year. The variable theilit refers to the urban–rural consumption inequality; netit is the Internet development index; n e t i t 2 represents the squared term of the Internet development index; Zit stands for a series of control variables; λ t , u i , and ε i t reflect the year fixed effects, the city fixed effects, and the random disturbance term, respectively.

3.3.2. Threshold Regression

To further investigate the potential threshold effect in the nonlinear relationship between the Internet development and the urban–rural consumption inequality, we construct a dynamic panel single–threshold model. Based on our theoretical analysis in the previous section, we utilize the level of urbanization as a threshold variable in the following model:
t h e i l i t = α 0 + α 1 n e t i t × I ( urb θ ) + α 2 n e t i t × I ( urb θ ) + γ Z i t + λ t + u i + ε i t
In model (3), urb represents the threshold variable; θ represents the value at which to test the threshold; and I(·) is an indicator function for the threshold model that equals 1 if true in parentheses and 0 otherwise.

4. Results

4.1. Benchmark Regression Model Estimation

The regression estimation was performed on the benchmark regression model (2), and the results are presented in Table 2. To ensure the robustness of the findings, we examined the regression results by transitioning from linear mixed OLS regression to nonlinear mixed OLS regression, two-way fixed effect and two-way fixed effect with control variables added. Columns (1)–(4) display the regression results for each model variation. The consistent and significant negative effect of Internet development on urban–rural consumption inequality is observed at the 5% significance level, but its squared term consistently and significantly indicates a positive effect. We give precedence to the findings presented in column (4). In the context of low Internet development, a linear relationship prevails, wherein each unit increase in the level of Internet development corresponds to a 0.5% decrease in the urban–rural consumption inequality index. Conversely, in high Internet development scenarios, a nonlinear relationship emerges, where each unit increase leads to a 0.2% increase in the urban–rural consumption inequality index. This finding confirms the existence of a U-shaped relationship between Internet development and urban–rural consumption inequality, thus supporting Hypothesis 1. These findings align with the research of Tian et al. [39] and Jiang et al. [40], who previously established a U-shaped relationship between Internet development and both the urban–rural gap and premature industrial structure. The digital divide and digital dividends resulting from Internet development vary across different stages of its evolution, highlighting the importance of investigating the potential nonlinear relationship between Internet development and urban–rural consumption inequality.

4.2. Threshold Regression Model Estimation

Initially, we conduct a regression analysis using the three-threshold model, and the results indicate that only the p-value associated with the single threshold is less than 0.1, suggesting the suitability of the single threshold model for this study. In Table 3, we analyze the impact of Internet development on urban–rural consumption inequality by incorporating the level of urbanization as a threshold variable. The results indicate a threshold effect of urbanization on the impact of Internet development on urban–rural consumption inequality, providing support for Hypothesis 2. Additionally, our regression analysis reveals that Internet development has a significant impact on increasing urban–rural consumption inequality when the level of urbanization is below 0.3474. However, this impact becomes insignificant once the level of urbanization exceeds 0.3474. These findings suggest that a low level of urbanization is not conducive to the Internet’s role in bridging the consumption gap between urban and rural areas.
To validate the threshold results, we perform a likelihood ratio test (LR test) on the outcomes obtained from the single threshold model, as illustrated in Figure 2. The results demonstrate that the LR statistical graph of the single threshold intersects with the horizontal line, indicating the passing of the significance test and ensuring the authenticity of the threshold result.

4.3. Robustness Analysis

4.3.1. Change of Samples

Municipalities directly under the central government possess comparative advantages in political, economic, transportation, and technological resources [38], which results in well-developed Internet infrastructure. This simultaneous change in the Internet development index and urban–rural consumption inequality in these municipalities may introduce a self-selection bias in estimation results. In the benchmark regression, we control for city-level fixed effects to mitigate this issue. Furthermore, to enhance the robustness of our analysis, we exclude samples of municipalities directly under the central government. The regression results presented in column (1) of Table 4 are consistent with the benchmark regression results, confirming their robustness.

4.3.2. Endogenous Test

To address endogeneity, we carefully select appropriate instrumental variables that can effectively control for potential confounding effects. The infrastructure of local post offices affects subsequent stages of Internet development, as it serves as a continuation of traditional communication technology. Its influence on economic development gradually diminishes with decreased frequency of use, ensuring exclusivity. Following methods used by Zhao et al. [41] and Nunn and Qian [42], we construct an interaction term using the number of Internet users in China from the previous year and the number of Post Offices Per Billion People in each city in 1984 as instrumental variables for measuring Internet development during that year. In examining the U-shaped relationship using the instrumental variable approach, we include a squared term of the instrumental variable. The 2SLS estimation is manually conducted in this study, which may increase the variance of the estimation coefficients in the second stage; nevertheless, the results remain unbiased estimators [43].
The regression results are shown in columns (2)–(4) of Table 4. The variables post and post2 represent the instrumental variable and its squared term, respectively. From the regression results of the second stage, we observe that the U-shaped effect of Internet development on urban–rural consumption inequality remains significant even after considering endogeneity. The first-stage results demonstrate that the instrumental variables exhibit a high correlation with the core explanatory variable, as indicated by their highly significant coefficients. The overall F statistic of the model rejects the null hypothesis, indicating the absence of a weak instrumental variable problem. In general, the validity of the instrumental variables is verified.

4.4. The Empirical Tests of Influence Mechanism

4.4.1. Urban–Rural Income Inequality

This study calculates the Theil index of urban–rural income inequality and regresses it with the level of Internet development as the explained variable. The control variables are consistent with the benchmark regression. The regression results are presented in columns (1)–(2) of Table 5. The coefficient of the core explanatory variable exhibits a negative but insignificant effect, whereas the quadratic term displays a positive and significant impact. This indicates that the impact of Internet development on urban–rural income inequality is not nonlinear. Therefore, the present study employs a linear regression model. Column (2) reveals that the impact of Internet development on urban–rural income inequality is significantly positive, which, in turn, affects urban–rural consumption inequality through the former [3]. Hence, Hypothesis 3 is confirmed.

4.4.2. Premature Industrial Structure

Referring to the research of Tian et al. [39], it is evident that a U-shaped relationship exists between Internet development and the precocity of the industrial structure. Therefore, this study continues to use nonlinear models to verify the relationship between the two. The regression results presented in column (3) of Table 5 reveal a U-shaped relationship between Internet development and premature industrial structure, providing support for Hypothesis 4, and reinforcing the causal relationship posited in Hypothesis 1. The potential explanation for these findings is that in contexts of low Internet development, the utilization of this technology can enhance labor productivity and manufacturing efficiency within enterprises (industries). Concurrently, the Internet has expedited labor market mobility and bolstered employment rates within the service industry [44,45,46,47,48,49], thereby promoting the upgrading of the industrial structure and reducing consumption inequality. However, a high level of Internet development may lead to an excessive transfer of production factors from primary and secondary industries to the tertiary industry. When the national capacity for innovation and overall labor productivity within the tertiary industry are insufficient to sustain high-quality economic growth over time, it can result in a premature industrial structure and aggravate urban–rural consumption inequality.

4.5. Heterogeneity Analysis

The spatial distribution of Internet development in China exhibits clear agglomeration characteristics, prompting this study to investigate the heterogeneity of its impact on urban–rural consumption inequality across different city clusters. Urban agglomerations, as the most dynamic and competitive core areas in economic development patterns, typically exhibit compact spatial organization and close economic links [50]. Owing to significant regional heterogeneity in industrial structure and development stage, the impact of Internet development on urban–rural consumption inequality may vary greatly. With consideration of the development planning and level of urban agglomerations in China, this study primarily examines the heterogeneity in five major agglomerations: Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YD), the Pearl River Delta (PRD), Chengdu–Chongqing (CC) and the middle reaches of Yangtze River (MY). Considering the consistency in sample size and geographical location of city clusters, this study classifies the three major urban agglomerations (BTH, YD, and PRD) into one group, while dividing CC and MY into another group. All cities outside of these aforementioned city clusters are classified into a third group for regression analysis.
The grouped regression results are shown in columns (4)–(6) of Table 5. A significant difference is noted in the results between city clusters and other cities. Specifically, in other cities, the impact of Internet development on urban–rural consumption inequality exhibits a significant U-shaped relationship, consistent with the regression results of the full sample. For the cities of BTH, YD and PRD, Internet development significantly exacerbates the urban–rural consumption inequality. However, for the cities of CC and MY, it significantly alleviates such inequality. The reason for this disparity may lie in the fact that BTH, YD, and MY, as China’s three world-class agglomerations, boast a relatively high level of economic and Internet development. However, they lack sufficient support for innovation and productivity, resulting in significant urban–rural income inequality and a premature industrial structure, which ultimately exacerbates consumption inequality. On the other hand, for the agglomerations in the central region, namely CC and MY, they have not yet reached the standard to lead to premature industrial structure. Instead, they promote industrial upgrading and reduce urban–rural consumption inequality.

5. Discussion

China’s internet penetration rate reached nearly 50% in 2014, marking the advent of the “Internet+” era and presenting a unique opportunity for development. However, it is crucial to remain cognizant of the double–edged sword effect that comes with internet development. Internet development can directly facilitate consumption upgrading and market renewal. Simultaneously, by improving information exchange and resource allocation, it can promote industrial upgrading, economic growth, employment increase, and indirectly provide stable support for expanding domestic demand. However, the digital divide between urban and rural residents has a significant impact on urban–rural consumption inequality caused by Internet development. Building upon the relevant research conducted by Tian (2021) and Luo (2021), we systematically examine the nonlinear relationship and influencing mechanism between Internet development and urban–rural consumption inequality.
The research conclusions are as follows: First, a U-shaped relationship exists between Internet development and urban–rural consumption inequality. That is, with Internet development, the urban–rural consumption inequality index will first decline and then rise. The main reason is that Internet development that is not matched with technological breakthrough and product innovation will lead to a premature industrial structure and further affect economic development and urban–rural consumption inequality. In the whole country and other cities except for the five major urban agglomerations, the U-shaped relationship between Internet development and urban–rural consumption inequality is statistically consistent and stable, indicating that Internet development is a double-edged sword. Second, the level of urbanization exhibits a threshold effect on the relationship between Internet development and urban–rural consumption inequality. When the level of urbanization is below 0.3474, Internet development will exacerbate urban–rural consumption inequality. Finally, Internet development will affect urban–rural consumption inequality by influencing urban–rural income inequality and premature industrial structure.

5.1. Practical Implications

Based on the above research conclusions and the previous theoretical analysis, this paper proposes the following policy implications: First, we must fully leverage the positive impact of the Internet on urban–rural consumption inequality. The Internet has penetrated into all links of the intra-product division of labor and has different degrees of impact on employment in the labor market and the labor productivity of the production link. We should fully exploit the role of the Internet in resource reallocation, deeply explore and continuously unleash the dividends of IT, digitalization and networking development, and maximize the scale effect and network effect of Internet development. For example, we can expand internet services by improving their application content, enhance societal digital capabilities, reinforce governmental efforts toward digital transformation and promote data-driven governance.
Second, a precise analysis of regional disparities must be conducted, taking into full account the U-shaped impact of Internet development on urban–rural consumption inequality. For BTH, YD and other economically developed regions, the primary focus should be on bolstering their internet development advantages by augmenting investments in technological innovation and enhancing production efficiency within the service industries. For the cities with average or poor development in central and western China, they should promptly address the deficiencies in their information infrastructure construction, leverage the positive impact of Internet development, and establish an innovative model for urban–rural interaction development, both inter-regionally and intra-regionally. This can effectively mitigate resource allocation disparities across different areas and address the issue of unbalanced regional and urban–rural development.
Third, as we increase the scale and intensity of our investments in the Internet, regional disparities in urbanization development must be considered. All regions should adhere to the objective law of coordinated economic development, conduct policy research on integrating the Internet and urbanization based on local conditions, and establish and improve relevant guarantee mechanisms. Only by surpassing the threshold of urbanization can we fully leverage the constraining effect of Internet development on urban–rural consumption inequality.
Fourth, we should facilitate the effective integration of the Internet with industry, particularly in manufacturing, to compensate for the dearth of technological breakthroughs and product innovation. We must harness the innovative potential of the Internet to drive evolution toward a “Kuznets” industrial structure within both the manufacturing and service sectors, thereby achieving sustained growth in macroeconomic conditions and domestic demand.
Fifth, the digital divide among vulnerable groups must be addressed and digital dividends should be created for them. Enhancing the digital skills of marginalized populations such as farmers and elderly individuals is crucial in bridging this gap. We should reinforce effective linkages between poverty alleviation and rural revitalization policies to narrow the digital divide across all regions during the process of Internet development. Fiscal and urbanization policies must be coordinated to promote equitable access to digital dividends and vigorously advocate for “equal internet speed” in both rural and urban areas. In addition, the equalization of basic public services should be promoted. We should improve educational resource allocation and provide training programs to enhance digital literacy, thereby enabling individuals to increase their income levels and fully realize their consumption potential.

5.2. Limitations and Future Research

There are several limitations to this study. First, the sample scope is limited. Due to the lack of some indicators, the selection of cities within China may not be inclusive, potentially leading to biased results. Future research can consider expanding the sample size and incorporating a more diverse range of cities to enhance the generalizability of the findings.
Second, the data in this study only begin from 2016 due to the availability and accuracy of Internet development index data. As such, this study fails to explore the relationship between Internet development and urban–rural consumption inequality before then. Future research can consider incorporating earlier data to provide a more comprehensive understanding of the long-term trends and dynamics.
Third, although we find that the level of urbanization exhibits a threshold effect on the relationship between Internet development and urban–rural consumption inequality, we do not examine whether other factors also influence this relationship. Future research can explore the interaction effects between Internet development and various contextual factors to gain a deeper understanding of the complex dynamics at play.
In future research, a multidimensional index system can be established to comprehensively evaluate the development status and trends of the Internet in China. Additionally, other factors that impact the relationship between Internet development and urban–rural consumption inequality can be further investigated to promote their mutually beneficial growth.

Author Contributions

Conceptualization, J.Z.; formal analysis, Z.L. and H.W.; writing—original draft, Z.L. and J.Z.; writing—review and editing, Z.L. and H.W. All authors have read and agreed to the published version of the manuscript.


This research was funded by Hunan Provincial Innovation Foundation for Postgraduate (Grant Number: CX20220629).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data resources are clear in Section 3.2 of this article. No new data were created or analyzed in this study. Data sharing is not applicable to this article due to privacy.


We sincerely thank Francoise Fu and the three anonymous reviewers for their constructive comments and great support throughout the reviewing process.

Conflicts of Interest

The authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article.


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Figure 1. Theoretical mechanisms.
Figure 1. Theoretical mechanisms.
Sustainability 15 09755 g001
Figure 2. LR test of single threshold model.
Figure 2. LR test of single threshold model.
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Table 1. Descriptive statistical results of the main variables.
Table 1. Descriptive statistical results of the main variables.
Variable SignVariable ImplicationMeanStdMinimumMaximumVIF1/VIF
theilUrban–Rural Consumption Inequality0.0520.0350.0010.319
netLogarithmic of Internet Development Index0.5610.5140.0483.6042.490.402
theil inUrban–Rural Disposable Income Inequality0.0680.0330.0050.186
ISPIndustrial Structure Precocity1.1730.5880.3705.1541.730.577
urbanUrbanization Rate0.5770.1380.2231.0003.230.310
postPost Offices Per Billion People of year 1984 × Time Trend Term0.3080.2200.0001.4091.110.898
emsBusiness Volume of Post Services Per Capita0.0280.0300.0030.1181.650.605
pgdpLogarithmic of Per
Capita GDP
indIndustrial Structure0.5550.1310.1660.9111.870.534
expFiscal Expenditure as a Percentage of GDP0.2050.0880.0660.7043.340.300
eduEducation Expenditure as a Percentage of GDP0.1700.0350.0630.3041.630.614
fdiIndustrial Output Value of Foreign–Invested Enterprises’ output as a Percentage of GDP0.0980.1430.0000.9461.490.672
lostUnemployment Rate0.0060.0040.0010.0251.610.619
Table 2. The benchmark regression results.
Table 2. The benchmark regression results.
net−0.008 ***−0.014 ***−0.005 **−0.005 **
net2 0.003 ***0.002 **0.002 **
pgdp −0.007 **
ind −0.007
exp 0.039 ***
edu −0.004
fdi −0.017 **
lost 0.201
Constant0.054 ***0.056 ***0.054 ***0.125 ***
Number of code263263263263
R-squared 0.1950.225
Hausman testProb > Chi2 = 0.0457
Note: *** and ** respectively indicate significance at the levels of 1% and 5%. The values in brackets are the t-statistics—the same as below.
Table 3. Threshold effects of urbanization.
Table 3. Threshold effects of urbanization.
net (urban < 0.3474)0.041 *** (4.81)
net (urban > 0.3474)0.001(0.82)
control variablesYES
Constant0.122 *** (2.97)
Number of code263
Note: *** indicates significance at the level of 1%.
Table 4. Robustness test results.
Table 4. Robustness test results.
Change Sample2SLS First Stage2SLS First Stage2SLS Second Stage
net−0.006 * −0.057 **
(−1.90) (−2.36)
net20.002 ** 0.022 ***
(2.29) (2.68)
post 4.063 ***11.350 ***
post2 −1.908 ***−4.422 ***
pgdp−0.006 *0.0670.203−0.010 **
ind−0.0080.2201.028 *−0.013 *
exp0.040 ***−0.286−0.6430.030*
edu−0.0051.291 *8.461 ***−0.136 ***
fdi−0.016 **−0.753 **−3.405 ***0.005
lost0.213−7.501 ***−48.085 ***0.789 ***
Constant0.121 ***−1.030−5.398−0.057 **
Number of code259266266262
Note: ***, ** and *, respectively indicate significance at the levels of 1%, 5% and 10%.
Table 5. Results of the mechanism test and heterogeneity test.
Table 5. Results of the mechanism test and heterogeneity test.
Theil_inTheil_inISPBTH + YD + PRDCC+ MYOther Cities
net−0.0010.002 ***−0.206 **0.007 ***−0.010 *−0.017 ***
net20.001 *** 0.037 *0.0000.0010.006 ***
(3.15) (1.67)(0.03)(1.27)(2.62)
pgdp−0.003−0.003−0.380 ***0.0010.000−0.012 **
ind0.0050.005*0.1430.023 ***−0.001−0.022 **
exp−0.011−0.0112.203 ***−0.008−0.059 *0.049 **
edu0.024 **0.028 **1.805 **0.006−0.091 **−0.004
fdi−0.014 ***−0.015 ***0.426 *−0.0050.049 **−0.020 *
lost0.012−0.013−2.4740.171 *−0.0100.161
Constant0.096 ***0.094 ***3.320 **0.0090.0660.193 ***
Number of code0.6212650.4750.5750.4080.244
Note: ***, ** and *, respectively indicate significance at the levels of 1%, 5% and 10%.
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Zhu, J.; Li, Z.; Wang, H. Internet Development and Urban–Rural Consumption Inequality: Evidence from Chinese Cities. Sustainability 2023, 15, 9755.

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Zhu J, Li Z, Wang H. Internet Development and Urban–Rural Consumption Inequality: Evidence from Chinese Cities. Sustainability. 2023; 15(12):9755.

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Zhu, Jian, Zifang Li, and Hui Wang. 2023. "Internet Development and Urban–Rural Consumption Inequality: Evidence from Chinese Cities" Sustainability 15, no. 12: 9755.

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