Background of old revolutionary base areas in China

Old revolutionary base areas refer to those areas that were led and founded by the Communist Party of China during the Second Revolutionary Civil War and the War of Resistance against Japan in the 1930s and 1940s. The regions comprise 28 provinces and 1,599 counties, accounting for 55% of China’s population and 33% of its total land area (He et al Reference He, Zhou and Wen2024). Historically, because of the needs of the revolutionary struggle, most of the old revolutionary zones were built in remote mountainous areas with dangerous terrain, typical of areas adjacent to provincial borders. The characteristics of the difficult terrain and weak natural endowments have led to the fact that the local modern industrial system has not yet been formed, the level of basic public services is not high, and there has been a serious brain drain, all of which have seriously hampered the economic development of the old revolutionary areas. The backwardness of the old revolutionary areas runs counter to the sustainable development goals of the United Nations. Moreover, the old revolutionary areas belong to a specific type of area, in terms of their geographic, demographic, and economic structures, and are evidently different from that of other rural areas. As a result, some of the ways of increasing the income of residents in other areas may not be applicable to the old revolutionary areas. Therefore, it is crucial to explore how to make farmers in the old revolutionary areas earn more as well as realise high-quality development processes.

Certainly, e-commerce has flourished in rural areas. It has provided a new impetus and a new carrier for the construction of ‘digital villages’, and has gradually become one of the most important tools for promoting farmers’ income growth. In order to promote the development of the old revolutionary areas and respond to the sustainable development goals, studies have been conducted to assess the contribution of e-commerce to the speed and quality of economic growth in the old revolutionary areas. However, there are fewer studies on the empirical analysis of its intrinsic mechanism. Selecting the old revolutionary areas as the research object will fill the research gap, enrich the exploration of regional development planning with Chinese characteristics, and provide valuable perspectives and experiences for promoting the economic development of the same type of impoverished areas for e-commerce.

Policy of e-commerce development in China

In order to promote the development of e-commerce nationwide, China promulgated the Twelfth Five-Year Plan for the Development of E-Commerce in 2012 with the aim of improving the e-commerce system and creating a secure, trustworthy, standardised, and orderly environment. In 2016, the 13th Five-Year Plan for the Development of E-Commerce was promulgated with the objective of accelerating the pace of e-commerce and upgrading and promoting deep integration of e-commerce into traditional industries.

In order to promote the development of e-commerce in the old revolutionary areas, China’s Ministry of Finance and Ministry of Commerce jointly issued the Circular on the Comprehensive Demonstration of E-commerce in Rural Areas in 2014. The policy increased the number of e-commerce platforms in the old revolutionary areas in many ways, especially the number of so-called Taobao villages, which became the main rural e-commerce platforms. Figure 1 shows in concrete terms that the policy has significantly increased the number of Taobao villages, and their communities.

Figure 1. Results of the effect of policy on the number of Taobao villages.

Theoretical analysis of e-commerce effects on farmers’ well-being

The development of e-commerce in old revolutionary areas has helped to increase both farmers’ trust and awareness of society, and their income levels, thus enhancing their well-being. The use of the internet has thus had a significant positive impact on agricultural product prices and farmer welfare (Khanal et al Reference Khanal, Mishra and Koirala2015). E-commerce development can both enhance an individual’s well-being through the direct effect of receiving self-affirmation (Yin et al Reference Yin, Li and Yang2021), as well as by facilitating the efficient transfer of information, breaking down the information barriers between farmers and the market, so solving the problems of asymmetric information and poor marketing for disadvantaged groups (Sun et al Reference Sun, Zhang and Jiang2017). The development of rural e-commerce can effectively help farmers achieve local employment, which in turn improves their social capital and entrepreneurship (Wang and He Reference Wang and He2020; Liu and Han Reference Liu and Han2018). There are numerous positive effects including the enabling of agricultural production to be more scientific, the simplification of intermediate links, and capacity to make smarter sales decisions that reduce the blindness of agricultural production. All of these effects improve transaction efficiency and promote farmers’ income (Correa et al Reference Correa, Pavez and Contreras2017; Cui et al Reference Cui, Pan, Newell and Cui2017).

The development of e-commerce also fosters equal opportunities between urban and rural areas (Xu and Wen Reference Xu, Lv and Wen2015). It is not surprising then that e-commerce has become increasingly prominent in the rural areas of many developing countries. E-commerce not only breaks the spatial distance amongst economic activities and significantly reduces transaction costs but also lowers the threshold of rural economic development and creates more employment opportunities for migrant workers, thereby narrowing the digital divide and increasing the information dividend (Larose et al Reference LaRose, Strover, Gregg and Straubhaar2011). These advantages make e-commerce a potent tool to increase farmers’ income and encourage rural residents’ participation. The development of e-commerce promotes the modernisation of rural communities (Leong et al Reference Leong, Pan, Newell and Cui2016). The development of e-commerce platform has promoted the local rural revitalisation, urban-rural integration, and community governance transformation, which will further enhance the driving role of residents’ income, even in the remote old revolutionary base areas (Zhou 2019).

The increase in the level of farmers’ income not only more fully meets the basic needs of farmers and improves their quality of life but also helps to slow down the disparity between the rich and the poor; in other words, e-commerce can enhance social equality and fairness. Rural e-commerce allows resources to be effectively distributed across space and geography, which is conducive to alleviating the inequality of opportunity faced by farmers, which will improve their sense of well-being, making it possible to enhance the well-being of farmers. Figure 2 shows the mechanism by which e-commerce affects the income.

Figure 2. Mechanism of e-commerce development affecting the rural income.

Theoretical analysis of mechanism function

Variable definition and description

The explained variable is the per capita disposable income in old revolutionary base areas (Rural-Income). Given that raising farmers’ income is the fundamental purpose of promoting rural development, income serves as a metric to determine whether the presence of e-commerce enhances residents’ living standards. This metric also provides a basis for this study to explore the mechanism of action.

The core explanatory variable is policy intervention (DID _it ), that is, the. Comprehensive demonstration policy for e-commerce in rural areas. The quasi-experimental event impact is treated as the proxy variable to eliminate the disturbance effect of other factors and to accurately capture the effect of the policy.

The control variables are as follows: (1) Population age structure (Pop_Stru) reflects the scale of local primary and secondary school students; (2) government expenditure (Gov_Scale) reflects the proportion of fiscal expenditure in total expenditure; (3) household deposit rate (Finance) reflects the ratio of urban and rural resident savings balance to gross domestic product (GDP); (4) local PM2.5 annual mean (PM2_5) reflects the local natural ecological environment; (5) per capita regional GDP (lnRGDP) represents the degree of local economic development; (6) per capita grain output (lnFood); (7) proportion of service industry in local GDP (Ser_Industry) reflects the development level and contribution of the tertiary industry; and (8) number of patents (lnInnovation) reflects independent innovation ability.

Mechanism variables. With total grain output as its main focus, this study uses total grain output (AO) as a proxy for the level of local agricultural revitalisation. Non-agricultural employment (OE) reflects the share of local non-agricultural employment in the total population. Given that the financial capacity of the local government is measured by fiscal revenue (Besley and Persson Reference Besley and Persson2009), government support (GS) is used as a proxy for the fiscal capacity of local governments. To mitigate the impact of outliers and excessive standard deviation on the results, this study applies logarithmic transformations on total grain output (AO) and general budget revenue (Fiscal_Income). The specific calculations of the variables are shown in Table 1.

Data sources

This study uses panel data from 321 county-level old revolutionary base areas in China from 2003 to 2020 for an empirical analysis in order to explore the intrinsic mechanism of the pilot policy of the Comprehensive Demonstration Project for E-commerce in Rural Areas seeking to increase the number of farmers in old revolutionary areas. Since the policy was enacted in 2014, new pilot counties have been added every year, so this study uses a time-varying DID model. First, the availability of data on relevant indicators in each county. Second, it is not appropriate to assess the policy effects over too long a time period; if the time span is too long, many policy and event interferences will arise in the middle, making the regression results erroneous. Considering the above two points, this study uses data from 2003 to 2020 to ensure the reasonableness of the results. Data sources include the Easy Professional Superior database, China County Statistical Yearbook and List of pilot counties issued by the General Office of the Ministry of Finance of China. The sample is processed as follows. Firstly, representative old revolutionary base areas are selected, and an 18-year time span is set as the research period, thereby forming a robust data foundation for micro-research on the impact of e-commerce development on residents’ income in these areas. Secondly, those old revolutionary base areas with limited data are eliminated from the sample, and those samples with missing data are interpolated. The final sample consists of 5778 observations from 321 counties across 17 provinces. The descriptive statistics for each variable are shown in Table 2.

Table 1. Variable definition

Table 2. Descriptive statistics

Note. Corr1 represents the correlation coefficient with Rural_Income. Corr2 represents the correlation coefficient with Treat × Post.

Model specification

The DID model can reduce the interference of irrelevant variables. Therefore, this method not only effectively identifies the effects of the policy individually but also accurately analyses the influence mechanism of policies related to e-commerce development on the income increase of rural residents in old revolutionary areas. This model defines the interaction term between the county group dummy variable (Treat) and the policy time point virtual variable before and after the implementation of the policy (Post). Subsequently, the DID model assesses the effect of place-based policies on income. The empirical model is specified as follows:

(1) $${{Rural}}\_{{Incom}}{{\rm{e}}_{it}} = {\alpha _0} + \delta {{DI}}{{{D}}_{it}} + {\mu _i} + {{\bf{\lambda }}_t} + \mathop \sum \nolimits_{k = 1}^K {\gamma _k}{{Contro}}{{{l}}_{kit}} + {\varepsilon _{it}}$$

DID = 1 when the old revolutionary area is a pilot area and after t is the pilot time. The coefficient of the interaction term (δ) is treated as the main estimator. If the coefficient δ is significantly positive, then the relevant policies have a positive effect on the income of residents in old revolutionary base areas; otherwise, these policies have a negative policy effect. Rural_Incomeit indicates the per capita disposable income of region i in period t, Controlsk _it represents the kth control variable, μ _i represents the city-fixed effects and λ _t represents the time-fixed effects. These variables aim to control the interference of individual variables that remain constant over time and those time variables that remain constant across individuals. ${\rm{\varepsilon }}$ _it is a random disturbance term on policy evaluation.

According to hypotheses 1–3, e-commerce development increases rural income in old revolutionary base areas through three mechanisms. To further examine whether the potential mediating variables have a significant impact on the relationship between e-commerce development and rural income, this paper adopts the empirical framework of the intermediary effect model based on model (1) and then establishes the following regression models:

(2) $${{Mediato}}{{{r}}_{it}} = {\rm{\;}}{\beta _1}{{DI}}{{{D}}_{it}} + \mathop \sum \nolimits_{k = 1}^K {\gamma _k}{{Contro}}{{{l}}_{kit}} + {\mu _i} + {\delta _t} + {\varepsilon _{it}}$$

(3) $${{Rural}}\_{{Income}}{_{it}} = {\rm{\;}}{{\rm{\alpha }}_1}{{DI}}{{{D}}_{it}}\; + {\rm{\;}}{{\rm{\alpha }}_3}{{Mediato}}{{{r}}_{it}} + \mathop \sum \nolimits_{k = 1}^K {\gamma _k}{{Contro}}{{{l}}_{kit}} + {{\rm{\lambda }}_t} + {\mu _i} + {\varepsilon _{it}}$$

where Mediator _it is the potential mediating variable, Control _kit denotes the relevant control variables, DID _it and Rural_Income _it are the core explanatory variable and dependent variable, respectively, and ${{\rm{\alpha }}_3}$ reflects the indirect effect of the potential mediating variables on rural income. The magnitude of the mediating effect is jointly measured by β₁ and ${{\rm{\alpha }}_3}$ .

To ensure that the DID model aligns with the parallel trend assumption, the following model is established.

(4) $${{Rural}}\_{{Incom}}{{{e}}_{it}} = \Sigma _{k = - 6,K \ne - 1}^5{\beta _k}{{Yea}}{{{r}}_{Y + k}} \times {{Pos}}{{{t}}_{it}} + \mathop \sum \nolimits_{k = 1}^K {\gamma _k}{{Contro}}{{{l}}_{kit}} + {\mu _i} + {\lambda _t} + {\varepsilon _{it}}$$

where Year _Y+k represents a series of dummy variables, k represents the years after the policy impact (a negative k represents the years before the policy impact) and Y denotes the year in which the policy is implemented. The definitions of the other variables are consistent with those in the benchmark model. The year before the policy shock is selected as the base group, thus excluding pre_1.

Benchmark regression results and analysis

The results of the benchmark regression are shown in Table 3. First, some regression models do not control for county-fixed effects. Given the small variability of e-commerce development in some old revolutionary areas during the sample period, the regression controlling for county-fixed effects may exclude the influence of differences in e-commerce development characteristics on residents’ income growth. Second, this study adds all control variables in columns (5) and (6). The failure to effectively control these factors may result in omitted variable bias. Third, clustering standard error is applied to relax the random error assumption. Given that the data are panel data, the error items may possibly be correlated. If this correlation is not considered, then the standard error of the estimation may be biased.

Table 3. Empirical results of baseline regression*

Note. Symbols refer to statistical significance at the corresponding levels.

* (10%).

** (5%).

*** (1%).

Table 3, column (6) shows the highest DID term coefficient, thereby suggesting that after the introduction of e-commerce, the per capita disposable income of residents in old revolutionary base areas has increased by 4.29%, representing a substantial economic significance. These results effectively prove that e-commerce can contribute to the economic development of old revolutionary base areas, thereby guiding policymakers towards strengthening the role of e-commerce in enhancing the per capita disposable income of residents. These successful experiences may also guide other countries in addressing poverty issues.

The control variables also have impacts on rural income. For instance, population age structure (Pop_Stru) has a negative impact on increasing the per capita disposable income of residents in old revolutionary areas. A higher proportion of primary and secondary school students brings burden to support systems, reduces the proportion of the labour force, and subsequently decreases per capita disposable income (Bloom and Williamson, Reference Bloom and Williamson1998). The effect of innovation ability (lnInnovation) on per capita disposable income is significantly positive. Innovation not only upgrades technology to reduce unemployment but also increases farmers’ income (Adriani and Wildayana Reference Adriani and Wildayana2019).

Parallel trend test

A prerequisite for using the DID model is that the parallel trend assumption must be met; otherwise, the results cannot represent the net effect of the policy. The relative policy time dummy variable coefficients before the policy are not significant and have small values, and the trends in the time-trend charts are basically consistent. In other words, before the implementation of the policy, no significant differences can be observed in the dynamic effects of the policy between the treatment and control groups, thus satisfying the parallel trend assumption. The results are shown in Figure 3. From the perspective of the dynamic effect, after the implementation of the policy, the dynamic effect of the policy suddenly increases. These results indicate that relevant policies have a positive effect on increasing the per capita disposable income of residents. The growth rate of the group without controlling for other factors is greater than that of the group that controls these factors, thereby implying that control variables encompass those factors that can increase income. Three years after the policy implementation, the dynamic effect of the policy shows a downward trend. These results highlight the need to formulate policies that are consistent with the specific situation across different stages of development.

Figure 3. Parallel trend test results. Note. Control variables were added to the right panel, and no control variables were added to the left panel.

Regression results using matching samples

The selection of e-commerce pilots may be affected by geographic location and information infrastructure, thus, non-random sample selection is possible. To reduce the impact of sample selection bias and omitted variable bias, this study uses radius matching, kernel matching, and nearest neighbour matching to re-match the scores of the samples and ran a regression with the matched data.

To address endogenous deviations in the sample and the significant differences in economic characteristics between the experimental and control groups, propensity score matching (PSM) and entropy balancing matching (EBM) are applied to generate matched subsamples, and these subsamples are then used to conduct a DID regression analysis. EBM not only enhances the effectiveness of regression and the policy evaluation robustness but also ensures the randomness and exogeneity of the experiment (Hainmueller Reference Hainmueller2012). Based on the key characteristics of the old revolutionary base areas, eight matching variables are selected. The corresponding descriptive statistics are shown in Tables 4 and 5.

Table 4. Descriptive statistics before entropy matching

Table 5. Descriptive statistics after entropy matching

Tables 4 and 5 compare the variable balance between the treatment and control groups before and after EBM. Some imbalances are observed in the first three moment measures of multiple samples before matching. By weighting the control group data and adjusting the differences in the first-, second-, and third-order moments of the covariates, the covariate matrix of each variable in each control group is consistent with that of the experimental group. The kernel matching method in PSM is also applied to address data and selection biases and to prevent the interference from confounding factors. Only those samples with tendencies towards overlapping scores are retained in the sample to improve matching quality.

After overcoming the characteristic differences of covariates via PSM and EBM, a DID regression is applied using the matched data, and the results are presented in Table 6. Columns (1)–(3) present the regression results after EBM, whilst columns (4)–(6) present the results of the regression after kernel matching. Regression with matched data can minimise the interference of irrelevant factors and the differences between group characteristics. Table 6 shows that the coefficients of the DID term are all significantly positive at the 1% confidence level. Following the introduction of control variables, the coefficient of the DID term can accurately reflect the policy effect. These results indicate that the relevant policies have significantly improved the level of per capita disposable income in old revolutionary base areas. The development of electronic commerce has brought considerable economic and social benefits in terms of farmers’ flexible employment and poverty alleviation, thus contributing to the future development of rural and underdeveloped areas (Li and Qin Reference Li and Qin2022). These results support the hypotheses.

Table 6. Regression results based on matched samples

Note. Symbols refer to statistical significance at the corresponding levels.

* (10%).

** (5%).

*** (1%).

Placebo test

The placebo test performed by Abadie is replicated in this study to enhance the robustness of the results. This placebo test involves a fictitious treatment group and a fictitious policy impact year. Those groups that are not affected by this policy (Treatd=0) are selected as fictional treatment groups. The cross-terms are then constructed for regression, and the regression coefficients are recorded. This process is repeated 1000 times for the analysis. Theoretically, given that the virtual experimental groups are randomly generated, their crossover terms do not have a significant effect on the model-dependent variable.

Figure 4 presents the P value coefficient scatter plot, which shows that the estimated coefficients in the virtual experimental group exhibit an approximate normal distribution. The horizontal dotted line indicates P=0.1. After randomisation, the P value of most coefficients exceeds the P value=0.1 line, thereby suggesting that these coefficients are not significant at the 10% level. The majority of the scattered points are concentrated near 0, and only a few scattered points are in the vertical line of the real value, thereby indicating that the real value is an outlier after randomisation. To a certain extent, these results rule out the influence of other random factors on changes in the explained variable per capita disposable income (Rural_Income), thereby further verifying the robustness of the conclusions.

Figure 4. Results of placebo test.

Instrumental variable test

There is a potential for endogenous problems with county e-commerce development indicators. First, there may be a greater number of Taobao villages in the more economically developed old revolutionary areas. Second, per capita income and e-commerce development may be affected by unobservable factors. Therefore, this study attempts to mitigate the endogeneity problem with the help of instrumental variables. Referring to Kolko (Reference Kolko2012), the topographical relief of the county is used as an instrumental variable for e-commerce development in the old revolutionary areas. Firstly, to satisfy the assumption of correlation, topographical relief is closely related to e-commerce development in the county because the greater the topographical relief, the higher the cost of construction and maintenance of network communication, which will affect the Internet penetration, which will have a direct impact on the development of e-commerce. Secondly, to satisfy the assumption of exogeneity, the terrain undulation in the old revolutionary areas is completely natural, and there is no direct correlation with other economic variables. In summary, the topographic relief of the county will be a valid instrumental variable.

Considering that the degree of topographic relief of counties is cross-sectional data, this study, following Manacorda and Tesei (Reference Manacorda and Tesei2020), uses the interaction term of cross-sectional instrumental variables and time-trend variable in the fixed effect model of panel data and uses two-stage regression method for estimation. As can be seen Table 7, the results of the first stage regression show that: terrain relief is highly correlated with the development of e-commerce, the Cragg-Donald Wald F value is greater than the critical value of Stock-Yogo’s 10% level, which indicates that the instrumental variables do not have the problem of weak instrumental variables, and from the results, it can be seen that it meets the instrumental variables’ identifiability. The results of the second stage regression show that the development of e-commerce significantly contributes to the income of the population. Benchmark regression results are robust.

Exclusion of other policy effects

The increase in per capita income in the old revolutionary areas may be affected not only by the development of e-commerce platforms but also by other policies. In order to minimise the impact of other interfering policies, representative policies enacted in the same period were chosen for this study. China has been attacking the difficulty of increasing the per capita income of poor counties in recent years, and most of the old revolutionary areas are poor counties. Therefore, this study selects China’s ‘National List of 832 Poverty-stricken Counties’ issued in 2012 as the intervention policy. Drawing on Duan et al. (Reference Duan, Jiang and Qi2024). On the basis of the original baseline regression model, ‘China’s poor counties’ (DID1) is added as an interaction term in order to control the effect of the disturbance policy as much as possible. As can be seen from the results in Table 8, the policy effects of e-commerce in rural areas are significant and the coefficients are basically unchanged before and after the introduction of other policies, indicating that the results of the benchmark regression are robust.

Table 7. Result of instrument variable regression

Note. Symbols represent significance levels. The values in parentheses are standard errors.

* (10%).

** (5%).

*** (1%).

Table 8. Results of exclusion of other policy effects

Note. Symbols represent significance levels. The values in parentheses are standard errors.

* (10%).

** (5%).

*** (1%).

Other robustness tests

The robustness must be further tested for two important effects. On the one hand, there is a need to take into account the fact that during the period under study, large events took place in China, which may have affected the assessment of policy effects. Secondly, and on the other hand, it is important to control for time trends in different individual explanatory variables that may exist and are not explained by other control variables as well as fixed effects. This study adds a time-trend term to the baseline regression. The results are shown in column (1) of Table 9. To control for the effect of time trend, this study further incorporates an interaction term between the time trend term and the control variables based on the baseline model. The results are shown in column (2) of Table 9.

Table 9. Other robustness test results**

Note. Symbols refer to statistical significance at the corresponding levels. trend indicates a time-trend item. Controls×trend denotes the interaction term between the time trend term and the control variable. CDV×trend denotes the interaction term between the time-trend term with the county dummy. PSV×year FE denotes the interaction term between the pilot selection variable and time-fixed effects.

* (10%).

** (5%).

*** (1%).

The demonstration programme was first implemented in 2014, but new counties are added each year, which can lead to possible differences in the rules for conducting the pilot programme from year to year, so this study draws on Wang et al (Reference Wang, Yin and Jiang2023) to include a time trend term and an interaction term for the county dummy variable in the regression. The results are shown in column (3) of Table 9. Finally, to address the issue that the selection of the demonstration counties is not completely randomised with reference to Li et al (Reference Li, Lu and Wang2016), an interaction of the pilot selection variable with time-fixed effects is introduced. The results are shown in column (4) of Table 9. All the above results show that e-commerce significantly increases farmers’ income.

Mediating effect of agricultural revitalisation

Column (1) of Table 10 shows that the coefficient of the DID term is positive, indicating that the development of e-commerce can significantly increase the food production in the old revolutionary areas, and the increase in food production will increase the personal income of farmers. Income from the agricultural sector has always dominated the total income of the residents in the old revolutionary areas. The development of rural e-commerce also drives the development of industries and upgrades the rural industrial structure, thereby increasing the development potential for rural revitalisation (Feng and Zhang Reference Feng and Zhang2022). Columns (2) show that the coefficients of total grain output are all significantly positive, thereby suggesting that an increase in grain output significantly boosts rural income. The coefficients of the DID term and grain output are significant, thereby indicating that this mechanism passes the mediating effect test (Chao et al Reference Chao, Biao and Zhang2021). In sum, e-commerce development in old revolutionary base increases the income of residents by increasing their total grain output, thus supporting Hypothesis 1.

Table 10. Results of the mechanism test*

Note. Symbols represent significance levels. The values in parentheses are standard errors.

* (10%).

** (5%).

*** (1%).

Mechanism of non-agricultural employment

When non-farm employment is regressed as a mechanism variable, the coefficient of the DID term is significantly positive. E-commerce promotes the return of labour, reduces the cost of entrepreneurship, and improves economic vitality, which will increase the non-farm employment rate in the old revolutionary areas. Poor households with sufficient IT skills can easily start a business or be employed on e-commerce platforms (Cui et al Reference Cui, Pan, Newell and Cui2017). The development of e-commerce not only brings entrepreneurial opportunities to residents in old revolutionary areas but also gives them diversified choices, thereby increasing non-agricultural employment. The increase in employment opportunities and forms gives residents more ways to earn income. Internet access facilitates the establishment of information channels and the accumulation of intellectual capital for entrepreneurial activities, thereby increasing residents’ entrepreneurship probabilities and income-earning capacity. Professional information and digital human capital can also increase these residents’ entrepreneurial income (Liu Reference Liu2018). By relying on e-commerce, farmers can gain diversified ways to start a business and increase their income, thereby supporting Hypothesis 2.

Mechanism of fiscal capacity

Table 10 shows that the policy of e-commerce in rural areas has significantly increased the government’s financial capacity. Fiscal capacity is the basis for the strength of government financial support. On the one hand, it will ensure the completeness of local infrastructure related to e-commerce, which is the basic condition for the locality to be able to benefit from the development of e-commerce, and on the other hand, the government’s construction of an incentive mechanism will increase the rate of farmers’ participation and enhance the role of e-commerce in increasing farmers’ income. The sustained and rapid development of platform economy has also given birth to many new economic forms, which play an important role in improving the efficiency of resource allocation, promoting industrial upgrading, and opening up employment channels (Xu et al Reference Xu, Lv and Wen2015; Ju et al Reference Ju, Zhao and Sun2020). From the result of Column (6), it can be seen that the enhancement of financial capacity does promote the increase of personal income, and Hypothesis 3 is valid.