Can household inclusive finance alleviate the relative poverty of rural residents? Evidence from static and dynamic perspectives

Abstract

Effectively addressing the current forms of relative poverty and preventing future household poverty are essential for the long-term success of global poverty reduction efforts. Using microdata from the China Household Finance Survey (CHFS), this study adopts an integrated analytical framework that examines relative poverty from static and dynamic perspectives. Using Probit estimation and Propensity Score Matching (PSM), the study empirically analyzes the impact and heterogeneity of household inclusive finance on rural relative poverty. First, household inclusive finance significantly reduces both static and dynamic relative poverty among rural residents, with a more pronounced effect on the dynamic dimension. Second, its poverty-reduction effects exhibit considerable heterogeneity, varying significantly across regions, the age of the household head, family structure, and levels of debt. Third, inclusive finance alleviates poverty by fostering the accumulation of livelihood capital and promoting the diversification of livelihood strategies. These findings provide valuable insights for the design of future poverty alleviation strategies. Strengthening the effectiveness of inclusive finance, addressing its current limitations, implementing tailored policies, and enhancing its linkage with household livelihoods are crucial for improving poverty-targeted financial strategies. The findings contribute to ensuring the long-term sustainability of global poverty alleviation efforts.

Introduction

The governance of poverty represents a critical global issue that has garnered sustained attention from the international community (Wang and Sun 2021). Since 1978, China has progressively intensified its poverty reduction efforts, achieving notable milestones, and in 2021, officially declared the eradication of absolute poverty (as shown in Fig. 1). While absolute poverty has been successfully addressed, relative poverty continues to present a significant national challenge for China and remains a shared concern globally. Consolidating the achievements of poverty alleviation and addressing the challenges posed by relative poverty remain central priorities for both current and future global poverty governance. A comparison of income ratios and Gini coefficients between urban and rural areas in China (as shown in Fig. 2) reveals that relative poverty is more pronounced in rural areas, particularly in those with inadequate infrastructure, which demands more urgent attention than in urban areas. This issue has emerged as a significant obstacle to effective poverty reduction efforts in both China and the broader international context. As a result, ensuring the long-term effectiveness of rural relative poverty alleviation is essential for promoting comprehensive rural development and achieving the goal of common prosperity. Dynamic relative poverty refers to the anticipated risk that an individual or household will, over a specified period (typically 3–5 years), experience adverse events that result in a decline in income or living conditions, pushing them below the established relative poverty threshold. This concept focuses on forward-looking vulnerability, capturing the potential instability and fragility of socio-economic well-being over time. When studying relative poverty reduction, both static and dynamic aspects should be considered in an integrated manner to formulate more targeted and effective strategies.

Fig. 1: Changes in China’s poverty population and poverty incidence rate between 1978 and 2020.

Note: Fig. 1 highlights three key time points corresponding to major adjustments in China’s poverty line standard: the establishment of the poverty line in 1978, the first significant revision in 2008, and the formulation of the poverty eradication baseline in 2010. These adjustments reflect China’s progressively deepening understanding of poverty issues and the evolution of poverty standards throughout various phases of economic and social development.

Fig. 2: Comparison of relative poverty status between urban and rural areas in China.

Notes: (1) All original data in Fig. 2 are sourced from the China Statistical Yearbook. (2) The income ratio in (a) is calculated based on quintile grouping data from the China Statistical Yearbook. It represents the ratio of the per capita disposable income of the top 20% of households (high-income group) to the per capita disposable income of the bottom 20% of households (low-income group). This indicator aims to measure the size of the income distribution gap by comparing the income levels of different income classes. Its calculation methodology aligns with the principles of the OECD and World Bank’s income disparity indicators (such as P90/P10). (3) The calculation method for the income Gini coefficient in (b) follows Tian (2012), a method recognized by the academic community (Huang and Zuo 2023). The Gini coefficient ranges from 0 to 1, where higher values indicate greater income inequality. A Gini coefficient closer to 1 signifies a highly uneven income distribution and more severe relative poverty.

Inclusive finance plays a pivotal role in alleviating rural poverty and fostering equitable development (Abramova et al. 2021). Recognized as a cornerstone for achieving the SDGs (Demirgüç-Kunt et al. 2017; Sahay et al. 2015), inclusive finance is defined in the World Bank’s 2014 report as the provision of low-cost, accessible, efficient, and equitable financial services (Leyshon et al. 2008). This concept emphasizes financial inclusion for all, particularly for those in marginalized areas and lower-income brackets, thereby expanding access to financial tools for the most vulnerable populations (Corrado and Corrado 2017). Inclusive financial mechanisms provide essential support to underdeveloped rural areas, mitigate risks, and offer opportunities to stabilize development (Koomson et al. 2020). The emergence of new digital financial models, such as e-wallets, mobile banking, and online payment platforms, has enhanced access to financial services (Pradhan et al. 2021), providing new momentum for the role of inclusive finance. It boosts residents’ entrepreneurial activities (Yue et al. 2025), improves their risk management (Li et al. 2022), and opens new avenues for inclusive finance to reduce income disparities and alleviate household vulnerability to poverty (Liu and Guo 2023).

Current measures of financial inclusion predominantly adopt a macroeconomic perspective (Tram et al. 2023). Research has primarily focused on the macroeconomic outcomes of financial inclusion, using national or regional data (Han and Melecky 2013; Inoue and Hamori 2016). Rumbogo et al. (2021) identified a significant positive relationship between financial inclusion and regional development in Indonesia. Du et al. (2023) used Chinese data to demonstrate that inclusive finance facilitates technological innovation and boosts consumption, thereby enhancing economic resilience. Meanwhile, macro-level data have also been used to infer micro-level effects, particularly at the household level. For instance, Nanziri (2020), through an analysis of Zambian data, found that financial service accessibility reduces gender wealth gaps and supports women’s empowerment. Existing research confirms that financial inclusion can elevate household savings, promote educational investment, stimulate entrepreneurship, empower women, and improve public health outcomes (Beck et al. 2007; Dupas and Robinson 2013; Bruhn and Love 2014; Angelucci et al. 2015; Banerjee et al. 2015). However, relying solely on macro data may not provide policymakers with a comprehensive understanding of household finance, thus undermining the validity of the assessment. There is a need to recognize rural finance as an integral part of household livelihood finance (Jones 2008) and focus on how positive changes in household financial status after accessing inclusive financial services can positively impact the stability of household livelihoods and economic status (Kochar et al. 2022). Therefore, this study aims to address the gap at the micro level by answering the following questions: What is the role of household inclusive finance in relation to the relative poverty of rural residents? By what mechanisms is this impact realized? How does this impact manifest differently across various rural households? The study seeks to shift the focus of inclusive finance from “whether it exists” to “whether it is effective” in the new era, thereby contributing effectively to the achievement of the SDGs in different countries.

The potential marginal contribution of this study includes the following: First, in contrast to previous macro-level research, it enhances the relevance and specificity of the results by measuring the inclusive finance index from a micro perspective at the household level, emphasizing access to and usage of inclusive finance rather than regional supply. This approach helps identify heterogeneity among households, analyze the effects of inclusive financial policies at the household level, and address the limitations of macro-level research. Second, most research examines relative poverty from a single perspective, potentially leading to an incomplete understanding of poverty alleviation. This study explores the role of inclusive finance from both static and dynamic perspectives, investigating its immediate and potential impacts on relative poverty and offering deeper and more comprehensive insights. This will facilitate a better understanding and a more effective approach to solving relative poverty. Third, when exploring the mechanism of inclusive finance in poverty reduction, existing research primarily focuses on a single aspect of livelihood, generally overlooks the overall structure of livelihood capital, and often fails to fully consider the importance of households’ overall livelihood strategic tendencies. Such research, focusing on individual livelihoods, may not fully reveal the complex impact of inclusive finance on household livelihoods. This study explores the internal operational mechanisms underlying the role of household inclusive finance from the perspective of farmers’ livelihoods, providing valuable theoretical support for the effective fulfillment of inclusive finance’s role.

Figure 3 illustrates the structure of the paper, which includes theoretical analysis (Part 2), methodology (Part 3), results of empirical analysis (Part 4), mechanism analysis (Part 5), and conclusions and implications (Part 6).

Fig. 3

The structure of the paper.

Theoretical analysis

Theoretical foundation

Poverty remains a significant challenge in humanity’s pursuit of a better life. Addressing both relative poverty and vulnerability is critical to a country’s development. The concept of vulnerability was incorporated into the World Bank’s poverty definition in 2000: poverty is not only reflected in the reduction of income-based indicators of social well-being below socially acceptable levels but also encompasses a wide range of risks and situations of vulnerability to them, as well as the lack of effective expression of individual requirements and the absence of participation in social activities (World Bank 2004). Livelihoods are the means by which households meet their basic necessities and are closely linked to vulnerability. In 1999, the Sustainable Livelihoods Approach (SLA) framework, developed by the UK Department for International Development (DFID), became the most widely used framework for analyzing sustainable livelihoods. The SLA framework defines sustainable livelihoods as those that can withstand pressures, shocks, or sudden changes, retaining and strengthening their capacities over the long term, without damaging natural resources. The elements covered by this framework are shown in Fig. 4. The theory of sustainable livelihoods provides a foundation for this study.

Fig. 4: Sustainable livelihoods framework.

Source: DFID (1999).

The SLA offers a comprehensive framework for analyzing diverse livelihood situations in rural societies (Kunjuraman 2024). Livelihoods are essential for rural economic development in many developing countries (Mpandeli and Maponya 2014). Ensuring the sustainability of livelihoods is critical for promoting sustainable development (Zhang et al. 2019) and remains a long-term aspiration for developing countries (Tripathy et al. 2022; Kumar et al. 2023). The core objective of a sustainable livelihoods strategy is to enhance sustainability by enabling households to adopt self-centered investment approaches (Mensah 2011), leveraging the asset bases of poor households to better seize opportunities, reduce vulnerability, and improve well-being (Foresti et al. 2007).

The SLA has been widely applied in national research and practice globally over the past decades (Opiyo et al. 2024). Some scholars have integrated the concept of resilience into the SLA (Scoones 2009), exploring the balance between vulnerability and resilience within the livelihoods framework (Ye et al. 2022) to better understand changing livelihood dynamics. Nonetheless, the SLA continues to face criticism, mainly due to its perceived overemphasis on micro perspectives at the household level, which limits the comprehensiveness and depth of its analysis at the macro-policy level (Hussein 2002). Some scholars have advocated for the integration of multidimensional elements such as markets, institutions, and technologies into the strategic framework to enhance its macro-level explanatory power (Dorward et al. 2003). Inclusive finance, as a prime example of integrating market forces, institutional design, and technological innovation, offers new perspectives for sustainable livelihood strategies, with its role in promoting livelihood sustainability receiving growing attention (Datta and Sahu 2022; Ding et al. 2023). Accordingly, the innovative focus on household inclusive finance practices not only does not diminish the overall assessment of financial inclusion as a macro-policy but also provides a more nuanced perspective on the effectiveness of inclusive financial policies at the micro-level, offering a clearer view of its contribution to sustainable livelihoods.

Research hypotheses

Household inclusive financial and relative poverty of rural residents

Relative poverty, characterized by chronicity and complexity, cannot be resolved in the short term. In addition to relative poverty, vulnerability to future relative poverty in households must also be addressed. Enhanced levels of household inclusive finance can sustainably address relative poverty. Household finance, introduced by John Campbell during the AFA presidential address (Campbell 2006), is closely linked to residents’ daily productive lives. In addition to enhancing regional financial services, the financial status of households is pivotal to their well-being (Addury 2018), directly influencing farm households’ access to and use of financial resources, thereby impacting their relative poverty status.

On one hand, access to and utilization of money transfers, payments, savings, microcredit, as well as more accessible insurance and financial management services can improve rural households’ economic conditions and expand their income sources. For example, microcredit, entrepreneurial support, and other services enable low-income individuals to start businesses or engage in more activities. Access to financial services, such as education loans and vocational training, can empower low-income individuals to enhance their skills and increase their competitiveness. As the income-generating abilities of low-income rural populations improve, income disparities will decrease, thereby reducing the incidence of relative rural poverty (Omar and Inaba 2020).

On the other hand, expanding inclusive financial services at the rural household level can equip households to withstand future economic risks and livelihood shocks, mitigating the incidence of disease and poverty caused by disasters. For example, savings help manage crises and promote self-esteem (Tang and Baker 2016); low-interest loans, such as microcredit, preempt risks, alleviate financial stress, stabilize income, and enhance affordability (Chua et al. 2000); insurance plays a crucial role in diversifying and transferring risks (Dercon et al. 2008). Concurrent diversification of income channels, facilitated by financial services, makes income increases for low-income rural groups sustainable, enabling them to navigate life’s uncertainties and risks. It fosters a virtuous cycle of improved living standards and reduced likelihood of falling into poverty traps. Furthermore, as the penetration of household inclusive finance increases, rural households will reduce their dependence on traditional financial services, curb high-cost private lending, and lessen their “future vulnerability” (Xu et al. 2023), contributing to alleviating their dynamic relative poverty. Accordingly, Hypothesis 1 is proposed:

Hypothesis 1: Household inclusive finance contributes to the alleviation of static and dynamic relative poverty among rural residents.

The role of livelihood capital

First, based on the theory of sustainable livelihoods, the accumulation of household livelihood capital is linked to whether a household is experiencing relative poverty and its potential to fall back into poverty. Subsequently, scholars introduced psychological capital (Li et al. 2020). On one hand, the accumulation of household livelihood capital acts as an endogenous driver of relative poverty alleviation. Abundant natural capital ensures stable production and living conditions, thereby alleviating relative poverty (Mahapatra 2010). Sufficient physical capital enhances productivity and quality of life, thereby reducing relative poverty (Hohmann and Goldblatt 2021). Adequate financial capital provides essential financial support, alleviating liquidity constraints and enhancing well-being (Qiao and Cai 2023), thereby reducing the level of relative poverty. Higher levels of human capital boost employment competitiveness and income levels, thereby reducing the incidence of relative poverty (Mihai et al. 2015). Robust social capital enables individuals or families to access social resources and support, thereby alleviating relative poverty (Abdul-Hakim et al. 2010). Rural residents possessing greater psychological capital can more effectively cope with life’s challenges (Bloem et al. 2018), thereby contributing to poverty reduction. On the other hand, the six categories of livelihood capital mentioned above constitute sources of households’ “anti-fragile capacity” to manage unknown risks (Wuepper and Lybbert 2017). Generally, the higher the level of livelihood capital and the stronger its portfolio characteristics, the more resilient and risk-tolerant rural residents are (Li et al. 2022). This increases their affordability and likelihood of achieving a better life, while lowering their probability of future poverty vulnerability.

Additionally, rural households, as key players in rural financial markets, can perceive changes in the financial environment and respond swiftly with livelihood capital, thereby influencing their relative poverty status. First, by utilizing inclusive finance services, households manage natural capital through purchase or lease, facilitating the accumulation of physical and financial capital. This process alleviates liquidity constraints (Winter-Nelson and Temu 2005), mitigates resource scarcity in rural areas, and enhances development opportunities for farming households. Second, inclusive finance, especially the intervention of digital technology, provides expanded opportunities for households to accumulate social capital (Borja 2014). Farmers gain access to information, resources, and support to promote social capital accumulation, including social networks, trust relationships, and the ability to participate in community activities. Third, the diversity of inclusive finance products helps households manage finances, leading to increased spending on education and healthcare (Rahmi and Aliasuddin 2020). This access fosters higher education and healthcare, enhancing human capital, improving employability and income, and helping households escape relative poverty. Fourth, inclusive financial services help families reduce financial burdens, boost self-confidence, and cultivate higher psychological capital (Ajefu et al. 2020). Accordingly, Hypothesis 2 is proposed:

Hypothesis 2: Household inclusive finance can alleviate static and dynamic relative poverty among rural residents by facilitating the accumulation of livelihood capital.

The role of livelihood diversification

First, according to the SLA, household livelihood strategies are intrinsically linked to relative poverty status. The SLA emphasizes the importance of diversifying strategies to manage risks and achieve sustainable development. These strategies enhance income, mitigate risks, and strengthen resilience, enabling households to sustain their livelihoods and pursue long-term development amidst external shocks.

Scholars have categorized livelihood strategies into agricultural and non-farm employment, with the latter further divided into entrepreneurship and labor. Currently, it is common for rural residents to run businesses at the household level or work outside their villages, with livelihoods increasingly reliant on off-farm wages and self-employment income from non-farm activities (Davis et al. 2017). Non-farm employment refers to the intentional participation of household members in the labor market to enhance income and expand investment channels (Reardon 1997). It is a crucial strategy for income diversification, reducing income volatility, and managing risks (Mishra and Sandretto, 2002). Entrepreneurship and labor often provide higher wages and more stable income than traditional agriculture, raising living standards and diminishing powerlessness in poverty alleviation efforts (Martin and Lorenzen 2016). Simultaneously, as off-farm employment opportunities grow, household livelihood strategies encounter a range of diversified options. On the one hand, diversified livelihood strategies offer multiple income sources, enhancing household income and preventing relative poverty (Oyinbo and Olaleye 2016). On the other hand, relying solely on a single livelihood often proves insufficient in the face of market volatility, natural disasters, and other risks. Diversified livelihood strategies mitigate risks through various means, helping households manage uncertainties, enhancing their capacity to withstand risks, and increasing livelihood stability and sustainability, ultimately reducing poverty vulnerability (Kassegn and Endris 2021).

Second, inclusive finance offers households a wider range of financing channels, enabling them to manage assets and risks more effectively through diverse financial products. Consequently, households become more capable of exploring various livelihood strategies (Arowolo et al. 2022). Research indicates that inclusive financial development influences the efficiency of non-farm business activities, decreasing rural households’ willingness to engage in agricultural production (Liu et al. 2021). Migrant workers, due to their new environments and working conditions, often have a more urgent need for financial services. Universal access to financial services enables them to make inter-regional remittances more conveniently, while managing wages and increasing wealth. In contrast to traditional finance, which is prone to exclusion and low returns, household inclusive finance has equipped migrant workers with products such as money funds and insurance, enhancing stability, accumulating assets, and managing finances effectively (Thomas and Bella 2023). This alleviates the concerns of migrant workers and broadens options for family members to choose livelihood strategies. For entrepreneurship, the central tenet of financial inclusion is to ensure all segments of society have access to accessible, equitable, and convenient financial services, promoting household entrepreneurship. Despite risks and uncertainties in rural entrepreneurship, reducing thresholds for financial services can catalyze entrepreneurship and offer farmers opportunities to establish businesses (Chen et al. 2023). Therefore, this study proposes Hypothesis 3:

Hypothesis 3: Household inclusive finance can alleviate static relative poverty and dynamic relative poverty among rural residents by enhancing livelihood diversification.

Methodology

Model design

Given that the central variable of this study is whether rural households will experience relative poverty currently or in the future, both explanatory variables present a typical binary choice problem, necessitating analysis via a binary Probit model. The model setup is as follows:

$${Prob}\left({S\_{poverty}}_{i}=1\right)={\alpha }_{0}+{\alpha }_{1}{{HIFI}}_{i}+{\alpha }_{2}{C{ontrol}}_{i}+{\varepsilon }_{i}$$

(1)

$${Prob}({D\_{poverty}}_{i}=1)={\beta }_{0}+{\beta }_{1}{{HIFI}}_{i}+{\beta }_{2}{C{ontrol}}_{i}+{\epsilon }_{i}$$

(2)

In Eq. (1), ${{S\_poverty}}_{i}$ represents the static relative poverty of the $i$ rural household. If the household is currently in relative poverty, then ${{S\_poverty}}_{i}$ = 1, otherwise ${{S\_poverty}}_{i}$ = 0. In Eq. (2), ${{D\_poverty}}_{i}$ denotes the dynamic relative poverty of the $i$ rural household, reflecting the household’s vulnerability to poverty. If the household is likely to become relatively poverty in the future, then ${{D\_poverty}}_{i}$ = 1, otherwise ${{D\_poverty}}_{i}$ = 0. ${{HIFI}}_{i}$ represents the household inclusive finance, ${C{ontrol}}_{i}$ is a series of control variables, ${\varepsilon }_{i}$ and ${\epsilon }_{i}$ are random error terms.

Selection of variables

Explanatory variable

Academics classify inclusive finance indices into three dimensions: penetration, availability, and usage (Sarma 2008), which are widely accepted by researchers (Mialou et al. 2017; Park and Mercado 2018). Unlike macro and supply perspectives, this study builds on Sarma’s (2008) indicator system, incorporating household-level indicators (Liu et al. 2021) and selecting micro-indicators: access, usage, and digitization. This approach creates a more refined household inclusive finance index, enhancing the objectivity and accuracy of evaluations. This system also avoids the problem of variable covariance in econometric analysis. Accessibility and utilization are frequently used indicators to measure inclusive finance (Beck et al. 2007). Accessibility refers to the availability of financial services in a region or group, linked to its capacity for financial service supply (Maity and Sahu 2018). It can be assessed using indicators such as the number of financial institutions, their staff, and service outlet distribution (Yang and Fu 2019). Achieving universal access to financial services is not enough; measuring utilization is equally important (Kempson et al. 2004), as it reflects the frequency and extent of usage. Utilization can be assessed using indicators such as loans, savings, and financial instruments at the demand level (Kumar and Mishra 2011). When shifting the perspective from the supply level to the household demand level, measures should focus on whether residents can effectively access and use financial services (Han et al. 2019), such as owning and using bank cards. With technological advancements, digital financial services have become a key driver of deeper financial inclusion (Siddik and Kabiraj 2020), and digitization has been integrated into the measurement of inclusive finance (Han et al. 2025). Whether users engage with digital financial services, which services they use, and the frequency and depth of their use reflect the popularity and impact of digital financial services. Existing studies have analyzed three types of digital services: digital payment, digital investment, and digital credit (Zhao et al. 2022). Based on these studies, this study selects the indicators for household inclusive finance as shown in Table 1.

Table 1 Description of indicators for the household inclusive finance index.

Selecting an appropriate methodology is crucial for measuring the inclusive finance index. Academia employs various methods to measure indicator systems, including the geometric mean, principal component analysis, hierarchical analysis, factor analysis, and entropy weighting. However, each method has its shortcomings, particularly in the calculation of weights, which may lack inherent stability. The application of average Euclidean distances to construct an inclusive finance index effectively circumvents the issue of complete substitutability among sub-indicators (Sarma and Pais 2011), satisfying the characteristics of being unit-independent, bounded, and monotonous. This method offers intuitive significance, ease of calculation, and comparability, making it suitable for measuring the inclusive finance index.

First, non-percentage inclusive finance sub-indicators are standardized to ensure that the indicators achieve unit independence and boundedness.

$${d}_{i}=\left[\begin{array}{l}{A}_{i},\,i={1}{,}{2}{,}{3}{,}{4}{,}{10}{,}{11}{,}{13}{,}{15}\\ \left({A}_{i}-{m}_{i}\right)/\left({M}_{i}-{m}_{i}\right),i={5}{,}{6}{,}{7}{,}{8}{,}{9}{,}{12}{,}{14}\end{array}\right.$$

(3)

In Eq. (3), ${A}_{i}$ is the actual value of the $i$ variable indicator. The indicators $i$ are listed in column 4 of Table 1. For indicator $i={\textstyle \text{''}}1,\,2,\,3,\,4,\,10,\,11,\,13,\,15{\textstyle \text{''}}$, since ${A}_{i}$ is already within the range $[\mathrm{0,1}]$, there is no need for standardization, and thus ${d}_{i}={A}_{i}$. When ${A}_{i}$[does not fall within the range $[\mathrm{0,1}]$, ${d}_{i}$ becomes the final standardized value of the $i$ indicator.

Second, the weights of the various indicators must be determined before synthesizing the index. Sarma (2016) emphasizes that the essence of inclusive finance lies in the balanced and concurrent advancement of basic service areas, suggesting equal weight allocation for each sub-indicator. On one hand, the equal weight system implies that all aspects of financial services are equally important, avoiding bias due to improper weight allocation and strengthening the reliability and persuasiveness of the findings; on the other hand, it helps simplify the assessment process, improving efficiency and reducing subjectivity and complexity. This study uses the equal weight assumption as the foundational principle in constructing the inclusive finance index, assigning equal weights to each dimension and indicator, ensuring fairness and balance in considering each sub-indicator and comprehensively reflecting the overall development status of inclusive finance. However, this method does not account for the differences and time-varying characteristics among the indicators, which could affect the credibility of the results. To address this limitation, this study will employ the entropy weight method (a non-equal weight approach) to remeasure the index and enhance the rigor and credibility of the analysis.

Specifically, this study assigns weight 1 to each dimension (${{\rm{w}}}_{i}$ = 1 for all $i$). In this case, the optimal state is represented by the point $w=\left(\mathrm{1,1,1},\cdots ,1\right)$ in the n-dimensional space. The calculation is performed with a weight of $w=\left(\mathrm{1,1,1},\cdots ,1\right)$ for the four tertiary indicators of the access index, $w=\left(\mathrm{1,1,1},\cdots ,1\right)$ for the five tertiary indicators of the usage index, and $w=\left(\mathrm{1,1,1},\cdots ,1\right)$ for the six tertiary indicators of the degree of digitization index.

Third, the average Euclidean distance method is used to calculate the indices of the three sub-indices and the overall index of inclusive finance. The equation is:

$${X}_{1}=\sqrt{{\sum }_{i=1}^{n}{d}_{i}^{2}}/\sqrt{{\sum }_{i=1}^{n}{w}_{i}^{2}},{X}_{2}=1-\sqrt{{\sum }_{i=1}^{n}\left(1-{d}_{i}^{2}\right)}/\sqrt{{\sum }_{i=1}^{n}{w}_{i}^{2}},{X}_{z}=\frac{{X}_{1}+{X}_{2}}{2}$$

(4)

In Eq. (4), $n$ represents the number of indicators corresponding to this dimension, and ${{\rm{w}}}_{i}$ denotes the weight assigned to each indicator. Based on Eq. (4), this study employs Eqs. (5), (6), and (7) to synthesize the access, usage, and degree of digitization indices, respectively. Subsequently, the overall index of inclusive finance is further synthesized using Eq. (8).

Specifically, after applying the Euclidean distance method three times to individually synthesize the access index, the usage index, and the degree of digitization index, the overall index of inclusive finance is synthesized.

$${HIF}{I}_{11}=\sqrt{\mathop{\sum }\limits_{i=1}^{4}{d}_{i}^{2}}/\sqrt{4},{HIF}{I}_{12}=1-\sqrt{\mathop{\sum }\limits_{i=1}^{4}\left(1-{d}_{i}^{2}\right)}/\sqrt{4},{HIF}{I}_{1}=\frac{{HIF}{I}_{11}+{HIF}{I}_{12}}{2}$$

(5)

$${HIF}{I}_{21}=\sqrt{\mathop{\sum }\limits_{i=1}^{5}{d}_{i}^{2}}/\sqrt{5},{HIF}{I}_{22}=1-\sqrt{\mathop{\sum }\limits_{i=1}^{5}\left(1-{d}_{i}^{2}\right)}/\sqrt{5},{HIF}{I}_{2}=\frac{{HIF}{I}_{21}+{HIF}{I}_{22}}{2}$$

(6)

$${HIF}{I}_{31}=\sqrt{\mathop{\sum }\limits_{i=1}^{6}{d}_{i}^{2}}/\sqrt{6},{HIF}{I}_{32}=1-\sqrt{\mathop{\sum }\limits_{i=1}^{6}\left(1-{d}_{i}^{2}\right)}/\sqrt{6},{HIF}{I}_{3}=\frac{{HIF}{I}_{31}+{HIF}{I}_{32}}{2}$$

(7)

$${HIF}{I}_{z1}=\sqrt{\mathop{\sum }\limits_{i=1}^{3}{d}_{i}^{2}}/\sqrt{3},{HIF}{I}_{z2}=1-\sqrt{\mathop{\sum }\limits_{i=1}^{3}\left(1-{d}_{i}^{2}\right)}/\sqrt{3},{HIF}{I}_{z}=\frac{{HIF}{I}_{z1}+{HIF}{I}_{z2}}{2}$$

(8)

In Eqs. (5)-(8), ${HIF}{I}_{11}$ and ${HIF}{I}_{12}$ represent the distance from the actual point to the worst point and the reverse distance to the best point, respectively. ${HIF}{I}_{1}$ is the average distance between the two, representing the access index. Similarly, ${HIF}{I}_{2}$ is the usage index, ${HIF}{I}_{3}$ is the degree of digitization index. In this study, the correlation index will be linearly normalized to range from 0 to 100. This enhances the convenience of regression analysis without changing the skewness and kurtosis of the index. Each increase of 1 in the converted value represents a 1% increase in the index.

Explained variable

Static relative poverty (${S\_{poverty}}_{i}$) and dynamic relative poverty (${D\_{poverty}}_{i}$). The fundamental aspect of analyzing relative poverty from a static perspective is the establishment of a “relative poverty line”. The OECD introduced a relative poverty criterion in 1976, namely a certain proportional value to the median or mean income of a country or region, which is commonly used in academia (Alkire and Santos 2014). The value of the proportion chosen varies, but typically ranges between 40% and 60% (Ravallion 2010). Koomson and Danquah (2021) highlight the need to combine international experience with national conditions to determine the appropriate proportion for the relative poverty line. Considering the actual situation in rural China, the static rural relative poverty line in this study is set at 40% of the median income of rural residents (Dang et al. 2024; Wang and Sun 2021).

The essential aspect of analyzing relative poverty vulnerability from a dynamic perspective involves calculating the “likelihood of future relative poverty,” which can be assessed using the “poverty vulnerability” concept proposed by the World Bank. This refers to the probability of suffering a loss of wealth or decreased living standards due to certain risks. This concept includes poverty characterized by low well-being, primarily measured by income, and poverty induced by external shocks such as natural disasters, diseases, and wars. A prevalent approach for measuring poverty vulnerability is the VEP model (Chaudhuri et al. 2002). The methodology for estimating relative poverty vulnerability requires setting two standardized lines: the relative poverty line and the vulnerability line. The likelihood of a rural household being relatively poor in the future is calculated based on the relative poverty line, and it is compared with a predefined vulnerability line. If this probability surpasses the vulnerability line, the rural household is classified as dynamically relatively poor. The specific measurement steps are outlined below.

Initially, the household income equation is estimated using OLS, and the residual squares obtained from the regression are used as income fluctuations for another OLS estimation:

$$ln{Y}_{i}={\alpha }_{i}{X}_{i}+{e}_{i}$$

(9)

$${\hat{e}}_{i}^{2}={\beta }_{i}{X}_{i}+{\mu }_{i}$$

(10)

In Eq. (9), ${Y}_{i}$ denotes the per capita household income, ${X}_{i,t}$ represents the observable characteristic variables affecting the per capita household income. These characteristics are categorized into three types: household head, household, and region. The household head variables include age, gender, marital status, education, and health; the household variables comprise household size, savings, debts, total assets, and happiness; and the regional variables include per capita GDP and traditional financial levels.

Second, the weights are constructed using the estimates ${\hat{\beta }}_{{OLS},i}$ obtained from Eq. (10), and Feasible Generalized Least Squares (FGLS) is applied to re-estimate $ln{Y}_{i}$ and ${\hat{e}}_{i}^{2}$ to derive ${\hat{\alpha }}_{{FGLS}}$ and ${\hat{\beta }}_{{FGLS}}$, which in turn estimates consistent estimates of the expected value ($E$) and the variance ($V$) of logarithmic future per capita income of the household:

$$\hat{E}(ln{Y}_{i}|{X}_{i})={X}_{i}{\hat{\alpha }}_{{FGLS}}$$

(11)

$$\hat{V}(ln{Y}_{i}|{X}_{i})={\hat{\sigma }}_{e,i}^{2}={X}_{i}{\hat{\beta }}_{{FGLS}}$$

(12)

Finally, based on the assumption that the logarithm of per capita household income follows a normal distribution, the poverty vulnerability of households is calculated according to the established relative poverty and vulnerability lines:

$$\hat{{V}_{i}}=\hat{P}\left(ln{Y}_{i}\le lnline|{X}_{i}\right)=\phi \left(\frac{{lnline}-{X}_{i}{\hat{\beta }}_{{FGLS}}}{\sqrt{{X}_{i}{\hat{\theta }}_{{FGLS}}}}\right)$$

(13)

In Eq. (13), $\mathrm{ln}{line}$ represents the logarithm of the relative poverty line, which is designated here as corresponding to the previous static relative poverty measure. Concerning the vulnerability line, which facilitates a more accurate assessment of a household’s vulnerability, the prevalent probability values are set at 29% and 50%. However, Ward (2016) indicates that employing a 50% probability value as the vulnerability line may overlook temporarily impoverished households. Therefore, following Guenther and Harttgen (2009), this study adopts a 29% probability value as the vulnerability line.

Control variables

Similar to the existing study (Mwangi and Atieno 2018), this study integrates three dimensions—head of household, family, and geographic factors—in the selection of control variables. First, the household head dimension includes culture, gender, age, marital status, and health. These factors influence household stability, their ability to acquire information, and openness to new ideas, leading to varying behaviors in accessing and utilizing inclusive financial resources, which in turn affect relative poverty status. Second, the household dimension includes size, debt, and dependency ratio. These factors reflect economic pressure, determining basic needs and consumption patterns, thus influencing the level and objectives of inclusive finance applications. Third, the regional dimension includes economic development, traditional finance, and financial support for agriculture. Economic development determines the availability and quality of financial services for rural households. More developed regions tend to have better financial facilities and a broader range of inclusive financial products, enabling households to better utilize resources to improve poverty. Traditional finance is the foundation of inclusive financial development and is linked to the accessibility and efficiency of financial services. An increase in financial support for agriculture improves rural financial services, influencing employment and income, with a complementary effect between agricultural finance and inclusive finance. Including these variables in the empirical analysis improves the precision and credibility of the study’s findings.

Data

The paper uses data from the 2017 and 2019 waves of the China Household Finance Survey (CHFS), which is updated every two years. Data from 2020 onward are unavailable due to licensing restrictions. These two waves provide the most recent data on household finances and relative poverty, supporting this study. CHFS is a national tracking survey launched by the Southwestern University of Finance and Economics (SWUFE), covering household-level data on assets, liabilities, income, expenditures, and member demographics and employment. Rigorous sampling and high-quality, representative data provide solid support for this study. The sample includes 38,322 rural households in 29 provinces, after filtering invalid, missing, or extreme data. The consumer price index is used to deflate monetary data to control for the influence of price factors. Additional relevant macroeconomic data are primarily sourced from the China Statistical Yearbook. Descriptive statistics of the variables are presented in Table 2.

Table 2 Variable statistical description.

Results

Baseline regression analysis results

Household inclusive finance and relative poverty

This section analyzes how household inclusive finance affects static and dynamic relative poverty, as shown in Table 3. Due to the Probit model’s nonlinearity, estimated coefficients do not directly indicate relationships. Therefore, marginal effects are calculated to reflect how changes in explanatory variables influence the dependent variable. Columns (1) and (3) show regressions, while columns (2) and (4) show marginal effects; the analysis focuses on the latter.

Table 3 Results of benchmark regression.

Table 3 shows a significant negative effect of inclusive finance on both types of poverty. Hypothesis 1 is supported; the impact on dynamic poverty exceeds that on static poverty (0.0114 > 0.0080). Static poverty is shaped by structural factors, limiting the role of inclusive finance. In contrast, inclusive finance builds household capacity, mitigates risks, and prevents future poverty.

Impact of different dimensions of household inclusive finance

Analyzing sub-indicators of household inclusive finance facilitates a comprehensive understanding and reveals potential patterns. To better depict the link between household inclusive finance and relative poverty, we conduct regressions using its accessibility, usage, and digitalization measures. Table 4 reports Probit regression results on how inclusive finance dimensions affect static and dynamic relative poverty. Depth exerts the greatest impact, while digitalization has the least impact on both types of poverty. Although accessibility matters, screening and using financial services have a far greater effect on outcomes. Only high-quality, broad financial services can truly help rural households escape poverty. The digital divide weakens digital finance’s role, as shown by its low impact.

Table 4 Impact of different dimensions of household inclusive finance.

Robustness tests

Reverse causation problems

A causal link may exist between financial inclusion and both relative poverty and future vulnerability. Relative poverty and risk tolerance may also affect inclusive finance, suggesting potential endogeneity, which can be addressed using IV-Probit. The effectiveness of IV-Probit depends on the proper selection of instruments. Based on prior studies, two instrumental variable approaches are constructed. China’s digital economy, initially centered in Hangzhou and led by Alibaba, triggered a spatial diffusion of digital finance, creating a gradient from Hangzhou. Following Hao and Zhao (2025), we use a time-varying IV (IV₁) formed by interacting provincial distance to Hangzhou with the financial inclusion index outside the province. Households are grouped by age, literacy, education, and location, as per Dong (2024). IV₂ is computed as the cluster-average participation rate, excluding the focal household, to mitigate endogeneity.

Table 5 shows significant endogeneity, confirmed by the Wald test rejection. This confirms baseline bias, justifying the use of IV. First-stage F-statistics exceed 10, and t-values are significant at the 1% level. Significant Wald and AR tests suggest no weak instruments, supporting identification. Overall, these results substantiate the robustness of the core findings reported in Table 3.

Table 5 Results of instrumental variable regression.

Self-selection problems

Household inclusive finance may reflect self-selection, requiring bias controls. Propensity Score Matching (PSM) better mitigates bias and confounding, improving inference validity and reliability. PSM is used to address sample self-selection, with nearest-neighbor (1:4) matching applied. Based on the median, the index is split into treatment and control groups for matching. Figure 5 shows post-match differences near zero (<10%), confirming balance and PSM reliability. Kernel and radius matching results (ATT) are in Table 6. All matching methods show significantly negative ATT for poverty. After adjusting for heterogeneity, Hypothesis 1 holds.

Fig. 5

Visualization of covariate deviations before and after PSM matching.

Table 6 Results of ATT estimation under different matching methods.

Replacing core variables

To address potential measurement bias associated with equal weighting, the entropy weight method—a widely recognized unequal weighting approach—is employed to re-estimate the explanatory variables (Ramaian Vasantha et al. 2023). Results appear in Table 7, columns (1) and (4). The poverty line is reset at 50% and 60% of median income to rebuild the dependent variables, with results shown in columns (2), (5), and (3), (6). In all cases, inclusive finance significantly reduces static and dynamic poverty, thus confirming Hypothesis 1.

Table 7 Results of replacing the estimated core variables.

Replacing regression methods

Robustness is tested using OLS and Logit. OLS offers causal insights despite limitations for binary outcomes (Hellevik 2009). Logit is suitable for binary outcomes (Hahn and Soyer 2005) and ensures consistency with the main model. Table 8 shows that inclusive finance significantly reduces both types of relative poverty, confirming the robustness of baseline results.

Table 8 Results of replacing regression methods.

Heterogeneity analysis

Heterogeneity in regions

To address regional disparity, the sample is split into East and Midwest. Figure 6 shows that inclusive finance has greater poverty-reduction effects in the Midwest, likely due to deeper poverty and weaker financial access. Accordingly, financial access boosts income, living standards, and resilience for disadvantaged groups.

Fig. 6: Heterogeneity in regions.

Note: The results of Fisher’s test for Fig. 6 are 0.090 and 0.000, respectively.

Heterogeneity in the age of the head of household

Access to and use of inclusive financial services may affect farmers of different ages differently. The United Nations World Health Organization (WHO) defines 44 as the cutoff between young and middle-aged. Figure 7 shows that inclusive finance reduces poverty more effectively among middle-aged and elderly households. This is likely due to their limited income-generating capacity and heightened exposure to risks such as illness, which make inclusive finance more necessary and beneficial for them than for younger groups.

Fig. 7: Heterogeneity in the age of the household head.

Note: The results of Fisher’s test for Fig. 7 are 0.000 and 0.000, respectively.

Heterogeneity in household structure

Dependency ratios are associated with the size of household burdens, which are closely linked to relative poverty, potentially resulting in varied effects on households with differing dependency ratios. Based on household structure, families are grouped as “No elderly and no children” or “Elderly or children.” Fig. 8 shows that inclusive finance has stronger poverty-reducing effects for the latter, likely due to their greater financial stress. Financial inclusion helps manage caregiving costs and risk burdens.

Fig. 8: Heterogeneity in household structure.

Note: The results of Fisher’s test for Fig. 8 are 0.000 and 0.000, respectively.

Heterogeneity in household indebtedness

Household over-indebtedness not only exacerbates relative poverty but also weakens the positive impact of financial inclusion and compromises the sustainability and inclusiveness of financial services. Using a 30% debt-to-income ratio threshold (Anioła-Mikołajczak, 2016), households are classified as over-indebted or non-over-indebted. Figure 9 shows stronger poverty-reducing effects for the latter, likely due to their better financial condition. In contrast, debt burdens delay benefits for over-indebted households.

Fig. 9: Heterogeneity in household indebtedness.

Note: The results of Fisher’s test for Fig. 9 are 0.000 and 0.000, respectively.

Mechanism analysis

Previous research has confirmed that household-inclusive finance can alleviate relative poverty in rural areas and reduce the risk of future poverty. However, its operating mechanism remains unclear. This study uses the causal stepwise regression (CSR) method of Baron and Kenny (1986) to further establish a mechanism regression model based on model (1). The CSR method has been widely used (Huang 2024). The equation is:

$${M}_{i}={\gamma }_{0}+{\gamma }_{1}{{HIFI}}_{i}+{\gamma }_{2}{C{ontrol}}_{i}+{\rho }_{i}$$

(14)

$${Prob}({S\_{poverty}}_{i}=1)={\delta }_{0}+{\delta }_{1}{{HIFI}}_{i}+{\delta }_{2}{M}_{i}+{\delta }_{2}{C{ontrol}}_{i}+{\sigma }_{i}$$

(15)

$${Prob}({D\_{poverty}}_{i}=1)={\mu }_{0}+{\mu }_{1}{{HIFI}}_{i}+{\mu }_{2}{M}_{i}+{\mu }_{2}{C{ontrol}}_{i}+{\tau }_{i}$$

(16)

In the equation, ${M}_{i}$ is a livelihood-based mechanism variable including capital and strategies. The CSR method involves: confirming significance of ${\alpha }_{1}$ and ${\beta }_{1}$; testing financial inclusion’s effect on ${M}_{i}$; and validating ${M}_{i}$’s mediating role in models (15–16). Significant $\delta$ and $\mu$ coefficients imply partial mediation; significance only in ${\delta }_{2}$ or ${\mu }_{2}$ suggests full mediation.

Mechanisms of livelihood capital

The previous theoretical analysis indicates that the accumulation of livelihood capital has a direct and vital effect on the survival and development of family members, as well as their behavioral decisions. Considering the actual situation in rural China, this study quantifies family capital accumulation across six dimensions: household natural, physical, human, social, financial, and psychological capital. Specifically, natural capital (${cap}\_A$) is quantified by the logarithm of household land capital, physical capital (${cap\_}B$) by the logarithms of the market value of housing and the sum of the market value of household automobiles, human capital (${cap\_}C$) by the household’s average education level, financial capital (${cap\_}D$) by the logarithm of household financial assets, social capital (${cap\_}E$) by the logarithm of the value of the number of favors and gifts exchanged, and psychological capital (${cap\_}F$) by the “happiness” ¹. ${LC}$ is constructed via entropy weighting and scaled from 0 to 100. Columns (1)–(3) of Table 9 shows livelihood capital’s mediating effects.

Table 9 Estimated results of livelihood capital mechanisms.

Column (1) in Table 9 shows a positive link between inclusive finance and livelihood capital. Columns (2–3) indicate that inclusive finance still reduces relative poverty after controlling for livelihood capital, which also shows a negative effect, confirming Hypothesis 2. Livelihood capital mediates the impact, with a stronger effect on dynamic poverty (12.71% <16.06%). This is because dynamic poverty focuses on future risk prevention, while livelihood capital boosts motivation and opportunity, directly reducing poverty.

Table 10 shows the mediating roles of six livelihood capitals. All except natural capital significantly mediate the relationship. For static poverty, the mediating effects rank as follows: financial > social > human > physical > psychological. For dynamic poverty, the ranking is: financial > physical > human > social > psychological. Financial capital mediates most strongly due to its direct economic link. Physical capital aids long-term poverty reduction, especially under dynamic poverty. Natural capital lacks a significant impact, likely due to its dependence on fixed endowments. These insights help craft targeted policy.

Table 10 The role of different types of livelihood capital accumulation.

Mechanisms of livelihood diversification

Livelihood strategy choices affect household poverty and welfare. Diversification is measured using indices like Simpson or Shannon-Wiener. The Shannon-Wiener index captures variety and is simple and reliable, hence widely used (Duy Linh et al. 2023). Accordingly, this study used the Shannon-Wiener index to measure livelihood diversification:

$${LD}=-\mathop{\sum }\limits_{j=1}^{3}\left({p}_{j}\times ln{p}_{j}\right)$$

(14)

In Eq. (14), ${LD}$ represents the livelihood diversification index and ${p}_{j}$ is the share of the $j$ household income in total income. Total household income is primarily classified into three types: agricultural income, industrial and commercial income, and wage income. The estimated results of the mechanism role of livelihood diversification are presented in columns (4)–(6) of Table 9.

Column (1) shows a significant positive link between inclusive finance and diversification. Columns (2–3) show that both inclusive finance and diversification reduce poverty, supporting Hypothesis 3. Diversification mediates the effect, especially for static poverty (2.47% > 0.02%). Diversification directly boosts income and reduces poverty, with this direct effect being particularly effective against static poverty.

Following Walelign et al. (2017), K-means is used to classify households by income types and occupations. Three strategies are identified: ${A\_led}$, ${E\_led}$, and ${L\_led}$². Table 11 assesses these strategies, showing that inclusive finance mainly reduces poverty by weakening agriculture-led approaches. However, entrepreneurship-led strategies initially mask static poverty reduction. For static poverty, agriculture-led mediation is the highest (16.43%); for dynamic poverty, labor-led (1.41%) is the highest. Early entrepreneurship is often unprofitable and burdensome, obscuring the effects of financial inclusion. Agricultural support yields limited gains due to sector constraints. Non-farm jobs offer more income stability. Financial inclusion can effectively improve poverty reduction by optimizing employment strategies for rural residents.

Table 11 The role of different dominant types of livelihood strategies.

Conclusions and implications

Conclusions

Drawing on micro-survey data from the 2017 and 2019 CHFS, this study constructed indices for household inclusive finance, rural relative poverty, and poverty vulnerability, and conducted an empirical analysis of the poverty alleviation effects, heterogeneity, and mechanisms of inclusive finance from both static and dynamic perspectives. The main conclusions are as follows: First, household inclusive finance significantly reduces both static and dynamic relative poverty, with a stronger effect observed on dynamic poverty. This conclusion is supported by robustness tests. Second, poverty alleviation effects vary significantly across regions, household head age groups, family structures, and debt burdens, revealing the asymmetric impact of inclusive finance on rural poverty. Third, inclusive finance alleviates poverty by enhancing rural households’ livelihood capital and promoting livelihood diversification.

Theoretical contribution

Researchers and policymakers have long examined the relationship between financial development and poverty reduction, with inclusive finance increasingly recognized as a core strategy for promoting inclusive growth. Numerous studies indicate that macro-level inclusive finance contributes to reducing income inequality (Verma and Giri 2024) and stimulating economic growth (Shrestha and Nursamsu 2021). At the micro level, financial inclusion facilitates savings, supports employment (Bruhn and Love 2014), and alleviates poverty (Jin 2017). Building on these findings, this study supports Atta-Ankomah et al.’s (2024) assertion that inclusive finance enhances rural livelihoods, while also affirming its poverty-reduction effects (Koomson et al. 2020). Notably, while the study aligns with macro-level conclusions, it distinguishes itself by focusing on household inclusive finance. It addresses relative poverty from dual perspectives and examines its mechanisms through the lens of rural households’ livelihood structures. The study reveals heterogeneous effects across different household types in accessing inclusive financial services, providing nuanced insights for policymaking. This perspective not only deepens the theoretical understanding of inclusive finance’s role in poverty reduction but also offers empirical guidance for the formulation of targeted policies aligned with the Sustainable Development Goals (SDGs).

Policy implications

This study examines how household inclusive finance reduces both static and dynamic relative poverty among rural residents, while uncovering the heterogeneity of its effects and mechanisms. The findings provide empirical evidence of its crucial role and align with global poverty reduction strategies and the Sustainable Development Goals (SDGs). By enhancing rural livelihood assets and promoting diversification, inclusive finance fosters sustainable rural development, mitigates poverty and vulnerability, and makes a significant contribution to achieving global poverty reduction targets. These results offer valuable policy insights for relevant stakeholders.

First, establish a dynamic monitoring and responsive support mechanism for poverty-prone households. Addressing relative poverty requires the creation of long-term, effective dynamic monitoring systems and responsive support mechanisms, complemented by targeted inclusive financial instruments. A sustainable, data-driven monitoring system should be developed to address dynamic relative poverty by tracking fluctuations in household income, expenditures, and assets. The early identification of vulnerable households enables the timely deployment of tailored inclusive financial instruments, such as emergency loans, flexible credit lines, and seed capital. The integration of digital technologies and early warning systems can further enhance the accuracy and responsiveness of poverty prevention efforts.

Second, efforts should focus on addressing deficiencies and implementing differentiated policies. Inclusive financial services should be tailored to accommodate the diverse characteristics of households. The impacts of financial inclusion vary by region, demographic profile, and household structure. In underdeveloped rural areas of western China, in addition to offering microcredit products with lower entry thresholds and interest rates, the spatial distribution of financial service outlets should be optimized by increasing outlet density and extending service hours to improve accessibility. At the same time, age- and education-specific financial literacy programs are crucial for bridging the digital divide. Younger households benefit more from financial planning and asset-building services, while complex households require modular and bundled financial products to meet their multidimensional needs.

Third, the positive relationship between inclusive finance and household livelihoods should be further strengthened. Inclusive finance is closely linked to the accumulation of household livelihood capital and plays a crucial role in helping rural households choose appropriate livelihood strategies. It should be integrated into broader rural welfare and development strategies to enhance its positive interaction with farm-based livelihood approaches. Efforts should focus on transforming the traditionally poverty-inducing nature of agriculture-dependent livelihoods into a driver of relative poverty reduction. For instance, phased agricultural production loans could be designed to meet financing needs for seeds, fertilizers, and irrigation systems. Innovations in agricultural insurance, such as income protection and price index-based products, can stabilize farm household incomes and promote sustained poverty alleviation and prevention in rural areas.

Limitations and future recommendations

Future research should be further expanded and refined along several dimensions. First, this study relies on CHFS data from 2017 and 2019 due to data limitations, which restricts its ability to capture the long-term dynamics between inclusive finance and relative poverty. Future studies should incorporate more recent and longitudinal data—either by updating existing databases or using high-quality, continuously monitored panel datasets—to generate more accurate and stable conclusions. Second, future research should enhance the precision of variable selection, diversify indicator systems, and improve the rigor of measurement models. Although this study adopts a relatively comprehensive household-level framework, further refinement is necessary to strengthen the analytical foundation. Finally, digital finance is becoming a key driver of inclusive finance, expanding the reach and efficiency of financial services. Future research should explore its multifaceted effects, particularly in improving rural welfare and enabling sustainable development, to better understand its transformative potential.

Notes

1. The “favors and gifts exchanged” is cash or non-cash received or given by the household to other relatives and non-relatives besides parents and in-laws/parents-in-law; the “consumption expenditure on socialization” includes household communication expenditure, transportation expenditure, and travel expenditure; and” happiness” is measured by the questionnaire’s subjective assessment of household happiness, with 1=very unhappy; 2=unhappy; 3=average; 4=happy; and 5=very happy.

2. Agriculture-led livelihood strategy is “Whether to choose farming as the dominant livelihood strategy”: yes=1, no=0; Entrepreneurship-led livelihood strategy is “Whether to choose entrepreneurship as the dominant livelihood strategy”: yes=1, no=0; Labor-led livelihood strategy is “Whether to choose labor as the dominant livelihood strategy”: yes=1, no=0.