Closing the income gap: The mediating effect of financial inclusion in the linkage between technological advancement and income inequality in BRICS economies

Closing the income gap: The mediating effect of financial inclusion in the linkage between technological advancement and income inequality in BRICS economies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Closing the income gap: The mediating effect of financial inclusion in the linkage between technological advancement and income inequality in BRICS economies muhammad suhrab, Chen Pinglu, Ningyu Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3826008/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the relationship between technological advancement and income inequality in the BRICS countries (Brazil, Russia, India, China, and South Africa) with a particular focus on the mediating role of financial inclusion. Employing statistical techniques such as two-stage least squares regression and principal component analysis, the research analyzes data from reliable sources between 2011 and 2021. The findings indicate a negative relationship between technological progress and income inequality, suggesting that as technology advances, income gaps will narrow slightly. Furthermore, the study reveals a positive relationship between technological advancement and financial inclusion, as well as a negative impact of financial inclusion on income inequality. These results have significant implications for policymakers, emphasizing the importance of promoting financial inclusivity to reduce income inequality in these countries. However, the study also acknowledges certain limitations and suggests future research to consider controlling for other potential factors and conducting longitudinal studies to better understand the dynamic relationship between these variables. Figures Figure 1 1. Introduction In today's interconnected world, access to financial services is no longer viewed as a privilege, but rather as a fundamental right for people and enterprises. Unfortunately, conventional financial frameworks have often overlooked marginalized groups such as those without bank accounts or living in developing nations, leaving them excluded from mainstream financial systems (Erel, & Liebersohn, 2022 ). To bridge this gap, leveraging cutting-edge technologies for financial inclusivity has become crucial for fostering sustainable development, alleviating poverty, and advancing the United Nations' SDGs. In recent times, advancements in technology have significantly impacted the financial sector, altering how financial services are delivered and accessed (Jalal et al. 2023 ). Research has consistently shown that technological innovations can significantly broaden accessibility, reduce costs, and enhance the effectiveness of financial services (Arner et al., 2022 ; Mushtaq et al., 2022; Chitimira, & Warikandwa., 2023). This is particularly evident in the BRICS nations (Brazil, Russia, India, China, and South Africa), where technological breakthroughs have revolutionized the financial sector and contributed to expanded financial inclusivity (Biyase et al. 2023; Senyo et al. 2023 ; Chishti, & Sinha, 2022 ). The proliferation of digital technologies such as mobile banking, online payment systems, and cryptocurrencies has brought about increased emphasis on financial inclusivity across the globe (Gallenstein et al. 2023 ). Financial inclusiveness refers to the provision and utilization of reasonably priced financial services by both individual and business entities at various strata of society (Kanga et al. 2022 ). This concept holds immense significance due to its capacity to promote economic expansion and development through enabling individuals and micro-enterprises to participate in the formal financial framework, secure loans, and save for the future (Mishi, & Anakpo 2022 ; Ozili et al. 2023 ). Financial inclusiveness has gained considerable attention in recent years due to its potential to eradicate poverty, empower marginalized populations, and foster economic progress (Lythreatis et al. 2022 ). As estimated by the World Bank, approximately two billion adults globally lack access to basic financial services, thereby hindering their ability to engage in productive economic activities and attain financial stability (Kouladoum et al. 2022 ). Nevertheless, while technological progress holds immense potential for promoting equitable and sustainable financial systems, it is important to recognize that infrastructural development, encompassing both physical and digital infrastructure, plays a vital role in fostering financial inclusion. To address this issue, innovative solutions are necessary to fill the existing gap and reach disadvantaged areas. Technological advancements have significantly impacted financial inclusivity by providing greater access to financial services (Coffie, & Hongjiang, 2023 ). The widespread adoption of digital technologies has enabled financial institutions to extend their reach to previously unserved or under-resourced areas, such as rural regions or those without physical bank branches (Asif et al. 2023 ). Mobile banking, in particular, has facilitated financial transactions, lending, and payment systems, making it easier for people living in remote locations to manage their finances (Ndassi et al. 2023). Through mobile devices, individuals can now access essential financial services with increased ease and convenience, particularly in underserved communities (Demirgüç et al. 2020; Lashitew, et al. 2019 ). The purpose of this study is to investigate the impact of technological innovation on financial inclusion and how access to financial services can reduce income inequality, examining the potential and difficulties that result from the increased use of technology in the financial sector. The BRICS nations, comprising rapidly developing economies, are simultaneously witnessing rapid technological progress and grappling with a pressing issue of income disparity. To gain a deeper comprehension of how technology influences income inequality within these countries, where a substantial portion of the global population resides, it is essential to investigate the connection between technological advancements and income inequality. Additionally, considering the growing emphasis placed upon inclusive economic expansion, examining the function that financial incorporation plays in this dynamic can offer valuable insights into fostering more equitable development in these nations. By conducting this study, we will be contributing to an already extensive body of work on income inequality, technological advancement, and financial inclusion, while providing a distinct viewpoint on the BRICS nations, which are poised to significantly influence global economic growth in the years to come. Notwithstanding a plethora of investigations into the effect of innovative advances on income inequality, scholarly works centered exclusively on emergent marketplaces like those represented by the BRICS countries are scarce. Furthermore, even fewer studies have delved into the possible role of financial inclusion in bridging the gap between technological progression and income disparities within these nations. As such, there exists a considerable knowledge deficit regarding the impact of technological advancements on income inequality and the potential functions that monetary incorporation might serve in mitigating or exacerbating this phenomenon in the specific context of the BRICS nations. 2. Literature Review 2.1. Income Inequality & Theoretical Review Inequality refers to the disparities in resources, opportunities, and privileges among distinct social groups within a society (Suhrab et al. 2023 ). This concept encompasses various forms of inequality, including economic, social, political, and cultural inequality (Bapuji et al. 2020 ). While injustice focuses on specific instances of unfair or unlawful behavior directed at specific individuals or groups, inequality focuses on the systematic differences in outcomes and opportunities experienced by various social categories. The study of inequality has been a major area of research across multiple disciplines, including economics, sociology, anthropology, political science, and psychology (Bapuji et al., 2020 ). Researchers have employed various methods to quantify and analyze these forms of inequality, with economic inequality typically measured through income and wealth gaps between individuals, households, and countries. Social inequality is often evaluated by assessing access to influential positions, public goods and services, such as education and healthcare, while political inequality involves examining representation and participation in governance structures and decision-making process (Jaravel, 2021 ; Solt, 2020 ; Sánchez et al. 2023). Cultural inequality can be identified through assessments of group membership and the presence of biases and discriminatory attitudes that impede equitable treatment and opportunities for marginalized populations(Chancel et al., 2022). At both the domestic and global spheres, inequality has been increasing steadily over recent years. Societal struggles with addressing this issue date back to ancient times, and it has been linked to an array of socioeconomic and political factors including poverty, subpar healthcare, limited access to education, and insufficient employment prospects (Deaton, 2021). The consequences of inequality are far-reaching and profoundly affect not only individual wellbeing but also societal stability. Consequently, devising a multifaceted approach that caters to diverse populations is imperative (Yenn, 2022). Furthermore, grasping the underlying reasons and repercussions of inequality is indispensable for designing targeted interventions aimed at reducing disparities effectively (Deo et al., 2022). 2.2. Empirical Review Nexus between technology diffusion and financial inclusion The past decade has witnessed an unprecedented surge in technological progress, transforming various aspects of human existence. Notably, this acceleration has affected the financial sector, particularly through the proliferation of Financial Technology (Fintech) and digital payment systems. Consequently, financial inclusivity or the accessibility and utilization of affordable financial services tailored to individual and business needs has expanded globally. This literature review seeks to investigate the intricate connection between technological innovation and financial inclusion, focusing on how technology can promote inclusive financial systems, alter existing financial architectures, and identify potential impediments hindering universal financial inclusion. In the realm of research on the relationship between technology dissemination and financial inclusivity, there is ample evidence demonstrating technology's pivotal role in expanding financial inclusion. A plethora of studies have underscored the beneficial impact of technology on this frontier. To cite an example, Konte, & Tetteh ( 2023 ) and Shaikh et al. ( 2023 ) revealed a significant correlation between the availability of mobile phones and the utilization of formal financial services in developing nations. Specifically, these findings suggest that mobile devices act as a conduit to financial access for the underprivileged populace. Similarly, Lashitew, et al. ( 2019 ) and Hasan et al. ( 2022 ) conducted a study that examined the impact of technology on financial inclusion. Their results indicated that the adoption of technological innovations, including mobile money, significantly improved the availability and accessibility of conventional financial services. Moreover, the study highlighted the potential of technology in lowering the costs associated with financial services and augmenting access for low-income individuals. This theme is echoed by the World Bank (2022), which notes that technology has been instrumental in advancing financial inclusion in developing economies. Specifically, mobile money services have facilitated outreach to previously unserved or hard-to-reach populations, thereby granting them access to financial services and enabling them to execute transactions sans the requirement of a brick-and-mortar bank branch (Kass-Hanna et al. 2022 ; Kouladoum et al. 2022 ; Demirgüç-Kunt; Shaikh et al. 2023 ). Numerous research studies have underscored the significant role of digital payment systems in promoting financial inclusivity across various nations. As highlighted by Al-Smadi, ( 2023 ), higher frequencies of digital payments are directly correlated with enhanced levels of financial inclusion within countries. Furthermore, digital payments have been found to enhance consumer financial stability, especially in developing regions where cash-based transactions are inherently riskier. The diffusion of technology has been credited with reducing disparities in accessing financial services and expanding financial inclusiveness (Jalal et al. 2023 ). A primary factor responsible for unequal access to financial services lies in the exorbitant costs associated with serving rural and impoverished populations (Bekele, 2023 ). These expenses include physical infrastructure investments, such as branch locations, as well as administrative overheads. Notwithstanding, the growing popularity of mobile and digital financial services has led to a substantial decrease in these costs, thereby facilitating the operation of financial institutions in distant areas and improving their ability to serve underprivileged segments of society (Coffie et al. 2023; Demirgüç-Kunt, et al. 2020 ). In recent times, rapid technological developments have significantly enhanced the delivery of financial services, thereby appealing to an increasing number of clients. To illustrate, digital financial platforms like mobile money have streamlined transaction processes by providing fast and convenient access to financial services without the need for physical visits to financial institutions (Lashitew et al. 2019 ). As a direct consequence, there has been a shift away from traditional cash-based transactions toward digital ones, which reduces both the risks associated with managing actual currency and the expenses linked with handling it. Moreover, advances in technology have empowered financial organizations to gather and examine data regarding consumer spending habits within specific geographic areas or market segments. By leveraging this knowledge, financial institutions can create tailored financial solutions designed specifically for diverse populations, including those living in underserved or remote regions (Demir et al., 2022 ; Chen et al. 2021 ). Through comprehensive understanding of local financial behaviors and preferences, financial service providers may now design offerings that better suit the needs of these clienteles, thereby promoting greater inclusivity throughout the industry. Technological advancements have shown promise in expanding financial inclusivity; however, several challenges and limitations must be addressed. A significant concern is the digital divide, where individuals residing in remote and impoverished regions encounter difficulties accessing technology or possess insufficient digital proficiency to utilize digital financial services (Asif, et al. 203). This hinders the spread of technology and excludes marginalized populations from acquiring financial services. Moreover, the integration of technology presents security and privacy concerns, particularly in developing nations with subpar digital infrastructure and regulatory frameworks (Goswami et al. 2022 ). These issues might lead to mistrust in digital financial services, thereby limiting their adoption and financial inclusiveness. H1: The diffusion of technology in the BRICS countries has a positive relationship with the degree of financial inclusivity, as advances in technology improve accessibility to and usage of financial services for those who are disadvantaged. Nexus between financial inclusion and income inequality The notions of financial inclusion and income inequality have garnered increasing interest in recent times. Financial inclusion pertains to the delivery of financial services and products to those with restricted access to conventional financial institutions (Yu, & Tang 2023 ). Meanwhile, income inequality refers to the disparate distribution of income and wealth within a society, where some individuals or households possess more resources than others (Wang, Li, & Li, 2023 ). Many studies investigated into the association between financial inclusion and income inequality and discovered that they are inextricably linked. This review of the literature aims to provide a comprehensive overview of the various studies that have investigated this nexus. Financial inclusion has been demonstrated to exert a pivotal influence in mitigating income inequality. According to Bansah, & Mohsin ( 2023 ), expanding financial access through credit and savings opportunities fosters an equitable disbursement of income. Specifically, financial inclusion enables individuals and households to accrue wealth and assets, which in turn reduces income gaps. Moreover, Demir et al. ( 2022 ) contend that financial inclusion directly affects income inequality by lowering the obstacles and costs associated with participation for those with limited means. Through provision of affordable financial services, financial inclusion facilitates increased earnings and improved economic welfare for these individuals. Furthermore, beyond its direct effect on income inequality, financial inclusion has been shown to exert an indirect influence on income distribution through various channels. One such channel involves the positive correlation between financial inclusion and improved socioeconomic indicators, as demonstrated by Kim, ( 2022 ) and Koomson, & Danquah ( 2021 ) findings in developing countries. Specifically, they discovered that enhancing financial inclusion leads to better educational and health outcomes, ultimately contributing to a more equitable distribution of income. In contrast, certain investigations have underscored the possibility that financial inclusion might amplify income disparity. According to Biyase, & Chisadza ( 2023 ) findings, expanded financial access might result in higher danger-taking and earnings instability among low-income people, thereby deepening wealth gaps. Similarly, Yin, & Choi, ( 2023 ) study revealed that monetary incorporation can negatively affect revenue equality in creating nations because of the subpar caliber of economic services and goods offered. As a consequence of their reliance on unmanageable degrees of borrowing, this can increase poverty. The body of research pertaining to the interplay between financial inclusion and income inequality reveals an intricate and multifaceted association. On one hand, expanded access to financial services has been found to contribute towards narrowing the gap between the rich and the poor by providing opportunities for individuals to improve their economic well-being (Mushtaq, & Bruneau; Koomson, et al. 2020 ; Tchamyou, et al. 2019 ; Huang et al. 2023 ). However, this relationship is not without its caveats as financial inclusion can sometimes exacerbate existing income disparities through various mechanisms. Moreover, the degree to which financial inclusion influences income inequality varies across different national contexts. For example, studies have demonstrated that financial inclusion has a more pronounced effect on reducing income inequality in nations experiencing higher levels of poverty and income inequality (Park, & Mercado 2021 ; Huang et al. 2023 ). H2: Increased financial inclusion reduces income inequality; however, the extent of this linkage varies by country. There exists an evidential void within existing scholarly discourse surrounding the intersectionality of technological innovation and economic inequality in the context of the BRICS nations (Brazil, Russia, India, China, and South Africa). Specifically, limited research has been conducted to investigate how the dissemination of advanced technologies influences the distribution of financial services across these emerging economies. This scarcity of investigations has resulted in a dearth of knowledge regarding the specific challenges faced by each country in addressing issues related to monetary exclusion. Our analysis aimed to rectify this lacuna through a comprehensive assessment of the relationship between technological adoption and financial inclusivity in the BRICS countries. By doing so, we provide valuable insights that can be leveraged to develop targeted policies focused on reducing income-based disparities in access to financial services throughout these pivotal regions. 3. Methodology 3.1. Data Collection In this investigation, we sourced data for the BRICS nations (Brazil, Russia, India, China, and South Africa) from a variety of trustworthy sources spanning the decade of 2011 to 2021. This extensive time frame enabled us to assess a broad array of technological developments and economic markers. To examine income inequality, we utilized the Standardized World Income Inequality Database (SWIID), which offers standardized measurements of income disparity across countries and through time. For financial inclusivity metrics, we turned to the Global Financial Inclusion Database (Findex) and world bank database, providing detailed and comparative data on individual-level access to and use of financial services across more than 140 countries, whereas data regarding technological innovation originated from the World Intellectual Property Organization (WIPO). Control variables such as employment rates and regulatory quality were acquired via the World Development Indicators dataset. A thorough list of the precise datasets and sources employed can be found in Table 1 . To prepare the data for analysis, we conducted rigorous quality control measures on each variable's raw information. We standardized all variables by transforming them into consistent units of measurement, corrected inconsistencies that arose from deviations from these standards, and eliminated any anomalous readings (outliers) that could have skewed our results. When necessary, we employed statistical methods to fill in gaps or missing values using an educated guess based on patterns observed within the available data (Little, & Rubin 2019 ; Meeker et al. 2022 ). As a result, our comprehensive dataset spanned one decade across multiple variables with 55 individual observations. 3.2. Variables and Descriptions The main variables of interest in this study are are income disparity, technological advancement, and financial inclusion. Income inequality is quantified utilizing the Gini coefficient, which spans from 0 (perfect equality) to 1 (absolute inequality). Technological development is evaluated through an index formulated from indicators including telecommunications, digital communication, computer technology, and IT-based management systems. Financial inclusion is measured via a composite index assembled from variables like the number of bank accounts, mobile banking transactions, internet bill payments, and outstanding loans from credit unions and cooperatives and number of debit and credit cards. Principal Component Analysis (PCA) is employed to create indices that measure financial inclusion and technological advancement. In this process, the original variables are first standardized, and their weights or importance are determined through the loadings represented in a matrix called Λ. These loaded variables are then combined using the formula given below, resulting in a composite index that reflects both factors. This composite index is obtained by simply adding up the standardized values of all the variables involved (Jolliffe, 2002 ). $${Z}_{i}= {{\Lambda }{\Theta }}_{i}$$ To improve the accuracy of our analysis of income inequality, we added controls for variables that could potentially influence the outcome. These included the unemployment rate, regulatory quality, and self-employment rate, which we chose because they have the potential to affect how income is distributed. A full list of each variable's definition and source can be found in Table 1 . By adding these control variables, we sought to gain a clearer picture of what determines income inequality by considering other relevant factors that may otherwise skew the results. Table 1 Measurement and sources details of variables Variable Data Sources Previous research Measurement Income Inequality Standardized World Income Inequality Database. Suhrab et al. ( 2023 ), Feng et al. ( 2022 ) and Ratnawati, ( 2020 ). The Gini coefficient is a widely used statistical measure to quantify income inequality across different populations. A Gini coefficient of 0 represents perfect equality, while a value of 1 indicates complete inequality. Technological Advancement World Intellectual Property Organization (WIPO) Abid et al. ( 2022 ), Popkova et al. ( 2022 ), Akram et al. ( 2023 ). We employed principal component analysis (PCA). PCA, experts can identify patterns and trends within massive datasets. Telecommunication WIPO number of mobile phone subscriptions per capita. Digital communication WIPO The most data that can be transmitted within a given bandwidth. Computer technology WIPO The amount of data that can be stored on a hard drive or other storage device in a computer. IT-base management System WIPO The amount of data that a database can store. The design and usability of the system's user interface. Internet access WIPO the total number of internet users. Broadband internet WIPO the percentage of households with access to broadband internet. Financial Inclusion Global Financial Inclusion Database (Findex) and World Bank. Lee et al. ( 2022 ), Omar, & Inaba ( 2020 ) and Yu, & Tang ( 2023 ). We employed principal component analysis (PCA). PCA, experts can identify patterns and trends within massive datasets. Number of bank accounts Findex Total number of accounts of customers in a country. Mobile banking transaction Findex The number of transactions every day. Internet bill payments Findex Total amount of bill paid for internet usage. Credit union and cooperation Findex The number of credit unions are available. Debit and credit cards Findex Outstanding loans from credit unions Findex Amount of credit distributed by credit unions in the market. Unemployment rate The Organization for Economic Co-operation and Development (OECD) Chan, & Dong ( 2022 ), Ahmad et al. ( 2023 ) and Wang, & Li ( 2021 ). This indicator is seasonally adjusted and measures the number of unemployed as a percentage of the labor force. The labor force is defined as the total number of unemployed people plus those who are employed. Regulatory quality World bank (Worldwide governance data.) Adedoyin et al. ( 2020 ), Obobisa et al. ( 2022 ) Individual and aggregate governance indicators for different governance dimensions. Self-employment rate The Organization for Economic Co-operation and Development (OECD) Fritsch et al. ( 2023 ), Giménez et al. (2022) and Fossen ( 2021 ). Self-employment is referred to as the employment of employers, self-employed workers, members of producer co-operatives, and unpaid family workers. 3.3. Econometric Techniques This research employed a two-stage least squares (2SLS) regression approach to investigate the connection between technological advancement and income inequality, taking into consideration the possibility that technological development may be related to both variables through an indirect mechanism. The 2SLS method is commonly utilized in statistical analysis when there is a correlation between the independent variable and the error term due to endogeneity. By using instrumental variables that are associated with the endogenous variable but unrelated to the error term, it seeks to address this issue (Timpone, 2003 ; Ullah et al. 2018 ). Specifically, we made use of lags of technological advancements as instruments in the initial stage, which demonstrated high correlation with contemporary levels of technological advancement yet had no significant association with the error term. To investigate the potential mediation role of financial inclusion in the relationship between technological progress and income disparity, we employed Sobel test (Sobel, 1982 ), which determines whether the impact of the independent variable on the dependent variable goes through the mediator variable. In other words, the Sobel test assesses if there is a significant indirect effect of the independent variable on the dependent variable via the mediator. Additionally, in each of the three models being examined, we will incorporate lagged values of the technological advancement variable as instrumental variables in the initial stage of the two-stage least squares (2SLS) regression analysis. This technique helps to mitigate the possibility that the technological variable may be endogenous since lagged values of the variable are less likely to be influenced by current levels of income inequality. \({GINI}_{i,t}=\beta 0+ {\beta }_{1}({TA)}_{i,t}+ {\epsilon }_{i,t}\) Model 1 In this equation, we are estimating the relationship between income inequality (measured by the Gini coefficient) and technological advancement (represented by TA) across different countries (i). \({GINI}_{i,t}=\beta 0+ {\beta }_{1}({TA)}_{i,t}+{\beta }_{2}({UR)}_{i,t}+{\beta }_{3}({RQ)}_{i,t}+{\beta }_{4}({SE)}_{i,t}+ {\epsilon }_{i,t}\) Model 2 In this equation, we are estimating the relationship between income inequality (measured by the Gini coefficient) and technological advancement (represented by TA) across different countries (i). To account for potential factors that may influence this relationship, we have included a set of control variables, including the unemployment rate (UR), regulatory quality (RQ), and self-employment rate (SE) for each country. The coefficients in the equation (β) represent the relative importance of these control variables in determining the relationship between technological advancement and income inequality, while the error term (ε) captures any other factors that may affect the relationship. \({FI}_{i,t}={\delta }0+ {{\delta }}_{1}({TA)}_{i,t}+{{\delta }}_{2}({UR)}_{i,t}+{{\delta }}_{3}({RQ)}_{i,t}+{{\delta }}_{4}({SE)}_{i,t}+ {\epsilon }_{i,t}\) Model 3 In this equation, we're attempting to identify the connection between financial innovation (FI) and technological advancement (represented by TA) across various nations (i) and we used the same control variables applied in the above equation. Specifically, the coefficients (δ) in the equation illustrate how much each control variable modifies the relationship between technological progress and financial innovation, while the error term (ε) accounts for any further factors that might shape their interaction. \({GINI}_{i,t}=\beta 0+ {\beta }_{1}({TA)}_{i,t}+{\beta }_{2}({FI)}_{i,t}+{\beta }_{3}({UR)}_{i,t}+{\beta }_{4}({RQ)}_{i,t}+{\beta }_{5}({SE)}_{i,t}+ {\epsilon }_{i,t}\) Model 4 In this equation, we are estimating the relationship between financial inclusion (measured by an index called FI), technological advancement (TA) and income inequality (measured by the Gini coefficient) for each country i. and employed the same control variables as used in the above equations. 4. Results & Discussions Table 2 presented in this study illustrates the statistical data about the BRICS nations (Brazil, Russia, India, China, and South Africa) over the period spanning from 2011 to 2021. A cursory examination of the data reveals that despite some degree of income inequality, the distribution of wealth within these countries was generally less extreme compared to other developing economies. Specifically, the mean Gini coefficient for the BRICS nations stood at approximately 0.51 during this timeframe, indicating a relatively modest level of income disparity. However, our analysis uncovered additional insights into the economic dynamics of the BRICS nations beyond just income inequality. Notably, we found evidence of substantial technological progress across these countries, as reflected by an average technological advancement score of 1.11. Moreover, the BRICS nations displayed a moderate level of financial inclusivity, with an average financial inclusion index value of 0.38. Meanwhile, unemployment rates among these countries remained relatively low, settling at an average of 5.63%, while self-employment rates were slightly higher at 23.95%. These findings suggest that although income inequality exists within the BRICS economies, it is not the sole determinant of their economic growth patterns. Other factors, such as technological innovation, financial inclusion, and labor market conditions, play equally important roles in shaping their development trajectories. Therefore, policymakers should consider addressing multiple aspects of economic policy to foster sustainable and equitable growth in these emerging markets. Table 2 Descriptive Statistics Variable Mean Standard Error Minimum Maximum GINI 0.513 0.050 0.401 0.632 TA 1.112 0.163 0.701 1.550 FI 0.381 0.061 0.241 0.501 UR 0.056 0.024 0.023 0.093 RQ 0.454 0.132 0.234 0.724 SE 0.241 0.063 0.130 0.360 Note: The author incorporated the above table based on the research dataset Pairwise correlation presented in Table 3 , there are several relationships between the primary variables of interest and various control variables. Notably, the correlation between income inequality and technological advancement is negligible but negative, suggesting that as technology develops, income inequality may decrease slightly. A similar pattern emerges when comparing income inequality to financial inclusion; the two exhibit a significant and negative connection. On the other hand, technological advancement seems to have a solidly positive impact on financial inclusion, with a substantial correlation coefficient of 0.549. Furthermore, examining the connections between the control variables and the key variables reveals some intriguing patterns. Unemployment rates show a noticeable association with income inequality, with a moderate positive correlation of 0.274. Regulatory quality appears to have a relatively minor negative correlation with both income inequality and financial inclusion, with values of -0.119 and − 0.103, respectively. Lastly, self-employment rates display slight links with all three major variables, including income inequality, technological development, and financial inclusion. Table 3 Pairwise correlation GINI FI TA UE RQ SE GINI 1.000 FI -0.216*** 1.000 TA -0.227*** 0.549** 1.000 UE 0.274** -0.059** -0.131** 1.000 RQ -0.119* -0.040 -0.114 -0.184 1.000 SE -0.170 -0.136 -0.103 -0.171** 0.027 1.000 Note: * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata. In Table 4 , we conducted two regression analyses to investigate the relationship between technological advancement and income inequality while controlling for potential confounding variables. Our findings indicate that, upon initial analysis (Model 1), there exists a significant negative association between technological advancement and income equality, with greater technological development tending to reduce income disparity (coefficient = -0.917; p < 0.01). However, when additional factors known to influence income inequality were included in the second model (Model 2), this relationship was found to diminish (coefficient = -0.725; p < 0.01), suggesting that factors such as unemployment rates, regulatory environments, and self-employment rates may also play a role in determining income inequality patterns. The inclusion of supplementary control variables in Model 2 provides valuable insight into the relationships between income inequality and various factors. Of particular note is the statistically significant positive linkage between income inequality and unemployment rate (p < 0.01), which aligns with previous research (Chan & Dong, 2022 ; Ahmad et al., 2023 ; Wang & Li, 2021 ). This finding suggests that increases in unemployment are associated with greater income disparities. Contrarily, regulatory quality exhibited a positive but nonsignificant association with income inequality, which could be attributed to the possibility that regulations disproportionately impact different income groups, leading to an inconclusive overall effect. Moreover, no significant connection was found between self-employment rates and income inequality, indicating that self-employment does not directly contribute to income distribution. In summary, these results imply that technological advancements have the potential to alleviate income inequality, although this association relies on numerous other variables. Thus, policymakers should consider multiple factors, such as joblessness levels and regulatory frameworks, when designing interventions aimed at reducing wealth disparities. Table 4 Benchmark model of Technological advancement (Index) and Income inequality Variables GINI Model 1 GINI Model 2 TA -0.917*** -0.725*** (0.038) (0.039) UR -0.112*** (0.032) RQ 0.051 (0.029) SE 0.001 (0.026) The standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata. Table 5 provides an analysis of the effects of various aspects of technological advancements on income inequality using two regression models. The first model reveals a statistically significant negative relationship (-0.016, p < 0.05) between telecommunications and income inequality, implying that nations with greater telecommunications infrastructure exhibit less income inequality. Nevertheless, after adjusting for additional variables in the second model, the connection weakens and loses statistical significance, suggesting that alternative elements like digital communication, computer technology, and IT-based management systems might exert a more profound influence on reducing income inequality. While the other dimensions of technological development did not demonstrate any notable effect on income inequality, the coefficients associated with them remained unfavorable, which could imply that they might indirectly affect income inequality through their broader societal ramifications. Notably, while neither the Sargan nor Hansen tests revealed any proof of endogeneity in either model, the R-squared values increased marginally in Model 2, signifying that the control variables satisfactorily clarified most of the changes in income inequality. These findings propose that policymakers must adopt a multifaceted strategy when formulating rules targeted toward diminishing wealth gaps by considering the diverse facets of technological advancement. Table 5 The impact of different dimensions of technological advancement on income inequality Variables GINI Model 1 GINI Model 2 Telecommunication -0.016** -0.013 (0.005) (0.009) Digital communication -0.005 -0.008 (0.005) (0.009) Computer technology -0.007*** -0.005 (0.003) (0.011) IT-base management System -0.004*** -0.003 (0.003) (0.009) Internet access -0.002 -0.006 (0.003) (0.008) Broadband internet 0.001 -0.002 (0.003) (0.007) Control variables Yes Yes R-squared 0.459 0.473 Sargan test (p-value) 0.631 0.379 Hansen test (p-value) 0.456 0.786 The standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata. Table 6 displays the findings of a statistical model that explored the relationship between technology development, financial accessibility, and income disparity through regression analysis. Specifically, we aimed to assess the extent to which technological advancement influences financial inclusion and income inequality while evaluating whether financial inclusion acts as a mediator in this relationship. Our findings indicate a substantial linkage between technological development and increased financial inclusion, suggesting that advancements in technology tend to enhance access to financial services among marginalized populations. Concurrently, our outcome revealed a pronounced negative association between technological progress and income inequality (-0.010, p < 0.05), indicating that technological developments contribute to a reduction in income disparities. Additionally, the relationship between Technology advancement and financial inclusion is significantly positive (0.005, p < 0.05). Which endorses that an increase in technological advancement increases financial inclusion. Moreover, our results substantiate the notion that financial inclusion serves as a mediating variable in the relationship between technological advancement and income inequality, with statistical evidence supporting the mediation hypothesis. These findings align with prior studies (Adedoyin et al., 2020 ; Obobisa et al., 2022 ) and underscore the significance of fostering financial inclusivity as a strategy for alleviating income inequality. Notably, our analysis also implied that other macroeconomic factors, such as unemployment rates, play a considerable role in shaping income inequality. Therefore, policymakers ought to consider integrating policy initiatives targeting both technological innovation and financial inclusivity when attempting to mitigate income disparities. Table 6 Influence of technological advancement on financial inclusion and income inequality Financial Inclusion Income Inequality TA 0.005*** -0.010*** (0.001) (-0.003) Financial inclusion -0.006*** (0.001) UR 0.004 0.007 (0.002) (0.004) RQ -0.003** -0.006*** (-0.001) (-0.001) SE 0.014 0.018*** (0.006) (0.003) Sobel test for mediation of financial inclusion p < 0.01. p < 0.01. The standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata. Robustness test It is fundamental to ascertain the durability of the discovered relationships by thoroughly investigating any latent biases resulting from utilizing a binary variable signifying low socioeconomic standing (SES), which is determined by the poverty line threshold. Variations in how destitution lines are characterized might have an impact on the data gathered, potentially compromising the trustworthiness and generality of the outcomes. In light of this, it is indispensable to supplement the principal investigation with extra confirmation strategies intended to analyze the connection among monetary inclusion (FI) and destitution over various impoverishment line limits. This will improve our conviction in the connections between FI and GINI2, all while maintaining the dependability and logical exactness of the discoveries. This research adopts a strategic methodology to examine the relationship between financial inclusion (FI) and multiple socioeconomic variables by replacing the initial explanatory variable of the Financial Inclusion Index with a binary variable denoting ownership of a credit card. The justification for this substitution is rooted in the notion that acquiring a credit card requires a rigorous evaluation of an individual's creditworthiness, which aligns closely with the standards employed to gauge financial participation. Furthermore, individuals who possess credit cards typically demonstrate heightened levels of engagement within the financial sector, thus creating a more robust link between credit card use and immersion in the digital financial landscape. Finally, due to the interconnected nature of credit cards within the larger financial system, their utilization can function as a suitable proxy for quantifying overall exposure to FI. Through analysis of these data, we may observe how credit card adoption acts as a reasonable substitute for FI as an explanatory variable to a certain extent. Table 7 , illustrates two regression models to investigate the connection between credit card use and income inequality. The findings indicate a substantial unfavorable association between credit card utilization and income inequality, with a coefficient of -0.250 (p < 0.001) in Model 1 and − 0.166 (p < 0.001) in Model 2. These outcomes suggest that as credit card adoption rises, income inequality decreases. This discovery aligns with prior studies like those of Ozili et al. ( 2023 ) and Wang, & Fu ( 2022 ), which have shown how credit cards may lessen income gaps by offering people access to credit and other financial services that might enhance their economic situation. findings indicate that controlling variables, specifically the unemployment rate, significantly influence income disparities. These results underscore the significance of evaluating economic factors when attempting to mitigate income inequality. Specifically, our data suggests that credit card usage can act as an effective proxy for assessing financial inclusion and may potentially contribute towards reducing income differences. Therefore, policymakers ought to deliberate on encouraging the proliferation and utilization of credit cards to enhance financial involvement and lessen income inequality. Nonetheless, it is imperative to acknowledge that while credit cards hold promise in expanding financial inclusion and alleviating income disparities, their indiscriminate implementation could lead to undesirable consequences, such as elevated indebtedness and financial instability (Dogan et al. 2022 ; Markose et al. 2022 ). Consequently, prudent regulatory frameworks and surveillance mechanisms must be instituted to guarantee responsible credit card usage and promote equitable outcomes. Table 7 Effects of credit usage (Credit cards) on income inequality Model 1 Model 2 CC -0.250*** -0.166*** (0.038) (0.038) UR -0.045* (0.024) RQ -0.031 (0.029) SE -0.008 (0.025) Constant 0.600*** (0.054) Adjusted R-squared 0.344 0.615 The standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata. Table 8 reveals the fascinating insights from a sophisticated regression analysis that investigates how various aspects of technological progress influence mobile banking, the number of bank accounts, and income disparity. The study uncovers an intriguing pattern where technological advancements yield a remarkable increase in mobile banking adoption and the number of bank accounts held by individuals. This suggests that as technology improves, people gain easier access to essential financial services, bridging the gap in inclusivity. Furthermore, the data illustrates a notable inverse relationship between technological progress and income equality, with each increment in technological advancement resulting in a commensurate decline in income disparity. Interestingly, when analyzing each dimension of technological advancement individually, we observe that digital communication, computer technology, IT-based management systems, and internet connectivity all exert significantly positive influences on both mobile banking penetration and the total number of bank accounts. Our study reveals that certain technological advancements hold particular significance when it comes to promoting financial inclusion. Specifically, we found that the mediating effect of financial inclusion is crucial across all aspects of technological progress, suggesting that expanded access to financial services could play a vital role in alleviating income disparity. These results align with earlier research conducted by Singh ( 2017 ), Kanga et al. ( 2022 ) and Demir et al. ( 2022 ), which highlighted the transformative potential of technology in enhancing financial inclusivity and mitigating income inequality. In essence, our findings underscore the importance of harnessing technological innovations to bolster financial inclusion and narrow the wealth gap, especially within the domains of digital communication, computer technology, IT-based management systems, and internet connectivity. Table 8 linkage between the different dimensions of technological advancement and financial inclusion and income inequality Variables Mobile banking Number of Bank Accounts Income Inequality TA (Total index) 0.004* 0.009*** -0.010** (0.002) (0.002) (0.003) Digital communication 0.002** 0.003*** -0.004* (0.001) (0.001) (0.002) Computer technology 0.002* 0.004*** -0.005** (0.001) (0.001) (0.002) IT-base management System 0.001 0.002** -0.003* (0.001) | (0.001) (0.002) Internet access 0.001** 0.002*** -0.004*** (0.001) (0.001) (0.002) Sobel test for mediation of financial inclusion p < 0.01 p < 0.01 p < 0.01 The standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata. 5. Conclusion This study investigated the complex relationship between technological advancements, financial inclusion, and income inequality among the BRICS countries (Brazil, Russia, India, China, and South Africa) during the time frame of 2011–2021 using statistical analysis. Findings revealed that despite relatively low levels of income inequality throughout the studied period, these nations exhibited significant levels of technological growth and moderately high levels of economic coverage. However, there was a negative correlation observed between technological progress and income disparity, suggesting that as technology advances, income gaps may narrow slightly. Nevertheless, this correlation weakened when other relevant factors such as labor productivity and regulatory quality were taken into consideration. Moreover, the results also showed a strong positive association between technological progress and financial security, as well as a negative relationship between technological advancement and income inequality. Furthermore, the findings suggested that promoting financial inclusivity could help reduce income inequality. In conclusion, this study sheds light on the intricate dynamics between technological progress, monetary inclusion, and income inequality within the BRICS nations. By employing rigorous statistical methods, the investigation uncovered both positive and negative correlations between these variables, highlighting their interconnected nature. These insights can inform policy decisions aimed at fostering sustainable economic growth and reducing income inequality through strategies that promote financial inclusivity. In this study, we investigate the intricate interplay between technological progress, monetary inclusion, and earnings inequality amongst BRICS countries (Brazil, Russia, India, China, and South Africa). By leveraging sophisticated statistical approaches, including two-stage least squares regression and principal element evaluation, we contribute to the present body of literature and enhance the validity and generalizability of our findings. Moreover, conducting a robustness check to evaluate the effect of credit score utilization on earnings inequality offers additional assist to our conclusions. Our findings have significant ramifications for policymakers and practitioners looking to deal with revenue inequality in BRICS international locations. Firstly, our effects recommend that promoting technical development and expanding economic inclusiveness may want to lessen source of revenue inequality. This might be accomplished via guidelines and packages aimed toward growing get right of entry to to and use of era and financial offerings within the populace, specifically in marginalized and low-profits groups. Moreover, the research underlines the significance of tackling different influential elements, along with unemployment charges and regulatory great, while searching out to lessen resource of revenue disparities. It is essential to note that this study has some limitations. Firstly, the use of secondary data limited the study's ability to control for all potential confounding variables that may impact the relationship between technological advancement, financial inclusion, and income inequality. Additionally, the study focused on a specific period and may not account for potential changes over time. Therefore, future research should consider conducting longitudinal studies to understand how the relationship between these variables evolves. 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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-3826008","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264693471,"identity":"c2a0b0cf-ae9a-408b-b13c-2e3b98cdaebf","order_by":0,"name":"muhammad suhrab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYFACxgYILcHAwMxQAWQwMzcQ0tLYwJAA03IGpAVmCF5rYFoY25DtxQEMbh9uf/Dxh409v3Tvw8+F82qj+duBWn5UbMOt5VxiY+OMhLTEmXOOG0vP3HY8d8ZhxgbGnjO3cWoxO8PY2MyTcDjB4EYagzTvtmO5DUAtQBcS0PIn4bA9UAvzb945x3LnE6WFIeEw44YbaWzSvA01uRsIabEHapnZkwb0y4w0NusZxw7kbgRqOYjPL5I97A8+/LABhphEGvPtgpq63HnnDx988KMCtxZ0cBhMHiBaPRDUkaJ4FIyCUTAKRggAAF76XaITaLyyAAAAAElFTkSuQmCC","orcid":"","institution":"Huazhong University of science and technology","correspondingAuthor":true,"prefix":"","firstName":"muhammad","middleName":"","lastName":"suhrab","suffix":""},{"id":264693472,"identity":"618b4ed7-010d-4f94-92d7-300139b5d90f","order_by":1,"name":"Chen Pinglu","email":"","orcid":"","institution":"Huazhong University of science and technology","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Pinglu","suffix":""},{"id":264693473,"identity":"fcb76703-7141-4f03-93d0-657668c9ef29","order_by":2,"name":"Ningyu Qian","email":"","orcid":"","institution":"Huazhong University of science and technology","correspondingAuthor":false,"prefix":"","firstName":"Ningyu","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2023-12-31 14:30:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3826008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3826008/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49138262,"identity":"3131adf1-baaa-4bbd-a044-b95f4618ca70","added_by":"auto","created_at":"2024-01-03 17:46:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":351579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Above figure 1 is incorporated by author as per research empirical approach\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3826008/v1/798440c4ea722b3556b58fd9.jpg"},{"id":52873935,"identity":"34f6d1fa-46cc-466c-9493-26725a884dc7","added_by":"auto","created_at":"2024-03-18 07:47:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":491360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3826008/v1/7a9067d3-f6e1-477f-8f39-bb6784a0dcf2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Closing the income gap: The mediating effect of financial inclusion in the linkage between technological advancement and income inequality in BRICS economies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn today's interconnected world, access to financial services is no longer viewed as a privilege, but rather as a fundamental right for people and enterprises. Unfortunately, conventional financial frameworks have often overlooked marginalized groups such as those without bank accounts or living in developing nations, leaving them excluded from mainstream financial systems (Erel, \u0026amp; Liebersohn, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To bridge this gap, leveraging cutting-edge technologies for financial inclusivity has become crucial for fostering sustainable development, alleviating poverty, and advancing the United Nations' SDGs.\u003c/p\u003e \u003cp\u003eIn recent times, advancements in technology have significantly impacted the financial sector, altering how financial services are delivered and accessed (Jalal et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research has consistently shown that technological innovations can significantly broaden accessibility, reduce costs, and enhance the effectiveness of financial services (Arner et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mushtaq et al., 2022; Chitimira, \u0026amp; Warikandwa., 2023). This is particularly evident in the BRICS nations (Brazil, Russia, India, China, and South Africa), where technological breakthroughs have revolutionized the financial sector and contributed to expanded financial inclusivity (Biyase et al. 2023; Senyo et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chishti, \u0026amp; Sinha, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The proliferation of digital technologies such as mobile banking, online payment systems, and cryptocurrencies has brought about increased emphasis on financial inclusivity across the globe (Gallenstein et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Financial inclusiveness refers to the provision and utilization of reasonably priced financial services by both individual and business entities at various strata of society (Kanga et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This concept holds immense significance due to its capacity to promote economic expansion and development through enabling individuals and micro-enterprises to participate in the formal financial framework, secure loans, and save for the future (Mishi, \u0026amp; Anakpo \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ozili et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinancial inclusiveness has gained considerable attention in recent years due to its potential to eradicate poverty, empower marginalized populations, and foster economic progress (Lythreatis et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As estimated by the World Bank, approximately two billion adults globally lack access to basic financial services, thereby hindering their ability to engage in productive economic activities and attain financial stability (Kouladoum et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, while technological progress holds immense potential for promoting equitable and sustainable financial systems, it is important to recognize that infrastructural development, encompassing both physical and digital infrastructure, plays a vital role in fostering financial inclusion. To address this issue, innovative solutions are necessary to fill the existing gap and reach disadvantaged areas.\u003c/p\u003e \u003cp\u003eTechnological advancements have significantly impacted financial inclusivity by providing greater access to financial services (Coffie, \u0026amp; Hongjiang, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The widespread adoption of digital technologies has enabled financial institutions to extend their reach to previously unserved or under-resourced areas, such as rural regions or those without physical bank branches (Asif et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mobile banking, in particular, has facilitated financial transactions, lending, and payment systems, making it easier for people living in remote locations to manage their finances (Ndassi et al. 2023). Through mobile devices, individuals can now access essential financial services with increased ease and convenience, particularly in underserved communities (Demirg\u0026uuml;\u0026ccedil; et al. 2020; Lashitew, et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The purpose of this study is to investigate the impact of technological innovation on financial inclusion and how access to financial services can reduce income inequality, examining the potential and difficulties that result from the increased use of technology in the financial sector.\u003c/p\u003e \u003cp\u003eThe BRICS nations, comprising rapidly developing economies, are simultaneously witnessing rapid technological progress and grappling with a pressing issue of income disparity. To gain a deeper comprehension of how technology influences income inequality within these countries, where a substantial portion of the global population resides, it is essential to investigate the connection between technological advancements and income inequality. Additionally, considering the growing emphasis placed upon inclusive economic expansion, examining the function that financial incorporation plays in this dynamic can offer valuable insights into fostering more equitable development in these nations.\u003c/p\u003e \u003cp\u003eBy conducting this study, we will be contributing to an already extensive body of work on income inequality, technological advancement, and financial inclusion, while providing a distinct viewpoint on the BRICS nations, which are poised to significantly influence global economic growth in the years to come. Notwithstanding a plethora of investigations into the effect of innovative advances on income inequality, scholarly works centered exclusively on emergent marketplaces like those represented by the BRICS countries are scarce. Furthermore, even fewer studies have delved into the possible role of financial inclusion in bridging the gap between technological progression and income disparities within these nations. As such, there exists a considerable knowledge deficit regarding the impact of technological advancements on income inequality and the potential functions that monetary incorporation might serve in mitigating or exacerbating this phenomenon in the specific context of the BRICS nations.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Income Inequality \u0026amp; Theoretical Review\u003c/h2\u003e \u003cp\u003eInequality refers to the disparities in resources, opportunities, and privileges among distinct social groups within a society (Suhrab et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This concept encompasses various forms of inequality, including economic, social, political, and cultural inequality (Bapuji et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While injustice focuses on specific instances of unfair or unlawful behavior directed at specific individuals or groups, inequality focuses on the systematic differences in outcomes and opportunities experienced by various social categories.\u003c/p\u003e \u003cp\u003eThe study of inequality has been a major area of research across multiple disciplines, including economics, sociology, anthropology, political science, and psychology (Bapuji et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Researchers have employed various methods to quantify and analyze these forms of inequality, with economic inequality typically measured through income and wealth gaps between individuals, households, and countries. Social inequality is often evaluated by assessing access to influential positions, public goods and services, such as education and healthcare, while political inequality involves examining representation and participation in governance structures and decision-making process (Jaravel, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Solt, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S\u0026aacute;nchez et al. 2023). Cultural inequality can be identified through assessments of group membership and the presence of biases and discriminatory attitudes that impede equitable treatment and opportunities for marginalized populations(Chancel et al., 2022).\u003c/p\u003e \u003cp\u003eAt both the domestic and global spheres, inequality has been increasing steadily over recent years. Societal struggles with addressing this issue date back to ancient times, and it has been linked to an array of socioeconomic and political factors including poverty, subpar healthcare, limited access to education, and insufficient employment prospects (Deaton, 2021). The consequences of inequality are far-reaching and profoundly affect not only individual wellbeing but also societal stability. Consequently, devising a multifaceted approach that caters to diverse populations is imperative (Yenn, 2022). Furthermore, grasping the underlying reasons and repercussions of inequality is indispensable for designing targeted interventions aimed at reducing disparities effectively (Deo et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Empirical Review\u003c/h2\u003e \u003cp\u003e \u003cb\u003eNexus between technology diffusion and financial inclusion\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe past decade has witnessed an unprecedented surge in technological progress, transforming various aspects of human existence. Notably, this acceleration has affected the financial sector, particularly through the proliferation of Financial Technology (Fintech) and digital payment systems. Consequently, financial inclusivity or the accessibility and utilization of affordable financial services tailored to individual and business needs has expanded globally. This literature review seeks to investigate the intricate connection between technological innovation and financial inclusion, focusing on how technology can promote inclusive financial systems, alter existing financial architectures, and identify potential impediments hindering universal financial inclusion.\u003c/p\u003e \u003cp\u003eIn the realm of research on the relationship between technology dissemination and financial inclusivity, there is ample evidence demonstrating technology's pivotal role in expanding financial inclusion. A plethora of studies have underscored the beneficial impact of technology on this frontier. To cite an example, Konte, \u0026amp; Tetteh (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Shaikh et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) revealed a significant correlation between the availability of mobile phones and the utilization of formal financial services in developing nations. Specifically, these findings suggest that mobile devices act as a conduit to financial access for the underprivileged populace. Similarly, Lashitew, et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Hasan et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted a study that examined the impact of technology on financial inclusion. Their results indicated that the adoption of technological innovations, including mobile money, significantly improved the availability and accessibility of conventional financial services.\u003c/p\u003e \u003cp\u003eMoreover, the study highlighted the potential of technology in lowering the costs associated with financial services and augmenting access for low-income individuals. This theme is echoed by the World Bank (2022), which notes that technology has been instrumental in advancing financial inclusion in developing economies. Specifically, mobile money services have facilitated outreach to previously unserved or hard-to-reach populations, thereby granting them access to financial services and enabling them to execute transactions sans the requirement of a brick-and-mortar bank branch (Kass-Hanna et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kouladoum et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Demirg\u0026uuml;\u0026ccedil;-Kunt; Shaikh et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous research studies have underscored the significant role of digital payment systems in promoting financial inclusivity across various nations. As highlighted by Al-Smadi, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), higher frequencies of digital payments are directly correlated with enhanced levels of financial inclusion within countries. Furthermore, digital payments have been found to enhance consumer financial stability, especially in developing regions where cash-based transactions are inherently riskier. The diffusion of technology has been credited with reducing disparities in accessing financial services and expanding financial inclusiveness (Jalal et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A primary factor responsible for unequal access to financial services lies in the exorbitant costs associated with serving rural and impoverished populations (Bekele, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These expenses include physical infrastructure investments, such as branch locations, as well as administrative overheads. Notwithstanding, the growing popularity of mobile and digital financial services has led to a substantial decrease in these costs, thereby facilitating the operation of financial institutions in distant areas and improving their ability to serve underprivileged segments of society (Coffie et al. 2023; Demirg\u0026uuml;\u0026ccedil;-Kunt, et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent times, rapid technological developments have significantly enhanced the delivery of financial services, thereby appealing to an increasing number of clients. To illustrate, digital financial platforms like mobile money have streamlined transaction processes by providing fast and convenient access to financial services without the need for physical visits to financial institutions (Lashitew et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As a direct consequence, there has been a shift away from traditional cash-based transactions toward digital ones, which reduces both the risks associated with managing actual currency and the expenses linked with handling it. Moreover, advances in technology have empowered financial organizations to gather and examine data regarding consumer spending habits within specific geographic areas or market segments. By leveraging this knowledge, financial institutions can create tailored financial solutions designed specifically for diverse populations, including those living in underserved or remote regions (Demir et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Through comprehensive understanding of local financial behaviors and preferences, financial service providers may now design offerings that better suit the needs of these clienteles, thereby promoting greater inclusivity throughout the industry.\u003c/p\u003e \u003cp\u003eTechnological advancements have shown promise in expanding financial inclusivity; however, several challenges and limitations must be addressed. A significant concern is the digital divide, where individuals residing in remote and impoverished regions encounter difficulties accessing technology or possess insufficient digital proficiency to utilize digital financial services (Asif, et al. 203). This hinders the spread of technology and excludes marginalized populations from acquiring financial services. Moreover, the integration of technology presents security and privacy concerns, particularly in developing nations with subpar digital infrastructure and regulatory frameworks (Goswami et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These issues might lead to mistrust in digital financial services, thereby limiting their adoption and financial inclusiveness.\u003c/p\u003e \u003cp\u003eH1: The diffusion of technology in the BRICS countries has a positive relationship with the degree of financial inclusivity, as advances in technology improve accessibility to and usage of financial services for those who are disadvantaged.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNexus between financial inclusion and income inequality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe notions of financial inclusion and income inequality have garnered increasing interest in recent times. Financial inclusion pertains to the delivery of financial services and products to those with restricted access to conventional financial institutions (Yu, \u0026amp; Tang \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Meanwhile, income inequality refers to the disparate distribution of income and wealth within a society, where some individuals or households possess more resources than others (Wang, Li, \u0026amp; Li, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Many studies investigated into the association between financial inclusion and income inequality and discovered that they are inextricably linked. This review of the literature aims to provide a comprehensive overview of the various studies that have investigated this nexus.\u003c/p\u003e \u003cp\u003eFinancial inclusion has been demonstrated to exert a pivotal influence in mitigating income inequality. According to Bansah, \u0026amp; Mohsin (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), expanding financial access through credit and savings opportunities fosters an equitable disbursement of income. Specifically, financial inclusion enables individuals and households to accrue wealth and assets, which in turn reduces income gaps. Moreover, Demir et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) contend that financial inclusion directly affects income inequality by lowering the obstacles and costs associated with participation for those with limited means. Through provision of affordable financial services, financial inclusion facilitates increased earnings and improved economic welfare for these individuals. Furthermore, beyond its direct effect on income inequality, financial inclusion has been shown to exert an indirect influence on income distribution through various channels. One such channel involves the positive correlation between financial inclusion and improved socioeconomic indicators, as demonstrated by Kim, (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Koomson, \u0026amp; Danquah (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) findings in developing countries. Specifically, they discovered that enhancing financial inclusion leads to better educational and health outcomes, ultimately contributing to a more equitable distribution of income.\u003c/p\u003e \u003cp\u003eIn contrast, certain investigations have underscored the possibility that financial inclusion might amplify income disparity. According to Biyase, \u0026amp; Chisadza (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) findings, expanded financial access might result in higher danger-taking and earnings instability among low-income people, thereby deepening wealth gaps. Similarly, Yin, \u0026amp; Choi, (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) study revealed that monetary incorporation can negatively affect revenue equality in creating nations because of the subpar caliber of economic services and goods offered. As a consequence of their reliance on unmanageable degrees of borrowing, this can increase poverty.\u003c/p\u003e \u003cp\u003eThe body of research pertaining to the interplay between financial inclusion and income inequality reveals an intricate and multifaceted association. On one hand, expanded access to financial services has been found to contribute towards narrowing the gap between the rich and the poor by providing opportunities for individuals to improve their economic well-being (Mushtaq, \u0026amp; Bruneau; Koomson, et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tchamyou, et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this relationship is not without its caveats as financial inclusion can sometimes exacerbate existing income disparities through various mechanisms. Moreover, the degree to which financial inclusion influences income inequality varies across different national contexts. For example, studies have demonstrated that financial inclusion has a more pronounced effect on reducing income inequality in nations experiencing higher levels of poverty and income inequality (Park, \u0026amp; Mercado \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eH2: Increased financial inclusion reduces income inequality; however, the extent of this linkage varies by country.\u003c/p\u003e \u003cp\u003eThere exists an evidential void within existing scholarly discourse surrounding the intersectionality of technological innovation and economic inequality in the context of the BRICS nations (Brazil, Russia, India, China, and South Africa). Specifically, limited research has been conducted to investigate how the dissemination of advanced technologies influences the distribution of financial services across these emerging economies. This scarcity of investigations has resulted in a dearth of knowledge regarding the specific challenges faced by each country in addressing issues related to monetary exclusion. Our analysis aimed to rectify this lacuna through a comprehensive assessment of the relationship between technological adoption and financial inclusivity in the BRICS countries. By doing so, we provide valuable insights that can be leveraged to develop targeted policies focused on reducing income-based disparities in access to financial services throughout these pivotal regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Data Collection\u003c/h2\u003e\n\u003cp\u003eIn this investigation, we sourced data for the BRICS nations (Brazil, Russia, India, China, and South Africa) from a variety of trustworthy sources spanning the decade of 2011 to 2021. This extensive time frame enabled us to assess a broad array of technological developments and economic markers. To examine income inequality, we utilized the Standardized World Income Inequality Database (SWIID), which offers standardized measurements of income disparity across countries and through time. For financial inclusivity metrics, we turned to the Global Financial Inclusion Database (Findex) and world bank database, providing detailed and comparative data on individual-level access to and use of financial services across more than 140 countries, whereas data regarding technological innovation originated from the World Intellectual Property Organization (WIPO). Control variables such as employment rates and regulatory quality were acquired via the World Development Indicators dataset. A thorough list of the precise datasets and sources employed can be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTo prepare the data for analysis, we conducted rigorous quality control measures on each variable's raw information. We standardized all variables by transforming them into consistent units of measurement, corrected inconsistencies that arose from deviations from these standards, and eliminated any anomalous readings (outliers) that could have skewed our results. When necessary, we employed statistical methods to fill in gaps or missing values using an educated guess based on patterns observed within the available data (Little, \u0026amp; Rubin \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Meeker et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, our comprehensive dataset spanned one decade across multiple variables with 55 individual observations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Variables and Descriptions\u003c/h2\u003e\n\u003cp\u003eThe main variables of interest in this study are are income disparity, technological advancement, and financial inclusion. Income inequality is quantified utilizing the Gini coefficient, which spans from 0 (perfect equality) to 1 (absolute inequality). Technological development is evaluated through an index formulated from indicators including telecommunications, digital communication, computer technology, and IT-based management systems. Financial inclusion is measured via a composite index assembled from variables like the number of bank accounts, mobile banking transactions, internet bill payments, and outstanding loans from credit unions and cooperatives and number of debit and credit cards.\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) is employed to create indices that measure financial inclusion and technological advancement. In this process, the original variables are first standardized, and their weights or importance are determined through the loadings represented in a matrix called \u0026Lambda;. These loaded variables are then combined using the formula given below, resulting in a composite index that reflects both factors. This composite index is obtained by simply adding up the standardized values of all the variables involved (Jolliffe, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$${Z}_{i}= {{\\Lambda }{\\Theta }}_{i}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eTo improve the accuracy of our analysis of income inequality, we added controls for variables that could potentially influence the outcome. These included the unemployment rate, regulatory quality, and self-employment rate, which we chose because they have the potential to affect how income is distributed. A full list of each variable's definition and source can be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. By adding these control variables, we sought to gain a clearer picture of what determines income inequality by considering other relevant factors that may otherwise skew the results.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMeasurement and sources details of variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eData Sources\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePrevious research\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMeasurement\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncome Inequality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStandardized World Income Inequality Database.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuhrab et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Feng et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Ratnawati, (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe Gini coefficient is a widely used statistical measure to quantify income inequality across different populations. A Gini coefficient of 0 represents perfect equality, while a value of 1 indicates complete inequality.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTechnological Advancement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWorld Intellectual Property Organization (WIPO)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbid et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), Popkova et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), Akram et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWe employed principal component analysis (PCA). PCA, experts can identify patterns and trends within massive datasets.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTelecommunication\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWIPO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003enumber of mobile phone subscriptions per capita.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDigital communication\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWIPO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe most data that can be transmitted within a given bandwidth.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComputer technology\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWIPO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe amount of data that can be stored on a hard drive or other storage device in a computer.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIT-base management System\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWIPO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe amount of data that a database can store. The design and usability of the system's user interface.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInternet access\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWIPO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ethe total number of internet users.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBroadband internet\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWIPO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ethe percentage of households with access to broadband internet.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinancial Inclusion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlobal Financial Inclusion Database (Findex) and World Bank.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLee et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), Omar, \u0026amp; Inaba (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Yu, \u0026amp; Tang (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWe employed principal component analysis (PCA). PCA, experts can identify patterns and trends within massive datasets.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of bank accounts\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFindex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal number of accounts of customers in a country.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMobile banking transaction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFindex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe number of transactions every day.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInternet bill payments\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFindex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal amount of bill paid for internet usage.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCredit union and cooperation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFindex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe number of credit unions are available.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDebit and credit cards\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFindex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOutstanding loans from credit unions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFindex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmount of credit distributed by credit unions in the market.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnemployment rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe Organization for Economic Co-operation and Development (OECD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChan, \u0026amp; Dong (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), Ahmad et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Wang, \u0026amp; Li (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThis indicator is seasonally adjusted and measures the number of unemployed as a percentage of the labor force. The labor force is defined as the total number of unemployed people plus those who are employed.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulatory quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWorld bank (Worldwide governance data.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdedoyin et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), Obobisa et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndividual and aggregate governance indicators for different governance dimensions.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSelf-employment rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe Organization for Economic Co-operation and Development (OECD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFritsch et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Gim\u0026eacute;nez et al. (2022) and Fossen (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSelf-employment is referred to as the employment of employers, self-employed workers, members of producer co-operatives, and unpaid family workers.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Econometric Techniques\u003c/h2\u003e\n\u003cp\u003eThis research employed a two-stage least squares (2SLS) regression approach to investigate the connection between technological advancement and income inequality, taking into consideration the possibility that technological development may be related to both variables through an indirect mechanism. The 2SLS method is commonly utilized in statistical analysis when there is a correlation between the independent variable and the error term due to endogeneity. By using instrumental variables that are associated with the endogenous variable but unrelated to the error term, it seeks to address this issue (Timpone, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ullah et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Specifically, we made use of lags of technological advancements as instruments in the initial stage, which demonstrated high correlation with contemporary levels of technological advancement yet had no significant association with the error term.\u003c/p\u003e\n\u003cp\u003eTo investigate the potential mediation role of financial inclusion in the relationship between technological progress and income disparity, we employed Sobel test (Sobel, \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e), which determines whether the impact of the independent variable on the dependent variable goes through the mediator variable. In other words, the Sobel test assesses if there is a significant indirect effect of the independent variable on the dependent variable via the mediator. Additionally, in each of the three models being examined, we will incorporate lagged values of the technological advancement variable as instrumental variables in the initial stage of the two-stage least squares (2SLS) regression analysis. This technique helps to mitigate the possibility that the technological variable may be endogenous since lagged values of the variable are less likely to be influenced by current levels of income inequality.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({GINI}_{i,t}=\\beta 0+ {\\beta }_{1}({TA)}_{i,t}+ {\\epsilon }_{i,t}\\)\u003c/span\u003e \u003c/span\u003e Model 1\u003c/p\u003e\n\u003cp\u003eIn this equation, we are estimating the relationship between income inequality (measured by the Gini coefficient) and technological advancement (represented by TA) across different countries (i).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({GINI}_{i,t}=\\beta 0+ {\\beta }_{1}({TA)}_{i,t}+{\\beta }_{2}({UR)}_{i,t}+{\\beta }_{3}({RQ)}_{i,t}+{\\beta }_{4}({SE)}_{i,t}+ {\\epsilon }_{i,t}\\)\u003c/span\u003e \u003c/span\u003e Model 2\u003c/p\u003e\n\u003cp\u003eIn this equation, we are estimating the relationship between income inequality (measured by the Gini coefficient) and technological advancement (represented by TA) across different countries (i). To account for potential factors that may influence this relationship, we have included a set of control variables, including the unemployment rate (UR), regulatory quality (RQ), and self-employment rate (SE) for each country. The coefficients in the equation (\u0026beta;) represent the relative importance of these control variables in determining the relationship between technological advancement and income inequality, while the error term (\u0026epsilon;) captures any other factors that may affect the relationship.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({FI}_{i,t}={\\delta }0+ {{\\delta }}_{1}({TA)}_{i,t}+{{\\delta }}_{2}({UR)}_{i,t}+{{\\delta }}_{3}({RQ)}_{i,t}+{{\\delta }}_{4}({SE)}_{i,t}+ {\\epsilon }_{i,t}\\)\u003c/span\u003e \u003c/span\u003e Model 3\u003c/p\u003e\n\u003cp\u003eIn this equation, we're attempting to identify the connection between financial innovation (FI) and technological advancement (represented by TA) across various nations (i) and we used the same control variables applied in the above equation. Specifically, the coefficients (\u0026delta;) in the equation illustrate how much each control variable modifies the relationship between technological progress and financial innovation, while the error term (\u0026epsilon;) accounts for any further factors that might shape their interaction.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({GINI}_{i,t}=\\beta 0+ {\\beta }_{1}({TA)}_{i,t}+{\\beta }_{2}({FI)}_{i,t}+{\\beta }_{3}({UR)}_{i,t}+{\\beta }_{4}({RQ)}_{i,t}+{\\beta }_{5}({SE)}_{i,t}+ {\\epsilon }_{i,t}\\)\u003c/span\u003e \u003c/span\u003e Model 4\u003c/p\u003e\n\u003cp\u003eIn this equation, we are estimating the relationship between financial inclusion (measured by an index called FI), technological advancement (TA) and income inequality (measured by the Gini coefficient) for each country i. and employed the same control variables as used in the above equations.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results \u0026 Discussions","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presented in this study illustrates the statistical data about the BRICS nations (Brazil, Russia, India, China, and South Africa) over the period spanning from 2011 to 2021. A cursory examination of the data reveals that despite some degree of income inequality, the distribution of wealth within these countries was generally less extreme compared to other developing economies. Specifically, the mean Gini coefficient for the BRICS nations stood at approximately 0.51 during this timeframe, indicating a relatively modest level of income disparity.\u003c/p\u003e\n\u003cp\u003eHowever, our analysis uncovered additional insights into the economic dynamics of the BRICS nations beyond just income inequality. Notably, we found evidence of substantial technological progress across these countries, as reflected by an average technological advancement score of 1.11. Moreover, the BRICS nations displayed a moderate level of financial inclusivity, with an average financial inclusion index value of 0.38. Meanwhile, unemployment rates among these countries remained relatively low, settling at an average of 5.63%, while self-employment rates were slightly higher at 23.95%.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that although income inequality exists within the BRICS economies, it is not the sole determinant of their economic growth patterns. Other factors, such as technological innovation, financial inclusion, and labor market conditions, play equally important roles in shaping their development trajectories. Therefore, policymakers should consider addressing multiple aspects of economic policy to foster sustainable and equitable growth in these emerging markets.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive Statistics\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard Error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMinimum\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMaximum\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGINI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.401\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.632\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.550\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.501\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.360\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eNote: The author incorporated the above table based on the research dataset\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePairwise correlation presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, there are several relationships between the primary variables of interest and various control variables. Notably, the correlation between income inequality and technological advancement is negligible but negative, suggesting that as technology develops, income inequality may decrease slightly. A similar pattern emerges when comparing income inequality to financial inclusion; the two exhibit a significant and negative connection. On the other hand, technological advancement seems to have a solidly positive impact on financial inclusion, with a substantial correlation coefficient of 0.549. Furthermore, examining the connections between the control variables and the key variables reveals some intriguing patterns. Unemployment rates show a noticeable association with income inequality, with a moderate positive correlation of 0.274. Regulatory quality appears to have a relatively minor negative correlation with both income inequality and financial inclusion, with values of -0.119 and \u0026minus;\u0026thinsp;0.103, respectively. Lastly, self-employment rates display slight links with all three major variables, including income inequality, technological development, and financial inclusion.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePairwise correlation\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGINI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTA\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRQ\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGINI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.216***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.227***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.549**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.274**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.059**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.131**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.119*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.171**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eNote: * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, we conducted two regression analyses to investigate the relationship between technological advancement and income inequality while controlling for potential confounding variables. Our findings indicate that, upon initial analysis (Model 1), there exists a significant negative association between technological advancement and income equality, with greater technological development tending to reduce income disparity (coefficient = -0.917; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, when additional factors known to influence income inequality were included in the second model (Model 2), this relationship was found to diminish (coefficient = -0.725; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that factors such as unemployment rates, regulatory environments, and self-employment rates may also play a role in determining income inequality patterns.\u003c/p\u003e\n\u003cp\u003eThe inclusion of supplementary control variables in Model 2 provides valuable insight into the relationships between income inequality and various factors. Of particular note is the statistically significant positive linkage between income inequality and unemployment rate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which aligns with previous research (Chan \u0026amp; Dong, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ahmad et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang \u0026amp; Li, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This finding suggests that increases in unemployment are associated with greater income disparities. Contrarily, regulatory quality exhibited a positive but nonsignificant association with income inequality, which could be attributed to the possibility that regulations disproportionately impact different income groups, leading to an inconclusive overall effect. Moreover, no significant connection was found between self-employment rates and income inequality, indicating that self-employment does not directly contribute to income distribution. In summary, these results imply that technological advancements have the potential to alleviate income inequality, although this association relies on numerous other variables. Thus, policymakers should consider multiple factors, such as joblessness levels and regulatory frameworks, when designing interventions aimed at reducing wealth disparities.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBenchmark model of Technological advancement (Index) and Income inequality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGINI\u003c/p\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGINI\u003c/p\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.917***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.725***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.038)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.039)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.112***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.032)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.026)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eThe standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e provides an analysis of the effects of various aspects of technological advancements on income inequality using two regression models. The first model reveals a statistically significant negative relationship (-0.016, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between telecommunications and income inequality, implying that nations with greater telecommunications infrastructure exhibit less income inequality. Nevertheless, after adjusting for additional variables in the second model, the connection weakens and loses statistical significance, suggesting that alternative elements like digital communication, computer technology, and IT-based management systems might exert a more profound influence on reducing income inequality.\u003c/p\u003e\n\u003cp\u003eWhile the other dimensions of technological development did not demonstrate any notable effect on income inequality, the coefficients associated with them remained unfavorable, which could imply that they might indirectly affect income inequality through their broader societal ramifications. Notably, while neither the Sargan nor Hansen tests revealed any proof of endogeneity in either model, the R-squared values increased marginally in Model 2, signifying that the control variables satisfactorily clarified most of the changes in income inequality. These findings propose that policymakers must adopt a multifaceted strategy when formulating rules targeted toward diminishing wealth gaps by considering the diverse facets of technological advancement.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe impact of different dimensions of technological advancement on income inequality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGINI\u003c/p\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGINI\u003c/p\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTelecommunication\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.016**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDigital communication\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComputer technology\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.007***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.011)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIT-base management System\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.004***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInternet access\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBroadband internet\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl variables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.459\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.473\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSargan test (p-value)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.379\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHansen test (p-value)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.786\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eThe standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e displays the findings of a statistical model that explored the relationship between technology development, financial accessibility, and income disparity through regression analysis. Specifically, we aimed to assess the extent to which technological advancement influences financial inclusion and income inequality while evaluating whether financial inclusion acts as a mediator in this relationship. Our findings indicate a substantial linkage between technological development and increased financial inclusion, suggesting that advancements in technology tend to enhance access to financial services among marginalized populations.\u003c/p\u003e\n\u003cp\u003eConcurrently, our outcome revealed a pronounced negative association between technological progress and income inequality (-0.010, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that technological developments contribute to a reduction in income disparities. Additionally, the relationship between Technology advancement and financial inclusion is significantly positive (0.005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Which endorses that an increase in technological advancement increases financial inclusion. Moreover, our results substantiate the notion that financial inclusion serves as a mediating variable in the relationship between technological advancement and income inequality, with statistical evidence supporting the mediation hypothesis. These findings align with prior studies (Adedoyin et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Obobisa et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and underscore the significance of fostering financial inclusivity as a strategy for alleviating income inequality. Notably, our analysis also implied that other macroeconomic factors, such as unemployment rates, play a considerable role in shaping income inequality. Therefore, policymakers ought to consider integrating policy initiatives targeting both technological innovation and financial inclusivity when attempting to mitigate income disparities.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eInfluence of technological advancement on financial inclusion and income inequality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFinancial Inclusion\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIncome Inequality\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.010***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinancial inclusion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.006***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.003**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.006***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSobel test for mediation of financial inclusion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eThe standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRobustness test\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eIt is fundamental to ascertain the durability of the discovered relationships by thoroughly investigating any latent biases resulting from utilizing a binary variable signifying low socioeconomic standing (SES), which is determined by the poverty line threshold. Variations in how destitution lines are characterized might have an impact on the data gathered, potentially compromising the trustworthiness and generality of the outcomes. In light of this, it is indispensable to supplement the principal investigation with extra confirmation strategies intended to analyze the connection among monetary inclusion (FI) and destitution over various impoverishment line limits. This will improve our conviction in the connections between FI and GINI2, all while maintaining the dependability and logical exactness of the discoveries.\u003c/p\u003e\n\u003cp\u003eThis research adopts a strategic methodology to examine the relationship between financial inclusion (FI) and multiple socioeconomic variables by replacing the initial explanatory variable of the Financial Inclusion Index with a binary variable denoting ownership of a credit card. The justification for this substitution is rooted in the notion that acquiring a credit card requires a rigorous evaluation of an individual's creditworthiness, which aligns closely with the standards employed to gauge financial participation. Furthermore, individuals who possess credit cards typically demonstrate heightened levels of engagement within the financial sector, thus creating a more robust link between credit card use and immersion in the digital financial landscape. Finally, due to the interconnected nature of credit cards within the larger financial system, their utilization can function as a suitable proxy for quantifying overall exposure to FI. Through analysis of these data, we may observe how credit card adoption acts as a reasonable substitute for FI as an explanatory variable to a certain extent.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, illustrates two regression models to investigate the connection between credit card use and income inequality. The findings indicate a substantial unfavorable association between credit card utilization and income inequality, with a coefficient of -0.250 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in Model 1 and \u0026minus;\u0026thinsp;0.166 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in Model 2. These outcomes suggest that as credit card adoption rises, income inequality decreases. This discovery aligns with prior studies like those of Ozili et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Wang, \u0026amp; Fu (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), which have shown how credit cards may lessen income gaps by offering people access to credit and other financial services that might enhance their economic situation. findings indicate that controlling variables, specifically the unemployment rate, significantly influence income disparities. These results underscore the significance of evaluating economic factors when attempting to mitigate income inequality.\u003c/p\u003e\n\u003cp\u003eSpecifically, our data suggests that credit card usage can act as an effective proxy for assessing financial inclusion and may potentially contribute towards reducing income differences. Therefore, policymakers ought to deliberate on encouraging the proliferation and utilization of credit cards to enhance financial involvement and lessen income inequality. Nonetheless, it is imperative to acknowledge that while credit cards hold promise in expanding financial inclusion and alleviating income disparities, their indiscriminate implementation could lead to undesirable consequences, such as elevated indebtedness and financial instability (Dogan et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Markose et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, prudent regulatory frameworks and surveillance mechanisms must be instituted to guarantee responsible credit card usage and promote equitable outcomes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEffects of credit usage (Credit cards) on income inequality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.250***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.166***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.038)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.038)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.045*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.025)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.600***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.054)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdjusted R-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.615\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eThe standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e reveals the fascinating insights from a sophisticated regression analysis that investigates how various aspects of technological progress influence mobile banking, the number of bank accounts, and income disparity. The study uncovers an intriguing pattern where technological advancements yield a remarkable increase in mobile banking adoption and the number of bank accounts held by individuals. This suggests that as technology improves, people gain easier access to essential financial services, bridging the gap in inclusivity. Furthermore, the data illustrates a notable inverse relationship between technological progress and income equality, with each increment in technological advancement resulting in a commensurate decline in income disparity. Interestingly, when analyzing each dimension of technological advancement individually, we observe that digital communication, computer technology, IT-based management systems, and internet connectivity all exert significantly positive influences on both mobile banking penetration and the total number of bank accounts.\u003c/p\u003e\n\u003cp\u003eOur study reveals that certain technological advancements hold particular significance when it comes to promoting financial inclusion. Specifically, we found that the mediating effect of financial inclusion is crucial across all aspects of technological progress, suggesting that expanded access to financial services could play a vital role in alleviating income disparity. These results align with earlier research conducted by Singh (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), Kanga et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Demir et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), which highlighted the transformative potential of technology in enhancing financial inclusivity and mitigating income inequality. In essence, our findings underscore the importance of harnessing technological innovations to bolster financial inclusion and narrow the wealth gap, especially within the domains of digital communication, computer technology, IT-based management systems, and internet connectivity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003elinkage between the different dimensions of technological advancement and financial inclusion and income inequality\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMobile banking\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of Bank Accounts\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIncome Inequality\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTA (Total index)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.010**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDigital communication\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.004*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComputer technology\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.005**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIT-base management System\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.003*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001) |\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInternet access\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.004***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSobel test for mediation of financial inclusion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eThe standard errors are shown in brackets, * p 0.10, ** p 0.05, *** p 0.01, and each statistic is from Stata.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study investigated the complex relationship between technological advancements, financial inclusion, and income inequality among the BRICS countries (Brazil, Russia, India, China, and South Africa) during the time frame of 2011\u0026ndash;2021 using statistical analysis. Findings revealed that despite relatively low levels of income inequality throughout the studied period, these nations exhibited significant levels of technological growth and moderately high levels of economic coverage. However, there was a negative correlation observed between technological progress and income disparity, suggesting that as technology advances, income gaps may narrow slightly.\u003c/p\u003e \u003cp\u003eNevertheless, this correlation weakened when other relevant factors such as labor productivity and regulatory quality were taken into consideration. Moreover, the results also showed a strong positive association between technological progress and financial security, as well as a negative relationship between technological advancement and income inequality. Furthermore, the findings suggested that promoting financial inclusivity could help reduce income inequality.\u003c/p\u003e \u003cp\u003eIn conclusion, this study sheds light on the intricate dynamics between technological progress, monetary inclusion, and income inequality within the BRICS nations. By employing rigorous statistical methods, the investigation uncovered both positive and negative correlations between these variables, highlighting their interconnected nature. These insights can inform policy decisions aimed at fostering sustainable economic growth and reducing income inequality through strategies that promote financial inclusivity.\u003c/p\u003e \u003cp\u003eIn this study, we investigate the intricate interplay between technological progress, monetary inclusion, and earnings inequality amongst BRICS countries (Brazil, Russia, India, China, and South Africa). By leveraging sophisticated statistical approaches, including two-stage least squares regression and principal element evaluation, we contribute to the present body of literature and enhance the validity and generalizability of our findings. Moreover, conducting a robustness check to evaluate the effect of credit score utilization on earnings inequality offers additional assist to our conclusions.\u003c/p\u003e \u003cp\u003eOur findings have significant ramifications for policymakers and practitioners looking to deal with revenue inequality in BRICS international locations. Firstly, our effects recommend that promoting technical development and expanding economic inclusiveness may want to lessen source of revenue inequality. This might be accomplished via guidelines and packages aimed toward growing get right of entry to to and use of era and financial offerings within the populace, specifically in marginalized and low-profits groups. Moreover, the research underlines the significance of tackling different influential elements, along with unemployment charges and regulatory great, while searching out to lessen resource of revenue disparities.\u003c/p\u003e \u003cp\u003eIt is essential to note that this study has some limitations. Firstly, the use of secondary data limited the study's ability to control for all potential confounding variables that may impact the relationship between technological advancement, financial inclusion, and income inequality. Additionally, the study focused on a specific period and may not account for potential changes over time. Therefore, future research should consider conducting longitudinal studies to understand how the relationship between these variables evolves.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1 Muhammad Suhrab, Established the research problem, compiled relevant material and data, developed and tested hypotheses, generated original ideas and concepts, and prepared and reviewed the manuscript. 2 Prof. Chen Pinglu; Analyzed, interpreted, and presented the results, carried out and reported the findings, identified and discussed limitations and implications, and evaluated the research methods used. 3 Dr. Ningyu Qian; Updated and synthesized existing literature, co-authored abstract, introduction and conclusion, Wrote, reviewed, and edited drafts of the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbid, N., Marchesani, F., Ceci, F., Masciarelli, F., \u0026amp; Ahmad, F. (2022). 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Does financial inclusion improve energy efficiency? \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e, \u003cem\u003e186\u003c/em\u003e, 122110.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-3826008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3826008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the relationship between technological advancement and income inequality in the BRICS countries (Brazil, Russia, India, China, and South Africa) with a particular focus on the mediating role of financial inclusion. Employing statistical techniques such as two-stage least squares regression and principal component analysis, the research analyzes data from reliable sources between 2011 and 2021. The findings indicate a negative relationship between technological progress and income inequality, suggesting that as technology advances, income gaps will narrow slightly. Furthermore, the study reveals a positive relationship between technological advancement and financial inclusion, as well as a negative impact of financial inclusion on income inequality. These results have significant implications for policymakers, emphasizing the importance of promoting financial inclusivity to reduce income inequality in these countries. However, the study also acknowledges certain limitations and suggests future research to consider controlling for other potential factors and conducting longitudinal studies to better understand the dynamic relationship between these variables.\u003c/p\u003e","manuscriptTitle":"Closing the income gap: The mediating effect of financial inclusion in the linkage between technological advancement and income inequality in BRICS economies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 17:46:20","doi":"10.21203/rs.3.rs-3826008/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":"763e6efe-d498-429d-bd9a-dbc4c2ba72b7","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-18T07:39:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 17:46:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3826008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3826008","identity":"rs-3826008","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}