From Access to Impact: How Digital Financial Inclusion Drives Sustainable Development

Abstract

This study examines the combined impact of fintech and financial inclusion on achieving the United Nations’ Sustainable Development Goals (SDGs). Previous research has emphasized the role of financial inclusion in reducing poverty, strengthening resilience, and promoting economic stability; however, its interaction with fintech in advancing sustainability remains less examined. Using four composite indices incorporating updated variables, expanded country coverage, and a broader temporal scope, this analysis evaluates digital financial channels, including formal access, mobile money, digital credit, transfers, and rural finance, across SDGs 3, 4, 8, and 9. The findings indicate that formal access is associated with lower maternal mortality (SDG 3) and contributes positively to decent work and economic growth (SDG 8), as well as industry, innovation, and infrastructure (SDG 9). Digital credit and transfers help ease liquidity constraints in high-inequality regions, while mobile money enhances education outcomes (SDG 4) under robust governance, supporting informal labor markets. Rural finance strengthens innovation and infrastructure development in underserved areas, reinforcing SDG 9. A simultaneous equation model provides evidence of bidirectional relationships among financial inclusion, fintech adoption, and sustainable development, underscoring their mutual reinforcement rather than strict causality. Overall, the study highlights the systemic interconnection between finance and sustainability and emphasizes the importance of governance, infrastructure, and regulation in maximizing developmental benefits.

1. Introduction

Digital financial inclusion (DFI) refers to the use of digital technologies—such as mobile banking, digital credit, and online transfers—to extend affordable and convenient financial services to all population segments, particularly underserved groups. Fintech, or financial technology, has emerged as a transformative force in promoting financial inclusion and advancing the Sustainable Development Goals (SDGs). The intersection of fintech and financial inclusion plays a pivotal role in bolstering economic growth, reducing poverty, and addressing inequalities, especially in underbanked and underserved populations. Research indicates that fintech’s innovative solutions enhance access to financial services, thereby fostering inclusivity, which is essential for achieving several SDGs, including no poverty (SDG 1) and reduced inequalities (SDG 10) [1,2,3].

Financial inclusion, defined as affordable access to financial products for all population segments, has become a crucial tool for reducing poverty and promoting economic empowerment [4]. Fintech-driven financial inclusion is increasingly recognized for its potential to support sustainable economic development. By leveraging digital technologies, fintech can significantly reduce transaction costs, enhance access to financial services, and empower individuals to manage their finances more effectively [5,6]. Additionally, providing affordable financial services can promote equity, aligning with the goals of the SDGs to foster inclusive social systems [2,3,5].

In imperfect markets, financial systems are vital for allocating capital efficiently; however, formal financial systems have fallen short in achieving this goal. Digital Financial Inclusion, which combines inclusive finance and digital innovation, plays a key role in addressing household-level issues such as unemployment, poverty, health, aging, and gender disparities [7]. Access to digital financial services can help tackle global development challenges outlined in the UN Sustainable Development Goals (SDGs) [8].

Additionally, integrating fintech into traditional financial systems can accelerate the development of the infrastructure needed to support inclusive financial ecosystems. The infrastructure must be robust to ensure that fintech innovations reach marginalized populations effectively, thereby contributing to an environment conducive to sustainable growth [9,10]. As noted by various scholars, creating a favorable regulatory and operational environment is crucial for fintech adoption, ensuring alignment with the SDGs while also enhancing the efficacy of financial services globally [11,12].

The relationship between digital financial inclusion is not merely transactional; it encapsulates a socio-economic transformation that seeks to balance economic growth with social equity and environmental sustainability. This integrated approach suggests that fintech should be embraced as a strategic tool in the policy-making arsenal of countries aiming to meet their SDGs [10,12].

Therefore, in this study, we examine how digital financial inclusion—through its various dimensions such as formal access, mobile money, digital credit, and transfers, as well as rural outreach—influences progress toward the SDGs. We employ a panel data model for 148 countries, spanning 2011, 2014, 2017, and 2021, based on the Global Findex Survey [13], and utilize quantile regression to examine the effects across varying levels of sustainable development. As part of our control and instrumental macroeconomic variables, we include the Gini Index (SWIID) [14] to capture inequality and the six governance dimensions from the World Governance Indicators (WGI/WDI), which encompass voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. Incorporating these governance indicators allows us to better account for institutional quality.

Our study builds on existing research in several important ways. First, we introduce new composite indices that measure the impact of primary diffusion channels—digital innovations, formal financial inclusion, and rural outreach. These indices are adapted and extended from Choudhary (2025) [15] by incorporating updated variables, expanding the country coverage from 86 to 148 countries, and applying a three-stage PCA. Second, by integrating multiple dimensions of digital financial inclusion into a cross-country framework, this study contributes new empirical insights into the mechanisms linking financial technology and sustainable development. Third, the study explores the vulnerabilities of emerging economies amid turbulent global conditions through the lens of financial inequality and digital inclusion, and proposes innovative strategies for economic stabilization via digital diffusion channels.

Specifically, our study demonstrates that the Formal Financial Access (FFA) Index plays a crucial role in advancing SDG 8 and SDG 9 by fostering job creation, investment in human capital, and infrastructure development that supports innovation. Drawing on insights from finance, economics, and governance, our findings provide both theoretical groundwork and practical policy recommendations to enhance resilience and promote sustainable growth in global economies. The remainder of the paper is organized as follows: Section 2 presents the theoretical framework, followed by the method and research design in Section 3. Results and Discussion are presented in Section 4. Robustness tests are highlighted in Section 5. Section 6 concludes the paper.

2. Theoretical Framework

Digital financial inclusion, propelled by digital finance, holds significant potential to contribute to reducing income disparity and promoting economic growth. It improves access to financial services for the traditionally excluded population segments of the formal financial system [8]. The broad social and economic benefits of providing better access to financial services include increased household consumption, higher levels of domestic savings, increases in production output, more equitable income distribution, and general improvements in the quality of life [16].

DFI encompasses various dimensions such as access, usage, and the depth of financial services, which are crucial for evaluating its effectiveness. In consequence, Digital financial inclusion has become a transformative driver of the Sustainable Development Goals (SDGs) 3, 4, 8, and 9. By enabling affordable healthcare payments and savings options, it improves health outcomes and supports SDG 3, while helping families invest in school fees and learning resources, thereby promoting SDG 4 [17]. Access to digital finance also boosts entrepreneurship and job creation by reducing transaction costs and enhancing liquidity management, thereby strengthening economic growth in line with SDG 8 [18,19]. Additionally, digital platforms drive infrastructure development and innovation, expanding financial access in underserved areas and reshaping the payments landscape to encourage industrial progress and technological advancement [17,19]. The coverage and the depth of use are significant contributors to the overall impact of DFI, particularly in rural and underdeveloped areas [20]. Overall, these effects demonstrate that digital financial inclusion serves as both a social equalizer and an economic driver, underscoring its crucial role in achieving inclusive and sustainable development.

Hypothesis 1.

Digital financial inclusion dimensions drive the achievement of Sustainable Development: SDG3 (Health), SDG 4 (Education), SDG 8 (Decent Work and Economic Growth), and SDG 9 (Industry, Innovation, and Infrastructure).

The effectiveness of DFI varies significantly across levels of development, with stronger impacts observed in areas with advanced innovation capabilities and established technological infrastructure [21]. Different countries exhibit varying levels of DFI, influenced by local factors such as human and social capital [22]. New technologies and innovations spread differently across society, market, or system through time. The timing and context of technological adoption play a crucial role, as periods of global turbulence often reshape both the pace of diffusion and the depth of impact of new digital technologies across economies. The COVID-19 pandemic has accelerated the adoption of digital financial services, yet challenges remain, particularly in developing countries where disparities exist between different demographics regarding access to these services [23]. Furthermore, time matters in the technology of acceptance to adapt innovations by individuals [16]. Rogers (2003) [24] measures how the advancement of technology at different socioeconomic costs is relevant in the current environmental setting. Furthermore, academics [25] showed that, in the presence of imperfect credit markets, poor households cannot borrow to invest in their education. Some studies [8] quantify and provide evidence, using fintech and financial inclusion proxy variables, of a link between mobile finance, financial inclusion, and income inequality at the international level. Greater income inequality can hinder economic growth and negatively affect health and educational outcomes. Several studies emphasize the importance of addressing income inequality to achieve positive development outcomes [26,27]. DFI particularly benefits those in eastern regions and households with lower debt-to-income ratios [28]. Some studies posit that the relationship between digital finance and income mobility is significant, particularly in rural areas [29].

Hypothesis 2.

The impact of distinct dimensions of digital financial inclusion varies significantly depending on development levels, suggesting societies experience differentiated outcomes from financial and technological access.

To explain the objectives of this study, a diagram in Appendix A.4 is included to better illustrate the relationship between digital inclusive finance and sustainable development.

3. Method

3.1. Sample and Data Sources

We use proxies to measure the impact on each development goal: maternal deaths [30] for SDG 3, school enrollment [15] for SDG 4, GDP per capita [16] for SDG 8, and internet usage [31] for SDG 9. To construct digital financial inclusion dimensions (indices), this study uses data from the World Bank Global Findex (Findex) database. These were created using three-stage Principal Component Analysis (3PCA) to reduce dimensionality and identify underlying structures [15]. Data for control variables were sourced from the World Bank database, the International Communication Union, and the World Inequality Database. We collected data for 148 countries from the Global Findex survey waves for the years 2011, 2014, 2017, and 2021.

Table 1. Variables description.

Variable	Definition and Measurement	Source
SDG 3	Log of Maternal Death	World Development Indicators (WDI)
SDG 4	School Enrollment (% population)	WDI
SDG 8	Log GDP per capita	WDI
SDG 9	Individuals using the Internet (% of population)	ITU
Inequality	Standardized World Income Inequality Database (SWIID). Gini Index: a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.	World Income Inequality Database
Formal Financial Access (Index)	“Used a mobile phone or the internet to check account balance (% age 15+), Made or received a digital payment (% age 15+), Financial institution account (% age 15+), Saved at a financial institution (% age 15+), Made a utility payment: using a financial institution account (% age 15+), Owns a debit card, (% age 15+), Owns a debit card, poorer (% age 15+), Received wages, into a financial institution account (% age 15+), Received wages, poorer (% age 15+).	Global Findex
Mobile Money Index	“Mobile money account (% age 15+), Mobile money account, income, poorest 40% (% ages 15+), Mobile money account, rural (% age 15+)	Global Findex
Digital Credit & Transfers (Index)	Use a mobile phone or the internet to make payment, Used a mobile phone or the internet to send money (% age 15+), Borrowed any money from a formal financial institution or using a mobile money account (% age 15+)	Global Findex
Rural Finance (Index)	Owns a debit card, rural (% ages 15–24), Received wages, rural (% ages 15–24)	Global Findex
Trade	Trade (%GDP)	WDI
Urbanisation	Urban Population (% of Total)	WDI
Population Growth	Population growth (annual %)	WDI
Government Expenditure	Government Expenditure (% of GDP)	WDI
Water Facility	People using at least basic drinking water services (% of population)	WDI
Health Expenditure	Domestic general government health expenditure (% of GDP)	WDI
GDP growth	GDP per capita growth (% annual)	WDI
Voice and Accountability	Voice and Accountability captures perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e., ranging from approximately −2.5 to 2.5.	World Governance Indicators (WGI)
Government Effectiveness	Government Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e., ranging from approximately −2.5 to 2.5.	WGI
Regulator Quality	The Regulatory Quality estimate in the World Development Indicators (WDI) reflects the perceived ability of a country’s government to formulate and implement sound policies and regulations that support private sector development. It is one of the six dimensions of governance measured by the Worldwide Governance Indicators (WGI) (WGI), which are produced by the World Bank. The Regulatory Quality estimate is presented as a standardized score, typically ranging from −2.5 to 2.5, with higher scores indicating better regulatory quality.	WGI
Rule of Law	Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e., ranging from approximately −2.5 to 2.5.	WGI
Political Stability	Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. Estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e., ranging from approximately −2.5 to 2.5	WGI
Corruption Control	Measures the extent to which public power is exercised for private gain, including petty and grand corruption, and the capture of the state by elites and private interests. It is an aggregate indicator ranging from approximately −2.5 to 2.5, with higher scores indicating lower levels of corruption.	WGI

summarizes the definition of each variable, along with details of the indicators and data sources [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49].

The Global Findex database utilizes the World Bank’s income classification, which categorizes countries into low, lower-middle, upper-middle, and high-income groups based on their Gross National Income [40]. The list of countries, along with their respective GINI Coefficients, is presented in Appendix A.1. The data for high-income countries indicates that Chile possesses the highest GINI coefficient at 46.32. Among upper-middle-income countries, Colombia exhibits a GINI coefficient of 55.1. For lower-middle-income countries, Eswatini records a GINI coefficient of 54.6, and among lower-income countries, Mozambique has a GINI coefficient of 55.2. The Global Findex Report (2025) [50] shows a rise in formal savings, mainly due to the convenience, accessibility, and affordability of mobile financial services that facilitate savings deposits. Mobile money and other digital accounts are key drivers of this growth. Regional differences may be due to varying levels of integration between mobile platforms and traditional banking systems, with regions showing higher increases likely having stronger links between these channels.

3.2. Variable Design

3.2.1. Dependent Variables

Sustainable Development Goal: This study presents a description of all variables used in our model in Table 1, along with details of the indicators, their definitions, and data sources. We use one proxy for each SDG, following the guidance provided in the Sustainable Development Report [30,51], which emphasizes the importance of selecting measurable and policy-relevant indicators.

3.2.2. Explanatory Variables

Digital Financial Inclusion Dimensions: To measure Digital Financial Inclusion, we use four composite indices [15] as the main explanatory variables: Formal Financial Access—FFA, Mobile Money Inclusion—MMI, Digital Credit & Transfers—DCT, and Rural Financial Index—Rural. These were created using three-stage Principal Component Analysis (3PCA) to reduce dimensionality and identify underlying structures [15]. To handle missing data and ensure consistency across time points, this study employs Multiple Imputation by Chained Equations (MICE) and linear interpolation.

3.2.3. Control Variables

Control variables: encompass macroeconomic, technological, and demographic indicators. These include the GINI Coefficient (estimated via the SWIID), GDP growth, Water Facility, Trade, Government Expenditure, Health Expenditure, urbanization, and Population growth. Additionally, the study considers the six dimensions of the Governance indicator developed by the World Bank: Rule of Law, Government Effectiveness, Regulator Quality, Voice, and Accountability.

The GINI Coefficient, estimated from the SWIID, ranges from 0 (perfect equality) to 100 (maximum inequality) and provides insights into income distribution within a society. A higher Gini coefficient signifies greater inequality, which can hinder economic growth and negatively affect health and educational outcomes. Several studies emphasize the importance of addressing income inequality to achieve positive development outcomes [20,21].

As a key measure of income inequality, the Gini coefficient critically influences progress toward SDGs 3, 4, 8, and 9 by affecting health, education, economic growth, and infrastructure. Greater inequality is associated with poorer health indicators, limited educational attainment, fewer economic opportunities, and unequal access to infrastructure, thereby sustaining poverty cycles and impeding sustainable development [52,53].

Financial inclusion is a powerful tool to reduce these disparities by expanding access to health services, educational resources, and entrepreneurial opportunities. This fosters inclusive growth and innovation. Including the Gini coefficient in analysis frameworks offers valuable insights into how income inequality interacts with financial inclusion, highlighting that without directly addressing inequality, financial inclusion alone might not be enough to promote equitable progress across the SDGs [26,27].

GDP growth is a crucial economic indicator that reflects a country’s overall financial health and development. According to some academics [54,55], by controlling GDP growth, we account for the influence of economic performance, education, health and population growth. Existing studies also indicate that internet penetration promotes economic growth by offering innovative applications and new opportunities for information exchange [31,56].

When examining the effects of financial technologies (fintech) and financial inclusion on maternal mortality, especially in developing countries, it is essential to control for access to water facilities. Incorporating this variable helps to mitigate confounding influences stemming from inadequate water and sanitation infrastructure, thereby isolating the specific contributions of fintech and financial inclusion. This approach facilitates a clearer understanding of how financial innovations can enhance maternal health outcomes once basic infrastructural needs are met [57].

Trade reflects the accumulation of both physical and human capital, serving as a key driver of economic growth [16]. Countries with stronger trade linkages generally enjoy greater access to technological advancements and foreign investment, which can enhance internet infrastructure [58]. Accordingly, we include trade as a control variable to capture the influence of international economic exchanges on infrastructure development and innovation [59], thereby allowing for a more accurate assessment of the relationship between fintech, financial inclusion, and internet penetration.

Government expenditure on health and education plays a crucial role in enabling financial inclusion. By improving population health and strengthening human capital, public spending reduces reliance on informal mechanisms and equips individuals with the skills needed to engage in formal financial systems [60,61]. However, the strength of this relationship may vary across countries, as some studies report mixed effects between government spending and financial development [62].

Health expenditure strongly supports SDG 3 by lowering maternal and infant mortality while reducing households’ financial vulnerability [63]. These improvements foster economic stability and create conditions that enable fintech and financial inclusion initiatives to be more effective. Thus, government spending not only enhances social outcomes but also reinforces the ecosystem in which digital finance can drive sustainable development.

Urbanization is closely linked to disparities in healthcare access, infrastructure, and broader socioeconomic conditions [64]. By controlling for urbanization, we account for these structural differences, ensuring that variations in maternal mortality rates are not solely driven by urban–rural divides but also reflect the role of fintech diffusion and the expansion of financial inclusion [15].

Population growth exerts significant pressure on health, education, employment, and infrastructure systems, thereby influencing progress toward SDGs 3, 4, 8, and 9. Rapid increases often strain healthcare and education services, heighten unemployment risks, and intensify infrastructure demands, particularly in resource-limited settings [65,66]. Financial inclusion emerges as a crucial tool for mitigating these challenges by expanding access to essential services, including health, education, and entrepreneurial opportunities. Effective governance ensures that such benefits are equitably distributed [67,68].

Moreover, family planning and reproductive health services directly improve maternal and child health, reduce pressure on education systems, and support sustainable economic growth [69]. Ultimately, the interaction of population dynamics, financial inclusion, and governance quality underscores the need for integrated policies that transform demographic pressures into opportunities for sustainable development.

The World Bank’s governance indicators provide a critical framework for examining how institutional quality influences financial inclusion and progress toward the SDGs. A strong rule of law fosters secure financial transactions, promoting investment and participation in the formal economy [68]. Government effectiveness enhances the delivery of public services and policy implementation, ensuring that expenditures in health and education contribute directly to SDGs 3 and 4 [70]. Similarly, high regulatory quality creates an enabling environment for financial innovations and consumer protections, supporting the growth of fintech solutions that expand access to finance [71]. Finally, voice and accountability empower citizens to shape expenditure priorities, leading to improved health and education outcomes that reinforce sustainable development objectives [72].

3.2.4. Descriptive Statistics

The descriptive statistics are presented in

Table 2. Descriptive Statistics.

Variable	Mean	Std. Dev.	Min	Max
SDG 3-Health	163.7942	242.6934	0	1662
SDG 4-Education	0.6493123	0.442752	0	1.62422
SFG 8-Decent work and economic growth	14,503.58	21,676.79	0	134,965.8
SDG 9-Industry, innovation and infrastructure	0.4804598	0.3093509	0	1
Formal Financial Access	−4.84 × 10−16	1	−2.318284	2.470754
Mobile Money	−3.49 × 10−19	1	−2.689258	6.418031
Direct Credit and Transfers	−3.93 × 10−17	1	−3.038604	4.72196
Rural Financial Access	−2.93 × 10−18	1	−2.560755	7.514234
Inequality	36.23246	9.670869	0	63
Trade	0.7961813	0.5014696	0	3.931412
Urbanisation	0.5794366	0.2192012	0	0.99278
Population Growth	0.012752	0.0155345	0.1092744	0.1158171
Government Expenditure	0.1474412	0.065312	0	0.3968057
Water Facility	0.8437025	0.209835	0	1
Health Expenditure	0.0343905	0.0237946	0	0.1020335
GDP growth	3.021634	3.994628	−15.33669	33.76856
Voice Accountability	−0.084633	0.9600761	−2.259265	1.738036
Government Effectiveness	−0.064277	0.9695092	−2.440221	2.235192
Regulator Quality	−0.016336	0.9475239	−2.102013	2.040489
Rule of Law	−0.10765	0.9660785	−2.097953	2.124762
Political Stability	−0.225219	0.9340703	−2.934317	1.402653
Corruption Control	−0.138072	0.9793363	−1.836981	2.392884

Source: Authors’ computation based on secondary data.

, revealing substantial heterogeneity across countries in the dataset. For the Sustainable Development Goals (SDGs), SDG 3 (health) shows a mean value of 163.79 with a wide dispersion (SD = 242.69), ranging from 0 to 1662, indicating significant disparities in health outcomes across countries. Similarly, SDG 8 (economic growth and employment) presents a mean of 14,503.58 with a standard deviation of 21,676.79 and a maximum of 134,965.8, reflecting the dominance of large economies as outliers.

In contrast, SDG 4 (education) and SDG 9 (industry, innovation, and infrastructure) exhibit more moderate averages of 0.65 and 0.48, respectively, with bounded ranges (0–1.62 and 0–1.0), suggesting that while progress differs, most countries cluster within comparable levels of development. These patterns are consistent with prior research, which shows that SDG performance is highly uneven, with economic indicators tending to be more skewed due to the influence of larger economies [51,73,74].

Financial access indicators, standardized around zero, highlight uneven diffusion: mobile money (−2.68 to 6.41) and rural access (−2.56 to 7.51) show the widest disparities, underscoring the uneven penetration of digital finance. These findings reinforce the existing literature on digital finance as a transformative yet unevenly distributed channel for inclusion [67,68,74]. Socioeconomic measures also vary substantially: inequality averages 36.23 (0–63), trade openness 0.79 (0–3.93), and urbanization 0.58 (0–0.99). Fiscal priorities differ, with government expenditure averaging 14.7% of GDP (ranging from a maximum of 39.7% to a minimum of 0.0%) and health expenditure at 3.4% (ranging from a maximum of 10.2% to a minimum of 0.0%). GDP growth is positive on average (3.02%), but volatile (−15.3% to 33.8%), reflecting emerging economy cycles and volatility typical of emerging economies [75]. Governance indicators cluster slightly below zero (e.g., government effectiveness, M = −0.06, rule of law, M = −0.11), with extremes ranging from −2.93 to +2.39, confirming the results of Kaufmann & Lafarre (2021) [76], who highlight governance quality as a persistent constraint in developing regions.

The correlation matrix of financial inclusion indicators and SDGs is presented in

Table 3. Correlation Matrix.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)	(20)	(21)	(22)
(1) Inequality	1.00
(2) Formal Financial Access	−0.20	1.00
(3) Mobile Money Index	0.17	−0.10	1.00
(4) Digital Credit & Transfers	−0.06	0.53	0.19	1.00
(5) Rural Finance	0.06	0.03	0.39	0.30	1.00
(6) SDG 3	0.40	−0.58	0.33	−0.15	0.08	1.00
(7) SDG 4	−0.16	0.48	−0.25	0.20	0.01	−0.58	1.00
(8) SDG 8	−0.25	0.67	−0.28	0.25	−0.07	−0.81	0.60	1.00
(9) SDG 9	−0.27	0.70	−0.13	0.35	0.10	−0.73	0.63	0.84	1.00
(10) Trade	−0.15	0.31	−0.12	0.10	−0.03	−0.39	0.25	0.40	0.39	1.00
(11) Urbanisation	−0.07	0.45	−0.20	0.16	−0.04	−0.57	0.50	0.76	0.73	0.31	1.00
(12) Population Growth	0.16	−0.36	0.19	−0.19	0.01	0.47	−0.40	−0.40	−0.43	−0.13	−0.24	1.00
(13) Government Expenditure	−0.01	0.34	−0.12	0.14	−0.07	−0.42	0.35	0.41	0.43	0.43	0.38	−0.21	1.00
(14) Water Facility	−0.22	0.37	−0.20	0.13	−0.01	−0.52	0.50	0.61	0.62	0.26	0.59	−0.31	0.25	1.00
(15) Health Expenditure	−0.12	0.65	−0.24	0.31	−0.05	−0.65	0.59	0.77	0.74	0.28	0.62	−0.40	0.61	0.47	1.00
(16) GDP growth	−0.03	0.13	−0.06	0.27	0.12	−0.13	0.17	0.12	0.16	0.16	0.00	−0.30	0.05	0.09	0.13	1.00
(17) Voice and Accountability	−0.07	0.54	−0.15	0.19	−0.04	−0.51	0.56	0.69	0.60	0.30	0.49	−0.32	0.44	0.39	0.73	0.11	1.00
(18) Government Effectiveness	−0.21	0.65	−0.22	0.21	−0.06	−0.73	0.62	0.86	0.77	0.39	0.60	−0.33	0.47	0.54	0.75	0.14	0.78	1.00
(19) Regulator Quality	−0.13	0.63	−0.23	0.20	−0.06	−0.68	0.61	0.82	0.74	0.40	0.59	−0.35	0.46	0.50	0.74	0.11	0.84	0.93	1.00
(20) Rule of Law	−0.16	0.66	−0.19	0.22	−0.07	−0.66	0.58	0.82	0.72	0.37	0.56	−0.30	0.47	0.48	0.75	0.10	0.84	0.95	0.94	1.00
(21) Political Stability	−0.12	0.49	−0.15	0.16	−0.06	−0.55	0.45	0.69	0.59	0.43	0.45	−0.27	0.36	0.39	0.62	0.20	0.70	0.75	0.71	0.77	1.00
(22) Corruption Control	−0.15	0.62	−0.20	0.23	−0.06	−0.63	0.55	0.70	0.69	0.36	0.56	−0.27	0.48	0.45	0.75	0.08	0.80	0.92	0.89	0.96	0.75	1.00

Source: Authors’ computation based on secondary data.

. The correlation analysis reveals that inequality is negatively associated with formal financial access (−0.20) and digital credit & transfers (−0.06), suggesting that greater integration into the formal financial system may contribute to reducing disparities. Interestingly, inequality is positively correlated with mobile money (0.17), indicating that in contexts with higher inequality, individuals tend to rely more heavily on alternative financial technologies rather than traditional banking. This finding highlights the dual role of fintech: while it broadens access, it may also signal institutional gaps in formal financial inclusion [57].

Financial inclusion variables display distinct patterns of association. Formal financial access shows strong positive correlations with SDG8 (0.67) and SDG9 (0.70), underlining its role in supporting economic growth and infrastructure development [77]. It is also strongly associated with governance quality indicators such as government effectiveness (0.65) and rule of law (0.66), emphasizing the institutional foundations required for financial deepening. By contrast, mobile money is only moderately linked to rural finance (0.39), suggesting it primarily complements outreach in underserved areas [78]. Digital credit & transfers correlate positively with formal access (0.53), reinforcing their dependence on an established financial ecosystem [1].

The SDGs exhibit distinct interlinkages. SDG3 (health) correlates positively with inequality (r = 0.40), suggesting that health outcomes are weaker in unequal contexts, despite improvements in financial access [79]. SDG4 (education) and SDG8 (economic growth) are strongly connected (r = 0.60), and both exhibit robust positive correlations with urbanization, governance, and financial access, indicating complementarities between human capital and economic expansion. SDG9 (infrastructure and innovation) emerges as the most interconnected dimension, displaying high correlations with SDG8 (0.84), SDG4 (0.63), and formal access (0.70). These results underscore the importance of infrastructure as a central node for achieving multiple development objectives simultaneously [80,81,82].

Governance variables consistently demonstrate high internal correlations (above 0.70), reflecting institutional coherence across dimensions such as regulatory quality, the rule of law, and political stability [83]. They are also strongly linked to formal financial access and SDGs, particularly SDG8 and SDG9. For instance, government effectiveness correlates 0.86 with SDG8 and 0.77 with SDG9, illustrating the enabling role of institutional strength in advancing sustainable development. This alignment between financial and governance systems suggests that institutional quality is not only complementary but essential to realizing the benefits of financial inclusion [84].

Finally, social expenditures and infrastructure services show positive correlations with both governance and development outcomes. Health expenditure is strongly associated with governance quality (0.75, particularly with the rule of law, and formal access (0.65). Water facilities, on the other hand, correlate positively with SDG4 (0.50), SDG8 (0.61), and urbanization (0.59) [85]. Trade and GDP growth, by contrast, exhibit weaker correlations, suggesting that short-term macroeconomic performance does not fully capture the dynamics of sustainable development. Collectively, these results indicate that institutional strength, financial inclusion, and infrastructure are the principal drivers of progress toward the SDGs. At the same time, inequality remains a persistent barrier that fintech alone cannot overcome [86].

To evaluate possible multicollinearity among the variables, we computed the variance inflation factor (VIF) (See

Table 4. Variance Inflation Factor.

Variable	VIF	1/VIF
Rule of Law	27.09	0.037
Government Efficiency	15.22	0.066
Regulator Quality	12.76	0.078
Corruption Control	12.55	0.080
Voice and Accountability	4.56	0.219
Health Expenditure	4.55	0.220
Political Stability	2.98	0.336
Formal Financial Access	2.85	0.350
Urbanisation	2.25	0.444
Government Expenditure	2.01	0.498
Water Facility	1.83	0.546
Digital Credit and Transfers	1.82	0.548
Trade	1.64	0.610
Population Growth	1.42	0.706
Mobile Money	1.40	0.713
GDP growth	1.31	0.765
Rural Index	1.30	0.770
Inequality	1.20	0.830
Mean VIF	5.49

). As posited by Anane & Nie (2022) [87], VIF measurements provide crucial diagnostic insight to identify correlated predictors that might distort model interpretation. We find that the mean value of VIF is 5.49, which is below the conventional threshold of 10 [88]. However, several institutional quality variables exhibit substantially high VIF values, including Rule of Law (27.09), Government Effectiveness (15.22), Regulatory Quality (12.76), and Control of Corruption (12.55). These values suggest a high degree of collinearity among governance indicators, consistent with their conceptual and empirical overlap [89].

By contrast, most financial inclusion variables (e.g., Formal Financial Access, 2.85; Mobile Money Index, 1.40; Digital Credit & Transfers, 1.82) and socio-economic controls (e.g., Population Growth, 1.42; GDP growth, 1.31) exhibit low VIF values, indicating that multicollinearity is not a concern for these dimensions. The moderate values for variables such as Health Expenditure (4.55) and Voice and Accountability (4.56) remain within acceptable limits.

3.3. Empirical Model

The general model is presented as follows:

$${SDG\_3}_{i,t}={\beta }_0+{\beta }_1{FFA}_{i,t}+{\beta }_2{MobileMoney}_{i,t}+{\beta }_3{DigitalCreditT}_{i,t}+{\beta }_4{RuralFI}_{i,t}+{\sum }_{m=1}^M{\beta }_m{X}_{m,i,t}+{\eta }_i+{\mu }_{i,t}$$

(1)

$${SDG\_4}_{i,t}={\beta }_0+{\beta }_1{FFA}_{i,t}+{\beta }_2{MobileMoney}_{i,t}+{\beta }_3{DigitalCreditT}_{i,t}+{\beta }_4{RuralFI}_{i,t}+{\sum }_{m=1}^M{\beta }_m{X}_{m,i,t}+{\eta }_i+{\mu }_{i,t}$$

(2)

$${SDG\_8}_{i,t}={\beta }_0+{\beta }_1{FFA}_{i,t}+{\beta }_2{MobileMoney}_{i,t}+{\beta }_3{DigitalCreditT}_{i,t}+{\beta }_4{RuralFI}_{i,t}+{\sum }_{m=1}^M{\beta }_m{X}_{m,i,t}+{\eta }_i+{\mu }_{i,t}$$

(3)

$${SDG\_9}_{i,t}={\beta }_0+{\beta }_1{FFA}_{i,t}+{\beta }_2{MobileMoney}_{i,t}+{\beta }_3{DigitalCreditT}_{i,t}+{\beta }_4{RuralFI}_{i,t}+{\sum }_{m=1}^M{\beta }_m{X}_{m,i,t}+{\eta }_i+{\mu }_{i,t}$$

(4)

where SDG 3 for health, SDG 4 for education, SDG 8 for decent work, and SDG 9 for innovation represent the dependent variables in each model, respectively, the primary exploratory variables are four composite indices created using 3-Stage Principal Component Analysis to capture different dimensions of digital financial inclusion for $i$ country in period $t$: Formal Financial Access Index ${FFA}_{i,t}$, Mobile Money Inclusion Index ${MobileMoney}_{i,t}$, Digital Credit and Transfers Index ${DigitalCreditT}_{i,t}$ and Rural Financial Index ${RuralFI}_{i,t}$.

Additionally, the model includes a set of control variables $X_{m,i,t}$ for each country in each period to account for country-specific economic, demographic, and institutional conditions. These include the income inequality index standardized (SWIID), trade openness, GDP per capita, health expenditure, urbanization, and several governance dimensions from the World Bank Indicators, such as control of corruption, government expenditure, rule of law, and regulatory quality. Finally, ${\eta }_i$ and ${\mu }_{i,t}$ represent the time effect and the error term, respectively, for unobserved shocks or country-specific effects not captured by the model’s variables.

To examine the influence of digital financial inclusion on SDG outcomes, we employ a panel data technique estimated using fixed effects. This approach enables us to capture heterogeneous effects across the conditional distribution of the dependent variables, acknowledging that the impact of financial access and technology may vary between low-performing and high-performing countries [15].

To determine the most appropriate panel data specification, we conducted a Hausman test to compare fixed-effects and random-effects estimators. The null hypothesis of the Hausman test is that random effects are consistent and efficient. At the same time, the alternative suggests that fixed effects are preferred because regressors are correlated with unobserved individual effects [90]. The test results were statistically significant ($X^2=107.65;$ p leading us to reject the null hypothesis and conclude that fixed effects estimation is more suitable for our dataset. This implies that unobserved country-specific characteristics are correlated with the explanatory variables, and therefore, controlling for these time-invariant effects is necessary to obtain consistent parameter estimates.

Additionally, as the sample includes countries with specific characteristics that influence the normality of the distribution and different populations [91], a quantile regression was conducted to test for heterogeneity. The model pioneered by Koenker & Bassett (1978) [92] complemented this analysis by examining the impact of fintech across different levels of the dependent variables, recognizing that the effects may vary throughout the distribution (lower vs. higher achievement of each SDG). This approach addresses potential heteroscedasticity and provides a more nuanced understanding of how financial inclusion impacts less developed areas compared to more developed ones [8,93,94]. Furthermore, to account for unobserved heterogeneity in panel data and ensure consistent estimation under fixed effects, we applied a two-step estimator based on a transformation that removes location-shifting effects, following the framework proposed for quantile regression models with fixed effects [91].

4. Results and Discussions

The direct effects of the digital financial inclusion (DFI) dimensions on the achievement of the Sustainable Development Goals (SDGs) are presented in

Table 5. Impact of Digital Financial Inclusion and Inequality in SDG 3-Health.

Variables	(1) Panel Data (FE)		(2) (0.25)		(3) (0.50)		(4) (0.75)
Formal Financial Access (Index)	−0.028 **	(0.014)	−0.006	(0.009)	−0.013	(0.01)	−0.031 ***	(0.01)
Mobile Money (Index)	−0.021 **	(0.01)	−0.005	(0.006)	−0.014 **	(0.007)	−0.028 ***	(0.006)
Digital Credit & Transfers (Index)	0.023 ***	(0.009)	0.006	(0.007)	0.027 ***	(0.007)	0.040 ***	(0.007)
Rural Finance (Index)	0.019 **	(0.008)	0.011 *	(0.006)	0.014 **	(0.006)	0.031 ***	(0.006)
Inequality	−0.100 ***	(0.038)	−0.099 ***	(0.005)	−0.103 ***	(0.006)	−0.101 ***	(0.006)
Trade	0.039 *	(0.022)	0.032 ***	(0.006)	0.030 ***	(0.007)	0.039 ***	(0.007)
Urbanisation	−0.318 **	(0.143)	−0.255 ***	(0.007)	−0.245 ***	(0.008)	−0.233 ***	(0.008)
Population Growth	−0.013	(0.01)	−0.011 *	(0.006)	−0.015 **	(0.006)	−0.018 ***	(0.006)
Government Expenditure	−0.074 ***	(0.023)	−0.062 ***	(0.007)	−0.067 ***	(0.008)	−0.068 ***	(0.007)
Water Facility	−0.025	(0.02)	−0.024 ***	(0.007)	−0.036 ***	(0.007)	−0.034 ***	(0.007)
Health Expenditure	0.124 ***	(0.033)	0.121 ***	(0.01)	0.118 ***	(0.011)	0.117 ***	(0.011)
GDP growth	0.044 ***	(0.009)	0.044 ***	(0.006)	0.038 ***	(0.006)	0.037 ***	(0.006)
Voice and Accountability	−0.008	(0.05)	0.000	(0.01)	−0.008	(0.011)	−0.011	(0.011)
Government Effectiveness	−0.066	(0.048)	−0.073 ***	(0.019)	−0.047 **	(0.021)	−0.090 ***	(0.02)
Regulator Quality	−0.049	(0.047)	−0.047 ***	(0.018)	−0.029	(0.019)	−0.047 **	(0.018)
Rule of Law	0.068	(0.061)	0.036	(0.026)	0.032	(0.028)	0.095 ***	(0.027)
Political Stability	−0.016	(0.027)	−0.013	(0.008)	−0.007	(0.009)	−0.013	(0.009)
Corruption Control	−0.064	(0.05)	−0.038 **	(0.017)	−0.060 ***	(0.019)	−0.056 ***	(0.018)
Time effect	−0.001	(0.004)	−0.028 ***	(0.006)	−0.020 ***	(0.007)	−0.007	(0.006)
Constant	1.910	(7.218)	−0.004	(0.016)	0.042 **	(0.018)	0.076 ***	(0.017)
Observations	589		589		589		589
Pseudo R2 (for quantile regressions)			0.6938		0.6918		0.6765
R2 Within
R2 Between	0.1688
R2 Overall	0.1673

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard errors are in parentheses.

,

Table 6. Impact of Digital Financial Inclusion and Inequality in SDG 4-Education.

Variables	(1) Panel Data (FE)		(2) (0.25)		(3) (0.50)		(4) (0.075)
Formal Financial Access (Index)	0.032	(0.052)	0.010	(0.035)	0.027	(0.016)	0.031	(0.042)
Mobile Money (Index)	−0.001	(0.038)	0.014	(0.023)	0.001	(0.011)	−0.008	(0.028)
Digital Credit & Transfers (Index)	−0.015	(0.034)	−0.027	(0.026)	−0.037 ***	(0.012)	−0.003	(0.031)
Rural Finance (Index)	0.020	(0.03)	−0.004	(0.022)	0.003	(0.01)	−0.003	(0.026)
Inequality	−0.123	(0.144)	−0.107 ***	(0.021)	−0.123 ***	(0.01)	−0.104 ***	(0.025)
Trade	0.104	(0.083)	0.107 ***	(0.024)	0.084 ***	(0.011)	0.082 ***	(0.029)
Urbanisation	0.080	(0.547)	0.049 *	(0.028)	0.093 ***	(0.013)	0.079 **	(0.034)
Population Growth	0.009	(0.038)	−0.003	(0.023)	−0.002	(0.011)	−0.023	(0.027)
Government Expenditure	−0.135	(0.086)	−0.119 ***	(0.027)	−0.121 ***	(0.013)	−0.133 ***	(0.032)
Water Facility	0.012	(0.078)	0.003	(0.026)	0.007	(0.012)	0.029	(0.031)
Health Expenditure	0.140	(0.125)	0.132 ***	(0.04)	0.123 ***	(0.019)	0.110 **	(0.048)
GDP growth	0.007	(0.034)	0.006	(0.022)	0.016	(0.01)	0.005	(0.026)
Voice and Accountability	0.356 *	(0.192)	0.448 ***	(0.04)	0.347 ***	(0.019)	0.286 ***	(0.048)
Government Effectiveness	0.279	(0.185)	0.313 ***	(0.074)	0.221 ***	(0.035)	0.109	(0.089)
Regulator Quality	0.266	(0.182)	0.328 ***	(0.067)	0.273 ***	(0.032)	0.230 ***	(0.081)
Rule of Law	−0.282	(0.236)	−0.378 ***	(0.098)	−0.217 ***	(0.046)	−0.145	(0.118)
Political Stability	−0.035	(0.104)	−0.069 **	(0.033)	−0.027 ***	(0.015)	−0.027	(0.039)
Corruption Control	0.021	(0.192)	0.053	(0.067)	0.021	(0.031)	0.126	(0.08)
Time effect	0.009	(0.014)	0.034	(0.024)	0.017	(0.011)	0.028	(0.028)
Constant	−18.486	(27.71)	−0.215 ***	(0.062)	−0.021	(0.029)	0.115	(0.074)
Observations	589		589		589		589
Pseudo R2 (for quantile regressions)			0.5475		0.6233		0.6053
R2 Within	0.0568
R2 Between	0.5243
R2 Overall	0.4207

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard errors are in parentheses.

,

Table 7. Impact of Digital Financial Inclusion and Inequality in SDG 8- Decent Work and Economic Growth.

Variables	(1) Panel Data (FE)		(2) (0.25)		(3) (0.50)		(4) (0.75)
Formal Financial Access (Index)	−0.009	(0.011)	−0.006	(0.009)	−0.010	(0.007)	−0.022 **	(0.01)
Mobile Money (Index)	−0.005	(0.008)	−0.005	(0.006)	−0.005	(0.005)	−0.010	(0.007)
Digital Credit & Transfers (Index)	0.018 **	(0.007)	0.018 ***	(0.007)	0.018 ***	(0.005)	0.031 ***	(0.008)
Rural Finance (Index)	0.003	(0.006)	0.004	(0.006)	0.004	(0.004)	0.003	(0.007)
Inequality	0.039	(0.03)	0.039 ***	(0.005)	0.045 ***	(0.004)	0.039 ***	(0.006)
Trade	0.006	(0.018)	0.008	(0.006)	0.005	(0.005)	0.006	(0.007)
Urbanisation	0.057	(0.115)	0.058 ***	(0.007)	0.068 ***	(0.006)	0.079 ***	(0.008)
Population Growth	0.015 *	(0.008)	0.018 ***	(0.006)	0.021 ***	(0.005)	0.014 **	(0.007)
Government Expenditure	0.020	(0.018)	0.029 ***	(0.007)	0.018 ***	(0.005)	0.008	(0.008)
Water Facility	−0.001	(0.016)	0.000	(0.007)	−0.002	(0.005)	−0.003	(0.008)
Health Expenditure	0.016	(0.026)	0.008	(0.01)	0.026 ***	(0.008)	0.025 **	(0.012)
GDP growth	0.014 **	(0.007)	0.011 **	(0.006)	0.017 ***	(0.004)	0.020 ***	(0.007)
Voice and Accountability	−0.121 ***	(0.041)	−0.104 ***	(0.01)	−0.120 ***	(0.008)	−0.129 ***	(0.012)
Government Effectiveness	0.100 **	(0.039)	0.094 ***	(0.019)	0.110 ***	(0.015)	0.115 ***	(0.022)
Regulator Quality	0.024	(0.038)	0.039 **	(0.017)	0.023 ***	(0.014)	−0.014	(0.02)
Rule of Law	0.184 ***	(0.05)	0.159 ***	(0.025)	0.167 ***	(0.02)	0.207 ***	(0.029)
Political Stability	0.032	(0.022)	0.029 ***	(0.008)	0.028 ***	(0.007)	0.033 ***	(0.01)
Corruption Control	0.009	(0.041)	0.028	(0.017)	0.017	(0.014)	−0.002	(0.02)
Time Effect	0.005 *	(0.003)	0.018 ***	(0.006)	0.015 ***	(0.005)	0.014 **	(0.007)
Constant	−10.72 *	(5.845)	−0.096 ***	(0.016)	−0.037 ***	(0.013)	0.022	(0.019)
Observations	589		589		589		589
Pseudo R2 (for quantile regressions)			0.7200		0.7367		0.7414
R2 Within	0.2376
R2 Between	0.7451
R2 Overall	0.7271

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard errors are in parentheses.

and

Table 8. Impact of Digital Financial Inclusion and Inequality in SDG 9- Industry, Innovation and Infrastructure.

Variables	(1) Panel Data (FE)		(2) (0.25)		(3) (0.50)		(4) (0.75)
Formal Financial Access (Index)	−0.030	(0.026)	−0.016	(0.021)	−0.022	(0.016)	−0.068 **	(0.027)
Mobile Money (Index)	0.038 **	(0.019)	0.026 *	(0.014)	0.056 ***	(0.011)	0.027	(0.018)
Digital Credit & Transfers (Index)	−0.060 ***	(0.017)	−0.061 ***	(0.016)	−0.056 ***	(0.012)	−0.035 *	(0.02)
Rural Finance (Index)	0.065 ***	(0.015)	0.059 ***	(0.013)	0.072 ***	(0.01)	0.083 ***	(0.017)
Inequality	−0.043	(0.071)	−0.035 ***	(0.013)	−0.051 ***	(0.01)	−0.066 ***	(0.016)
Trade	0.077 *	(0.041)	0.087 ***	(0.015)	0.085 ***	(0.011)	0.092 ***	(0.018)
Urbanisation	0.530 **	(0.267)	0.520 ***	(0.017)	0.556 ***	(0.013)	0.527 ***	(0.022)
Population Growth	−0.021	(0.019)	−0.023	(0.014)	−0.029 ***	(0.01)	−0.008	(0.017)
Government Expenditure	−0.001	(0.043)	−0.006	(0.016)	−0.008	(0.012)	0.010	(0.02)
Water Facility	−0.026	(0.038)	−0.028 *	(0.016)	−0.040 ***	(0.012)	−0.035 *	(0.02)
Health Expenditure	0.169 ***	(0.062)	0.173 ***	(0.025)	0.182 ***	(0.019)	0.161 ***	(0.031)
GDP growth	−0.015	(0.017)	−0.007	(0.013)	−0.013	(0.01)	−0.016	(0.017)
Voice and Accountability	0.086	(0.095)	0.115 ***	(0.025)	0.062 ***	(0.019)	0.085 ***	(0.031)
Government Effectiveness	0.120	(0.091)	0.128 ***	(0.046)	0.106 ***	(0.035)	0.094 *	(0.057)
Regulator Quality	0.089	(0.09)	0.092 **	(0.041)	0.076 ***	(0.031)	0.032	(0.052)
Rule of Law	0.129	(0.116)	0.136 **	(0.06)	0.158 ***	(0.046)	0.151 **	(0.075)
Political Stability	0.024	(0.052)	0.023	(0.02)	0.006	(0.015)	0.007	(0.025)
Corruption Control	−0.098	(0.095)	−0.138 ***	(0.041)	−0.098 ***	(0.031)	−0.030	(0.051)
Time effect	0.275 ***	(0.022)	0.261 ***	(0.015)	0.255 ***	(0.011)	0.289 ***	(0.018)
Constant	−0.688	(0.055)	−0.787 ***	(0.038)	−0.642 ***	(0.029)	−0.577 ***	(0.048)
Observations	589		589		589		589
Pseudo R2 (for quantile regressions)			0.7906		0.8076		0.7908
R2 Within	0.7186
R2 Between	0.8238
R2 Overall	0.8035

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard errors are in parentheses.

. The fixed-effects panel regression results for SDG 3 (Health) are reported in column (1) of Table 5. All four dimensions of digital financial inclusion exhibit statistical significance at least at the 5 percent level. On one hand, the Formal Financial Access (FFA) and Mobile Money dimensions exhibit negative coefficients, suggesting that greater DFI contributes to a reduction in maternal mortality. On the other hand, Digital Credit and Transfers (DCT) and Rural Financial Inclusion show positive coefficients. This combined pattern may be explained by the inverted U-shaped relationship of DFI, indicating that beyond a certain threshold, excessive financial inclusion may generate instability or underinvestment for vulnerable populations —thereby underscoring the need for balanced policy strategies [95].

To examine heterogeneity across levels of development, columns (2)–(4) report the results for the lower, median, and upper quantiles, respectively. In the lower quantile, only the Rural Index displays a marginal level of significance, suggesting that DFI effects are concentrated in rural areas. High maternal mortality rates persist in rural and underserved areas, particularly in regions like Eastern Europe and sub-Saharan Africa, due to inadequate access to healthcare services [96,97]. In the median quantile regression—representing economies at mid-level stages of development—the Mobile Money Inclusion, Digital Credit and Transfers, and Rural Financial Inclusion indices are significant at the 5% and 1% levels, respectively.

In the upper quantiles, where the number of maternal deaths is higher, the FFA exhibits a negative coefficient, as expected, indicating that greater access to formal financial services reduces maternal deaths by facilitating financial security and access to health-related resources. Additionally, the Mobile Money Index is negative at the 95% level of confidence, suggesting that access to mobile money may reduce maternal mortality. This effect may be linked to improved liquidity, enabling households to afford medical services, transportation, or emergency care when needed [98]. These results are in accordance with other studies, demonstrating that fragmentation of health financing and services often leads to disparities in maternal health outcomes, particularly among marginalized populations [99].

Table 6 examines the influence of digital financial inclusion on education. For SDG 4, the panel regression results for the entire sample indicate, column (1), that none of the financial inclusion indices are statistically significant, except for the governance dimension of Voice and Accountability. These results are consistent with studies related to DFI and SDG impact [15]. Individuals who may not have the skills to navigate digital financial services effectively could potentially be susceptible to overspending and poor financial management, which may not affect their social mobility [100].

However, when accounting for heterogeneity through quantile regression (columns 2–4), the DCT Index becomes relevant in the median quantile, indicating that digital financial services play a role regardless of whether countries are educated. This suggests that digital finance significantly promotes high-quality education development, with its effects varying by regional economic levels [101]. In regions where education is low, the introduction of digital finance does not always translate into effective financial inclusion. The complexity of digital tools can deter users, especially those with lower educational backgrounds, from fully utilizing these services, which can perpetuate existing financial exclusion and may reflect the continued importance of addressing vulnerable populations, even in contexts with higher overall educational attainment [102,103].

Moreover, the Government Effectiveness dimension shows strong relevance in both median and less educated countries, highlighting the critical role of institutional quality in achieving educational outcomes. Finally, in less educated countries, the Formal Financial Access (FFA) Index emerges as significant, suggesting that expanding access to the formal financial sector can contribute to increased access to education and a reduction in illiteracy [104].

Table 7 presents the results for SDG 8 (Decent Work and Economic Growth). For both the regression using the full sample (column 1) and the heterogeneity tests (columns 2 to 4), the DCT dimension is statistically positive and significant at the 5% level in the full-sample fixed effects model, and at the 1% level across all three quantiles. This underscores that economic and financial factors exert a greater influence on decent work outcomes in contexts where the informal employment sector prevails. The DCT Index proves particularly relevant, indicating that digital financial tools, including mobile money and digital accounts, are closely tied to improvements in employment and labor market participation. Financial inclusion is crucial for improving employment levels, particularly in low- and middle-income countries, where it has a greater impact on marginalized groups [105]. The integration of digital finance is crucial for improving the competitiveness of industry sectors, as seen in the UAE’s banking industry during the COVID-19 pandemic [106]. The findings suggest that digital finance usage can significantly boost employment opportunities and promote entrepreneurship, thereby contributing to economic resilience [107,108].

Conversely, DFI is shown to have heterogeneous impacts across income groups, with significant effects observed in upper-middle-income economies with more formal and decent work structures [109]. At the upper quantile, the Formal Financial Access (FFA) Index has a negative and significant impact, reflecting the constraints imposed by formal financial intermediaries on formal job creation, labor market participation, and investment in human capital. This insight aligns with the findings of Malkova et al. (2021) [110], who demonstrate that a relaxation of credit constraints increases the probability of transitioning from an informal to a formal job, thereby enhancing the likelihood that informal sector workers will formalize their employment.

Macroeconomic indicators consistently exhibit significant effects across quantiles, as expected given the intrinsic relationship between economic growth and employment embodied in SDG 8. Governance dimensions also play a substantial role, albeit with mixed effects: Voice and Accountability shows negative associations, whereas Government Effectiveness, Political Stability, and Rule of Law show positive associations.

Finally, Table 8 presents the results for SDG 9 (Industry, Innovation, and Infrastructure). The results (columns 1 to 4) indicate a substantial impact on most explanatory variables. The Rural Financial Index is positive and significant at the 99% confidence level across all three quantiles, suggesting that investment in rural-focused financial products promotes innovation and contributes to national development. Moreover, innovation is recognized as a key aspect of development, particularly in rural territories, where it can fulfill citizens’ needs and promote development [111]. Traditionally, rural access has been associated with physical infrastructure, such as roads or urbanization; however, our findings confirm that expanding specialized rural financial services also drives innovation and enhances social well-being. Conversely, the DCT index is negative and significant at 1% across the lower and median quantiles, where the economies present minor levels of technological innovation. This suggests that the interdependence of financial inclusion, technological infrastructure, and governance is emphasized as essential for achieving long-term economic growth in emerging economies [112]. DCT’s negative compensatory effects may primarily stem from behavioral changes due to inefficient economic policy and taxation [113].

In countries where innovation (in the upper quantiles) is more supported, governance indicators do not show a substantial impact. The index Mobile Money Inclusion demonstrates positive and significant associations with SDG 9 outcomes, underscoring the role of financial services in providing firms and households with the liquidity and credit necessary for technological adoption and infrastructure investment [114,115]. This aligns with prior evidence that access to finance stimulates business creation, innovation, and long-term productivity growth. Given the nature of SDG 9, the influence of digital financial inclusion indices is particularly notable (column 1), highlighting their central role in supporting innovation-led development.

5. Robustness Tests

5.1. Income Analysis

Consistent with the literature [15], this study estimates the models using the OLS method for income level. The cross-country analysis reveals firm heterogeneity in how digital financial inclusion and governance affect SDG outcomes across income groups. In high-income countries, digital finance—particularly digital credit, transfers, and rural finance—plays a central role in supporting economic growth (SDG 8) and infrastructure (SDG 9), while formal financial access mainly benefits education (SDG 4). Yet, inequality persists as a barrier to health and infrastructure, and governance quality shows mixed effects, requiring redistributive and institutional policies to complement digital finance.

In upper-middle-income countries, digital finance channels, such as mobile money and formal access to support infrastructure (SDG 9) and healthcare (SDG 3), are often adopted, albeit at the cost of economic growth. In lower-middle-income countries, formal financial access and rural finance play broader roles, improving education (SDG 4), development (SDG 8), and infrastructure, while inequality remains a significant obstacle. In low-income countries, financial inclusion has a narrower scope: formal access benefits only infrastructure, while trade, urbanization, and institutional quality are stronger determinants of growth and SDG progress.

Taken together, the results suggest that digital finance is more effective in high and middle-income economies, where infrastructure and governance are relatively stronger. In contrast, low-income economies rely more heavily on structural reforms, effective institutions, and equitable service delivery to advance the SDGs. The results of the Income Analysis in

Table A2. Impact of Digital Financial Inclusion and Inequality on SDGs for High-Income countries.

Variables	SDG 3		SDG 4		SDG 8		SDG 9
Formal Financial Access (Index)	−0.060	(0.081)	0.189 **	(0.085)	−0.025	(0.054)	0.369 ***	(0.054)
Mobile Money (Index)	0.020	(0.064)	−0.087	(0.067)	−0.071 *	(0.042)	0.078 *	(0.043)
Digital Credit & Transfers (Index)	0.057	(0.08)	−0.023	(0.084)	0.139 **	(0.053)	0.111 **	(0.054)
Rural Finance (Index)	−0.024	(0.066)	0.006	(0.069)	−0.053	(0.044)	0.138 ***	(0.044)
Inequality	0.10 ***	(0.018)	−0.025	(0.019)	−0.008	(0.012)	−0.038 ***	(0.012)
Trade	0.21 ***	(0.082)	0.075	(0.086)	0.157 ***	(0.055)	0.021	(0.055)
Urbanisation	−0.059	(0.081)	0.139	(0.085)	0.096 *	(0.054)	0.135 **	(0.055)
Population Growth	−0.007	(0.07)	−0.065	(0.073)	0.100 **	(0.046)	0.098 **	(0.047)
Government Expenditure	−0.110	(0.111)	0.196 *	(0.116)	−0.119	(0.074)	0.064	(0.075)
Water Facility	0.093	(0.066)	−0.020	(0.069)	0.008	(0.044)	0.028	(0.044)
Health Expenditure	0.27 ***	(0.105)	−0.079	(0.111)	0.134 *	(0.07)	0.002	(0.071)
GDP growth	0.096	(0.082)	0.013	(0.086)	0.009	(0.055)	0.120 **	(0.055)
Voice and Accountability	0.012	(0.11)	0.39 ***	(0.116)	−0.256 ***	(0.073)	−0.342 ***	(0.074)
Government Effectiveness	−0.007	(0.21)	−0.50 **	(0.22)	0.441 ***	(0.139)	0.168	(0.141)
Regulator Quality	−0.181	(0.176)	−0.241	(0.185)	−0.319 ***	(0.117)	−0.036	(0.118)
Rule of Law	0.158	(0.298)	0.150	(0.313)	0.493 **	(0.198)	0.302	(0.201)
Political Stability	0.143	(0.097)	−0.151	(0.102)	−0.119 *	(0.064)	0.053	(0.065)
Corruption Control	−0.273	(0.209)	0.348	(0.22)	0.326 **	(0.139)	0.102	(0.141)
Constant	−3.52 ***	(0.589)	0.871	(0.619)	0.237	(0.392)	1.236 ***	(0.397)
Observations	172		172		172		172
R2	0.3688		0.2243		0.7569		0.7457

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard Errors are in parentheses.

,

Table A3. Impact of Digital Financial Inclusion and Inequality on SDGs for Upper-Middle Income countries.

Variables	SDG3		SDG4		SDG8		SDG9
Formal Financial Access (Index)	−0.198 ***	(0.069)	−0.001	(0.09)	0.081	(0.087)	0.253 ***	(0.072)
Mobile Money (Index)	0.141 **	(0.066)	0.012	(0.087)	−0.233 ***	(0.084) ***	0.152 **	(0.069)
Digital Credit & Transfers (Index)	0.159 **	(0.073)	−0.080	(0.095)	0.101	(0.092)	0.011	(0.076)
Rural Finance (Index)	0.022	(0.059)	0.100	(0.077)	−0.024	(0.074)	0.139 **	(0.061)
Inequality	0.010 ***	(0.005)	−0.011 *	(0.007)	−0.007	(0.006)	−0.023 ***	(0.005)
Trade	−0.215 ***	(0.061)	0.023	(0.079)	−0.091	(0.076)	−0.075	(0.063)
Urbanisation	0.084	(0.058)	0.311 ***	(0.076)	0.369 ***	(0.074)	0.357 ***	(0.061)
Population Growth	0.462 ***	(0.059)	−0.377 ***	(0.077)	0.360 ***	(0.075)	−0.137 **	(0.061)
Government Expenditure	0.217 ***	(0.069)	−0.321 ***	(0.09)	−0.115	(0.087)	−0.024	(0.072)
Water Facility	0.306 **	(0.054)	0.067	(0.071)	−0.140 **	(0.068)	0.064	(0.056)
Health Expenditure	−0.186 **	(0.073)	0.240 **	(0.095)	0.361 ***	(0.092)	0.394 ***	(0.075)
GDP growth	0.138 ***	(0.053)	−0.132 *	(0.069)	−0.037	(0.067)	0.064	(0.055)
Voice and Accountability	0.452 ***	(0.08)	0.230 **	(0.104)	−0.373 ***	(0.101)	−0.213 **	(0.083)
Government Effectiveness	0.131	(0.097)	0.268 **	(0.126)	0.161	(0.122)	0.041	(0.101)
Regulator Quality	0.033	(0.111)	−0.003	(0.144)	0.073	(0.14)	0.445 ***	(0.115)
Rule of Law	0.051	(0.14)	−0.040	(0.183)	0.523 ***	(0.177)	0.076	(0.146)
Political Stability	−0.040	(0.067)	−0.144	(0.087)	0.117	(0.085)	−0.027	(0.07)
Corruption Control	−0.246 **	(0.098)	−0.051	(0.127)	−0.151	(0.123)	−0.149	(0.101)
Constant	−0.377 **	(0.189)	0.411 *	(0.247)	0.256	(0.239)	0.842 ***	(0.197)
Observations	168		168		168		168
R2	0.6456		0.3982		0.436		0.6174

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard Errors are in parentheses.

,

Table A4. Impact of Digital Financial Inclusion and Inequality on SDGs for Low-Middle Income countries.

Variables	SDG3		SDG4		SDG8		SDG9
Formal Financial Access (Index)	−0.115	(0.074)	0.152 **	(0.08)	0.178 ***	(0.065)	0.271 ***	(0.07)
Mobile Money (Index)	0.036	(0.069)	−0.161 **	(0.075)	−0.058	(0.061)	0.103	(0.065)
Digital Credit & Transfers (Index)	0.147 *	(0.087)	−0.053	(0.095)	−0.009	(0.077)	−0.069	(0.083)
Rural Finance (Index)	−0.047	(0.068)	0.092	(0.074)	0.044	(0.06)	0.283 ***	(0.064)
Inequality	0.021 **	(0.01)	−0.022 **	(0.011)	0.039 ***	(0.009)	−0.026 ***	(0.01)
Trade	0.045	(0.078)	−0.316 ***	(0.085)	−0.070	(0.069)	−0.048	(0.074)
Urbanisation	−0.033	(0.069)	−0.073	(0.075)	0.534 ***	(0.06)	0.384 ***	(0.065)
Population Growth	−0.005	(0.064)	0.083	(0.07)	−0.110 *	(0.057)	−0.137 **	(0.061)
Government Expenditure	0.005	(0.087)	0.213 **	(0.094)	−0.151 *	(0.076)	0.039	(0.082)
Water Facility	−0.373 ***	(0.075)	0.378 ***	(0.082)	0.074	(0.066)	0.175 **	(0.071)
Health Expenditure	−0.264 ***	(0.077)	−0.051	(0.084)	0.129 *	(0.068)	0.166 **	(0.073)
GDP growth	0.003	(0.065)	0.145 **	(0.071)	−0.056	(0.057)	−0.109 *	(0.061)
Voice and Accountability	0.320 ***	(0.072)	−0.063	(0.079)	−0.219 ***	(0.063)	−0.127 *	(0.068)
Government Effectiveness	−0.213 *	(0.108)	0.194	(0.118)	0.409 ***	(0.095)	0.149	(0.102)
Regulator Quality	−0.172 *	(0.098)	0.295 ***	(0.107)	−0.172 **	(0.087)	0.131	(0.093)
Rule of Law	−0.136	(0.143)	−0.446 ***	(0.156)	0.343 ***	(0.126)	−0.109	(0.135)
Political Stability	−0.123	(0.079)	0.181 **	(0.086)	−0.154 **	(0.069)	0.102	(0.074)
Corruption Control	0.279 **	(0.122)	0.285 **	(0.133)	−0.109	(0.108)	0.017	(0.115)
Constant	−0.831 **	(0.397)	0.836 **	(0.433)	−1.516 ***	(0.35)	1.019 ***	(0.375)
Observations	164		164		164		164
R2	0.5287		0.4393		0.6343		0.5793

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard Errors are in parentheses.

and

Table A5. Impact of Digital Financial Inclusion and Inequality on SDGs for Low-Income Countries.

Variables	SDG3		SDG4		SDG8		SDG9
Formal Financial Access (Index)	−0.127	(0.095)	−0.137	(0.129)	0.054	(0.104)	0.324	(0.106) ***
Mobile Money (Index)	0.011	(0.111)	−0.117	(0.151)	−0.244	(0.121) **	0.192	(0.124)
Digital Credit & Transfers (Index)	0.015	(0.124)	0.158	(0.169)	0.100	(0.135)	−0.127	(0.139)
Rural Finance (Index)	0.012	(0.143)	−0.253	(0.195)	0.054	(0.156)	0.019	(0.161)
Inequality	−0.024	(0.02)	0.027	(0.027)	−0.011	(0.021)	−0.008	(0.022)
Trade	−0.084	(0.121)	0.155	(0.166)	0.616 ***	(0.133)	0.181	(0.136)
Urbanisation	−0.164	(0.13)	0.136	(0.178)	−0.102	(0.142)	0.337 **	(0.146)
Population Growth	0.049	(0.099)	0.234 *	(0.135)	−0.344 ***	(0.108)	−0.155	(0.111)
Government Expenditure	−0.028	(0.131)	−0.014	(0.178)	−0.753 ***	(0.143)	−0.010	(0.146)
Water Facility	−0.431 ***	(0.138)	0.218	(0.188)	0.299 *	(0.151)	0.238	(0.155)
Health Expenditure	−0.183	(0.124)	0.178	(0.169)	−0.065	(0.135)	0.069	(0.138)
GDP growth	0.025	(0.094)	0.169	(0.129)	−0.035	(0.103)	−0.059	(0.106)
Voice and Accountability	0.010	(0.135)	0.042	(0.184)	−0.020	(0.147)	0.027	(0.151)
Government Effectiveness	−0.944 ***	(0.285)	0.045	(0.389)	−0.517	(0.311)	0.323	(0.319)
Regulator Quality	0.373 *	(0.218)	−0.214	(0.297)	0.440 *	(0.238)	−0.330	(0.244)
Rule of Law	−0.133	(0.322)	0.223	(0.44)	0.972 ***	(0.352)	0.350	(0.361)
Political Stability	0.460 ***	(0.15)	0.004	(0.205)	−0.559 ***	(0.164)	−0.240	(0.169)
Corruption Control	0.413 **	(0.18)	−0.022	(0.246)	−0.147	(0.197)	0.032	(0.202)
Constant	0.942	(0.767)	−1.056	(1.047)	0.446	(0.838)	0.324	(0.86)
Observations	84		84		84		84
R2	0.5165		0.1001		0.4234		0.3924

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard Errors are in parentheses.

are presented in Appendix A.2.

5.2. Simultaneous Equation Model

To ensure the validity of our results and address potential reverse causality, we further implement a robustness check using a simultaneous equation model (SEM), which mitigates concerns about endogeneity arising from bidirectional causality between financial inclusion, FinTech adoption, and SDG progress [116]. This SEM strengthens the reliability of our findings and aligns with best practices in the emerging literature on FinTech and sustainable development [117,118].

We estimate a system of five equations in which the SDG proxies and the digital financial inclusion indices are each treated as dependent variables, allowing us to capture the mutual relationships among them. The simultaneous equation model is estimated using a three-stage least squares (3SLS) regression technique, which combines the strengths of the instrumental variables (2SLS) and seemingly unrelated regression (SUR) approaches to obtain consistent and efficient estimates. In this framework, selected control variables serve as instruments to address potential endogeneity: the governance indicators [15] act as the primary instruments and Urbanization, while Water Facility is used for the FFA equation, the one-period lag of MMI, DCT, and Rural index for their respective equations, and Health Expenditure for the SDG equations. This approach ensures robust parameter estimation and captures the interdependencies between financial inclusion and sustainable development [119,120]. The results of the SEM in

Table A6. Reverse Causality analysis for SDG 3- Health.

	(1) SDG3		(2) FFA		(3) MMI		(4) DCT		(5) Rural FI
SDG3			−1.335 ***	(0.173)	1.194 ***	(0.378)	−0.729 **	(0.391)	0.457	(0.423)
FFA	−1.030 ***	(0.173)
MMI	0.122 **	(0.047)
DCT	−0.573 **	(0.247)
Rural FI	0.202	(0.375)
Inequality	0.177 ***	(0.35)	0.257 ***	(0.076)	−0.193	(0.103)	0.248	(0.114)	−0.009	(0.12)
Trade	0.015	(0.045)	−0.017	(0.044)	0.073	(0.052)	−0.089	(0.061)	0.098	(0.063)
Urbanisation			−0.032	(0.04)	0.158 *	(0.088)	−0.224 **	(0.108)	−0.096	(0.093)
Population Growth	0.119 ***	(0.04)	0.191 **	(0.078)	−0.181 **	(0.086)	0.108	(0.099)	−0.058	(0.102)
Government Expenditure	−0.102 **	(0.049	−0.100 *	(0.052)	0.074	(0.064)	−0.109	(0.067)	−0.039	(0.076)
Water Facility	−0.140 ***	(0.038)	−0.146 **	(0.045)	0.031	(0.055)	−0.043	(0.076)	0.181 ***	(0.065)
Health Expenditure	−0.166 ***	(0.054)
GDP growth	−0.031	(0.036)	−0.020	(0.04)	−0.151 ***	(0.051)	0.108	(0.099)	0.059	(0.062)
Voice and Accountability			0.084	(0.084)	0.063	(0.113)	−0.185	(0.122)	0.214	(0.137)
Government Effectiveness			−0.126	(0.133)	0.311	(0.23)	0.261	(0.2)	−0.102	(0.273)
Regulator Quality			−0.071	(0.085)	−0.132	(0.155)	0.126	(0.126)	−0.176	(0.19)
Rule of Law			−0.053	(0.112)	0.170	(0.179)	0.018	(0.142)	−0.196	(0.25)
Political Stability			−0.110 *	(0.065)	0.153 *	(0.081)	0.018	(0.052)	−0.008	(0.097)
Corruption Control			0.263 **	(0.118)	−0.136	(0.131)	−0.044	(0.094)	0.416 **	(0.168)
MMI_lag1					0.807 ***	(0.083)
DCT_lag1							0.037	(0.062)
RuralFI_lag1									−0.070	(0.148)
Constant			−1.011	(0.092)	−0.205	(0.187)	−2.234 ***	(0.237)	−1.279	(0.23)
Observations	589		581		443		443		443
R2	0.4217		0.2422		0.2759		0.1767		0.0650

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard Errors are in parentheses.

,

Table A7. Reverse Causality analysis for SDG 4- Education.

	(1) SDG4		(2) FFA		(3) MMI		(4) DCT		(5) Rural FI
SDG4			−0.034	(0.819)	−0.575	(1.427)	−1.249	(1.008)	0.457	(0.423)
FFA	0.305 *	(0.175)
MMI	−0.166 **	(0.061)
DCT	0.211	(0.243)
Rural FI	0.202	0.375
Inequality	−0.008	(0.036)	−0.042	(0.074)	0.116	(0.089)	0.004	(0.092)	−0.009	(0.12)
Trade	0.008	(0.045)	0.036	(0.082)	−0.027	(0.134)	−0.128	(0.112)	0.098	(0.063)
Urbanisation			0.159	(0.077)	0.108	(0.19)	−0.066	(0.176)	−0.096	(0.093)
Population Growth	−0.160 ***	(0.04)	0.015	(0.149)	−0.080	(0.177)	−0.133	(0.155)	−0.058	(0.102)
Government Expenditure	−0.028	(0.049)	−0.012	(0.092)	0.018	(0.111)	−0.025	(0.106)	−0.039	(0.076)
Water Facility			0.283	(0.127)	0.131	(0.237)	0.084	(0.191)	0.181 ***	(0.065)
Health Expenditure	0.224 **	(0.087)
GDP growth			−0.052	(0.082)	−0.033	(0.134)	0.114	(0.119)	0.059	(0.062)
Voice and Accountability			0.326	(0.163)	−0.205	(0.286)	0.509 ***	(0.192)	0.214	(0.137)
Government Effectiveness			0.889	(0.359)	−0.369	(0.468)	0.953 ***	(0.329)	−0.102	(0.273)
Regulator Quality			0.223 *	(0.124)	−0.532	(0.37)	0.865 **	(0.305)	−0.176	(0.19)
Rule of Law			−0.257	(0.275)	0.680	(0.509)	−0.952 ***	(0.385)	−0.196	(0.25)
Political Stability			−0.176	(0.118)	0.168	(0.132)	−0.144 ***	(0.083)	−0.008 ***	(0.097)
Corruption Control			−0.478	(0.214)	0.267	(0.208)	0.203 ***	(0.147)	0.416 ***	(0.168)
MMI_lag1					0.888 ***	(0.301)
DCT_lag1							−0.072	(0.092)
RuralFI_lag1									−0.070	(0.148)
Constant	0.234	(0.167)	−0.911	(0.183)	−0.347	(0.44)	−2.193 ***	(0.326)	−1.279	(0.23)
Observations	589		581		443		443		443
R2	0.4087		0.4089		0.2662		0.4045		0.0650

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard Errors are in parentheses.

,

Table A8. Reverse Causality analysis for SDG 8- Decent work and economic growth.

	(1) SDG8		(2) FFA		(3) MMI		(4) DCT		(5) Rural FI
SDG8			0.658 **	(0.293)	−0.991 ***	(0.345)	0.141	(0.34)	0.285	(0.411)
FFA	0.908 ***	(0.146)
MMI	−0.006	(0.032)
DCT	0.835 ***	(0.251)
Rural FI	−0.577	(0.44)
Inequality	−0.026	(0.03)	−0.009	(0.043)	0.026	(0.052)	0.064	(0.056)	0.150	(0.064)
Trade	0.019	(0.038)	0.016	(0.038)	0.043	(0.048)	−0.052	(0.05)	0.075	(0.059)
Urbanisation			0.106	(0.111)	0.313 **	(0.141)	−0.146	(0.152)	−0.510 ***	(0.149)
Population Growth	−0.020	(0.034)	−0.012	(0.044)	−0.041	(0.052)	−0.019	(0.057)	0.043	(0.065)
Government Expenditure	−0.075 *	(0.041)	0.025	(0.04)	−0.088 *	(0.052)	0.006	(0.053)	−0.025	(0.063)
Water Facility			−0.003	(0.024)	0.016 ***	(0.051)	−0.094 *	(0.055)	0.277 ***	(0.06)
Health Expenditure	0.108	(0.072)
GDP growth	0.014	(0.03)	−0.023	(0.033)	−0.124 ***	(0.046)	0.060	(0.05)	0.051	(0.058)
Voice and Accountability			−0.025	(0.039)	0.127 *	(0.077)	−0.065	(0.049)	0.121 *	(0.072)
Government Effectiveness			0.148	(0.148)	0.364 *	(0.214)	0.402 **	(0.159)	−0.621 ***	(0.232)
Regulator Quality			0.009	(0.061)	−0.199	(0.136)	0.050	(0.066)	−0.114	(0.096)
Rule of Law			0.114	(0.123)	0.270	(0.198)	0.164	(0.133)	−0.308	(0.228)
Political Stability			−0.128 **	(0.055)	0.150 **	(0.073)	0.045	(0.041)	−0.003	(0.09)
Corruption Control			−0.109	(0.102)	−0.155	(0.136)	−0.123	(0.078)	0.740 ***	(0.137)
MMI_lag1					0.927 ***	(0.067)
DCT_lag1							0.114 **	(0.061)
RuralFI_lag1									−0.185 *	(0.112)
Constant	0.794 ***	(0.139)	−0.863 ***	(0.076)	−0.373 **	(0.168)	−2.046 ***	(0.175)	−1.456 ***	(0.201)
Observations	589		581		443		443		443
R2	0.5906		0.5860		0.4586		0.3563		0.0409

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard Errors are in parentheses.

and

Table A9. Reverse Causality analysis for SDG 9- Industry, innovation and infrastructure.

	(1) SDG9		(2) FFA		(3) MMI		(4) DCT		(5) Rural FI
SDG9			0.662 **	(0.322)	−1.174 ***	(0.401)	0.387	(0.459)	0.955 **	(0.473)
FFA	0.840 ***	(0.155)
MMI	−0.020	(0.034)
DCT	0.414 **	(0.182)
Rural FI	−0.157	(0.288)
Inequality	−0.045	(0.032)	0.003	(0.049)	0.007	(0.059)	0.086	(0.069)	0.212 ***	(0.072)
Trade	0.048	(0.04)	−0.011	(0.043)	0.070	(0.051)	−0.092	(0.059)	0.109 *	(0.063)
Urbanisation			0.114	(0.1)	0.365 **	(0.155)	−0.229	(0.191)	−0.679 **	(0.177)
Population Growth	−0.068 *	(0.036)	0.024	(0.056)	−0.086	(0.063)	0.015	(0.077)	0.117	(0.077)
Government Expenditure	−0.009	(0.044)	−0.015	(0.045)	0.009	(0.055)	0.003	(0.065)	−0.064	(0.068)
Water Facility			0.029	(0.037)	0.162 **	(0.072)	−0.161 *	(0.085)	0.166	(0.089)
Health Expenditure	0.199 ***	(0.039)
GDP growth	−0.012	(0.032)	−0.006	(0.039)	−0.144 ***	(0.05)	0.074	(0.058)	0.066	(0.062)
Voice and Accountability			−0.066	(0.058)	0.112	(0.095)	−0.246 **	(0.096)	0.433 ***	(0.113)
Government Effectiveness			0.149	(0.133)	0.263	(0.193)	0.363 **	(0.162)	−0.491 **	(0.241)
Regulator Quality			0.091	(0.096)	−0.168	(0.169)	0.281 **	(0.138)	−0.556 ***	(0.197)
Rule of Law			0.064	(0.122)	0.223	(0.192)	0.139	(0.15)	−0.325	(0.249)
Political Stability			−0.094	(0.062)	0.147 *	(0.08)	0.048	(0.054)	−0.184 **	(0.086)
Corruption Control			−0.046	(0.115)	−0.181	(0.146)	−0.190	(0.116)	0.573 ***	(0.185)
MMI_lag1					0.919 ***	(0.071)
DCT_lag1							0.220 **	(0.105)
RuralFI_lag1									−0.341 **	(0.165)
Constant	0.028	(0.148)	−0.398	(0.25)	−1.303	(0.373)	−1.695 ***	(0.372)	−0.732 *	(0.415)
Observations	581		581		443		443		443
R2	0.5393		0.502		0.3432		0.275		0.0386

Source: Authors’ computation based on secondary data. *** p < 0.01, ** p < 0.05, * p < 0.01. Standard Errors are in parentheses.

are presented in Appendix A.3.

Results in Table A6 confirm the bidirectional links between financial inclusion and health outcomes (SDG 3). The coefficient for the impact of health on digital financial inclusion is significant and higher, suggesting that while improvements in digital financial inclusion do contribute to better health, the effect is modest compared to how better health enhances inclusion. Formal financial access reduces poor health outcomes (β = −1.335, p < 0.01). In contrast, mobile money (β = 1.194, p < 0.01) and digital credit (β = −0.729, p < 0.05) show positive and compensating effects, suggesting that digital channels complement formal finance. Rural finance remains insignificant. Feedback effects show that improved health reduces formal finance dependence (β = −1.030, p < 0.01) but increases mobile money use (β = 0.122, p < 0.05), indicating mutual causality. Governance factors such as corruption control and political stability play a reinforcing role. Our robustness tests confirm the consistency of these findings across model specifications

Results in Table A7 reveal heterogeneous effects of financial inclusion on education (SDG 4). Formal financial access positively affects educational outcomes (β = 0.305, p < 0.05), while mobile money shows a negative association (β = −0.166, p < 0.05), implying that rapid digitalization may create trade-offs for education access or quality. Feedback effects indicate that better education reduces mobile money use (β = –0.166, p < 0.05) but strengthens formal financial access (β = 0.305, p < 0.05). Institutional quality enhances education, as government effectiveness (β = 0.889, p < 0.01) and regulatory quality (β = 0.223, p < 0.05) exert strong positive effects. Access to water facilities (β = 0.283, p < 0.05) supports education, while population growth undermines it (β = −0.160, p < 0.01). Overall, the results highlight the complementary role of rural finance and governance in educational outcomes, while our robustness tests confirm the direction and stability of these effects.

Results in Table A8 show that financial inclusion dimensions exert diverse effects on SDG 8. Formal financial access promotes growth and employment (β = 0.908, p < 0.01), whereas mobile money use has a negative effect (β = −0.991, p < 0.01), suggesting substitution rather than complementarity with productive investment. Feedback dynamics confirm that higher growth reinforces formal finance (β = 0.658, p < 0.05) but reduces mobile money adoption (β = −0.006, p < 0.05). Inequality shows a slightly negative but not significant association with growth, while trade and urbanisation support it. Government expenditure remains adverse, whereas government effectiveness (β = 0.364, p < 0.05) and voice and accountability (β = 0.127, p < 0.1) contribute positively. Overall, the SEM indicates that finance, governance, and structural factors interact to shape inclusive growth. Robustness checks validate these relationships across income groups and estimators.

Finally, the results in Table A9 highlight the critical role of financial inclusion in fostering innovation and infrastructure (SDG 9). Formal financial access (β = 0.840, p < 0.01), digital credit (β = 0.414, p < 0.05), and rural finance (β = 0.955, p < 0.05) positively influence SDG 9 outcomes, while mobile money has mixed effects. Feedback effects indicate a reinforcing loop, with stronger industrial development promoting financial access (β = 0.662, p < 0.05) and digital credit expansion (β = 0.220, p < 0.05). Governance quality also matters, as regulatory quality (β = 0.281, p < 0.05) supports industrial progress, while government effectiveness shows a positive but not statistically significant association. Inequality (β = −0.045, p < 0.05) and population growth hinder outcomes, while water access (β = 0.162, p < 0.05) and health expenditure (β = 0.199, p < 0.01) enhance them. Robustness tests reaffirm the consistency and validity of these findings across all models.

6. Conclusions

The literature has long recognized that financial inclusion offers significant economic benefits, including reducing poverty, enhancing resilience, and improving financial stability. However, despite the rapid expansion of fintech solutions and the broader reach of inclusive finance, little attention has been paid to their combined effect on achieving the Sustainable Development Goals (SDGs). This study addresses this gap by developing a composite financial inclusion index and analyzing, through quantile regression across 148 countries, how fintech and financial inclusion influence various SDGs. This research demonstrates that digital financial inclusion channels—such as formal financial access, mobile money, digital credit and transfers, and rural finance—are essential in advancing multiple SDGs, albeit their impacts vary depending on context and different points in the distribution.

Our findings highlighted that for SDG 3 (health and well-being), formal financial access consistently reduces maternal mortality by providing economic security and access to healthcare resources. Digital credit and transfers become particularly relevant in contexts of high inequality, mitigating liquidity constraints and improving access to emergency healthcare. However, this effect largely reflects a reactive mechanism among marginal and vulnerable populations, highlighting underlying structural development challenges.

Regarding SDG 4 (quality education), quantile regressions reveal that DCT is a significant driver in some development educational contexts. This study aligns with several authors who suggest that while education improves the understanding and use of digital financial services, it does not uniformly translate into increased financial stability or reduced poverty across all demographics [121,122]. Institutional quality, measured by government effectiveness and voice and accountability, further strengthens educational outcomes, emphasizing the interplay between finance and governance.

For SDG 8 (decent work and economic growth), mobile money inclusion promotes labor market participation in countries with larger informal sectors. Meanwhile, formal financial access has a greater impact in economies with stronger formal institutions. Reducing inequality is linked to better labor outcomes, highlighting the dual importance of finance and fairness in achieving decent work. Governance factors have mixed effects: while government effectiveness and regulatory quality improve employment outcomes, corruption control and weak accountability may hinder progress.

Regarding SDG 9 (industry, innovation, and infrastructure), rural finance stands out as a key driver of growth fueled by innovation across all income levels, highlighting the need to improve financial access in underserved areas. Mobile money also has strong positive effects, supporting investment, liquidity, and the adoption of technology. Structural factors, such as urbanization and infrastructure spending, exacerbate these effects.

Our results underscore that the Formal Financial Access (FFA) Index is a critical driver of progress toward SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). Specifically, greater access to formal financial services enhances job creation, supports labor market participation, and promotes investment in human capital, thereby promoting inclusive economic growth. At the same time, formal finance facilitates the development of digital and physical infrastructure, narrowing the innovation and connectivity gaps that hinder sustainable development. These findings align with prior evidence [110] showing that easing credit constraints enhances the transition from informal to formal employment, further reinforcing the role of financial intermediaries in promoting structural transformation. Taken together, the evidence highlights that expanding formal financial access is not only a tool for individual financial security but also a systemic enabler of decent work opportunities and resilient infrastructure, both of which are essential pillars for achieving the 2030 Agenda.

The simultaneous equation model (SEM) provides evidence of bidirectional relationships among financial inclusion, fintech adoption, and SDG progress. Stronger SDG performance appears to enhance financial inclusion, particularly in formal finance, rural outreach, and mobile money, while increased financial access, in turn, supports SDG advancement. This finding reflects associations rather than strict causality, given that SEM identification depends on model assumptions and available instruments.

Despite the promising benefits of fintech and financial inclusion, this study acknowledges certain limitations. The SDGs are represented by proxy variables that may not fully capture their multidimensional nature. Likewise, the construction of composite indices involves subjective weighting and normalization, which can introduce measurement uncertainty. Although multiple governance and economic controls were incorporated, potential omitted variables—such as innovation capacity or cultural factors—may remain unobserved. Additionally, data imputation across years and countries could affect coefficient precision [123,124]. These factors suggest that the results should be interpreted as robust associations rather than definitive causal effects. Future research should adopt a broader approach to digital innovation by integrating diverse forms of digital finance and evaluating their differential impacts on various development outcomes. Extending the analysis to include SDGs related to environmental sustainability (SDG 13) and gender equality (SDG 5) would also provide deeper insights into the transformative potential of digital finance across different socio-economic contexts. Additionally, the mediation and moderation mechanisms of DFI and the SDGs could be explored. Regarding SDG 4, our findings suggest that the proxy currently used in global SDG reports may not fully capture the educational dimensions influenced by digital finance. Therefore, a promising avenue for future research should be to identify or design a more adequate education-related variable that better reflects the relationship with DFI.

From a policy perspective, the most stable results across quantile regressions suggest that expanding rural finance programs can strengthen innovation and infrastructure (SDG 9), while improving government effectiveness and accountability is crucial to enhance health and education outcomes (SDG 3 and SDG 4). Moreover, promoting financial literacy and interoperability frameworks can ensure that fintech innovations reach excluded populations while maintaining consumer protection and institutional trust. By aligning these interventions with the empirical findings, policymakers can foster an inclusive financial ecosystem that advances sustainable growth and accelerates progress toward the 2030 Agenda.

Furthermore, examining regional and socioeconomic differences will be crucial in tailoring policies that maximize the developmental benefits of fintech and financial inclusion. Areas with different levels of economic development may experience varying impacts from fintech innovations, requiring a context-sensitive approach to policymaking [125,126]. By considering local economic conditions and demographic factors, stakeholders can better align fintech initiatives with the specific needs and capabilities of diverse communities.

Integrating fintech into development strategies calls for a comprehensive regulatory framework that encourages innovation, protects consumers, and promotes financial literacy [127]. Such policies can build public trust in digital financial services, encouraging wider adoption among populations previously hesitant to use financial technologies. Policymakers should also prioritize infrastructure improvements to support digital finance, especially in rural and underserved urban areas, to ensure equal access to financial resources [126,128]. Additionally, improving the interoperability of financial systems will enable seamless transactions across platforms, expanding financial access. Governments can support initiatives that foster collaboration between the public and private sectors to strengthen financial literacy programs, equipping users with the skills needed to navigate various fintech services [128,129].