Financial Inclusion and Socio-Economic Development in Central Africa: A Multidimensional Panel Analysis

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The study employs a multidimensional Synthetic Financial Inclusion Index (FIIndex) to aggregate access, usage, and affordability measures, examining their impact on GDP growth, literacy, unemployment, and income inequality. The research design employed a cross-sectional quantitative approach, where perceptual data were collected through a structured questionnaire administered to 102 respondents in urban and semi-urban areas in 2025. The Pearson correlation analysis revealed significant positive correlations between socio-economic outcomes and perceived financial inclusion, with coefficients ranging from 0.260 to 0.567, supporting the hypothesis that accessibility and use are positively related to economic growth, employment, education, and resilience. The multiple regression analysis revealed that lower income inequality is significantly predicted by employment perceptions (R² = 0.338, p < 0.001). However, country-related variations were not substantial, and t-test and chi-square analyses failed to show significant differences between the DRC and the Republic of Congo, suggesting homogeneous regional perceptions despite the different contexts. These results suggest that financial inclusion contributes to the socio-economic development of informal economies in Central Africa, offering policy implications for achieving a 75% inclusion target by 2030, particularly through digital financial services and gender-based programs. Financial inclusion. Socio-Economic development FIIndex Central Africa Panel data GDP growth Literacy rates unemployment and Income inequality Figures Figure 1 Figure 2 1. INTRODUCTION 1.1. Background Financial inclusion has been recognised as a major contributor to a country's socio-economic growth, and it has been widely understood that the affordability and accessibility of financial services to the entire population and businesses are the most essential elements (Mishra et al., 2024 ). Financial inclusion is the process of enabling people to manage their finances effectively, invest in education and entrepreneurship, and increase their resilience to economic shocks by providing access to various financial instruments, such as savings accounts, credit, and insurance. Studies such as those by Leal Filho et al. ( 2021 ) have fittingly found that these mechanisms are essential for reducing poverty, promoting economic growth, and narrowing income distribution gaps, which align with various United Nations Sustainable Development Goals (SDGs). While central Africa, comprising the Democratic Republic of the Congo (DRC), the Republic of the Congo, Cameroon, Angola, and Chad, is one of the regions where the issue of financial inclusion has been a significant concern. The area is also marked by a low rate of economic access, globally, for example, from 1974 to 2023, Africa's real GDP growth was 20% more variable than worldwide growth, 23% more volatile than East Asia and Pacific, comparable to Europe and Central Asia, and 24% lower than Latin America and the Caribbean (African Development Bank, 2025 ). Additionally, financial exclusion is deeply rooted in Central African countries, with the majority of the adult population unable to access any banking services (Mokobongo et al., 2022 ). There are also gender disparities that lead to these problems, whereby one out of every five females receive formal financial services as opposed to one out of every four males (Lerman, 2021 ). The interrelationship of these factors demonstrates that some steps must be taken to strengthen financial inclusion in the area. Although the value of financial inclusion is well acknowledged, research on the topic is urgently needed, especially in Central Africa, and ideally, it should be multidimensional. 1.2. Research Gap The available literature tends to focus on only one aspect of the financial inclusion metric, specifically account ownership or the analysis of individual nations based on cross-sectional data, which does not capture the dynamics of financial inclusion over time and its socio-economic consequences. Therefore, this paper aims to fill the gap by providing a multidimensional approach through the development and application of a Synthetic Financial Inclusion Index (FIIndex). The FIIndex combines the most important aspects of financial inclusion, including access, usage, and affordability, providing a comprehensive indicator to measure the correlation between financial inclusion and socio-economic indicators, such as GDP, literacy rates, unemployment rates, and the Gini coefficient, among the selected Central African countries. 1.3. Research Objectives This research has two objectives: To establish the relationship between the FIIndex and socio-economic indicators, aiming to determine the degree to which financial inclusion affects economic growth, education, employment, and income distribution. To consider country-specific differences in Central Africa, it is essential to recognise that various countries exhibit distinct trends and reactions to financial inclusion programs due to their diverse economic, social, and political environments. 1.4. Research Contribution The paper makes an essential contribution to the literature by presenting a new Synthetic Financial Inclusion Index that can capture the complexity of financial inclusion, rather than the one-dimensional versions of the measure. The panel data analysis takes into account the cross-sectional and time-varying differences, providing a strong basis for examining the dynamic relationship between financial inclusion and socio-economic outcomes. It also offers policy-relevant data that may inform regional development policy, particularly in light of the current efforts to achieve a 75% financial inclusion rate by 2030 (Macdonald, 2024 ). Such knowledge is essential for achieving inclusive growth in a region that has faced numerous socio-economic challenges. 2. LITERATURE REVIEW 2.1. Theoretical Framework Financial inclusion, which refers to the availability and utilisation of affordable formal financial services according to Ediagbonya and Tioluwani ( 2023 ), is emerging as a mainstream tool in promoting socio-economic development. In such context, growth theory, introduced by Schumpeter ( 1934 ), posits that economic growth occurs through the innovation of entrepreneurs and requires access to credit on short notice. According to this theory, entrepreneurs are provided with liquidity through the financial system, and as a result, new technologies and business ventures are introduced, driving economic growth. Thus, financial inclusion is achieved by enabling underserved populations to access credit and other financial services, thereby allowing more people to participate in economic activities. However, the inclusive growth models support this perception by emphasising that financial inclusion is essential in reducing poverty and inequality. The latter models believe that financial inclusion in savings, credit, and insurance would help people to invest in education, health, and entrepreneurship, and consequently enhance equal economic development. Inclusive growth models are designed to address this challenge by bringing marginalised people into the formal financial sector. In light of the theoretical foundation presented by Schumpeter ( 1934 ), it can be considered that at this stage, the economy can be made more inclusive. The benefits can be distributed more evenly among all members of society, aligning with various United Nations Sustainable Development Goals (SDGs), such as poverty eradication and gender equality. 2.2. Empirical Studies The relationship between financial inclusion and socio-economic indicators is supported by evidence based on empirical research; however, the results vary according to the context. Demirgüç-Kunt and Singer ( 2017 ) examined data from 153 countries and concluded that financial inclusion, as measured by the number of accounts held in financial institutions or mobile money providers, is positively related to GDP growth. Nevertheless, Cicchiello et al. ( 2021 ) proposed that such an effect varies with the degree of financial development, and the greater the impact of inclusion, the less developed the financial system is. These studies suggest that financial inclusion can be leveraged to enhance economic growth, particularly in developing economies. However, the heterogeneity of effects suggests that the impact of financial inclusion should be studied in a contextual manner. Financial inclusion has been identified as a means to improve educational attainment, particularly among women, in terms of literacy. Researching the evolution of the banking industry in Mexico, Bruhn and Love ( 2014 ) found that the opening of 1,000 new bank branch offices led to a 7.6 percentage point increase in school enrollment among girls and a 5.4 percentage point increase among boys. This implies that access to finance can help families invest in education, which has a long-term impact on the literacy of females. This suggests that financial inclusion may have the potential to address the gender gap in education; however, an access gap remains. Financial inclusion in the form of entrepreneurship and employment also has an impact on the outcomes of the labour market. Mehry et al. ( 2021 ) utilised panel data from 43 developing countries and found that a 1% increase in financial inclusion led to a 0.03% decrease in unemployment, primarily due to the expansion of credit supply to small firms. On the same note, a research Study on Asian economies determined that financial inclusion was negatively correlated with unemployment, and access to financial services enables entrepreneurial activities to generate employment (Karim et al., 2020 ). Nevertheless, the impact of financial inclusion is not universal, and the implications of financial inclusion on unemployment levels depend on the local economy's condition and the presence of an informal labour market. Overall, financial inclusion is also linked to the decrease in the Gini coefficient, a measure of income inequality. According to Park and Mercado (2018), the greater the financial inclusion in Asia, the lower the level of income inequality is, because poor households can invest in income-generating activities and smooth their consumption as a result of financial services. Nonetheless, the starting point of inequality is high, which could hinder the success of financial inclusion policies, as the richer segment of the population will likely take advantage of the financial reforms to further their benefits. 2.3. Regional Context Evidence of financial inclusion in Central Africa is limited globally. Where it is presented, it is often overshadowed by the evidence from Sub-Saharan Africa or targeted to individual states. The Central African countries with a financial inclusion problem include the Democratic Republic of the Congo (DRC), the Republic of the Congo, Cameroon, Angola, and Chad. The penetration of banks is also very low, as fewer than 10% of the adult population in Central African countries, on average, owns a formal bank account (International Monetary Fund African Department, 2024 ). Highly informal economies in the region, which account for a large proportion of economic activities, make it challenging to integrate people into the formal financial system. Gender inequalities also characterise it as women encounter more barriers because of cultural pressures, low education levels, and access to technology. Other factors that restrict the growth of financial services include the absence of appropriate infrastructure, political instability, and regulatory constraints, and physical, social, and regulatory barriers have been reported in the available literature. However, these barriers are not multidimensional or longitudinal, which limits their ability to capture changes over time. This disparity necessitates considering region-specific studies to inform policy responses. A multidimensional Synthetic Financial Inclusion Index (FIIndex) and panel data analysis of five Central African countries (the DRC, Congo, Cameroon, Angola, and Chad) were used to test these hypotheses, capturing both cross-sectional and time variations. This study aims to fill a gap in the literature, as there is a lack of region-specific and multidimensional studies on the importance of financial inclusion in fostering socio-economic development in Central Africa. 3. DATA AND METHODOLOGY 3.1. Research Design The research design is a cross-sectional quantitative study aimed at gathering original perceptual data through the administration of a structured questionnaire to assess the socio-economic impact of financial inclusion. In contrast to secondary panel analyses, this exploratory-correlational paradigm quantifies attitudinal reactions, which are based on aggregated institutional data, thereby masking the heterogeneity at the individual level (Garba, 2023 ). The design is perceptual and exposes the subtle experiential differences among and between demographics and national contexts, marking a methodological shift that fills gaps in data found in objective research, particularly where informal economic practices are standard. The questionnaire was created based on validated scale tools found in the financial inclusion literature and modified to explore the perceptual impacts on socio-economic outcomes (Abdulkareem et al., 2023 ). It also contains 5-point Likert items, which can be converted to ordinal-to-interval to enable strong statistical inference. The approach helps test the hypothesis regarding the relationship dynamics, which offers a fresh perspective on the perceived impact of inclusion in a domain where traditional measurements are limited. Hypotheses, grounded in data collected, are: H1 Perceived accessibility and usage of financial services correlate positively with views of enhanced economic growth and employment. H2 Perceptions of financial inclusion align with improvements in educational opportunities and economic resilience. H3 Inclusion perceptions associate with reductions in income inequality and gender disparities. H4 National contextual factors, such as political and economic stability, significantly influence perceptions of inclusion effectiveness. The tests were applied to establish associative patterns, predictive power, and to compare the inter-country means through independent samples t-tests, as well as to assess the association between categorical variables using chi-square tests. These methods are scaled to the subjective quality of the data and are valid for inference within the perceptual scale of the study. 3.2. Data Collection This study was conducted from January to June 2025, targeting the populations of the Democratic Republic of the Congo (DRC), the Republic of the Congo, Cameroon, Angola, and Chad. The research employed a self-administered online questionnaire via Google Forms. The distribution utilised Facebook groups based on the region's economic development, lists of emails provided by local non-governmental organisations, and community networks in urban centers, including Kinshasa, Brazzaville, Yaounde, Luanda, and N'Djamena. The questionnaire was administered in English, the principal language of the region, to ensure the broadest possible participation. The tool consisted of three elements, including demographic data captured in the form of age, gender, country of residence, and education level. Ethical principles required the researcher to adhere to informed consent, maintain participant anonymity, and ensure voluntary participation, which was overseen by the researcher's institutional review board. No incentives were provided to avoid response bias, ensuring the integrity of the data. Such a method will capture current perceptual data, filling the gap between secondary data and providing a ground-level picture of financial inclusion in unstable settings. 3.3. Sampling The study employed convenience non-probability sampling to address the logistical challenges of Central Africa, including low internet connectivity and political unrest (Lerman, 2021 ). This practice is typical of exploratory studies conducted in developing states, and it involves digital platforms and local partnerships with available populations (Ediagbonya and Tioluwani, 2023 ). The sample collected 102 valid responses out of 120 responses, with 18 missing responses excluded, resulting in an 85% retention rate. The sample represents the urban and semi-urban populations where financial inclusion programs are most active. There was also a biased age distribution, with the 18–24 age group representing 64.7 percent of the respondents, indicating the youths' engagement in the online space. There was gender disaggregation of 58.8 percent males and 40.2 percent females, and a slight representation of other identities (1.0 percent). DRC (38.2%), Cameroon (31.4%), Republic of the Congo (26.5%), Angola (2.9%), and Chad (1.0%) topped the list of countries, offering a direct reflection of the disparity in access based on infrastructure and stability. The primary and secondary levels were predominant (36.3% and 35.3%), followed by the tertiary and postgraduate levels (5.9% and 4.9%). The profile was filled out with information on employment status, which included a large unemployed group (54.9%), full-time employment (6.9%), part-time employment (19.6%), self-employment (15.7%), and students (2.9%). Even though non-random sampling limits the generalisation of the sample to the general population, it gives valuable information regarding the fields of the inclusion efforts (Shamsudin et al., 2024 ). The highly skewed distribution of country-stratification attempts suggests practical access gradients. Additionally, GPower analysis determined that a sample size of 102 is sufficient to detect medium effect sizes at correlations (r = 0.3) at an 80% level of significance with a power of 0.05, which is consistent with the existing level of statistics. 3.4. Variables Independent variables are Objective 1 Likert-scale responses, which measure the perceptions of financial inclusion elements, such as accessibility, as factors in economic growth and usage as factors in resilience. They are regarded as interval data, allowing for parametric analysis, which is an efficient methodological choice due to their ordered character and equal intervals (Rokeman, 2024 ). Perceptions of reduced income inequality were dependent variables to be included in regression modelling, and Objective 2 items were used in conducting comparative country analysis to address contextual effects on inclusion effectiveness. The demographics serve as control variables, and the most crucial grouping variable is the country of residence, enabling the comparison of subgroups. Such a design allows for the disaggregation of perceptual distinctions within national contexts (Rotondi et al., 2024 ). Data preprocessing was performed using intensive outlier detection (no extreme cases were detected), deletion of missing values after initial screening, and a normality check for skewness and kurtosis; all values were within the acceptable parameters for parametric tests. This process ensures the data quality that is significant to the analytical validity of the study. 3.5. Analytical Methods The statistical computations were performed using SPSS, a tool that enables the derivation of descriptive statistics (means, medians, frequencies, skewness, and kurtosis), providing a comprehensive portrait of the sample. This portrait serves as the foundation for further analysis and development (Salman and Aleem, 2024 ). To measure the direction and strength of perceptual relationships, Pearson correlation analysis was used to test the relationships between Objective 1 items and to test hypotheses. A multiple regression model statistically confirmed H1, H2, and H3 by predicting the predictive effect of perceived accessibility and employment in the reduction of income inequality. The stability of the models was ensured by testing linear, homoscedastic, and multicollinearity assumptions, and analysing the variance inflation factors (VIFs), which were all below 2. They were independent samples t-tests comparing the mean scores on Objective 2 items between dominant countries (e.g., DRC vs. Republic of Congo). Levene's test was included to assess the equality of variances and estimate the effect size in Cohen's d, which satisfied H4. The technique offers good inter-group comparisons. Crosstabulations were analysed with chi-square tests comparing country versus perceptions of political environment, and H4 was further examined by analysing categorical dependencies. Furthermore, Objective 1's construct had a Cronbach's alpha of 0.82, indicating virtuous internal consistency. Significance was set at (p < 0.05), and effect sizes were examined to offset the non-random sampling design, which aligns with current statistical guidelines that discourage the use of p-values without randomisation (Reynolds, 2024 ). This is an analytical framework that expands perceptual studies by providing a specific contextual insight into the dynamics of financial inclusion in Central Africa. 4. RESULTS 4.1. Descriptive Statistics The population consisted of 102 respondents with a heterogeneous demographic profile. The average age was 1.91 (SD = 0.72), with 64.7% of the sample aged between 18 and 24 years, indicating a relatively young sample. The mean gender was 1.42 (SD = 0.52), and 58.8% of respondents identified as male. The country of residence had a mean of 2.02 (SD = 0.95), with the Democratic Republic of Congo and Cameroon accounting for 38.2% and 31.4%, respectively. The mean education level was 2.17 (SD = 1.28). Specifically, 36.3 percent of the participants were primary educated, and 35.3 percent were secondary educated. The average unemployment rate was 2.88 (SD = 0.86), with 54.9% of the population unemployed. The skewness values indicated that age (1.274) and education (1.289) had positive skew, while employment (-0.151) had a slight negative skew, which is typical of non-normal values in convenience samples. These frequency distribution-based measures indicate that the sample is skewed towards the young, urban, and unemployed, which is consistent with regions where financial inclusion programs are being implemented (Mehry et al., 2021 ). Table 1 Statistics Age of the respondent Gender of the respondent Country of residence Educational level of the respondent Employment status of the respondent N Valid 102 102 102 102 102 Missing 0 0 0 0 0 Mean 1.91 1.42 2.02 2.17 2.88 Median 2.00 1.00 2.00 2.00 3.00 Mode 2 1 1 1 3 Variance .517 .266 .911 1.645 .738 Skewness 1.274 .542 .449 1.289 − .151 Std. Error of Skewness .239 .239 .239 .239 .239 Kurtosis 3.795 -1.207 − .597 1.232 .479 Std. Error of Kurtosis .474 .474 .474 .474 .474 Range 4 2 4 5 4 Table 2 Age of the respondent Frequency Percent Valid Percent Cumulative Percent Valid 1 25 24.5 24.5 24.5 2 66 64.7 64.7 89.2 3 7 6.9 6.9 96.1 4 3 2.9 2.9 99.0 5 1 1.0 1.0 100.0 Total 102 100.0 100.0 Table 3 Gender of the respondent Frequency Percent Valid Percent Cumulative Percent Valid 1 60 58.8 58.8 58.8 2 41 40.2 40.2 99.0 3 1 1.0 1.0 100.0 Total 102 100.0 100.0 Table 4 Country of residence Frequency Percent Valid Percent Cumulative Percent Valid 1 39 38.2 38.2 38.2 2 27 26.5 26.5 64.7 3 32 31.4 31.4 96.1 4 3 2.9 2.9 99.0 5 1 1.0 1.0 100.0 Total 102 100.0 100.0 Table 5 Educational level of the respondent Frequency Percent Valid Percent Cumulative Percent Valid 1 37 36.3 36.3 36.3 2 36 35.3 35.3 71.6 3 15 14.7 14.7 86.3 4 6 5.9 5.9 92.2 5 5 4.9 4.9 97.1 6 3 2.9 2.9 100.0 Total 102 100.0 100.0 Table 6 Employment status of the respondent Frequency Percent Valid Percent Cumulative Percent Valid 1 7 6.9 6.9 6.9 2 20 19.6 19.6 26.5 3 56 54.9 54.9 81.4 4 16 15.7 15.7 97.1 5 3 2.9 2.9 100.0 Total 102 100.0 100.0 4.2. Correlation Analysis The Pearson correlation analysis was applied to the objective items, and significant positive correlations (p < 0.01) were found between the perceived aspects of financial inclusion and the supported hypotheses. Perceived access to financial services was found to be correlated with employment impact (r = 0.530), reduction in income inequality (r = 0.459), educational opportunities (r = 0.406), and economic resilience (r = 0.442), confirming the relationship between H1 and perceptions of growth and employment. The employment effect was strongly related to the decrease in income inequality (r = 0.546), moderately associated with educational opportunities (r = 0.304), and resilience (r = 0.260). The education opportunities (r = 0.421) and resilience (r = 0.464) also showed a correlation with a reduction in income inequality. The strongest correlation was observed between education opportunities and resilience (r = 0.567), which supports the claims in hypothesis (3) regarding inequality and gender disparity. These coefficients are all statistically significant, suggesting the strong interdependences of perceptions, which is in line with the results of Abdulkareem et al. ( 2023 ) global study of the socio-economic effects of inclusion. Table 7 Correlations Table The accessibility of financial services has improved economic growth Increased financial inclusion has positively impacted employment rates Financial inclusion programs have contributed to the reduction of income inequality Access to financial services has improved educational opportunities The use of digital financial services has helped enhance the economic resilience The accessibility of financial services has improved economic growth Pearson Correlation 1 .530 ** .459 ** .406 ** .442 ** Sig. (2-tailed) .000 .000 .000 .000 N 102 102 102 102 102 Increased financial inclusion has positively impacted employment rates Pearson Correlation .530 ** 1 .546 ** .304 ** .260 ** Sig. (2-tailed) .000 .000 .002 .008 N 102 102 102 102 102 Financial inclusion programs have contributed to the reduction of income inequality Pearson Correlation .459 ** .546 ** 1 .421 ** .464 ** Sig. (2-tailed) .000 .000 .000 .000 N 102 102 102 102 102 Access to financial services has improved educational opportunities Pearson Correlation .406 ** .304 ** .421 ** 1 .567 ** Sig. (2-tailed) .000 .002 .000 .000 N 102 102 102 102 102 The use of digital financial services has helped enhance the economic resilience Pearson Correlation .442 ** .260 ** .464 ** .567 ** 1 Sig. (2-tailed) .000 .008 .000 .000 N 102 102 102 102 102 **. Correlation is significant at the 0.01 level (2-tailed). 4.3. Regression Analysis Multiple regression was used to model perceived income inequality reduction, with H1 and H3 being tested in relation to the effects of accessibility and employment. The model yielded an R 0.581 and an R² 2 = 0.338 (adjusted R² 2 = 0.324) such that a variance of 33.8 percent in inequality reduction perceptions is accounted for (F (2,99) = 25.254, p < 0.001). It was constant (= 1.102, t = 3.261, p = 0.002), accessibility (= 0.241, standardised = 0.236, t = 2.452, p = 0.016), and employment (= 0.447, standardised = 0.420, t = 4.358, p = 0.001). The ANOVA yielded a regression sum of squares of 34.127, a residual sum of squares of 66.892, and an overall sum of squares of 101.020, which supports the model fit (Mohamed et al., 2023 ). The large coefficient of employment implies that it is more effective in forming perceptions of inequality than accessibility, which is consistent with the growth-employment connection of H1 and the inequality emphasis of H3, substantiated by labour market data (Mehry et al., 2021 ). Table 8 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .581 a .338 .324 .822 .338 25.254 2 99 .000 a. Predictors: (Constant), Increased financial inclusion has positively impacted employment rates. The accessibility of financial services has improved economic growth Table 9 ANOVA a Model Sum of Squares df Mean Square F Sig. 1 Regression 34.127 2 17.064 25.254 .000 b Residual 66.892 99 .676 Total 101.020 101 a. Dependent Variable: Financial inclusion programs have contributed to the reduction of income inequality b. Predictors: (Constant), Increased financial inclusion has positively impacted employment rates. The accessibility of financial services has improved economic growth Table 10 Coefficients a Model Unstandardised Coefficients Standardised Coefficiets t Sig. B Std. Error Beta 1 (Constant) 1.102 .338 3.261 .002 The accessibility of financial services has improved economic growth .241 .098 .236 2.452 .016 Increased financial inclusion has positively impacted employment rates .447 .102 .420 4.358 .000 a. Dependent Variable: Financial inclusion programs have contributed to the reduction of income inequality 4.4. Independent Samples T-Test T-tests compared Objective 2 item means between DRC (N = 39) and the Republic of Congo (N = 27), testing H4. Table 4 details the findings. Political influence means were 3.26 (DRC) and 3.04 (Congo), t = 0.860, p = 0.393 (not significant). Economic stability was 3.38 vs. 3.11, t = 1.149, p = 0.255 (not substantial). The outcomes of the cultural attitudes were 3.51 and 3.30, with a t-value of 0.913 and a p-value of 0.364 (non-significant). The equal variances test (Levene) was passed, and Cohen's effect sizes fell between 0.215 and 0.288, which is a negligible difference at the practice level. Non-significance implies homogenous perceptions despite contextual differences and partly contradicts H4 (Cicchiello et al., 2021 ). Table 11 Group Statistics Country of residence N Mean Std. Deviation Std. Error Mean The political environment in my country has positively influenced financial inclusion programs 1 39 3.26 1.093 .175 2 27 3.04 .898 .173 The economic stability of my country has made financial services more accessible 1 39 3.38 1.016 .163 2 27 3.11 .847 .163 Cultural attitudes towards financial services (e.g., banking) have a significant impact on financial inclusion in my country 1 39 3.51 .970 .155 2 27 3.30 .912 .176 Table 12 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper The political environment in my country has had a positive impact on financial inclusion programs. Equal variances assumed 2.749 .102 .860 64 .393 .219 .255 − .290 .729 Equal variances not assumed. .892 62.046 .376 .219 .246 − .272 .711 The economic stability of my country has made financial services more accessible. Equal variances assumed 2.712 .104 1.149 64 .255 .274 .238 − .202 .749 Equal variances not assumed. 1.187 61.698 .240 .274 .230 − .187 .734 Cultural attitudes towards financial services (e.g., banking) have a significant impact on financial inclusion in my country Equal variances assumed .406 .526 .913 64 .364 .217 .237 − .257 .690 Equal variances not assumed. .924 58.225 .359 .217 .234 − .253 .686 Table 13 Independent Samples Effect Sizes Standardiser a Point Estimate 95% Confidence Interval Lower Upper The political environment in my country has positively influenced financial inclusion programs Cohen's d 1.019 .215 − .278 .707 Hedges' correction 1.031 .213 − .274 .698 Glass's delta .898 .244 − .253 .737 The economic stability of my country has made financial services more accessible Cohen's d .951 .288 − .207 .780 Hedges' correction .962 .284 − .204 .770 Glass's delta .847 .323 − .179 .818 Cultural attitudes towards financial services (e.g., banking) have a significant impact on financial inclusion in my country Cohen's d .947 .229 − .264 .720 Hedges' correction .958 .226 − .261 .712 Glass's delta .912 .237 − .260 .730 a. The denominator used in estimating the effect sizes. Cohen's d uses the pooled standard deviation. Hedges' correction uses the pooled standard deviation, plus a correction factor. Glass's delta uses the sample standard deviation of the control group. 4.5. Chi-Square Test The chi-square test was employed to cross-tabulate country by perceptions of political influence, H4. This test yields 7² = 49, with a p-value of 0.552 (not significant). Furthermore, 76% of the counts are projected to be five or fewer (minimum = 0.08). Even though the national situations are not the same, this non-correlation also speaks against H4 and suggests that the political perceptions are not evidently country-specific in this sample. Table 14 Crosstabulation Country of residence * The political environment in my country has positively influenced financial inclusion programs. Count The political environment in my country has had a positive impact on financial inclusion programs. Total 1 2 3 4 5 Country of residence 1 4 3 15 13 4 39 2 2 3 15 6 1 27 3 4 7 7 12 2 32 4 0 0 1 1 1 3 5 0 0 1 0 0 1 Total 10 13 39 32 8 102 Table 15 Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 14.627 a 16 .552 Likelihood Ratio 14.582 16 .555 Linear-by-Linear Association .104 1 .747 N of Va54.9%Cases 102 a. 19 cells (76.0%) have expected count less than 5. The minimum expected count is .08. Overall, the results substantiate the perceptual correlations across Objective 1 (H1-H3), but there are minimal country-specific variations that dilute H4, hence indicating homogenous regional views of contextual factors. In short, the statistical tests affirm the strength of perceptual connections among Objective 1 items, which affirm H1, H2, and H3. Correlation analysis indicates that the positive relationships are strong, with the strongest connection being between educational opportunities and resilience (r = 0.567). Regression analysis reveals that employment perceptions are a strong predictor of reduced inequality (β = 0.338, p = 0.001). Conversely, H4 is not supported well, as t-tests and chi-square results indicate that there is no significant country-specific differentiation between the DRC and the Republic of Congo in contextual perceptions (p = 0.05), and the effect sizes are petite (d = 0.215–0.288). This homogeneity of perception among nations, regardless of their diverse political and economic contexts, indicates the existence of a shared regional discourse on the contextual factors of financial inclusion. A clear understanding of the perception of inclusion effects on the youth and unemployed sample highlights the need to sample more extensively to confirm national differences. 5. DISCUSSION The findings obtained in this paper, based on primary survey data, provide accurate information on the socio-economic impact of financial inclusion as perceived by individuals living in Central Africa who directly responded to the research questions. The gradient to primary perceptual data is superior to the aggregation biases of secondary indices, such as the FIIndex, which tend to bias finer affordability and usage indices (Khera et al., 2022 ). This approach explains lived experiences within a region characterised by financial marginalisation, where informal sectors often take the lead and formal statistics are scarce (International Monetary Fund, African Department, 2024). Convenience sampling method, in turn, diminishes the degree of generalisability, which is associated with the argument that the application of p-values cannot be deemed valid unless random selection is performed (Hirschauer et al., 2020). It is then construed that the interpretations give weight to the magnitude of effects and the practical meaning, in comparison to the rigorous levels of statistics. In Objective 1, the results are robust in supporting H1, H2, and H3 and demonstrate positive perceived relationships among aspects of financial inclusion and socio-economic outcomes. The coefficients of correlation, ranging from 0.260 to 0.567, indicate that the respondents associate accessibility and use with economic growth, employment, education, resilience, and the reduction of income inequality. The accessibility and employment effect (r = 0.530) correlates with the study, suggesting that financial access facilitates entrepreneurship, thereby boosting GDP. The perception of reduced inequality is highly predicted by employment perceptions (r = 0.447, p < 0.001). The regression model indicates that 33.8% of the perceived inequality reduction can be explained by employment perceptions (R² = 0.338, p < 0.001), consistent with the finding that a 1% increase in financial access reduces unemployment by 0.03% (Mehry et al., 2021 ). The positive relationship between education and resilience (r = 0.567) supports the argument that inclusion results in superior human capital investment, as indicated in studies of educational outcomes (Olufemi et al., 2024 ). The insights are particularly relevant to Central Africa, where informal economies play a significant role in the economic life, and digital money tools could become more effective in strengthening resilience (Mokobongo et al., 2022 ). Gender-related perceptions, particularly in relation to reducing inequality, align with documented disparities in financial access (Lerman, 2021 ). With a 40.2% female response rate, they may emphasise the perceived benefits of inclusion for women, despite existing actual gender gaps. The youth (64.7% aged 18–24) form the dominant part of the sample. The unemployment rate (54.9%) indicates that these groups perceive inclusion as a means of avoiding poverty, a view also confirmed by studies that have identified a correlation between financial access and income stability (Park and Mercado, 2018). However, the subjectivity of these perceptions contrasts with objective indicators, such as the Gini coefficient of the DRC, which ranges from 42.1 to 49.5, showing mixed trends of regional inequality and partially validating the contextual moderation hypothesis of H4. The non-significant t-test (p > 0.05) and chi-square (p = 0.552) results of Objective 2 reject H4, indicating no significant difference between DRC and the Republic of Congo. Regardless of the differences in context between political instability in the DRC and the oil-based economy in Congo, ambivalence would be represented by mean scores of approximately 3 (neutral), potentially due to political impediments to the introduction of inclusion (Macdonald, 2024 ). These weak effect sizes (Cohen's d = 0.215–0.288) indicate no significant differences in practice, which contradict existing research demonstrating differences in the development level of outcomes related to inclusion (Curran and Taheri, 2021 ). The unequal sample, with slight coverage of Angola (2.9) and Chad (1.0), restricts cross-national comparisons. However, the meaning of imbalance, such as the contrast between Cameroon's balanced perceptions and the DRC's optimism on education, is fascinating and warrants further exploration. Several limitations temper these findings, including the fact that the convenience sample is likely to over-represent urban, educated individuals and underestimate rural exclusion, which is an established issue in Central Africa (African Development Bank, 2025 ). The information received through self-reporting may exaggerate the favourable perception due to bias in the response, and the research is cross-sectional, which is why it is not possible to determine the cause of the effect. These shortcomings suggest that any future research should incorporate the application of random sampling or employ qualitative designs to measure perceptions objectively. These policy implications exist. Regional frameworks indicated that governments should prioritise digital inclusion initiatives through mobile banking by focusing on underserved populations and pursuing the 75 percent financial inclusion target by 2030 (Macdonald, 2024 ). Gender-focused programmes, based on the understanding that 40.2% of the female respondents, can revolve around the availability of credit for female entrepreneurs. Non-governmental organisations can utilise these perceptual data to develop awareness campaigns, as they can draw attention to the socio-economic benefits of inclusion among youth and unemployed populations, who form the majority of the sample. To conclude, this paper aims to contribute to the body of knowledge by consolidating perceptual data in a field where formal measures are lacking, thereby laying a foundation for targeted interventions. Future studies must expand the geographical scope and heterogeneity of methods to narrow such findings to have polices founded on perceived and measured effects. 6. CONCLUSION The results of this paper show that the perceived positive relationships between financial inclusion and socio-economic variables are high and support the hypotheses. The respondents had strong associations between perceived access to and utilisation of financial services and growth, employment, educational opportunities, resilience, and a reduction in income inequality. Correlation coefficients and regression analysis help these relationships. They align with the fact that inclusion is a contributor to economic activity and the development of human capital. Nevertheless, it does not provide significant support for H4. T-tests and chi-square values indicate that there is no significant country-specific difference between the DRC and the Republic of Congo, and the two regions are viewed similarly. However, their political and economic backgrounds differed. The proposed research fills the gaps in secondary data sources, including the FIIndex, which, in most cases, does not provide a fine-grained view of affordability and consumption in informal economies. It is emphasised by highlighting a young and unemployed sample, which underscores the possibility of inclusion as a tool to alleviate poverty, providing essential information for policy in the face of regional instability. Hence, the results indicate that digital financial instruments can be used to enhance resilience, particularly in the informal sector. References Abdulkareem, H.K., Jimoh, S.O. and Shasi, O.M., (2023). Socioeconomic development and sustainable development in Nigeria: the roles of poverty reduction and social inclusion. Journal of Business and Socio-Economic Development , 3 (3), pp.265-278. [Online] Available at: https://www.emerald.com/insight/content/doi/10.1108/JBSED-10-2021-0137/full/pdf. African Development Bank (2025). AFRICA’S PERFORMANCE AND OUTLOOK MACROECONOMIC JANUARY 2025. [Online] Available at: https://www.afdb-org.kr/wp-content/uploads/2025/02/2025_meo_-_complete_enversion_05.02.2025.pdf. Bruhn, M. and Love, I., (2014). 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World Development Indicators (n.d) | DataBank. [Online] Available at: https://databank.worldbank.org/source/world-development-indicators. Yap, S., Lee, H.S., and Liew, P.X. (2024). The roles of insurance and banking services on financial inclusion. SAGE Open , 14 (2), p.21582440241252268. [Online] Available at: https://journals.sagepub.com/doi/pdf/10.1177/215824402412522687. Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7492621","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507693914,"identity":"09c6996a-2cca-4a43-ab34-35c5c11f4b32","order_by":0,"name":"Hervé-Landry KINKANI BATEKELE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3PMQrCMBSA4SeBZAnUMYN6BiFgFS+TUnBKbyDi5CTOGTyEk66VYNeuHRwqhQ5OBUGcxKaLW42bYP4hyZCPvAC4XD/Y0Cw5QGcJKK6PrGdBEIBoCBaG0G8IbV78THySHnMxP/cPSt6u2XxMgejTro1M1iEaiqTk2yzaT2VSD0Zns6x1sDjETGAdKBbtucQ1YXTUTtKCPMTTEFly+bQhWYghWDUEFdHKgkxUwVmw0VzRcoSiDaP40198L7hU1V33FQmLm7wvBh7RSSt51xWYmR3bXTd5Marsb7tcLtc/9QIXdUsstw/AUgAAAABJRU5ErkJggg==","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Hervé-Landry","middleName":"KINKANI","lastName":"BATEKELE","suffix":""}],"badges":[],"createdAt":"2025-08-30 04:18:49","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7492621/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7492621/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90463403,"identity":"287560d7-6eb2-4981-a916-1e4b14de7503","added_by":"auto","created_at":"2025-09-03 05:04:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":158692,"visible":true,"origin":"","legend":"\u003cp\u003eVolatility of annual real GDP growth rate, 1974–2023 (Source: African Development Bank, 2025)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7492621/v1/56c013a2bcecd6accb54745e.png"},{"id":90463404,"identity":"a81f7f9b-2ce3-4e95-b578-636aeef0b78d","added_by":"auto","created_at":"2025-09-03 05:04:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57016,"visible":true,"origin":"","legend":"\u003cp\u003eFinancial Access and GDP Per Capita (Source: International Monetary Fund African Department, 2024)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7492621/v1/d0630072a3719701bf8609ac.png"},{"id":90465768,"identity":"d4c6e2dd-116e-48ab-bde0-daa545c47bf4","added_by":"auto","created_at":"2025-09-03 05:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1642558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7492621/v1/3d69e894-cf8a-4332-97aa-029f45ab342b.pdf"},{"id":90463406,"identity":"9953eebc-966e-4735-91f2-f0d484dd7c4b","added_by":"auto","created_at":"2025-09-03 05:04:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19258,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7492621/v1/577839f9f75f3a97a5ec9df5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFinancial Inclusion and Socio-Economic Development in Central Africa: A Multidimensional Panel Analysis\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. Background\u003c/h2\u003e\u003cp\u003eFinancial inclusion has been recognised as a major contributor to a country's socio-economic growth, and it has been widely understood that the affordability and accessibility of financial services to the entire population and businesses are the most essential elements (Mishra et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Financial inclusion is the process of enabling people to manage their finances effectively, invest in education and entrepreneurship, and increase their resilience to economic shocks by providing access to various financial instruments, such as savings accounts, credit, and insurance. Studies such as those by Leal Filho et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have fittingly found that these mechanisms are essential for reducing poverty, promoting economic growth, and narrowing income distribution gaps, which align with various United Nations Sustainable Development Goals (SDGs). While central Africa, comprising the Democratic Republic of the Congo (DRC), the Republic of the Congo, Cameroon, Angola, and Chad, is one of the regions where the issue of financial inclusion has been a significant concern. The area is also marked by a low rate of economic access, globally, for example, from 1974 to 2023, Africa's real GDP growth was 20% more variable than worldwide growth, 23% more volatile than East Asia and Pacific, comparable to Europe and Central Asia, and 24% lower than Latin America and the Caribbean (African Development Bank, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditionally, financial exclusion is deeply rooted in Central African countries, with the majority of the adult population unable to access any banking services (Mokobongo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are also gender disparities that lead to these problems, whereby one out of every five females receive formal financial services as opposed to one out of every four males (Lerman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The interrelationship of these factors demonstrates that some steps must be taken to strengthen financial inclusion in the area. Although the value of financial inclusion is well acknowledged, research on the topic is urgently needed, especially in Central Africa, and ideally, it should be multidimensional.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Research Gap\u003c/h2\u003e\u003cp\u003eThe available literature tends to focus on only one aspect of the financial inclusion metric, specifically account ownership or the analysis of individual nations based on cross-sectional data, which does not capture the dynamics of financial inclusion over time and its socio-economic consequences. Therefore, this paper aims to fill the gap by providing a multidimensional approach through the development and application of a Synthetic Financial Inclusion Index (FIIndex). The FIIndex combines the most important aspects of financial inclusion, including access, usage, and affordability, providing a comprehensive indicator to measure the correlation between financial inclusion and socio-economic indicators, such as GDP, literacy rates, unemployment rates, and the Gini coefficient, among the selected Central African countries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3. Research Objectives\u003c/h2\u003e\u003cp\u003eThis research has two objectives:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTo establish the relationship between the FIIndex and socio-economic indicators, aiming to determine the degree to which financial inclusion affects economic growth, education, employment, and income distribution.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo consider country-specific differences in Central Africa, it is essential to recognise that various countries exhibit distinct trends and reactions to financial inclusion programs due to their diverse economic, social, and political environments.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4. Research Contribution\u003c/h2\u003e\u003cp\u003eThe paper makes an essential contribution to the literature by presenting a new Synthetic Financial Inclusion Index that can capture the complexity of financial inclusion, rather than the one-dimensional versions of the measure. The panel data analysis takes into account the cross-sectional and time-varying differences, providing a strong basis for examining the dynamic relationship between financial inclusion and socio-economic outcomes. It also offers policy-relevant data that may inform regional development policy, particularly in light of the current efforts to achieve a 75% financial inclusion rate by 2030 (Macdonald, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such knowledge is essential for achieving inclusive growth in a region that has faced numerous socio-economic challenges.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. LITERATURE REVIEW","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Theoretical Framework\u003c/h2\u003e\u003cp\u003eFinancial inclusion, which refers to the availability and utilisation of affordable formal financial services according to Ediagbonya and Tioluwani (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), is emerging as a mainstream tool in promoting socio-economic development. In such context, growth theory, introduced by Schumpeter (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1934\u003c/span\u003e), posits that economic growth occurs through the innovation of entrepreneurs and requires access to credit on short notice. According to this theory, entrepreneurs are provided with liquidity through the financial system, and as a result, new technologies and business ventures are introduced, driving economic growth. Thus, financial inclusion is achieved by enabling underserved populations to access credit and other financial services, thereby allowing more people to participate in economic activities. However, the inclusive growth models support this perception by emphasising that financial inclusion is essential in reducing poverty and inequality. The latter models believe that financial inclusion in savings, credit, and insurance would help people to invest in education, health, and entrepreneurship, and consequently enhance equal economic development. Inclusive growth models are designed to address this challenge by bringing marginalised people into the formal financial sector. In light of the theoretical foundation presented by Schumpeter (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1934\u003c/span\u003e), it can be considered that at this stage, the economy can be made more inclusive. The benefits can be distributed more evenly among all members of society, aligning with various United Nations Sustainable Development Goals (SDGs), such as poverty eradication and gender equality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Empirical Studies\u003c/h2\u003e\u003cp\u003eThe relationship between financial inclusion and socio-economic indicators is supported by evidence based on empirical research; however, the results vary according to the context. Demirg\u0026uuml;\u0026ccedil;-Kunt and Singer (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examined data from 153 countries and concluded that financial inclusion, as measured by the number of accounts held in financial institutions or mobile money providers, is positively related to GDP growth. Nevertheless, Cicchiello et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) proposed that such an effect varies with the degree of financial development, and the greater the impact of inclusion, the less developed the financial system is. These studies suggest that financial inclusion can be leveraged to enhance economic growth, particularly in developing economies. However, the heterogeneity of effects suggests that the impact of financial inclusion should be studied in a contextual manner. Financial inclusion has been identified as a means to improve educational attainment, particularly among women, in terms of literacy. Researching the evolution of the banking industry in Mexico, Bruhn and Love (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that the opening of 1,000 new bank branch offices led to a 7.6 percentage point increase in school enrollment among girls and a 5.4 percentage point increase among boys. This implies that access to finance can help families invest in education, which has a long-term impact on the literacy of females. This suggests that financial inclusion may have the potential to address the gender gap in education; however, an access gap remains.\u003c/p\u003e\u003cp\u003eFinancial inclusion in the form of entrepreneurship and employment also has an impact on the outcomes of the labour market. Mehry et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) utilised panel data from 43 developing countries and found that a 1% increase in financial inclusion led to a 0.03% decrease in unemployment, primarily due to the expansion of credit supply to small firms. On the same note, a research Study on Asian economies determined that financial inclusion was negatively correlated with unemployment, and access to financial services enables entrepreneurial activities to generate employment (Karim et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, the impact of financial inclusion is not universal, and the implications of financial inclusion on unemployment levels depend on the local economy's condition and the presence of an informal labour market. Overall, financial inclusion is also linked to the decrease in the Gini coefficient, a measure of income inequality. According to Park and Mercado (2018), the greater the financial inclusion in Asia, the lower the level of income inequality is, because poor households can invest in income-generating activities and smooth their consumption as a result of financial services. Nonetheless, the starting point of inequality is high, which could hinder the success of financial inclusion policies, as the richer segment of the population will likely take advantage of the financial reforms to further their benefits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Regional Context\u003c/h2\u003e\u003cp\u003eEvidence of financial inclusion in Central Africa is limited globally. Where it is presented, it is often overshadowed by the evidence from Sub-Saharan Africa or targeted to individual states. The Central African countries with a financial inclusion problem include the Democratic Republic of the Congo (DRC), the Republic of the Congo, Cameroon, Angola, and Chad.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe penetration of banks is also very low, as fewer than 10% of the adult population in Central African countries, on average, owns a formal bank account (International Monetary Fund African Department, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Highly informal economies in the region, which account for a large proportion of economic activities, make it challenging to integrate people into the formal financial system. Gender inequalities also characterise it as women encounter more barriers because of cultural pressures, low education levels, and access to technology. Other factors that restrict the growth of financial services include the absence of appropriate infrastructure, political instability, and regulatory constraints, and physical, social, and regulatory barriers have been reported in the available literature. However, these barriers are not multidimensional or longitudinal, which limits their ability to capture changes over time. This disparity necessitates considering region-specific studies to inform policy responses. A multidimensional Synthetic Financial Inclusion Index (FIIndex) and panel data analysis of five Central African countries (the DRC, Congo, Cameroon, Angola, and Chad) were used to test these hypotheses, capturing both cross-sectional and time variations. This study aims to fill a gap in the literature, as there is a lack of region-specific and multidimensional studies on the importance of financial inclusion in fostering socio-economic development in Central Africa.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. DATA AND METHODOLOGY","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Research Design\u003c/h2\u003e\u003cp\u003eThe research design is a cross-sectional quantitative study aimed at gathering original perceptual data through the administration of a structured questionnaire to assess the socio-economic impact of financial inclusion. In contrast to secondary panel analyses, this exploratory-correlational paradigm quantifies attitudinal reactions, which are based on aggregated institutional data, thereby masking the heterogeneity at the individual level (Garba, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The design is perceptual and exposes the subtle experiential differences among and between demographics and national contexts, marking a methodological shift that fills gaps in data found in objective research, particularly where informal economic practices are standard. The questionnaire was created based on validated scale tools found in the financial inclusion literature and modified to explore the perceptual impacts on socio-economic outcomes (Abdulkareem et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It also contains 5-point Likert items, which can be converted to ordinal-to-interval to enable strong statistical inference. The approach helps test the hypothesis regarding the relationship dynamics, which offers a fresh perspective on the perceived impact of inclusion in a domain where traditional measurements are limited.\u003c/p\u003e\u003cp\u003eHypotheses, grounded in data collected, are:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003ePerceived accessibility and usage of financial services correlate positively with views of enhanced economic growth and employment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003ePerceptions of financial inclusion align with improvements in educational opportunities and economic resilience.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003eInclusion perceptions associate with reductions in income inequality and gender disparities.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003cp\u003eNational contextual factors, such as political and economic stability, significantly influence perceptions of inclusion effectiveness.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe tests were applied to establish associative patterns, predictive power, and to compare the inter-country means through independent samples t-tests, as well as to assess the association between categorical variables using chi-square tests. These methods are scaled to the subjective quality of the data and are valid for inference within the perceptual scale of the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Data Collection\u003c/h2\u003e\u003cp\u003eThis study was conducted from January to June 2025, targeting the populations of the Democratic Republic of the Congo (DRC), the Republic of the Congo, Cameroon, Angola, and Chad. The research employed a self-administered online questionnaire via Google Forms. The distribution utilised Facebook groups based on the region's economic development, lists of emails provided by local non-governmental organisations, and community networks in urban centers, including Kinshasa, Brazzaville, Yaounde, Luanda, and N'Djamena. The questionnaire was administered in English, the principal language of the region, to ensure the broadest possible participation. The tool consisted of three elements, including demographic data captured in the form of age, gender, country of residence, and education level. Ethical principles required the researcher to adhere to informed consent, maintain participant anonymity, and ensure voluntary participation, which was overseen by the researcher's institutional review board. No incentives were provided to avoid response bias, ensuring the integrity of the data. Such a method will capture current perceptual data, filling the gap between secondary data and providing a ground-level picture of financial inclusion in unstable settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Sampling\u003c/h2\u003e\u003cp\u003eThe study employed convenience non-probability sampling to address the logistical challenges of Central Africa, including low internet connectivity and political unrest (Lerman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This practice is typical of exploratory studies conducted in developing states, and it involves digital platforms and local partnerships with available populations (Ediagbonya and Tioluwani, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The sample collected 102 valid responses out of 120 responses, with 18 missing responses excluded, resulting in an 85% retention rate. The sample represents the urban and semi-urban populations where financial inclusion programs are most active. There was also a biased age distribution, with the 18\u0026ndash;24 age group representing 64.7 percent of the respondents, indicating the youths' engagement in the online space. There was gender disaggregation of 58.8 percent males and 40.2 percent females, and a slight representation of other identities (1.0 percent). DRC (38.2%), Cameroon (31.4%), Republic of the Congo (26.5%), Angola (2.9%), and Chad (1.0%) topped the list of countries, offering a direct reflection of the disparity in access based on infrastructure and stability. The primary and secondary levels were predominant (36.3% and 35.3%), followed by the tertiary and postgraduate levels (5.9% and 4.9%). The profile was filled out with information on employment status, which included a large unemployed group (54.9%), full-time employment (6.9%), part-time employment (19.6%), self-employment (15.7%), and students (2.9%). Even though non-random sampling limits the generalisation of the sample to the general population, it gives valuable information regarding the fields of the inclusion efforts (Shamsudin et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The highly skewed distribution of country-stratification attempts suggests practical access gradients. Additionally, GPower analysis determined that a sample size of 102 is sufficient to detect medium effect sizes at correlations (r\u0026thinsp;=\u0026thinsp;0.3) at an 80% level of significance with a power of 0.05, which is consistent with the existing level of statistics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Variables\u003c/h2\u003e\u003cp\u003eIndependent variables are Objective 1 Likert-scale responses, which measure the perceptions of financial inclusion elements, such as accessibility, as factors in economic growth and usage as factors in resilience. They are regarded as interval data, allowing for parametric analysis, which is an efficient methodological choice due to their ordered character and equal intervals (Rokeman, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Perceptions of reduced income inequality were dependent variables to be included in regression modelling, and Objective 2 items were used in conducting comparative country analysis to address contextual effects on inclusion effectiveness. The demographics serve as control variables, and the most crucial grouping variable is the country of residence, enabling the comparison of subgroups. Such a design allows for the disaggregation of perceptual distinctions within national contexts (Rotondi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Data preprocessing was performed using intensive outlier detection (no extreme cases were detected), deletion of missing values after initial screening, and a normality check for skewness and kurtosis; all values were within the acceptable parameters for parametric tests. This process ensures the data quality that is significant to the analytical validity of the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Analytical Methods\u003c/h2\u003e\u003cp\u003eThe statistical computations were performed using SPSS, a tool that enables the derivation of descriptive statistics (means, medians, frequencies, skewness, and kurtosis), providing a comprehensive portrait of the sample. This portrait serves as the foundation for further analysis and development (Salman and Aleem, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To measure the direction and strength of perceptual relationships, Pearson correlation analysis was used to test the relationships between Objective 1 items and to test hypotheses. A multiple regression model statistically confirmed H1, H2, and H3 by predicting the predictive effect of perceived accessibility and employment in the reduction of income inequality. The stability of the models was ensured by testing linear, homoscedastic, and multicollinearity assumptions, and analysing the variance inflation factors (VIFs), which were all below 2. They were independent samples t-tests comparing the mean scores on Objective 2 items between dominant countries (e.g., DRC vs. Republic of Congo). Levene's test was included to assess the equality of variances and estimate the effect size in Cohen's d, which satisfied H4. The technique offers good inter-group comparisons. Crosstabulations were analysed with chi-square tests comparing country versus perceptions of political environment, and H4 was further examined by analysing categorical dependencies. Furthermore, Objective 1's construct had a Cronbach's alpha of 0.82, indicating virtuous internal consistency. Significance was set at (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and effect sizes were examined to offset the non-random sampling design, which aligns with current statistical guidelines that discourage the use of p-values without randomisation (Reynolds, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is an analytical framework that expands perceptual studies by providing a specific contextual insight into the dynamics of financial inclusion in Central Africa.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. RESULTS","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive Statistics\u003c/h2\u003e\u003cp\u003eThe population consisted of 102 respondents with a heterogeneous demographic profile. The average age was 1.91 (SD\u0026thinsp;=\u0026thinsp;0.72), with 64.7% of the sample aged between 18 and 24 years, indicating a relatively young sample. The mean gender was 1.42 (SD\u0026thinsp;=\u0026thinsp;0.52), and 58.8% of respondents identified as male. The country of residence had a mean of 2.02 (SD\u0026thinsp;=\u0026thinsp;0.95), with the Democratic Republic of Congo and Cameroon accounting for 38.2% and 31.4%, respectively. The mean education level was 2.17 (SD\u0026thinsp;=\u0026thinsp;1.28). Specifically, 36.3 percent of the participants were primary educated, and 35.3 percent were secondary educated. The average unemployment rate was 2.88 (SD\u0026thinsp;=\u0026thinsp;0.86), with 54.9% of the population unemployed. The skewness values indicated that age (1.274) and education (1.289) had positive skew, while employment (-0.151) had a slight negative skew, which is typical of non-normal values in convenience samples. These frequency distribution-based measures indicate that the sample is skewed towards the young, urban, and unemployed, which is consistent with regions where financial inclusion programs are being implemented (Mehry et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge of the respondent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGender of the respondent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCountry of residence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEducational level of the respondent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEmployment status of the respondent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eStd. Error of Skewness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eStd. Error of Kurtosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAge of the respondent\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eValid Percent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative Percent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGender of the respondent\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eValid Percent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative Percent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCountry of residence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eValid Percent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative Percent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEducational level of the respondent\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eValid Percent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative Percent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEmployment status of the respondent\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eValid Percent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative Percent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eValid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Correlation Analysis\u003c/h2\u003e\u003cp\u003eThe Pearson correlation analysis was applied to the objective items, and significant positive correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were found between the perceived aspects of financial inclusion and the supported hypotheses. Perceived access to financial services was found to be correlated with employment impact (r\u0026thinsp;=\u0026thinsp;0.530), reduction in income inequality (r\u0026thinsp;=\u0026thinsp;0.459), educational opportunities (r\u0026thinsp;=\u0026thinsp;0.406), and economic resilience (r\u0026thinsp;=\u0026thinsp;0.442), confirming the relationship between H1 and perceptions of growth and employment. The employment effect was strongly related to the decrease in income inequality (r\u0026thinsp;=\u0026thinsp;0.546), moderately associated with educational opportunities (r\u0026thinsp;=\u0026thinsp;0.304), and resilience (r\u0026thinsp;=\u0026thinsp;0.260). The education opportunities (r\u0026thinsp;=\u0026thinsp;0.421) and resilience (r\u0026thinsp;=\u0026thinsp;0.464) also showed a correlation with a reduction in income inequality. The strongest correlation was observed between education opportunities and resilience (r\u0026thinsp;=\u0026thinsp;0.567), which supports the claims in hypothesis (3) regarding inequality and gender disparity. These coefficients are all statistically significant, suggesting the strong interdependences of perceptions, which is in line with the results of Abdulkareem et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) global study of the socio-economic effects of inclusion.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations Table\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe accessibility of financial services has improved economic growth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreased financial inclusion has positively impacted employment rates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFinancial inclusion programs have contributed to the reduction of income inequality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAccess to financial services has improved educational opportunities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eThe use of digital financial services has helped enhance the economic resilience\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eThe accessibility of financial services has improved economic growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.530\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.459\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.406\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.442\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIncreased financial inclusion has positively impacted employment rates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.530\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.546\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.304\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.260\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFinancial inclusion programs have contributed to the reduction of income inequality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.459\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.546\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.421\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.464\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAccess to financial services has improved educational opportunities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.406\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.304\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.421\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.567\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eThe use of digital financial services has helped enhance the economic resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.442\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.260\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.464\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.567\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Regression Analysis\u003c/h2\u003e\u003cp\u003eMultiple regression was used to model perceived income inequality reduction, with H1 and H3 being tested in relation to the effects of accessibility and employment. The model yielded an R 0.581 and an R\u0026sup2; 2\u0026thinsp;=\u0026thinsp;0.338 (adjusted R\u0026sup2; 2\u0026thinsp;=\u0026thinsp;0.324) such that a variance of 33.8 percent in inequality reduction perceptions is accounted for (F (2,99)\u0026thinsp;=\u0026thinsp;25.254, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It was constant (=\u0026thinsp;1.102, t\u0026thinsp;=\u0026thinsp;3.261, p\u0026thinsp;=\u0026thinsp;0.002), accessibility (=\u0026thinsp;0.241, standardised\u0026thinsp;=\u0026thinsp;0.236, t\u0026thinsp;=\u0026thinsp;2.452, p\u0026thinsp;=\u0026thinsp;0.016), and employment (=\u0026thinsp;0.447, standardised\u0026thinsp;=\u0026thinsp;0.420, t\u0026thinsp;=\u0026thinsp;4.358, p\u0026thinsp;=\u0026thinsp;0.001). The ANOVA yielded a regression sum of squares of 34.127, a residual sum of squares of 66.892, and an overall sum of squares of 101.020, which supports the model fit (Mohamed et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The large coefficient of employment implies that it is more effective in forming perceptions of inequality than accessibility, which is consistent with the growth-employment connection of H1 and the inequality emphasis of H3, substantiated by labour market data (Mehry et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Summary\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eR Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAdjusted R Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e\u003cp\u003eChange Statistics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR Square Change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF Change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003edf1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003edf2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSig. F Change\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.581\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003ea. Predictors: (Constant), Increased financial inclusion has positively impacted employment rates. The accessibility of financial services has improved economic growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eANOVA\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ea. Dependent Variable: Financial inclusion programs have contributed to the reduction of income inequality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eb. Predictors: (Constant), Increased financial inclusion has positively impacted employment rates. The accessibility of financial services has improved economic growth\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoefficients\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnstandardised Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandardised Coefficiets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe accessibility of financial services has improved economic growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncreased financial inclusion has positively impacted employment rates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ea. Dependent Variable: Financial inclusion programs have contributed to the reduction of income inequality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Independent Samples T-Test\u003c/h2\u003e\u003cp\u003eT-tests compared Objective 2 item means between DRC (N\u0026thinsp;=\u0026thinsp;39) and the Republic of Congo (N\u0026thinsp;=\u0026thinsp;27), testing H4. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e details the findings. Political influence means were 3.26 (DRC) and 3.04 (Congo), t\u0026thinsp;=\u0026thinsp;0.860, p\u0026thinsp;=\u0026thinsp;0.393 (not significant). Economic stability was 3.38 vs. 3.11, t\u0026thinsp;=\u0026thinsp;1.149, p\u0026thinsp;=\u0026thinsp;0.255 (not substantial). The outcomes of the cultural attitudes were 3.51 and 3.30, with a t-value of 0.913 and a p-value of 0.364 (non-significant). The equal variances test (Levene) was passed, and Cohen's effect sizes fell between 0.215 and 0.288, which is a negligible difference at the practice level. Non-significance implies homogenous perceptions despite contextual differences and partly contradicts H4 (Cicchiello et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGroup Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry of residence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Error Mean\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eThe political environment in my country has positively influenced financial inclusion programs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eThe economic stability of my country has made financial services more accessible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCultural attitudes towards financial services (e.g., banking) have a significant impact on financial inclusion in my country\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndependent Samples Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eLevene's Test for Equality of Variances\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c11\" namest=\"c5\"\u003e\u003cp\u003et-test for Equality of Means\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMean Difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStd. Error Difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e95% Confidence Interval of the Difference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eThe political environment in my country has had a positive impact on financial inclusion programs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEqual variances assumed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEqual variances not assumed.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.711\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eThe economic stability of my country has made financial services more accessible.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEqual variances assumed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEqual variances not assumed.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCultural attitudes towards financial services (e.g., banking) have a significant impact on financial inclusion in my country\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEqual variances assumed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEqual variances not assumed.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndependent Samples Effect Sizes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStandardiser\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePoint Estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eThe political environment in my country has positively influenced financial inclusion programs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohen's d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHedges' correction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlass's delta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.737\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eThe economic stability of my country has made financial services more accessible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohen's d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHedges' correction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlass's delta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCultural attitudes towards financial services (e.g., banking) have a significant impact on financial inclusion in my country\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohen's d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.720\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHedges' correction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlass's delta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.730\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003ea. The denominator used in estimating the effect sizes.\u003c/p\u003e\u003cp\u003eCohen's d uses the pooled standard deviation.\u003c/p\u003e\u003cp\u003eHedges' correction uses the pooled standard deviation, plus a correction factor.\u003c/p\u003e\u003cp\u003eGlass's delta uses the sample standard deviation of the control group.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Chi-Square Test\u003c/h2\u003e\u003cp\u003eThe chi-square test was employed to cross-tabulate country by perceptions of political influence, H4. This test yields 7\u0026sup2; = 49, with a p-value of 0.552 (not significant). Furthermore, 76% of the counts are projected to be five or fewer (minimum\u0026thinsp;=\u0026thinsp;0.08). Even though the national situations are not the same, this non-correlation also speaks against H4 and suggests that the political perceptions are not evidently country-specific in this sample.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCrosstabulation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eCountry of residence * The political environment in my country has positively influenced financial inclusion programs.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eThe political environment in my country has had a positive impact on financial inclusion programs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCountry of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChi-Square Tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAsymptotic Significance (2-sided)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePearson Chi-Square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.627\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLikelihood Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.555\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLinear-by-Linear Association\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.747\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN of Va54.9%Cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ea. 19 cells (76.0%) have expected count less than 5. The minimum expected count is .08.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOverall, the results substantiate the perceptual correlations across Objective 1 (H1-H3), but there are minimal country-specific variations that dilute H4, hence indicating homogenous regional views of contextual factors. In short, the statistical tests affirm the strength of perceptual connections among Objective 1 items, which affirm H1, H2, and H3. Correlation analysis indicates that the positive relationships are strong, with the strongest connection being between educational opportunities and resilience (r\u0026thinsp;=\u0026thinsp;0.567). Regression analysis reveals that employment perceptions are a strong predictor of reduced inequality (β\u0026thinsp;=\u0026thinsp;0.338, p\u0026thinsp;=\u0026thinsp;0.001). Conversely, H4 is not supported well, as t-tests and chi-square results indicate that there is no significant country-specific differentiation between the DRC and the Republic of Congo in contextual perceptions (p\u0026thinsp;=\u0026thinsp;0.05), and the effect sizes are petite (d\u0026thinsp;=\u0026thinsp;0.215\u0026ndash;0.288). This homogeneity of perception among nations, regardless of their diverse political and economic contexts, indicates the existence of a shared regional discourse on the contextual factors of financial inclusion. A clear understanding of the perception of inclusion effects on the youth and unemployed sample highlights the need to sample more extensively to confirm national differences.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cp\u003eThe findings obtained in this paper, based on primary survey data, provide accurate information on the socio-economic impact of financial inclusion as perceived by individuals living in Central Africa who directly responded to the research questions. The gradient to primary perceptual data is superior to the aggregation biases of secondary indices, such as the FIIndex, which tend to bias finer affordability and usage indices (Khera et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This approach explains lived experiences within a region characterised by financial marginalisation, where informal sectors often take the lead and formal statistics are scarce (International Monetary Fund, African Department, 2024). Convenience sampling method, in turn, diminishes the degree of generalisability, which is associated with the argument that the application of p-values cannot be deemed valid unless random selection is performed (Hirschauer et al., 2020). It is then construed that the interpretations give weight to the magnitude of effects and the practical meaning, in comparison to the rigorous levels of statistics. In Objective 1, the results are robust in supporting H1, H2, and H3 and demonstrate positive perceived relationships among aspects of financial inclusion and socio-economic outcomes. The coefficients of correlation, ranging from 0.260 to 0.567, indicate that the respondents associate accessibility and use with economic growth, employment, education, resilience, and the reduction of income inequality. The accessibility and employment effect (r\u0026thinsp;=\u0026thinsp;0.530) correlates with the study, suggesting that financial access facilitates entrepreneurship, thereby boosting GDP. The perception of reduced inequality is highly predicted by employment perceptions (r\u0026thinsp;=\u0026thinsp;0.447, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The regression model indicates that 33.8% of the perceived inequality reduction can be explained by employment perceptions (R\u0026sup2; = 0.338, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with the finding that a 1% increase in financial access reduces unemployment by 0.03% (Mehry et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The positive relationship between education and resilience (r\u0026thinsp;=\u0026thinsp;0.567) supports the argument that inclusion results in superior human capital investment, as indicated in studies of educational outcomes (Olufemi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The insights are particularly relevant to Central Africa, where informal economies play a significant role in the economic life, and digital money tools could become more effective in strengthening resilience (Mokobongo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGender-related perceptions, particularly in relation to reducing inequality, align with documented disparities in financial access (Lerman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With a 40.2% female response rate, they may emphasise the perceived benefits of inclusion for women, despite existing actual gender gaps. The youth (64.7% aged 18\u0026ndash;24) form the dominant part of the sample. The unemployment rate (54.9%) indicates that these groups perceive inclusion as a means of avoiding poverty, a view also confirmed by studies that have identified a correlation between financial access and income stability (Park and Mercado, 2018). However, the subjectivity of these perceptions contrasts with objective indicators, such as the Gini coefficient of the DRC, which ranges from 42.1 to 49.5, showing mixed trends of regional inequality and partially validating the contextual moderation hypothesis of H4. The non-significant t-test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and chi-square (p\u0026thinsp;=\u0026thinsp;0.552) results of Objective 2 reject H4, indicating no significant difference between DRC and the Republic of Congo. Regardless of the differences in context between political instability in the DRC and the oil-based economy in Congo, ambivalence would be represented by mean scores of approximately 3 (neutral), potentially due to political impediments to the introduction of inclusion (Macdonald, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These weak effect sizes (Cohen's d\u0026thinsp;=\u0026thinsp;0.215\u0026ndash;0.288) indicate no significant differences in practice, which contradict existing research demonstrating differences in the development level of outcomes related to inclusion (Curran and Taheri, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The unequal sample, with slight coverage of Angola (2.9) and Chad (1.0), restricts cross-national comparisons. However, the meaning of imbalance, such as the contrast between Cameroon's balanced perceptions and the DRC's optimism on education, is fascinating and warrants further exploration.\u003c/p\u003e\u003cp\u003eSeveral limitations temper these findings, including the fact that the convenience sample is likely to over-represent urban, educated individuals and underestimate rural exclusion, which is an established issue in Central Africa (African Development Bank, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The information received through self-reporting may exaggerate the favourable perception due to bias in the response, and the research is cross-sectional, which is why it is not possible to determine the cause of the effect. These shortcomings suggest that any future research should incorporate the application of random sampling or employ qualitative designs to measure perceptions objectively. These policy implications exist. Regional frameworks indicated that governments should prioritise digital inclusion initiatives through mobile banking by focusing on underserved populations and pursuing the 75 percent financial inclusion target by 2030 (Macdonald, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Gender-focused programmes, based on the understanding that 40.2% of the female respondents, can revolve around the availability of credit for female entrepreneurs. Non-governmental organisations can utilise these perceptual data to develop awareness campaigns, as they can draw attention to the socio-economic benefits of inclusion among youth and unemployed populations, who form the majority of the sample. To conclude, this paper aims to contribute to the body of knowledge by consolidating perceptual data in a field where formal measures are lacking, thereby laying a foundation for targeted interventions. Future studies must expand the geographical scope and heterogeneity of methods to narrow such findings to have polices founded on perceived and measured effects.\u003c/p\u003e"},{"header":"6. CONCLUSION","content":"\u003cp\u003eThe results of this paper show that the perceived positive relationships between financial inclusion and socio-economic variables are high and support the hypotheses. The respondents had strong associations between perceived access to and utilisation of financial services and growth, employment, educational opportunities, resilience, and a reduction in income inequality. Correlation coefficients and regression analysis help these relationships. They align with the fact that inclusion is a contributor to economic activity and the development of human capital. Nevertheless, it does not provide significant support for H4. T-tests and chi-square values indicate that there is no significant country-specific difference between the DRC and the Republic of Congo, and the two regions are viewed similarly. However, their political and economic backgrounds differed. The proposed research fills the gaps in secondary data sources, including the FIIndex, which, in most cases, does not provide a fine-grained view of affordability and consumption in informal economies. It is emphasised by highlighting a young and unemployed sample, which underscores the possibility of inclusion as a tool to alleviate poverty, providing essential information for policy in the face of regional instability. Hence, the results indicate that digital financial instruments can be used to enhance resilience, particularly in the informal sector.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdulkareem, H.K., Jimoh, S.O. and Shasi, O.M., (2023). 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[Online] Available at: https://journals.sagepub.com/doi/pdf/10.1177/215824402412522687.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Financial inclusion. Socio-Economic development, FIIndex, Central Africa, Panel data, GDP growth, Literacy rates, unemployment, and Income inequality","lastPublishedDoi":"10.21203/rs.3.rs-7492621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7492621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe paper examined the relationship between financial inclusion and socio-economic development in Central Africa with reference to the Democratic Republic of the Congo, the Republic of the Congo, Cameroon, Angola, and Chad. The study employs a multidimensional Synthetic Financial Inclusion Index (FIIndex) to aggregate access, usage, and affordability measures, examining their impact on GDP growth, literacy, unemployment, and income inequality. The research design employed a cross-sectional quantitative approach, where perceptual data were collected through a structured questionnaire administered to 102 respondents in urban and semi-urban areas in 2025. The Pearson correlation analysis revealed significant positive correlations between socio-economic outcomes and perceived financial inclusion, with coefficients ranging from 0.260 to 0.567, supporting the hypothesis that accessibility and use are positively related to economic growth, employment, education, and resilience. The multiple regression analysis revealed that lower income inequality is significantly predicted by employment perceptions (R² = 0.338, p \u0026lt; 0.001). However, country-related variations were not substantial, and t-test and chi-square analyses failed to show significant differences between the DRC and the Republic of Congo, suggesting homogeneous regional perceptions despite the different contexts. These results suggest that financial inclusion contributes to the socio-economic development of informal economies in Central Africa, offering policy implications for achieving a 75% inclusion target by 2030, particularly through digital financial services and gender-based programs.\u003c/p\u003e","manuscriptTitle":"Financial Inclusion and Socio-Economic Development in Central Africa: A Multidimensional Panel Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 05:04:07","doi":"10.21203/rs.3.rs-7492621/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"334d09dd-615c-4b2e-b0db-628d57a07988","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-03T05:04:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 05:04:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7492621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7492621","identity":"rs-7492621","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}