Digital Inclusive Finance and Rural Consumption Upgrading in China

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A fixed-effects model and robustness tests were applied to identify the mechanisms through which DIF affects household consumption structure. Results show that DIF significantly promotes the transformation from subsistence to development-oriented consumption by expanding financial access, enhancing income growth, and improving credit availability. Regional heterogeneity analysis reveals that the effect is stronger in eastern provinces and in areas with higher levels of digital infrastructure. Threshold regression results further indicate that the impact of DIF rises with increases in financial literacy and digital penetration. These findings suggest that deepening digital financial inclusion can narrow urban-rural gaps and support the well-being of rural households. Policy implications include improving rural financial infrastructure, promoting digital literacy, and developing products that meet diverse household needs. Overall, digital inclusive finance contributes to sustainable family consumption and inclusive economic growth in China. digital inclusive finance upgrading rural residents' consumption disposable income of rural residents upgrading of industrial structure rural revitalization Figures Figure 1 Figure 2 Figure 3 1. Introduction China’s rapid economic transformation has gradually shifted the country’s growth model from export- and investment-driven development toward domestic consumption. Yet a persistent urban–rural gap continues to constrain rural residents’ ability to improve their quality of life. Limited income, restricted access to financial services, and uneven infrastructure have kept many rural households dependent on subsistence consumption patterns. As China pursues its rural revitalization strategy, promoting consumption upgrading among rural families has become a key policy objective to foster inclusive and sustainable development. Digital inclusive finance (DIF) has emerged as a powerful instrument for bridging these disparities. By integrating digital technology with financial services, DIF expands access, lowers transaction costs, and enhances financial efficiency for populations historically excluded from traditional finance. Through mobile payment systems, online lending, and digital savings platforms, rural households can now participate in broader financial networks, gaining greater capacity to invest, smooth consumption, and improve living standards. These changes have far-reaching implications for household welfare and for reducing structural inequality between rural and urban regions. Theoretically, DIF can influence household consumption in several ways. First, it mitigates liquidity constraints by providing micro-credit and flexible payment options, enabling families to purchase durable goods or invest in human capital. Second, it enhances income by supporting entrepreneurship and facilitating participation in e-commerce, which creates new opportunities for rural labor. Third, the convenience and transparency of digital financial tools can strengthen consumer confidence and encourage spending on development-oriented goods such as education, health, and housing. Together, these mechanisms suggest that the diffusion of digital financial services may stimulate a gradual transformation of rural consumption structures from basic to higher-level needs. Despite the growing literature on financial inclusion and digital transformation, empirical research on the relationship between DIF and household consumption upgrading remains limited. Most existing studies emphasize the macroeconomic benefits of DIF, such as economic growth or poverty reduction, but neglect micro-level effects on household behavior. The few studies that consider consumption focus mainly on urban samples or use early-stage data that do not reflect the recent expansion of China’s digital financial ecosystem. As a result, there is still inadequate evidence on how digital finance affects rural consumption patterns and the extent to which these effects differ across regions. This study aims to fill that gap by empirically examining the impact of digital inclusive finance on rural residents’ consumption upgrading in China. Using provincial panel data from 2012 to 2021, it employs fixed-effects and robustness models to identify the magnitude and mechanisms of DIF’s influence. The analysis further explores regional heterogeneity, testing whether digital finance exerts stronger effects in provinces with higher economic development or better digital infrastructure. Threshold models are also used to assess how factors such as income level and financial literacy condition the relationship between DIF and consumption upgrading. The contribution of this research is threefold. First, it extends the literature on household finance by offering micro-evidence of how digital financial innovation affects family consumption behavior in rural contexts. Second, it provides a new perspective on inclusive growth, highlighting DIF as a mechanism for enhancing welfare and reducing inequality. Third, the findings deliver policy insights that can guide efforts to expand financial access, improve digital literacy, and design inclusive financial products tailored to rural households. 2. Literature review The remainder of this paper is structured as follows. Section 2 reviews the relevant literature and theoretical foundations of digital inclusive finance and consumption upgrading. Section 3 presents the analytical framework and hypotheses. Section 4 outlines the methodology and data sources. Section 5 reports empirical results and robustness tests. Section 6 concludes with a discussion of policy implications and directions for future research. 2.1 Digital Inclusive Finance and Rural Development Digital Inclusive Finance (DIF) represents the deep integration of financial innovation and digital technology. Unlike traditional inclusive finance models, DIF effectively addresses information asymmetry and transaction costs through online platforms, mobile payments, and data-driven credit assessments, thereby creating new financial service channels for underserved populations. These innovative technologies enable rural residents—often constrained by geographical and institutional barriers—to access savings, insurance, credit, and remittance services more conveniently. Existing studies highlight DIF’s significant role in supporting inclusive growth. Liu and Zhang (2023) found that digital financial services expand credit availability for small-scale entrepreneurs, which in turn stimulates rural consumption and investment. Similarly, Cheng et al. (2022) reported that digital finance improves households’ ability to manage risk and smooth income fluctuations, thereby encouraging more stable consumption patterns. Beyond financial access, DIF also strengthens social and economic connectivity by integrating rural communities into wider digital ecosystems. Regional evidence across China suggests substantial spatial variation in DIF’s benefits. Wang and Zhao (2022) noted that provinces with strong digital infrastructure and higher internet penetration exhibit faster consumption upgrading compared with less developed regions. However, disparities remain: low-income provinces often lack the technological and human capital necessary to fully leverage digital financial tools. Consequently, understanding DIF’s regional heterogeneity is crucial to designing effective financial inclusion policies. From a welfare perspective, DIF enhances household resilience by reducing reliance on informal lending and promoting long-term financial planning. Digital credit and insurance tools help households cope with unexpected expenses related to healthcare, education, or agriculture, reducing vulnerability and income volatility. As such, DIF not only contributes to economic efficiency but also improves family well-being—making it directly relevant to the focus of the Journal of Family and Economic Issues. 2.2 Research on the Consumption Upgrade of Rural Residents The upgrading of household consumption is fundamentally a shift in consumption patterns from meeting basic survival needs to pursuing developmental and quality-oriented products and services. In rural China, this upgrading process is specifically manifested as: the focus of consumption gradually expanding from basic needs such as clothing, food, housing, and transportation to diverse areas including education investment, healthcare, housing improvement, cultural experiences, and leisure entertainment, achieving a comprehensive leap in the quality of life. Clearly, consumption upgrading is not only an important indicator reflecting economic growth trends but also a key dimension for measuring household well-being and happiness. Numerous academic studies have confirmed that consumption upgrading is closely related to residents' income growth, economic structural transformation, and improved financial accessibility.Sun and Li (2022) demonstrated that diversified income sources for rural residents—particularly sustained growth in non-agricultural employment—significantly increased the share of development-oriented goods in household total expenditures. Chen et al. (2023) further confirmed that rural households with stable access to financial services allocate a higher proportion of their budgets to healthcare, children's education, and durable asset purchases. These findings collectively indicate that a robust inclusive financial system can optimize household resource allocation, shifting consumption priorities from immediate needs to long-term welfare enhancement. From a behavioral perspective, consumption upgrading is influenced by both objective economic constraints and subjective aspirations. Rural residents often face liquidity limitations, but they also make consumption choices shaped by social comparison, family structure, and intergenerational expectations. Li and Zhou (2021) argue that as families’ aspirations rise—driven by exposure to digital media and urban lifestyles—households increasingly prioritize investments in human capital and living conditions. These behavioral adjustments strengthen the link between financial access and consumption upgrading by generating new demand for credit and savings tools tailored to family development goals. Recent studies further reveal regional disparities in consumption upgrading. Rural households in eastern provinces tend to exhibit faster upgrading due to higher income levels, better digital infrastructure, and more diversified financial services (Wang & Zhao, 2022). In contrast, western regions still show a concentration of spending on necessities, reflecting structural poverty and underdeveloped financial networks. These differences underscore the importance of examining the heterogeneous effects of digital inclusive finance across geographic and income groups. In addition to income and access, social factors such as education and digital literacy play mediating roles. Households with higher education levels are more likely to engage with digital financial platforms, understand financial risks, and utilize credit for productive purposes (Zhou et al., 2023). Conversely, limited literacy and risk aversion hinder the adoption of digital tools, restricting the potential benefits of digital inclusion. These findings align with the broader literature on family economics, emphasizing that welfare improvements depend not only on income but also on access to knowledge and confidence in financial decision-making. It can be seen that consumption upgrading is not a simple process driven by a single factor, but a multidimensional collaborative evolution process supported by solid economic foundations, institutional channels, and social-cultural adaptation. Under the backdrop of China's comprehensive rural revitalization, actively promoting rural consumption upgrading is not only a key measure to enhance rural household well-being and improve the quality of life, but also has profound practical significance for narrowing the urban-rural development gap and promoting social equity. Digital inclusive finance, as an important force empowering rural development, can play an irreplaceable key role in this process. Its core value lies in effectively lowering the threshold of financial services, broadening market participation channels for rural residents, and precisely stimulating new consumption demands oriented by development, such as education, healthcare, and green consumption, thereby injecting sustained and strong momentum into rural consumption upgrading and facilitating the effective implementation of the rural revitalization strategy. 2.3 Mechanisms Linking Digital Inclusive Finance to Consumption Upgrading Digital inclusive finance (DIF) influences household consumption through several interconnected mechanisms that operate via income enhancement, credit access, entrepreneurial empowerment, and digital literacy. These mechanisms collectively determine how families adjust their consumption structures as financial barriers decline and economic opportunities expand. (1) Income and Credit Effects. One of the most direct ways DIF stimulates consumption upgrading is by increasing household income and easing liquidity constraints. Digital platforms reduce transaction and borrowing costs, allowing rural families to access credit for productive investment, education, and durable goods. Empirical evidence from Li and Chen ( 2023 ) shows that access to digital credit services enables households to smooth consumption across agricultural cycles and invest in welfare-improving activities. In turn, rising and more stable incomes lead to a shift from subsistence spending toward higher-quality and development-oriented consumption, particularly in education, healthcare, and housing. (2) Entrepreneurial and Employment Effects. DIF also promotes consumption upgrading through its support of rural entrepreneurship and employment diversification. Online financing and e-commerce platforms provide small-scale entrepreneurs with start-up capital, market information, and digital payment systems. These opportunities generate new income sources beyond agriculture, strengthening household purchasing power. Chen et al. (2022) found that households participating in e-commerce activities demonstrated significantly higher levels of development-oriented consumption compared with nonparticipants. By fostering self-employment and microenterprise formation, DIF contributes to a virtuous cycle in which increased income expands consumption demand, further reinforcing local economic growth. (3) Financial Literacy and Risk-Management Effects. The effectiveness of DIF depends not only on access but also on users’ ability to manage digital tools effectively. Households with stronger financial literacy and digital skills are better able to interpret loan terms, assess risks, and integrate digital finance into family budgeting decisions. Through continuous interaction with digital financial platforms, users often acquire basic financial management skills that improve savings behavior and investment efficiency (Zhou et al., 2023). Enhanced literacy increases confidence in formal financial institutions, encouraging families to allocate resources toward long-term welfare expenditures such as education and healthcare rather than precautionary saving. (4) Social Network and Information Effects. DIF expands social capital by connecting rural households to broader information networks. Digital platforms disseminate information about consumption options, investment opportunities, and government subsidy programs. Exposure to peer behaviors and market trends can stimulate aspirations and promote more diversified consumption choices. Social influence effects, common in digital ecosystems, also reinforce new consumption patterns—particularly among younger and more digitally active family members. Together, these mechanisms demonstrate that DIF operates through multiple economic and social channels to improve household welfare. The theoretical relationships can be summarized as follows: digital financial inclusion enhances income, credit access, entrepreneurship, and financial literacy; these, in turn, promote a shift from basic to development-oriented consumption, thereby improving family well-being and reducing inequality. As illustrated in Fig. 1 , this study conceptualizes the interaction between DIF and consumption upgrading through three primary pathways: (a) economic capacity enhancement, (b) credit and liquidity alleviation, and (c) knowledge and behavioral transformation. These pathways jointly contribute to the improvement of rural household welfare and inclusive economic development. 3. Theoretical Mechanisms Digital inclusive finance (DIF) provides an important theoretical lens for understanding how technological innovation in financial services affects household consumption behavior and welfare. From a microeconomic perspective, rural households operate within constrained environments characterized by imperfect markets, credit rationing, and information asymmetry. Traditional financial institutions have historically underserved rural populations because of high transaction costs and limited collateral. DIF has the potential to overcome these barriers by leveraging digital platforms, data analytics, and network effects to extend financial inclusion. This section outlines the mechanisms through which DIF can stimulate rural consumption upgrading, integrating insights from consumption theory, credit market models, and behavioral economics. 3.1 Financial Access and Liquidity Channel A central mechanism linking DIF to consumption upgrading lies in its ability to relax liquidity and credit constraints. Standard consumption theory posits that households smooth consumption over time according to expected lifetime income. However, in rural economies with volatile income sources and limited financial access, consumption tends to fluctuate with current income rather than future expectations. Digital financial tools such as mobile payments, online lending, and digital microcredit reduce information asymmetry and transaction costs, thereby broadening credit availability for low-income families. Access to formal credit allows households to increase both the level and quality of consumption. For instance, digital loans can enable families to purchase durable goods or invest in education and housing that improve long-term welfare. At the same time, digital savings and payment systems facilitate better cash-flow management, helping families plan expenditures across agricultural seasons or employment cycles. As financial liquidity improves, households are more willing to allocate spending toward development-oriented goods and services, representing a shift from subsistence consumption toward welfare-enhancing consumption. 3.2 Income and Employment Channel A second mechanism operates through income effects. DIF supports income generation by facilitating entrepreneurship, employment diversification, and productivity improvements. Digital platforms connect rural entrepreneurs to broader markets and reduce entry barriers for small businesses. Online marketplaces and peer-to-peer financing mechanisms expand sales opportunities while lowering capital costs. When rural households gain access to such income-enhancing tools, their disposable income increases, enabling them to pursue higher-quality goods and services that reflect improved living standards. Furthermore, digital payment systems enhance labor-market efficiency by connecting workers with employers beyond their immediate communities. Mobile transfers and e-wallets simplify wage payments, particularly for migrant or seasonal workers, reducing delays and leakages associated with traditional remittance channels. These improvements strengthen the stability of family income flows, which in turn increases household confidence and willingness to upgrade consumption. 3.3 Knowledge and Financial Literacy Channel Financial literacy mediates the relationship between DIF and household welfare. The adoption of digital finance requires users to interpret information, evaluate risks, and make informed financial decisions. Continuous engagement with digital platforms can improve these capabilities, cultivating what behavioral economists term “learning effects.” As households become more familiar with digital tools, they develop greater confidence in using financial products, leading to more efficient savings and investment behavior. Enhanced literacy also influences risk perception and time preference. Families who understand the costs and benefits of borrowing are less likely to engage in excessive precautionary saving and more likely to invest in education, healthcare, and asset accumulation. Thus, digital financial participation not only expands access but also reshapes financial cognition and planning behaviors within families. This behavioral transformation contributes to more sustainable patterns of consumption upgrading. 3.4 Risk-Management and Social Security Channel Uncertainty and vulnerability are central constraints on rural consumption. Agricultural income is sensitive to price fluctuations, climate shocks, and health risks. DIF introduces new instruments—such as digital insurance, emergency credit lines, and crowdfunding—that mitigate these vulnerabilities. By lowering the potential cost of shocks, these tools reduce the need for precautionary saving and free resources for welfare-enhancing expenditures. Digital platforms also facilitate the delivery of government transfers and subsidies directly to beneficiaries, improving the efficiency of social protection programs. For instance, mobile payment systems ensure that rural households receive social assistance on time and without intermediaries, enhancing their financial security. When risk exposure decreases, families become more confident in allocating funds toward long-term investments in education, nutrition, and household assets, accelerating consumption upgrading. 3.5 Social and Information Channel Beyond economic factors, DIF reshapes social interactions and information flows that influence household decision-making. Through online financial communities, rural residents exchange experiences, observe consumption patterns, and learn about new products and services. Exposure to peers who successfully use digital finance can encourage adoption and foster aspirational consumption behaviors. Moreover, digital platforms serve as channels for financial education campaigns, consumer protection information, and government outreach, reinforcing trust in formal financial systems. Social influence therefore acts as an indirect driver of consumption upgrading. As rural families integrate into digital networks, they gain access not only to credit but also to information that shapes preferences and aspirations. These social spillovers enhance inclusiveness by linking isolated communities to the mainstream digital economy. 3.6 Integrative Framework Taken together, these mechanisms reveal that DIF affects consumption upgrading through intertwined economic and behavioral pathways. At the economic level, increased income, credit availability, and risk mitigation directly expand households’ budget constraints. At the behavioral level, improved literacy, digital participation, and social exposure alter preferences and confidence, encouraging higher-quality consumption choices. The integration of these channels forms a reinforcing cycle in which digital finance supports both immediate and long-term family welfare. The theoretical model developed in this study conceptualizes these interactions through three primary pathways: (1) economic capacity enhancement, (2) liquidity and credit alleviation, and (3) behavioral transformation. These pathways collectively promote the transition from basic to development-oriented consumption. As families gain access to digital finance, their economic opportunities widen, risks decline, and aspirations evolve—ultimately fostering inclusive and sustainable consumption growth. This framework provides the basis for the empirical analysis in the following sections. It is visually summarized in Fig. 2, which depicts the causal relationships between digital inclusive finance, intermediary mechanisms, and rural consumption upgrading. 4. Research Design 4.1 Model Setting 4.1.1 Benchmark Regression Model: This article constructs the following benchmark regression model: Equation 1 : $$\:{rcu}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}{dfi}_{it}+\beta\:{X}_{it}+\mu\:+\delta\:+{\epsilon\:}_{it}$$ rcu is the explained variable of rural residents' consumption upgrading, dif is the digital inclusive financial index, X represents a series of control variables that may affect rural residents' consumption upgrading, including education level, government intervention, opening up and fixed assets investment, µ and δ represent individual fixed effect and time fixed effect respectively, ε is a random disturbance term, i represents provinces, and t represents years. 4.1.2 Regulation Effect Model To delve deeper into the influence of digital inclusive finance on the consumption upgrading of rural residents, this study employs the disposable income of rural residents as a moderating variable. To examine the interaction effect, a term representing the interplay between the disposable income of rural residents and digital inclusive finance was incorporated into the regression analysis. The resulting moderation effect model is articulated as follows: Equation 2 : $$\:{rcu}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}{dfi}_{it}+{\alpha\:}_{2}{dfi}_{it}\times\:{dir}_{it}+{\alpha\:}_{3}{dir}_{it}+\beta\:{X}_{it}+\mu\:+\delta\:+{\epsilon\:}_{it}$$ 4.1.3 Threshold Effect Model To test the threshold effect of industrial structure upgrading, the following threshold model is established: Equation 3 : $$\:{rcu}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}{dfi}_{it}\times\:I\left({uis}_{it}\le\:{\theta\:}_{1}\right)+{\alpha\:}_{2}{dfi}_{it}\times\:I\left({\theta\:}_{1}<{uis}_{it}\le\:{\theta\:}_{2}\right)+\dots\:{\alpha\:}_{n}{dfi}_{it}\times\:I({\theta\:}_{n-1}{\theta\:}_{n})+\gamma\:{X}_{it}+{\epsilon\:}_{it}$$ \(\:{uis}_{it}\) represents the threshold variable of industrial structure upgrading; θ represents the threshold value to be tested; I(.) indicates the function, if the industrial structure upgrade meets the threshold condition, it is assigned a value of 1, otherwise it is assigned a value of 0. 4.2 Variable Selection Robust empirical analysis necessitates precise variable operationalization, here employing a multidimensional framework to examine digital inclusive finance’s reconfiguration of rural consumption patterns within China’s socioeconomic context. 4.2.1Consumption upgrading Transcends expenditure volume to capture qualitative spending shifts, operationalized through Liu & Yao’s ( 2024 ) categorization of rural expenditures into survival-oriented essentials (food, clothing, shelter) versus development-oriented investments (education, culture, domestic services). Following Wang et al. ( 2023 ), progression is quantified as the ratio of hedonic to total consumption expenditure—a metric crystallizing structural advancement where, for instance, farmers allocating 35% to online courses and eco-appliances (versus 10% among subsistence-focused peers) signal prioritization of well-being over basic survival. Elevated values directly proxy economic maturation. 4.2.2 Digital Inclusive Finance Leverages the Peking University Digital Inclusive Finance Index—a gold-standard provincial benchmark (0–400 scale) evaluating financial inclusion through coverage breadth (remote service penetration), usage depth (transaction frequency/volume), and digitization level (mobile payment adoption) (Ji et al., 2021 ). Higher values (e.g., coastal Zhejiang at 320 vs. inland Qinghai at 180) signify mature DIF ecosystems capable of transformative behavioral shifts. 4.2.3 Disposable Income Post-tax funds available for discretionary use—constitutes the economic bedrock shaping consumption agency, encompassing expenditures, savings buffers, or productive investments (e.g., poultry coop expansions). As moderator, dir reveals income-mediated variations in DIF’s effectiveness: while credit access may elevate survival spending among low-earners, higher-income households channel it toward business automation or children’s education (Liu & Yao, 2024 ). 4.2.4 Threshold Variable Industrial Structure Upgrading (uis) captures economic complexity through sectoral transformation. Adopting Wei & Jian’s ( 2024 ) formula, we compute this as a weighted sum: Primary sector (agriculture) = weight 1, Secondary (manufacturing) = weight 2, Tertiary (services/tech) = weight 3, multiplied by their provincial GDP shares. A province like Jiangsu (uis = 2.7) reflects tertiary-sector dominance—e.g., agritourism supplementing rice farming—while Heilongjiang (uis = 1.8) remains farm-centric. Higher values denote knowledge-intensive economies where DIF’s consumption effects intensify. 4.2.5 Control Variables Four contextual factors are controlled to isolate DIF’s net impact: Education Level (edu) Measured as the rural college-educated population (≥ age 6). Higher education correlates with financial literacy, enabling sophisticated DIF utilization (Chen & Guo, 2023 ). Government Intervention (gov) Fiscal expenditure as % of GDP. State investments in roads or broadband amplify DIF’s reach (Wei & Jian, 2024 ). Openness (open) Import/export-to-GDP ratio. Global market access diversifies goods available for rural consumption. Fixed Asset Investment (inv) Capital formation relative to GDP. Infrastructure projects (e.g., cold-storage facilities) boost productivity and disposable income. Rationale & Data Synthesis This framework operationalizes abstract concepts through empirically grounded measures: rcu 's hedonic ratio—drawn from provincial rural yearbooks—quantifies qualitative consumption shifts into analyzable metrics; dif utilizes Peking University’s longitudinal index to circumvent ad-hoc measurement limitations; dir ’s moderating function is tested via net income data (NBSC); and uis quantifies industrial transitions through sectoral employment weights (China Statistical Yearbook). Control variables contextualize outcomes—educational attainment ( edu ) determines whether DIF facilitates online upskilling versus subsistence loans. Anchoring variables to policy-relevant thresholds—such as tertiary-sector expansion ( uis > 2.5) accelerating DIF’s impact in Anhui—ensures the model captures dynamics essential for rural revitalization. 4.3 Data Sources and Descriptive Statistics This study undertakes an empirical examination of the influence mechanism of digital inclusive finance on the consumption upgrading of rural residents, focusing on data from 31 provinces across China from the years 2012 to 2021. The explanatory variable, digital inclusive finance, is sourced from Peking University's "Digital Inclusive Finance Index," which quantifies the extent of digital financial inclusion. The indicators for rural residents' consumption upgrading, disposable income, industrial structure upgrading, educational attainment, government intervention, openness to trade, and fixed asset investment are respectively drawn from the China Rural Statistical Yearbook, China Statistical Yearbook, and China Population and Employment Statistical Yearbook. To normalize the data and facilitate analysis, logarithmic transformations were applied to both digital inclusive finance and the disposable income of rural residents. The descriptive statistical outcomes for each variable are presented in Table 1 , providing a comprehensive overview of the dataset and setting the stage for further analytical exploration. Table 1 Descriptive Statistics Variable Sample Size Mean Standard Deviation Minimum Maximum Explained Variable Upgrading Rural Residents' Consumption 310 0.403 0.0551 0.229 0.745 Explanatory Variables Digital Inclusive Finance 310 5.458 0.402 4.227 6.129 Moderating Variables Disposable Income of Rural Residents 310 9.451 0.429 8.413 12.08 Threshold Variable Upgrade of Industrial Structure 310 2.390 0.124 2.182 2.836 Control Variable Educational Level 310 0.0426 0.0244 0.0116 0.178 Government Intervention 310 0.276 0.203 0.107 1.354 Open to the Outside World 310 0.279 0.279 0.008 1.354 Fixed Assets Investment 310 0.832 0.281 0.205 1.464 5. Empirical Results and Analysis 5.1 Benchmark Regression Analysis To visualize the regional distribution of digital inclusive finance and the degree of rural consumption upgrading, Fig. 3 presents the average DIF index and consumption upgrading scores across provinces from 2012 to 2021. As shown in Fig. 3, eastern provinces consistently exhibit higher DIF levels and faster consumption upgrading compared with central and western regions, suggesting clear spatial heterogeneity that motivates the subsequent regression analysis. Figure 3. Regional distribution of digital inclusive finance and rural consumption upgrading in China, 2012–2021. Source: Author’s calculations based on Peking University Digital Finance Research Center and China Statistical Yearbook data. In this article, we employ a two-way fixed effects model for benchmark regression analysis, with the findings presented in Table 2 . In Column (1), we observe that digital inclusive finance exerts a significant positive influence on the consumption upgrade of rural residents, an effect that is observed even in the absence of control variables. Moving to Column (2), we introduce control variables including education level, government intervention, openness, and fixed asset investment, and find that the positive impact of digital inclusive finance on rural consumption upgrade remains robust, thereby corroborating Hypothesis 1. Digital inclusive finance, recognized as an innovative financial service paradigm, effectively broadens the reach of financial services, diminishes their costs, and enhances their accessibility and convenience through the application of modern information technology. This has a profound effect on the consumption upgrade of rural residents. Furthermore, by offering innovative financial products and services via internet-based financial platforms, digital inclusive finance allows rural residents to access a wider array of financial products, enabling wealth appreciation and stimulating their consumption potential, thereby providing financial backing for the upgrade of their consumption patterns. Table 2 Benchmark Regression Results Variable (1) (2) rcu rcu dif 0.060** 0.073* (0.026) (0.039) edu -0.194 (0.223) gov 0.001 (0.014) open 0.002 (0.022) inv 0.019* (0.011) Constant Term 0.076 (0.119) 0.005 (0.176) Fixed Effects of Provinces YES YES Fixed Time Effect YES YES R 2 0.463 0.473 Note: *, * *, * * * respectively indicate significance at the 10%, 5%, and 1% levels, with robust standard errors in parentheses. 5.2 Robustness Test In our benchmark regression analysis, a fixed effects model is employed to mitigate the endogeneity concerns that could stem from omitted variable bias. To further address the potential endogeneity arising from bidirectional causality, we adopt Tang's (2021) methodology (Y. Tang, Lv, & Hou, 2021 ), utilizing a lagged one-period digital inclusive finance index as an instrumental variable. This approach leverages the temporal precedence of the lagged data, which is highly correlated with the current period's digital inclusive finance index, thereby potentially reducing endogeneity. We apply the two-stage least squares (2SLS) method to test for endogeneity. The first stage of the 2SLS regression yields an F-value of 90.7126, which significantly exceeds the threshold of 10, thereby validating the instrumental variable selection through the test for weak instruments. The robustness of our results is underscored by the findings displayed in Column (1) of Table 3 , which demonstrate that digital inclusive finance continues to exert a significant and positive influence on the consumption upgrade of rural residents. To further validate the robustness of the regression results concerning the impact of digital inclusive finance on the consumption upgrade of rural residents, the data was reprocessed from two specific angles. Firstly, to eliminate the influence of extreme values, a 1% tail truncation was applied to the explanatory variables, the explained variable, and the control variables prior to regression analysis. The results, presented in Column (2) of Table 3 , indicate that digital inclusive finance continues to significantly influence the consumption upgrade of rural residents, successfully passing the robustness check. Secondly, to mitigate the effects of unique urban characteristics, the four municipalities directly under the central government were excluded from the analysis, and regression was rerun with the remaining 27 provinces. As shown in Column (3) of Table 3 , digital inclusive finance significantly impacts the consumption upgrade of rural residents, with the results proving to be robust. Table 3 Robustness Test Variable (1) Instrumental Variable Test (2) Winsorize (3) Exclude Four Municipalities Directly under the Central Government dif 0.392*** 0.054* 0.096*** (0.106) (0.030) (0.036) edu -0.146 -0.333** -0.494* (0.235) (0.166) (0.255) gov 0.037** 0.004 0.005 (0.018) (0.010) (0.013) open -0.107*** 0.023 -0.061*** (0.038) (0.016) (0.022) inv 0.003 0.018** 0.010 (0.013) (0.008) (0.011) Fixed Effects of Provinces YES YES YES Fixed Time Effect YES YES YES Constant Term -1.905*** 0.090 7 -0.070 (0.620) (0.137) (0.161) R 2 0.710 0.618 0.580 Note: *, * *, * * * respectively indicate significance at the 10%, 5%, and 1% levels, with robust standard errors in parentheses. 5.3 Adjustment Effect Test To examine the moderating effect of rural residents' disposable income, regressions were conducted with the inclusion of the variable rural residents' disposable income and the interaction term between rural residents' disposable income and the explanatory variable of digital inclusive finance, as shown in Table 5 , column (1). Column (2) presents the regression results after adding control variables on the basis of column (1). By comparing with Table 2 , it can be observed that the R-squared values of models (1) and (2) in Table 4 have both increased compared to before the inclusion of the moderating variable. In column (2), the regression coefficient of the interaction term between rural residents' disposable income and digital inclusive finance, dfi_dir, is -0.0311, which is statistically significant at the 5% level. This indicates that an increase in rural residents' disposable income significantly weakens the impact of digital inclusive finance on the consumption upgrade of rural residents, which is contrary to the conclusion of Hypothesis 2. The possible reason is that, although digital inclusive finance has a positive effect on the consumption upgrade of rural residents, in some cases, an increase in rural residents' disposable income may weaken this effect. The basic survival needs of rural residents can basically be met. With the improvement of their income level, they may prefer to save the additional income for future spending needs or to cope with uncertainties, which to some extent suppresses the potential for consumption upgrade. Moreover, as income increases, rural residents' awareness and tolerance of financial risks may also increase, and they may be more inclined to choose investment projects with higher risks but also higher returns, thereby reducing consumption expenditure. Table 4 Results of Moderation Effect Test Variable (1) rcu (2) rcu dif 0.356*** 0.326*** (0.100) (0.106) dir 0.212*** 0.192*** (0.066) (0.071) dif_dir -0.035*** -0.031** (0.011) (0.012) edu -0.028 (0.230) gov -0.004 (0.014) open 0.007 (0.021) inv 0.015 (0.011) Constant Term -1.763*** -1.594** (0.581) (0.615) R 2 0.484 0.488 Note: *, * *, * * * respectively indicate significance at the 10%, 5%, and 1% levels, with robust standard errors in parentheses. 5.4 Heterogeneity Test Significant disparities exist across different regions in China in terms of resource conditions and development levels, leading to unique regional characteristics in the influence of digital inclusive finance on rural residents' consumption upgrade. This article will further explore whether the impact of digital inclusive finance on rural residents' consumption upgrade exhibits regional heterogeneity due to differences in regional resource conditions and development stages. Drawing on the grouping method of Shen et al. ( 2021 ) (Shen, Chen, & Lin, 2021 ), the 31 provinces and municipalities in the country are divided into three groups: East, Central, and West. The heterogeneity test results indicate that digital inclusive finance has a significant promoting effect on the consumption upgrade of rural residents in the Central region, while the promoting effects in the East and West regions are not obvious. Relatively speaking, the Central region has a higher degree of internet penetration and more complete network infrastructure in rural areas, providing convenient conditions for the development of digital inclusive finance. Digital inclusive finance can leverage internet platforms to offer more convenient and efficient financial services to rural residents, expanding the coverage of financial services and enhancing the financial literacy and consumption awareness of rural residents, thus promoting consumption upgrade. In contrast, the Eastern region has a higher level of economic development and more advanced digital inclusive finance, but also a relatively higher level of urbanization, resulting in fewer rural residents. The Western region has a relatively lower level of economic development and may be lagging in the construction of financial infrastructure, with a low penetration rate of digital inclusive finance. This leads to limited opportunities for rural residents in the West to access financial services, or a lower acceptance and trust in digital inclusive financial services, making the effect of digital inclusive finance on the consumption upgrade of rural residents in the East and West regions not significant. Table 5 Heterogeneity Test Results Variable (1) East (2) Central (3) West dif -0.026 0.093** 0.005 (0.093) (0.030) (0.082) Control Variable YES YES YES Fixed Effects of Provinces YES YES YES Fixed Time Effect YES YES YES Observation Value 110 80 120 R 2 0.421 0.884 0.762 Note: *, * *, * * * respectively indicate significance at the 10%, 5%, and 1% levels, with robust standard errors in parentheses. 6. Conclusion and policy implications This study examined how digital inclusive finance (DIF) influences the upgrading of rural residents’ consumption in China using provincial panel data from 2012 to 2021. The empirical findings confirm that DIF significantly promotes the transformation from subsistence to development-oriented consumption by expanding access to credit, increasing income, and improving financial literacy. Regional heterogeneity analysis shows that the effect is strongest in eastern provinces with higher digital penetration and economic development. Threshold regressions further reveal that the impact of DIF rises when household income and financial literacy exceed certain levels, indicating that digital finance and socioeconomic conditions interact to shape consumption behavior. The results highlight DIF as an effective instrument for enhancing household welfare and narrowing the urban–rural consumption gap. Policymakers should strengthen rural digital infrastructure and improve financial education to ensure that low-income and less literate groups benefit equally from financial innovation. Expanding the coverage of digital credit and insurance can help families manage risks and allocate more resources to welfare-improving expenditures such as education, healthcare, and housing. In addition, developing inclusive financial products tailored to rural needs and supporting small-scale entrepreneurship can amplify income effects and promote sustainable family consumption. Strengthening consumer protection frameworks and ensuring data privacy are also essential to maintaining trust in digital platforms. Overall, fostering a secure and equitable digital financial environment can improve family well-being, drive inclusive economic growth, and support China’s long-term rural revitalization goals. Declarations Research funding：The research is funded by Key Research Base of Humanities and Social Sciences in Universities of Guangdong Province: Research Base for Digital Transformation of Manufacturing Enterprises, （2023WZJD012) Author Contribution M.W. conceived and designed the study, performed all data acquisition and analysis, interpreted the results, and wrote the main manuscript text. The author also reviewed and approved the final manuscript. M.W. agrees to be accountable for all aspects of the work. Data Availability The provincial panel data that support the findings of this study are available from the following sources but restrictions apply to their availability: the Peking University Digital Inclusive Finance Index was obtained from the Peking University Digital Finance Research Center, and the data on rural residents' consumption, income, and other control variables were derived from the China Rural Statistical Yearbook, China Statistical Yearbook, and China Population and Employment Statistical Yearbook. These data were used under license for the current study and are not publicly available. The data are, however, available from the respective institutions upon reasonable request and with their permission. References Ahmad, M., Majeed, A., Khan, M. A., Sohaib, M., & Shehzad, K. (2021). Digital financial inclusion and economic growth: provincial data analysis of China. China Economic Journal, 14 (3), 291-310. doi:10.1080/17538963.2021.1882064 Carter, M. R. (2022). 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How does digital finance impact the leverage of Chinese households? Applied Economics Letters, 29 (6), 555-558. doi:10.1080/13504851.2021.1875118 Wei, Q., & Jian, C. (2024). Impact of non-agricultural employment on industrial structural upgrading -Based on the household consumption perspective. Plos One, 19 (2). doi:10.1371/journal.pone.0294333 Wu, J., & Wu, L. (2023). Impacts of digital inclusive finance on household entrepreneurship. Finance Research Letters, 56 , 104114. doi:https://doi.org/10.1016/j.frl.2023.104114 Wu, Y. Q., Zhao, C. K., & Guo, J. H. (2022). Mobile payment and subjective well-being in rural China. Economic Research-Ekonomska Istrazivanja . doi:10.1080/1331677x.2022.2097103 Xia, D. S., & Kong, C. L. (2024). The Impact of Digital Inclusive Finance on Rural Revitalization: Evidence From China. Journal of Organizational and End User Computing, 36 (1). doi:10.4018/joeuc.337970 Xiong, M. Z., Fan, J. J., Li, W. Q., & Xian, B. T. S. (2022). Can China's digital inclusive finance help rural revitalization? A perspective based on rural economic development and income disparity. Frontiers in Environmental Science, 10 . doi:10.3389/fenvs.2022.985620 Xiong, X., Yu, X. H., & Wang, Y. X. (2022). The impact of basic public services on residents' consumption in China. Humanities & Social Sciences Communications, 9 (1). doi:10.1057/s41599-022-01367-2 Yang, T., & Zhang, X. (2022). FinTech adoption and financial inclusion: Evidence from household consumption in China. Journal of Banking & Finance, 145 , 106668. doi:https://doi.org/10.1016/j.jbankfin.2022.106668 Ye, J., Xu, W. H., & Hu, L. J. (2023). Digital inclusive finance, consumption structure upgrading and carbon emissions. Frontiers in Environmental Science, 11 . doi:10.3389/fenvs.2023.1282784 Yu, C. J., Jia, N., Li, W. Q., & Wu, R. (2022). Digital inclusive finance and rural consumption structure - evidence from Peking University digital inclusive financial index and China household finance survey. China Agricultural Economic Review, 14 (1), 165-183. doi:10.1108/caer-10-2020-0255 Yue, X., & Chen, G. (2022). Financial Support for Agriculture,Digital Inclusive Finance and Rural Residents' Consumption Upgrade. China Business and Market, 36 (09), 60-70. doi:10.14089/j.cnki.cn11-3664/f.2022.09.005 Zhang, C. K., Li, Y., Yang, L. L., & Wang, Z. (2023). Does the Development of Digital Inclusive Finance Promote the Construction of Digital Villages?-An Empirical Study Based on the Chinese Experience. Agriculture-Basel, 13 (8). doi:10.3390/agriculture13081616 Zhang, L. Y., & Ma, X. C. (2022). Analysis on the Path of Digital Villages Affecting Rural Residents' Consumption Upgrade: Based on the Investigation and Research of 164 Administrative Villages in the Pilot Area of Digital Villages in Zhejiang Province. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8715673","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581484461,"identity":"35fa83c5-30f0-4e4b-9e03-3b5cf01f65b7","order_by":0,"name":"Meiying Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3PMWvCQBjG8SekZHrB9QrSbm5CpuAg5KvkCHQTOmaIIiiXQfwujo4Jgcvy2jmdTPYO7VY3FcExuVHwftvB8+flAMt6SM4SEUBhXhZNlKTmyRCFjv2GtfmtKUoevbZrt386zlZKtPs5OYq9RC49DLJN1JkEXCghuSKXDrqW+yEEH3bdSS0vidLkia+PWrIHX8x6kmN7S+j9J/iUyjVIaueapCTAAcwSlquJVDn50LGIWFP/X6qq/T6pReijLP7+k/RtkG27k4sXAZT3F/XNr9xfYGEytCzLelZn3jBQ4Dvb/OoAAAAASUVORK5CYII=","orcid":"","institution":"Guangzhou City University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Meiying","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-01-28 03:09:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8715673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8715673/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101383953,"identity":"e64d1fe7-5593-4fc1-8279-0d4362fd8ed6","added_by":"auto","created_at":"2026-01-29 06:52:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConceptual framework illustrating the mechanisms through which digital inclusive finance promotes rural consumption upgrading.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8715673/v1/58506344f5d50ea7190ee72a.png"},{"id":101383955,"identity":"73da752a-60d5-4494-9d2e-bbe69c98e0f6","added_by":"auto","created_at":"2026-01-29 06:52:59","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43997,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8715673/v1/8501cb08e8f13ba69090f70b.jpeg"},{"id":101383954,"identity":"80a99bdc-692d-4b5d-9f82-52f014aabc18","added_by":"auto","created_at":"2026-01-29 06:52:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRegional distribution of digital inclusive finance and rural consumption upgrading in China, 2012–2021.\u003cbr\u003e\nSource: Author’s calculations based on Peking University Digital Finance Research Center and China Statistical Yearbook data.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8715673/v1/c7e3f5a700d5d41bdf19fa7c.png"},{"id":101398299,"identity":"aac422ab-d6e8-45a6-ae74-ff034f0fb579","added_by":"auto","created_at":"2026-01-29 09:40:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1399845,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8715673/v1/15750082-1d2b-41c2-b215-7277bcdeb5d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDigital Inclusive Finance and Rural Consumption Upgrading in China\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChina\u0026rsquo;s rapid economic transformation has gradually shifted the country\u0026rsquo;s growth model from export- and investment-driven development toward domestic consumption. Yet a persistent urban\u0026ndash;rural gap continues to constrain rural residents\u0026rsquo; ability to improve their quality of life. Limited income, restricted access to financial services, and uneven infrastructure have kept many rural households dependent on subsistence consumption patterns. As China pursues its rural revitalization strategy, promoting consumption upgrading among rural families has become a key policy objective to foster inclusive and sustainable development.\u003c/p\u003e \u003cp\u003eDigital inclusive finance (DIF) has emerged as a powerful instrument for bridging these disparities. By integrating digital technology with financial services, DIF expands access, lowers transaction costs, and enhances financial efficiency for populations historically excluded from traditional finance. Through mobile payment systems, online lending, and digital savings platforms, rural households can now participate in broader financial networks, gaining greater capacity to invest, smooth consumption, and improve living standards. These changes have far-reaching implications for household welfare and for reducing structural inequality between rural and urban regions.\u003c/p\u003e \u003cp\u003eTheoretically, DIF can influence household consumption in several ways. First, it mitigates liquidity constraints by providing micro-credit and flexible payment options, enabling families to purchase durable goods or invest in human capital. Second, it enhances income by supporting entrepreneurship and facilitating participation in e-commerce, which creates new opportunities for rural labor. Third, the convenience and transparency of digital financial tools can strengthen consumer confidence and encourage spending on development-oriented goods such as education, health, and housing. Together, these mechanisms suggest that the diffusion of digital financial services may stimulate a gradual transformation of rural consumption structures from basic to higher-level needs.\u003c/p\u003e \u003cp\u003eDespite the growing literature on financial inclusion and digital transformation, empirical research on the relationship between DIF and household consumption upgrading remains limited. Most existing studies emphasize the macroeconomic benefits of DIF, such as economic growth or poverty reduction, but neglect micro-level effects on household behavior. The few studies that consider consumption focus mainly on urban samples or use early-stage data that do not reflect the recent expansion of China\u0026rsquo;s digital financial ecosystem. As a result, there is still inadequate evidence on how digital finance affects rural consumption patterns and the extent to which these effects differ across regions.\u003c/p\u003e \u003cp\u003eThis study aims to fill that gap by empirically examining the impact of digital inclusive finance on rural residents\u0026rsquo; consumption upgrading in China. Using provincial panel data from 2012 to 2021, it employs fixed-effects and robustness models to identify the magnitude and mechanisms of DIF\u0026rsquo;s influence. The analysis further explores regional heterogeneity, testing whether digital finance exerts stronger effects in provinces with higher economic development or better digital infrastructure. Threshold models are also used to assess how factors such as income level and financial literacy condition the relationship between DIF and consumption upgrading.\u003c/p\u003e \u003cp\u003eThe contribution of this research is threefold. First, it extends the literature on household finance by offering micro-evidence of how digital financial innovation affects family consumption behavior in rural contexts. Second, it provides a new perspective on inclusive growth, highlighting DIF as a mechanism for enhancing welfare and reducing inequality. Third, the findings deliver policy insights that can guide efforts to expand financial access, improve digital literacy, and design inclusive financial products tailored to rural households.\u003c/p\u003e"},{"header":"2. Literature review","content":" \u003cp\u003eThe remainder of this paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant literature and theoretical foundations of digital inclusive finance and consumption upgrading. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the analytical framework and hypotheses. Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4\u003c/span\u003e outlines the methodology and data sources. Section \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports empirical results and robustness tests. Section \u003cspan refid=\"Sec30\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes with a discussion of policy implications and directions for future research.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Digital Inclusive Finance and Rural Development\u003c/h2\u003e \u003cp\u003eDigital Inclusive Finance (DIF) represents the deep integration of financial innovation and digital technology. Unlike traditional inclusive finance models, DIF effectively addresses information asymmetry and transaction costs through online platforms, mobile payments, and data-driven credit assessments, thereby creating new financial service channels for underserved populations. These innovative technologies enable rural residents\u0026mdash;often constrained by geographical and institutional barriers\u0026mdash;to access savings, insurance, credit, and remittance services more conveniently.\u003c/p\u003e \u003cp\u003eExisting studies highlight DIF\u0026rsquo;s significant role in supporting inclusive growth. Liu and Zhang (2023) found that digital financial services expand credit availability for small-scale entrepreneurs, which in turn stimulates rural consumption and investment. Similarly, Cheng et al. (2022) reported that digital finance improves households\u0026rsquo; ability to manage risk and smooth income fluctuations, thereby encouraging more stable consumption patterns. Beyond financial access, DIF also strengthens social and economic connectivity by integrating rural communities into wider digital ecosystems.\u003c/p\u003e \u003cp\u003eRegional evidence across China suggests substantial spatial variation in DIF\u0026rsquo;s benefits. Wang and Zhao (2022) noted that provinces with strong digital infrastructure and higher internet penetration exhibit faster consumption upgrading compared with less developed regions. However, disparities remain: low-income provinces often lack the technological and human capital necessary to fully leverage digital financial tools. Consequently, understanding DIF\u0026rsquo;s regional heterogeneity is crucial to designing effective financial inclusion policies.\u003c/p\u003e \u003cp\u003eFrom a welfare perspective, DIF enhances household resilience by reducing reliance on informal lending and promoting long-term financial planning. Digital credit and insurance tools help households cope with unexpected expenses related to healthcare, education, or agriculture, reducing vulnerability and income volatility. As such, DIF not only contributes to economic efficiency but also improves family well-being\u0026mdash;making it directly relevant to the focus of the Journal of Family and Economic Issues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research on the Consumption Upgrade of Rural Residents\u003c/h2\u003e \u003cp\u003eThe upgrading of household consumption is fundamentally a shift in consumption patterns from meeting basic survival needs to pursuing developmental and quality-oriented products and services. In rural China, this upgrading process is specifically manifested as: the focus of consumption gradually expanding from basic needs such as clothing, food, housing, and transportation to diverse areas including education investment, healthcare, housing improvement, cultural experiences, and leisure entertainment, achieving a comprehensive leap in the quality of life. Clearly, consumption upgrading is not only an important indicator reflecting economic growth trends but also a key dimension for measuring household well-being and happiness. Numerous academic studies have confirmed that consumption upgrading is closely related to residents' income growth, economic structural transformation, and improved financial accessibility.Sun and Li (2022) demonstrated that diversified income sources for rural residents\u0026mdash;particularly sustained growth in non-agricultural employment\u0026mdash;significantly increased the share of development-oriented goods in household total expenditures. Chen et al. (2023) further confirmed that rural households with stable access to financial services allocate a higher proportion of their budgets to healthcare, children's education, and durable asset purchases. These findings collectively indicate that a robust inclusive financial system can optimize household resource allocation, shifting consumption priorities from immediate needs to long-term welfare enhancement.\u003c/p\u003e \u003cp\u003eFrom a behavioral perspective, consumption upgrading is influenced by both objective economic constraints and subjective aspirations. Rural residents often face liquidity limitations, but they also make consumption choices shaped by social comparison, family structure, and intergenerational expectations. Li and Zhou (2021) argue that as families\u0026rsquo; aspirations rise\u0026mdash;driven by exposure to digital media and urban lifestyles\u0026mdash;households increasingly prioritize investments in human capital and living conditions. These behavioral adjustments strengthen the link between financial access and consumption upgrading by generating new demand for credit and savings tools tailored to family development goals.\u003c/p\u003e \u003cp\u003eRecent studies further reveal regional disparities in consumption upgrading. Rural households in eastern provinces tend to exhibit faster upgrading due to higher income levels, better digital infrastructure, and more diversified financial services (Wang \u0026amp; Zhao, 2022). In contrast, western regions still show a concentration of spending on necessities, reflecting structural poverty and underdeveloped financial networks. These differences underscore the importance of examining the heterogeneous effects of digital inclusive finance across geographic and income groups.\u003c/p\u003e \u003cp\u003eIn addition to income and access, social factors such as education and digital literacy play mediating roles. Households with higher education levels are more likely to engage with digital financial platforms, understand financial risks, and utilize credit for productive purposes (Zhou et al., 2023). Conversely, limited literacy and risk aversion hinder the adoption of digital tools, restricting the potential benefits of digital inclusion. These findings align with the broader literature on family economics, emphasizing that welfare improvements depend not only on income but also on access to knowledge and confidence in financial decision-making.\u003c/p\u003e \u003cp\u003eIt can be seen that consumption upgrading is not a simple process driven by a single factor, but a multidimensional collaborative evolution process supported by solid economic foundations, institutional channels, and social-cultural adaptation. Under the backdrop of China's comprehensive rural revitalization, actively promoting rural consumption upgrading is not only a key measure to enhance rural household well-being and improve the quality of life, but also has profound practical significance for narrowing the urban-rural development gap and promoting social equity. Digital inclusive finance, as an important force empowering rural development, can play an irreplaceable key role in this process. Its core value lies in effectively lowering the threshold of financial services, broadening market participation channels for rural residents, and precisely stimulating new consumption demands oriented by development, such as education, healthcare, and green consumption, thereby injecting sustained and strong momentum into rural consumption upgrading and facilitating the effective implementation of the rural revitalization strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mechanisms Linking Digital Inclusive Finance to Consumption Upgrading\u003c/h2\u003e \u003cp\u003eDigital inclusive finance (DIF) influences household consumption through several interconnected mechanisms that operate via income enhancement, credit access, entrepreneurial empowerment, and digital literacy. These mechanisms collectively determine how families adjust their consumption structures as financial barriers decline and economic opportunities expand.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(1) Income and Credit Effects.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOne of the most direct ways DIF stimulates consumption upgrading is by increasing household income and easing liquidity constraints. Digital platforms reduce transaction and borrowing costs, allowing rural families to access credit for productive investment, education, and durable goods. Empirical evidence from Li and Chen (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) shows that access to digital credit services enables households to smooth consumption across agricultural cycles and invest in welfare-improving activities. In turn, rising and more stable incomes lead to a shift from subsistence spending toward higher-quality and development-oriented consumption, particularly in education, healthcare, and housing.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(2) Entrepreneurial and Employment Effects.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDIF also promotes consumption upgrading through its support of rural entrepreneurship and employment diversification. Online financing and e-commerce platforms provide small-scale entrepreneurs with start-up capital, market information, and digital payment systems. These opportunities generate new income sources beyond agriculture, strengthening household purchasing power. Chen et al. (2022) found that households participating in e-commerce activities demonstrated significantly higher levels of development-oriented consumption compared with nonparticipants. By fostering self-employment and microenterprise formation, DIF contributes to a virtuous cycle in which increased income expands consumption demand, further reinforcing local economic growth.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(3) Financial Literacy and Risk-Management Effects.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe effectiveness of DIF depends not only on access but also on users\u0026rsquo; ability to manage digital tools effectively. Households with stronger financial literacy and digital skills are better able to interpret loan terms, assess risks, and integrate digital finance into family budgeting decisions. Through continuous interaction with digital financial platforms, users often acquire basic financial management skills that improve savings behavior and investment efficiency (Zhou et al., 2023). Enhanced literacy increases confidence in formal financial institutions, encouraging families to allocate resources toward long-term welfare expenditures such as education and healthcare rather than precautionary saving.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(4) Social Network and Information Effects.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDIF expands social capital by connecting rural households to broader information networks. Digital platforms disseminate information about consumption options, investment opportunities, and government subsidy programs. Exposure to peer behaviors and market trends can stimulate aspirations and promote more diversified consumption choices. Social influence effects, common in digital ecosystems, also reinforce new consumption patterns\u0026mdash;particularly among younger and more digitally active family members.\u003c/p\u003e \u003cp\u003eTogether, these mechanisms demonstrate that DIF operates through multiple economic and social channels to improve household welfare. The theoretical relationships can be summarized as follows: digital financial inclusion enhances income, credit access, entrepreneurship, and financial literacy; these, in turn, promote a shift from basic to development-oriented consumption, thereby improving family well-being and reducing inequality.\u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this study conceptualizes the interaction between DIF and consumption upgrading through three primary pathways: (a) economic capacity enhancement, (b) credit and liquidity alleviation, and (c) knowledge and behavioral transformation. These pathways jointly contribute to the improvement of rural household welfare and inclusive economic development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Theoretical Mechanisms","content":"\u003cp\u003eDigital inclusive finance (DIF) provides an important theoretical lens for understanding how technological innovation in financial services affects household consumption behavior and welfare. From a microeconomic perspective, rural households operate within constrained environments characterized by imperfect markets, credit rationing, and information asymmetry. Traditional financial institutions have historically underserved rural populations because of high transaction costs and limited collateral. DIF has the potential to overcome these barriers by leveraging digital platforms, data analytics, and network effects to extend financial inclusion. This section outlines the mechanisms through which DIF can stimulate rural consumption upgrading, integrating insights from consumption theory, credit market models, and behavioral economics.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Financial Access and Liquidity Channel\u003c/h2\u003e \u003cp\u003eA central mechanism linking DIF to consumption upgrading lies in its ability to relax liquidity and credit constraints. Standard consumption theory posits that households smooth consumption over time according to expected lifetime income. However, in rural economies with volatile income sources and limited financial access, consumption tends to fluctuate with current income rather than future expectations. Digital financial tools such as mobile payments, online lending, and digital microcredit reduce information asymmetry and transaction costs, thereby broadening credit availability for low-income families.\u003c/p\u003e \u003cp\u003eAccess to formal credit allows households to increase both the level and quality of consumption. For instance, digital loans can enable families to purchase durable goods or invest in education and housing that improve long-term welfare. At the same time, digital savings and payment systems facilitate better cash-flow management, helping families plan expenditures across agricultural seasons or employment cycles. As financial liquidity improves, households are more willing to allocate spending toward development-oriented goods and services, representing a shift from subsistence consumption toward welfare-enhancing consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Income and Employment Channel\u003c/h2\u003e \u003cp\u003eA second mechanism operates through income effects. DIF supports income generation by facilitating entrepreneurship, employment diversification, and productivity improvements. Digital platforms connect rural entrepreneurs to broader markets and reduce entry barriers for small businesses. Online marketplaces and peer-to-peer financing mechanisms expand sales opportunities while lowering capital costs. When rural households gain access to such income-enhancing tools, their disposable income increases, enabling them to pursue higher-quality goods and services that reflect improved living standards.\u003c/p\u003e \u003cp\u003eFurthermore, digital payment systems enhance labor-market efficiency by connecting workers with employers beyond their immediate communities. Mobile transfers and e-wallets simplify wage payments, particularly for migrant or seasonal workers, reducing delays and leakages associated with traditional remittance channels. These improvements strengthen the stability of family income flows, which in turn increases household confidence and willingness to upgrade consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Knowledge and Financial Literacy Channel\u003c/h2\u003e \u003cp\u003eFinancial literacy mediates the relationship between DIF and household welfare. The adoption of digital finance requires users to interpret information, evaluate risks, and make informed financial decisions. Continuous engagement with digital platforms can improve these capabilities, cultivating what behavioral economists term \u0026ldquo;learning effects.\u0026rdquo; As households become more familiar with digital tools, they develop greater confidence in using financial products, leading to more efficient savings and investment behavior.\u003c/p\u003e \u003cp\u003eEnhanced literacy also influences risk perception and time preference. Families who understand the costs and benefits of borrowing are less likely to engage in excessive precautionary saving and more likely to invest in education, healthcare, and asset accumulation. Thus, digital financial participation not only expands access but also reshapes financial cognition and planning behaviors within families. This behavioral transformation contributes to more sustainable patterns of consumption upgrading.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Risk-Management and Social Security Channel\u003c/h2\u003e \u003cp\u003eUncertainty and vulnerability are central constraints on rural consumption. Agricultural income is sensitive to price fluctuations, climate shocks, and health risks. DIF introduces new instruments\u0026mdash;such as digital insurance, emergency credit lines, and crowdfunding\u0026mdash;that mitigate these vulnerabilities. By lowering the potential cost of shocks, these tools reduce the need for precautionary saving and free resources for welfare-enhancing expenditures.\u003c/p\u003e \u003cp\u003eDigital platforms also facilitate the delivery of government transfers and subsidies directly to beneficiaries, improving the efficiency of social protection programs. For instance, mobile payment systems ensure that rural households receive social assistance on time and without intermediaries, enhancing their financial security. When risk exposure decreases, families become more confident in allocating funds toward long-term investments in education, nutrition, and household assets, accelerating consumption upgrading.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Social and Information Channel\u003c/h2\u003e \u003cp\u003eBeyond economic factors, DIF reshapes social interactions and information flows that influence household decision-making. Through online financial communities, rural residents exchange experiences, observe consumption patterns, and learn about new products and services. Exposure to peers who successfully use digital finance can encourage adoption and foster aspirational consumption behaviors. Moreover, digital platforms serve as channels for financial education campaigns, consumer protection information, and government outreach, reinforcing trust in formal financial systems.\u003c/p\u003e \u003cp\u003eSocial influence therefore acts as an indirect driver of consumption upgrading. As rural families integrate into digital networks, they gain access not only to credit but also to information that shapes preferences and aspirations. These social spillovers enhance inclusiveness by linking isolated communities to the mainstream digital economy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Integrative Framework\u003c/h2\u003e \u003cp\u003eTaken together, these mechanisms reveal that DIF affects consumption upgrading through intertwined economic and behavioral pathways. At the economic level, increased income, credit availability, and risk mitigation directly expand households\u0026rsquo; budget constraints. At the behavioral level, improved literacy, digital participation, and social exposure alter preferences and confidence, encouraging higher-quality consumption choices. The integration of these channels forms a reinforcing cycle in which digital finance supports both immediate and long-term family welfare.\u003c/p\u003e \u003cp\u003eThe theoretical model developed in this study conceptualizes these interactions through three primary pathways: \u003cb\u003e(1)\u003c/b\u003e economic capacity enhancement, \u003cb\u003e(2)\u003c/b\u003e liquidity and credit alleviation, and \u003cb\u003e(3)\u003c/b\u003e behavioral transformation. These pathways collectively promote the transition from basic to development-oriented consumption. As families gain access to digital finance, their economic opportunities widen, risks decline, and aspirations evolve\u0026mdash;ultimately fostering inclusive and sustainable consumption growth.\u003c/p\u003e \u003cp\u003eThis framework provides the basis for the empirical analysis in the following sections. It is visually summarized in Fig.\u0026nbsp;2, which depicts the causal relationships between digital inclusive finance, intermediary mechanisms, and rural consumption upgrading.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Research Design","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Model Setting\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Benchmark Regression Model:\u003c/h2\u003e \u003cp\u003eThis article constructs the following benchmark regression model:\u003c/p\u003e \u003cp\u003e \u003cem\u003eEquation 1\u003c/em\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{rcu}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{dfi}_{it}+\\beta\\:{X}_{it}+\\mu\\:+\\delta\\:+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ercu is the explained variable of rural residents' consumption upgrading, dif is the digital inclusive financial index, X represents a series of control variables that may affect rural residents' consumption upgrading, including education level, government intervention, opening up and fixed assets investment, \u0026micro; and δ represent individual fixed effect and time fixed effect respectively, ε is a random disturbance term, i represents provinces, and t represents years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Regulation Effect Model\u003c/h2\u003e \u003cp\u003eTo delve deeper into the influence of digital inclusive finance on the consumption upgrading of rural residents, this study employs the disposable income of rural residents as a moderating variable. To examine the interaction effect, a term representing the interplay between the disposable income of rural residents and digital inclusive finance was incorporated into the regression analysis. The resulting moderation effect model is articulated as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eEquation 2\u003c/em\u003e:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{rcu}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{dfi}_{it}+{\\alpha\\:}_{2}{dfi}_{it}\\times\\:{dir}_{it}+{\\alpha\\:}_{3}{dir}_{it}+\\beta\\:{X}_{it}+\\mu\\:+\\delta\\:+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Threshold Effect Model\u003c/h2\u003e \u003cp\u003eTo test the threshold effect of industrial structure upgrading, the following threshold model is established:\u003c/p\u003e \u003cp\u003e \u003cem\u003eEquation 3\u003c/em\u003e:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{rcu}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{dfi}_{it}\\times\\:I\\left({uis}_{it}\\le\\:{\\theta\\:}_{1}\\right)+{\\alpha\\:}_{2}{dfi}_{it}\\times\\:I\\left({\\theta\\:}_{1}\u0026lt;{uis}_{it}\\le\\:{\\theta\\:}_{2}\\right)+\\dots\\:{\\alpha\\:}_{n}{dfi}_{it}\\times\\:I({\\theta\\:}_{n-1}\u0026lt;{uis}_{it}\\le\\:{\\theta\\:}_{n})+{\\alpha\\:}_{n+1}{dfi}_{it}\\times\\:I({uis}_{it}\u0026gt;{\\theta\\:}_{n})+\\gamma\\:{X}_{it}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{uis}_{it}\\)\u003c/span\u003e \u003c/span\u003erepresents the threshold variable of industrial structure upgrading; θ represents the\u003c/p\u003e \u003cp\u003ethreshold value to be tested; I(.) indicates the function, if the industrial structure upgrade meets the threshold condition, it is assigned a value of 1, otherwise it is assigned a value of 0.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variable Selection\u003c/h2\u003e \u003cp\u003eRobust empirical analysis necessitates precise variable operationalization, here employing a multidimensional framework to examine digital inclusive finance\u0026rsquo;s reconfiguration of rural consumption patterns within China\u0026rsquo;s socioeconomic context.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1Consumption upgrading\u003c/h2\u003e \u003cp\u003eTranscends expenditure volume to capture qualitative spending shifts, operationalized through Liu \u0026amp; Yao\u0026rsquo;s (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) categorization of rural expenditures into survival-oriented essentials (food, clothing, shelter) versus development-oriented investments (education, culture, domestic services). Following Wang et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), progression is quantified as the ratio of hedonic to total consumption expenditure\u0026mdash;a metric crystallizing structural advancement where, for instance, farmers allocating 35% to online courses and eco-appliances (versus 10% among subsistence-focused peers) signal prioritization of well-being over basic survival. Elevated values directly proxy economic maturation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Digital Inclusive Finance\u003c/h2\u003e \u003cp\u003eLeverages the Peking University Digital Inclusive Finance Index\u0026mdash;a gold-standard provincial benchmark (0\u0026ndash;400 scale) evaluating financial inclusion through coverage breadth (remote service penetration), usage depth (transaction frequency/volume), and digitization level (mobile payment adoption) (Ji et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Higher values (e.g., coastal Zhejiang at 320 vs. inland Qinghai at 180) signify mature DIF ecosystems capable of transformative behavioral shifts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Disposable Income\u003c/h2\u003e \u003cp\u003ePost-tax funds available for discretionary use\u0026mdash;constitutes the economic bedrock shaping consumption agency, encompassing expenditures, savings buffers, or productive investments (e.g., poultry coop expansions). As moderator, dir reveals income-mediated variations in DIF\u0026rsquo;s effectiveness: while credit access may elevate survival spending among low-earners, higher-income households channel it toward business automation or children\u0026rsquo;s education (Liu \u0026amp; Yao, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4 Threshold Variable\u003c/h2\u003e \u003cp\u003eIndustrial Structure Upgrading (uis) captures economic complexity through sectoral transformation. Adopting Wei \u0026amp; Jian\u0026rsquo;s (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) formula, we compute this as a weighted sum:\u003c/p\u003e \u003cp\u003ePrimary sector (agriculture) = weight 1, Secondary (manufacturing) = weight 2, Tertiary (services/tech) = weight 3, multiplied by their provincial GDP shares.\u003c/p\u003e \u003cp\u003eA province like Jiangsu (uis\u0026thinsp;=\u0026thinsp;2.7) reflects tertiary-sector dominance\u0026mdash;e.g., agritourism supplementing rice farming\u0026mdash;while Heilongjiang (uis\u0026thinsp;=\u0026thinsp;1.8) remains farm-centric. Higher values denote knowledge-intensive economies where DIF\u0026rsquo;s consumption effects intensify.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.2.5 Control Variables\u003c/h2\u003e \u003cp\u003eFour contextual factors are controlled to isolate DIF\u0026rsquo;s net impact:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEducation Level (edu)\u003c/strong\u003e \u003cp\u003eMeasured as the rural college-educated population (\u0026ge;\u0026thinsp;age 6). Higher education correlates with financial literacy, enabling sophisticated DIF utilization (Chen \u0026amp; Guo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGovernment Intervention (gov)\u003c/strong\u003e \u003cp\u003eFiscal expenditure as % of GDP. State investments in roads or broadband amplify DIF\u0026rsquo;s reach (Wei \u0026amp; Jian, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOpenness (open)\u003c/strong\u003e \u003cp\u003eImport/export-to-GDP ratio. Global market access diversifies goods available for rural consumption.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFixed Asset Investment (inv)\u003c/strong\u003e \u003cp\u003eCapital formation relative to GDP. Infrastructure projects (e.g., cold-storage facilities) boost productivity and disposable income.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRationale \u0026amp; Data Synthesis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis framework operationalizes abstract concepts through empirically grounded measures: \u003cb\u003ercu\u003c/b\u003e's hedonic ratio\u0026mdash;drawn from provincial rural yearbooks\u0026mdash;quantifies qualitative consumption shifts into analyzable metrics; \u003cb\u003edif\u003c/b\u003e utilizes Peking University\u0026rsquo;s longitudinal index to circumvent ad-hoc measurement limitations; \u003cb\u003edir\u003c/b\u003e\u0026rsquo;s moderating function is tested via net income data (NBSC); and \u003cb\u003euis\u003c/b\u003e quantifies industrial transitions through sectoral employment weights (China Statistical Yearbook). Control variables contextualize outcomes\u0026mdash;educational attainment (\u003cb\u003eedu\u003c/b\u003e) determines whether DIF facilitates online upskilling versus subsistence loans. Anchoring variables to policy-relevant thresholds\u0026mdash;such as tertiary-sector expansion (\u003cb\u003euis\u003c/b\u003e\u0026thinsp;\u0026gt;\u0026thinsp;2.5) accelerating DIF\u0026rsquo;s impact in Anhui\u0026mdash;ensures the model captures dynamics essential for rural revitalization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Data Sources and Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThis study undertakes an empirical examination of the influence mechanism of digital inclusive finance on the consumption upgrading of rural residents, focusing on data from 31 provinces across China from the years 2012 to 2021. The explanatory variable, digital inclusive finance, is sourced from Peking University's \"Digital Inclusive Finance Index,\" which quantifies the extent of digital financial inclusion. The indicators for rural residents' consumption upgrading, disposable income, industrial structure upgrading, educational attainment, government intervention, openness to trade, and fixed asset investment are respectively drawn from the China Rural Statistical Yearbook, China Statistical Yearbook, and China Population and Employment Statistical Yearbook.\u003c/p\u003e \u003cp\u003eTo normalize the data and facilitate analysis, logarithmic transformations were applied to both digital inclusive finance and the disposable income of rural residents. The descriptive statistical outcomes for each variable are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, providing a comprehensive overview of the dataset and setting the stage for further analytical exploration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplained Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpgrading Rural Residents' Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplanatory Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Inclusive Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerating Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisposable Income of Rural Residents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpgrade of Industrial Structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eControl Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment Intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen to the Outside World\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed Assets Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical Results and Analysis","content":"\u003cdiv class=\"Heading\"\u003e\u003cb\u003e\u003c/b\u003e\u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Benchmark Regression Analysis\u003c/h2\u003e \u003cp\u003eTo visualize the regional distribution of digital inclusive finance and the degree of rural consumption upgrading, Fig.\u0026nbsp;3 presents the average DIF index and consumption upgrading scores across provinces from 2012 to 2021.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;3, eastern provinces consistently exhibit higher DIF levels and faster consumption upgrading compared with central and western regions, suggesting clear spatial heterogeneity that motivates the subsequent regression analysis.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 3. Regional distribution of digital inclusive finance and rural consumption upgrading in China, 2012\u0026ndash;2021.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Author\u0026rsquo;s calculations based on Peking University Digital Finance Research Center and China Statistical Yearbook data.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this article, we employ a two-way fixed effects model for benchmark regression analysis, with the findings presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In Column (1), we observe that digital inclusive finance exerts a significant positive influence on the consumption upgrade of rural residents, an effect that is observed even in the absence of control variables. Moving to Column (2), we introduce control variables including education level, government intervention, openness, and fixed asset investment, and find that the positive impact of digital inclusive finance on rural consumption upgrade remains robust, thereby corroborating Hypothesis 1.\u003c/p\u003e \u003cp\u003eDigital inclusive finance, recognized as an innovative financial service paradigm, effectively broadens the reach of financial services, diminishes their costs, and enhances their accessibility and convenience through the application of modern information technology. This has a profound effect on the consumption upgrade of rural residents. Furthermore, by offering innovative financial products and services via internet-based financial platforms, digital inclusive finance allows rural residents to access a wider array of financial products, enabling wealth appreciation and stimulating their consumption potential, thereby providing financial backing for the upgrade of their consumption patterns.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBenchmark Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ercu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ercu\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edif\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.060**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eedu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.223)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eopen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003einv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003cp\u003e(0.119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003cp\u003e(0.176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effects of Provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Time Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: *, * *, * * * respectively indicate significance at the 10%, 5%, and 1% levels, with robust standard errors in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Robustness Test\u003c/h2\u003e \u003cp\u003eIn our benchmark regression analysis, a fixed effects model is employed to mitigate the endogeneity concerns that could stem from omitted variable bias. To further address the potential endogeneity arising from bidirectional causality, we adopt Tang's (2021) methodology (Y. Tang, Lv, \u0026amp; Hou, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), utilizing a lagged one-period digital inclusive finance index as an instrumental variable. This approach leverages the temporal precedence of the lagged data, which is highly correlated with the current period's digital inclusive finance index, thereby potentially reducing endogeneity. We apply the two-stage least squares (2SLS) method to test for endogeneity. The first stage of the 2SLS regression yields an F-value of 90.7126, which significantly exceeds the threshold of 10, thereby validating the instrumental variable selection through the test for weak instruments. The robustness of our results is underscored by the findings displayed in Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which demonstrate that digital inclusive finance continues to exert a significant and positive influence on the consumption upgrade of rural residents.\u003c/p\u003e \u003cp\u003eTo further validate the robustness of the regression results concerning the impact of digital inclusive finance on the consumption upgrade of rural residents, the data was reprocessed from two specific angles. Firstly, to eliminate the influence of extreme values, a 1% tail truncation was applied to the explanatory variables, the explained variable, and the control variables prior to regression analysis. The results, presented in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicate that digital inclusive finance continues to significantly influence the consumption upgrade of rural residents, successfully passing the robustness check. Secondly, to mitigate the effects of unique urban characteristics, the four municipalities directly under the central government were excluded from the analysis, and regression was rerun with the remaining 27 provinces. As shown in Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, digital inclusive finance significantly impacts the consumption upgrade of rural residents, with the results proving to be robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eInstrumental Variable Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003eWinsorize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003cp\u003eExclude Four Municipalities Directly under the Central Government\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edif\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.392***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.036)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eedu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.333**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.494*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.255)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.037**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eopen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.061***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003einv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effects of Provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Time Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.905***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.090 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.620)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *, * *, * * * respectively indicate significance at the 10%, 5%, and 1% levels, with robust standard errors in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Adjustment Effect Test\u003c/h2\u003e \u003cp\u003eTo examine the moderating effect of rural residents' disposable income, regressions were conducted with the inclusion of the variable rural residents' disposable income and the interaction term between rural residents' disposable income and the explanatory variable of digital inclusive finance, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, column (1). Column (2) presents the regression results after adding control variables on the basis of column (1). By comparing with Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be observed that the R-squared values of models (1) and (2) in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e have both increased compared to before the inclusion of the moderating variable. In column (2), the regression coefficient of the interaction term between rural residents' disposable income and digital inclusive finance, dfi_dir, is -0.0311, which is statistically significant at the 5% level. This indicates that an increase in rural residents' disposable income significantly weakens the impact of digital inclusive finance on the consumption upgrade of rural residents, which is contrary to the conclusion of Hypothesis 2. The possible reason is that, although digital inclusive finance has a positive effect on the consumption upgrade of rural residents, in some cases, an increase in rural residents' disposable income may weaken this effect. The basic survival needs of rural residents can basically be met. With the improvement of their income level, they may prefer to save the additional income for future spending needs or to cope with uncertainties, which to some extent suppresses the potential for consumption upgrade. Moreover, as income increases, rural residents' awareness and tolerance of financial risks may also increase, and they may be more inclined to choose investment projects with higher risks but also higher returns, thereby reducing consumption expenditure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Moderation Effect Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003ercu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003ercu\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edif\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.356***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.106)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.212***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.192***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.071)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edif_dir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.031**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eedu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.230)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eopen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003einv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.763***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.594**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.615)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: *, * *, * * * respectively indicate significance at the 10%, 5%, and 1% levels, with robust standard errors in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Heterogeneity Test\u003c/h2\u003e \u003cp\u003eSignificant disparities exist across different regions in China in terms of resource conditions and development levels, leading to unique regional characteristics in the influence of digital inclusive finance on rural residents' consumption upgrade. This article will further explore whether the impact of digital inclusive finance on rural residents' consumption upgrade exhibits regional heterogeneity due to differences in regional resource conditions and development stages. Drawing on the grouping method of Shen et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Shen, Chen, \u0026amp; Lin, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the 31 provinces and municipalities in the country are divided into three groups: East, Central, and West. The heterogeneity test results indicate that digital inclusive finance has a significant promoting effect on the consumption upgrade of rural residents in the Central region, while the promoting effects in the East and West regions are not obvious.\u003c/p\u003e \u003cp\u003eRelatively speaking, the Central region has a higher degree of internet penetration and more complete network infrastructure in rural areas, providing convenient conditions for the development of digital inclusive finance. Digital inclusive finance can leverage internet platforms to offer more convenient and efficient financial services to rural residents, expanding the coverage of financial services and enhancing the financial literacy and consumption awareness of rural residents, thus promoting consumption upgrade. In contrast, the Eastern region has a higher level of economic development and more advanced digital inclusive finance, but also a relatively higher level of urbanization, resulting in fewer rural residents. The Western region has a relatively lower level of economic development and may be lagging in the construction of financial infrastructure, with a low penetration rate of digital inclusive finance. This leads to limited opportunities for rural residents in the West to access financial services, or a lower acceptance and trust in digital inclusive financial services, making the effect of digital inclusive finance on the consumption upgrade of rural residents in the East and West regions not significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edif\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.082)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effects of Provinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Time Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *, * *, * * * respectively indicate significance at the 10%, 5%, and 1% levels, with robust standard errors in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion and policy implications","content":"\u003cp\u003eThis study examined how digital inclusive finance (DIF) influences the upgrading of rural residents\u0026rsquo; consumption in China using provincial panel data from 2012 to 2021. The empirical findings confirm that DIF significantly promotes the transformation from subsistence to development-oriented consumption by expanding access to credit, increasing income, and improving financial literacy. Regional heterogeneity analysis shows that the effect is strongest in eastern provinces with higher digital penetration and economic development. Threshold regressions further reveal that the impact of DIF rises when household income and financial literacy exceed certain levels, indicating that digital finance and socioeconomic conditions interact to shape consumption behavior.\u003c/p\u003e \u003cp\u003eThe results highlight DIF as an effective instrument for enhancing household welfare and narrowing the urban\u0026ndash;rural consumption gap. Policymakers should strengthen rural digital infrastructure and improve financial education to ensure that low-income and less literate groups benefit equally from financial innovation. Expanding the coverage of digital credit and insurance can help families manage risks and allocate more resources to welfare-improving expenditures such as education, healthcare, and housing.\u003c/p\u003e \u003cp\u003eIn addition, developing inclusive financial products tailored to rural needs and supporting small-scale entrepreneurship can amplify income effects and promote sustainable family consumption. Strengthening consumer protection frameworks and ensuring data privacy are also essential to maintaining trust in digital platforms. Overall, fostering a secure and equitable digital financial environment can improve family well-being, drive inclusive economic growth, and support China\u0026rsquo;s long-term rural revitalization goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eResearch funding：The research is funded by Key Research Base of Humanities and Social Sciences in Universities of Guangdong Province: Research Base for Digital Transformation of Manufacturing Enterprises, （2023WZJD012)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.W. conceived and designed the study, performed all data acquisition and analysis, interpreted the results, and wrote the main manuscript text. The author also reviewed and approved the final manuscript. M.W. agrees to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe provincial panel data that support the findings of this study are available from the following sources but restrictions apply to their availability: the Peking University Digital Inclusive Finance Index was obtained from the Peking University Digital Finance Research Center, and the data on rural residents' consumption, income, and other control variables were derived from the China Rural Statistical Yearbook, China Statistical Yearbook, and China Population and Employment Statistical Yearbook. These data were used under license for the current study and are not publicly available. The data are, however, available from the respective institutions upon reasonable request and with their permission.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAhmad, M., Majeed, A., Khan, M. A., Sohaib, M., \u0026amp; Shehzad, K. (2021). 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Digital inclusive finance, industrial structure, and economic growth: An empirical analysis of Beijing-Tianjin-Hebei region in China. \u003cem\u003ePlos One, 19\u003c/em\u003e(3). doi:10.1371/journal.pone.0299206\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital inclusive finance, upgrading rural residents' consumption, disposable income of rural residents, upgrading of industrial structure, rural revitalization","lastPublishedDoi":"10.21203/rs.3.rs-8715673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8715673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing provincial panel data from 2012\u0026ndash;2021, this study examines how digital inclusive finance (DIF) influences rural residents\u0026rsquo; consumption upgrading in China. A fixed-effects model and robustness tests were applied to identify the mechanisms through which DIF affects household consumption structure. Results show that DIF significantly promotes the transformation from subsistence to development-oriented consumption by expanding financial access, enhancing income growth, and improving credit availability. Regional heterogeneity analysis reveals that the effect is stronger in eastern provinces and in areas with higher levels of digital infrastructure. Threshold regression results further indicate that the impact of DIF rises with increases in financial literacy and digital penetration. These findings suggest that deepening digital financial inclusion can narrow urban-rural gaps and support the well-being of rural households. Policy implications include improving rural financial infrastructure, promoting digital literacy, and developing products that meet diverse household needs. Overall, digital inclusive finance contributes to sustainable family consumption and inclusive economic growth in China.\u003c/p\u003e","manuscriptTitle":"Digital Inclusive Finance and Rural Consumption Upgrading in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 06:52:55","doi":"10.21203/rs.3.rs-8715673/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c7bb544-68c8-40f7-bcc2-4c8713276082","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-29T13:54:58+00:00","index":19,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-07T08:39:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 06:52:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8715673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8715673","identity":"rs-8715673","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}