Has Electronic Commerce Growth Narrowed the Urban–Rural Income Gap? The Intermediary Effect of the Technological Innovation

Abstract

In the internet era, the development of e-commerce has had an important impact on all aspects of society. Many previous studies focused on the social and economic effects of the development of e-commerce. However, previous studies paid little attention to the impact of e-commerce development on the urban–rural income gap. Here, the influence of electronic commerce growth on the urban–rural income gap is investigated based on Chinese provincial panel data. The result reveals that electronic commerce growth measured by per capita express volume (pair value) can effectively explain the urban–rural income gap, i.e., the income gap between cities and the countryside is reduced with the rise of the electronic commerce growth level after a series of possible factors for the urban–rural income gap (e.g., urbanization rate, industrial proportion, dependency ratio, and human capital) are controlled. In the abovementioned conclusions, the influence of electronic commerce growth on the income gap between cities and the countryside still takes on significance even after the use of the post office number and the mileage of grade roads in 1991 as the instrumental variables of the current electronic commerce growth and the robustness test. The growth of electronic commerce is a vital new approach for adjusting the urban–rural income distribution pattern. The study further reveals that electronic commerce growth does not directly affect the urban–rural income gap by affecting technological innovation. The research results of this study provide a reference for electronic commerce growth and the income distribution of cities and the countryside.

1. Introduction

The economic growth of China has been significantly advancing since the introduction of Reform and Opening-Up. From 2001 to 2019, China’s GDP increased by 9.04% on a year-to-year basis on average (2000 = 100), ranking it as the second largest economy and the largest developing country worldwide. However, behind the abovementioned significant advance in economic growth, a considerable number of distortions remain from a structural perspective (e.g., the urban–rural income imbalance), which have been a bottleneck, restricting Chinese social stability and economic growth that could have been sustained [1]. For instance, in 2018, the average difference between the urban–rural income gap between the five provinces exhibiting the largest income gap (the Theil index) was 5.167 times larger than that of the provinces exhibiting the smallest income gap, and the income gap between cities and the countryside continues to be persistent and severe. Accordingly, the way to effectively reduce the income gap between cities and the countryside has become a vital issue facing current Chinese economists and policymakers.

Since the Second World War, the world has been continuously facing an energy crisis, a population crisis (from population expansion to population decline), a resource consumption crisis, as well as other problems (e.g., environmental pollution, global warming, unemployment, poverty, and the wealth gap), which have aroused the interest of numerous scientists, sociologists, politicians, etc., [2,3]. In the above-described context, the United Nations has addressed several issues regarding sustainable growth, and the theory of sustainable growth has been continuously applied in real life. The theory of sustainable growth can be summarized with the following three points of consensus regarding the exploration and practice of sustainable growth over the past three decades: (1) It is imperative to follow scientific and technological innovation and provide a novel impetus for economic growth; (2) The accumulation of wealth should not ensure high-quality economic growth by ignoring the environment; (3) Fair and orderly growth and social harmony should be facilitated, and social unrest and imbalance should be reduced [4]. Sustainable growth covers a wide range of contents (e.g., politics, economy, culture, society, and other aspects), while the economic basis (economic growth) determines the superstructure (e.g., politics and culture), which is the most basic point of consensus. The growth inequity caused by the imbalance and insufficiency in the process of economic growth is one of the important subjects of sustainable growth research [5]. Thus, the reasons for the income gap between cities and the countryside investigated here further expand the theory of sustainable growth, and have important practical and theoretical significance for achieving sustainable growth. The development of e-commerce belongs to the economic category, and the income gap between urban and rural areas is related to social equity and belongs to the social category. Therefore, it is of great significance to study this topic for the sustainable development of China’s economy and society. As a world power and an important part of the global market, China’s development is of great significance in the sustainable development of the global society and economy. At the same time, the influencing factors of the urban–rural income gap studied in this paper can expand and enrich the theory of sustainable development.

In developing countries, the urban–rural dual structure and the significant income gap of residents in cities and the countryside are common phenomena. Rural residents’ income, social insurance, welfare, and other financial aspects are far less than those of residents living in cities. The urban–rural growth imbalance has created inequality between incomes, which is not conducive to the sustainable growth of the economy, and even threatens the stability of society. The enormous income inequality boosts the sustainable growth of the national economy while threatening social stability in the long term. On 19 February 2019, the No. 1 central document proposed to implement the strategy of the digital village and further facilitate “Internet plus”. On 26 January 2022, the Central Cyberspace Office and other departments issued the 14th Five-Year Plan for the digital village, aiming to balance the urban–rural growth gap and achieve common prosperity. Like the traditional economy, the electronic commerce economy has also brought about GDP growth and improved people’s lives, which has taken on critical significance in reducing the growth imbalance in cities and the countryside. Premier Li Keqiang has repeatedly stressed that the growth of rural electronic commerce is an effective way of facilitating sustainable living for poor people in rural areas, shake off poverty, and become rich. However, will e-commerce help narrow the urban–rural income gap? To be specific, does the growth of electronic commerce realize the universal improvement and fair sharing of income or welfare? Have rural residents shared the dividends brought by the rapid growth of electronic commerce? Is the growth of rural electronic commerce worth promoting? These are problems that need to be solved urgently on a theoretical level. At present, research on the income distribution effect of electronic commerce economic growth is quite rare at home and abroad. This study will systematically answer the above questions based on empirical evidence from China.

The contributions of this study to the existing relevant research are as follows: (1) Making up for the lack of research on electronic commerce applications for solving developing countries’ income distribution in cities and the countryside. At present, the research on the economic impact of electronic commerce mainly focuses on production, exchange (trade), consumption, and other aspects, and there is little research on its distribution effect; (2) It provides evidence that support the fact that electronic commerce is an effective way (technological innovation) to promote growth in rural areas; (3) Since some factors (e.g., missing variables or reverses causality) may result in endogenous problems for explanatory variables, an instrumental variable method has been adopted to perform a two-stage test to make the result estimation more accurate.

This study is structured as follows. Part 2 is largely intended as a literature review of the electronic commerce growth and urban–rural income gap. Part 3 illustrates the empirical strategies, basic models, and main data sources to create a foundation for subsequent empirical research. In Part 4, the preliminary regressions and robustness tests are conducted. Part 5 analyzes technological innovation as the mechanism for electronic commerce growth and the urban–rural income gap. Part 6 draws conclusions and suggestions and highlights the limitations of the current research of this study.

2. Literature Review

The development of e-commerce has an impact on the global social economy, especially on economic development, which plays an important role in promoting, but also causes some social equity problems. As the largest developing country in the world, since the Reform and Opening-Up economic reforms, the gap between cities and the countryside in China has continued to expand [6,7,8], and the internal causes have always been the focus of academic research and debate. Most scholars have suggested that the growth policy of “agriculture supports industry and rural supports cities” and its supporting system (i.e., urban biased policy system) that China has long adhered to are the crux of the widening urban–rural gap [9,10,11,12]. Urban-biased policies comprise social security, labor employment, social welfare, financial distribution [13], education fund allocation, and so forth. These will inevitably expand the gap between cities and the countryside while promoting rapid economic growth. To be specific, the registered residence system, which hinders the free flow and migration of labor through administrative means, is the most emblematic policy; it is one of the main reasons for the growing urban–rural income gap.

Taobao villages and Taobao towns (a Taobao village is a village where the number of active online stores exceeds 10% of the local households, or the annual electronic commerce turnover exceeds RMB 10 million. A Taobao town represents a village where three Taobao villages or more exist in a township or street, or where the annual electronic commerce sales in a township exceed RMB 30 million, and the number of active online stores exceeds 300, regardless of whether there are Taobao villages), rural electronic commerce service centers, and other novel phenomena have emerged over the past few years. As depicted in Figure 1 (the number of Taobao towns in 2014–2018, published by Alibaba Research Institute), the number of Taobao towns has grown tremendously in 2018. Compared with the traditional economy, the electronic commerce economy formed by the growth of communication technologies (e.g., the internet) has also rapidly boosted GDP growth. The business logic of the internet era refers to a platform model in accordance with the logic of community [14], breaking the spatial distance of some economic activities (e.g., consumption) while achieving information matching across space–time constraints, such that transaction costs are reduced significantly [15]. Electronic commerce is increasingly becoming a vital method of increasing farmers’ income, improving the participation of rural residents, and integrating the growth of cities and the countryside. The electronic commerce economy is becoming a participatory economy [16]. The spatial distribution map in Figure 2 indicates that the number of Taobao towns and the spatial distribution of the proportion of online retail sales in GDP are larger in the east than in the west. The imbalance of growth and the rise of rural electronic commerce makes it possible for us to explore the correlation between electronic commerce growth and the urban–rural income gap.

In the existing analysis, the research on the correlation between the electronic commerce economy and enterprise operation cost and performance [17,18], enterprise production efficiency [19], enterprise competition and competitiveness [20,21], industrial structure [22], trade and market shares [23], and consumption growth [24] reflects the efficiency and advantages of the electronic commerce economy. Does this electronic commerce economy, where everyone engages in “We Media, We Finance, and We Enterprise”, improve the welfare of the society as a whole? Is the digital dividend shared by all residents? Is the digital divide between residents living in cities and residents living in the countryside insurmountable? The abovementioned problem has triggered a heated debate in the academic community, but there has been no conclusion on which side affects the growth of the electronic commerce economy the most, in regard to the income of residents living in cities and the countryside.

Some research has suggested that the use of the internet and electronic equipment has a significant positive effect on product sales, agricultural product prices, and farmers’ welfare [25]. A great majority of farmers in countries under development are facing product marketing challenge, and the emergence of electronic commerce enables farmers to enter domestic and global markets on the basis of internet trade [26]. In addition, electronic commerce markedly affects the sales decisions of small farmers. Electronic commerce achieves the production and marketing connection of producers, consumers, and intermediaries, and other subjects increase the transaction efficiency, thus becoming conducive to increasing farmers’ income [27,28]. In general, the benefits for farmers originate from the reduction in information asymmetry and information gain, i.e., in the “platform economy” mode; farmers can master production demand and price information through electronic commerce and obtain benefits. Electronic commerce offers farmers opportunities to start their businesses and overcome poverty [29], which helps them overcome geographical disadvantages, makes them more effective market participants, and improves their income. On that basis, the income gap between cities and the countryside is reduced [30,31].

In addition, some scholars argue that the growth of communication technology will increase the urban–rural income gap, and it is difficult for rural enterprises and farmers to share digital dividends. The first reason is the lack of access to information technology, and the second is the lack of ability to use information technology [32]. There are still significant differences between provinces and between cities and the countryside in the popularity and application of the internet, thus undoubtedly further increasing the income and employment gap of provinces and between cities and the countryside. The construction of network facilities in different countries is initiated in cities. Subsequently, it tends to be extended to rural areas. As a result, the “primary digital divide” between cities and the countryside has widened, i.e., a gap in the accessibility of information technology. The “platform effect” of electronic commerce will intensify the concentration of interests and values to a small number of people and significantly reduce the surplus of producers, thus making rural areas benefit less. Empirical evidence from Japan and the United States also shows that when the cost of electronic commerce economic growth is high, social welfare is reduced [33].

Others believe that the influence of the popularization of the internet on the urban–rural income gap shows an inverted U-shaped trend. In the first stage, the income gap between cities and the countryside will be expanded, and in the second stage it will be narrowed. In the beginning, people will gather in more developed areas, such as cities and the countryside (with superior geographical locations and perfect infrastructure), further widening the gap between cities and the countryside. The delayed growth advantages and the huge potential of electronic commerce will bring more dividends to rural residents. At this stage, the vigorous growth of electronic commerce will reduce the income gap between cities and the countryside to a certain extent.

The development of e-commerce belongs to the category of economic development, while the income gap between urban and rural areas belongs more to the category of social equity [1,2,3,4,5]. Economic development prioritizes efficiency. With the development of the economy, market competition becomes increasingly fierce, which leads to the unbalanced initial distribution of assets, the unequal distribution among social members, the widening of the gap between the rich and the poor, and the intensification of social contradictions [10]. The continuous development of the economy does not lead to social equity. On the contrary, it often widens the income gap between regions and leads to social inequality. However, the development of e-commerce can increase the income of poor areas, thus narrowing the economic gap with developed areas and eventually narrowing the regional or urban–rural gap [6,7,8,9]. Therefore, there are two hypotheses:

Hypothesis 1:

The development of e-commerce widens the urban–rural income gap.

Hypothesis 2:

The development of e-commerce reduces the urban–rural income gap.

To sum up, there is no consensus on the correlation between electronic commerce growth and the urban–rural income gap. Until now, most empirical studies have studied the influence of electronic commerce growth on production, exchange (trade), consumption, and other aspects from the perspective of the accessibility, permeability, access to the internet, and ownership of computers and mobile phones, while most of the research on distribution has been focused on the internal research of Guangdong and other developed regions. Developing an electronic commerce economy is an important new way to adjust China’s income distribution pattern. Analyzing the distribution of electronic commerce economic growth is of great significance in China and in developing countries. Based on the literature review, this study uses inter-provincial panel data from 2002 to 2017 to test the important role of electronic commerce growth in narrowing the gap between cities and the countryside, and enriches the relevant research in electronic commerce growth and urban–rural income distribution.

3. Empirical Model, Variable Selection, and Data Sources

3.1. Model Setting

To study the influence of electronic commerce growth on the urban–rural income gap, we refer to the practices of Cheng, M.; Zhang, J. (2019) [34] and Guo, J.; and Luo, P. (2016) [35] to build the following model to test

$$G{\mathrm{ap}}_{it}={\alpha }_1+{\beta }_{1}e\_commerc{e}_{it}+{\lambda }_1{X}_{it}+{\epsilon }_{it}$$

(1)

In the benchmark model (1), Gap_it is the explained variable, representing the urban–rural income gap in the tth year of the ith province; e_commerce_it, as the core explanatory variable, represents the electronic commerce growth level in the tth year of the ith province, and X_it is the control variable in the model. In Model (1), when β₁ is less than zero, the electronic commerce growth inhibits the expansion of the urban–rural income gap; when β₁ is greater than zero, the growth of electronic commerce facilitates the expansion of an income gap between cities and the countryside.

3.2. Variable Selection and Data Sources

This article selects Chinese provinces from the 2002 to 2017 data of the 2002–2017 statistical yearbook and the National Bureau of Statistics (http://www.stats.gov.cn/tjsj/, accessed on 1 January 2023). Xinjiang, Tibet, Yunnan, and Chongqing lacked complete data from 2002–2017, while data from Hong Kong, Macao, and Taiwan were unavailable. Therefore, the Chinese provinces of Xinjiang, Tibet, Yunnan, Chongqing, Hong Kong, Macao, and Taiwan are not included in this study.

3.2.1. Explained Variables

Given the characteristics of China’s dual economic structure and the objective fact that China’s rural population accounts for a large proportion of the total population, this study uses the Theil index as the main indicator for measuring the income gap between cities and the countryside. The specific calculation formula of the Theil index is

$$\begin{array}{l}\\ Thei{l}_t={\sum }^2{}_{j=1}(\frac{W_{j.t}}{W_t})(\ln \frac{W_{j.t}}{W_t}-\ln \frac{p_{j.t}}{p_t})\end{array}$$

(2)

where j = 1 expresses cities and the countryside, and j = 2 is rural areas; W_t expresses the total income in the tth period, and W_j,t is the total income in the tth period of the Jth region; P_t is the total population in the tth period, and P_j,t denotes the total population in the tth period of the Jth region.

3.2.2. Core Explanatory Variables

Both the growth of main electronic commerce enterprises and the electronic commerce growth of traditional enterprises need the media of logistics to be delivered to consumers. Because the population of each province is different, the absolute value of express delivery volume cannot reflect the growth level of electronic commerce in a region. Accordingly, to better represent the application level of electronic commerce growth, the alternative indicator of per capita express volume to measure the growth level of electronic commerce (i.e., the practice of Han Lei and Zhang Lei [15]) was used as the core explanatory variable of this study. Given the explained variable ranges from 0–1, the logarithm of per capita express volume is the explanatory variable.

3.2.3. Control Variables

Since the purpose of the model is to estimate the influence of electronic commerce growth on the urban–rural income gap, we have added control variables such as human capital, urbanization rate, the proportion of the secondary industry, the proportion of the tertiary industry, and dependency ratio [36,37,38].

Table 1. The statistical description of major variables.

Variable Name	Full Sample	2002–2005	2006–2009	2010–2013	2014–2017
Theil index	0.118	0.142	0.134	0.111	0.086
	(0.057)	(0.062)	(0.059)	(0.050)	(0.036)
e_commerce	6.175	0.178	1.179	4.201	19.143
	(16.824)	(0.202)	(2.488)	(7.135)	(29.117)
Human capital	8.649	8.050	8.402	8.897	9.247
	(1.014)	(0.872)	(0.930)	(0.914)	(0.915)
Urbanization rate	0.521	0.454	0.496	0.546	0.590
	(0.149)	(0.158)	(0.148)	(0.136)	(0.118)
Proportion of secondary industry	0.460	0.445	0.479	0.487	0.430
	(0.082)	(0.074)	(0.077)	(0.081)	(0.083)
Proportion of tertiary industry	0.426	0.412	0.405	0.412	0.477
	(0.797)	(0.068)	(0.082)	(0.093)	(0.092)
Dependency rate	0.365	0.395	0.365	0.337	0.363
	(0.070)	(0.069)	(0.067)	(0.069)	(0.061)
GDP per capita	10.191	9.344	9.978	10.566	10.877
	(0.768)	(0.583)	(0.539)	(0.444)	(0.401)
Per capita GDP square	104.453	87.652	99.853	111.845	118.462
	(15.496)	(11.106)	(10.856)	(9.429)	(8.809)
Sample size	432	108	108	108	108

Explanation: 1. The value outside the bracket represents the mean value, and the value inside the bracket represents the standard deviation. 2. (1) Theil index: calculated by Formula (2); (2) Electronic commerce growth: measured by per capita express delivery volume; (3) Human capital: expressed as average years of education, and the specific calculation method is calculated by referring to the weighted years of education at the respective stage of the National Bureau of Statistics; (4) Urbanization rate: expressed as permanent urban population/total population; (5) Proportion of secondary industry: added value of secondary industry/GDP × 100%; (6) Proportion of tertiary industry: ratio of tertiary industry/GDP × 100%; (7) Dependency ratio: expressed as the ratio of the non-working-age population to the working-age population; (8) Per capita GDP: local GDP/total local population × 100%. 3. Data source: The Ali Research Institute and Statistical Yearbook of China’s Provinces 2002–2017, National Bureau of Statistics (http://www.stats.gov.cn (accessed on 1 January 2022)).

lists the statistical description of the whole sample and the small sample classified by time. The data of all control variables originate from the China Statistical Yearbook and The Ali Institute. Furthermore, Table 1 lists the statistical descriptions and data sources of major variables.

As depicted in Table 1, the urban–rural resident income gap in China is progressively narrowing, whereas the urban–rural resident income gap remains high. Among the variables, the per capita express business volume (representing the level of electronic commerce economic growth) has grown most rapidly. The average per capita express business volume of all provinces in China was 0.178 from 2002 to 2005, and it increased to 19.143 from 2014 to 2017. The above-described results suggest that electronic commerce, i.e., an emerging economy, has developed very rapidly over the past few years.

4. An Empirical Analysis of the Influence of Electronic Commerce Growth on the Urban–Rural Income Gap

4.1. Preliminary Regression Results

First, mixed regression, bilateral fixed effect, and random effect are employed for estimation, and

Table 2. Effect of electronic commerce growth on the income gap between cities and the countryside.

Variable Name:	Urban–Rural Income Gap
	(1) Pool	(2) Fe	(3) Re	(4) Pool	(5) Fe	(6) Re
Core explanatory variables:
e_commerce	−0.019 ***	−0.008 *	−0.017 ***	−0.008 **	−0.009 *	−0.012 ***
	(0.001)	(0.004)	(0.004)	(0.003)	(0.005)	(0.004)
Control variables:
Human capital				−0.012 ***	0.016 *	0.001
				(0.004)	(0.008)	(0.007)
Urbanization rate				−0.175 ***	0.264 ***	−0.125 *
				(0.033)	(0.090)	(0.007)
The proportion of secondary industry				0.176 ***	0.094	0.168 **
				(0.046)	(0.114)	(0.084)
The proportion of tertiary industry				0.305 ***	0.047	0.093
				(0.057)	(0.133)	(0.096)
Dependency rate				0.141 ***	0.243 ***	0.231 ***
				(0.043)	(0.061)	(0.055)
GDP per capita				0.107 *	0.029	0.022
				(0.065)	(0.073)	(0.068)
Per capita GDP square				−0.005	−0.004	−0.004
				(0.003)	(0.004)	(0.003)
Time fixed effect	No	Yes	Yes	No	Yes	Yes
Regional fixed effect	No	Yes	Yes	No	Yes	Yes
Constant term	0.118 ***	0.118	0.098 ***	−0.482	0.215	0.007
	(0.002)	(0.011)	(0.012)	(0.333)	(0.374)	(0.349)
Observed value	432	432	432	432	432	432
R-squared	0.371	0.413	0.408	0.564	0.459	0.444

Explanation: 1. (1)–(3) in Table 2 denote mixed regression, bilateral fixed effect regression, and random effect regression without control variables, respectively; (4)–(6) Denote mixed regression, bilateral fixed effect regression, and random effect regression with control variables. 2. The electronic commerce growth is measured using the natural logarithm of the per capita express volume. 3. Data source: The Ali Research Institute and National Bureau of Statistics (http://www.stats.gov.cn (accessed on 1 January 2023)); 4. *** p < 0.01, ** p < 0.05, * p < 0.1.

lists the preliminary regression result. The logarithm of per capita express delivery volume is a measure of electronic commerce growth, examining its effect on the urban–rural income gap. The first, second, and third columns are employed for testing the correlation between mixed regression (benchmark regression), fixed effect, and random effect models without adding control variables. The regression results show that the growth of electronic commerce in the three models has significantly inhibited the urban–rural income gap, and the results are −0.018, −0.0084, and −0.017, respectively. The fourth, fifth, and sixth columns list the regression results with control variables, which still show that the growth of electronic commerce will inhibit the further expansion of the income gap between cities and the countryside. The income gap between cities and the countryside has decreased by 5.133 times with the rise of electronic commerce growth (the logarithm of per capita express delivery volume) from the minimum value of −3.208 (Guizhou) to the maximum value of 4.943 (Zhejiang). As revealed by the above results, the growth of electronic commerce explains China’s urban–rural income gap to a great extent, and it is an important reason for the presence of the income gap between cities and the countryside.

For other control variables, its role in the income gap between cities and the countryside is basically consistent with expectations. It should be noted that the sign of the urbanization rate level is negative in mixed regression and random effects, which can be explained by the excessive urbanization in some parts of China [39,40].

4.2. Robustness Test

4.2.1. Endogenous Test

Although we have added a series of control variables that may have an effect on the income gap between cities and the countryside in the model to reduce the accuracy of missing variables in the result estimation, we still cannot exclude the endogenous problems of the electronic commerce growth indicators. To be specific, in regions with a small urban–rural income gap, the growth of local electronic commerce is facilitated due to the balanced internal growth of the region; in this case, the urban–rural income gap as the explained variable hurts the growth of electronic commerce as the explanatory variable. For this reason, simple linear regression results fail to indicate the causal effect of electronic commerce growth on the urban–rural income gap. Accordingly, the instrumental variable method is adopted to eliminate the effect of endogenous problems so as to increase the robustness of the results. The mileage of grade roads and the post office number in 1991 serve as instrumental variables. The two abovementioned indicators have been selected as the instrumental variables mainly because the growth of electronic commerce cannot be separated from the support of the corresponding infrastructure. The increase in the mileage of grade roads and the post office number will facilitate the growth of local electronic commerce, whereas it will not have any direct effect on the expansion of the urban–rural income gap, consistent with the conditions for selecting instrumental variables. Thus, the mileage of grade roads and the post office number in 1991 were selected as the instrumental variables of electronic commerce growth to estimate the causal effect of electronic commerce growth on the urban–rural income gap.

Thus, the two-stage regression model is set below, and the mileage of classified highways and the post office number in 1991 are employed as the instrumental variables to estimate the causal effect of electronic commerce growth on the urban–rural income gap.

$$\mathrm{Stage} 2: e\_commerc{e}_{it}={\alpha }_1+{\beta }_1{Z}_{it}+{\lambda }_1{X}_{it}+{\epsilon }_{it}$$

(3)

$$\mathrm{Stage} 1: Ga{p}_{it}={\alpha }_2+{\beta }_{2}e\_commerceit+{\lambda }_2{X}_{it}+{\epsilon }_{it}$$

(4)

To be specific, Model (3) is the regression of the first stage, and its explanatory variable is electronic commerce growth; Z_it represents the instrumental variable expressed by the mileage of classified highways and the post office number in 1991. If the mileage of classified highways or the post office number facilitate electronic commerce growth, β₁ is greater than zero and significant; Model (4) is the second stage regression, and its setting is the same as that of Model (1). The difference is that the explanatory variables in Model (4) are derived from the estimated values of the results of the first-stage regression.

Table 3. Regression results of electronic commerce growth on the urban–rural income gap.

Variable Name:	Stage 1	Stage 2	Stage 1	Stage 2
	E_Commerce	Urban–Rural Income Gap	E_Commerce	Urban–Rural Income Gap
Instrumental variables:
Post office number	0.0003 ***
	(0.00004)
Mileage of classified highways			0.127 ***
			(0.007)
F test	53.04		311.57
Core explanatory variables:
e_commerce		−0.033 ***		−0.012 ***
		(0.005)		(0.002)
Control variables:	Yes	Yes	Yes	Yes
Observed value	432	432	432	432
R-squared		0.292		0.556

Explanation: 1. The instrumental variables in the first two columns refer to the post office number in 1991; The instrumental variable in the last two columns is the mileage of classified highways; 2. Data source: The Ali Research Institute and National Bureau of Statistics (http://www.stats.gov.cn (accessed on 1 January 2023)); 3. *** p < 0.01, ** p < 0.05, * p < 0.1.

lists the two-stage regression results of the electronic commerce growth and the urban–rural income gap. The first-stage regression results indicate that the post office number and the mileage of grade roads notably boost electronic commerce. The more significant the difference between the post office number or the mileage of grade roads in the two regions, the greater the level of electronic commerce growth in the two regions. As indicated by the second-stage regression results, the coefficient of electronic commerce growth is significantly negative after the two-stage regression with the post office number or the mileage of grade roads as the instrumental variable of electronic commerce growth, suggesting that electronic commerce growth, excluding endogenous problems, can markedly reduce the regional urban–rural income gap. On that basis, through the regression of instrumental variables, the endogenous issues arising from historical factors (e.g., the post office number in 1991) and infrastructure factors (e.g., the mileage of classified roads) are eliminated, and the growth of electronic commerce still takes on great significance in curbing the expansion of the income gap between cities and the countryside.

4.2.2. Robustness Test of Different Samples

Subsequently, this study conducts a robustness test by replacing samples and core explanatory variable indicators, and chooses online retail sales/GDP as the proxy indicator to measure electronic commerce growth. Since the online retail sales data of all provinces in China have been published in 2015, the sample period of the robustness test data is the period from 2015 to 2018 (Chongqing, Tibet, Xinjiang, and Yunnan were excluded since the data in some provinces were not sufficient). The urban–rural growth gap is measured using the Theil index, and the electronic commerce growth is measured using the online retail sales/GDP.

Table 4. Robustness test—A.

Variable Name:	Urban–Rural Income Gap
	(1) Pool	(2) Fe	(3) GMM	(4) Pool	(5) Fe	(6) GMM
Core explanatory variables:
Urban–rural Income Gap L1.			0.968 ***			0.915 ***
			(0.012)			(0.038)
Online retail sales /GDP	−0.536 ***	−0.093 ***	−0.089 ***	−0.276 ***	−0.078 **	−0.068 **
	(0.073)	(0.033)	(0.021)	(0.098)	(0.032)	(0.028)
Control variables:	No	No	No	Yes	Yes	Yes
Time fixed effect	No	Yes	Yes	No	Yes	No
Regional fixed effect	No	Yes	Yes	No	Yes	No
Constant term	0.152 ***	0.121	0.011 ***	0.406 ***	0.041	0.053 *
	(0.006)	(0.001)	(0.003)	(0.082)	(0.046)	(0.028)
Observed value	108	108	81	108	108	81
R-squared	0.332	0.536		0.638	(0.612)

Explanation: 1. Data source: 2015–2018 Statistical Yearbook of Chinese Provinces, National Bureau of Statistics (http://www.stats.gov.cn (accessed on 1 January 2022)); 2. *** p < 0.01, ** p < 0.05, * p < 0.1.

lists the results of mixed regression, fixed effect regression, and SYS-GMM regression. We find that every 1% increase in the electronic commerce growth level will lead to a significant reduction of 0.08–0.54% in the urban–rural income gap, thus confirming the regression result’s robustness.

The emergence of “farmers’ online merchants” over the past few years has aroused widespread concern, especially with the rise of “Taobao Village” and “Taobao Town”. A growing number of scholars have begun to study the phenomenon of “agglomeration” of electronic commerce. This type of agglomeration is the agglomeration of enterprises and personnel on the supply side of the electronic commerce market in a certain region, as well as the agglomeration of basic elements of electronic commerce growth. “Taobao Village” and “Taobao Town”, a novel form of economic agglomeration, have significant social effects, and should be investigated in depth. Thus, the influence of electronic commerce agglomeration on the urban–rural income gap was examined. On that basis, the explanatory variables (with the proportion of Taobao towns, specifically the number of Taobao towns/the number of towns in the region, and online retail sales/GDP as the alternative indicators of rural electronic commerce growth level) and the explanatory variables (expressed in the urban–rural income ratio and Theil index, respectively) were replaced, given the availability of data. The sample data from 2015 to 2018 (excluding Beijing, Shanghai, Chongqing, Tianjin, Qinghai, Tibet, Xinjiang, and Yunnan) were selected for testing. As indicated by the mixed regression results in

Table 5. Robustness test—B.

Variable Name:	Theil Index	Urban–Rural Income Ratio	Urban–Rural Income Ratio
Core explanatory variables:
Taobao Town Ratio	−0.160 ** (0.075)	−1.290 ** (0.606)
Online retail sales/GDP			−1.380 *** (0.807)
Control variables:	Yes	Yes	Yes
Constant term	0.320 *** (0.105)	1.380 * (0.842)	1.387 * (0.851)
Observed value R-squared	92 0.628	92 0.475	92 0.465

Explanation: 1. Baseline regression is used for robustness test; 2. Data source: National Bureau of Statistics (http://www.stats.gov.cn (accessed on 1 January 2023)); 3. *** p < 0.01, ** p < 0.05, * p < 0.1.

, every 1% increase in electronic commerce growth level will trigger a significantly reduced urban–rural income gap of 0.16–1.38%, thus confirming the regression result’s robustness.

5. Mechanism Testing

With the advancements of the internet, the growth of electronic commerce, a product of the internet era, is progressively affecting the economic and social growth of countries worldwide as an emerging economy [41,42,43]. In China, electronic commerce has tended to significantly facilitate economic growth. The statistics of the Department of Electronic Commerce and Information Technology of the Ministry of Commerce suggest that the national electronic commerce transaction volume reached RMB 34.81 trillion in 2019. To be specific, the online retail volume was RMB 10.63 trillion, marking an increase of 165% on a year-to-year basis, taking up 20.7% of the total retail sales of social consumer goods. It can contribute to 45.6% of the growth of the total retail sales of social consumer goods. Especially since electronic commerce has gradually become a vital means of rural revitalization, it has provided a novel impetus and a new carrier for rural revitalization over the past few years. The above empirical research reveals that the growth of electronic commerce is indeed a vital factor contributing to the existence of the urban–rural gap. The growth of electronic commerce boosts the “dominant” and “soft” innovation of various new formats and novel technologies, reflecting an innovative effect. Accordingly, there may be a transmission mechanism as the electronic commerce → technological innovation → can alleviate the inequality of the urban–rural gap. Thus, the technological innovation effect of electronic commerce on the urban–rural gap is elucidated to provide a scientific reference for the formulation of China’s current economic policies.

5.1. Mechanism Test Model Setting

Electronic commerce has the effect of reducing poverty and increasing income, especially the rural electronic commerce growth, which has effectively improved the economic and social outlook of rural areas and improved farmers’ income. The electronic commerce growth can reduce the income gap between cities and the countryside by improving the quality of regional innovation and increasing the number of regional innovations. As reflected by the quality-of-life levels, technology changes lives. This is the optimal interpretation of the transformation of Chinese lifestyle, and it is capable of explaining the success of the strategy of the “Pinduoduo” electronic commerce to a certain extent. In addition, it has been verified that technological innovation is the mechanism that influences electronic commerce growth, which alleviates the income gap between cities and the countryside.

To verify whether electronic commerce growth has an effect on the urban–rural income gap through the technological innovation effect, the following model has been built to explore the technological innovation effect of the urban–rural gap of electronic commerce growth.

$$Tech\_inn{o}_i{}_t={\alpha }_2+{\beta }_{2}e\_commerc{e}_{it}+{\gamma }_2{X}_{it}+{\epsilon }_{it}$$

(5)

$$Ga{p}_{it}={\alpha }_1+{\beta }_{1}e\_commerc{e}_{it}+{\varphi }_{1}e\_commerc{e}_{it}\times Tech\_inn{o}_i{}_t+{\gamma }_1{X}_{it}+{\epsilon }_{it}$$

(6)

In Model (5), the number of new patents authorized and the total number of patents authorized in the tth year in the Ith province are the explained variable technological innovation effect (Tech_inno_it); the core explanatory variable e_commerce_it is the electronic commerce growth level of the tth year in the Ith province (expressed as a logarithm of express delivery volume per capita); the explained variable in Model (6) complies with that in Model (2), and the core explanatory variable conforms to Model (2) by adding the technological innovation effect (Tech_inno_it) and the interaction between electronic commerce growth and the technological innovation effect (e_commerce_it × Tech_inno_it).

5.2. Empirical Results of Mechanism Test

The correlation between the growth of electronic commerce and the effect of technological innovation regresses when using the bilateral fixed effect model. Subsequently, the bilateral fixed effect regresses when using the interaction between the growth of electronic commerce and the effect of technological innovation.

Table 6. Analysis of technological innovation effect of electronic commerce growth on the urban–rural income gap.

Variable Name:	Patents	Utility Patents	Urban–Rural Gap
	(1)	(2)	(3)	(4)
Core explanatory variables:
e_commerce	0.196 ***	0.196 ***	−0.040 ***	−0.036 ***
	(0.044)	(0.043)	(0.014)	(0.013)
patents			−0.010 *
			(0.006)
e_commerce × patents			0.002 ***
			(0.001)
utility patent				−0.012 *
				(0.006)
e_commerce × utility patents				0.002 **
				(0.001)
Control variables:
Human capital	0.302 ***	0.153 **	0.016 *	0.015 *
	(0.067)	(0.066)	(0.009)	(0.008)
Urbanization rate	3.961 ***	3.737 ***	0.318 ***	0.320 ***
	(0.728)	(0.717)	(0.093)	(0.092)
The proportion of secondary industry	−0.798	2.091 **	0.110	0.158
	(0.922)	(0.908)	(0.114)	(0.115)
The proportion of tertiary industry	0.872	2.018 *	0.066	0.115
	(1.078)	(1.062)	(0.133)	(0.134)
Dependency rate	0.124	−1.873 ***	0.207 ***	0.175 ***
	(0.495)	(0.488)	(0.062)	(0.065)
GDP per capita	1.358 **	−0.060	0.125	0.106
	(0.592)	(0.583)	(0.080)	(0.079)
Per capita GDP square	−0.060 **	−0.001	−0.008 **	−0.008 **
	(0.030)	(0.029)	(0.004)	(0.004)
Time fixed effect	Yes	Yes	Yes	Yes
Regional fixed effect	Yes	Yes	Yes	Yes
Constant term	-3.156	4.189	=0.708 *	−0.605 **
	(3.019)	(2.975)	(0.412)	(0.411)
Observed value	432	432	432	432
R-squared	0.953	0.959	0.472	0.473

Explanation: 1. Baseline regression is used for robustness test; 2. Data source: The Ali Research Institute and National Bureau of Statistics (http://www.stats.gov.cn (accessed on 1 January 2023)); 3. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the result. The regression result of the electronic commerce growth of the technological innovation effect with the total number of patents authorized as the substitute indicator of a technological innovation effect is illustrated in the first column, and the second column is the regression result of electronic commerce growth of the technological innovation effect with the total number of utility model patents authorized as the substitute indicator of the technological innovation effect. As indicated by the results, the number of patents (e.g., utility models) will increase more significantly (both 0.196) with the increase in the electronic commerce growth index, suggesting that the growth of electronic commerce will facilitate the formation of a technological innovation effect. The third and fourth columns indicate the effect of electronic commerce growth on technological innovation in the urban–rural gap (the alternative indicators are the total number of patent licenses and the total number of utility model patents). As indicated by the result, the growth of electronic commerce reduces the urban–rural growth gap expansion (−0.04 and −0.036, respectively), whereas this reduction will be restrained by the technological innovation effect (0.002). A possible reason for the above result is that the technological innovation effect with the number of patents authorized (including utility models) as an alternative indicator mostly occurs in cities and the countryside, and the radiation effect on rural areas is not significant, thus weakening the role of electronic commerce growth in narrowing the urban–rural gap.

6. Conclusions and Suggestions

The economy advancing in China after the introduction of the Reform and Opening-Up economic reforms, whereas the widening income gap between cities and the countryside has been a significant bottleneck restricting the sustainable growth of China’s economy and social stability, hindering common prosperity. The income gap between cities and the countryside tends to be narrowed from the perspective of equality. Measurement methods have been adopted for studying the correlation between electronic commerce growth and the urban–rural income gap, and the following conclusions have been drawn:

(1)

The growth of electronic commerce notably reduces the urban–rural income gap, and the results are stable. This shows that the growth of electronic commerce can effectively explain China’s urban–rural income gap, and it is an important factor influencing the urban–rural income gap;

(2)

The growth of electronic commerce reduces the urban–rural income gap by increasing the income of residents, especially rural residents. Accordingly, encouraging electronic commerce growth, especially in rural areas, is an effective way of minimizing the urban–rural income gap, and an important measure to alleviate the imbalance of China’s regional economic growth.

Our study confirms the hypothesis that the development of e-commerce reduces the urban–rural income gap in China. Therefore, the Chinese government should promote the development of rural e-commerce through various policies and measures and the implementation of air traffic control means in order to narrow the gap between urban and rural China. Our results also show that technological innovation plays a mediating role. Therefore, in this process, the Chinese government needs to pay special attention to the development of e-commerce technology and the improvement of relevant innovation abilities.

To reduce the gap between cities and the countryside is to decrease the negative effect of economic growth imbalance within a controllable range, instead of eliminating inequality. Based on the special institutional background of China, this study provides a novel perspective for gaining insights into the vast urban–rural growth imbalance in China, and also provides useful inspiration for the current formulation of policies for balanced regional growth. Given the above results, the suggestions are given in terms of electronic commerce growth:

(1)

Aiming at the closed geographical environment of underdeveloped areas (e.g., rural areas), we should focus on improving infrastructure construction in underdeveloped areas, encouraging electronic commerce, forming more Taobao villages and towns, encouraging electronic commerce businesses to settle in rural areas, actively cultivating a competitive innovation environment and business environment, cultivating competitive innovation subjects, radically increasing the income of rural residents, and reducing the urban–rural gap;

(2)

Technological progress is the fundamental driving force of economic and social growth, and so is the electronic commerce industry. For rural areas, in addition to improving infrastructure, we should also encourage technological innovation, accelerate patent technology application and authorization, and gain a voice in the field of digital economy, improve the product after-sales guarantee system, and implement the negative list system of rural electronic commerce platforms to facilitate the establishment of electronic commerce platforms in rural areas to facilitate economic growth and achieve common prosperity. In addition, electronic commerce, as an important part of the digital economy, relying on advanced technologies (e.g., the internet and big data) must actively cultivate innovative talents and develop key digital technologies to enhance core competitiveness.

Even though the instrumental variable method has been adopted to address the endogenous problem and find the intermediary effect of technological innovation between electronic commerce growth and the income gap between cities and the countryside, this study still has certain limitations, such as the lack of data (e.g., Xinjiang, Tibet, Yunnan, and Chongqing) and other Chinese regions (e.g., Hong Kong, Macao, and Taiwan). Although the abovementioned limitations do not affect the research results and conclusions, they indicate the aspects that need to be improved in future research.