Examining the Mediating Role of Digital Finance in the Relationship Between Financial Inclusion and Poverty Alleviation

Examining the Mediating Role of Digital Finance in the Relationship Between Financial Inclusion and Poverty Alleviation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Examining the Mediating Role of Digital Finance in the Relationship Between Financial Inclusion and Poverty Alleviation Dilgasa Bedada Gonfa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8391843/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study explores the mediating role of digital finance in enhancing financial inclusion and alleviating poverty across nine East Africa countries from 2008 to 2022 and considering country-specific effects, the study fills a gap in the literature by empirically examining how digital finance mediates the relationship between financial inclusion and poverty reduction. The research, based on secondary data from the IMF, UNDP, and World Bank, Using a quantitative approach with Feasible Generalized Least Squares (FGLS) regression model, the study investigates the impact of independent variables on poverty alleviation. Additionally, bootstrap fixed-effects and Sobel Z-value tests are employed to assess the significance of the mediation approach, followed by robustness checks using Generalized Method of Moments (GMM) and Quantile regression. The findings reveal that digital finance and financial inclusion have a positive impact on poverty alleviation. The study highpoints that digital finance significantly mediates the effect of financial inclusion on poverty alleviation, accounting for about 38.3% of the total impact. Urban areas benefit substantially from digital finance, while rural areas face barriers like poor infrastructure and low digital literacy, which limit the effectiveness of these interventions. The research emphasizes the need for governments to focus on strengthening digital infrastructure, fostering public-private partnerships, improving digital literacy, and developing targeted policies for rural areas to bridge the urban-rural divide. These measures will maximize the impact of digital finance, foster inclusive growth, and reduce poverty across East Africa. Finance Agricultural Economics & Policy Development Economics Economic Theory Digital Finance FGLS Financial Inclusion GMM and Poverty Alleviation 1. Introduction In East Africa, access to financial services remains limited, especially for a large portion of the population in rural areas. Despite the vast majority of adults in the world lacking access to formal banking services, the demand for financial tools and services continues to grow exponentially. Digital finance, which includes mobile banking, mobile money, and automated teller machines (ATMs), has emerged as a crucial enabler of financial inclusion. However, billions of people remain excluded from accessing these services, creating an imbalance between the demand and supply of financial services. This study explores the significant role that digital finance plays in enhancing financial inclusion and alleviating poverty, particularly in East African countries, by analyzing its impact over a fifteen-year period (2008-2022). The study fills a gap in the existing literature by investigating how digital finance mediates the relationship between financial inclusion and poverty reduction in the East African context. By focusing on nine East African countries, this research provides a comprehensive empirical analysis to understand the underlying dynamics and challenges that shape financial inclusion and poverty alleviation efforts. Digital finance plays a pivotal role in advancing financial inclusion and alleviating poverty, aligning with the 2030 Agenda for Sustainable Development (SDGs). It enhances access to financial services such as mobile banking, digital wallets, and online payments, particularly for underserved and marginalized populations. This contributes directly to several SDGs, including SDG 1 (No Poverty), SDG 8 (Decent Work and Economic Growth), SDG 10 (Reduced Inequality), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 17 (Partnerships for the Goals). Digital finance empowers individuals with tools for savings, credit, and insurance, fostering economic stability and reducing poverty (Jack & Suri, 2011). It also supports entrepreneurship, job creation, and sustainable economic growth, particularly in underserved communities (Sarma & Pais, 2011). By bridging financial gaps for marginalized groups, digital finance promotes financial equity and inclusion (Narain & Schreiber, 2017). Furthermore, it fosters innovation and strengthens financial infrastructure, driving inclusive growth and resilience (Ozili, 2018). Collaboration between governments, private sector actors, and international organizations further expands the reach and impact of digital finance, ensuring no one is left behind in achieving sustainable development (Beck, 2013). Digital finance, as a strategy for financial inclusion, has been significantly enhanced by Industry 4.0, which improves data transmission and processing, leading to faster and better decision-making (Al-Smadi, 2023). Industry 4.0 has introduced new competencies for both individuals and machines, transforming the financial sector through technology-driven changes, which have led to innovative financial services and digital advertising methods (Mhlanga, 2020). The 2015 UNDP Millennium Development Agenda aimed to eradicate extreme poverty and reduce the number of people living on less than one dollar per day. However, a World Bank report (2021) revealed that, in 2018, 80% of the poor lived in rural areas, with over 40% residing in regions affected by stagnation or conflict. While global poverty decreased by nearly 1% annually from 1990 to 2015, the pace slowed to under 0.6% annually between 2013 and 2017, raising concerns about meeting the UNDP's poverty reduction targets. Millions of "new poor" are emerging, particularly in semi-urban areas, due to skill gaps and job loss. These vulnerable populations face shrinking social protection and job opportunities. Despite financial growth, poverty reduction remains slow in these regions, largely due to the ineffective distribution of resources. The lack of progress is linked to fragile institutions, which, due to weak infrastructure and inefficiency, struggle to address poverty effectively, especially in conflict-affected areas (Acemoglu and Robinson, 2012). Internet-based banking services offer a practical and cost-effective alternative to traditional banking for low-income individuals in underdeveloped countries. These services improve financial security, simplify transactions, and reduce the need for cash handling (Haider, 2018). Financial technologies lower service provision costs, driving greater financial inclusion, ensuring that everyone, regardless of wealth, has access to affordable financial services (Agelyne & Musau, 2021). Digital finance provides more affordable access to financial services compared to traditional banks, with technology-driven platforms playing a significant role in financial inclusion. However, access alone is insufficient; service quality and usage are critical. Cultural or religious factors may deter some individuals from using digital finance (Thaddeus & Ngong, 2020). To foster inclusive growth, financial inclusion must be complemented by financial security, as both must work synergistically for effective development (Iddrisu et al., 2023). Since 2010, the World Bank and the Group of Twenty have spearheaded efforts to enhance financial accessibility in developing and emerging economies, aiming to reduce poverty (GPFI, 2016). Financial inclusion has become a critical global issue, garnering increasing attention from financial institutions, governments, and other stakeholders (Mhlanga et al., 2020). Both developed and developing nations now recognize financial exclusion as a significant socioeconomic challenge (Mhlanga & Denhere, 2020). As part of the 2030 Agenda, countries have committed to reducing poverty and vulnerability to crises, including natural disasters and conflicts. Achieving this requires addressing key challenges through country-specific plans, robust management structures, and international cooperation, with the active involvement of all relevant stakeholders (United, 2018). The World Bank (2017) projects that by 2030, over 90% of people in East Africa will have access to savings and financial services, up from 63% in 2017. This growth is intended to lower costs, remove economic barriers, and improve financial access for low-income populations. However, the implementation of digital finance, financial inclusion, and poverty alleviation in underdeveloped countries faces significant challenges, particularly regarding accessibility and participation. The full impact of these solutions remains unclear, emphasizing the need for further study into the barriers East African countries face in integrating digital finance for improved financial inclusion and poverty reduction. 1.1 Statement of the Problem Africa has one of the lowest levels of financial inclusion globally. While approximately 2.7 billion people worldwide have access to financial services, more than 80% of families in many African countries still lack sufficient income to meet basic needs (Chibba, 2014). To address this disparity, financial inclusion became a key objective of the United Nations' 2015 Sustainable Development Agenda, which aims to improve living standards, reduce poverty, and accelerate development in participating nations. By enabling middle- and low-income individuals to access the formal financial system, financial inclusion promotes prosperity, contributing to economic development and poverty reduction. In countries like Ethiopia, where 80% of the population resides in rural or semi-rural areas, financial services are concentrated in urban centers, leaving millions in underserved regions without access to essential financial services. This gap underscores the need for greater financial inclusion in rural areas to meet growing demand and foster economic development (Bedada, 2020). Digital technologies are improving access to financial services for underbanked populations in developing countries, creating profitable opportunities for financial institutions while empowering women, reducing poverty, and strengthening institutions (Tafesework, 2020). According to Emon & Chowdhury (2023), many in less developed nations rely on informal financial services due to the limited availability of formal institutions, hindering economic growth and worsening wealth inequality. While online banking services provide greater comfort, security, and safety compared to cash reliance (Lewis, 2015), digital finance remains less widespread in emerging economies (Ozili, 2022). The debate surrounding digital finance and financial inclusion is ongoing, with varied opinions from policymakers, academics, and industry experts, highlighting the need for a deeper understanding of its challenges and potential for achieving true financial inclusion. Several studies using the global Findex database have examined global disparities in financial inclusion (Zins & Weill, 2016; Fatoki & Wokabi, 2019). While financial inclusion is widely recognized as vital for economic growth, limited research has focused on its drivers in East African countries. This study aims to identify the factors influencing financial activity in East Africa to inform policies that enhance financial inclusion. A key limitation in existing studies is the lack of detailed methodology, hindering their validity and reproducibility. Additionally, there is insufficient analysis on the impact of financial inclusion and digital finance (Soni et al., 2021). Challenges include the digital divide between urban and rural areas, low financial literacy, and weak last-mile data networks. The research seeks to address the gap in understanding the connection between digital finance, financial inclusion, and poverty reduction in East African Community (EAC) countries. Most studies are country-specific, limiting their applicability to the broader region (James et al., 2020). Digital finance, including mobile banking and digital wallets, has improved access to financial services, particularly for underserved populations, and has shown success in breaking the poverty cycle, as seen with M-Pesa in Kenya (Jack & Suri, 2011). However, barriers like low digital literacy, poor infrastructure, and limited internet access, especially in rural areas, prevent full adoption, perpetuating economic inequality (Narain & Schreiber, 2017). Even in countries like Kenya, marginalized groups, including women and low-income earners, still face difficulties accessing formal financial networks, hindering their ability to save, invest, and build wealth (Sarma & Pais, 2011). While digital finance has potential for fostering entrepreneurship and economic growth, its inconsistent impact on job creation and sustainable development highlights the need to address these access barriers for broader, equitable use. Still these gaps are particularly important when mediating digital finance by assessing the relationship between financial inclusion and poverty alleviation in East African countries. 1.2 Designed Research Questions. Focused inquiries that direct a study, reducing its scope and ensuring that it is in line with its goals are known as designed research questions. They fill in knowledge gaps, define the issue, pinpoint important factors, and describe connections or results. The purpose alongside effects of the study are defined by these questions. The queries posed through that research consisted thoughtfully crafted to correspond with the main issue, guaranteeing a thorough examination of fundamental concerns and offering insightful information. 1. What is the impact of financial inclusion on poverty alleviation in East African? 2. How does the interaction between digital finance and financial inclusion influence poverty alleviation in East Africa? 3. What is the mediating effect of digital finance on the relationship between financial inclusion and poverty alleviation in East Africa? 1.3 Objectives of Study Study objectives are clear and specific declarations that specify the desired results of a study. They act as a road map for the studies process, outlining the parameters of the study and directing the investigation. By laying out the steps a study will employ to solve the issues under study, such goals enable an in-depth look at particular facets of the subject being studied? Study objectives allow the researcher to methodically investigate, evaluate, and understand the topic at hand by forming precise, quantifiable goals. The particular aims of this research are delineated with the following objectives: 1. To assess the impact of financial inclusion on poverty alleviation in East Africa. 2. To examine how the interaction between digital finance and financial inclusion influences poverty alleviation in East Africa. 3. To investigate the mediating effect of digital finance on the relationship between financial inclusion and poverty alleviation in East Africa. 2. Literature Review The relationship between financial inclusion and poverty alleviation has been widely discussed in the literature, but with digital finance increasingly recognized as a key driver of financial inclusion. Financial inclusion is defined as the access of individuals to useful and affordable financial products and services that meet their needs, including payments, savings, credit, and insurance. On the other hand, poverty alleviation refers to efforts aimed at reducing the incidence of poverty, enhancing human development, and improving economic well-being. Digital finance, which encompasses mobile banking and mobile money services, has become a popular tool for increasing financial inclusion, especially in developing countries. Scholars have noted that digital finance helps bridge the gap by providing underserved populations with access to banking services through their mobile phones, thereby enhancing financial accessibility and increasing savings, investments, and access to credit (Beck, Demirguc-Kunt, & Levine, 2007). However, despite the potential of digital finance, there is a lack of consensus regarding its definition and its role in poverty alleviation. Some studies argue that digital finance alone may not be sufficient to eliminate poverty, as it must be accompanied by improved infrastructure, digital literacy, and strong financial institutions (Narayan, 2018). Others suggest that digital finance can be a powerful tool for poverty reduction, particularly in areas with limited physical banking infrastructure, such as East Africa. In recent years, researchers have explored the mediating role of digital finance in the relationship between financial inclusion and poverty alleviation, but there remains a need for empirical studies that investigate this mediation, especially in the context of East Africa. Empirical studies suggest that digital finance serves as a bridge between financial inclusion and poverty reduction. Financial inclusion theories emphasize the importance of accessible and affordable financial services, such as banking, insurance, savings, and payments, to improve individuals' financial situations and reduce poverty. Digital finance, through smartphones and the internet, enhances financial inclusion by overcoming barriers like geographical limitations and high infrastructure costs. The mediation effect theory posits that digital finance can increase savings, investment, and entrepreneurship, thereby directly impacting poverty levels. Inclusive growth theory highlights digital finance's role in promoting broad economic growth, benefiting the poor. According to Tang et al. (2023), shows that digital finance reduces financial constraints, supporting innovation in green technologies by improving access to funding. The study finds that digital finance fosters green innovation, especially in Eastern China, where government-owned enterprises benefit more from internet banking. Similarly, Huang et al. (2023) explore how digital financial inclusion supports rural businesses in China, showing that digital finance aids in rural development by improving access to financing and technology. These studies highlight the importance of digital finance in fostering economic inclusion and rural industrial development. James et al. (2020) investigate the mediating role of financial deepening in the relationship between economic growth and poverty reduction in five East African Community countries from 1989 to 2018. The study reveals a significant inverse relationship between financial growth and poverty levels, demonstrating that economic growth generally contributes to poverty alleviation through increased incomes. Financial deepening, operationalized as credit access for individuals and businesses, is identified as a critical intermediary mechanism that enhances access to financial services, promotes savings, and stimulates investment. The findings underscore that financial deepening, facilitated by economic growth, plays a pivotal role in poverty reduction. This research offers important contributions to understanding how financial deepening can drive economic growth and mitigate poverty, addressing a largely underexplored issue within the context of the East African Community. The transformative role of financial inclusion and digital finance in alleviating poverty and promoting economic development, especially in underserved regions. Studies show that financial inclusion offers alternative strategies for poverty reduction, complementing traditional methods (Chibba, 2014; Tuesta et al., 2015), and plays a key role in mitigating income inequality and enhancing economic stability (Saraswati et al., 2020; Thaddeus & Ngong, 2020). In Sub-Saharan Africa, financial inclusion is found to significantly reduce poverty and inequality, while digital finance, particularly through mobile technology, improves access to secure and affordable financial services (Soumaré et al., 2016; Durai & Stella, 2019). Factors such as income, education, gender, and access to technology are crucial determinants of financial inclusion, especially in rural areas (Badu et al., 2018; Wokabi & Fatoki, 2019). Further research across regions like Asia and Africa highlights that financial inclusion can reduce poverty, with governments encouraged to leverage it for poverty alleviation (Kumar & Jie, 2023; Gao, 2023). Innovations in digital finance and robust financial policies are essential for reducing inequalities and fostering growth (Polloni et al., 2021; Evans, 2023). Additionally, the impact of fintech on financial inclusion in emerging economies and the influence of factors like internet access and bank infrastructure demonstrate the potential for digital finance to drive inclusive economic development (Banna & Roy, 2023; Pandey et al., 2023). The existing literature on digital finance, financial inclusion, and poverty alleviation provides valuable insights into the role of digital tools in improving access to financial services. However, much of the research has been conducted in isolated contexts or without a clear focus on the mediating role of digital finance in the relationship between financial inclusion and poverty alleviation. Furthermore, while East Africa has witnessed significant growth in mobile banking and mobile money services, studies that comprehensively assess the impact of digital finance on financial inclusion and poverty alleviation across multiple East African countries remain scarce. This study fills this gap by empirically examining how digital finance mediates the relationship between financial inclusion and poverty alleviation in nine East African countries. 2.1 Formulation Hypothesis A hypothesis is a testable statement that predicts a relationship between two or more variables, based on existing theories or observations. It suggests a potential outcome that can be validated or disproven through data analysis. An effective hypothesis is precise, grounded in theory, and testable through empirical research, with statistical methods used to accept or reject the hypothesis. Based on these criteria, the following research hypothesis has been formulated: H1: Financial inclusion has a significant positive impact on poverty alleviation in East Africa. H2: The interaction between digital finance and financial inclusion significantly influences poverty alleviation in East Africa. H3: Digital finance mediates the relationship between financial inclusion and poverty alleviation in East Africa. 3. Methodology This study adopts a quantitative approach using a balanced statistical panel data methodology, drawing data from secondary sources such as the International Monetary Fund (IMF), the United Nations Development Programme (UNDP), and the World Bank. Covering nine East African countries from 2008 to 2022, the dataset creates a panel structure with multiple annual observations for each country, enabling an analysis of both cross-country (differences between countries) and temporal (changes over time) variations in key variables, including financial inclusion, poverty alleviation, mobile banking, internet usage, population density, and rural population growth. The study employs Fixed Effects and Feasible Generalized Least Squares (FGLS) models to address statistical challenges. The Fixed Effects model accounts for unobserved country-specific factors that remain constant over time, isolating the impact of key variables such as financial inclusion. The FGLS model corrects for common panel data issues like heteroscedasticity and autocorrelation, enhancing the precision and reliability of the estimates. Additionally, bootstrap fixed-effects and Sobel Z-value tests are used to assess the significance of the mediation approach, which examines how digital finance influences poverty alleviation through financial inclusion. The bootstrap fixed-effects method provides a more robust estimation of the coefficients by resampling the data, allowing for better inference regarding the significance of the mediation effect. The Sobel Z-value test is applied to test the significance of the indirect effect of digital finance on poverty alleviation through financial inclusion. Following these, robustness checks are performed using the Generalized Method of Moments (GMM) and Quantile regression. The GMM method helps address potential endogeneity by providing consistent estimates in the presence of endogenous explanatory variables, while Quantile regression is used to examine the effects at different points in the distribution of the dependent variable, ensuring that the results are not driven by outliers or skewed data. These steps enhance the reliability and robustness of the findings, providing more confidence in the conclusions drawn. The study also conducts robustness checks, such as sensitivity analyses, to ensure the stability of the findings under various model specifications and assumptions. By integrating these models, the study aims to provide a comprehensive and robust analysis of how financial inclusion and digital finance contribute to poverty alleviation in East Africa. 4. Empirical Analysis and Results 4.1 Introduction The primary objective of this study is to examine the heterogeneity and robustness of digital finance's mediation role in promoting financial inclusion and alleviating poverty across East African countries. To achieve this, the study employs a Feasible Generalized Least Squares (FGLS) regression model to assess the impact of various independent variables on poverty alleviation in nine East African countries, using a sample of 135 observations over a 15-year period (2008–2022), while accounting for country-specific effects to capture the unique characteristics of each nation. To evaluate the significance of digital finance’s mediation role, bootstrap fixed-effects and Sobel Z-value tests are employed, assessing the reliability of the indirect effects of digital finance on poverty alleviation through financial inclusion. Additionally, robustness checks are conducted using the Generalized Method of Moments (GMM) to address potential endogeneity concerns, and Quantile regression is used to analyze effects at different points in the poverty distribution. This chapter includes descriptive statistics and empirical analysis, endogeneity testing, and robustness checks, with findings interpreted through various models, providing a comprehensive understanding of how digital finance enhances financial inclusion and reduces poverty in East Africa. 4.2 Descriptive Analysis The researcher performed descriptive statistical analyses for selected variables to ascertain the statistical characteristics of the information prior to estimate. This involved the utilization of descriptive statistics instruments such as mean that deviation from the mean, a minimum, as well as maximum values. The summary of statistical information is provided below: Table 4.1 Description of statistical data (1) (2) (3) (4) (5) Variables N Mean SD Min Max HDI 135 0.485 0.0551 0.310 0.581 FII 135 -0.113 0.781 -1.730 1.555 DF 135 -4.44e-09 1.000 -1.847 2.809 Source: STATA 18.0 Results, 2025 The table presents the mean, standard deviation, and range for the dataset, with 135 observations derived from a 15-year study period across nine countries. The average Human Development Index (HDI) for poverty reduction was 0.485, indicating that over 48.5% of the population in these East African countries remain below the Low Human Development threshold, reflecting high poverty levels. The minimum HDI score of 0.310 suggests extreme poverty, while the highest score of 0.558 shows some progress in poverty alleviation, though challenges remain. A standard deviation of 0.0551 indicates significant disparities in human development across the countries. The Financial Inclusion Index had a mean of -0.113, with a high standard deviation of 0.781, indicating substantial variability, ranging from − 1.730 to 1.555, showing significant differences in financial inclusion. Digital finance showed a near-zero mean of -4.44e-09, with a standard deviation of 1.000, reflecting considerable volatility and variation in usage, with scores ranging from − 1.847 to 2.809. 6.3 Analysis of correlation Researchers use a correlation matrix to assess the relationship between explanatory variables. According to Brooks (2008), a correlation coefficient above 0.80 suggests potential multi-col-linearity, which can lead to unreliable regression estimates. In this study, the correlation coefficient is used to examine the relationship between poverty alleviation, measured by the Human Development Index (HDI), and financial inclusion, with digital finance acting as a mediating variable. Table 4.2 Analysis of Correlation (1) HDI FII DF HDI 1.00 FII 0.33 *** 1.00 DF 0.41 *** 0.46 *** 1.00 Source: STATA 18.0 Results, 2025 The analysis explores how digital finance (DF) mediates the relationship between financial inclusion (FII) and poverty alleviation which measured by the Human Development Index (HDI). The findings show a significant positive correlation between HDI, financial inclusion, and digital finance. As human development improves, access to financial services increases, and the use of digital finance grows. Countries with higher HDI are more likely to adopt digital financial tools, which enhances financial inclusion and contributes to poverty reduction. Digital finance offers a more accessible and cost-effective way to deliver financial services to marginalized populations, improving access, affordability, and efficiency. By strengthening the connection between financial inclusion and poverty alleviation, digital finance helps create economic opportunities, reduce vulnerability, and improve living conditions, particularly for low-income groups in East Africa. 4.3 Investigation of FGLS Model Econometric Results Feasible Generalized Least Squares (FGLS) is an econometric technique employed to address issues such as heteroskedasticity and autocorrelation in regression models, which can render Ordinary Least Squares (OLS) estimators inefficient and result in biased standard errors. By estimating the error variance-covariance matrices, FGLS enhances the precision and reliability of regression estimates. In this study, FGLS is utilized to examine the mediating role of digital finance in the relationship between financial inclusion and poverty reduction across nine East African countries, using data spanning from 2008 to 2022. Table 4.3 FGLS Econometric Results (1) (2) (3) (4) (5) (6) (7) (8) (9) Variables HDI HDI HDI HDI HDI HDI HDI HDI HDI FII 0.0124** 0.0171*** 0.0216*** 0.00620** 0.0113*** 0.0145*** 0.0125*** 0.0115*** 0.0214*** (2.025) (2.895) (3.733) (2.198) (4.014) (5.269) (40.22) (5.831) (12.55) DF 0.0182*** 0.0143*** 0.0123*** 0.0287*** 0.0131*** 0.0117*** 0.0179*** 0.00840*** 0.0105*** (3.790) (3.333) (2.846) (12.20) (6.364) (5.739) (52.63) (5.265) (8.588) Constant 0.486*** 0.481*** 0.458*** 0.497*** 0.496*** 0.472*** 0.486*** 0.481*** 0.467*** (113.0) (26.73) (41.54) (207.9) (59.40) (65.10) (1,298) (57.15) (67.96) Obs 135 135 135 135 135 135 135 135 135 No. Country 9 9 9 9 9 9 9 9 9 Z-statistics are presented in parenthesis; significance levels are indicated as follows: a *** p < 0.01, ** p < 0.05, and * p < 0.1 Source: STATA 18.0, Results 2025 According to the results from columns 1, 2, and 3 in the aforementioned table, column 3 is the superior column as it possesses the greatest FII coefficient (0.0216), with both FII and DF coefficient demonstrating high statistical significance shown by the highest Z-statistics. Column 3 indicates the most stable positive correlation between inclusion in financial services, digital finance, and the Human Development Index, rendering it the superior model for both the significance and strength of the coefficients, in accordance with theoretical expectations. The researcher analyzed the coefficients, Z-statistics, and statistical significance levels over Columns 4, 5, and 6, resulting in display outcomes for the regression approaches, with HDI (Human Development Indices) with the response variable, FII (Financial Inclusiviness Indices) for the autonomous parameter, and DF (Digital Finance) as the mediating variable. Column 6 represents the optimal specification due to its greatest and most important value for FII (0.0145), indicating inclusion in finance exerts more substantial significant effect on HDI compared to other columns. Overall, FII and DF were of statistical significance at a level of one percent, demonstrating a strong correlation with HDI. The most logical and reliable statistical evidence shows that both financial inclusion and digital financial services are beneficial for human development. Column 6 supports this argument by demonstrating the most significant effect of measuring financial inclusion's overall HDI, a metric of human development. Making it easier for individuals acquire finance goods and offerings, whether through traditional financial inclusiviness or digital finance, leads to better human development and, in turn, more effective poverty reduction. This is shown by the fact that both FII and DF have significant and positive coefficients. Because column 9 has the highest coefficients for FII (0.0214), which exhibits a robust positive connection with HDI, it is best model from columns 7, 8, and 9. This indicates that financial inclusion exerts the most significant impact on the humanity Development Indexes (HDI), which is under this model. Digital Finance positively influences HDI; however, its effect is marginally less significant compared to the significance of FII across Column Nine. Both FII and DF exhibit strong significance at the 1% level, rendering this approach the most statistically robust. The idea behind column 9 is that including finance and digital finance makes it easier to fight poverty and improve people's lives. As a result, Column 9 is the most stable model, showing the biggest positive effects of including finance with digital financial in general on grassroots development, which is in line with the idea of reducing poverty. 4.4 Effect of Digital Finance as a Mediator between Financial Inclusion and Poverty Alleviation 4.4.1 Analysis of Mediating Effects This study presents findings and the researcher investigates the potential mediating role of digital financing in connection to the inclusion of finance and its impact for poverty alleviation through East African nations. The Bootstrap fixed-effects analysis approach seeks to analyze the effect of financial inclusion on poverty alleviation with the Sobel Z-test using for determine importance of mediation within the model. This modification guarantees the accuracy and reliability of tests for hypothesis and confidence intervals, even in the presence of heteroscedasticity among the inaccuracies. A table displays how outcomes of Bootstrap fixed-effect regression approaches for which Human Development Index, which assesses poverty reduction, indicating the findings. Analyzing the influence about inclusion in finance affects poverty reduction through mediation of digitally finance. This research revealed the results for the coefficient test across the three channels. The researcher precisely formulated the three equations presented below: $$\:\text{Y}\text{i}\text{t}\:={\beta\:}0\:+\:{\beta\:}1\text{X}\text{i}\text{t}\:+{\epsilon\:}\text{i}\text{t}-------\left(1\right)$$ $$\:\text{U}\text{i}\text{t}=\:{\beta\:}0\:+\:{\beta\:}1\text{X}\text{i}\text{t}\:+{\epsilon\:}\text{i}\text{t}--------\left(2\right)$$ $$\:\text{Y}\text{i}\text{t}=\:{\beta\:}0\:+\:{\beta\:}1\text{X}\text{i}\text{t}\:+{\beta\:}2\text{U}\text{i}\text{t}+{\epsilon\:}\text{i}\text{t}--------\left(3\right)$$ Where; Y = Human Development Index X = Financial Inclusion U = Digital Finance (Mediator Variable) The equation use X to denote the variable independent of the financial inclusion indexes and Y to signify the dependent factor during the human growth index. Furthermore, U represents mediating mutable to be analyzed, which encompass the dimensional channels promoting FII, Human Developmental Indexes channel for poor alleviation and considering the accessibility of finances services channel (Digital financing). Specifically, for test z-statistics constructed: $$\:z=\frac{ab}{sab}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:.\left(4\right)$$ Where a, b, and sab are the symbols in the Eq. (4) that stand for the estimated value of a, the estimated technique for b as well as the standard errors for each estimate ab. This study created a 95% confidence interval by calculating the standard errors via bootstrapping, resulting in fairly precise standard errors. Removal of zero from confidence interval indicates that the combination considering coefficients was sufficiently significant, suggesting for presence a mediating influence. Tables 6.8 illustrates the results concerning the indirect mediated effect. This study initially identified DF as a mediator variable concerning accessibility to financial service channels. The bootstrap technique shows that the 95% confidence interval doesn't include 0 and that z-statistic implies significantly not equal to Zero, which means that its coefficient was affirmative. Thus, This is significance favorable unintended endorsement via digital financing. The findings of an analysis of mediation analyzing the intermediary function of digital finance in the relationship with inclusiveness in finance and reducing destitution, which assessed Human Development Index (HDI). 1. The initial regression involves regressing digital finance (DF) on financial inclusion index (FII) and the interaction term FIIDF. The initial model examines the effects of the financial inclusion index and an interaction term, FIIDF, which represents the relationship between financial inclusion and digital finance, on digital finance as a mediator. The coefficient for financial inclusion is 0.5871, with a p-value of less than 0.01, indicating that financial inclusion has a strong positive and statistically significant impact on digital finance. This means that for every one-unit increase in financial inclusion, digital finance increases by 0.5871 units. This suggests that expanding financial inclusion encourages the adoption and use of digital financial technologies. Additionally, the coefficient for the FIIDF interaction term is 0.0274, with a p-value of 0.029, which is statistically significant. This shows that the interaction between financial inclusion and digital finance has a meaningful and direct effect on digital finance, reinforcing the idea that these two factors work together to drive the implementation and acceptance of digital financial services. Table 4.4 Mediating Effects of Digital Finance DF Coefficients Bootstrapping Standard errors T-stat P-Value 〔95% Conf. Interval〕 FII 0. 5870611 0. 1026329 5.72 0.000 (0.3840288 0.7900934) FIIDF 0. 0273895 0. 1021436 0.27 0.029 (0.2294539 0.1746748) Cons 0. 0689362 0. 0850228 0.81 0.419 (0.0992592 0.2371317) Source: STATA 18.0 Results, 2025 2. The second regression involves regressing the Human Development Index (HDI) on the financial inclusion index (FII), digital finance (DF), and the interaction term FIIDF The second model analyzes the impact of financial inclusion, digital finance (DF), and the interaction term FIIDF on poverty alleviation, measured by the Human Development Index (HDI). Financial inclusion has a statistically significant positive effect on HDI, particularly in relation to poverty reduction. A one-unit increase in the financial inclusion index (FII) results in a 0.0169 increase in HDI, indicating that improving financial inclusion contributes to poverty alleviation. Digital finance (DF) also has a positive and statistically significant effect on HDI, with a one-unit increase in DF leading to a 0.0182 increase in HDI, suggesting that greater access to digital finance plays a role in reducing poverty. The interaction term FIIDF is statistically significant, demonstrating that digital finance strengthens the relationship between financial inclusion and poverty alleviation (HDI). This finding highlights that digital finance acts as a mediating factor, enhancing the positive impact of financial inclusion on poverty reduction. Table 4.5 Interaction Term Effects HDI Coefficients Bootstrap Standard Error t- Statistic p-Value 〔95% Conf. Interval〕 FII DF 0.0168628 0.0182007 0.0062558 0.0047637 2.70 3.82 0.008 0.000 (0.0044865 0.0292391) (0.0087763 0.0276252) FIIDF 0.0158162 0.0055707 2.84 0.005 (0.0047952 0.0268372) Cons 0.4804656 0.0046473 103.39 0.000 (0.4712714 0.4896598) Source: STATA 18.0 Results, 2025 3. Mediation Effects The Average Causal Mediation Effect (ACME) is 0.0108, with a 95% confidence interval ranging from 0.0049 to 0.0184. This value represents the portion of the overall impact of financial inclusion on poverty alleviation that occurs through digital finance. The positive value and the confidence interval excluding zero show that digital finance significantly mediates the relationship between financial inclusion and poverty reduction. The Direct Effect is 0.0172, with a confidence interval from 0.0048 to 0.0294, indicating that financial inclusion directly influences the Human Development Index (HDI), contributing to poverty alleviation even without the mediation of digital finance. The Total Effect is 0.0280, with a confidence interval between 0.0163 and 0.0392, reflecting the combined impact of financial inclusion on HDI, both directly and through digital finance. The Indirect Effect is 0.0108, which indicates that a part of financial inclusion’s impact on poverty reduction (0.0108) happens through digital finance. This means that while financial inclusion directly improves poverty alleviation, a significant portion of its effect is facilitated by digital financial services, such as mobile banking or online payment systems, which help further reduce poverty. Therefore, digital finance is key in enhancing the positive effects of financial inclusion on poverty reduction. This total effect is substantial, demonstrating the strong overall impact of financial inclusion on poverty alleviation. Furthermore, 38.3% of the total effect is mediated by digital finance, meaning that digital finance significantly enhances the positive effects of financial inclusion in reducing poverty. Table 4.6 Mediation Effects both direct and indirect Effects Mean 〔95% Conf. Interval〕 ACME 0.0108124 (0.0048994 0.0184256) Direct Effect 0.0172253 (0.0047802 0.0293774) Total Effect 0.0280377 (0.016345 0.0391923) % of Total Eff Mediated 0.3829994 (0.27588 0.6615292) Source: STATA 18.0 Results, 2025 Summary Analysis of mediating Digital finance substantially influences the correlation within inclusion in finance and alleviating poorness (HDI). A wide-ranging effects of inclusion in finance upon alleviating poorness (HDI) is both affirmative and significant. Digital finance (DF) accounts for approximately 38.3% of the total effect, highlighting its critical role in amplifying the effects of inclusion in finances on human development and poverty alleviation. Finally both financial inclusiveness and digital finance (DF) support to reduce poverty indirectly and directly approaches. Though, digital finance's role as a mediator makes it even more important for increasing the positive outcomes of financial inclusiveness. This research shows how important digitally finance for boosting the impact for financial inclusiveness on alleviating poverty. This makes digital finance significant tool for economic advancement and societal development well-being. 4.4.2 Sobel Z analysis for the mediation impact DF upon FII and HDI A Sobel Z-test for significance evaluates the importance regarding an indirect (mediated) impact within a mediation paradigm. It is generally utilized in social science research to comprehend the correlation with mediation variable beside a dependent factor. Overview Mediator Model Independent factor (X): a predictive or causal variable. The mediation's function (M): a factor which clarifies the relationship between both dependent as well as independent factors. Dependent factor (Y): This impact that is being assessed. A Sobel was a tests assesses statistical significance concerning the indirect influence of X upon Y via a mediating, M. Following indirect outcome determines by multiplication comprising two a coefficient: A route between X to M- (a) A route between M to Y - (b) Consequently, indirect influence is represented as a × b = ab. Formula for Sobel Z-testing The Sobel test calculates that Z-value for analyzing the significance of this indirect impact, expressed as: $$\:\text{Z}\:\:=\frac{\text{a}\text{*}\text{b}}{\sqrt{{b}^{2}\text{*}\text{S}{a}^{2}+{a}^{2\text{*}}}\text{S}{b}^{2}}$$ Where it is: a and b represent both of the coefficients of regression Sa & Sb denote standard errors both a & b, correspondingly. Table 4.7 Indirectly Impact of DF concerning FII with HDI: Sobel Z-testing Variable DF (a) HDI (b) FII 0.589*** 0.012*** (0.099) (0.006) DF 0.018*** (0.005) Constant 0.067*** 0.486*** (0.077) (0.004) Observations No. of Country 135 9 135 9 Z- V Testing = 17.81 Notes: standard errors in parenthesis, *** p < 0.01; ** p < 0.05; * p < 0.1 Source: STATA 18.0, Results 2024 According to aforementioned Z-value analysis calculation: $$\:\text{Z}\:\:=\frac{\text{a}\text{*}\text{b}}{\sqrt{{b}^{2}\text{*}\text{S}{a}^{2}+{a}^{2\text{*}}}\text{S}{b}^{2}}$$ Where: a stands for coefficient of independence sa indicates the standard errors of the variables b represents a coefficient for mediating factor sb denotes the standard errors of the mediating variables. Therefore; a equal 0.589; sa equal 0.099; b equal 0.018; sb equal 0.005 $$\:\text{Z}\:\:=\frac{0.589\text{*}0.018}{\sqrt{({0.018)}^{2}\text{*}{\left(0.099\right)}^{2}+{\left(0.589\right)}^{2\text{*}}}{\left(0.005\right)}^{2}}$$ Z- Value Test = 17.81 The Sobel Z-test is used to assess the statistical significance of the indirect effect in a mediation model. A Z-score greater than 1.960 or less than − 1.960 indicates that the indirect effect is statistically significant, suggesting a strong mediation, while a small Z-score close to zero implies a weak or insignificant indirect effect. The Sobel test involves two linear regression models: the first regresses digital finance (DF) on financial inclusion (FII), with DF as the dependent variable and FII as the independent variable; the second regresses the Human Development Index (HDI) on both FII and DF, with HDI as the dependent variable and FII and DF as independent variables. The results from these models are used to calculate the Sobel Z-score, which determines the significance of the indirect effect of financial inclusion on poverty alleviation through digital finance. According to the aforementioned value of Z test of the Sobel modeling principle: If the value of Z exceeds 1.96, then "M" strongly mediates the relationship between X and Y. The Z-value result was 17.81 based on the Sobel test which indicates a highly significant outcome. The Sobel test evaluates the statistical impact of a mediating influence, specifically determining if indirectly influence pertaining to a variable that was independent considering a variable that depends via mediation that's of statistical significance. Final assessment derived from a Z-score result and finding: The Z-score of 17.81 is highly significant, indicating that the indirect effect is statistically meaningful. With the critical threshold for a two-tailed test at ± 1.96, the Z-score of 17.81 is far above this value, confirming the presence of a strong mediation effect. The findings clearly demonstrate that the mediator variable plays a significant role in influencing the relationship between the dependent and independent variables. Additionally, the associated p-value, given the high Z-score, is exceedingly small (well below 1%), suggesting that the null hypothesis (which assumes no mediation effect) can be confidently rejected, further supporting the existence of a substantial mediation effect. Therefore, the Sobel value for the Z result is 17.81, exceeding 1.96. Thus, DF serves a vital mediating role in the relationship between FII and alleviating poverty, as assessed by HDI. Policymakers should concentrate on strategies that attract digital finance as a means to improve inclusion in finances with alleviate poverty. 4.5 Resilience Assessment and Heterogeneity Analysis Resilience Assessment involves evaluating how stable or reliable a study's findings are under varying conditions, examining whether the results hold true when assumptions, models, or data are adjusted. This process is essential for ensuring that conclusions are not simply influenced by specific assumptions or data characteristics, thus increasing confidence in their generalizability to different settings or populations. On the other hand, Heterogeneity Analysis focuses on understanding how subgroups or variables within a sample might influence the study's outcomes. It explores whether the effects observed are consistent across different groups or if they vary based on factors such as age, gender, or income, providing insights into conditions where effects may be stronger or weaker. Together, resilience assessment and heterogeneity analysis enhance the validity of research findings by confirming their reliability across different scenarios and revealing variations in effects among subgroups, ensuring that conclusions can be broadly applied and are not limited to specific data or assumptions. 4.5.1 Robustness checks A robustness check is a technique used in statistical, econometric, and other qualitative studies to assess the reliability and consistency of the main findings. It involves evaluating whether the results hold true across different assumptions, scenarios, or data specifications. The goal is to determine whether the outcomes are sensitive to minor changes or remain consistent, which strengthens trust in the conclusions. Robustness checks are important because they affirm the reliability of the results, identify potential biases or limitations in the analysis, and ensure the generalizability of the findings to broader contexts. In this study, several robustness checks were conducted to verify the validity of the results. The Feasible Generalized Least Squares (FGLS) methodology was initially applied to reassess the approach, enhancing the reliability of the conclusions. The findings confirmed that both Digital Finance (DF) and the Financial Inclusion Index (FII) significantly contributed to poverty reduction, as measured by the Human Development Index (HDI). To further test the robustness of the models, the study employed model replacements and variable substitution techniques to ensure that the results were not overly dependent on any specific model or variable. Additionally, the study used the Generalized Method of Moments (GMM) and quantile regression techniques to address potential issues like endogeneity and provide more reliable estimates across different data quantiles. Table 4.8 The Robustness checks (1) (2) (3) (4) (5) (6) VARIABLES HDI GMM HDI 10th HDI 25th HDI 50th HDI 75th HDI 90th HDI 0.5704*** (6.32) FII 0.0061*** 0.0272*** 0.0235*** 0.0205*** 0.0155** 0.0112** (2.66) (3.637) (4.680) (4.635) (2.222) (1.053) DF 0.0068*** 0.0215*** 0.0221*** 0.0225*** 0.0232*** 0.0238*** (2.00) (5.757) (8.891) (10.31) (6.682) (4.498) Observations 135 135 135 135 135 135 No. Countries 9 9 9 9 9 9 Z-statistics are presented in parenthesis; significance levels are indicated as follows: *** with a p < 0.01; ** with a p < 0.05, * p < 0.1 Source: STATA 18.0, Results 2025 The analysis reveals a strong positive relationship between the Human Development Index (HDI) and financial inclusion, indicating that countries with higher financial inclusion tend to experience better human development outcomes, with the relationship being statistically significant at the 1% level. Digital finance (DF) also significantly enhances HDI, further supporting human development. A Generalized Method of Moments (GMM) analysis confirms a positive link between HDI, the financial inclusion index (FII), and DF, showing that both variables significantly boost HDI. To address potential endogeneity, the study used GMM to mitigate the risk of biased estimates, ensuring more accurate causal effects. Additionally, robustness checks, including quantile regression at various percentiles (10th, 25th, 50th, and 75th), demonstrated that financial inclusion has a greater impact on human development in countries with lower HDI, highlighting its crucial role in poverty alleviation, particularly in poorer nations. These robustness checks ensure the stability and reliability of the results, confirming that financial inclusion and digital finance are key drivers of human development and poverty reduction, with their effects varying across different levels of HDI. 4.5.2 Heterogeneity Analysis Heterogeneity analysis involves scrutinizing the disparities or variations in the impact of a specific variable or intervention across several subgroups or circumstances. Heterogeneity analysis is an essential instrument for comprehending the diverse effects of factors across various subgroups or situations. It assists researchers and policymakers in understanding that the impact of a variable may not be consistent, facilitating more precise and effective responses. In empirical research, it pertains to the investigation of how the correlations between variables may fluctuate based on particular data characteristics, such as disparities between urban and rural demographic components in East Africa. Table 4.9 Test of Heterogeneity Analysis (1) (2) VARIABLES Urban Population Rural Population HDI 0. 5125** -0.1134** (2.11) (-2.57) FII 0.7342** -0-8970** (2.13) (-2.63) DF 0.17297*** -0.3280* Cons (7.24) 0.26367*** (7.47) (-8.0) -0.1799*** (-2.97) Observations 135 135 No. Countries 9 9 Z-statistics are presented in parenthesis; significance levels are indicated as follows: *** with a p < 0.01, ** with a p < 0.05, * p < 0.1 Source: STATA 18.0, Results 2024 The analysis explores the impact of the Human Development Index (HDI), financial inclusion, and digital finance on poverty alleviation in both urban and rural populations. In urban areas, all three factors HDI, financial inclusion, and digital finance positively contribute to poverty reduction, with HDI showing a coefficient of 0.5125, financial inclusion at 0.7342, and digital finance at 0.17297, reflecting the benefits of improved infrastructure and better access to services. In contrast, rural areas experience less positive outcomes, with HDI showing a negative coefficient of -0.1134, and both financial inclusion (-0.8970) and digital finance (-0.3280) showing negative impacts due to challenges such as inadequate infrastructure, low digital literacy, and limited access to financial services. The comparative analysis reveals that while urban areas benefit significantly from these factors, rural communities in countries like Rwanda, Uganda, and Kenya face obstacles that hinder the effectiveness of these interventions. To improve poverty reduction in rural areas, policies should focus on bridging infrastructure gaps, improving digital literacy, and expanding access to financial services. 4.6 Conclusion This investigation examines the mediating function of digital financing in the correlation between the inclusion of finance and alleviating poverty among nine East African nations over a 15-year span (2008–2022). The econometric analysis employs FGLS and Bootstrapping fixed-effects regression approaches. This section analyzed how function pertaining to digitally finance mediating the financial inclusiveness and alleviating poverty, employing a diverse perspective. The research utilized an extensive array of diagnostic assessments and regression analyses to evaluate the correlations among the measures of human developmental, the financial inclusiveness indices, and Digital financial, emphasizing the mediating effect the impact of digital financing on improving financial inclusiveness with mitigating poverty. Descriptive statistics indicated significant variations in development of people and financial inclusion among countries, with numerous nations exhibiting elevated poverty rates and restricted financial access. Notwithstanding these obstacles, digital finance emerged as a promising instrument for enhancing financial accessibility and, hence, alleviating poverty. Correlation analysis revealed statistically significant positive associations between HDI and both FII and DF, indicating that as human development rises, financial inclusion and digital finance acceptance enhance. Digital finance serves a crucial intermediary function in amplifying the positive effects about financial inclusiveness on poverty alleviation. Digital finance greatly enhances the impact of financial inclusion upon poverty reduction, particularly during East African nations. The FGLS regression model, following the resolution of heteroskedasticity and cross- sectional dependence, yielded strong evidence for the mediating role of digital finance. The results indicate that finances available digital are essential to enhancing financial prospects with alleviating poorness in context, especially for vulnerable populations. Financial Inclusiveness with Digital finances Influence: Both financial inclusiveness and digitally finances parade a substantial beneficial effect on human development, indicating that accessibility to financial facilities was essential in alleviating poverty. Financial inclusiveness Index and Digital Finance enhance Human Development Index both directly and indirectly, with digital finance augmenting the beneficial impacts of business presence. A mediation effect of digital financial indicates that it accounts for roughly 38.3% that was overall effects that financial inclusiveness upon alleviating poverty. A signifies the digital finance amplifies the advantages for financial inclusiveness, serving in the capacity of vital facilitator with the mitigation of impoverishment. A models continuously demonstrate the FII and DF are of statistically significant at the one percent value, so affirming that robustness and dependability for both of these factors in fostering human growth and mitigating poverty. This investigation highlights the crucial importance of the inclusion of finances and digital financial in mitigating poverty, especially among urban residents. The Sobel Z-value test verifies a considerable and statistically significant mediator effect of DF regarding the correlation within inclusion in finance along with poverty alleviation, as assessed by the Human Indexes, which was the Z-value for 17.81 signifies that DF is crucial in amplifying the effect of financial inclusiveness upon poverty alleviation. The endogeneity analyses besides quantile regression analysis confirm the robustness of these findings, indicating that the two variables financing and DF significantly impact HDI across different data specifications. The heterogeneity analysis reveals that urban residents derive substantial advantages through these interventions, with the inclusion of finance and DF presenting robust positive relationships with HDI. In contrast, rural communities encounter obstacles that impede the efficacy of these interventions, such as restricted accessibility to financial services, inadequate infrastructure, and insufficient digital literacy, leading to a negative association between HDI and alleviating poverty in rural regions. In a nutshell digital finance is an essential facilitator that amplifies positive impacts for financial inclusiveness upon alleviating poorness. Digital finance serves as a potent instrument for fostering economic progress, advancing human development, and attaining poverty alleviation objectives in East Africa by augmenting accessibility for financing. Although metropolitan regions significantly benefit for improvements in financial inclusion as well as digital financial services, rural communities have distinct obstacles that hinder the efficacy of these instruments. Policymakers must prioritize rectifying infrastructural deficiencies, augmenting digital literacy, and facilitating accessibility to financing in remote areas to exploit positive concerning to finance inclusion and digital financing for alleviating poverty. 5. Contribution This research contributes to the understanding of how digital finance serves as a mediator in the relationship between financial inclusion and poverty alleviation in East Africa. It provides new empirical evidence on the role of digital finance in reducing poverty, particularly in the context of urban and rural differences. The study extends the existing literature by offering insights into the impact of digital finance on financial inclusion and poverty alleviation, highlighting its significance in economic development and social well-being. 6. Recommendations To enhance financial inclusion and alleviate poverty in East Africa, several strategic measures are essential. First, governments should prioritize the development of reliable digital infrastructure, particularly in rural areas, to ensure that all populations have access to digital financial services. Public-private partnerships between governments, tech companies, financial institutions, and NGOs are crucial for creating sustainable digital finance ecosystems that effectively serve underserved communities. Additionally, capacity-building initiatives aimed at improving both digital and financial literacy are necessary to empower individuals, especially in rural areas, to participate in the digital economy. Policymakers should also focus on developing targeted interventions for rural populations, addressing issues such as infrastructure gaps and low digital literacy, to ensure the effective use of digital finance. Furthermore, investment in mobile networks is vital to bridging the urban-rural divide, ensuring that mobile services are accessible and reliable across all regions. By addressing these strategies, governments can harness the power of digital finance to drive inclusive growth, reduce poverty, and foster overall economic development in East Africa. 7. References Abel, S., Mutandwa, L., & Le Roux, P. (2018). A review of determinants of financial inclusion. International Journal of Economics and Financial Issues, 8 (3), 1–8. https://www.econjournals.com Al-Smadi, M. O. (2023). Examining the relationship between digital finance and financial inclusion: Evidence from MENA countries. 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Introduction","content":"\u003cp\u003eIn East Africa, access to financial services remains limited, especially for a large portion of the population in rural areas. Despite the vast majority of adults in the world lacking access to formal banking services, the demand for financial tools and services continues to grow exponentially. Digital finance, which includes mobile banking, mobile money, and automated teller machines (ATMs), has emerged as a crucial enabler of financial inclusion. However, billions of people remain excluded from accessing these services, creating an imbalance between the demand and supply of financial services. This study explores the significant role that digital finance plays in enhancing financial inclusion and alleviating poverty, particularly in East African countries, by analyzing its impact over a fifteen-year period (2008-2022). The study fills a gap in the existing literature by investigating how digital finance mediates the relationship between financial inclusion and poverty reduction in the East African context. By focusing on nine East African countries, this research provides a comprehensive empirical analysis to understand the underlying dynamics and challenges that shape financial inclusion and poverty alleviation efforts. Digital finance plays a pivotal role in advancing financial inclusion and alleviating poverty, aligning with the 2030 Agenda for Sustainable Development (SDGs). It enhances access to financial services such as mobile banking, digital wallets, and online payments, particularly for underserved and marginalized populations. This contributes directly to several SDGs, including SDG 1 (No Poverty), SDG 8 (Decent Work and Economic Growth), SDG 10 (Reduced Inequality), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 17 (Partnerships for the Goals). Digital finance empowers individuals with tools for savings, credit, and insurance, fostering economic stability and reducing poverty (Jack \u0026amp; Suri, 2011). It also supports entrepreneurship, job creation, and sustainable economic growth, particularly in underserved communities (Sarma \u0026amp; Pais, 2011). By bridging financial gaps for marginalized groups, digital finance promotes financial equity and inclusion (Narain \u0026amp; Schreiber, 2017). Furthermore, it fosters innovation and strengthens financial infrastructure, driving inclusive growth and resilience (Ozili, 2018). Collaboration between governments, private sector actors, and international organizations further expands the reach and impact of digital finance, ensuring no one is left behind in achieving sustainable development (Beck, 2013). Digital finance, as a strategy for financial inclusion, has been significantly enhanced by Industry 4.0, which improves data transmission and processing, leading to faster and better decision-making (Al-Smadi, 2023). Industry 4.0 has introduced new competencies for both individuals and machines, transforming the financial sector through technology-driven changes, which have led to innovative financial services and digital advertising methods (Mhlanga, 2020). The 2015 UNDP Millennium Development Agenda aimed to eradicate extreme poverty and reduce the number of people living on less than one dollar per day. However, a World Bank report (2021) revealed that, in 2018, 80% of the poor lived in rural areas, with over 40% residing in regions affected by stagnation or conflict. While global poverty decreased by nearly 1% annually from 1990 to 2015, the pace slowed to under 0.6% annually between 2013 and 2017, raising concerns about meeting the UNDP's poverty reduction targets.\u003c/p\u003e\n\u003cp\u003eMillions of \"new poor\" are emerging, particularly in semi-urban areas, due to skill gaps and job loss. These vulnerable populations face shrinking social protection and job opportunities. Despite financial growth, poverty reduction remains slow in these regions, largely due to the ineffective distribution of resources. The lack of progress is linked to fragile institutions, which, due to weak infrastructure and inefficiency, struggle to address poverty effectively, especially in conflict-affected areas (Acemoglu and Robinson, 2012). Internet-based banking services offer a practical and cost-effective alternative to traditional banking for low-income individuals in underdeveloped countries. These services improve financial security, simplify transactions, and reduce the need for cash handling (Haider, 2018). Financial technologies lower service provision costs, driving greater financial inclusion, ensuring that everyone, regardless of wealth, has access to affordable financial services (Agelyne \u0026amp; Musau, 2021). Digital finance provides more affordable access to financial services compared to traditional banks, with technology-driven platforms playing a significant role in financial inclusion. However, access alone is insufficient; service quality and usage are critical. Cultural or religious factors may deter some individuals from using digital finance (Thaddeus \u0026amp; Ngong, 2020). To foster inclusive growth, financial inclusion must be complemented by financial security, as both must work synergistically for effective development (Iddrisu et al., 2023).\u003c/p\u003e\n\u003cp\u003eSince 2010, the World Bank and the Group of Twenty have spearheaded efforts to enhance financial accessibility in developing and emerging economies, aiming to reduce poverty (GPFI, 2016). Financial inclusion has become a critical global issue, garnering increasing attention from financial institutions, governments, and other stakeholders (Mhlanga et al., 2020). Both developed and developing nations now recognize financial exclusion as a significant socioeconomic challenge (Mhlanga \u0026amp; Denhere, 2020). As part of the 2030 Agenda, countries have committed to reducing poverty and vulnerability to crises, including natural disasters and conflicts. Achieving this requires addressing key challenges through country-specific plans, robust management structures, and international cooperation, with the active involvement of all relevant stakeholders (United, 2018). The World Bank (2017) projects that by 2030, over 90% of people in East Africa will have access to savings and financial services, up from 63% in 2017. This growth is intended to lower costs, remove economic barriers, and improve financial access for low-income populations. However, the implementation of digital finance, financial inclusion, and poverty alleviation in underdeveloped countries faces significant challenges, particularly regarding accessibility and participation. The full impact of these solutions remains unclear, emphasizing the need for further study into the barriers East African countries face in integrating digital finance for improved financial inclusion and poverty reduction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1\u0026nbsp; Statement of the Problem\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfrica has one of the lowest levels of financial inclusion globally. While approximately 2.7 billion people worldwide have access to financial services, more than 80% of families in many African countries still lack sufficient income to meet basic needs (Chibba, 2014). To address this disparity, financial inclusion became a key objective of the United Nations' 2015 Sustainable Development Agenda, which aims to improve living standards, reduce poverty, and accelerate development in participating nations. By enabling middle- and low-income individuals to access the formal financial system, financial inclusion promotes prosperity, contributing to economic development and poverty reduction. In countries like Ethiopia, where 80% of the population resides in rural or semi-rural areas, financial services are concentrated in urban centers, leaving millions in underserved regions without access to essential financial services. This gap underscores the need for greater financial inclusion in rural areas to meet growing demand and foster economic development (Bedada, 2020). Digital technologies are improving access to financial services for underbanked populations in developing countries, creating profitable opportunities for financial institutions while empowering women, reducing poverty, and strengthening institutions (Tafesework, 2020). According to Emon \u0026amp; Chowdhury (2023), many in less developed nations rely on informal financial services due to the limited availability of formal institutions, hindering economic growth and worsening wealth inequality. While online banking services provide greater comfort, security, and safety compared to cash reliance (Lewis, 2015), digital finance remains less widespread in emerging economies (Ozili, 2022). The debate surrounding digital finance and financial inclusion is ongoing, with varied opinions from policymakers, academics, and industry experts, highlighting the need for a deeper understanding of its challenges and potential for achieving true financial inclusion. Several studies using the global Findex database have examined global disparities in financial inclusion (Zins \u0026amp; Weill, 2016; Fatoki \u0026amp; Wokabi, 2019). While financial inclusion is widely recognized as vital for economic growth, limited research has focused on its drivers in East African countries. This study aims to identify the factors influencing financial activity in East Africa to inform policies that enhance financial inclusion. A key limitation in existing studies is the lack of detailed methodology, hindering their validity and reproducibility. Additionally, there is insufficient analysis on the impact of financial inclusion and digital finance (Soni et al., 2021). Challenges include the digital divide between urban and rural areas, low financial literacy, and weak last-mile data networks. The research seeks to address the gap in understanding the connection between digital finance, financial inclusion, and poverty reduction in East African Community (EAC) countries. Most studies are country-specific, limiting their applicability to the broader region (James et al., 2020). Digital finance, including mobile banking and digital wallets, has improved access to financial services, particularly for underserved populations, and has shown success in breaking the poverty cycle, as seen with M-Pesa in Kenya (Jack \u0026amp; Suri, 2011). However, barriers like low digital literacy, poor infrastructure, and limited internet access, especially in rural areas, prevent full adoption, perpetuating economic inequality (Narain \u0026amp; Schreiber, 2017). Even in countries like Kenya, marginalized groups, including women and low-income earners, still face difficulties accessing formal financial networks, hindering their ability to save, invest, and build wealth (Sarma \u0026amp; Pais, 2011). While digital finance has potential for fostering entrepreneurship and economic growth, its inconsistent impact on job creation and sustainable development highlights the need to address these access barriers for broader, equitable use. Still these gaps are particularly important when mediating digital finance by assessing the relationship between financial inclusion and poverty alleviation in East African countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2\u0026nbsp;Designed Research Questions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFocused inquiries that direct a study, reducing its scope and ensuring that it is in line with its goals are known as designed research questions. They fill in knowledge gaps, define the issue, pinpoint important factors, and describe connections or results. The purpose alongside effects of the study are defined by these questions. The queries posed through that research consisted thoughtfully crafted to correspond with the main issue, guaranteeing a thorough examination of fundamental concerns and offering insightful information.\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;What is the impact of financial inclusion on poverty alleviation in East African?\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;How does the interaction between digital finance and financial inclusion influence poverty alleviation in East Africa?\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;What is the mediating effect of digital finance on the relationship between financial inclusion and poverty alleviation in East Africa?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eObjectives of Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy objectives are clear and specific declarations that specify the desired results of a study. They act as a road map for the studies process, outlining the parameters of the study and directing the investigation. By laying out the steps a study will employ to solve the issues under study, such goals enable an in-depth look at particular facets of the subject being studied? Study objectives allow the researcher to methodically investigate, evaluate, and understand the topic at hand by forming precise, quantifiable goals. The particular aims of this research are delineated with the following objectives:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp; To assess the impact of financial inclusion on poverty alleviation in East Africa.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;To examine how the interaction between digital finance and financial inclusion influences poverty alleviation in East Africa.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; To investigate the mediating effect of digital finance on the relationship between financial inclusion and poverty alleviation in East Africa.\u003c/p\u003e"},{"header":"2.\tLiterature Review","content":"\u003cp\u003eThe relationship between financial inclusion and poverty alleviation has been widely discussed in the literature, but with digital finance increasingly recognized as a key driver of financial inclusion. Financial inclusion is defined as the access of individuals to useful and affordable financial products and services that meet their needs, including payments, savings, credit, and insurance. On the other hand, poverty alleviation refers to efforts aimed at reducing the incidence of poverty, enhancing human development, and improving economic well-being. Digital finance, which encompasses mobile banking and mobile money services, has become a popular tool for increasing financial inclusion, especially in developing countries. Scholars have noted that digital finance helps bridge the gap by providing underserved populations with access to banking services through their mobile phones, thereby enhancing financial accessibility and increasing savings, investments, and access to credit (Beck, Demirguc-Kunt, \u0026amp; Levine, 2007). However, despite the potential of digital finance, there is a lack of consensus regarding its definition and its role in poverty alleviation. Some studies argue that digital finance alone may not be sufficient to eliminate poverty, as it must be accompanied by improved infrastructure, digital literacy, and strong financial institutions (Narayan, 2018). Others suggest that digital finance can be a powerful tool for poverty reduction, particularly in areas with limited physical banking infrastructure, such as East Africa. In recent years, researchers have explored the mediating role of digital finance in the relationship between financial inclusion and poverty alleviation, but there remains a need for empirical studies that investigate this mediation, especially in the context of East Africa. Empirical studies suggest that digital finance serves as a bridge between financial inclusion and poverty reduction. Financial inclusion theories emphasize the importance of accessible and affordable financial services, such as banking, insurance, savings, and payments, to improve individuals' financial situations and reduce poverty. Digital finance, through smartphones and the internet, enhances financial inclusion by overcoming barriers like geographical limitations and high infrastructure costs. The mediation effect theory posits that digital finance can increase savings, investment, and entrepreneurship, thereby directly impacting poverty levels. Inclusive growth theory highlights digital finance's role in promoting broad economic growth, benefiting the poor.\u003c/p\u003e\n\u003cp\u003eAccording to Tang et al. (2023), shows that digital finance reduces financial constraints, supporting innovation in green technologies by improving access to funding. The study finds that digital finance fosters green innovation, especially in Eastern China, where government-owned enterprises benefit more from internet banking. Similarly, Huang et al. (2023) explore how digital financial inclusion supports rural businesses in China, showing that digital finance aids in rural development by improving access to financing and technology. These studies highlight the importance of digital finance in fostering economic inclusion and rural industrial development. James et al. (2020) investigate the mediating role of financial deepening in the relationship between economic growth and poverty reduction in five East African Community countries from 1989 to 2018. The study reveals a significant inverse relationship between financial growth and poverty levels, demonstrating that economic growth generally contributes to poverty alleviation through increased incomes. Financial deepening, operationalized as credit access for individuals and businesses, is identified as a critical intermediary mechanism that enhances access to financial services, promotes savings, and stimulates investment. The findings underscore that financial deepening, facilitated by economic growth, plays a pivotal role in poverty reduction. This research offers important contributions to understanding how financial deepening can drive economic growth and mitigate poverty, addressing a largely underexplored issue within the context of the East African Community.\u003c/p\u003e\n\u003cp\u003eThe transformative role of financial inclusion and digital finance in alleviating poverty and promoting economic development, especially in underserved regions. Studies show that financial inclusion offers alternative strategies for poverty reduction, complementing traditional methods (Chibba, 2014; Tuesta et al., 2015), and plays a key role in mitigating income inequality and enhancing economic stability (Saraswati et al., 2020; Thaddeus \u0026amp; Ngong, 2020). In Sub-Saharan Africa, financial inclusion is found to significantly reduce poverty and inequality, while digital finance, particularly through mobile technology, improves access to secure and affordable financial services (Soumaré et al., 2016; Durai \u0026amp; Stella, 2019). Factors such as income, education, gender, and access to technology are crucial determinants of financial inclusion, especially in rural areas (Badu et al., 2018; Wokabi \u0026amp; Fatoki, 2019). Further research across regions like Asia and Africa highlights that financial inclusion can reduce poverty, with governments encouraged to leverage it for poverty alleviation (Kumar \u0026amp; Jie, 2023; Gao, 2023). Innovations in digital finance and robust financial policies are essential for reducing inequalities and fostering growth (Polloni et al., 2021; Evans, 2023). Additionally, the impact of fintech on financial inclusion in emerging economies and the influence of factors like internet access and bank infrastructure demonstrate the potential for digital finance to drive inclusive economic development (Banna \u0026amp; Roy, 2023; Pandey et al., 2023). The existing literature on digital finance, financial inclusion, and poverty alleviation provides valuable insights into the role of digital tools in improving access to financial services. However, much of the research has been conducted in isolated contexts or without a clear focus on the mediating role of digital finance in the relationship between financial inclusion and poverty alleviation. Furthermore, while East Africa has witnessed significant growth in mobile banking and mobile money services, studies that comprehensively assess the impact of digital finance on financial inclusion and poverty alleviation across multiple East African countries remain scarce. This study fills this gap by empirically examining how digital finance mediates the relationship between financial inclusion and poverty alleviation in nine East African countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;Formulation Hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA hypothesis is a testable statement that predicts a relationship between two or more variables, based on existing theories or observations. It suggests a potential outcome that can be validated or disproven through data analysis. An effective hypothesis is precise, grounded in theory, and testable through empirical research, with statistical methods used to accept or reject the hypothesis. Based on these criteria, the following research hypothesis has been formulated:\u003c/p\u003e\n\u003cp\u003eH1: Financial inclusion has a significant positive impact on poverty alleviation in East Africa.\u003c/p\u003e\n\u003cp\u003eH2: The interaction between digital finance and financial inclusion significantly influences poverty alleviation in East Africa.\u003c/p\u003e\n\u003cp\u003eH3: Digital finance mediates the relationship between financial inclusion and poverty alleviation in East Africa.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study adopts a quantitative approach using a balanced statistical panel data methodology, drawing data from secondary sources such as the International Monetary Fund (IMF), the United Nations Development Programme (UNDP), and the World Bank. Covering nine East African countries from 2008 to 2022, the dataset creates a panel structure with multiple annual observations for each country, enabling an analysis of both cross-country (differences between countries) and temporal (changes over time) variations in key variables, including financial inclusion, poverty alleviation, mobile banking, internet usage, population density, and rural population growth. The study employs Fixed Effects and Feasible Generalized Least Squares (FGLS) models to address statistical challenges. The Fixed Effects model accounts for unobserved country-specific factors that remain constant over time, isolating the impact of key variables such as financial inclusion. The FGLS model corrects for common panel data issues like heteroscedasticity and autocorrelation, enhancing the precision and reliability of the estimates.\u003c/p\u003e \u003cp\u003eAdditionally, bootstrap fixed-effects and Sobel Z-value tests are used to assess the significance of the mediation approach, which examines how digital finance influences poverty alleviation through financial inclusion. The bootstrap fixed-effects method provides a more robust estimation of the coefficients by resampling the data, allowing for better inference regarding the significance of the mediation effect. The Sobel Z-value test is applied to test the significance of the indirect effect of digital finance on poverty alleviation through financial inclusion. Following these, robustness checks are performed using the Generalized Method of Moments (GMM) and Quantile regression. The GMM method helps address potential endogeneity by providing consistent estimates in the presence of endogenous explanatory variables, while Quantile regression is used to examine the effects at different points in the distribution of the dependent variable, ensuring that the results are not driven by outliers or skewed data. These steps enhance the reliability and robustness of the findings, providing more confidence in the conclusions drawn. The study also conducts robustness checks, such as sensitivity analyses, to ensure the stability of the findings under various model specifications and assumptions. By integrating these models, the study aims to provide a comprehensive and robust analysis of how financial inclusion and digital finance contribute to poverty alleviation in East Africa.\u003c/p\u003e"},{"header":"4. Empirical Analysis and Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Introduction\u003c/h2\u003e\n \u003cp\u003eThe primary objective of this study is to examine the heterogeneity and robustness of digital finance\u0026apos;s mediation role in promoting financial inclusion and alleviating poverty across East African countries. To achieve this, the study employs a Feasible Generalized Least Squares (FGLS) regression model to assess the impact of various independent variables on poverty alleviation in nine East African countries, using a sample of 135 observations over a 15-year period (2008\u0026ndash;2022), while accounting for country-specific effects to capture the unique characteristics of each nation. To evaluate the significance of digital finance\u0026rsquo;s mediation role, bootstrap fixed-effects and Sobel Z-value tests are employed, assessing the reliability of the indirect effects of digital finance on poverty alleviation through financial inclusion. Additionally, robustness checks are conducted using the Generalized Method of Moments (GMM) to address potential endogeneity concerns, and Quantile regression is used to analyze effects at different points in the poverty distribution. This chapter includes descriptive statistics and empirical analysis, endogeneity testing, and robustness checks, with findings interpreted through various models, providing a comprehensive understanding of how digital finance enhances financial inclusion and reduces poverty in East Africa.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Descriptive Analysis\u003c/h2\u003e\n \u003cp\u003eThe researcher performed descriptive statistical analyses for selected variables to ascertain the statistical characteristics of the information prior to estimate. This involved the utilization of descriptive statistics instruments such as mean that deviation from the mean, a minimum, as well as maximum values. The summary of statistical information is provided below:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of statistical data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.44e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe table presents the mean, standard deviation, and range for the dataset, with 135 observations derived from a 15-year study period across nine countries. The average Human Development Index (HDI) for poverty reduction was 0.485, indicating that over 48.5% of the population in these East African countries remain below the Low Human Development threshold, reflecting high poverty levels. The minimum HDI score of 0.310 suggests extreme poverty, while the highest score of 0.558 shows some progress in poverty alleviation, though challenges remain. A standard deviation of 0.0551 indicates significant disparities in human development across the countries. The Financial Inclusion Index had a mean of -0.113, with a high standard deviation of 0.781, indicating substantial variability, ranging from \u0026minus;\u0026thinsp;1.730 to 1.555, showing significant differences in financial inclusion. Digital finance showed a near-zero mean of -4.44e-09, with a standard deviation of 1.000, reflecting considerable volatility and variation in usage, with scores ranging from \u0026minus;\u0026thinsp;1.847 to 2.809.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e6.3 Analysis of correlation\u003c/h2\u003e\n \u003cp\u003eResearchers use a correlation matrix to assess the relationship between explanatory variables. According to Brooks (2008), a correlation coefficient above 0.80 suggests potential multi-col-linearity, which can lead to unreliable regression estimates. In this study, the correlation coefficient is used to examine the relationship between poverty alleviation, measured by the Human Development Index (HDI), and financial inclusion, with digital finance acting as a mediating variable.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of Correlation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFII\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe analysis explores how digital finance (DF) mediates the relationship between financial inclusion (FII) and poverty alleviation which measured by the Human Development Index (HDI). The findings show a significant positive correlation between HDI, financial inclusion, and digital finance. As human development improves, access to financial services increases, and the use of digital finance grows. Countries with higher HDI are more likely to adopt digital financial tools, which enhances financial inclusion and contributes to poverty reduction. Digital finance offers a more accessible and cost-effective way to deliver financial services to marginalized populations, improving access, affordability, and efficiency. By strengthening the connection between financial inclusion and poverty alleviation, digital finance helps create economic opportunities, reduce vulnerability, and improve living conditions, particularly for low-income groups in East Africa.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Investigation of FGLS Model Econometric Results\u003c/h2\u003e\n \u003cp\u003eFeasible Generalized Least Squares (FGLS) is an econometric technique employed to address issues such as heteroskedasticity and autocorrelation in regression models, which can render Ordinary Least Squares (OLS) estimators inefficient and result in biased standard errors. By estimating the error variance-covariance matrices, FGLS enhances the precision and reliability of regression estimates. In this study, FGLS is utilized to examine the mediating role of digital finance in the relationship between financial inclusion and poverty reduction across nine East African countries, using data spanning from 2008 to 2022.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFGLS Econometric Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0124**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0171***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0216***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00620**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0113***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0145***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0125***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0115***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0214***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2.895)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.733)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(40.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.831)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(12.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0182***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0143***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0123***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0287***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0131***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0117***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0179***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00840***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0105***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3.790)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(3.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.846)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(12.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.364)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(52.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.588)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.486***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.481***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.458***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.497***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.496***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.472***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.481***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.467***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(113.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(26.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(41.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(207.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(59.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(65.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1,298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(57.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(67.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo. Country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eZ-statistics are presented in parenthesis; significance levels are indicated as follows: a *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 Source: STATA 18.0, Results 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAccording to the results from columns 1, 2, and 3 in the aforementioned table, column 3 is the superior column as it possesses the greatest FII coefficient (0.0216), with both FII and DF coefficient demonstrating high statistical significance shown by the highest Z-statistics. Column 3 indicates the most stable positive correlation between inclusion in financial services, digital finance, and the Human Development Index, rendering it the superior model for both the significance and strength of the coefficients, in accordance with theoretical expectations. The researcher analyzed the coefficients, Z-statistics, and statistical significance levels over Columns 4, 5, and 6, resulting in display outcomes for the regression approaches, with HDI (Human Development Indices) with the response variable, FII (Financial Inclusiviness Indices) for the autonomous parameter, and DF (Digital Finance) as the mediating variable. Column 6 represents the optimal specification due to its greatest and most important value for FII (0.0145), indicating inclusion in finance exerts more substantial significant effect on HDI compared to other columns. Overall, FII and DF were of statistical significance at a level of one percent, demonstrating a strong correlation with HDI.\u003c/p\u003e\n \u003cp\u003eThe most logical and reliable statistical evidence shows that both financial inclusion and digital financial services are beneficial for human development. Column 6 supports this argument by demonstrating the most significant effect of measuring financial inclusion\u0026apos;s overall HDI, a metric of human development. Making it easier for individuals acquire finance goods and offerings, whether through traditional financial inclusiviness or digital finance, leads to better human development and, in turn, more effective poverty reduction. This is shown by the fact that both FII and DF have significant and positive coefficients. Because column 9 has the highest coefficients for FII (0.0214), which exhibits a robust positive connection with HDI, it is best model from columns 7, 8, and 9. This indicates that financial inclusion exerts the most significant impact on the humanity Development Indexes (HDI), which is under this model. Digital Finance positively influences HDI; however, its effect is marginally less significant compared to the significance of FII across Column Nine. Both FII and DF exhibit strong significance at the 1% level, rendering this approach the most statistically robust. The idea behind column 9 is that including finance and digital finance makes it easier to fight poverty and improve people\u0026apos;s lives. As a result, Column 9 is the most stable model, showing the biggest positive effects of including finance with digital financial in general on grassroots development, which is in line with the idea of reducing poverty.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Effect of Digital Finance as a Mediator between Financial Inclusion and Poverty Alleviation\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.1 Analysis of Mediating Effects\u003c/h2\u003e\n \u003cp\u003eThis study presents findings and the researcher investigates the potential mediating role of digital financing in connection to the inclusion of finance and its impact for poverty alleviation through East African nations. The Bootstrap fixed-effects analysis approach seeks to analyze the effect of financial inclusion on poverty alleviation with the Sobel Z-test using for determine importance of mediation within the model. This modification guarantees the accuracy and reliability of tests for hypothesis and confidence intervals, even in the presence of heteroscedasticity among the inaccuracies. A table displays how outcomes of Bootstrap fixed-effect regression approaches for which Human Development Index, which assesses poverty reduction, indicating the findings. Analyzing the influence about inclusion in finance affects poverty reduction through mediation of digitally finance. This research revealed the results for the coefficient test across the three channels. The researcher precisely formulated the three equations presented below:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{Y}\\text{i}\\text{t}\\:={\\beta\\:}0\\:+\\:{\\beta\\:}1\\text{X}\\text{i}\\text{t}\\:+{\\epsilon\\:}\\text{i}\\text{t}-------\\left(1\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\text{U}\\text{i}\\text{t}=\\:{\\beta\\:}0\\:+\\:{\\beta\\:}1\\text{X}\\text{i}\\text{t}\\:+{\\epsilon\\:}\\text{i}\\text{t}--------\\left(2\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:\\text{Y}\\text{i}\\text{t}=\\:{\\beta\\:}0\\:+\\:{\\beta\\:}1\\text{X}\\text{i}\\text{t}\\:+{\\beta\\:}2\\text{U}\\text{i}\\text{t}+{\\epsilon\\:}\\text{i}\\text{t}--------\\left(3\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere;\u003c/p\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;Human Development Index\u003c/p\u003e\n \u003cp\u003eX\u0026thinsp;=\u0026thinsp;Financial Inclusion\u003c/p\u003e\n \u003cp\u003eU\u0026thinsp;=\u0026thinsp;Digital Finance (Mediator Variable)\u003c/p\u003e\n \u003cp\u003eThe equation use X to denote the variable independent of the financial inclusion indexes and Y to signify the dependent factor during the human growth index. Furthermore, U represents mediating mutable to be analyzed, which encompass the dimensional channels promoting FII, Human Developmental Indexes channel for poor alleviation and considering the accessibility of finances services channel (Digital financing). Specifically, for test z-statistics constructed:\u003c/p\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:z=\\frac{ab}{sab}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:.\\left(4\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere a, b, and sab are the symbols in the Eq. (4) that stand for the estimated value of a, the estimated technique for b as well as the standard errors for each estimate ab. This study created a 95% confidence interval by calculating the standard errors via bootstrapping, resulting in fairly precise standard errors. Removal of zero from confidence interval indicates that the combination considering coefficients was sufficiently significant, suggesting for presence a mediating influence. Tables 6.8 illustrates the results concerning the indirect mediated effect. This study initially identified DF as a mediator variable concerning accessibility to financial service channels. The bootstrap technique shows that the 95% confidence interval doesn\u0026apos;t include 0 and that z-statistic implies significantly not equal to Zero, which means that its coefficient was affirmative. Thus, This is significance favorable unintended endorsement via digital financing. The findings of an analysis of mediation analyzing the intermediary function of digital finance in the relationship with inclusiveness in finance and reducing destitution, which assessed Human Development Index (HDI).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. The initial regression involves regressing digital finance (DF) on financial inclusion index (FII) and the interaction term FIIDF.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe initial model examines the effects of the financial inclusion index and an interaction term, FIIDF, which represents the relationship between financial inclusion and digital finance, on digital finance as a mediator. The coefficient for financial inclusion is 0.5871, with a p-value of less than 0.01, indicating that financial inclusion has a strong positive and statistically significant impact on digital finance. This means that for every one-unit increase in financial inclusion, digital finance increases by 0.5871 units. This suggests that expanding financial inclusion encourages the adoption and use of digital financial technologies. Additionally, the coefficient for the FIIDF interaction term is 0.0274, with a p-value of 0.029, which is statistically significant. This shows that the interaction between financial inclusion and digital finance has a meaningful and direct effect on digital finance, reinforcing the idea that these two factors work together to drive the implementation and acceptance of digital financial services.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMediating Effects of Digital Finance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBootstrapping Standard errors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT-stat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e〔95% Conf. Interval〕\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 5870611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 1026329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.3840288 0.7900934)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIIDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 0273895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 1021436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.2294539 0.1746748)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 0689362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 0850228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0992592 0.2371317)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cspan\u003e\n \u003cp\u003e2. The second regression involves regressing the Human Development Index (HDI) on the financial inclusion index (FII), digital finance (DF), and the interaction term FIIDF\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe second model analyzes the impact of financial inclusion, digital finance (DF), and the interaction term FIIDF on poverty alleviation, measured by the Human Development Index (HDI). Financial inclusion has a statistically significant positive effect on HDI, particularly in relation to poverty reduction. A one-unit increase in the financial inclusion index (FII) results in a 0.0169 increase in HDI, indicating that improving financial inclusion contributes to poverty alleviation. Digital finance (DF) also has a positive and statistically significant effect on HDI, with a one-unit increase in DF leading to a 0.0182 increase in HDI, suggesting that greater access to digital finance plays a role in reducing poverty. The interaction term FIIDF is statistically significant, demonstrating that digital finance strengthens the relationship between financial inclusion and poverty alleviation (HDI). This finding highlights that digital finance acts as a mediating factor, enhancing the positive impact of financial inclusion on poverty reduction.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInteraction Term Effects\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBootstrap Standard Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et- Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e〔95% Conf. Interval〕\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFII\u003c/p\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0168628\u003c/p\u003e\n \u003cp\u003e0.0182007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0062558\u003c/p\u003e\n \u003cp\u003e0.0047637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0044865 0.0292391)\u003c/p\u003e\n \u003cp\u003e(0.0087763 0.0276252)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIIDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0158162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0055707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0047952 0.0268372)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4804656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0046473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.4712714 0.4896598)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Mediation Effects","content":"\u003cp\u003eThe Average Causal Mediation Effect (ACME) is 0.0108, with a 95% confidence interval ranging from 0.0049 to 0.0184. This value represents the portion of the overall impact of financial inclusion on poverty alleviation that occurs through digital finance. The positive value and the confidence interval excluding zero show that digital finance significantly mediates the relationship between financial inclusion and poverty reduction. The Direct Effect is 0.0172, with a confidence interval from 0.0048 to 0.0294, indicating that financial inclusion directly influences the Human Development Index (HDI), contributing to poverty alleviation even without the mediation of digital finance. The Total Effect is 0.0280, with a confidence interval between 0.0163 and 0.0392, reflecting the combined impact of financial inclusion on HDI, both directly and through digital finance. The Indirect Effect is 0.0108, which indicates that a part of financial inclusion\u0026rsquo;s impact on poverty reduction (0.0108) happens through digital finance. This means that while financial inclusion directly improves poverty alleviation, a significant portion of its effect is facilitated by digital financial services, such as mobile banking or online payment systems, which help further reduce poverty. Therefore, digital finance is key in enhancing the positive effects of financial inclusion on poverty reduction. This total effect is substantial, demonstrating the strong overall impact of financial inclusion on poverty alleviation. Furthermore, 38.3% of the total effect is mediated by digital finance, meaning that digital finance significantly enhances the positive effects of financial inclusion in reducing poverty.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4.6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Effects both direct and indirect\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\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e〔95% Conf. Interval〕\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0108124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0048994 0.0184256)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0172253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0047802 0.0293774)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0280377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.016345 0.0391923)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of Total Eff Mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3829994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.27588 0.6615292)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSummary Analysis of mediating Digital finance substantially influences the correlation within inclusion in finance and alleviating poorness (HDI). A wide-ranging effects of inclusion in finance upon alleviating poorness (HDI) is both affirmative and significant. Digital finance (DF) accounts for approximately 38.3% of the total effect, highlighting its critical role in amplifying the effects of inclusion in finances on human development and poverty alleviation. Finally both financial inclusiveness and digital finance (DF) support to reduce poverty indirectly and directly approaches. Though, digital finance's role as a mediator makes it even more important for increasing the positive outcomes of financial inclusiveness. This research shows how important digitally finance for boosting the impact for financial inclusiveness on alleviating poverty. This makes digital finance significant tool for economic advancement and societal development well-being.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e4.4.2 Sobel Z analysis for the mediation impact DF upon FII and HDI\u003c/div\u003e \u003cp\u003eA Sobel Z-test for significance evaluates the importance regarding an indirect (mediated) impact within a mediation paradigm. It is generally utilized in social science research to comprehend the correlation with mediation variable beside a dependent factor.\u003c/p\u003e \u003cp\u003eOverview Mediator Model\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIndependent factor (X): a predictive or causal variable.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe mediation's function (M): a factor which clarifies the relationship between both dependent as well as independent factors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDependent factor (Y): This impact that is being assessed.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA Sobel was a tests assesses statistical significance concerning the indirect influence of X upon Y via a mediating, M. Following indirect outcome determines by multiplication comprising two a coefficient:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA route between X to M- (a)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA route between M to Y - (b)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eConsequently, indirect influence is represented as a \u0026times; b\u0026thinsp;=\u0026thinsp;ab.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFormula for Sobel Z-testing\u003c/p\u003e \u003cp\u003eThe Sobel test calculates that Z-value for analyzing the significance of this indirect impact, expressed as:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\text{Z}\\:\\:=\\frac{\\text{a}\\text{*}\\text{b}}{\\sqrt{{b}^{2}\\text{*}\\text{S}{a}^{2}+{a}^{2\\text{*}}}\\text{S}{b}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere it is: a and b represent both of the coefficients of regression\u003c/p\u003e \u003cp\u003eSa \u0026amp; Sb denote standard errors both a \u0026amp; b, correspondingly.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4.7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndirectly Impact of DF concerning FII with HDI: Sobel Z-testing\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\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF (a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDI (b)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.589***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.067***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.486***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003cp\u003eNo. of Country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZ- V Testing\u0026thinsp;=\u0026thinsp;17.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotes: standard errors in parenthesis, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003cp\u003eSource: STATA 18.0, Results 2024\u003c/p\u003e \u003cp\u003eAccording to aforementioned Z-value analysis calculation:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\text{Z}\\:\\:=\\frac{\\text{a}\\text{*}\\text{b}}{\\sqrt{{b}^{2}\\text{*}\\text{S}{a}^{2}+{a}^{2\\text{*}}}\\text{S}{b}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003ea stands for coefficient of independence\u003c/p\u003e \u003cp\u003esa indicates the standard errors of the variables\u003c/p\u003e \u003cp\u003eb represents a coefficient for mediating factor\u003c/p\u003e \u003cp\u003esb denotes the standard errors of the mediating variables.\u003c/p\u003e \u003cp\u003eTherefore; a equal 0.589; sa equal 0.099; b equal 0.018; sb equal 0.005\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\text{Z}\\:\\:=\\frac{0.589\\text{*}0.018}{\\sqrt{({0.018)}^{2}\\text{*}{\\left(0.099\\right)}^{2}+{\\left(0.589\\right)}^{2\\text{*}}}{\\left(0.005\\right)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eZ- Value Test\u0026thinsp;=\u0026thinsp;17.81\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Sobel Z-test is used to assess the statistical significance of the indirect effect in a mediation model. A Z-score greater than 1.960 or less than \u0026minus;\u0026thinsp;1.960 indicates that the indirect effect is statistically significant, suggesting a strong mediation, while a small Z-score close to zero implies a weak or insignificant indirect effect. The Sobel test involves two linear regression models: the first regresses digital finance (DF) on financial inclusion (FII), with DF as the dependent variable and FII as the independent variable; the second regresses the Human Development Index (HDI) on both FII and DF, with HDI as the dependent variable and FII and DF as independent variables. The results from these models are used to calculate the Sobel Z-score, which determines the significance of the indirect effect of financial inclusion on poverty alleviation through digital finance. According to the aforementioned value of Z test of the Sobel modeling principle: If the value of Z exceeds 1.96, then \"M\" strongly mediates the relationship between X and Y. The Z-value result was 17.81 based on the Sobel test which indicates a highly significant outcome. The Sobel test evaluates the statistical impact of a mediating influence, specifically determining if indirectly influence pertaining to a variable that was independent considering a variable that depends via mediation that's of statistical significance. Final assessment derived from a Z-score result and finding: The Z-score of 17.81 is highly significant, indicating that the indirect effect is statistically meaningful. With the critical threshold for a two-tailed test at \u0026plusmn;\u0026thinsp;1.96, the Z-score of 17.81 is far above this value, confirming the presence of a strong mediation effect. The findings clearly demonstrate that the mediator variable plays a significant role in influencing the relationship between the dependent and independent variables. Additionally, the associated p-value, given the high Z-score, is exceedingly small (well below 1%), suggesting that the null hypothesis (which assumes no mediation effect) can be confidently rejected, further supporting the existence of a substantial mediation effect.\u003c/p\u003e \u003cp\u003eTherefore, the Sobel value for the Z result is 17.81, exceeding 1.96. Thus, DF serves a vital mediating role in the relationship between FII and alleviating poverty, as assessed by HDI. Policymakers should concentrate on strategies that attract digital finance as a means to improve inclusion in finances with alleviate poverty.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Resilience Assessment and Heterogeneity Analysis\u003c/h2\u003e \u003cp\u003eResilience Assessment involves evaluating how stable or reliable a study's findings are under varying conditions, examining whether the results hold true when assumptions, models, or data are adjusted. This process is essential for ensuring that conclusions are not simply influenced by specific assumptions or data characteristics, thus increasing confidence in their generalizability to different settings or populations. On the other hand, Heterogeneity Analysis focuses on understanding how subgroups or variables within a sample might influence the study's outcomes. It explores whether the effects observed are consistent across different groups or if they vary based on factors such as age, gender, or income, providing insights into conditions where effects may be stronger or weaker. Together, resilience assessment and heterogeneity analysis enhance the validity of research findings by confirming their reliability across different scenarios and revealing variations in effects among subgroups, ensuring that conclusions can be broadly applied and are not limited to specific data or assumptions.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Robustness checks\u003c/h2\u003e \u003cp\u003eA robustness check is a technique used in statistical, econometric, and other qualitative studies to assess the reliability and consistency of the main findings. It involves evaluating whether the results hold true across different assumptions, scenarios, or data specifications. The goal is to determine whether the outcomes are sensitive to minor changes or remain consistent, which strengthens trust in the conclusions. Robustness checks are important because they affirm the reliability of the results, identify potential biases or limitations in the analysis, and ensure the generalizability of the findings to broader contexts. In this study, several robustness checks were conducted to verify the validity of the results. The Feasible Generalized Least Squares (FGLS) methodology was initially applied to reassess the approach, enhancing the reliability of the conclusions. The findings confirmed that both Digital Finance (DF) and the Financial Inclusion Index (FII) significantly contributed to poverty reduction, as measured by the Human Development Index (HDI). To further test the robustness of the models, the study employed model replacements and variable substitution techniques to ensure that the results were not overly dependent on any specific model or variable. Additionally, the study used the Generalized Method of Moments (GMM) and quantile regression techniques to address potential issues like endogeneity and provide more reliable estimates across different data quantiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4.8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Robustness checks\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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 \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003cp\u003eGMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003cp\u003e\u003cb\u003e10th\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003cp\u003e\u003cb\u003e25th\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003cp\u003e\u003cb\u003e50th\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003cp\u003e\u003cb\u003e75th\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003cp\u003e\u003cb\u003e90th\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5704***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0272***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0235***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0205***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0155**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0112**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.635)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.222)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.053)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0068***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0215***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0221***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0225***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0232***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0238***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(8.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(6.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.498)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eZ-statistics are presented in parenthesis; significance levels are indicated as follows: *** with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ** with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: STATA 18.0, Results 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis reveals a strong positive relationship between the Human Development Index (HDI) and financial inclusion, indicating that countries with higher financial inclusion tend to experience better human development outcomes, with the relationship being statistically significant at the 1% level. Digital finance (DF) also significantly enhances HDI, further supporting human development. A Generalized Method of Moments (GMM) analysis confirms a positive link between HDI, the financial inclusion index (FII), and DF, showing that both variables significantly boost HDI. To address potential endogeneity, the study used GMM to mitigate the risk of biased estimates, ensuring more accurate causal effects. Additionally, robustness checks, including quantile regression at various percentiles (10th, 25th, 50th, and 75th), demonstrated that financial inclusion has a greater impact on human development in countries with lower HDI, highlighting its crucial role in poverty alleviation, particularly in poorer nations. These robustness checks ensure the stability and reliability of the results, confirming that financial inclusion and digital finance are key drivers of human development and poverty reduction, with their effects varying across different levels of HDI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2 Heterogeneity Analysis\u003c/h2\u003e \u003cp\u003eHeterogeneity analysis involves scrutinizing the disparities or variations in the impact of a specific variable or intervention across several subgroups or circumstances. Heterogeneity analysis is an essential instrument for comprehending the diverse effects of factors across various subgroups or situations. It assists researchers and policymakers in understanding that the impact of a variable may not be consistent, facilitating more precise and effective responses. In empirical research, it pertains to the investigation of how the correlations between variables may fluctuate based on particular data characteristics, such as disparities between urban and rural demographic components in East Africa.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4.9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest of Heterogeneity Analysis\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\"\u003e\u0026nbsp;\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 \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUrban Population\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRural Population\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 5125**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1134**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7342**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0-8970**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17297***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.3280*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7.24)\u003c/p\u003e \u003cp\u003e0.26367***\u003c/p\u003e \u003cp\u003e(7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-8.0)\u003c/p\u003e \u003cp\u003e-0.1799***\u003c/p\u003e \u003cp\u003e(-2.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eZ-statistics are presented in parenthesis; significance levels are indicated as follows: *** with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** with a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: STATA 18.0, Results 2024\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis explores the impact of the Human Development Index (HDI), financial inclusion, and digital finance on poverty alleviation in both urban and rural populations. In urban areas, all three factors HDI, financial inclusion, and digital finance positively contribute to poverty reduction, with HDI showing a coefficient of 0.5125, financial inclusion at 0.7342, and digital finance at 0.17297, reflecting the benefits of improved infrastructure and better access to services. In contrast, rural areas experience less positive outcomes, with HDI showing a negative coefficient of -0.1134, and both financial inclusion (-0.8970) and digital finance (-0.3280) showing negative impacts due to challenges such as inadequate infrastructure, low digital literacy, and limited access to financial services. The comparative analysis reveals that while urban areas benefit significantly from these factors, rural communities in countries like Rwanda, Uganda, and Kenya face obstacles that hinder the effectiveness of these interventions. To improve poverty reduction in rural areas, policies should focus on bridging infrastructure gaps, improving digital literacy, and expanding access to financial services.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Conclusion\u003c/h2\u003e \u003cp\u003eThis investigation examines the mediating function of digital financing in the correlation between the inclusion of finance and alleviating poverty among nine East African nations over a 15-year span (2008\u0026ndash;2022). The econometric analysis employs FGLS and Bootstrapping fixed-effects regression approaches. This section analyzed how function pertaining to digitally finance mediating the financial inclusiveness and alleviating poverty, employing a diverse perspective. The research utilized an extensive array of diagnostic assessments and regression analyses to evaluate the correlations among the measures of human developmental, the financial inclusiveness indices, and Digital financial, emphasizing the mediating effect the impact of digital financing on improving financial inclusiveness with mitigating poverty.\u003c/p\u003e \u003cp\u003eDescriptive statistics indicated significant variations in development of people and financial inclusion among countries, with numerous nations exhibiting elevated poverty rates and restricted financial access. Notwithstanding these obstacles, digital finance emerged as a promising instrument for enhancing financial accessibility and, hence, alleviating poverty. Correlation analysis revealed statistically significant positive associations between HDI and both FII and DF, indicating that as human development rises, financial inclusion and digital finance acceptance enhance. Digital finance serves a crucial intermediary function in amplifying the positive effects about financial inclusiveness on poverty alleviation. Digital finance greatly enhances the impact of financial inclusion upon poverty reduction, particularly during East African nations. The FGLS regression model, following the resolution of heteroskedasticity and cross- sectional dependence, yielded strong evidence for the mediating role of digital finance. The results indicate that finances available digital are essential to enhancing financial prospects with alleviating poorness in context, especially for vulnerable populations.\u003c/p\u003e \u003cp\u003eFinancial Inclusiveness with Digital finances Influence: Both financial inclusiveness and digitally finances parade a substantial beneficial effect on human development, indicating that accessibility to financial facilities was essential in alleviating poverty. Financial inclusiveness Index and Digital Finance enhance Human Development Index both directly and indirectly, with digital finance augmenting the beneficial impacts of business presence. A mediation effect of digital financial indicates that it accounts for roughly 38.3% that was overall effects that financial inclusiveness upon alleviating poverty. A signifies the digital finance amplifies the advantages for financial inclusiveness, serving in the capacity of vital facilitator with the mitigation of impoverishment. A models continuously demonstrate the FII and DF are of statistically significant at the one percent value, so affirming that robustness and dependability for both of these factors in fostering human growth and mitigating poverty. This investigation highlights the crucial importance of the inclusion of finances and digital financial in mitigating poverty, especially among urban residents. The Sobel Z-value test verifies a considerable and statistically significant mediator effect of DF regarding the correlation within inclusion in finance along with poverty alleviation, as assessed by the Human Indexes, which was the Z-value for 17.81 signifies that DF is crucial in amplifying the effect of financial inclusiveness upon poverty alleviation. The endogeneity analyses besides quantile regression analysis confirm the robustness of these findings, indicating that the two variables financing and DF significantly impact HDI across different data specifications. The heterogeneity analysis reveals that urban residents derive substantial advantages through these interventions, with the inclusion of finance and DF presenting robust positive relationships with HDI. In contrast, rural communities encounter obstacles that impede the efficacy of these interventions, such as restricted accessibility to financial services, inadequate infrastructure, and insufficient digital literacy, leading to a negative association between HDI and alleviating poverty in rural regions.\u003c/p\u003e \u003cp\u003eIn a nutshell digital finance is an essential facilitator that amplifies positive impacts for financial inclusiveness upon alleviating poorness. Digital finance serves as a potent instrument for fostering economic progress, advancing human development, and attaining poverty alleviation objectives in East Africa by augmenting accessibility for financing. Although metropolitan regions significantly benefit for improvements in financial inclusion as well as digital financial services, rural communities have distinct obstacles that hinder the efficacy of these instruments. Policymakers must prioritize rectifying infrastructural deficiencies, augmenting digital literacy, and facilitating accessibility to financing in remote areas to exploit positive concerning to finance inclusion and digital financing for alleviating poverty.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Contribution","content":"\u003cp\u003eThis research contributes to the understanding of how digital finance serves as a mediator in the relationship between financial inclusion and poverty alleviation in East Africa. It provides new empirical evidence on the role of digital finance in reducing poverty, particularly in the context of urban and rural differences. The study extends the existing literature by offering insights into the impact of digital finance on financial inclusion and poverty alleviation, highlighting its significance in economic development and social well-being.\u003c/p\u003e"},{"header":"6. Recommendations","content":"\u003cp\u003eTo enhance financial inclusion and alleviate poverty in East Africa, several strategic measures are essential. First, governments should prioritize the development of reliable digital infrastructure, particularly in rural areas, to ensure that all populations have access to digital financial services. Public-private partnerships between governments, tech companies, financial institutions, and NGOs are crucial for creating sustainable digital finance ecosystems that effectively serve underserved communities. Additionally, capacity-building initiatives aimed at improving both digital and financial literacy are necessary to empower individuals, especially in rural areas, to participate in the digital economy. Policymakers should also focus on developing targeted interventions for rural populations, addressing issues such as infrastructure gaps and low digital literacy, to ensure the effective use of digital finance. Furthermore, investment in mobile networks is vital to bridging the urban-rural divide, ensuring that mobile services are accessible and reliable across all regions. By addressing these strategies, governments can harness the power of digital finance to drive inclusive growth, reduce poverty, and foster overall economic development in East Africa.\u003c/p\u003e"},{"header":"7. References","content":"\u003col\u003e\n\u003cli\u003eAbel, S., Mutandwa, L., \u0026amp; Le Roux, P. (2018). A review of determinants of financial inclusion. \u003cem\u003eInternational Journal of Economics and Financial Issues, 8\u003c/em\u003e(3), 1\u0026ndash;8. https://www.econjournals.com\u003c/li\u003e\n\u003cli\u003eAl-Smadi, M. O. (2023). 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Financial inclusion and extreme poverty in the MENA region: A gap analysis approach. \u003cem\u003eReview of Economics and Political Science, 5\u003c/em\u003e(3), 207\u0026ndash;230. https://doi.org/10.1108/reps-03-2020-0041\u003c/li\u003e\n\u003cli\u003eEvans, O. (2017). Determinants of financial inclusion in Africa: A dynamic panel data approach. \u003cem\u003eInternational Journal of Finance\u003c/em\u003e, 81326.\u003c/li\u003e\n\u003cli\u003eEvans, O., \u0026amp; Adeoye, B. (2016). Determinants of financial inclusion in Africa: A dynamic panel data approach. \u003cem\u003eInternational Journal of Financial Research\u003c/em\u003e, January. https://doi.org/10.5430/ijfr.v11n1p123\u003c/li\u003e\n\u003cli\u003eKamara, A. K. (2024). 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Digital financial inclusion, digital financial services tax and financial inclusion in the Fourth Industrial Revolution era in Africa. \u003cem\u003eEconomies, 10\u003c/em\u003e(8). https://doi.org/10.3390/economies10080184\u003c/li\u003e\n\u003cli\u003eNarayan, D. (2018). Poverty and financial inclusion. \u003cem\u003eWorld Bank Policy Research Paper\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eOzili, P. K. (2022). Digital financial inclusion. \u003cem\u003eWorld Bank Research Paper\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eThomi, J., \u0026amp; Mose, N. (2021). Financial inclusion in East Africa: Does economic growth matter? \u003cem\u003eJournal of Economics, Management and Trade, 27\u003c/em\u003e(2), 1\u0026ndash;8. https://doi.org/10.9734/jemt/2021/v27i230325\u003c/li\u003e\n\u003cli\u003eZins, A., \u0026amp; Weill, L. (2016). The determinants of financial inclusion in Africa. \u003cem\u003eReview of Development Finance, 6\u003c/em\u003e(1), 46\u0026ndash;57. https://doi.org/10.1016/j.rdf.2016.05.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bule Hora University","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 Finance, FGLS, Financial Inclusion, GMM and Poverty Alleviation","lastPublishedDoi":"10.21203/rs.3.rs-8391843/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8391843/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the mediating role of digital finance in enhancing financial inclusion and alleviating poverty across nine East Africa countries from 2008 to 2022 and considering country-specific effects, the study fills a gap in the literature by empirically examining how digital finance mediates the relationship between financial inclusion and poverty reduction. The research, based on secondary data from the IMF, UNDP, and World Bank, Using a quantitative approach with Feasible Generalized Least Squares (FGLS) regression model, the study investigates the impact of independent variables on poverty alleviation. Additionally, bootstrap fixed-effects and Sobel Z-value tests are employed to assess the significance of the mediation approach, followed by robustness checks using Generalized Method of Moments (GMM) and Quantile regression. The findings reveal that digital finance and financial inclusion have a positive impact on poverty alleviation. The study highpoints that digital finance significantly mediates the effect of financial inclusion on poverty alleviation, accounting for about 38.3% of the total impact. Urban areas benefit substantially from digital finance, while rural areas face barriers like poor infrastructure and low digital literacy, which limit the effectiveness of these interventions. The research emphasizes the need for governments to focus on strengthening digital infrastructure, fostering public-private partnerships, improving digital literacy, and developing targeted policies for rural areas to bridge the urban-rural divide. These measures will maximize the impact of digital finance, foster inclusive growth, and reduce poverty across East Africa.\u003c/p\u003e","manuscriptTitle":"Examining the Mediating Role of Digital Finance in the Relationship Between Financial Inclusion and Poverty Alleviation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 04:09:30","doi":"10.21203/rs.3.rs-8391843/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":"fd6664a1-9be3-4edd-9cec-b721d4deafbe","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59922977,"name":"Finance"},{"id":59922978,"name":"Agricultural Economics \u0026 Policy"},{"id":59922979,"name":"Development Economics"},{"id":59922980,"name":"Economic Theory"}],"tags":[],"updatedAt":"2025-12-19T04:09:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 04:09:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8391843","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8391843","identity":"rs-8391843","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}