Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Digital inclusive finance, rural revitalization and rural consumption

Abstract

With the rapid development of technology and the evolution of the global financial system, digital inclusive finance has become a new way to promote rural revitalization and rural residents’ consumption with the power of financial technology. This study explores the relationship between digitally inclusive finance, rural revitalization, and rural residents’ consumption. Based on the panel data of 30 provinces and cities in China from 2011 to 2021, this study empirically examines the impact of digitally inclusive finance on rural residents’ consumption and the mediating and threshold effects of rural revitalization. The results reveal that digitally inclusive finance is conducive to the enhancement of rural residents’ consumption, and rural revitalization acts as a mediator. Meanwhile, there is a nonlinear positive correlation between the impact of digitally inclusive finance and rural revitalization on rural residents’ consumption, in which there is a double-threshold effect of digitally inclusive finance and rural revitalization in the lagged period. Based on the above findings, we believe that while promoting digitally inclusive finance, it is important to promote rural revitalization strategy in a timely manner, improve rural infrastructure, and continuously stimulate the impact of digitally inclusive finance on rural residents’ consumption.

Citation: Li W, Zhang L, Pu M, Wang H (2025) Digital inclusive finance, rural revitalization and rural consumption. PLoS ONE 20(1): e0310064. https://doi.org/10.1371/journal.pone.0310064

Editor: Francisca Ortiz Ruiz, Universidad Mayor, CHILE

Received: April 1, 2024; Accepted: August 20, 2024; Published: January 3, 2025

Copyright: © 2025 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information.

Funding: Study on Green Finance Promoting the Adjustment of Energy Industry Structure in Anhui Province, SK2018A0649 Support Program for outstanding young people in universities, gxyqZD2021048 Research on the Mechanism and Path of Inclusive Finance to Help Anhui Rural Revitalization 2022CX199 General Project of Outstanding Young Talents Support Program, gxyq2022131 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

With the strategy of rural revitalization being incorporated into the overall development of the China, the comprehensive economic and social development of rural areas has become a key direction for future development. Further promotion and deepening of the strategy of rural revitalization will improve the infrastructure of the majority of rural areas and steadily increase the income of residents. The 2023 Central Economic Work Conference proposed stimulating current domestic consumption and further expanding domestic demand. However, the development of the vast rural areas is lagging, and most residents generally have a habit of saving. Based on the current stage of the country’s development, the 20th National Congress of the Communist Party of China (CPC) further emphasized the goal of modernization for the common prosperity of all people, coordinating urban and rural development, and realizing the goal of modern living conditions in the countryside. Consequently, it is essential to determine the role of financial resources in promoting rural modernization and achieving economic development and higher incomes for people in rural areas.

Through the rapid development of the Internet and the continuous improvement of digital infrastructure, the digital economy is becoming a new engine and kinetic energy source for the stable and high-quality development of China’s economy [1]. The predicament of the difficulty and high cost of financing for agriculture-related small, medium, and micro enterprises (SMEs) is widespread. The source of financing relies mainly on credit from banks, which cannot meet the demand for personalized financial products of SMEs. With the development of the Internet, blockchain, and other technologies and the development of digital infrastructure in rural areas, especially the deep integration and development of inclusive finance and fintech, the information asymmetry problem between the financing parties has been reduced to a greater extent [2]. In fact, financing constraints have been eased [3,4], which in turn accurately identifies the financing constraints [3,4], and thus accurate identification of financing subjects and stronger risk control [5,6], which not only meets the financial needs of agriculture-related SMEs for expanding reproduction, but also improves the employment and income level in rural areas.

The prosperous development of rural areas is closely related to the optimization of the rural industrial structure and the improvement in the income level of rural residents [7]. China’s long-term unbalanced development of urban and rural areas has led to the outflow of labor, raw materials, and other factors of production from rural areas, hindering the areas’ development, which in turn limits the growth of rural residents’ income and widens the development gap between urban and rural areas. As the global economic growth rate declines and the impact of epidemics increases, China is facing problems such as blocked exports, a surplus of domestic products, and a shortage of consumer demand. In the context of rural revitalization, how to promote consumption demand in rural areas has become an important issue, especially in terms of building a “double-cycle” development pattern. Subsequently, how to use the domestic macro-cycle to stimulate consumption demand in rural areas deserves in-depth consideration.

This study considers rural revitalization and rural residents’ consumption as the research objects and first adopts the fixed effect regression method and carries out the mediation effect test. In addition, it constructs a threshold model to empirically analyze the threshold effect between digitally inclusive finance, rural revitalization, and rural residents’ consumption. Through these methods, this study aims to explore the relationship between rural revitalization and rural residents’ consumption as well as the role of digitally inclusive finance in promoting rural residents’ consumption demand. The findings of this study will provide policymakers and relevant institutions with a basis for decision-making regarding rural revitalization and will also provide useful references for expanding rural residents’ consumption, optimizing the structure of rural industries, and raising the level of residents’ income.

The main contributions of this study are as follows: First, the relationship between digitally inclusive finance, rural revitalization, and rural residents’ consumption is examined from both linear and non-linear perspectives. Second, it extends the extant literature by examining not only digitally inclusive finance and rural revitalization as explanatory variables but also rural revitalization as a mediating variable to better clarify the relationship between the three factors.

2. Theoretical analysis and research hypothesis

2.1 Rural revitalization and rural consumption

The concept of rural revitalization was first proposed in 2017, and Xi Jinping proposed the comprehensive promotion of rural revitalization in a report of the 20th National Congress in 2022, emphasizing the solid promotion of rural industry, talent, culture, ecology, and organizational revitalization. At present, academic research on the rural revitalization of rural residents’ consumption is relatively limited, but some scholars have studied the impact of rural residents’ consumption from the dimension of rural revitalization. [8–12] analyzed the development of rural e-commerce from the perspective of the development of rural e-commerce, and concluded that improvement of rural infrastructure and development of rural e-commerce can increase the income of rural residents and promote the consumption and upgradation of rural residents. With regard to the industrial structure, [13] examined the shift in agricultural production from low-value food crops to high-value commodities in Indonesia over the past three decades and determined that the development of high-value agriculture was positively correlated with rural household income, which in turn improves the demand for life. [14,15] argued that optimizing the industrial structure is needed to achieve quality, expansion, and upgradation of rural consumption. In terms of financial support for agriculture, [16] analyzed the impact of government expenditure in a general equilibrium monopolistic competition model and found that an increase in government expenditure has a positive effect on residents’ consumption in both the short and long term. [17] found that financial support for agriculture has a certain effect on the consumption level of rural residents: with the positive impact of financial support for agriculture policy, the consumption level of rural residents will appear at a certain level of enhancement and shows a lag phenomenon; with the gradual prolongation of time, the impact gradually tends to level off. [18] analyzed the impact of financial support for agriculture on the consumption of rural residents from the level of consumption, consumption structure, and quality of consumption and found that the financial support for agricultural expenditure and the level of rural residents’ consumption has a segmented non-linear relationship, and the upgradation of the consumption structure of rural residents has a “U”-shaped non-linear relationship. With regard to the allocation of financial resources, [19,20] argued that credit constraints make consumers too sensitive to current consumption, which hinders the growth of their consumption. The realization of financial marketization can optimize the allocation of financial resources to facilitate consumers to make better use of financial resources to realize intertemporal consumption, thus effectively releasing the demand for residential consumption. Based on the above analyses, this study proposes the following:

  1. Hypothesis 1: Rural revitalization effectively promotes rural residents’ consumption.

2.2 Digitally inclusive finance and rural revitalization

The strategy of rural revitalization was proposed by Comrade Xi Jinping in the report of the 19th Party Congress on October 18, 2017, emphasizing the solution for the “three rural issues,” which mainly involves five dimensions: industrial prosperity, ecological livability, civilized countryside, effective governance, and affluent life. [21,22] used these five dimensions and measured the indicators of rural revitalization to construct indicators. With the development of financial technology and the promotion of rural revitalization strategies, scholars are first studying rural revitalization from the perspective of digitally inclusive finance. [23,24] found that digitally inclusive finance, with its technological advantages, enhances credit volume, achieves surface expansion, and ensures quality improvement, promotes the development of rural industries, and helps rural industrial revitalization. In terms of improving digital infrastructure, [25,26] found that digital financial inclusion significantly contributes toward the development level of rural revitalization, there is significant regional heterogeneity, and digital infrastructure construction and rural human capital significantly moderate the relationship between digitally inclusive finance and the development of rural revitalization. With regard to financing costs, [27,28] argued that the combination of digitally inclusive finance classes with modern technology can realize the cost effect and, therefore, meet the demand for financing rural industrial development. [29,30] believd that digitally inclusive finance, as an emerging product of the financial industry, can broaden financing channels and improve financing efficiency, compensate for defects in expensive traditional financial services, and effectively provide reliable financial support and convenient services for the innovative and entrepreneurial activities of various financing subjects. Based on the above analyses, this study proposes the following hypothesis:

  1. Hypothesis 2: Digitally inclusive finance can effectively promote rural revitalization.

Scholars have begun to conduct research on the five dimensions of digital financial inclusion for rural revitalization. In terms of industrial prosperity, [31] argued that digitally inclusive finance can directly affect rural industrial prosperity through the breadth of coverage, diversification of financial products, and low cost, and indirectly support rural industrial prosperity by supporting the regional economy. [32] found that digitally inclusive finance provides good support for the development of rural industrial integration by increasing capital supply, driving technological innovation, and guiding the return of labor. [33] used empirical analyses to prove that digitally inclusive finance has a positive spatial spillover effect on agricultural quality development. In terms of ecological livability, [34] constructed a rural ecological livability index system using the aspects of natural environment livability, artificial environment livability, and social environment livability and found that digitally inclusive finance can significantly improve the level of rural ecological livability, which and can be achieved through the effect of agricultural technology progress and urbanization. [35] showed that digitally inclusive finance can support farmers in planting green agricultural products with higher premiums and reduce the intensity of agricultural carbon emissions through channels such as improving the level of agricultural mechanization, optimizing the structure of agricultural cultivation, and expanding the scale of operation. In terms of rural civilization, [36] argued that digitally inclusive finance continuously records the credit activities of poverty-stricken people in an accurate, timely, and multi-dimensional way through digital credit collection, which helps farmers and low-income groups in poverty-stricken areas to establish a concept of credit and build and consolidate the concept of honesty and integrity in traditional culture. [37] argued that digitally inclusive finance reduces the burden on capital-constrained households, mitigates the problem of information asymmetry, and improves the educational attainment of low-income groups in terms of financial access. In terms of effective governance, [38] argued that cracking the “financial exclusion” and “threshold effect” of digital inclusive finance in China’s rural society can help innovate and improve the modernization and transformation of China’s rural governance model. [39] found that digitally inclusive finance can better help rural governance from the perspective of rural land contract disputes. In terms of livelihood affluence, [40] used an empirical analysis to determine that the development of digitally inclusive finance can effectively reduce the urban-rural income gap, while increasing Internet penetration can increase the inhibitory effect of digitally inclusive finance on the urban-rural income gap. [41] found that digitally inclusive finance empowers rural residents to increase their income through micro-mechanisms, such as easing the formal credit constraints of rural households, improving the production and operation of rural households, and raising the income level of rural households, thus reducing the urban-rural income gap. Based on the above analysis, this study proposes the following hypothesis:

  1. Hypothesis 2.1: Digitally inclusive finance can effectively contribute to the realization of the dimensions of rural revitalization.

2.3 Digitally inclusive finance, rural revitalization, and rural consumption

The dynamic development of digitally inclusive finance has had a far-reaching impact on financial constraints in rural areas, providing more accessible and convenient channels for credit funds required by agribusinesses and farm households, which boosts the development of the rural economy and the income of residents. This triggers the overall level of consumption and improves its structure. Existing literature mainly includes the following paths: First, digitally inclusive finance has taken advantage of its own technology to expand the coverage of inclusive finance, the depth of use in the region, and the digitization of financial products for the majority of rural areas, improving the experience of financial services. Simultaneously digitally inclusive finance has supported the construction of infrastructure in rural areas [42,43], enhanced agricultural production, and mitigated agricultural risk management of agriculture-related enterprises, promoting the development and revitalization of the countryside. Second, with the revitalization of the countryside, rural areas’ economies further develop with the support of digitally inclusive finance, which further optimizes the digital infrastructure of the countryside [44]. Digitally inclusive finance not only provides a good financing environment for the development of agro-related enterprises, but also stimulates the entrepreneurial enthusiasm of rural residents, which steadily increases their income [45,46]. Third, along with the sustained growth of the economy in rural areas and improvement of the residents’ income level, the residents’ consumption ability also improves, which further optimizes the consumption structure of rural areas. Consequently, the said consumption structure will be of high-quality and become more diversified, which will promote the development of the overall consumer market. Based on the above analysis, we propose the following hypothesis:

  1. Hypothesis 3: Rural revitalization mediates the relationship between digitally inclusive finance and rural residents’ consumption.

Fig 1 shows a schematic representation of the mechanisms of digitally inclusive finance, rural revitalization, and rural consumption.

thumbnail
Download:
Fig 1. Mechanism analysis diagram.

https://doi.org/10.1371/journal.pone.0310064.g001

Extant research is laden with the following shortcomings. First, research on digitally inclusive finance and rural revitalization is mainly based on a linear perspective and does not consider the impact of time lag and the possible non-linear impact. Second, existing studies mainly study the direct impact of digitally inclusive finance on rural revitalization and do not consider whether rural revitalization has a mediating role in the relationship between digitally inclusive finance and rural residents’ consumption. Therefore, this study attempts to fill these research gaps.

3. Model setting and data description

3.1 Basic model setting

This study conducted empirical analyses based on panel data from 30 provinces and cities in China from 2011 to 2021 and constructs a model as follows (1). Considering the differences in the impact of digitally inclusive finance on different dimensions of rural revitalization, we constructed models for each dimension as follows [(2) to (6)].

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

3.2 Mediating effects modelling

Combined with the above analysis, this study argued that digitally inclusive finance affects rural residents’ consumption, whereas rural revitalization mediates the impact of digitally inclusive finance on rural residents’ consumption. The specific mediation model was as follows (7)–(9): Considering that the rural revitalization indicator contains five dimensions, the mediating role of each dimension was further analyzed. Consequently, the following models were constructed [(10)–(14)].

(7)(8)(9)(10)(11)(12)(13)(14)

here Cons represents the consumption of rural residents, Finid stands for digitally inclusive finance, Rure denotes rural revitalization, Control signifies the control variables, α is a constant term, μit is an individual fixed effect, and εit is an error term.

3.3 Threshold effect modelling

This study draws on Hansen’s (2000) threshold regression model and [47] model construction approach to construct the model as follows [(15)–(16)]: (15) (16) where θ1, θ2⋯θn is the threshold for n levels, Controlit is a control variable, Rureit is a threshold variable, I(•) is a dummy variable, and μit is a random disturbance term.

3.4 Variable selection

3.4.1 Description of variables.

This study selected rural residents’ per-capita consumption expenditure as the core explanatory variable to reflect changes in rural consumption. Digitally inclusive finance and rural revitalization were selected as explanatory variables. To guarantee the accuracy of the empirical results, financial support for agriculture, rural old-age dependency ratio, rural juvenile dependency ratio, level of opening up to the outside world, urbanization rate, industrial structure, consumer price index of rural residents, and level of economic development were selected as control variables (Table 1).

thumbnail
Download:
Table 1. Variable definition.

https://doi.org/10.1371/journal.pone.0310064.t001

Fiscal support for agriculture signifies funds used to support rural development, involving all aspects of agriculture. In other words, not only improving rural infrastructure but also supporting the comprehensive development of agriculture, and generally promoting the stable development of agricultural and rural economies, which in turn promotes the growth of farmers’ income and demand [48].

Increases or decreases in both rural old-and young-age dependency ratios correspond to potential household cost expenditures for rural households. To better cope with old-age needs and the education of the next generation, there may be a reduction in consumption to increase savings and cope with future needs for money. Therefore, these two indicators impact rural residents’ consumption [49].

Level of openness to the outside world refers to the degree of communication between rural areas and domestic and foreign markets. A higher level of openness to the outside world means that rural areas can participate more extensively in international trade and external economic ties, which helps rural residents obtain more consumption choices, reduce consumption costs, and improve the quality and diversity of their consumption [50].

With the acceleration of urbanization in rural areas, rural residents are more easily exposed to urban consumption concepts, styles, and markets. Urbanization also enhances employment opportunities and opportunities to increase income, thus improving rural residents’ consumption capacity [51]. Therefore, urbanization rate significantly impacts rural residents’ consumption.

The transformation of the rural economy from traditional agriculture to modern agriculture, rural industry, and services not only increases employment opportunities and income levels in rural areas, but also provides a more diversified supply of goods. Therefore, the industrial structure may affect the consumption level and structure of rural residents [52].

The rural consumer price index directly reflects the changes in goods and services related to the daily lives of rural residents, and a sustained rise in this index will seriously affect residents’ daily lives and reduce consumption demand [53].

3.4.2 Measurement of the level of rural revitalization.

Rural revitalization is China’s development strategy and an inevitable requirement for achieving common prosperity; thus, the selection of rural revitalization indicators must fully reflect many aspects of development in rural areas. We referred to the methods of [54,55] and selected 29 basic indicators to quantify the level of rural revitalization using five dimensions: industrial prosperity, ecological livability, civilized rural culture, effective governance, and affluent life. The specific indicators are listed in Table 2.

thumbnail
Download:
Table 2. Indicator system of rural revitalization.

https://doi.org/10.1371/journal.pone.0310064.t002

4. Empirical analysis

4.1 Data selection and processing

This study mainly selected the panel data of 30 provinces, autonomous regions, and municipalities directly under the central government of China, excluding Tibet, Hong Kong, Macao, and Taiwan, for the period of 2011–2021. The data on digitally inclusive finance are from the Digital Finance Research Centre of Peking University, while the data on other indicators are from the China Statistical Yearbook, China Rural Statistical Yearbook, China Urban and Rural Construction Statistical Yearbook, and the Wind Database. For missing individual data, the trend replacement method was used as a supplement. The descriptive statistics of the data are presented in Table 3.

thumbnail
Download:
Table 3. Descriptive statistics of the variables.

https://doi.org/10.1371/journal.pone.0310064.t003

4.2 Measurement of rural revitalization index

Based on the calculation of 29 specific indicators according to five dimensions of rural revitalization, and using the entropy weighting method to calculate the rural revitalization development index [57,58], we found that the rural revitalization index of provinces and cities increased in 2021 compared with that in 2011. Shandong Province had the highest rural revitalization index (0.783), while Xinjiang had the lowest (0.523), with a mean value of 0.665. Guizhou showed the highest increase of 0.348, whereas Shanghai showed the lowest increase of 0.040. In terms of regional differences, the rural revitalization index of the eastern region was generally higher than that of the central region, and that of the central region was higher than that of the western region, with municipalities in the direct province of China having a certain impact due to their higher urbanization rate, smaller rural population, and smaller area. The results are summarized in Table 4.

thumbnail
Download:
Table 4. Index of rural revitalization.

https://doi.org/10.1371/journal.pone.0310064.t004

4.3 Basic regression and mediation effects analysis

This study first explored the relationship between digitally inclusive finance, rural revitalization, and rural residents’ consumption using an ordinary panel model. Since the LM and MLE tests strongly rejected the original hypothesis, and the p-value of the Hausman test was 0.000, which also strongly rejects the original hypothesis, this study chose the fixed-effects model for analysis.

In Table 5, Columns (1)–(4) test the impact of digitally inclusive finance on rural residents’ consumption and the mediating role of rural revitalization. Column (1) shows that digitally inclusive finance is significant at the 1% significance level for rural residents’ consumption, indicating that the development of digitally inclusive finance has a positive effect on rural residents’ consumption. This indicates that the advancement of digitally inclusive finance in the breadth of coverage, depth of use, and degree of digitization helps exert an income-generating effect, driving rural residents to increase their income and pursue higher levels of consumption demand. Column (2) indicates that the impact of digitally inclusive finance on rural revitalization is significant at the 1% significance level, with a coefficient of 0.025, thus verifying that Hypothesis 2 is valid; that is, digitally inclusive finance enhances rural residents’ consumption. Column (3) shows that the impact of rural revitalization on rural residents’ consumption is significant at the 1% significance level, with a coefficient of 2.015, thus verifying that Hypothesis 1 is valid; that is, rural revitalization promotes rural residents’ consumption. Column (4) shows that the effects of digitally inclusive finance and rural revitalization on rural residents’ consumption are also significant at the 1% significance level, with coefficients of 0.162 and 1.267, respectively, verifying that Hypothesis 3 is valid. This indicates that the development of digitally inclusive finance can bring about the redistribution of financial resources for rural development and promote the industrial development of the countryside, sustainable development of ecology, effectiveness of rural governance, and affluence of rural residents’ lives and alleviate the liquidity constraints of rural residents and enhance the consumption expenditure of rural residents.

thumbnail
Download:
Table 5. Results of basic regression and mediation effect analysis.

https://doi.org/10.1371/journal.pone.0310064.t005

We also examined the difference in the impact of digitally inclusive finance on the different dimensions of rural revitalization (Table 6). The results show that the difference in the impact of different dimensions of digitally inclusive finance on rural revitalization have a certain degree of significance, which verifies Hypothesis 2.1.

thumbnail
Download:
Table 6. Analysis results of different dimensions of rural revitalization.

https://doi.org/10.1371/journal.pone.0310064.t006

The mediation effect test for the different dimensions of rural revitalization is shown in Table 7. Columns (1)–(5) in Table 7 show the mediation effects of the five dimensions of rural revitalization on rural residents’ consumption through digitally inclusive finance. Among them, industrial prosperity was significant at the 10% level, rural civilization and affluent life were significant at the 1% level. However, the mediating effects of ecological livability and effective governance were not significant. This indicates that the impact of digitally inclusive finance on rural residents’ consumption through rural revitalization varies across dimensions, and the development of digitally inclusive finance mainly promotes the development of rural industries, improvement of rural civilization, and affluence of rural residents’ lives through the reallocation of financial resources. Overall, it eases the liquidity constraints of rural residents and increases rural residents’ consumption expenditure.

thumbnail
Download:
Table 7. Analysis of the mediating effect of the five dimensions of rural revitalization.

https://doi.org/10.1371/journal.pone.0310064.t007

4.4 Robustness checks

Considering the impact of extreme values of the sample of explanatory variables, we shrunk the explanatory variables by 1% [59] to ensure the robustness of the results. The regression results are shown in Table 8. The results show that digitally inclusive finance and rural revitalization have a facilitating effect on the consumption of rural residents, while the development of digitally inclusive finance can promote rural revitalization, thus indicating that rural revitalization has a mediating effect on the impact of digitally inclusive finance on rural residents’ consumption. This confirmed the robustness of the study’s results.

thumbnail
Download:
Table 8. Robustness checks results.

https://doi.org/10.1371/journal.pone.0310064.t008

4. 5 Heterogeneity analysis

Given that there are certain differences in economic development between Chinese provinces, the sample was divided into eastern, central, and western regions for the heterogeneity test. The test results are presented in Table 9, which shows that the east, central, and west are basically the same as the national results, mainly because of the relatively slow development of the countryside in the central and western regions, the serious exodus of the young population, the insufficient development of the industry, and the fact that the utilization of digitally inclusive finance is yet to be further improved. Therefore, it can be assumed that digitally inclusive finance has a promoting effect on rural residents’ consumption, while rural revitalization mediates the relationship between digital inclusive finance and rural residents’ consumption.

thumbnail
Download:
Table 9. Heterogeneity analysis.

https://doi.org/10.1371/journal.pone.0310064.t009

4.6 Analysis of threshold effects

Before regressing the threshold variables, we determined whether there was a threshold effect and the number of thresholds using bootstrap sampling (bootstrap) of 300 regressions (Tables 10 and 11).

thumbnail
Download:
Table 10. Threshold effect self-sampling test results.

https://doi.org/10.1371/journal.pone.0310064.t010

thumbnail
Download:
Table 11. Threshold estimates and confidence intervals.

https://doi.org/10.1371/journal.pone.0310064.t011

4.6.1 Threshold effect test for rural revitalization.

When rural revitalization was both an independent variable and a threshold variable, the results showed that rural revitalization has a single threshold, with an F-statistic of 32.18, which is significant at the 5% level. The single threshold value for rural revitalization was 0.400, with a 95% confidence interval of [0.388–0.404]. Considering that the promotion of rural revitalization requires a process, it will not be completed immediately; therefore, there will be a dual-threshold effect for the lag phase of the rural revitalization process, wherein the second threshold value is 0.658, with a 95% confidence interval of [0.649 0.659].

4.6.2 Threshold effect test for digital inclusive finance.

When the independent variable was digitally inclusive finance, rural revitalization was significant at the 1% significance level in the single-threshold test. The single-threshold value was 0.5520, with a 95% confidence interval of [0.549 0.554]. Considering that the development of digitally inclusive finance also requires a process, there was a dual-threshold effect for the lagging period of digitally inclusive finance, wherein the single threshold value was 0.520, with a 95% confidence interval of [0.508 0.522], and the second threshold value was 0.584, with a 95% confidence interval of [0.578 0.585]. After surpassing the first threshold value, as digitally inclusive finance develops and improves, the effect is further enhanced.

The parameters were estimated for the sample based on the values of each threshold indicator, and the results of the threshold regression are shown in Table 12.

thumbnail
Download:
Table 12. Threshold regression estimates.

https://doi.org/10.1371/journal.pone.0310064.t012

The regression results for rural revitalization and rural residents’ consumption were analyzed. Column (1) of Table 12 shows that when rural revitalization was less than or equal to 0.400, there was a significant positive impact on rural residents’ consumption. However, when rural revitalization was less than or equal to 0.400, its stimulating effect on consumption was significant. Further, when rural revitalization was greater than 0.400, its stimulating effect on consumption diminished, presenting an inverted “U”-shaped relationship. Column (2) in Table 12 shows the results of the lagged one period of rural revitalization regression: when rural revitalization was less than or equal to 0.552, there was a significant positive impact on residents’ consumption (the coefficient was 1.463). When rural revitalization was between 0.552 to 0.658, the positive impact on rural residents’ consumption became stronger. Further, when rural revitalization was greater than 0.658, the significant positive impact on rural consumption was further strengthened. Therefore, there is a nonlinear positive relationship between rural revitalization and rural consumption with a lag. Additionally, the control variables exhibited significant correlations with rural consumption. Urbanization rate and industrial structure positively affect consumption, implying that modern living aspirations and increased income drive consumption. Aging populations are increasing their spending on health, leisure, and driving. Conversely, the child dependency ratio negatively impacts consumption, as families save on future education expenses. Furthermore, the Consumer Price Index (CPI) negatively affects consumption because rising prices reduce rural residents’ purchasing and spending power.

4.6.3 Analysis of the regression results for digitally inclusive finance and rural residents’ consumption.

Columns (3) and (4) of Table 12 show the results for digitally inclusive finance in the current and lagged periods. The results in column (3) indicate that when rural revitalization was less than or equal to 0.552, there was a significant positive impact of digitally inclusive finance on rural residents’ consumption; when rural revitalization was greater than 0.552, there was a significant positive impact on rural residents’ consumption and the coefficient further increased. Therefore, there is a nonlinear positive relationship between digitally inclusive finance and rural residents’ consumption in the current period; that is, the higher the level of digitally inclusive finance development, the greater is the effect on rural residents’ consumption. Column (4) of Table 12 presents lagged one period of digitally inclusive finance regression results, indicating that when rural revitalization was less than or equal to 0.520, digitally inclusive finance had a significant positive impact on rural residents’ consumption. Further, when rural revitalization was between 0.520 and 0.584, the positive impact of digitally inclusive finance on rural residents’ consumption was strengthened, with the coefficient rising to 0.159. Finally, when rural revitalization was greater than 0.584, the positive impact on rural residents’ consumption increased. Essentially, when revitalization was greater than 0.584, there was a significant positive impact on rural residents’ consumption, with the coefficient increasing to 0.178. Therefore, there is a nonlinear positive relationship between digitally inclusive finance and rural residents’ consumption, with a lag period.

5. Conclusions of the study and recommendations for countermeasures

This study empirically investigates the impact of digitally inclusive finance on rural residents’ consumption and the mediating and threshold effects of rural revitalization based on panel data from 30 provinces and cities in China from 2011 to 2021. Heterogeneity analysis was conducted based on the regional differences in eastern, central, and western China. The empirical results show that: (1) there is a significant positive promotion effect of digitally inclusive finance on rural residents’ consumption, while rural revitalization shows a significant mediating effect; however, there are differences in the mediating effects based on the dimensions of rural revitalization. The mediating effects of ecological livability and effective governance are not significant. (2) The impact of rural revitalization on rural residents’ consumption exhibits a single-threshold effect, whereas the effect of rural revitalization lagged by one period on rural residents’ consumption shows a dual-threshold effect, with an overall increasing trend. (3) The impact of digitally inclusive finance on rural residents’ consumption exhibits a single-threshold effect of rural revitalization, demonstrating a clear promoting effect on rural residents’ consumption. The effect of digitally inclusive finance lagged by one period on rural residents’ consumption shows a dual-threshold effect of rural revitalization, with an overall trend of increasing gradient.

Based on these conclusions, this study puts forward the following two suggestions:

First, the government should prioritize the improvement of rural information sharing and digital infrastructure to boost digital financing and rural consumption. This implies increasing network investment for comprehensive coverage, especially in remote areas, to support digital finance. In addition, establishing information sharing mechanisms can address financing difficulties, reduce costs, and support rural economic development. Furthermore, regulating digital finance products is crucial for preventing wasteful development and excessive consumption in rural areas.

Second, to foster rural development, the government should boost policy support and resource allocation, while igniting the entrepreneurial spirit of rural residents. This involves promoting modern agricultural techniques, enhancing product quality, and increasing incomes. Diversifying rural industries to create jobs and income is crucial. Additionally, upgrading rural infrastructure, such as transportation and utilities, is essential for bridging the urban-rural divide. Prioritizing sustainable energy sources improves living standards and environmental sustainability. Furthermore, investing in rural education, healthcare, and social services enhances the well-being of rural residents. Developing rural tourism and cultural sectors enriches rural life and its appeal.

This study investigated the correlation between digitally inclusive finance, rural revitalization, and rural consumption based on existing theories. However, there are areas for improvement. First, the selection of rural revitalization indicators relies mainly on statistical yearbook data, which limits comprehensive insights. Future research should conduct practical studies to gather more extensive data and enhance the measurement of rural revitalization. Additionally, while this study employs a panel model for empirical analysis, future studies could explore alternative empirical methods such as spatial econometric or systematic GMM models for a deeper analysis of the relationships between digital finance, rural revitalization, and rural consumption.

Supporting information

S1 File. Dataset.

Data.

https://doi.org/10.1371/journal.pone.0310064.s001

(RAR)

References

  1. 1. Wang Yongyu, Wu Yubin. Digital economy empowers new development pattern:internal mechanism and policy options[J]. Gansu Social Science,2023,(01):218–227.
  2. 2. Xiong M, Li W, Xian B T S, et al. Digital inclusive finance and enterprise innovation-Empirical evidence from Chinese listed companies[J]. Journal of Innovation & Knowledge, 2023, 8(1): 100321.
  3. 3. Guo B, Feng Y, Lin J. Digital inclusive finance and digital transformation of enterprises[J]. Finance Research Letters, 2023, 57: 104270.
  4. 4. Zhang X, Li J, Xiang D, et al. Digitalization, financial inclusion, and small and medium-sized enterprise financing: evidence from China[J]. Economic Modelling, 2023, 126: 106410.
  5. 5. Huang Hengjun, Wang Ziyuan. The impact of digital inclusive finance on agroecological efficiency-theoretical framework, mechanism analysis and spatial spillover effect[J]. Modern Finance and Economics(Journal of Tianjin University of Finance and Economics),2024, 44(01):3–17. .
  6. 6. Zhao Jian,Wei Xin. Measurement of coupled coordination analysis of digital inclusive finance and rural revitalization: an example of six central provinces[J]. Statistics and Decision Making,2023,39(15):142–146.
  7. 7. Sun Wanyun. Role mechanism of digital new infrastructure on China’s residents’ consumption upgrading under the perspective of double intermediary effect[J]. Research on Business Economy,2023(21):46–50.
  8. 8. Jiang Yangge. The impact of rural e-commerce development on rural residents’ consumption upgrading in the context of rural revitalization—the moderating effect based on distribution specialization[J]. Research on Business Economics, 2023,(15): 99–102.
  9. 9. Yin Z H, Choi C H. Does e-commerce narrow the urban-rural income gap? Evidence from Chinese provinces[J]. Internet Research, 2022, 32(4): 1427–1452.
  10. 10. Li G, Qin J. Income effect of rural E-commerce: empirical evidence from Taobao villages in China[J]. Journal of Rural Studies, 2022, 96: 129–140.
  11. 11. Wang Xiaolong,Tang Jianrong. Rural E-commerce Logistics Layout and Rural Residents’ Consumption—Tracking Based on Rural Taobao[J]. Research on Business Economy, 2021, (23): 77–81.
  12. 12. Zhi Min, Lai Zuolian. The mechanism and path of e-commerce to promote rural consumption upgrading—Taking Shaanxi as an example[J]. Research on Business Economy,2023(04):98–102.
  13. 13. Sudaryanto T, Dermoredjo S K, Purba H J, et al. Regional rural transformation and its association with household income and poverty incidence in Indonesia in the last two decades[J]. Journal of Integrative Agriculture, 2023, 22(12): 3596–3609.
  14. 14. Wang Xiaoyan, Gao Yue. Impact of industrial structure upgrading on rural residents’ consumption in the context of rural revitalization[J]. Research on Business Economy, 2023, (13): 96–99.
  15. 15. Kong Fanbin, Wang Ning, Xu Caiyao. Influence mechanism of forestry industry development on urban-rural income gap in mountainous areas—Based on the perspective of employment and income mediation effect[J]. Journal of Natural Resources,2024,39(01):62–83.
  16. 16. Devereux M B, Head A C, Lapham B J. Monopolistic competition, increasing returns, and the effects of government spending[J]. Journal of Money, credit and Banking,1996, 28(2): 233–254.
  17. 17. Tan Xiaoxiao. Study on the Dynamic Effect and Threshold Effect of Fiscal Support to Agriculture on Rural Residents’ Consumption Upgrading[J]. Research on Business Economy, 2022, (17): 41–45.
  18. 18. Jiang Tuanbiao, Zhang Yaping. Influence Mechanism of Financial Expenditure on Rural Residents’ Consumption Upgrading[J]. East China Economic Management,2021, 35 (12): 1–9.
  19. 19. Campbell , John Y. Mankiw , Gregory N. the response of consumption to income: across-country investigation[J]. European Economic Review,1991,35 (4):723–756
  20. 20. Zhao Jing. Influence mechanism and balancing path of financial resource allocation on regional differences in rural residents’ consumption[J]. Business and Economic Research, 2021, (21): 139–142.
  21. 21. Xu Xue, Wang Yongyu. Measurement of China’s rural revitalization level, decomposition of regional differences and dynamic evolution[J]. Research on Quantitative and Technical Economics,2022,39(05):64–83.
  22. 22. Lu Fengying, Pang Zhiqiang, Dang Guangyao. Measurement of regional differences and formation mechanism of China’s rural revitalization development[J]. Exploration of Economic Issues,2022(04):19–36.
  23. 23. Liu Jinshuo, Yu Hongju, Zhan Yu et al. Mechanism, current situation and prospect of digital inclusive finance to help rural industry revitalization [J/O L]. Financial Theory and Practice,2023(12):82–91.
  24. 24. Chen Wenting, Xu Daheng, Yang Liling. Can the Coupling of Green Inclusive Finance and Rural Revitalization Boost Economic Development? [J]. Financial Theory and Practice,2023,44(06):100–107.
  25. 25. Yan Yuhao, Li Wei, Hu Xiquin et al. The impact effect of digital inclusive finance on rural revitalization and empirical evidence[J]. Statistics and Decision Making,2023,39(15):131–135.
  26. 26. Wang J. Digital inclusive finance and rural revitalization[J]. Finance Research Letters, 2023, 57: 104157. https://doi.org/10.1016/j.frl.2023.104157
  27. 27. Aziz A., & Naima U. (2021). Rethinking digital financial inclusion: Evidence from Bangladesh. Technology in Society, 64, 101509.
  28. 28. Manyika J, Lund S, Bughin J. Digital globalization: the new era global flows[R]. McKinsey Global Institute, 2016.
  29. 29. Maskus K E, Neumann R, Seidel T. How national and international financial development affect industrial R&D[J]. European Economic Review, 2012, 56(1): 72–83.
  30. 30. Bonfiglioli A. Financial integration, productivity and capital accumulation[J]. Journal of International Economics, 2008, 76(2): 337–355.
  31. 31. Guo Xinglei,Liu Tao. The impact effect and mechanism of digital inclusive finance on rural industrial prosperity[J]. Statistics and Decision Making,2023,39(19):132–136.
  32. 32. Xu Weiqi, Liu Song, Tan Zhuomin, et al. Digital inclusive finance enabling rural industrial integration: intrinsic mechanism and threshold effect[J]. Financial Theory and Practice,2024(02):75–84.
  33. 33. Zhao M, Guo WJ. Can digital financial inclusion promote high-quality agricultural development?—A study on the impact mechanism and spatial effect[J]. Journal of Hohai University (Philosophy and Social Science Edition),2024,26(02):79–94.
  34. 34. Cai Xuexiong,Li Mengqi. Research on the impact of digital financial inclusion on rural eco-livability[J]. Chongqing Social Science,2023(06):47–62.
  35. 35. Gao Guosheng,Wang Qizhen,Zhi Haibing. Analysis of the impact effect of digital inclusive finance on agricultural carbon emission intensity[J]. Economic Issues,2024(01):57–65.
  36. 36. Fu Qiaoling,LI Yuanyuan,Zhao Rui. Logic, Problems and Suggestions of Digital Inclusive Finance to Promote Comprehensive Rural Revitalisation in Poverty-Reduced Areas[J]. Macroeconomic Research,2022(06):49–56.
  37. 37. Sui Xiang,Xu Na. Current status and outlook of research on the development drivers and regional development differences of digital inclusive finance[J]. Science Decision,2024(04): 153–167.
  38. 38. Chen Xi,Zhang Ligang. Stimulating endogenous order: path optimisation of digital inclusive finance embedded in rural governance[J]. Jiangxi Social Science,2021,41(10):58–67.
  39. 39. Lv Yongbin,Wen Lin. Digital Inclusive Finance and Rural Governance-Evidence from Rural Land Contract Disputes[J]. Oriental Forum,2022(04):70–84.
  40. 40. Tu Pingping. Digital inclusive finance and narrowing the income gap between urban and rural residents[J]. Price Theory and Practice,2024(02):185–188+224.
  41. 41. Liu Songtao, Luo Weilin, Liang Yingxin. The impact of digital inclusive finance on urban-rural income gap and its mechanism[J]. Journal of Hunan Agricultural University (Social Science Edition),2024,25(01):103–112.
  42. 42. Shi J, Yang X. Sustainable development levels and influence factors in rural China based on rural revitalization strategy[J]. Sustainability, 2022, 14(14): 8908.
  43. 43. Zhao Y, Li R. Coupling and coordination analysis of digital rural construction from the perspective of rural revitalization: A case study from Zhejiang province of China[J]. province of China[J]. Sustainability, 2022, 14(6): 3638.
  44. 44. Luo G, Yang Y, Wang L. Driving rural industry revitalization in the digital economy era: Exploring strategies and pathways in China[J]. Plos one, 2023, 18(9): e0292241. pmid:37768942
  45. 45. Zhang Y, Xinyu X, Qiu X, et al. Study on Livelihood Resilience of Rural Residents under the Rural Revitalization Strategy in Ethnic Areas of Western Sichuan, China[J]. Agriculture, 2023, 13(10): 1957.
  46. 46. Wang C, Chen X, Hu J, et al. Poverty alleviation and rural revitalization: Perspective of fiscal spending and data evidence from 81 Chinese counties[J]. Heliyon, 2023, 9(7). pmid:37483731
  47. 47. Wu Zhiyong, Wang Zeren, Ma Yonghong. Analysis of the threshold effect of R&D investment, internationalization degree and manufacturing firm value[J].Science and Technology Progress and Countermeasures,2020,37(14):94–101.
  48. 48. Liu Xiaoli,Han Keyong. Financial support for agriculture, labour flow and agricultural economic growth[J]. Modern Economic Discussion,2023(07):115–122.
  49. 49. Lu Shanshan, Shi Shuhua. Research on the impact of age structure of rural population on rural residents’ consumption in China[J]. Research on Business Economy,2024(09):103–107.
  50. 50. Cai Jiping, Zeng Xiaohua, Zhang Mengtao. The impact of high-level opening-up on urban residents’ service consumption—a comparison based on trade opening and market opening[J]. Research on Business Economy,2023(15):130–134.
  51. 51. Cui Linhao, Feng Feng. Research on the impact of new urbanisation construction on releasing residents’ consumption potential[J]. Journal of Xi’an Jiaotong University (Social Science Edition),2024,44(02):156–167.
  52. 52. Wang Fang,Wang Jingdong. Industrial transformation and upgrading, urban-rural income gap and residents’ consumption[J]. Industrial Technology and Economics,2024,43(01):93–101.
  53. 53. Liu Jiajie. Analysis of the impact of consumer price index on consumer demand[J]. Business Times,2012,(25):19–20.
  54. 54. Zhang Mingdou,Zhou Chuan. Coordination degree of rural revitalization and urban-rural integrated development and its spatial correlation[J/OL]. Journal of Agricultural and Forestry Economics and Management:1–13[2024-01-27]. http://kns.cnki.net/kcms/detail/36.1328.F.20240119.1005.002.html
  55. 55. Yang Chaojuan, He Gaoxiang. Spatio-temporal evolution and prediction of the development level of rural revitalization in the Yangtze River Economic Belt[J]. Statistics and Decision Making,2023,39(11):77–82.
  56. 56. Ding Baogen, Zhao Yu, Dang Junhong. A study on the measurement, decoupling characteristics and driving factors of carbon emissions from China’s plantation sector[J]. China Agricultural Resources and Zoning,2022,43(05):1–11.
  57. 57. Feng G, Zhang M. The coupling coordination development of rural e-commerce and rural revitalization: a case study of 10 rural revitalization demonstration counties in Guizhou[J]. Procedia Computer Science, 2022, 199: 407–414.
  58. 58. Liu Y, Qiao J, Xiao J, et al. Evaluation of the effectiveness of rural revitalization and an improvement path: a typical old revolutionary cultural area as an example[J]. International Journal of Environmental Research and Public Health, 2022, 19(20): 13494. pmid:36294074
  59. 59. Ju Xiaoxian. Analysis of the impact of digital economy on rural residents’ consumption[J]. Research on Business Economy,2023(14):107–110.