1 Introduction

In the vast landscape of societal challenges, few issues resonate as profoundly as poverty. Its far-reaching implications permeate every facet of human life, from education attainment and health outcomes to economic development, political stability, and social mobility. According to the World Bank, around 9.2 per cent of the global population lived in extreme poverty in 2020, defined as living on less than $1.90 per day. The COVID-19 pandemic has further exacerbated poverty rates, with an estimated 120 million and 150 million people pushed into extreme poverty in 2020 and 2021, respectively (World Bank, 2020a). Knowing that the $1.90 per day international poverty line may be too low to determine people experiencing poverty in middle-income countries, the World Bank adjusted this line to $3.20 and $5.50 for lower-middle-income and upper-middle-income countries. Based on these poverty lines, nearly one-fourth of the global population lives below the $3.20 threshold, while over 40% of the world’s population, nearly 3.3 billion individuals, live below the $5.50 benchmark (World Bank, 2020b). The World Bank even stated that achieving the goal of eradicating extreme poverty by 2030 seems hard to meet (Reuters, 2022). These facts show that poverty is still a considerable challenge in the world.

The topic of poverty, with its inherent challenges and potential for positive impact, beckons researchers to engage in a meaningful exploration that transcends disciplines. Research on this topic is intellectually stimulating and carries profound implications for policymaking, social programs, and the well-being of millions worldwide. By shedding light on the nuances of poverty, researchers have the power to inform transformative interventions, challenge systemic inequalities, and pave the way for a more inclusive and equitable future. Inspired by this, our research aims to explore novel and potent instruments for eliminating poverty on a global scale.

The current literature has documented a wide range of factors associated with poverty reduction, including schooling (Hofmarcher, 2021; Zhang, 2014), employment and nonfarm employment (Lanjouw, 1999; Page & Shimeles, 2015; Thompson & Dahling, 2019), microfinance and financial inclusion (Koomson et al., 2020a; Li, 2018; Polloni-Silva et al., 2021), women empowerment (Tang, 2022), energy and renewable energy accessibility (Taghizadeh et al., 2023; Zhao et al., 2022), infrastructure development (Timilsina et al., 2020), health status and healthcare availability (Krishna, 2007; Silverman et al., 2016; Zhou et al., 2020), and others.

In recent years, there is a growing literature on the impact of financial literacy on poverty alleviation. Using the China Household Finance Survey (CHFS) from 2015 to 2017, Xu et al. (2023) show that financial literacy can effectively and efficiently reduce poverty among rural households in the short and long term. Similarly, financial literacy is also found to have positive impacts on poverty alleviation in Chinese households (Wang et al., 2022). Jappelli et al. (2014) even recognize financial literacy as essential to eradicating poverty. Besides poverty, some studies also provide evidence of the positive effects of financial literacy on income and wealth accumulation, which are closely related to poverty. Adopting a new instrument variable method, Van Rooij et al. (2012) confirm the positive effect of financial literacy on household wealth in the Netherlands. Sekita et al. (2022) also find the same result in Japan. Meanwhile, financial illiteracy is a crucial predictor of wealth inequality (Jappelli & Padula, 2013; Lusardi et al., 2017). In addition, Disney and Gathergood (2011) indicate that financially literate people have higher household income levels. These studies provide many meaningful insights into poverty reduction and the income and wealth of individuals and households. Unfortunately, the current literature only focuses on a single country, such as China, the Netherlands, and Japan.

Our study contributes to the current literature as follows. First, we are the first to explore whether financial literacy reduces the likelihood of individuals falling into poverty on a global scale. Our analyses confirm that financial literacy significantly and negatively affects poverty worldwide. Second, this is also the first attempt to provide thorough analyses of the relationship between financial literacy and poverty across different socio-demographic groups, regions, and country income levels. The results show that the negative association between financial literacy and poverty remains largely unchanged across different socio-demographic groups, regions, and country income levels, implying that our results can be applicable in various contexts. Last, various robustness checks are conducted to ensure that the impacts of financial literacy are not deviated by reverse causality, omitted variable bias and variation between countries. The coefficients of financial literacy on poverty are still significant and negative across robustness tests, including (i) probit models with Gaussian copula terms (addressing endogeneity concerns), (ii) multilevel probit models (considering variations across countries), and (iii) Rubin’s (1974) causal model (addressing omitted variable concerns).

Following this introduction, the remainder of this paper can be summarized as follows. Section 2 provides a detailed review of financial literacy and poverty studies. In Sect. 3, we describe the measures of poverty and the econometric strategy employed in this paper. Section 4 presents the empirical results, while Sect. 5 thoroughly discusses these results. Finally, we conclude and provide policy implications derived from our findings in Sect. 6.

2 Literature Review

We classify the literature under review into three strands of financial literacy and poverty studies. The first strand deals with the determinants of poverty at the macro level. The second strand is the determinants of poverty at the individual level. The third strand includes findings regarding the impacts of financial literacy.

2.1 Macro-Level Determinants of Poverty

First, the existing literature has documented various country-level factors influencing poverty, including economic growth (Adams, 2004; Klasen, 2008); trade openness and liberalization (Bhagwati & Srinivasan, 2002; Harrison et al., 2003; Hertel & Reimer, 2005); financial development (Bolarinwa et al., 2021; Jeanneney & Kpodar, 2011; Perez-Moreno, 2011), foreign direct investment (Gohou & Soumaré, 2012) the informationization level (James, 2006; Mora-Rivera & García-Mora, 2021), institutional factors such as political stability, and corruption (Han et al., 2022; Tebaldi & Mohan, 2010).

2.2 Individual-Level Determinants of Poverty

Regarding the second strand, there is a rich literature on individual-level factors that influence the likelihood of falling into poverty. Using a vast database of 32 European countries, Hofmarcher (2021) points out substantially and significantly mitigating the effects of education on poverty. Similarly, studies by Zhang (2014), Ladd (2012) and Zhang and Zhao (2006) also confirm that education is positively associated with poverty reduction. The literature also emphasizes the gendered dimensions of poverty that female and female-head households are often at higher risk of falling into and staying in poverty than males (Lewin & Stier, 2018; Millar & Glendinning, 1989). This phenomenon was noted as the “feminization of poverty”—the growing trend wherein individuals experiencing poverty are predominantly women—by Pearce (1990). Along with gender, marital status, and the number of children at home can exert significant effects on poverty. Unmarried people are found to be poorer than legally married ones in Mexico (Ortega-Díaz, 2020), but a reversed trend is observed in Nigeria (Anyanwu, 2014). Herbst-Debby et al. (2021) show that divorce heightens the likelihood of poverty for women and diminishes this likelihood for men. Nevertheless, for both genders, the combination of divorce and more children at home amplifies the risk of poverty. In contrast, divorce or separation is negatively correlated with the probability of falling into poverty in the study by Anyanwu (2014). Additionally, the study by Anyanwu (2014) points out that household size is an important factor influencing poverty. Besides, old age has also been found to be correlated with poverty in many countries (Kwan & Walsh, 2018; Lloyd-Sherlock, 2000). Lastly, employment and nonfarm employment are effective and efficient in helping people escape from poverty, especially in African countries (Lanjouw, 1999; Page & Shimeles, 2015; Thompson & Dahling, 2019).

2.3 The Impacts of Financial Literacy

Besides, there is a rapidly expanding body of research examining the effects of financial literacy on various aspects of individuals, households, and society. These impacts can be categorized into two main groups: (i) impacts on behaviours and (ii) impacts on financial capacity.

The literature analyzing the impacts of financial literacy on behaviours is elaborated first. Utilizing data from 143 countries worldwide in 2014, Grohmann et al. (2018) confirm the important role of financial literacy in improving financial inclusion under “all” circumstances. Specifically, financial literacy boosts bank account ownership, savings at a formal financial institution, debit card ownership, and usage among populations in countries with both high and low levels of financial depth. Similarly, studies by Cole et al. (2011) and Hogarth et al. (2005) also point out the negative correlation between financial literacy and the number of unbanked adults and inactive account holders. However, Cole and Shastry (2009) note an exception in the United States, where financial market participation is not influenced by state-mandated financial literacy education. Besides, Cohen and Nelson (2011) document the positive effects of financial literacy on people's awareness of available financial services and the ability to choose suitable financial services. Additionally, the higher rate of stock participation can be attributed to financial literacy, as it encourages the use of financial instruments such as insurance and credit to protect individuals from unexpected incidents and change household risk attitudes (Urrea & Maldonado, 2011; Koomson et al., 2020b). The positive effects of financial literacy on stock market participation are also confirmed in other studies, such as Almenberg and Dreber (2015), Van Rooij et al. (2011) and Christelis et al. (2010). The causal effects of economic education on stock market participation are even established by Christiansen et al. (2008). In terms of formal financial inclusion, financial literacy, on the one hand, helps those in need recognize the credit demand and their demand-based credit constraints (Lusardi & Tufano, 2015; Sol Murta & Miguel Gama, 2022; Stango & Zinman, 2009). On the other hand, financial literacy improves their understanding of policies and lending information (Bilal et al., 2021). Borrowers have a higher propensity to lend from formal financial institutions instead of from casual relationships such as family and friends or loans from informal lenders (Xu et al., 2020). This finding is noted as “increasing household credit access by breaking down the information barrier” by Wang et al. (2022). These favourable outcomes contribute significantly to the broader positive impact of financial literacy—promoting entrepreneurial behaviours (Ćumurović & Hyll, 2019). By addressing household demand for credit and removing credit constraints, financial literacy directly mitigates primary constraints on residents’ entrepreneurial activities (Karaivanov, 2012; Weng et al., 2022). Similarly, the higher propensity to buy insurance and credit for risk protection, along with better investment opportunities, can also boost households’ willingness to start a business (Bilal et al., 2021; Cude et al., 2020).

Financial literacy plays a pivotal role in equipping individuals with the essential skills and qualities necessary for undertaking entrepreneurial activities (Oggero et al., 2020), including better allocation decisions to different types of assets and better entrepreneurial choices. Additionally, Lusardi and Mitchell (2011a. 2011b) and Bucher-Koenen and Lusardi (2011) successfully prove the higher propensity to plan for retirement in financially literate individuals. At the same time, Niu et al. (2020) also discover the ability to build a comprehensive, long-term financial plan for people in this group. Despite the robust impacts of financial literacy on bank accounts and debit card usage, financial literacy’s impacts on savings and wealth accumulation vary in different studies. Karlan et al. (2014) assert no correlation between higher usages of savings products resulting from higher financial literacy and the increase in users’ net savings (due to the possibility of crowd-out and crowd-in) and/or the improvement in their overall wealth (due to the probability of trade-off between money for savings and money for other activities like borrowing, investment, health, and consumptions). This finding aligns with those reported in the study conducted by Dupas et al. (2018), which utilizes data from Chile, Malawi, and Uganda. Conversely, Banerjee (1992) and Hastings et al. (2013) still find a high correlation between financial illiteracy and low savings.

Besides behaviours, financial literacy also can influence individuals' financial statuses. First, financial literacy significantly contributes to a better financial decision-making process (Evans & Jovanovic, 1989; Santos et al., 2022). Specifically, a higher level of financial literacy empowers individuals to effectively utilize financial instruments by enabling them to evaluate the value of financial products and make well-informed decisions, such as those related to reverse mortgages (Davidoff et al., 2017; Duca & Kumar, 2014) and investments (Bucher-Koenen & Ziegelmeyer, 2014; Guiso & Viviano, 2015; Klapper et al., 2013). Moreover, research in Germany (Bucher-Koenen & Lusardi, 2011), the Netherlands (Van Rooij et al., 2011) and Russia (Klapper & Panos, 2011) show that individuals with basic financial understandings are more excel in planning and saving for retirement. Second, for debt management, Lusardi and Tufano (2015) and Stango and Zinman (2009) discover a strong relationship between debt literacy and debt load. Debt-illiterate borrowers usually struggle with transacting in high-cost ways (paying fees and borrowing at high interest rates) and end up borrowing more but saving less (Galariotis & Monne, 2023). Meanwhile, adults with higher debt literacy are less likely to be over-indebted (Gathergood, 2012) and to fall into the trap of fictitious billing and loan guarantee fraud (Kadoya et al., 2021). A possible explanation can be that financial literacy practices caution, decreased comfort with debt, and sensitivity to the framing of people (Lusardi & Messy, 2023). Financial knowledge can also facilitate the alignment of liabilities with debt obligations, a critical aspect of prudent mortgage management (Thorp et al., 2023).

2.4 Financial Literacy and Poverty

Because our research focuses on poverty, the impacts of financial literacy on poverty are separately presented in this subsection. Studies in rural Chinese areas (Wang et al., 2022; Xu et al., 2023) show that financial literacy alleviates the poverty probability of households in both short and long term. Besides poverty, researchers also point out the positive impacts of financial literacy on poverty-related factors such as wealth and income. Research on the impacts of financial literacy on wealth accumulation in Chile (Behrman et al., 2012), The Netherlands (Van Rooij et al., 2012) and Japan (Sekita et al., 2022) all support a consistent result: financial literacy significantly and positively contributes to the wealth accumulation. Conversely, financial illiteracy is positively correlated with wealth disparity (Jappelli & Padula, 2013; Lusardi et al., 2017) and negatively correlated with income (Disney & Gathergood, 2011, 2013; Gathergood, 2012).

In general, Jappelli et al. (2014) indicate financial literacy as a driving force in tackling impoverishment worldwide. However, as shown above, there are only two studies in a single country (Xu et al., 2023; and Wang et al., 2022) directly investigating the impact of financial literacy on poverty. The limited poverty literature can impede global progress in sustainable poverty reduction. As such, there is a growing need for research on the impacts of financial literacy on poverty in various countries and worldwide. This observation warrants our study to be conducted.

The current literature shows a variation in the definition of financial literacy. While it can be broadly defined as financial capability encompassing knowledge, behaviour, and self-efficacy (Xiao et al., 2022), it can also be narrowly defined as basic financial knowledge for decision-making (Lusardi & Mitchell, 2014). In this study, we utilise the narrow definition of financial literacy, capturing four fundamental concepts, including risk diversification, inflation, basic numeracy, and compound interest.

3 A Theoretical Framework and Hypothesis Development

The relationship between financial literacy and poverty reduction can be elucidated through the human capital theory. We establish the theoretical framework for this study in two steps. First, we review the human capital theory and specify its relevance to financial literacy and poverty reduction. Second, we build on existing literature to theorize the relationship between financial literacy and poverty reduction.

3.1 The Overview of Human Capital Theory

The concept of human capital can be dated back to Smith (1776) in the work of Adam Smith, while the first formal use in research using the term human capital belongs to Irving Fisher (1897), and then the theory was popularized by the work of Mincer (1958), Schultz (1961) and Becker (1962, 1964). The theory posits that economic outputs can be improved by bettering people's inputs, such as education and health (Baldacci et al., 2008), and treating these inputs as a form of capital. As such, similar to physical or financial capital, they can be invested. Hence, enhancing financial literacy can positively affect poverty alleviation, which is an economic outcome.

3.2 Financial Literacy, Financial Behaviour, and Poverty

Several studies highlight that for the poor, financial behaviour is more crucial than financial knowledge in improving financial well-being (Xiao & Porto, 2022) and, consequently, in reducing poverty. In other words, having knowledge without taking action appears insufficient to decrease the likelihood of falling into poverty. Nonetheless, this does not mean that financial knowledge plays no role in reducing poverty. Indeed, lacking basic financial knowledge can hinder people from adopting healthy financial behaviours such as formal borrowing with better terms, mortgage refinancing (Bialowolski et al., 2022), person-to-person (P2P) borrowing (Han et al., 2019), avoiding risky credit behaviour (Xiao et al., 2014) and financial asset holding (Zhu & Xiao, 2022). Grohmann et al. (2018) highlight the essential role of financial literacy in improving financial inclusion across all circumstances. It boosts bank account ownership and formal savings in both high- and low-financial depth countries.

As such, financial knowledge serves as a critical foundation for financial behaviours, considerably enhancing financial well-being (Xiao & Porto, 2022) and financial resilience (Klapper & Lusardi, 2020), thereby reducing the probability of falling into poverty. Additionally, by being equipped with financial literacy, people are more motivated and confident in dealing with financial matters; this can be deemed as an effect of self-efficacy—a “hidden” form of human capital (Roy et al., 2018). This form of capital implies that people are prone to engage in activities commensurate with the level of proficiency they perceive themselves to possess. Hence, financial literacy catalyzes changing financial behaviour, thereby alleviating poverty. Building on the theoretical model and the discussion of financial literacy in the literature review section, three main hypotheses are proposed:

H1

Financial literacy is negatively associated with the probability of falling into poverty.

H2

Financial literacy is positively associated with desirable financial behaviours, such as bank account ownership (H2a) and formal savings (H2b). In turn, these desirable financial behaviours are negatively associated with the probability of falling into poverty.

4 Data and Methodology

4.1 Data

Data used in the empirical analysis in this paper are collected by merging information from three datasets: (i) the S&P Global FinLit Survey 2014,Footnote 1 (ii) the Global Findex 2014Footnote 2 and (iii) the Gallup World Poll (GWP) 2014Footnote 3 to form a unique dataset of 150,000 adults across 142 countries. Because these three datasets were conducted jointly by Gallup, Inc., and the World Bank in 2014, they share the same sampling method and respondents. Researchers can easily merge these three datasets since each respondent has a unique identifier. With this rich dataset covering 142 countries in 2014, we can thoroughly examine the relationship between financial literacy and poverty reduction. Details of each dataset are discussed below.

Our empirical analysis centres on the S&P Global FinLit Survey, the most comprehensive global-scale survey on financial literacy (GFLEC, 2023). Notably, the S&P Global FinLit Survey was built on the collaboration among Gallup, Inc., the World Bank, and other stakeholders in 2014, leading to a shared sample and sampling method with the Gallup World Poll (GWP). With a universal approach, the S&P Global FinLit Survey measures the understanding of four fundamental financial concepts: (1) risk diversification, (2) inflation, (3) basic numeracy, and (4) interest compounding. Details of financial literacy questions are provided in Table 18 in the Appendix. These concepts closely relate to daily financial decision-making. Particularly, the knowledge of risk diversification is the understanding of reducing risk without sacrificing expected returns in business and investment (Reinholtz et al., 2021). Inflation knowledge alerts people of purchasing power fluctuation over time and encourages strategic decisions to cope with its menace. Basic numeracy is essential in financial market activities, particularly in calculating interest to prevent over-indebtedness (Lusardi & Tufano, 2015), mortgage delinquency, and default (Gerardi et al., 2013). Additionally, proficiency in interest compounding enables individuals to anticipate interest payments and make informed choices for the most beneficial financial products. In addition, we obtain the data on bank account ownership and formal savings from the Global Findex 2014 dataset, which shares the same respondents with the S&P Global FinLit.

Because the data on the socio-demographic characteristics of respondents are limited in the S&P Global FinLit Survey 2014, the GWP is utilized. The GWP is an annual statistical collection of nationally representative on a global scale regarding important issues worldwide (Gallup, 2016). From the GWP, we collect information on gender, age, education level, marital status, employment status, urbanicity, and household size. These variables collectively form the baseline model in all our regression analyses and become significantly important for investigating the heterogeneous impacts of financial literacy on the likelihood of falling into poverty across different subsamples regarding demographic and socioeconomic factors. With data from the S&P Global FinLit Survey and the GWP, we can ensure the unbiasedness and reliability of our results for several reasons. Firstly, the Kish grid method is employed to select interviewees randomly and directly interact with these interviewees through face-to-face interviews or 80% telephone coverage (Gallup, 2016). Additionally, as they pose uniform questions at the individual level worldwide and employ a robust translation and sampling scheme (Gallup, 2016), they mitigate our concern about potential minor errors, as seen in other global surveys.

During the data cleansing process, observations with missing values of used variables are eliminated. Additionally, all respondents are non-poor in some countries, such as Norway. This means that these countries would predict non-poor perfectly and would automatically be removed by the probit model. Therefore, we eliminate these countries from the sample to avoid potential bias. Ultimately, 115,336 observations from 113 countries remain for analysis, accounting for about 77% of the initial sample. The full list of 113 countries is provided in Table 17 in the Appendix. The summary statistics are presented in Table 1.

Table 1 Descriptive statistics and description of all variables
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4.2 Methodology

We construct the financial literacy score by aggregating correct answers from five financial concept questions (Table 18) in the S&P Global FinLit Survey. For each correct answer, respondents score one point, resulting in an aggregate score ranging from 0 to 5.

Next, we categorize respondents into poor and non-poor based on international poverty lines. The procedure to derive this variable is as follows. First, we divide each respondent’s annual income in local currency by the purchasing power parity (PPP) conversion factor (local currency per US$) in 2017, as determined by the World Bank, to estimate the annual income in 2017 US$ PPP. Then, we assign the value 1 to \(Poverty_{i}\) if the estimated annual income (2017 US$ PPP) is below the international poverty lines U$ 2.15 (for those in low-income countries), U$ 3.65 (for those in lower–upper-income countries) and U$ 6.85 (for those in upper-middle- and high-income countries) and 0 otherwise. Please refer to Jolliffe and Prdyz (2016) and Jolliffe et al. (2022) for rationales behind these international poverty lines.

Because the dependent variable, \(Poverty_{i}\) is binary, we utilize a probit regression. The estimation model is specified as follows:

$$\begin{array}{*{20}c} {Poverty_{i} = \beta_{0} + \beta_{1} Finlit_{ij} + \beta_{2} X_{i} + \beta_{3} Y_{i} + e_{i} } \\ \end{array}$$
(1)

where \(i\) represents the respondent. \(Poverty_{i}\) is a dummy variable which equals 1 if the respondent is classified as poor, 0 if otherwise. \(Finlit_{i}\) denotes the financial literacy score of the respondent. For each correct answer to the five financial concept questions in the S&P Global FinLit Survey, respondents score one point. The aggregate score ranges from 0 to 5. \(X_{i}\) is the matrix of control variables. Following Xu et al. (2023), we employ gender, age, urban residence, household size, educational attainment, marital status, and employment status as individual-level control variables. \(Y_{j}\) is the matrix of country dummies. Finally, \(e_{j}\) is the error term of the model.

5 Results

The paper now proceeds to report and discuss the findings. First, the empirical results regarding the association of financial literacy with the probability of populations across 113 countries falling into poverty are reported. This is followed by a heterogeneity analysis, an analysis of financial literacy components, and a mediation analysis.

5.1 Main Results

The estimated coefficients of financial literacy are shown in Table 2. Columns 1 and 2 show the empirical results from probit models. Socio-demographic factors (i.e., age, education, employment status, and others) are incorporated as control variables in all models. Furthermore, the country where respondents live can affect economic opportunities, access to education, and the availability of financial resources, which consequently affect poverty. Hence, country dummies are added to control variations across countries.

Table 2 Empirical results from probit models for the full sample—Financial literacy index
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Column 1 shows that financial literacy negatively affects the probability of falling into poverty. The slope coefficient of financial literacy is statistically significant at the 1 per cent level. Holding other things constant, a unit increase in the financial literacy index corresponds to a 6.2% decrease in the probability of falling into poverty. This coefficient even increases from 6.2 to 7.5% after controlling variations across countries (Column 2). Overall, the results indicate that financial literacy is negatively associated with the probability of falling into poverty, supporting Hypothesis 1.

5.2 Heterogeneity Analysis

Using probit models with Gaussian copula, we then analyze the heterogeneous association of financial literacy with the likelihood of being poor across sub-samples divided based on socio-demographic factors, regions and country income levels. The Shapiro–Wilk tests conducted in all models reject the null hypothesis of normality of financial literacy (the endogenous variable), suggesting that Gaussian copula estimations are appropriate (Park & Gupta, 2012). The results for demographic and socioeconomic groups are shown in Tables 3 and 4, respectively. Next, the results for different regions and income groups are provided in Tables 5 and 6, respectively.

Table 3 Empirical results from probit models for the demographic groups– Financial literacy index
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Table 4 Empirical results from probit models for the socioeconomic groups – Financial literacy index
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Table 5 Empirical results from Gaussian copula probit models for different regions– Financial literacy index
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Table 6 Empirical results from probit models for the different country income levels– Financial literacy index
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As shown in Table 3, financial literacy significantly and negatively affects poverty across different demographic groups. Specifically, financial literacy exerts a larger association with poverty among female, old and married populations than the opposite (male, young and unmarried). To start, we compared the association of financial literacy with the chances of escaping from poverty by gender of respondents. Results show that a financially literate woman can decrease the likelihood of falling into poverty by 17%, compared with the poverty reduction of 13.2% by a man having financial knowledge (Columns 1 and 2). A similar pattern is also observed in a 2014 World Bank report on traditional literacy terms, which found that each additional year of schooling boosts women’s earnings by an average of 11.7% versus 9.6% for men (Montenegro & Patrinos, 2014). Regarding age, financial literacy has a greater association with poverty reduction among old people (aged 60 and over) than young ones. Ceteris paribus, a unit increase in financial literacy can lead to a 14.2% decrease in the likelihood of falling into poverty for older people and a 13.8% decrease for younger ones. The association of an individual's financial understanding with the risk of falling into poverty, categorized by marital status, are presented in columns 5 and 6 of Table 3. The results show that financially literate married individuals have a 17.7% lower likelihood of falling into poverty, which is 5.5% higher compared to unmarried individuals.

Regarding socioeconomic groups, the results are reported in Table 4 concerning urban residence in Columns 1 and 2, employment status in Columns 3 and 4, and education attainment in Columns 5–7. Results show that the association of financial literacy with poverty are more pronounced among residents with low socioeconomic status (rural, unemployed, and low-education-level residents). Specifically, financial literacy exerts a larger association with poverty reduction among rural residents than urban ones. A financially literate person living in rural areas can decrease the likelihood of falling into poverty by 15.4%, compared to a decrease of 11.4% for those living in urban areas. Similarly, a one-unit increase in financial literacy is associated with a poverty reduction of 16.8% among employed individuals, while it just fell by 14% among those unemployed (Columns 3 and 4). Next, we examine in more detail how financial knowledge differentially affects poverty alleviation among individuals who have completed tertiary education or higher, those who completed secondary education and those who completed primary education or lower. Financial literacy has a smaller association with the chances of escape from poverty among people with a bachelor’s degree. Specifically, an increase in financial literacy corresponds to a decrease in the likelihood of falling into poverty by 18.8% for individuals who completed primary education (Column 7) and by 8% for those who completed secondary education (Column 6). These coefficients are statistically significant at 1% level. Meanwhile, the effect decreases to 6.8% for those with a bachelor’s degree. The coefficient of financial literacy even turns out to be insignificant for those with a bachelor’s degree, implying that financial literacy may have no effect in this group (Column 5).

Several studies indicate variations in financial knowledge and financial behaviours across countries with different cultures (Biolowalski et al., 2023) and developmental stages (Xiao & Biolowalski, 2023). Biolowalski et al. (2023) find that individualism, long-term orientation, and indulgence are positively correlated with financial capability (which includes financial knowledge and behaviour), whereas uncertainty avoidance exhibits a negative correlation. Interestingly, Xiao and Biolowalski (2023) show that financial capability exhibits a stronger correlation with human development in highly developed countries, implying that promoting financial capability is more cost-effective and beneficial in these countries. Therefore, we conduct further analysis to examine the heterogeneity in the relationship between financial literacy and poverty across regions and country income levels. Regarding regions, as shown in Table 5, the coefficients of financial literacy are negative and statistically significant across East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, North America, and Sub-Saharan Africa (Table 5). However, in South Asia and the Middle East & North Africa, although the coefficients remain negative, they are not statistically significant. Regarding country income levels, financial literacy has the largest association with poverty in low-income countries and the smallest association with poverty in high-income countries (Table 6). Holding other things constant, a unit increase in the financial literacy index can lead to a 32.5% (9.3%) decrease in the likelihood of falling into poverty in low-income (high-income) countries. The coefficients of financial literacy are statistically significant across all country income levels. These results suggest that while financial knowledge may be more beneficial to high-income countries in terms of human development—a broad measure encompassing education, health, and income (Xiao & Biolowalski, 2023), it is more advantageous for low-income countries in reducing poverty, which is a narrower measure capturing income levels and basic needs.

5.3 Robustness Check

This sub-section presents results from the robustness tests to assess whether the effects of financial literacy on the probability of falling into poverty are causal. First, to address potential endogeneity issues that can mislead financial literacy’s actual impacts on poverty, we follow Shin et al. (2022) to employ probit models with the Gaussian copula term. Specifically, the Gaussian copula term of financial literacy is \(Copula term_{Financial Literacy} = \emptyset^{ - 1} \left( {H_{Financial Literacy} } \right)\), where \(\emptyset^{ - 1}\) is the inverse of the cumulative normal distribution and \(H_{Financial Literacy}\) is the empirical distribution functions of financial literacy score. Eckert and Hohberger (2023) provide instructions on how to build the Gaussian copula term using Stata software. The Gaussian copula term can control the correlation between financial literacy and error terms. The results are reported in Columns 3 and 4 of Table 2. The Shapiro–Wilk test result indicates that financial literacy is non-normally distributed, confirming the appropriateness of Gaussian copula estimations (Park & Gupta, 2012). The alleviating effects of financial literacy on poverty remain consistent when Gaussian copula terms are included in models (Columns 3 and 4). After controlling for endogeneity concerns, the effect size of financial literacy even increases to 13.7% (Column 4). The Gaussian copula term is moderately statistically significant in Column 3 and turns out to be statistically insignificant in Column 4, where country dummies are added. These results indicate that endogeneity issues are not severe in probit models with country-fixed effects. Generally, these results suggest that financial literacy exerts alleviating effects on the probability of populations across more than 100 countries falling into poverty.

Second, in order to test whether the relationship between financial literacy and the probability of falling into poverty is affected by omitted variable bias, we employ a novel approach, namely, Rubin’s (1974) causal model. This model allows researchers to conduct a robustness of inference to replacement (RIR) analysis and the impact threshold of a confounding variable (ITCV) analysis (Frank, 2000; Frank et al., 2013; Xu et al., 2019). The RIR analysis quantifies how many observed cases need to be replaced with the opposite cases to invalidate the causal inference. The ITCV analysis indicates how correlated an omitted variable would have to be with the independent and dependent variables for the statistical inference to change. Indeed, the RIR analysis is a part of the ITCV analysis family (Frank, 2000). Xu et al. (2019) suggest that for a nonlinear model, the ITCV analysis should not be used because it is correlation-based and thus applies only to linear cases. Instead, the per cent bias to invalidate the inference (i.e., RIR) should be applied in this case (see also Busenbark et al., 2022 for further guidance on deciding whether to use ITCV or RIR); since our model is non-linear, we conduct the RIR analysis, using the konfound command (Xu et al., 2019) in Stata with the nonlinear option specified.

The results are presented in Table 7. In Column 1, it is shown that up to 85.89% of observations in our sample would need replacement to invalidate the effects of financial literacy on poverty. This corresponds to 99,062 out of 115,336 observations, as indicated in Column 2. In other words, to invalidate the inference, 85.89% (99,062) of the cases would have to be replaced with cases with an effect of 0. We thus conclude that omitted variable bias is not a major concern in our analysis.

Table 7 The Rubin’s (1974) causal model—Quantify the per cent bias necessary to invalidate an inference
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Similar patterns are also observed in sub-samples (Tables 8, 9 and 10). For demographic groups, from 75 to 84% of the cases would have to be replaced with cases for which there is an effect of 0 to invalidate the causal inference (Table 8). This implies that omitted variable bias is highly unlikely to affect the impacts of financial literacy on poverty in demographic groups. Table 9 also indicates that omitted variable bias is not severe among socioeconomic groups. However, for the group with tertiary or higher education, only 8.33% of the cases needed to be replaced with counterfactual cases to invalidate the impact of financial literacy on poverty. For regions, results from Table 10 indicate that the estimated effect of financial literacy on poverty is vulnerable to omitted variable bias in North America because only 1.87% of cases need to be replaced to invalidate the inference. In contrast, the effect of financial literacy is robust in other regions. Additionally, as shown in Table 11, the effect of financial literacy on poverty is also robust across different income levels.

Table 8 Results from the Rubin’s (1974) causal model for demographic groups—Quantify the per cent bias necessary to invalidate an inference
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Table 9 Results from the Rubin’s (1974) causal model for socioeconomic groups—Quantify the percentage bias necessary to invalidate an inference
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Table 10 Results from the Rubin’s (1974) causal model for regions—Quantify the per cent bias necessary to invalidate an inference
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Table 11 Results from the Rubin’s (1974) causal model for income groups—Quantify the percentage bias necessary to invalidate an inference
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Third, in line with Chaudhry and Shafiullah (2021), we also employ OLS and Lewbel’s (2012) instrumental variable approach to examine whether our results change under different estimation models. Unlike the traditional instrumental variable approach, which necessitates external instrumental variables for endogenous variables in the model, Lewbel’s (2012) instrumental variable approach self-generates internal variables from the heteroscedasticity of the model. This approach has been widely employed in the literature as a robustness check when traditional instruments are used or when external instruments are lacking (Churchill & Smyth, 2017; Wang & Cheng, 2022). Prior research suggests that the instrumental variable (IV) estimates obtained by this method are nearly identical to those obtained by using conventional validated IVs (Umberger et al., 2015). The Lewbel's (2012) approach is briefly outlined as below:

$$Y_{1} = \beta \tilde{\user2{X}} + Y_{2} \gamma + \varepsilon_{1} ; Y_{2} = \alpha \tilde{\user2{X}} + \varepsilon_{2}$$
$$\varepsilon_{1} = \alpha_{1} U + V_{1} ; \varepsilon_{2} = \alpha_{2} U + V_{2}$$

where \(Y_{1}\) stands for the dependent variable. \(Y_{2}\) refers to the endogenous variable. \(U\) denotes the unobserved characteristics that can affect both \(Y_{1}\) and \(Y_{2}\). \(V_{1}\) and \(V_{2}\) are idiosyncratic errors. Lewbel (2012) posits that there exists a vector \(Z\) of observed exogenous variables meeting the conditions that \(E\left( {X\varepsilon_{1} } \right) = 0\), \(E\left( {X\varepsilon_{2} } \right) = 0\), \(Cov = \left( {Z, \varepsilon_{1} \varepsilon_{2} } \right)\), with some degree of heteroskedasticity in \(\varepsilon_{j}\). The vector Z may be a subset of X or equivalent to X. Under these conditions, \(\left[ {Z - E\left( Z \right)} \right]\varepsilon_{2}\) can serve as a vector of valid instruments satisfying the standard rank condition. We regress financial literacy on \(\tilde{\user2{X}}\) and then obtain the residuals \(\widehat{{\varepsilon_{2} }}\), which are consistent estimates of the reduced form error \(\varepsilon_{2}\). The estimated residuals are then used to create \(\left[ {Z - E\left( Z \right)} \right]\widehat{{\varepsilon_{2} }}\) as self-generated internal instruments for estimation.

The results are presented in Columns 1 and 2 of Table 15. The Breusch–Pagan test rejects the null hypothesis of homoscedasticity, indicating that Lewbel’s (2012) approach is appropriate in this case. The coefficients of financial literacy are still statistically significant and negative. This implies that financial literacy exerts causal impacts on the likelihood of falling into poverty worldwide.

Finally, as our respondents are nested in countries, those from the same country may share some common characteristics that potentially affect their financial literacy and the likelihood of falling into poverty. If this holds true, the estimated effects of financial literacy on poverty can be biased and invalid. Multilevel modelling is often adopted to deal with such hierarchical data. As our dependent variable is a dummy variable, which equals 1 if the respondent is poor and 0 if otherwise, multilevel probit modelling with individuals at level 1 and countries at level 2 will be employed. In addition, because there is no obvious evidence that the effect sizes of financial literacy vary across countries, random intercept multilevel probit modelling is utilized. The estimation is specified below.

$$\begin{array}{*{20}c} {Poverty_{ij} = \beta_{0} + \beta_{1} Finlit_{ij} + \beta_{2} X_{ij} + \beta_{3} Y_{j} + u_{ij} + e_{j} } \\ \end{array}$$
(2)

where \(i\) and \(j\) represent the respondent and country, respectively. \(Poverty_{ij}\) is a dummy variable which equals 1 if the respondent is classified as poor, 0 if otherwise. \(Finlit_{ij}\) is the financial literacy score of the respondent. \(X_{ij}\) is the matrix of individual-level control variables. \(Y_{j}\) is the matrix of country-level control variables. Data on country-level control variables, including GDP per capita, the proportion of private credit to GDP, trade openness, foreign direct investment, internet users (% of the population), mobile cellular subscriptions (% of the population), rule of law index, political stability index, and control of corruption index, are collected from World Development Indicators (WDI). Finally, \(u_{ij}\) and \(e_{j}\) are level 1 (individual) and 2 (country) error terms.

The results are reported in Table 16. It can be observed that financial literacy has a statistically significant and negative effect on the likelihood of falling into poverty (Column 1). As shown in Columns 2–10, the coefficients of financial literacy remain statistically significant and negative when country-level variables are added to the model in Column 1. This implies that our results are robust to variations across countries.

5.4 Financial Literacy Components

In this sub-section, we examine each financial literacy component, including (i) risk diversification, (ii) inflation, (iii) numeracy (capacity to do simple calculations regarding interest rates) and (iv) compound interest. As shown in Table 12, all four components are significantly and negatively associated with the probability of falling into poverty. The rationales behind these results are as follows. First, numeracy is essential for everyone in managing everyday finance decisions, such as budgeting, comparing prices, and understanding bills. Second, understanding how interest accumulates can help individuals avoid predatory lending and seek more favourable credit terms, reducing the likelihood of falling into debt traps. Third, understanding how inflation erodes purchasing power may encourage seeking out interest-bearing accounts and other savings mechanisms that keep pace with inflation. This knowledge helps individuals protect their financial resources from losing value over time, resulting in a decrease in the probability of falling into poverty. Finally, knowledge of risk diversification can help individuals diversify assets and investments, reducing vulnerability to financial shocks and building resilience against unexpected events.

Table 12 Empirical results from probit models for the full sample—Financial literacy components
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However, there are differences in their magnitudes. Specifically, compound interest, numeracy, and inflation have the first, second, and third largest associations with poverty. In contrast, the coefficient of risk diversification is the smallest. This suggests that knowledge of compound interest, numeracy, and inflation is more essential for people than knowledge of risk diversification. On the one hand, as discussed above, compound interest, numeracy, and inflation are fundamental concepts that directly help in making daily financial decisions, avoiding predatory lending and obtaining better interest term loans (Bialowolski et al., 2022), all of which are significant factors that can contribute to the risk of falling into poverty. On the other hand, risk diversification is more relevant to individuals with surplus income or investments. For people without investments, the concept of risk diversification is less applicable, as they do not face the same level of financial risk as those with investments. Conversely, individuals without investments often rely on stable sources of income, such as wages, salaries, or government benefits, which are less susceptible to market fluctuations than investments such as stocks and bonds. Therefore, the immediate impact of risk diversification may be limited for individuals in poverty compared to the direct relevance of other concepts. Indeed, risk diversification is the least understood concept, with only 35% of adults answering correctly (Klapper & Lusardi, 2020).

5.5 Mediation Analysis

This sub-section examines possible mediating channels in the relationship between financial literacy and poverty. Two mediating channels are considered: bank account ownership and formal savings. To do so, we follow the procedure outlined in Barkat et al. (2023) and Alesina and Zhuravskaya (2011). The procedure imposes two conditions for bank account ownership and formal savings to serve as mediating channels. The first condition, known as the correlation condition, stipulates that financial literacy must be significantly correlated with bank account ownership and formal savings. The second condition, known as the magnitude condition, requires the magnitude of the coefficient of financial literacy to decrease when bank account ownership and formal savings are incorporated into the model with poverty as the dependent variable. Equations (1), (3) and (4) are used to verify the mediating channels.

$$\begin{array}{*{20}c} {Mediators_{i} = \beta_{0} + \beta_{1} Finlit_{ij} + \beta_{2} X_{i} + e_{i} } \\ \end{array}$$
(3)
$$\begin{array}{*{20}c} {Poverty_{i} = \beta_{0} + \beta_{1} Finlit_{ij} + \beta_{2} X_{i} + \beta_{3} Y_{i} + \beta_{4} Mediators_{i} + e_{i} } \\ \end{array}$$
(4)

where \(i\) represents the respondent. \(Poverty_{i}\), \(Finlit_{i}\), \(X_{i}\) and \(Y_{i}\) are poverty status, financial literacy score, the matrix of control variables and the matrix of country dummies, respectively. \(Mediators_{i}\) is the matrix of bank account ownership dummy (1 if the respondent owns a bank account, 0 if otherwise) and formal savings dummy (1 if the respondent saved at a formal institution in the past 12 months, 0 if otherwise).

The results of the correlation between financial literacy and mediators, as specified in Eq. (3), are presented in Table 13. Results presented in Columns 1 and 2 suggest that financial literacy significantly and positively correlates with bank account ownership and formal savings. Next, the results of the magnitude condition, as specified in Eqs. (1) and (4), are reported in Table 14. The magnitude of the coefficient of financial literacy decreases as bank account ownership and formal savings are incorporated into the model (Columns 1–3). As such, bank account ownership and formal savings are qualified as mediating variables in the association between financial literacy and poverty, supporting Hypothesis 2. Indeed, financial literacy encourages desirable financial behaviours (Grohmann et al., 2018), which may lead to a decrease in the probability of falling into poverty.

Table 13 Empirical results from probit models for the full sample—correlation condition
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Table 14 Empirical results from probit models for the full sample—magnitude condition
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6 Discussions

We have discovered a negative association between financial literacy and the likelihood of falling into poverty across 113 countries globally. Furthermore, we provide evidence supporting a causality from financial literacy to poverty reduction by adopting probit estimations with Gaussian copula terms. Overall, our findings suggest that financial literacy can serve as a pivotal instrument in the process of tackling poverty worldwide. Interestingly, financial literacy is found to have a heterogeneous association with poverty in different demographic groups, socioeconomic groups, regions, and country income levels. Moreover, we demonstrate that desirable financial behaviours, such as account ownership and formal savings, serve as the mediating variables in the relationship between financial literacy and poverty. This sub-section will further discuss and provide possible explanations for these findings.

To begin with, we will provide possible explanations for the mitigating effects of financial literacy on poverty. Regarding the underlying mechanisms through which financial literacy can reduce poverty, the empirical evidence is still relatively scarce. However, there are several potential mechanisms through which financial literacy can affect poverty. On the one hand, as mentioned in Sect. 2.3, financial literacy positively influences account ownership and usage, formal savings, formal borrowing, stock participation, portfolio performance, insurance usage, debt management ability and retirement planning. Furthermore, empirical analysis from this study verifies the mediating role of account ownership and formal savings in the relationship between financial literacy and poverty.

On the other hand, these impacts correlate with poverty reduction in multifaceted ways. First, savings facilitate escape from poverty by smoothing consumption and financing productive investments (Karlan et al., 2014; Pomeranz & Kast, 2022), preventing indebtedness and debt-trap situations (Lister, 2006). Especially those equipped with emergency savings possess the advantageous ability to shield themselves from prolonged financial hardships resulting from adverse economic events (Diwakar & Shepherd, 2022; Shah et al., 2012), thereby attaining financial resilience, reducing the poverty risk (Hasler et al., 2018; Lusardi et al., 2011). Second, insurance defences against the risk of future poverty among customers (Koomson et al., 2020a), improving risk-taking and managing capacities (Hong et al., 2020; Wang et al., 2022) to accumulate more in the financial market and entrepreneurship, taking great steps in the poverty alleviation process (Bucher-Koenen & Ziegelmeyer, 2014; Guiso & Viviano, 2015, Klapper et al., 2013). Third, shifting lending behaviour from informal sources to formal institutions is also associated with eliminating irrational economic behaviours and financial constraints, facilitating households' escape from poverty (Sarthak & Ashish, 2012). Fourth, implementing effective debt management also helps reduce the likelihood of over-indebtedness (Gathergood, 2012), mortgage delinquency, and default (Gerardi et al., 2013). The fifth mechanism entails the advantages of heightened demand for bank accounts and debit cards. While Kefela (2011) exhibits that having an account at a bank or other financial institution is an important first step for financially literate people to participate in the financial system, Lusardi and Messy (2023) assert the influence of this participation on the efficiency and soundness of financial systems. As the financial system improves with more financially literate participants, it facilitates economic growth and alleviates poverty in middle- and high-income countries (Dhrifi, 2015). Sixth, Setor et al. (2021) examine data from 111 developing countries from 2010 to 2018, conclusively identifying digital transactions as a valuable tool in mitigating corruption and poverty by enhancing transparency, given the bidirectional causality between corruption and poverty (Han et al., 2022; Justesen & Bjørnskov, 2014). Seventh, asset and livelihood diversification is confirmed to have a negative association with poverty (Martin & Lorenzen, 2016). Eighth, entrepreneurship, viewed through different lenses, addresses poverty by addressing resource scarcity (remediation), social exclusion (reform), and challenging capitalist tenets (revolution) (see Sutter et al., 2019 for a review of related literature). Ninth, the reduction of anxiety among individuals can also help them escape from the cyclic nature of poverty—mental disorder (Anakwenze & Zuberi, 2013; Lund et al., 2011). Finally, effective retirement plans are also associated with increased wealth accumulation and poverty alleviation, particularly during old age (Lusardi & Mitchell, 2011b; Behrman et al., 2012). Overall, the alleviating effect of financial literacy on poverty may be explained through many mechanisms, including savings, emergency funds, insurance usage, lending from formal institutions, debt management, usage of bank accounts and debit cards, digital transactions, asset diversification, entrepreneurship, and retirement plans.

7 Conclusions and Policy Implications

Our study explores how financial literacy affects the likelihood of individuals falling into poverty worldwide. Our analyses confirm that financial literacy significantly and negatively affects poverty. This result remains largely unchanged across different socio-demographic groups, regions, and country income levels, implying that our results can be applicable in various contexts. The coefficients of financial literacy are still statistically significant and consistent in terms of signs across robustness tests, including (i) probit models with Gaussian copula terms (addressing endogeneity concerns), (ii) multilevel probit models (considering variations across countries) and (iii) the Rubin’s (1974) causal model. Moreover, we find a notable heterogeneity in the impacts of financial literacy across subsamples. For demographic groups, the negative correlation between financial literacy and poverty is more pronounced among females, older individuals, and married individuals than their counterparts (males, young individuals, and unmarried individuals). In socioeconomic groups, residents of rural areas, those unemployed, and individuals with or without a primary degree are likely to derive greater benefits from financial literacy compared to their counterparts in the respective dimensions. Across regions, those living in East Asia, the Pacific, and Sub-Saharan Africa demonstrate the first and second largest decreases in the likelihood of poverty, with the same increase in financial literacy. Meanwhile, the coefficient of financial literacy turns out to be statistically insignificant in South Asia, the Middle East & North Africa, suggesting that financial literacy may not have any effect on poverty. Financial literacy has the most substantial impact on reducing the likelihood of poverty among citizens in lower-middle-income and low-income countries. Conversely, its association with the risk of poverty among individuals in high-income countries is the lowest.

Policy implications have emerged based on these insightful findings. First, governments should implement comprehensive financial education programs targeting various socio-demographic groups or incorporate financial topics into school curriculum. These programs should cover essential topics in finance, such as inflation, compound interest, risk diversification and debt management. Second, governments may consider providing funds for financial education initiatives such as community-based workshops, online courses, and financial educational materials to reach a broad audience. Moreover, financial literacy can serve as a powerful instrument to mitigate disparities in poverty rates among females, the elderly, the unmarried, rural residents, the unemployed, individuals with lower education levels, inhabitants in East Asia and Pacific and Sub-Saharan Africa, and those in lower-middle and low-income countries due to its larger association with poverty reduction in these groups compared to their counterparts. Therefore, these groups should receive targeted attention from governments to enhance their financial literacy levels and address poverty and disparities in poverty rates. In particular, we suggest specific policy implications that governments can take into consideration.

While females, the elderly, and the separated or divorced are the most beneficiaries of financial literacy in poverty reduction, their awareness and conditions to make use of these advantages are limited, given that they tend to be biased as they should not and cannot make sound financial decisions. That is, policymakers need to simultaneously break down social stereotypes about their financial ability and facilitate their financial literacy improvement. On the one hand, more research about the financial ability of people with these demographic factors should be implemented and widely popularized to dispel stereotypes and provide evidence-based insights. Popularization can be achieved through continuous dialogue and awareness campaigns in educational institutions, workplaces, and online platforms, ensuring universality in access. Additionally, celebrating the achievements of women, the elderly, and unmarried individuals who leverage their financial knowledge to build wealth and safeguard assets is crucial. Their success stories should be prominently featured in media and journals as exemplary cases, inspiring others and challenging prevailing stereotypes. This concerted effort contributes to a cultural shift that recognizes and values experiences and perspectives in the financial decision-making of females, people aged above 70, and the unmarried. On the other hand, policymakers should make more effort to facilitate better financial literacy among these target groups. As financial education is a fundamental measure to increase financial literacy, governments may expand policies to stimulate people's participation and interest in financial education programs. For example, people with unfavourable demographic factors could receive free government-sponsored financial education, offer tax incentives or subsidies to residents participating in financial education, integrate financial literacy requirements into welfare programs, provide additional benefits or incentives for participants in financial education, and so on. Through the implementation of diverse and targeted measures, governments have the potential to significantly enhance the representation of females, individuals over 70, and those who are separated or divorced in the financial market. This, in turn, can contribute to reducing poverty levels and foster a higher rate of economic development.

Recognizing the amplified association between financial literacy and poverty reduction in specific socioeconomic groups, governments may prioritize allocating more resources to financial literacy programs that target residents with low socioeconomic status (rural, unemployed, and low-education-level ones). Also, to ensure that the content is relevant and accessible, financial literacy programs should be customized to address the unique financial circumstances of individuals in rural areas, the unemployed, and those with low education levels. Moreover, given that residents of low socioeconomic status are usually beneficiaries of social assistance programs, financial education components should be integrated into existing programs, such as unemployment benefits or rural welfare-oriented programs. In this way, countries can achieve sustainable poverty reduction instead of temporarily reducing poverty. By empowering individuals to make informed financial decisions in the long term, this approach addresses immediate financial needs, temporarily reduces poverty, and aims to reduce poverty sustainably.

Given these heterogeneous associations for residents in low-income countries and those in East Asia and Pacific and Sub-Saharan Africa, officials of these nations have the privilege of bridging the gap with other nations in eradicating poverty and moving up the economic ladder. To capitalize on this advantage, policymakers in East Asia and the Pacific, Sub-Saharan Africa, and low-income countries may consider enhancing financial literacy as an important and urgent national task that requires prioritizing attention and resources. Concurrently, the government should increase investments in developing financial infrastructure while implementing stringent regulations to foster a secure and readily accessible financial market for individuals and businesses. National strategies must be implemented consistently, effectively, and creatively to turn their potential into reality. For example, Vietnam's Prime Minister has approved the National Financial Inclusion Strategy until 2025. It clearly states the viewpoints, goals, tasks, and solutions to promote access and usage of financial goods and services for all residents and businesses, with a focus on those living in rural areas, low-income people, women, and other disadvantaged groups (Viet Nam Government Portal, 2022).