Unlocking Opportunity: The Nexus Between Financial Inclusion and Poverty Reduction in Asian Economies

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Tabash, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7829629/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Discover Sustainability → Version 1 posted 19 You are reading this latest preprint version Abstract This study investigates the influence of financial inclusion (FIC) on poverty alleviation in various Asian economies from 2000 to 2023. It emphasizes the role of enhanced access to financial services in reducing poverty levels, employing Cross-Sectional Autoregressive Distributed Lag (CS-ARDL). This approach was selected for its effectiveness in addressing cross-sectional dependence, providing robust long-term and short-term estimates, and managing potential endogeneity issues. The results reveal a significant negative correlation between FIC and poverty levels, indicating that improved access to financial services is associated with lower poverty rates across the studied economies. This relationship can be attributed to several factors: FIC enhances individuals' capacity to manage financial risks, invest in essential human capital, i.e., education and healthcare, and encourages entrepreneurial endeavors. Inclusive financial systems bolster economic resilience and create opportunities for marginalized groups to generate income by facilitating access to savings, credit, and financial education. The findings suggest that policymakers should prioritize expanding access to financial services for underserved populations. This strategy empowers individuals, contributes to poverty reduction (PVT), and stimulates economic growth. The originality of this research lies in its focus on the long-term effects of FIC, providing valuable facts on how it can be integrated into broader PVT strategies. JEL Codes: I32; O15 Financial Inclusion Poverty Reduction Asian Economies Structural Issues CS-ARDL 1. Introduction Financial inclusion (FIC) considers as a foundation of economic development, shaping the financial perspective of economies and affecting various social and economic factors (Mishra, et al., 2024 ). Access to essential financial services, e.g., credit, savings, insurance, and payment systems, is fundamental for individual empowerment and economic participation. For years, a large portion of the global population, particularly in developing regions, has remained excluded from these services, hindering their ability to engage fully in the economy. Yet, as technology has evolved, the barriers to FIC have begun to diminish. The rise of mobile banking and digital finance has opened new doors for underserved communities, facilitating easier access to financial resources. These innovations are not only changing how people interact with financial institutions but are also reshaping the broader economic environment (Basnayake, et al., 2024 ). The ability to securely save, invest, or access credit offers individuals and businesses greater flexibility in managing risks and seizing opportunities. Additionally, financial inclusion contributes to the diversification of economic activities, fostering entrepreneurial ventures and small businesses, particularly in areas where traditional banking infrastructure is scarce ( Xi & Wang, 2023 ). Beyond the economic context, financial access plays a key role in social empowerment, enabling individuals to make informed decisions regarding health, education, and housing. As these digital financial services continue to evolve, understanding the broader implications of FIC on societal structures, economic behavior, and institutional frameworks becomes increasingly important. The dynamics of how these factors interconnect hold the potential to redefine economic models in the years to come. Therefore, this research aims to explore how these dynamics unfold, examining whether and to what extent FIC might contribute to long-term reductions in poverty. Poverty reduction (PVT) is a complicated issue connected to many social and economic factors (Cattaneo, et al., 2022 ). Although the world has made progress, many developing countries still face big challenges, with people struggling to access financial services that could help them improve their lives. The growth of digital banking and financial services has brought hope, offering more people a chance to participate in the economy. However, one important question remains: Is FIC enough on its own to make a lasting difference, or do other factors need to support it? Fighting poverty involves many different aspects, such as foreign direct investment (FDI), money sent home by workers abroad (remittances), reducing corruption, and increasing exports. Remittances can provide families with needed funds, FDI can create jobs and help the economy grow, and controlling corruption makes sure resources are used properly and builds trust. A strong export market can also show a stable economy and create job opportunities. This research aims to answer: How much does FIC help to lessen poverty? In addition, how do FDI, remittances, corruption control, and export strength either boost or limit the impact of FIC on dropping poverty rate? FIC and poverty alleviation are central themes in economic development research, each influencing and being influenced by a range of interconnected factors (Fonseca, et al., 2024 ). The concept of FIC involves guaranteeing that individuals and businesses, especially those historically underserved, can access affordable and beneficial financial products and services. These offerings may encompass savings accounts, credit options, insurance, and payment systems, all delivered in a responsible and sustainable manner (Chowdhury, et al., 2024). The growing use of mobile technology and digital finance has significantly expanded the scope of FIC, enabling millions to participate in the formal economy (Asif, et al., 2023 ). This access can empower individuals by enhancing their capacity to save, invest, and manage financial risks, contributing to overall economic resilience. Poverty alleviation, on the other hand, states to the strategies and policies aimed at reducing the incidence and severity of poverty. It is a multifaceted objective encompassing increased access to essential services, job creation, and sustainable economic growth (Yuan, et al., 2024 ). FIC is essential in enabling households to navigate economic uncertainties, invest in education and health, and pursue income-generating activities. The link between FIC and poverty alleviation is influenced by various factors, including economic conditions, the quality of governance, and the availability of infrastructure. These elements collectively determine how effectively FIC can contribute to reducing poverty. A comprehensive understanding of the context in which financial services are offered and utilized is crucial for assessing the potential impact of FIC on sustainable poverty alleviation. This study explores the transformative power of FIC in driving poverty alleviation, delving into how broadened access to financial services can reshape economic opportunities and uplift communities out of poverty across Asian economies from 2000 to 2023. Utilizing advanced econometric methodology, namely Cross-Sectional Autoregressive Distributed Lag (CS-ARDL), the analysis aims to produce reliable findings by considering long-term relationships and cross-sectional dependencies in panel data. Utilizing this methodology allows for a comprehensive assessment of both short-term fluctuations and long-term equilibrium connections between FIC and poverty alleviation. The findings indicate a negative correlation between FIC and poverty levels, suggesting that greater access to financial services is associated with lower poverty rates in these economies. This connection can be explained by the ability of FIC to empower individuals to better manage risks, invest in education and health, support small businesses, and engage more actively in economic activities. By facilitating savings, credit access, and financial literacy, inclusive financial systems enhance economic resilience and enable vulnerable populations to pursue income-generating opportunities. The evidence indicates that individuals with consistent access to financial resources are more likely to improve their economic status and actively participate in fostering sustainable economic development. Theoretical significance of this study lies in its contribution to the broader understanding of the mechanisms through which FIC affects poverty alleviation. By integrating economic theories related to financial development and PVT, the research enhances the conceptual framework underpinning the role of inclusive financial systems in fostering economic resilience and individual empowerment. It bridges the gap between theoretical constructs of financial access and real-world poverty outcomes, demonstrating how access to credit, savings, and insurance can serve as vital tools for income generation, risk management, and investment in human capital. This theoretical expansion provides a foundation for future scholarly work that aims to explore the nuanced relationships between FIC and socio-economic development. Empirically, the study offers significant insights through its comprehensive analysis of data from 48 Asian economies over a 23-year period, from 2000 to 2023. The use of advanced econometric techniques such as CS-ARDL and FMOLS ensures the robustness of the findings, contributing valuable evidence to the empirical discourse on FIC and PVT. The results highlight an inverse relationship between FIC and poverty levels, underscoring the importance of strategic policy frameworks that support financial access as a tool for poverty alleviation. This empirical contribution is crucial for policymakers, international development agencies, and financial institutions aiming to design effective interventions that maximize the potential of FIC to drive sustainable PVT. The study’s insights have implications for shaping economic policies that foster inclusive growth and strengthen the economic capabilities of underserved populations. The second Section of this study focuses on the theoretical literature review and third Section explores into the empirical literature review. The fourth Section is dedicated to the materials and methods and the fifth Section reports the results. In the sixth Section, these results are thoroughly explained and the final Section offers a comprehensive conclusion and providing future recommendations for policymakers. 2. Theoretical Literature Review 2.1 Financial Intermediation Theory Financial Intermediation theory was introduced by Allen and Santomero ( 1998 ). This theory emphasizes the crucial role of financial intermediaries in bridging the gap between savers and borrowers, which posits that intermediaries i.e., banks and financial institutions improve efficiency in the financial system by reducing transaction costs and addressing information asymmetries. Financial intermediaries facilitate the allocation of capital, which, in turn, fuels economic activities and growth. In relation to this study, this theory underlines the importance of financial institutions in promoting financial inclusion (FIC). By providing access to essential financial services, intermediaries can empower individuals to save, invest, and access credit, which helps reduce poverty. This further supports the idea that well-functioning financial intermediaries can enhance the economic participation of marginalized groups, contributing to poverty alleviation by enabling income-generating activities and fostering economic resilience. 2.2 Endogenous Growth Theory Romer ( 1990 ) developed the Endogenous Growth theory, which suggests that economic growth is driven mainly by internal factors rather than outside influences. This theory highlights the importance of human capital, innovation, and knowledge in creating sustained economic growth. Financial inclusion fits well with this theory because it allows people to access resources that can be used for education, skill development, and small business growth. When individuals and businesses have access to credit and other financial services, they are better prepared to innovate and contribute to economic growth. This study uses this theory to show how access to financial resources can lead to investments in human and physical capital, ultimately helping to reduce poverty. The theory also emphasizes that inclusive financial systems boost development by increasing productivity and supporting economic growth from within a country. 2.3 Capability Approach Sen ( 1985 ) introduced Capability Approach, which emphasizes the importance of expanding individuals’ freedoms, and capabilities to achieve better living standards. This approach shifts the focus from income-based measures to broader human development factors, suggesting that true progress lies in enhancing individuals' ability to pursue the life they value. Financial inclusion fits into this framework as it empowers individuals by providing the tools needed to improve their economic situation, make informed choices, and reduce vulnerability to economic shocks. Access to financial services can help people build assets, invest in health and education, and mitigate risks, thus expanding their capabilities. This study adopts the Capability Approach to understand how FIC not only influences poverty rates but also supports broader socio-economic empowerment. By enabling greater financial access, communities can enhance their economic and social potential, aligning with Sen’s emphasis on comprehensive human development. 2.4 Financial Deepening Theory Financial Deepening theory initially discussed in the work of Shaw ( 1974 ). This suggests that the development of financial markets and institutions is crucial for economic growth. The theory posits that financial deepening, where financial markets become more sophisticated and inclusive, leads to improved mobilization of savings and more efficient allocation of resources. This theory is relevant to the current study as it highlights the positive effects of expanding financial services to a broader segment of the population. Financial deepening supports FIC by ensuring that financial systems cater to the needs of different economic actors, from small entrepreneurs to low-income individuals. This further emphasizes that as financial systems grow more inclusive and sophisticated, they contribute to economic stability, investment opportunities, and PVT. This study incorporates this theory to demonstrate how enhanced access to financial services can promote sustainable economic growth and help reduce poverty levels. Among these theories, the Capability Approach is the most pertinent to this study. This is because it goes beyond the economic implications of FIC and emphasizes the broader impact on human development. By focusing on how financial access can expand individuals’ capabilities and freedoms, this approach aligns closely with the study's objective of understanding how FIC contributes to poverty alleviation and overall socio-economic well-being. 3. Empirical Literature Review This section outlined findings from previous studies, providing a foundation for understanding the relationship between financial inclusion, financial development, foreign capital inflows, and poverty alleviation. Cheraghlou (2017) reported that 75% of Muslim adults lacked formal financial accounts. The study emphasized that access to Islamic financial services, such as Qard Hassan, promoted financial inclusion and helped reduce poverty, as seen in the case of Pakistan’s Akhuwat Foundation. Similarly, Ali (2017) focused on microfinance in Kenya’s North Eastern Province and identified illiteracy and poor infrastructure as significant barriers. Despite these challenges, microfinance initiatives contributed to poverty reduction in underserved communities. Lal (2018) examined the role of cooperative banks in three northern Indian states and found that access to basic financial services through these institutions significantly improved the well-being of the poor. Building on this, Inoue (2019) employed GMM estimation to assess the role of financial deepening in India. The study revealed that public sector banks, rather than private ones, were more effective in reducing poverty through enhanced financial access. Mushtaq and Bruneau (2019) investigated the impact of ICT on financial inclusion across 62 countries. Their findings confirmed that digital technologies promote inclusion and subsequently help reduce poverty and inequality. In line with this, Churchill and Marisetty (2020) consistently observed that broader access to financial services significantly alleviated poverty across diverse contexts. Islamic banking also emerged as a key driver in promoting financial inclusion. Rahman (2020) examined the case of Bangladesh and found that although many citizens remained excluded, Islamic financial institutions held strong potential to improve access and stimulate GDP growth. Erlando et al. (2020) emphasized the multidimensional role of financial inclusion by confirming its positive relationship with economic growth, poverty alleviation, and income distribution. In a broader macroeconomic context, Bird and Choi (2020) assessed the effects of foreign financial inflows like FDI, remittances, and aid on growth. They found that while FDI promoted economic growth, remittances had a negative impact, and the effects of aid varied depending on country-specific characteristics. More recent studies have turned attention to institutional and policy dynamics. Settle (2022) traced the evolution of Pakistan’s financial inclusion policies, noting a shift from donor-driven to regulatory approaches aimed at managing informality and enhancing monetary stability. Ngong et al. (2022), using co-integration and ARDL methods, found that micro-financial inclusion had a strong long-run impact on poverty reduction, although some short-term lag effects showed mixed outcomes. Chen et al. (2022) echoed these findings, reinforcing the existence of both positive and negative lagged effects in financial inclusion's impact on poverty. Aracil et al. (2022) provided cross-country evidence showing that the poverty-reducing impact of financial inclusion was strengthened by high institutional quality, particularly in low-income nations. Similarly, Arogundade et al. (2022), using spatial econometrics, discovered that FDI from neighboring countries significantly influenced domestic poverty levels across 44 African nations, with institutional quality playing a moderating role. Turning to remittance-focused studies, Abduvaliev and Bustillo (2020/2023) showed that remittances played a vital role in reducing poverty by supporting household income and stabilizing consumption in recipient countries. Mawutor et al. (2023), analyzing Ghana’s economy, found that remittances contributed positively to economic growth, while FDI and exchange rates had negative effects. The study recommended reforms to optimize remittance channels and currency policies. Sen et al. (2023) contributed further by linking financial inclusion with energy poverty in Bangladesh, revealing that financially included households, particularly those led by women, were less vulnerable to energy deprivation, thus supporting SDG 7. Camara (2023) explored the relationship between FDI and tax revenue in 90 developing countries and found that FDI boosted revenues, though the impact was muted in resource-rich economies. Haruna et al. (2023), focusing on Nigeria, employed ARDL and NARDL models to demonstrate that FDI shocks, both positive and negative contributed to short- and long-term poverty reduction. Lee et al. (2023) analyzed provincial data from China and demonstrated that digital financial inclusion (DIFI) significantly reduced poverty. The study also identified spatial spillover effects and a U-shaped relationship, indicating that DIFI offers both development opportunities and risk of exclusion if not properly managed. Studies from 2024 continued to explore the moderating role of governance and readiness in determining the effectiveness of financial inflows. Sanli et al. (2024) found that economic, social, and governance readiness enhanced the poverty-reducing effects of FDI in Sub-Saharan Africa. Cuong et al. (2024) confirmed that remittances significantly reduced multidimensional poverty across Belt and Road Initiative countries, with political stability and agricultural development acting as important enablers. Omenihu et al. (2024) examined the roles of financial access, usage, and quality in Nigeria and concluded that financial access had the most substantial poverty-reducing impact, especially when aligned with users’ education levels. Aloui et al. (2024) explored the role of governance in the FDI-poverty nexus and found that regions with stronger governance frameworks saw greater benefits from foreign capital inflows, particularly in Africa and Latin America. Collectively, these studies underscore that financial inclusion, financial development, and foreign capital inflows can substantially alleviate poverty, especially when supported by strong institutions, governance, and inclusive technologies. Based on the above-mentioned literature, we may hypothesize that financial inclusion significantly reduce poverty in developing countries H 1 : A significant negative relationship exists between financial inclusion and poverty reduction. 4. Material and Methods The research investigates the liaison between financial inclusion (FIC) and poverty reduction (PVT) across 26 Asian countries from 2000 to 2023. Initially, 48 Asian countries were considered for inclusion in the study; however, after an extensive data screening process, only 26 countries were selected due to the unavailability of reliable data on key indicators such as FIC and poverty rates (See Table A1 in appendix). This study spans a period that has witnessed significant technological progress in various sectors, making the last two decades particularly relevant for understanding the evolving dynamics of FIC and its impact on poverty alleviation. The selected countries encompass both emerging and developed economies, offering a comprehensive view of the regional variation in FIC and its potential role in reducing poverty. Data for this research were sourced from the WDI, providing a robust and consistent dataset to analyze the dynamic liaison between financial services access and PVT. By focusing on this time frame and these economies, the study aims to offer valuable insights into how FIC can be leveraged as a tool for poverty alleviation in Asia. 4.1 Vriable Discussion Financial inclusion (FIC) is the process of providing access to affordable and essential financial services to all individuals, especially those excluded from traditional financial systems. Its emergence as a key development focus is tied to the growing recognition that access to financial services can empower individuals and foster economic stability. Technological advancements, including mobile banking and digital platforms, have significantly expanded FIC by reaching underserved populations in rural and remote areas (Aloulou, et al., 2024 ; Oyewole, et al., 2024 ). FIC can play a pivotal role in PVT by enabling people to manage their finances, invest in education and healthcare, and engage in business activities. In developing economies, it promotes economic growth, reduces income inequality, and facilitates social mobility, contributing to long-term poverty alleviation. Similarly, PVT involves efforts to decrease the proportion of people living below the poverty line and improve their living standards. This process includes strategies aimed at increasing income levels, providing access to essential services, and reducing social inequalities. Over recent decades, the focus has shifted towards inclusive growth, where economic opportunities are made accessible to all members of society. FIC is closely tied to PVT, as it allows individuals to invest in income-generating activities, build savings, and mitigate the effects of economic shocks. By improving financial access, individuals in poverty can gain the tools to improve their livelihoods, fostering sustainable development and reducing poverty (Abduvaliev & Bustillo, 2020 ; Sanli, et al., 2024 ). Foreign direct investment involves investments made by foreign entities in the form of establishing or expanding business operations in a host country. FDI plays a significant role in boosting economic development by providing capital, technology, and expertise. This inflow of resources can increase productivity, enhance innovation, and create employment opportunities. FDI can also contribute to PVT by providing jobs, improving infrastructure, and increasing the availability of goods and services. The positive impact of FDI on poverty is closely linked to the ability of the host country to manage and distribute the benefits of investment through sound governance, policies, and institutions that ensure broad-based economic benefits (Haruna, et al., 2023 ; Mawutor, et al., 2023 ). Foreign aid consists of resources provided by developed countries to support the development efforts of less-developed countries. Its origins can be traced back to the mid-20th century, aimed at addressing poverty, improving infrastructure, and promoting economic growth in the Global South. The impact of foreign aid on PVT remains a topic of debate, but it has played a crucial role in funding development projects, improving healthcare, and enhancing education. Properly allocated foreign aid helps developing nations build infrastructure, enhance human capital, and reduce the barriers that prevent people from escaping poverty. It is most effective when aligned with the specific needs and priorities of recipient countries and complements domestic policy efforts (Bird & Choi, 2020 ; Wen, et al., 2021 ). Government effectiveness describes the capacity of public institutions to implement policies that positively affect citizens' welfare. It involves the efficiency, transparency, and quality of public services, as well as the government's ability to maintain law and order. Over time, effective governance has been recognized as essential for sustainable development and PVT, as strong institutions are crucial for managing resources and ensuring the equitable distribution of opportunities. Governments that create favorable economic environments, maintain stable institutions, and promote good governance practices contribute significantly to economic growth and poverty alleviation. Effective governments can also manage foreign aid and FDI, ensuring that these resources are used effectively to support development goals (Mongi & Saidi, 2023 ). 4.2 Econometric Models This study employs a series of econometric models to explore how FIC, FDI, ODA, and GOE influence PVT across different Asian economies. PVT = f (FIC, FDI, ODA, GOE) Eq. (1) Equation 1 expresses the general functional relationship between PVT (PVT) and several key determinants: financial inclusion (FIC), foreign direct investment (FDI), foreign aid (ODA), and government effectiveness (GOE). In this equation, POV is seen as a dependent variable influenced by the levels of FIC, FDI, ODA, and GOE. $$\:{Y}_{jt}={\alpha\:}_{\theta\:}+{\beta\:}_{1}{IV}_{jt}+{\gamma\:}_{1}{CV}_{jt}+{\epsilon\:}_{jt}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:Eq.\:\left(2\right)$$ Equation 2 presents a general econometric specification for modeling the relationship between a dependent variable \(\:{Y}_{jt}\) and a set of independent variables, including key factors (IV) and control variables (CV). The dependent variable \(\:{Y}_{jt}\) represents the variable of interest, such as a measure of poverty or income levels for country j at time t. \(\:{\alpha\:}_{\theta\:}\) is the intercept term, representing the baseline value of the dependent variable. The coefficient \(\:{\beta\:}_{1}\) captures the impact of the independent variable \(\:{IV}_{jt}\) on the dependent variable. The error term \(\:{\epsilon\:}_{jt}\) accounts for unobserved factors affecting the outcome. $$\:{PVT}_{jt}={\alpha\:}_{\theta\:}+{\beta\:}_{1}{FIC}_{jt}+{\gamma\:}_{1}{FDI}_{jt}+{\gamma\:}_{2}{ODA}_{jt}+{\gamma\:}_{3}{GOE}_{jt}+{\epsilon\:}_{jt}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:Eq.\:\left(3\right)$$ Equation 3 refines the relationship expressed in Eq. 1 by specifying the linear impact of FIC, FDI, ODA, and GOE on PVT. Here, \(\:{PVT}_{jt}\) is the PVT measure for country j at time t, and \(\:{\alpha\:}_{\theta\:}\) is the intercept. This model hypothesizes that each of these factors contributes to PVT, and their relative impacts will be estimated through the coefficients. For CS-ARDL model $$\:\varDelta\:{PVT}_{jt}={\alpha\:}_{^\circ\:}+{\sum\:}_{i=1}^{p}{\beta\:}_{1}\varDelta\:{FIC}_{jt-1}+{\sum\:}_{i=1}^{p}{\gamma\:}_{1}\varDelta\:{FDI}_{jt-1}+{\sum\:}_{i=1}^{p}{\gamma\:}_{2}\varDelta\:{ODA}_{jt-1}+{\sum\:}_{i=1}^{p}{\gamma\:}_{3}\varDelta\:{GOE}_{jt-1}+{\epsilon\:}_{it}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:Eq.\:\left(4\right)$$ Equation 4 adopts a dynamic approach using the CS-ARDL (Cross-Sectional Autoregressive Distributed Lag) model, which explores the short-term and long-term effects of FIC, FDI, ODA, and GOE on PVT. The dependent variable \(\:\varDelta\:{PVT}_{jt}\) represents the change in PVT for country j at time t. The terms \(\:\varDelta\:{FIC}_{jt-1}\) , \(\:\varDelta\:{FDI}_{jt-1}\) , \(\:\varDelta\:{ODA}_{jt-1}\) and \(\:\varDelta\:{GOE}_{jt-1}\) indicate the lagged effects of FIC, FDI, ODA, and GOE on changes in PVT. The coefficients of these lagged variables show how past changes in these factors influence the current rate of PVT. Table 1 Variables of Study Acronym Variable Measurement Reference PVT Poverty Reduction Poverty headcount ratio at societal poverty line (% of population) (Fonseca, et al., 2024 ) FIC Financial Inclusion FIC is measured by the availability and usage of financial services like bank accounts, loans, insurance, ATMs, and mobile money across different demographics. Key indicators include the number of financial access points (e.g., branches, ATMs), service usage, and affordability. (Basnayake, et al., 2024 ) FDI Foreign Direct Investment FDI, net inflows (% of GDP) (Aloui, et al., 2024 ) ODA Foreign Aid Net ODA received (% of GNI) (Wen, et al., 2021 ) GOE Government Effectiveness Quality of public services, policy implementation, and the efficiency of governance. (Mongi & Saidi, 2023) Note : This table is revealing the information on variables, measurement, and sources of data. Table 2 discloses the statistics of the Cross-Section Dependence (CD) analysis for several key variables: PVT, FIC, FDI, ODA, and GOE. Both the Breusch-Pagan LM and Pesaran CD tests indicate significant cross-sectional dependence for PVT, FIC, FDI, and GOE, as evidenced by their low p-values (all less than 0.05). This suggests that these variables are correlated across countries, meaning that the residuals of these variables are not independent but influenced by factors shared among countries in the sample. The absence of results for ODA indicates missing or incomplete data for this variable in the cross-sectional dependence analysis. Overall, the significant CD implies that these variables effects on PVT may be interconnected across countries, which should be considered in further econometric modeling to avoid biased estimates. Table 2 Cross-Section Dependence (CD) Analysis Breusch-Pagan LM Pesaran CD Variables Statistic Probability Statistic Probability PVT 1642.913 0.000 19.316 0.000 FIC 1634.820 0.000 13.137 0.000 FDI 585.878 0.000 8.8453 0.000 ODA 1180.90 0.000 7.819 0.000 GOE 1083.450 0.000 3.3931 0.000 Source: self-estimation. Table 3 presents the results of the unit root tests for stationarity, using two different methods: CIPS (Cross-Sectionally Augmented IPS) and CADF (Cross-Sectional Augmented Dickey-Fuller) tests. Table 3 further shows the test statistics for the variable PVT, FIC, FDI, ODA, and GOE at both levels and first differences. For PVT, the CIPS test statistic at level is -0.860 (with a p-value of 0.194), indicating that it is non-stationary at level but becomes stationary at the first difference, where the p-value is 0.000. A similar pattern is observed for FIC, which is non-stationary at level (CIPS value 2.851, p-value 0.997) but stationary at first difference with a highly significant p-value (0.000). FDI and ODA are both stationary at level according to the CIPS and CADF tests, with p-values less than 0.05. However, GOE is non-stationary at level but becomes stationary at first difference, with the CADF test showing a marginal p-value of 0.051 at level and a significant p-value at first difference. These results suggest that most variables require differencing to achieve stationarity, which is important for reliable econometric analysis (Dickey & Fuller, 1979 ; Im, Pesaran, & Shin, 2003 ). Table 3 Analysis of Stationarity through Unit Root Testing CIPS CADF Variables At Level (0) At first difference (1) (0) (1) PVT (-0.860) 0.194 (-20.485) 0.000 (52.176) 0.000 (501.409) 0.000 FIC (2.851) 0.997 (-8.557) 0.000 (33.764) 0.868 (160.892) 0.000 FDI (-5.163) 0.000 -- (109.753) 0.000 -- ODA (-3.210) 0.000 -- (75.517) 0.000 -- GOE (-0.668) 0.252 (-8.995) 0.000 (60.308) 0.051 (104.721) 0.000 Source: self-analysis. Table 4 presents the results of the Kao residual cointegration test, which examines the long-term relationship between the variables. The ADF (Augmented Dickey-Fuller) statistic is -2.768 with a p-value of 0.002, indicating significant cointegration, meaning that a long-run equilibrium relationship exists between the variables. The Residual Variance is 17.473, and the HAC (Heteroskedasticity and Autocorrelation Consistent) variance is 13.811, though the probabilities for these statistics are not provided. Overall, the Kao test confirms the presence of a cointegrated relationship, suggesting that the variables move together in the long run (Kao, 1999 ). Table 4 Cointegration Analysis Cointegration Test of Kao Residual Test Name t-statistics Probability ADF -2.768 0.002 Residual Variance 17.473 - HAC Variance 13.811 - Source : self-estimation 4.3 Methodology To analyze the impact of FIC on PVT, along with the roles of foreign direct investment, foreign aid, and government effectiveness, several econometric methodologies were employed in this study. The initial step involved testing for cross-sectional dependence among the variables, which is crucial for panel data analysis. Table 2 presents the results of the Breusch-Pagan LM and Pesaran CD tests, which were conducted to assess whether there is any correlation in the residuals across countries. Significant cross-sectional dependence was found for most variables, suggesting that the assumptions of independence between countries were violated, and this needed to be accounted for in further analysis. The findings from Table 2 highlighted that the presence of cross-sectional dependence requires adjustment in the modeling approach to avoid bias in the estimates. Next, to ensure the robustness of the findings, unit root tests were applied to check for stationarity. Table 3 presents the results of the CIPS and CADF tests, which indicated that while some variables, such as FDI and foreign aid (ODA), were stationary at their levels, others, like PVT and FIC, required differencing to achieve stationarity. The results from Table 3 suggest that stationarity was achieved for most variables at their first differences, highlighting the importance of transforming the data to avoid potential issues with non-stationary variables. This step is crucial because non-stationary data can lead to spurious relationships if not properly addressed. Once stationarity was established, the analysis proceeded with cointegration testing using the Kao residual test to examine whether a long-term liaison exists between the variables. Table 4 reveals the statistics of the Kao residual technique, which confirmed the presence of cointegration, suggesting that a stable long-run relationship exists between FIC, foreign aid, government effectiveness, and PVT across the selected countries. The results in Table 4 indicate that these variables move together in the long-run, further supporting the hypothesis of a sustained relationship among them. This finding reinforces the idea that the effects of FIC, foreign aid, and government effectiveness are interlinked over time, contributing to PVT. Finally, the study employed three key econometric techniques to estimate the relationships: POLS, FMOLS, and CS-ARDL models. Table 7 presents the results from POLS and FMOLS, where POLS was used for initial estimations, and FMOLS provided more robust estimates by accounting for potential endogeneity and serial correlation. The findings in Table 7 offer valuable facts into the liaison between the variables in the absence of lag effects. Subsequently, the CS-ARDL model, shown in Table 8 , was applied to explore both the short-term and long-term dynamics between the variables, while also accounting for lagged effects and cross-sectional dependence. The results from Table 8 emphasize the importance of including lag effects in the model, as they reveal the temporal nature of the relationships. This comprehensive methodology ensures a thorough examination of the complex relationships between FIC and PVT in the context of developing Asian economies. Moreover, FMOLS was initially introduced (Phillips & Hansen, 1990 ). 5. Results and Discussion Descriptive statistics provide a summary of the main features of a dataset, offering a concise overview of its central tendency, dispersion, and shape. Table 5 Descriptive Statistics Variables Mean Median Skewness Kurtosis Maximum Minimum Std. Dev. PVT 27.371 26.300 0.288 3.311 62.000 1.134 10.449 FIC 38.555 29.248 1.497 5.088 194.674 -7.273 30.980 FDI 3.927 2.465 3.428 32.434 55.072 -37.172 6.152 ODA 3.630 0.682 7.323 62.623 117.469 -0.943 11.658 GOE -0.291 -0.281 0.138 3.400 1.237 -1.850 0.559 Source: self-analysis. Table 5 provides the descriptive statistics for the key variables used in the study. The mean values show that PVT has a mean of 27.371, with a relatively low skewness of 0.288, indicating a symmetric distribution. Similarly, FIC has a higher mean of 38.555, but its skewness value of 1.497 suggests a rightward skew, implying a few outlier values with higher FIC. FDI displays a high skewness of 3.428, indicating that the distribution is heavily skewed to the right, with some countries attracting much higher levels of investment. ODA has a mean of 3.630, but it’s extremely high kurtosis (62.623) suggests that the distribution is highly leptokurtic, with a concentration of extreme values. GOE shows a mean of -0.291 with low skewness, indicating a near-normal distribution. These statistics help to contextualize the variability and distribution patterns of the data before applying further econometric analyses. Table 6 Correlation Statistics Variables PVT FIC FDI ODA GOE PVT 1.000 FIC -0.189 1.000 FDI -0.212 -0.094 1.000 ODA 0.062 -0.181 0.028 1.000 GOE -0.162 0.548 -0.092 -0.417 1.000 Source: self-analysis. Table 6 presents the correlation coefficients between the key variables of interest in the study. The correlation between PVT and FIC is negative and modest at -0.189, suggesting a weak inverse relationship. Similarly, PVT is negatively correlated with FDI and GOE, with coefficients of -0.212 and − 0.162, respectively, indicating weak negative associations. There is no significant correlation between PVT and ODA, with a coefficient of 0.062, showing almost no relationship. FIC is positively correlated with GOE (0.548), implying that higher government effectiveness is associated with greater FIC. However, the correlations between FIC and the other variables (FDI and ODA) are weak, with values of -0.094 and − 0.181, respectively. These correlations provide initial understandings into the interrelationships among the variables and set the stage for further analysis to explore their causal effects. Table 7 Effect of FIC on PVT POLS FMOLS Variables Coefficients Probability Coefficients Probability FIC -0.053 a 0.003 -0.113 a 0.000 FDI -0.401 a 0.000 -0.061 a 0.000 ODA -0.000 0.995 -0.082 a 0.000 GOE -1.835 c 0.087 -11.086 a 0.000 Adjusted R-squared 0.088 0.678 S.E. of regression 9.978 5.778 Note : The superscripts a, b, and c denotes the significance level at 1%, 5%, and 10%. Table 7 presents the results from two key econometric techniques: Pooled Ordinary Least Squares (POLS) and Fully Modified Ordinary Least Squares (FMOLS), both of which are used to estimate the effect of financial inclusion (FIC) on poverty reduction (PVT). The POLS model estimates the relationships between variables without accounting for potential endogeneity, while FMOLS adjusts for endogeneity and serial correlation to provide estimates that are more robust. The POLS results show that FIC has a statistically significant negative effect on PVT, with a coefficient of -0.053 and a p-value of 0.003. This suggests that, according to the POLS estimation, as FIC increases, PVT tends to decrease slightly, although the effect is weak. The negative relationship could be attributed to several factors. For instance, while FIC may increase access to financial resources, it might not necessarily result in immediate PVT, especially if the benefits of FIC are not equitably distributed or if it is accompanied by growing inequality. Additionally, FIC alone may not address other structural factors such as education, healthcare, and employment, which are essential for PVT. In contrast, the FMOLS results show a more substantial and negative coefficient of -0.113 for FIC, with a highly significant p-value of 0.000. FIC initiatives provide the poor with greater access to financial services, i.e., savings accounts, credit, and insurance, which can enhance their economic opportunities. These tools can help individuals invest in businesses, smooth consumption during economic shocks, and reduce vulnerability to unexpected expenses. By promoting broader access to financial resources, FIC can directly contribute to reducing poverty rates, as it empowers the poor to engage more actively in the economy and improve their living standards. Furthermore, when paired with appropriate policies, FIC can reduce inequality by ensuring that the benefits of economic growth are more evenly distributed across society. The coefficients for other variables, i.e., FDI, ODA, and GOE, provide additional context. FDI has a significantly negative effect on PVT in both models, with a coefficient of -0.401 (POLS) and − 0.061 (FMOLS), suggesting that, in these economies, higher levels of FDI may not be directly associated with poverty alleviation. This could be due to the fact that FDI often flows to sectors that are not necessarily aligned with PVT, such as capital-intensive industries or export-oriented businesses. ODA has an almost negligible effect in the POLS model with a coefficient of -0.000, but in the FMOLS model, it becomes significantly negative (-0.082), suggesting that foreign aid may not be effectively contributing to PVT or might be misallocated. The GOE coefficient is negative in both models, with a larger magnitude in FMOLS (-11.086), indicating that ineffective governance may be hindering the positive effects of FIC on PVT. A lack of institutional quality and weak governance may prevent the full benefits of FIC from reaching the poor, reinforcing the need for efficient institutions and policies to support poverty alleviation. In summary, the results indicate that while FIC has the potential to reduce poverty, its effects are complex and depend heavily on other factors, including governance, foreign investment, and aid effectiveness. The negative relationships observed in the models highlight the need for comprehensive policies that address the broader structural issues affecting PVT. Table 8 Impact of FIC on PVT PVT as a dependent variable CS-ARDL Model Variable Coefficient Std. Error t-Statistic Prob.* Long Run Equation FIC -0.081 a 0.025 -4.437 a 0.000 FDI -1.084 0.248 -4.367 a 0.000 ODA -0.090 a 0.019 -4.601 a 0.000 GOE -2.932 1.123 -2.609 a 0.009 Short-run Equation COINTEQ01 -0.257 0.096 -2.681 0.007 D(PVT(-1)) -0.135 0.070 -1.915 0.056 D(FIC) -0.090 0.052 -1.733 0.084 D(FIC(-1)) -0.135 0.136 -0.988 0.323 D(FDI) 0.042 0.273 0.155 0.876 D(FDI(-1)) 0.067 0.337 0.200 0.841 D(ODA) -5.390 3.826 -1.408 0.160 D(ODA(-1)) -1.846 2.256 -0.818 0.414 D(GOE) -1.226 0.860 -1.425 0.155 D(GOE(-1)) -1.030 1.990 -0.517 0.605 C 6.300 2.521 2.498 0.013 Source: self-analysis. Table 8 portrays the statistics of the CS-ARDL technique, which is used to scrutinize the long-run and short-run impact of FIC on PVT across the selected 26 Asian economies. Table 8 is in two-fold: the long-run equation and the short-run equation. In the long-run equation, the coefficient for FIC is -0.081 with a t-statistic of -4.437, indicating that FIC has a significant negative effect on PVT. This suggests that, in the long run, increased FIC, by providing access to services i.e., savings, loans, and insurance, empowers the poor to build wealth, manage risks, and expand their economic opportunities. When combined with supportive policies, this access can significantly reduce poverty levels and wealth inequality by fostering broader participation in the economy. FDI also shows a negative long-run effect on PVT, with a coefficient of -1.084 and a t-statistic of -4.367, which implies that foreign direct investment does not necessarily translate into poverty alleviation in the selected economies. The negative coefficient for ODA (-0.090) with a t-statistic of -4.601 suggests that foreign aid may not effectively reduce poverty in these countries, possibly due to misallocation or ineffective use of resources. Lastly, GOE (government effectiveness) shows a significantly negative relationship with PVT in the long run, with a coefficient of -2.932 and a t-statistic of -2.609, highlighting that poor governance and ineffective institutions may hinder the poverty-reducing potential of FIC and other factors. In the short-run equation, the coefficient of COINTEQ01 is -0.257, with a t-statistic of -2.681, indicating a significant and negative adjustment to the long-run equilibrium, suggesting that the system converges back to the long-run equilibrium over time. The lagged effects of PVT, FIC, FDI, ODA, and GOE are generally not significant, with only a few coefficients approaching statistical significance. Notably, D(PVT(-1)) has a coefficient of -0.135, which is marginally significant (p-value = 0.056), indicating some persistence in poverty levels from the previous period. Similarly, D(FIC) shows a negative short-term effect on PVT with a coefficient of -0.090 and a t-statistic of -1.733, suggesting that FIC short-term impact on PVT is weak. The coefficients for D(FDI), D(ODA), and D(GOE) are not significant in the short run, further suggesting that the immediate effects of foreign investment, foreign aid, and government effectiveness are not directly associated with short-term PVT. The CS-ARDL model results show that while FIC and other factors like foreign direct investment and government effectiveness have significant long-term effects on PVT, their short-term impact is weak and sometimes insignificant. The results highlight the complexity of the liaison between these variables and PVT, suggesting that the benefits of FIC and foreign aid may take time to materialize and depend on broader institutional and policy factors. 6. Conclusion and Policy Recommendations This endeavor explored the liaison between financial inclusion (FIC) and poverty reduction (PVT) across Asian economies over the period from 2000 to 2023. The results suggest a mixed and nuanced impact of FIC on PVT, with FIC generally showing a negative relationship with PVT in both the short and long run. FIC is a powerful tool for reducing poverty by expanding access to financial resources; its full potential is realized when accompanied by strong governance and effective policies. With the right support systems in place, FIC can significantly reduce poverty by empowering individuals to invest, build assets, and improve their economic resilience, ultimately driving growth that is more inclusive. FDI and ODA also demonstrate negative relationships with PVT, highlighting that these external factors do not necessarily lead to improved living standards, especially when they are not effectively targeted or managed. The government effectiveness (GOE) variable is found to have a significant negative impact, underscoring the importance of institutional quality in shaping the outcomes of FIC policies. These findings highlight that while FIC plays a crucial role in economic development, it must be integrated into a comprehensive strategy that tackles governance challenges, ensures efficient resource allocation, and addresses the underlying structural factors of poverty. Based on these findings, policymakers should prioritize improving governance and institutional effectiveness to enhance the impact of FIC on PVT. FIC policies should be designed to ensure equitable access, particularly targeting marginalized populations i.e., rural communities and women. In addition, foreign aid and foreign direct investment should be channeled into poverty-reducing sectors like education, healthcare, and infrastructure, ensuring these resources are effectively used. Governments must focus on building institutional capacity, combating corruption, and improving public service delivery to create an enabling environment for sustainable PVT. These efforts, combined with well-targeted FIC strategies, can create long-term benefits for poverty alleviation in the region. 6.1 Study Limitations and Future Recommendation A key limitation of this study is the potential issue of generalizability, as the findings are based on data from only 26 Asian economies, which may not fully represent the diverse economic and institutional contexts of all developing regions. This limits the extent to which the results can be applied to other regions with different economic structures or stages of development. Future research could expand the sample to include more countries from diverse regions, enhancing the generalizability of the findings. Additionally, examining a broader range of economies with varying levels of FIC and governance could provide conclusions that are more robust. Declarations Author contributions K.K. (Khursid Khudoykulov) conceptualized the research idea, designed the methodology, and supervised the entire study. Z.M. (Zokir Mamadiyarov) contributed to the theoretical framework and literature review. U.F. (Umar Farooq) performed the econometric modeling, data estimation, and robustness analysis. M.I.T. (Mosab I. Tabash) reviewed the econometric results, contributed to interpretation, and refined the discussion of policy implications. M.M. (Mansur Mansurov) assisted in data validation, visualization, and cross-checking statistical outputs. I.I. (Inomiddin Inomov) contributed to the formulation of conclusions, editing, and overall manuscript revision. All authors read and approved the final manuscript. Funding Not applicable. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7829629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541938532,"identity":"eac2f879-bf3d-4180-9e1a-9fa8f693d7a2","order_by":0,"name":"Khurshid Khudoykulov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACCQkGZmYGBmYQm/EAA4MNaVoYgFrSJEjWcpiwFsnZDczGhW3WcgbHcwwOfGw7Xyc/I/kAM88f3FqkZQ4wJ89sSzc2OPPG4ODMttsSBjfSEph5eHBrkZNIYD7M23Y4ccONHIPDPGeAWiRyDJh58LgPXcs5CfkZ+R+YeQzwOAyoJRmhpeKABMONHAZmngQ83p+R2GzMcy7dWPLMs4KDMyqSJTeceWZwcM4B3FokbiQfluYps5bjO5688cEHAzt++fbkhw/e4AkxYJw3gCmFA0huwWMHEpBvwOP8UTAKRsEoGNkAAKrxUIzR+cfyAAAAAElFTkSuQmCC","orcid":"","institution":"Termez University of Economics and Service","correspondingAuthor":true,"prefix":"","firstName":"Khurshid","middleName":"","lastName":"Khudoykulov","suffix":""},{"id":541938533,"identity":"c4c69715-3621-41ad-993a-aa8c7f561d07","order_by":1,"name":"Zokir Mamadiyarov","email":"","orcid":"","institution":"Mamun University","correspondingAuthor":false,"prefix":"","firstName":"Zokir","middleName":"","lastName":"Mamadiyarov","suffix":""},{"id":541938534,"identity":"5d36698d-cdc6-4c75-bf99-dc776e7c4db2","order_by":2,"name":"Umar Farooq","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Umar","middleName":"","lastName":"Farooq","suffix":""},{"id":541938535,"identity":"815668ff-e10c-4452-a2c1-0ccd5d27ea50","order_by":3,"name":"Mosab I. Tabash","email":"","orcid":"","institution":"Al Ain University","correspondingAuthor":false,"prefix":"","firstName":"Mosab","middleName":"I.","lastName":"Tabash","suffix":""},{"id":541938536,"identity":"24eb0984-7184-49c1-8f5c-da6e654eaea4","order_by":4,"name":"Mansur Mansurov","email":"","orcid":"","institution":"Karshi State University","correspondingAuthor":false,"prefix":"","firstName":"Mansur","middleName":"","lastName":"Mansurov","suffix":""},{"id":541938537,"identity":"86db71af-db5a-4981-9a53-70a5f4924f73","order_by":5,"name":"Inomiddin Imomov","email":"","orcid":"","institution":"Tashkent State University of Economics","correspondingAuthor":false,"prefix":"","firstName":"Inomiddin","middleName":"","lastName":"Imomov","suffix":""}],"badges":[],"createdAt":"2025-10-10 18:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7829629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7829629/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-026-02748-2","type":"published","date":"2026-02-11T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95550996,"identity":"bb97a147-7508-4b2c-9357-69650df813b5","added_by":"auto","created_at":"2025-11-10 13:35:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79086,"visible":true,"origin":"","legend":"","description":"","filename":"financialinclusionandpovertyreduction20112024.docx","url":"https://assets-eu.researchsquare.com/files/rs-7829629/v1/411df2967a0c78e09c6b60c7.docx"},{"id":95550994,"identity":"d4180605-fa9e-4315-beac-22f3e27ace0f","added_by":"auto","created_at":"2025-11-10 13:35:16","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7704,"visible":true,"origin":"","legend":"","description":"","filename":"e2744194701c4e4a9410b4ab5a05f0ab.json","url":"https://assets-eu.researchsquare.com/files/rs-7829629/v1/6fee10807188342f229035bb.json"},{"id":95550995,"identity":"822cec50-1add-4f21-992e-fea235d2d1f5","added_by":"auto","created_at":"2025-11-10 13:35:16","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163006,"visible":true,"origin":"","legend":"","description":"","filename":"e2744194701c4e4a9410b4ab5a05f0ab1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7829629/v1/ce1f23e99c9e231b2c22b827.xml"},{"id":95550997,"identity":"3424485d-1c04-482f-9f20-34e97ab6c8c5","added_by":"auto","created_at":"2025-11-10 13:35:16","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162525,"visible":true,"origin":"","legend":"","description":"","filename":"e2744194701c4e4a9410b4ab5a05f0ab1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7829629/v1/fd74b5a218f8a1cbb0452a17.xml"},{"id":95550998,"identity":"1019deb3-575b-4d7a-b3f4-49cb28d01063","added_by":"auto","created_at":"2025-11-10 13:35:16","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174995,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7829629/v1/5e26146e5382bbf7e1d5832a.html"},{"id":102785186,"identity":"389b5f47-5571-457c-8742-8aea44c3d8d2","added_by":"auto","created_at":"2026-02-16 16:01:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1129868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7829629/v1/7e5e1612-7d1e-467f-814b-d4bb35d7e89e.pdf"},{"id":95550993,"identity":"81619d82-0c05-4ec1-adf1-ad7d7c0d2789","added_by":"auto","created_at":"2025-11-10 13:35:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18622,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7829629/v1/40025154b1f124645aa3d261.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unlocking Opportunity: The Nexus Between Financial Inclusion and Poverty Reduction in Asian Economies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFinancial inclusion (FIC) considers as a foundation of economic development, shaping the financial perspective of economies and affecting various social and economic factors (Mishra, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Access to essential financial services, e.g., credit, savings, insurance, and payment systems, is fundamental for individual empowerment and economic participation. For years, a large portion of the global population, particularly in developing regions, has remained excluded from these services, hindering their ability to engage fully in the economy. Yet, as technology has evolved, the barriers to FIC have begun to diminish. The rise of mobile banking and digital finance has opened new doors for underserved communities, facilitating easier access to financial resources. These innovations are not only changing how people interact with financial institutions but are also reshaping the broader economic environment (Basnayake, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The ability to securely save, invest, or access credit offers individuals and businesses greater flexibility in managing risks and seizing opportunities. Additionally, financial inclusion contributes to the diversification of economic activities, fostering entrepreneurial ventures and small businesses, particularly in areas where traditional banking infrastructure is scarce ( Xi \u0026amp; Wang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond the economic context, financial access plays a key role in social empowerment, enabling individuals to make informed decisions regarding health, education, and housing. As these digital financial services continue to evolve, understanding the broader implications of FIC on societal structures, economic behavior, and institutional frameworks becomes increasingly important. The dynamics of how these factors interconnect hold the potential to redefine economic models in the years to come. Therefore, this research aims to explore how these dynamics unfold, examining whether and to what extent FIC might contribute to long-term reductions in poverty.\u003c/p\u003e\u003cp\u003ePoverty reduction (PVT) is a complicated issue connected to many social and economic factors (Cattaneo, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although the world has made progress, many developing countries still face big challenges, with people struggling to access financial services that could help them improve their lives. The growth of digital banking and financial services has brought hope, offering more people a chance to participate in the economy. However, one important question remains: Is FIC enough on its own to make a lasting difference, or do other factors need to support it? Fighting poverty involves many different aspects, such as foreign direct investment (FDI), money sent home by workers abroad (remittances), reducing corruption, and increasing exports. Remittances can provide families with needed funds, FDI can create jobs and help the economy grow, and controlling corruption makes sure resources are used properly and builds trust. A strong export market can also show a stable economy and create job opportunities. This research aims to answer: How much does FIC help to lessen poverty? In addition, how do FDI, remittances, corruption control, and export strength either boost or limit the impact of FIC on dropping poverty rate?\u003c/p\u003e\u003cp\u003eFIC and poverty alleviation are central themes in economic development research, each influencing and being influenced by a range of interconnected factors (Fonseca, et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The concept of FIC involves guaranteeing that individuals and businesses, especially those historically underserved, can access affordable and beneficial financial products and services. These offerings may encompass savings accounts, credit options, insurance, and payment systems, all delivered in a responsible and sustainable manner (Chowdhury, et al., 2024). The growing use of mobile technology and digital finance has significantly expanded the scope of FIC, enabling millions to participate in the formal economy (Asif, et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This access can empower individuals by enhancing their capacity to save, invest, and manage financial risks, contributing to overall economic resilience. Poverty alleviation, on the other hand, states to the strategies and policies aimed at reducing the incidence and severity of poverty. It is a multifaceted objective encompassing increased access to essential services, job creation, and sustainable economic growth (Yuan, et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). FIC is essential in enabling households to navigate economic uncertainties, invest in education and health, and pursue income-generating activities. The link between FIC and poverty alleviation is influenced by various factors, including economic conditions, the quality of governance, and the availability of infrastructure. These elements collectively determine how effectively FIC can contribute to reducing poverty. A comprehensive understanding of the context in which financial services are offered and utilized is crucial for assessing the potential impact of FIC on sustainable poverty alleviation.\u003c/p\u003e\u003cp\u003eThis study explores the transformative power of FIC in driving poverty alleviation, delving into how broadened access to financial services can reshape economic opportunities and uplift communities out of poverty across Asian economies from 2000 to 2023. Utilizing advanced econometric methodology, namely Cross-Sectional Autoregressive Distributed Lag (CS-ARDL), the analysis aims to produce reliable findings by considering long-term relationships and cross-sectional dependencies in panel data. Utilizing this methodology allows for a comprehensive assessment of both short-term fluctuations and long-term equilibrium connections between FIC and poverty alleviation. The findings indicate a negative correlation between FIC and poverty levels, suggesting that greater access to financial services is associated with lower poverty rates in these economies. This connection can be explained by the ability of FIC to empower individuals to better manage risks, invest in education and health, support small businesses, and engage more actively in economic activities. By facilitating savings, credit access, and financial literacy, inclusive financial systems enhance economic resilience and enable vulnerable populations to pursue income-generating opportunities. The evidence indicates that individuals with consistent access to financial resources are more likely to improve their economic status and actively participate in fostering sustainable economic development.\u003c/p\u003e\u003cp\u003eTheoretical significance of this study lies in its contribution to the broader understanding of the mechanisms through which FIC affects poverty alleviation. By integrating economic theories related to financial development and PVT, the research enhances the conceptual framework underpinning the role of inclusive financial systems in fostering economic resilience and individual empowerment. It bridges the gap between theoretical constructs of financial access and real-world poverty outcomes, demonstrating how access to credit, savings, and insurance can serve as vital tools for income generation, risk management, and investment in human capital. This theoretical expansion provides a foundation for future scholarly work that aims to explore the nuanced relationships between FIC and socio-economic development. Empirically, the study offers significant insights through its comprehensive analysis of data from 48 Asian economies over a 23-year period, from 2000 to 2023. The use of advanced econometric techniques such as CS-ARDL and FMOLS ensures the robustness of the findings, contributing valuable evidence to the empirical discourse on FIC and PVT. The results highlight an inverse relationship between FIC and poverty levels, underscoring the importance of strategic policy frameworks that support financial access as a tool for poverty alleviation. This empirical contribution is crucial for policymakers, international development agencies, and financial institutions aiming to design effective interventions that maximize the potential of FIC to drive sustainable PVT. The study\u0026rsquo;s insights have implications for shaping economic policies that foster inclusive growth and strengthen the economic capabilities of underserved populations.\u003c/p\u003e\u003cp\u003eThe second Section of this study focuses on the theoretical literature review and third Section explores into the empirical literature review. The fourth Section is dedicated to the materials and methods and the fifth Section reports the results. In the sixth Section, these results are thoroughly explained and the final Section offers a comprehensive conclusion and providing future recommendations for policymakers.\u003c/p\u003e"},{"header":"2. Theoretical Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Financial Intermediation Theory\u003c/h2\u003e\u003cp\u003eFinancial Intermediation theory was introduced by Allen and Santomero (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This theory emphasizes the crucial role of financial intermediaries in bridging the gap between savers and borrowers, which posits that intermediaries i.e., banks and financial institutions improve efficiency in the financial system by reducing transaction costs and addressing information asymmetries. Financial intermediaries facilitate the allocation of capital, which, in turn, fuels economic activities and growth. In relation to this study, this theory underlines the importance of financial institutions in promoting financial inclusion (FIC). By providing access to essential financial services, intermediaries can empower individuals to save, invest, and access credit, which helps reduce poverty. This further supports the idea that well-functioning financial intermediaries can enhance the economic participation of marginalized groups, contributing to poverty alleviation by enabling income-generating activities and fostering economic resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Endogenous Growth Theory\u003c/h2\u003e\u003cp\u003eRomer (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) developed the Endogenous Growth theory, which suggests that economic growth is driven mainly by internal factors rather than outside influences. This theory highlights the importance of human capital, innovation, and knowledge in creating sustained economic growth. Financial inclusion fits well with this theory because it allows people to access resources that can be used for education, skill development, and small business growth. When individuals and businesses have access to credit and other financial services, they are better prepared to innovate and contribute to economic growth. This study uses this theory to show how access to financial resources can lead to investments in human and physical capital, ultimately helping to reduce poverty. The theory also emphasizes that inclusive financial systems boost development by increasing productivity and supporting economic growth from within a country.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Capability Approach\u003c/h2\u003e\u003cp\u003eSen (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) introduced Capability Approach, which emphasizes the importance of expanding individuals\u0026rsquo; freedoms, and capabilities to achieve better living standards. This approach shifts the focus from income-based measures to broader human development factors, suggesting that true progress lies in enhancing individuals' ability to pursue the life they value. Financial inclusion fits into this framework as it empowers individuals by providing the tools needed to improve their economic situation, make informed choices, and reduce vulnerability to economic shocks. Access to financial services can help people build assets, invest in health and education, and mitigate risks, thus expanding their capabilities. This study adopts the Capability Approach to understand how FIC not only influences poverty rates but also supports broader socio-economic empowerment. By enabling greater financial access, communities can enhance their economic and social potential, aligning with Sen\u0026rsquo;s emphasis on comprehensive human development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Financial Deepening Theory\u003c/h2\u003e\u003cp\u003eFinancial Deepening theory initially discussed in the work of Shaw (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). This suggests that the development of financial markets and institutions is crucial for economic growth. The theory posits that financial deepening, where financial markets become more sophisticated and inclusive, leads to improved mobilization of savings and more efficient allocation of resources. This theory is relevant to the current study as it highlights the positive effects of expanding financial services to a broader segment of the population. Financial deepening supports FIC by ensuring that financial systems cater to the needs of different economic actors, from small entrepreneurs to low-income individuals. This further emphasizes that as financial systems grow more inclusive and sophisticated, they contribute to economic stability, investment opportunities, and PVT. This study incorporates this theory to demonstrate how enhanced access to financial services can promote sustainable economic growth and help reduce poverty levels.\u003c/p\u003e\u003cp\u003eAmong these theories, the Capability Approach is the most pertinent to this study. This is because it goes beyond the economic implications of FIC and emphasizes the broader impact on human development. By focusing on how financial access can expand individuals\u0026rsquo; capabilities and freedoms, this approach aligns closely with the study's objective of understanding how FIC contributes to poverty alleviation and overall socio-economic well-being.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Empirical Literature Review","content":"\u003cp\u003eThis section outlined findings from previous studies, providing a foundation for understanding the relationship between financial inclusion, financial development, foreign capital inflows, and poverty alleviation. Cheraghlou (2017) reported that 75% of Muslim adults lacked formal financial accounts. The study emphasized that access to Islamic financial services, such as Qard Hassan, promoted financial inclusion and helped reduce poverty, as seen in the case of Pakistan\u0026rsquo;s Akhuwat Foundation. Similarly, Ali (2017) focused on microfinance in Kenya\u0026rsquo;s North Eastern Province and identified illiteracy and poor infrastructure as significant barriers. Despite these challenges, microfinance initiatives contributed to poverty reduction in underserved communities. Lal (2018) examined the role of cooperative banks in three northern Indian states and found that access to basic financial services through these institutions significantly improved the well-being of the poor. Building on this, Inoue (2019) employed GMM estimation to assess the role of financial deepening in India. The study revealed that public sector banks, rather than private ones, were more effective in reducing poverty through enhanced financial access.\u003c/p\u003e\n\u003cp\u003eMushtaq and Bruneau (2019) investigated the impact of ICT on financial inclusion across 62 countries. Their findings confirmed that digital technologies promote inclusion and subsequently help reduce poverty and inequality. In line with this, Churchill and Marisetty (2020) consistently observed that broader access to financial services significantly alleviated poverty across diverse contexts. Islamic banking also emerged as a key driver in promoting financial inclusion. Rahman (2020) examined the case of Bangladesh and found that although many citizens remained excluded, Islamic financial institutions held strong potential to improve access and stimulate GDP growth. Erlando et al. (2020) emphasized the multidimensional role of financial inclusion by confirming its positive relationship with economic growth, poverty alleviation, and income distribution. In a broader macroeconomic context, Bird and Choi (2020) assessed the effects of foreign financial inflows like FDI, remittances, and aid on growth. They found that while FDI promoted economic growth, remittances had a negative impact, and the effects of aid varied depending on country-specific characteristics.\u003c/p\u003e\n\u003cp\u003eMore recent studies have turned attention to institutional and policy dynamics. Settle (2022) traced the evolution of Pakistan\u0026rsquo;s financial inclusion policies, noting a shift from donor-driven to regulatory approaches aimed at managing informality and enhancing monetary stability. Ngong et al. (2022), using co-integration and ARDL methods, found that micro-financial inclusion had a strong long-run impact on poverty reduction, although some short-term lag effects showed mixed outcomes. Chen et al. (2022) echoed these findings, reinforcing the existence of both positive and negative lagged effects in financial inclusion\u0026apos;s impact on poverty. Aracil et al. (2022) provided cross-country evidence showing that the poverty-reducing impact of financial inclusion was strengthened by high institutional quality, particularly in low-income nations. Similarly, Arogundade et al. (2022), using spatial econometrics, discovered that FDI from neighboring countries significantly influenced domestic poverty levels across 44 African nations, with institutional quality playing a moderating role.\u003c/p\u003e\n\u003cp\u003eTurning to remittance-focused studies, Abduvaliev and Bustillo (2020/2023) showed that remittances played a vital role in reducing poverty by supporting household income and stabilizing consumption in recipient countries. Mawutor et al. (2023), analyzing Ghana\u0026rsquo;s economy, found that remittances contributed positively to economic growth, while FDI and exchange rates had negative effects. The study recommended reforms to optimize remittance channels and currency policies. Sen et al. (2023) contributed further by linking financial inclusion with energy poverty in Bangladesh, revealing that financially included households, particularly those led by women, were less vulnerable to energy deprivation, thus supporting SDG 7. Camara (2023) explored the relationship between FDI and tax revenue in 90 developing countries and found that FDI boosted revenues, though the impact was muted in resource-rich economies. Haruna et al. (2023), focusing on Nigeria, employed ARDL and NARDL models to demonstrate that FDI shocks, both positive and negative contributed to short- and long-term poverty reduction. Lee et al. (2023) analyzed provincial data from China and demonstrated that digital financial inclusion (DIFI) significantly reduced poverty. The study also identified spatial spillover effects and a U-shaped relationship, indicating that DIFI offers both development opportunities and risk of exclusion if not properly managed.\u003c/p\u003e\n\u003cp\u003eStudies from 2024 continued to explore the moderating role of governance and readiness in determining the effectiveness of financial inflows. Sanli et al. (2024) found that economic, social, and governance readiness enhanced the poverty-reducing effects of FDI in Sub-Saharan Africa. Cuong et al. (2024) confirmed that remittances significantly reduced multidimensional poverty across Belt and Road Initiative countries, with political stability and agricultural development acting as important enablers. Omenihu et al. (2024) examined the roles of financial access, usage, and quality in Nigeria and concluded that financial access had the most substantial poverty-reducing impact, especially when aligned with users\u0026rsquo; education levels. Aloui et al. (2024) explored the role of governance in the FDI-poverty nexus and found that regions with stronger governance frameworks saw greater benefits from foreign capital inflows, particularly in Africa and Latin America.\u003c/p\u003e\n\u003cp\u003eCollectively, these studies underscore that financial inclusion, financial development, and foreign capital inflows can substantially alleviate poverty, especially when supported by strong institutions, governance, and inclusive technologies. Based on the above-mentioned literature, we may hypothesize that financial inclusion significantly reduce poverty in developing countries\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003csub\u003e1\u003c/sub\u003e:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eA significant negative relationship exists between financial inclusion and poverty reduction.\u003c/em\u003e\u003c/p\u003e"},{"header":"4. Material and Methods","content":"\u003cp\u003eThe research investigates the liaison between financial inclusion (FIC) and poverty reduction (PVT) across 26 Asian countries from 2000 to 2023. Initially, 48 Asian countries were considered for inclusion in the study; however, after an extensive data screening process, only 26 countries were selected due to the unavailability of reliable data on key indicators such as FIC and poverty rates (See Table \u003cspan class=\"InternalRef\"\u003eA1\u003c/span\u003e in appendix). This study spans a period that has witnessed significant technological progress in various sectors, making the last two decades particularly relevant for understanding the evolving dynamics of FIC and its impact on poverty alleviation. The selected countries encompass both emerging and developed economies, offering a comprehensive view of the regional variation in FIC and its potential role in reducing poverty. Data for this research were sourced from the WDI, providing a robust and consistent dataset to analyze the dynamic liaison between financial services access and PVT. By focusing on this time frame and these economies, the study aims to offer valuable insights into how FIC can be leveraged as a tool for poverty alleviation in Asia.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Vriable Discussion\u003c/h2\u003e\n \u003cp\u003eFinancial inclusion (FIC) is the process of providing access to affordable and essential financial services to all individuals, especially those excluded from traditional financial systems. Its emergence as a key development focus is tied to the growing recognition that access to financial services can empower individuals and foster economic stability. Technological advancements, including mobile banking and digital platforms, have significantly expanded FIC by reaching underserved populations in rural and remote areas (Aloulou, et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Oyewole, et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). FIC can play a pivotal role in PVT by enabling people to manage their finances, invest in education and healthcare, and engage in business activities. In developing economies, it promotes economic growth, reduces income inequality, and facilitates social mobility, contributing to long-term poverty alleviation. Similarly, PVT involves efforts to decrease the proportion of people living below the poverty line and improve their living standards. This process includes strategies aimed at increasing income levels, providing access to essential services, and reducing social inequalities. Over recent decades, the focus has shifted towards inclusive growth, where economic opportunities are made accessible to all members of society. FIC is closely tied to PVT, as it allows individuals to invest in income-generating activities, build savings, and mitigate the effects of economic shocks. By improving financial access, individuals in poverty can gain the tools to improve their livelihoods, fostering sustainable development and reducing poverty (Abduvaliev \u0026amp; Bustillo, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sanli, et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eForeign direct investment involves investments made by foreign entities in the form of establishing or expanding business operations in a host country. FDI plays a significant role in boosting economic development by providing capital, technology, and expertise. This inflow of resources can increase productivity, enhance innovation, and create employment opportunities. FDI can also contribute to PVT by providing jobs, improving infrastructure, and increasing the availability of goods and services. The positive impact of FDI on poverty is closely linked to the ability of the host country to manage and distribute the benefits of investment through sound governance, policies, and institutions that ensure broad-based economic benefits (Haruna, et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mawutor, et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Foreign aid consists of resources provided by developed countries to support the development efforts of less-developed countries. Its origins can be traced back to the mid-20th century, aimed at addressing poverty, improving infrastructure, and promoting economic growth in the Global South. The impact of foreign aid on PVT remains a topic of debate, but it has played a crucial role in funding development projects, improving healthcare, and enhancing education. Properly allocated foreign aid helps developing nations build infrastructure, enhance human capital, and reduce the barriers that prevent people from escaping poverty. It is most effective when aligned with the specific needs and priorities of recipient countries and complements domestic policy efforts (Bird \u0026amp; Choi, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wen, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eGovernment effectiveness describes the capacity of public institutions to implement policies that positively affect citizens\u0026apos; welfare. It involves the efficiency, transparency, and quality of public services, as well as the government\u0026apos;s ability to maintain law and order. Over time, effective governance has been recognized as essential for sustainable development and PVT, as strong institutions are crucial for managing resources and ensuring the equitable distribution of opportunities. Governments that create favorable economic environments, maintain stable institutions, and promote good governance practices contribute significantly to economic growth and poverty alleviation. Effective governments can also manage foreign aid and FDI, ensuring that these resources are used effectively to support development goals (Mongi \u0026amp; Saidi, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Econometric Models\u003c/h2\u003e\n \u003cp\u003eThis study employs a series of econometric models to explore how FIC, FDI, ODA, and GOE influence PVT across different Asian economies.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePVT\u0026thinsp;=\u0026thinsp;f (FIC, FDI, ODA, GOE) Eq.\u0026nbsp;(1)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eEquation 1 expresses the general functional relationship between PVT (PVT) and several key determinants: financial inclusion (FIC), foreign direct investment (FDI), foreign aid (ODA), and government effectiveness (GOE). In this equation, POV is seen as a dependent variable influenced by the levels of FIC, FDI, ODA, and GOE.\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{Y}_{jt}={\\alpha\\:}_{\\theta\\:}+{\\beta\\:}_{1}{IV}_{jt}+{\\gamma\\:}_{1}{CV}_{jt}+{\\epsilon\\:}_{jt}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:Eq.\\:\\left(2\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eEquation 2 presents a general econometric specification for modeling the relationship between a dependent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{jt}\\)\u003c/span\u003e\u003c/span\u003e and a set of independent variables, including key factors (IV) and control variables (CV). The dependent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{jt}\\)\u003c/span\u003e\u003c/span\u003e represents the variable of interest, such as a measure of poverty or income levels for country j at time t. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{\\theta\\:}\\)\u003c/span\u003e\u003c/span\u003e is the intercept term, representing the baseline value of the dependent variable. The coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e captures the impact of the independent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{IV}_{jt}\\)\u003c/span\u003e\u003c/span\u003e on the dependent variable. The error term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{jt}\\)\u003c/span\u003e\u003c/span\u003e accounts for unobserved factors affecting the outcome.\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{PVT}_{jt}={\\alpha\\:}_{\\theta\\:}+{\\beta\\:}_{1}{FIC}_{jt}+{\\gamma\\:}_{1}{FDI}_{jt}+{\\gamma\\:}_{2}{ODA}_{jt}+{\\gamma\\:}_{3}{GOE}_{jt}+{\\epsilon\\:}_{jt}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:Eq.\\:\\left(3\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eEquation 3 refines the relationship expressed in Eq.\u0026nbsp;1 by specifying the linear impact of FIC, FDI, ODA, and GOE on PVT. Here, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PVT}_{jt}\\)\u003c/span\u003e\u003c/span\u003e is the PVT measure for country j at time t, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{\\theta\\:}\\)\u003c/span\u003e\u003c/span\u003e is the intercept. This model hypothesizes that each of these factors contributes to PVT, and their relative impacts will be estimated through the coefficients.\u003c/p\u003e\n \u003cp\u003eFor CS-ARDL model\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:\\varDelta\\:{PVT}_{jt}={\\alpha\\:}_{^\\circ\\:}+{\\sum\\:}_{i=1}^{p}{\\beta\\:}_{1}\\varDelta\\:{FIC}_{jt-1}+{\\sum\\:}_{i=1}^{p}{\\gamma\\:}_{1}\\varDelta\\:{FDI}_{jt-1}+{\\sum\\:}_{i=1}^{p}{\\gamma\\:}_{2}\\varDelta\\:{ODA}_{jt-1}+{\\sum\\:}_{i=1}^{p}{\\gamma\\:}_{3}\\varDelta\\:{GOE}_{jt-1}+{\\epsilon\\:}_{it}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:Eq.\\:\\left(4\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eEquation 4 adopts a dynamic approach using the CS-ARDL (Cross-Sectional Autoregressive Distributed Lag) model, which explores the short-term and long-term effects of FIC, FDI, ODA, and GOE on PVT. The dependent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{PVT}_{jt}\\)\u003c/span\u003e\u003c/span\u003e represents the change in PVT for country j at time t. The terms \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{FIC}_{jt-1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{FDI}_{jt-1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{ODA}_{jt-1}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{GOE}_{jt-1}\\)\u003c/span\u003e\u003c/span\u003e indicate the lagged effects of FIC, FDI, ODA, and GOE on changes in PVT. The coefficients of these lagged variables show how past changes in these factors influence the current rate of PVT.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariables of Study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAcronym\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasurement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\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\u003e\u003cstrong\u003ePVT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePoverty Reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoverty headcount ratio at societal poverty line (% of population)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Fonseca, et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFinancial Inclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIC is measured by the availability and usage of financial services like bank accounts, loans, insurance, ATMs, and mobile money across different demographics. Key indicators include the number of financial access points (e.g., branches, ATMs), service usage, and affordability.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Basnayake, et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eForeign Direct Investment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFDI, net inflows (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Aloui, et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eODA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eForeign Aid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNet ODA received (% of GNI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Wen, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGovernment Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuality of public services, policy implementation, and the efficiency of governance.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Mongi \u0026amp; Saidi, 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003eThis table is revealing the information on variables, measurement, and sources of data.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e discloses the statistics of the Cross-Section Dependence (CD) analysis for several key variables: PVT, FIC, FDI, ODA, and GOE. Both the Breusch-Pagan LM and Pesaran CD tests indicate significant cross-sectional dependence for PVT, FIC, FDI, and GOE, as evidenced by their low p-values (all less than 0.05). This suggests that these variables are correlated across countries, meaning that the residuals of these variables are not independent but influenced by factors shared among countries in the sample. The absence of results for ODA indicates missing or incomplete data for this variable in the cross-sectional dependence analysis. Overall, the significant CD implies that these variables effects on PVT may be interconnected across countries, which should be considered in further econometric modeling to avoid biased estimates.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCross-Section Dependence (CD) Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBreusch-Pagan LM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePesaran CD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStatistic\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eProbability\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStatistic\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eProbability\u003c/em\u003e\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\u003e\u003cstrong\u003ePVT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1642.913\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\u003e19.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1634.820\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\u003e13.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e585.878\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\u003e8.8453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eODA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1180.90\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\u003e7.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1083.450\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\u003e3.3931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e\u003cem\u003eself-estimation.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the unit root tests for stationarity, using two different methods: CIPS (Cross-Sectionally Augmented IPS) and CADF (Cross-Sectional Augmented Dickey-Fuller) tests. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e further shows the test statistics for the variable PVT, FIC, FDI, ODA, and GOE at both levels and first differences. For PVT, the CIPS test statistic at level is -0.860 (with a p-value of 0.194), indicating that it is non-stationary at level but becomes stationary at the first difference, where the p-value is 0.000. A similar pattern is observed for FIC, which is non-stationary at level (CIPS value 2.851, p-value 0.997) but stationary at first difference with a highly significant p-value (0.000). FDI and ODA are both stationary at level according to the CIPS and CADF tests, with p-values less than 0.05. However, GOE is non-stationary at level but becomes stationary at first difference, with the CADF test showing a marginal p-value of 0.051 at level and a significant p-value at first difference. These results suggest that most variables require differencing to achieve stationarity, which is important for reliable econometric analysis (Dickey \u0026amp; Fuller, \u003cspan class=\"CitationRef\"\u003e1979\u003c/span\u003e; Im, Pesaran, \u0026amp; Shin, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of Stationarity through Unit Root Testing\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCIPS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCADF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAt Level (0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAt first difference (1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\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\u003e\u003cstrong\u003ePVT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.860)\u003c/p\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-20.485)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(52.176)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(501.409)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.851)\u003c/p\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-8.557)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(33.764)\u003c/p\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(160.892)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.163)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(109.753)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eODA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.210)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(75.517)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.668)\u003c/p\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-8.995)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(60.308)\u003c/p\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(104.721)\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eself-analysis.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the Kao residual cointegration test, which examines the long-term relationship between the variables. The ADF (Augmented Dickey-Fuller) statistic is -2.768 with a p-value of 0.002, indicating significant cointegration, meaning that a long-run equilibrium relationship exists between the variables. The Residual Variance is 17.473, and the HAC (Heteroskedasticity and Autocorrelation Consistent) variance is 13.811, though the probabilities for these statistics are not provided. Overall, the Kao test confirms the presence of a cointegrated relationship, suggesting that the variables move together in the long run (Kao, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCointegration Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eCointegration Test of Kao Residual\u003c/em\u003e\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\u003e\u003cstrong\u003eTest Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003et-statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProbability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eADF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHAC Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eself-estimation\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Methodology\u003c/h2\u003e\n \u003cp\u003eTo analyze the impact of FIC on PVT, along with the roles of foreign direct investment, foreign aid, and government effectiveness, several econometric methodologies were employed in this study. The initial step involved testing for cross-sectional dependence among the variables, which is crucial for panel data analysis. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the Breusch-Pagan LM and Pesaran CD tests, which were conducted to assess whether there is any correlation in the residuals across countries. Significant cross-sectional dependence was found for most variables, suggesting that the assumptions of independence between countries were violated, and this needed to be accounted for in further analysis. The findings from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e highlighted that the presence of cross-sectional dependence requires adjustment in the modeling approach to avoid bias in the estimates. Next, to ensure the robustness of the findings, unit root tests were applied to check for stationarity. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the CIPS and CADF tests, which indicated that while some variables, such as FDI and foreign aid (ODA), were stationary at their levels, others, like PVT and FIC, required differencing to achieve stationarity. The results from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e suggest that stationarity was achieved for most variables at their first differences, highlighting the importance of transforming the data to avoid potential issues with non-stationary variables. This step is crucial because non-stationary data can lead to spurious relationships if not properly addressed.\u003c/p\u003e\n \u003cp\u003eOnce stationarity was established, the analysis proceeded with cointegration testing using the Kao residual test to examine whether a long-term liaison exists between the variables. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reveals the statistics of the Kao residual technique, which confirmed the presence of cointegration, suggesting that a stable long-run relationship exists between FIC, foreign aid, government effectiveness, and PVT across the selected countries. The results in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that these variables move together in the long-run, further supporting the hypothesis of a sustained relationship among them. This finding reinforces the idea that the effects of FIC, foreign aid, and government effectiveness are interlinked over time, contributing to PVT.\u003c/p\u003e\n \u003cp\u003eFinally, the study employed three key econometric techniques to estimate the relationships: POLS, FMOLS, and CS-ARDL models. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents the results from POLS and FMOLS, where POLS was used for initial estimations, and FMOLS provided more robust estimates by accounting for potential endogeneity and serial correlation. The findings in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e offer valuable facts into the liaison between the variables in the absence of lag effects. Subsequently, the CS-ARDL model, shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, was applied to explore both the short-term and long-term dynamics between the variables, while also accounting for lagged effects and cross-sectional dependence. The results from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e emphasize the importance of including lag effects in the model, as they reveal the temporal nature of the relationships. This comprehensive methodology ensures a thorough examination of the complex relationships between FIC and PVT in the context of developing Asian economies. Moreover, FMOLS was initially introduced (Phillips \u0026amp; Hansen, \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Results and Discussion","content":"\u003cp\u003eDescriptive statistics provide a summary of the main features of a dataset, offering a concise overview of its central tendency, dispersion, and shape.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Dev.\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\u003e\u003cstrong\u003ePVT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-37.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eODA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e\u003cem\u003eself-analysis.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e provides the descriptive statistics for the key variables used in the study. The mean values show that PVT has a mean of 27.371, with a relatively low skewness of 0.288, indicating a symmetric distribution. Similarly, FIC has a higher mean of 38.555, but its skewness value of 1.497 suggests a rightward skew, implying a few outlier values with higher FIC. FDI displays a high skewness of 3.428, indicating that the distribution is heavily skewed to the right, with some countries attracting much higher levels of investment. ODA has a mean of 3.630, but it\u0026rsquo;s extremely high kurtosis (62.623) suggests that the distribution is highly leptokurtic, with a concentration of extreme values. GOE shows a mean of -0.291 with low skewness, indicating a near-normal distribution. These statistics help to contextualize the variability and distribution patterns of the data before applying further econometric analyses.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation Statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePVT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eODA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGOE\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\u003e\u003cstrong\u003ePVT\u003c/strong\u003e\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\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\u003e\u003cstrong\u003eFIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.189\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\u0026nbsp;\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\u003e\u003cstrong\u003eFDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.094\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\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\u003e\u003cstrong\u003eODA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e\u003cem\u003eself-analysis.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the correlation coefficients between the key variables of interest in the study. The correlation between PVT and FIC is negative and modest at -0.189, suggesting a weak inverse relationship. Similarly, PVT is negatively correlated with FDI and GOE, with coefficients of -0.212 and \u0026minus;\u0026thinsp;0.162, respectively, indicating weak negative associations. There is no significant correlation between PVT and ODA, with a coefficient of 0.062, showing almost no relationship. FIC is positively correlated with GOE (0.548), implying that higher government effectiveness is associated with greater FIC. However, the correlations between FIC and the other variables (FDI and ODA) are weak, with values of -0.094 and \u0026minus;\u0026thinsp;0.181, respectively. These correlations provide initial understandings into the interrelationships among the variables and set the stage for further analysis to explore their causal effects.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of FIC on PVT\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePOLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFMOLS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\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\u003eProbability\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\u003eProbability\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\u003e\u003cstrong\u003eFIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.053\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.113\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.401\u003csup\u003ea\u003c/sup\u003e\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-0.061\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eODA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.082\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.835\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.086\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eS.E. of regression\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003eThe superscripts a, b, and c denotes the significance level at 1%, 5%, and 10%.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents the results from two key econometric techniques: Pooled Ordinary Least Squares (POLS) and Fully Modified Ordinary Least Squares (FMOLS), both of which are used to estimate the effect of financial inclusion (FIC) on poverty reduction (PVT). The POLS model estimates the relationships between variables without accounting for potential endogeneity, while FMOLS adjusts for endogeneity and serial correlation to provide estimates that are more robust. The POLS results show that FIC has a statistically significant negative effect on PVT, with a coefficient of -0.053 and a p-value of 0.003. This suggests that, according to the POLS estimation, as FIC increases, PVT tends to decrease slightly, although the effect is weak. The negative relationship could be attributed to several factors. For instance, while FIC may increase access to financial resources, it might not necessarily result in immediate PVT, especially if the benefits of FIC are not equitably distributed or if it is accompanied by growing inequality. Additionally, FIC alone may not address other structural factors such as education, healthcare, and employment, which are essential for PVT.\u003c/p\u003e\n\u003cp\u003eIn contrast, the FMOLS results show a more substantial and negative coefficient of -0.113 for FIC, with a highly significant p-value of 0.000. FIC initiatives provide the poor with greater access to financial services, i.e., savings accounts, credit, and insurance, which can enhance their economic opportunities. These tools can help individuals invest in businesses, smooth consumption during economic shocks, and reduce vulnerability to unexpected expenses. By promoting broader access to financial resources, FIC can directly contribute to reducing poverty rates, as it empowers the poor to engage more actively in the economy and improve their living standards. Furthermore, when paired with appropriate policies, FIC can reduce inequality by ensuring that the benefits of economic growth are more evenly distributed across society. The coefficients for other variables, i.e., FDI, ODA, and GOE, provide additional context. FDI has a significantly negative effect on PVT in both models, with a coefficient of -0.401 (POLS) and \u0026minus;\u0026thinsp;0.061 (FMOLS), suggesting that, in these economies, higher levels of FDI may not be directly associated with poverty alleviation. This could be due to the fact that FDI often flows to sectors that are not necessarily aligned with PVT, such as capital-intensive industries or export-oriented businesses. ODA has an almost negligible effect in the POLS model with a coefficient of -0.000, but in the FMOLS model, it becomes significantly negative (-0.082), suggesting that foreign aid may not be effectively contributing to PVT or might be misallocated.\u003c/p\u003e\n\u003cp\u003eThe GOE coefficient is negative in both models, with a larger magnitude in FMOLS (-11.086), indicating that ineffective governance may be hindering the positive effects of FIC on PVT. A lack of institutional quality and weak governance may prevent the full benefits of FIC from reaching the poor, reinforcing the need for efficient institutions and policies to support poverty alleviation. In summary, the results indicate that while FIC has the potential to reduce poverty, its effects are complex and depend heavily on other factors, including governance, foreign investment, and aid effectiveness. The negative relationships observed in the models highlight the need for comprehensive policies that address the broader structural issues affecting PVT.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImpact of FIC on PVT\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePVT as a dependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCS-ARDL Model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. 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\u003eProb.*\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eLong Run Equation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.081\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.437\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.367\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eODA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.090\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.601\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.609\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eShort-run Equation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOINTEQ01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(PVT(-1))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(FIC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(FIC(-1))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(FDI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(FDI(-1))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(ODA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(ODA(-1))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(GOE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD(GOE(-1))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eself-analysis.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e portrays the statistics of the CS-ARDL technique, which is used to scrutinize the long-run and short-run impact of FIC on PVT across the selected 26 Asian economies. Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e is in two-fold: the long-run equation and the short-run equation. In the long-run equation, the coefficient for FIC is -0.081 with a t-statistic of -4.437, indicating that FIC has a significant negative effect on PVT. This suggests that, in the long run, increased FIC, by providing access to services i.e., savings, loans, and insurance, empowers the poor to build wealth, manage risks, and expand their economic opportunities. When combined with supportive policies, this access can significantly reduce poverty levels and wealth inequality by fostering broader participation in the economy. FDI also shows a negative long-run effect on PVT, with a coefficient of -1.084 and a t-statistic of -4.367, which implies that foreign direct investment does not necessarily translate into poverty alleviation in the selected economies. The negative coefficient for ODA (-0.090) with a t-statistic of -4.601 suggests that foreign aid may not effectively reduce poverty in these countries, possibly due to misallocation or ineffective use of resources. Lastly, GOE (government effectiveness) shows a significantly negative relationship with PVT in the long run, with a coefficient of -2.932 and a t-statistic of -2.609, highlighting that poor governance and ineffective institutions may hinder the poverty-reducing potential of FIC and other factors.\u003c/p\u003e\n\u003cp\u003eIn the short-run equation, the coefficient of COINTEQ01 is -0.257, with a t-statistic of -2.681, indicating a significant and negative adjustment to the long-run equilibrium, suggesting that the system converges back to the long-run equilibrium over time. The lagged effects of PVT, FIC, FDI, ODA, and GOE are generally not significant, with only a few coefficients approaching statistical significance. Notably, D(PVT(-1)) has a coefficient of -0.135, which is marginally significant (p-value\u0026thinsp;=\u0026thinsp;0.056), indicating some persistence in poverty levels from the previous period. Similarly, D(FIC) shows a negative short-term effect on PVT with a coefficient of -0.090 and a t-statistic of -1.733, suggesting that FIC short-term impact on PVT is weak. The coefficients for D(FDI), D(ODA), and D(GOE) are not significant in the short run, further suggesting that the immediate effects of foreign investment, foreign aid, and government effectiveness are not directly associated with short-term PVT. The CS-ARDL model results show that while FIC and other factors like foreign direct investment and government effectiveness have significant long-term effects on PVT, their short-term impact is weak and sometimes insignificant. The results highlight the complexity of the liaison between these variables and PVT, suggesting that the benefits of FIC and foreign aid may take time to materialize and depend on broader institutional and policy factors.\u003c/p\u003e"},{"header":"6. Conclusion and Policy Recommendations","content":"\u003cp\u003eThis endeavor explored the liaison between financial inclusion (FIC) and poverty reduction (PVT) across Asian economies over the period from 2000 to 2023. The results suggest a mixed and nuanced impact of FIC on PVT, with FIC generally showing a negative relationship with PVT in both the short and long run. FIC is a powerful tool for reducing poverty by expanding access to financial resources; its full potential is realized when accompanied by strong governance and effective policies. With the right support systems in place, FIC can significantly reduce poverty by empowering individuals to invest, build assets, and improve their economic resilience, ultimately driving growth that is more inclusive. FDI and ODA also demonstrate negative relationships with PVT, highlighting that these external factors do not necessarily lead to improved living standards, especially when they are not effectively targeted or managed. The government effectiveness (GOE) variable is found to have a significant negative impact, underscoring the importance of institutional quality in shaping the outcomes of FIC policies. These findings highlight that while FIC plays a crucial role in economic development, it must be integrated into a comprehensive strategy that tackles governance challenges, ensures efficient resource allocation, and addresses the underlying structural factors of poverty.\u003c/p\u003e\u003cp\u003eBased on these findings, policymakers should prioritize improving governance and institutional effectiveness to enhance the impact of FIC on PVT. FIC policies should be designed to ensure equitable access, particularly targeting marginalized populations i.e., rural communities and women. In addition, foreign aid and foreign direct investment should be channeled into poverty-reducing sectors like education, healthcare, and infrastructure, ensuring these resources are effectively used. Governments must focus on building institutional capacity, combating corruption, and improving public service delivery to create an enabling environment for sustainable PVT. These efforts, combined with well-targeted FIC strategies, can create long-term benefits for poverty alleviation in the region.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Study Limitations and Future Recommendation\u003c/h2\u003e\u003cp\u003eA key limitation of this study is the potential issue of generalizability, as the findings are based on data from only 26 Asian economies, which may not fully represent the diverse economic and institutional contexts of all developing regions. This limits the extent to which the results can be applied to other regions with different economic structures or stages of development. Future research could expand the sample to include more countries from diverse regions, enhancing the generalizability of the findings. Additionally, examining a broader range of economies with varying levels of FIC and governance could provide conclusions that are more robust.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.K. (Khursid Khudoykulov) conceptualized the research idea, designed the methodology, and supervised the entire study. Z.M. (Zokir Mamadiyarov) contributed to the theoretical framework and literature review. U.F. (Umar Farooq) performed the econometric modeling, data estimation, and robustness analysis. M.I.T. (Mosab I. Tabash) reviewed the econometric results, contributed to interpretation, and refined the discussion of policy implications. M.M. (Mansur Mansurov) assisted in data validation, visualization, and cross-checking statistical outputs. I.I. (Inomiddin Inomov) contributed to the formulation of conclusions, editing, and overall manuscript revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is based on publicly available secondary data and does not involve any studies with human participants or animals performed by any of the authors. Therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbduvaliev, M., \u0026amp; Bustillo, R. (2020). Impact of remittances on economic growth and poverty reduction amongst CIS countries. \u003cem\u003ePost-Communist Economies, 32\u003c/em\u003e(4), 525-546. Retrieved from https://doi.org/10.1080/14631377.2019.1678094\u003c/li\u003e\n \u003cli\u003eAli, A. E. (2017). 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Foreign direct investment, remittances, real exchange rate, imports, and economic growth in Ghana: An ARDL approach. \u003cem\u003eCogent Economics \u0026amp; Finance, 11\u003c/em\u003e(1). Retrieved from https://doi.org/10.1080/23322039.2023.2185343\u003c/li\u003e\n \u003cli\u003eMishra, D., Kandpal, V., Agarwal, N., \u0026amp; Srivastava, B. (2024). Financial Inclusion and Its Ripple Effects on Socio-Economic Development: A Comprehensive Review. \u003cem\u003eJournal of Risk and Financial Management, 17\u003c/em\u003e(3), 105. Retrieved from https://doi.org/10.3390/jrfm17030105\u003c/li\u003e\n \u003cli\u003eMongi, C., \u0026amp; Saidi , K. (2023). The Impact of Corruption, Government Effectiveness, FDI, and GFC on Economic Growth: New Evidence from Global Panel of 48 Middle-Income Countries. \u003cem\u003eJournal of the Knowledge Economy\u003c/em\u003e. Retrieved from https://doi.org/10.1007/s13132-023-01509-0\u003c/li\u003e\n \u003cli\u003eMushtaq, R., \u0026amp; Bruneau, C. 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Digital financial inclusion and quality of economic growth. \u003cem\u003eHeliyon, 9\u003c/em\u003e(9). Retrieved from https://doi.org/10.1016/j.heliyon.2023.e19731\u003c/li\u003e\n \u003cli\u003eYuan, C., Ding, T., \u0026amp; Wu, B. (2024). Targetedness, effectiveness, and sustainability of China\u0026apos;s photovoltaic poverty alleviation programmes. \u003cem\u003eEnergy, 300\u003c/em\u003e. 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It emphasizes the role of enhanced access to financial services in reducing poverty levels, employing Cross-Sectional Autoregressive Distributed Lag (CS-ARDL). This approach was selected for its effectiveness in addressing cross-sectional dependence, providing robust long-term and short-term estimates, and managing potential endogeneity issues. The results reveal a significant negative correlation between FIC and poverty levels, indicating that improved access to financial services is associated with lower poverty rates across the studied economies. This relationship can be attributed to several factors: FIC enhances individuals' capacity to manage financial risks, invest in essential human capital, i.e., education and healthcare, and encourages entrepreneurial endeavors. Inclusive financial systems bolster economic resilience and create opportunities for marginalized groups to generate income by facilitating access to savings, credit, and financial education. The findings suggest that policymakers should prioritize expanding access to financial services for underserved populations. This strategy empowers individuals, contributes to poverty reduction (PVT), and stimulates economic growth. The originality of this research lies in its focus on the long-term effects of FIC, providing valuable facts on how it can be integrated into broader PVT strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes: \u003c/strong\u003eI32; O15\u003c/p\u003e","manuscriptTitle":"Unlocking Opportunity: The Nexus Between Financial Inclusion and Poverty Reduction in Asian Economies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 13:35:11","doi":"10.21203/rs.3.rs-7829629/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-01T06:44:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-01T05:37:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T06:12:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-30T16:25:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264636502796900314525407998145100122777","date":"2025-12-27T18:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39895743894213880698203789047422973740","date":"2025-12-27T09:29:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T18:18:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233733055896466659647237680090624849897","date":"2025-12-22T16:50:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321544658181890680227802308940394936885","date":"2025-12-22T12:36:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128461469209776076229382586412576584056","date":"2025-12-22T11:30:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212235607907080398426866303007230592596","date":"2025-12-22T10:11:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188440866270121163836183665085764866472","date":"2025-12-06T15:33:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T17:44:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329429212723976216068689677748715967375","date":"2025-12-04T17:33:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131231977343743018519604702203561327792","date":"2025-11-17T03:42:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-29T12:50:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-15T07:32:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-15T07:32:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-10-10T18:29:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9625c122-1667-462a-9c58-1189ac5843e4","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:00:18+00:00","versionOfRecord":{"articleIdentity":"rs-7829629","link":"https://doi.org/10.1007/s43621-026-02748-2","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2026-02-11 15:57:00","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2025-11-10 13:35:11","video":"","vorDoi":"10.1007/s43621-026-02748-2","vorDoiUrl":"https://doi.org/10.1007/s43621-026-02748-2","workflowStages":[]},"version":"v1","identity":"rs-7829629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7829629","identity":"rs-7829629","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}