Revisiting Financial Inclusion and Income Inequality Nexus: Evidences from Selected Economies in Asia

  • ALI, Jamshed (Department of Management Sciences, COMSATS University) ;
  • KHAN, Muhammad Arshad (Department of Economics, COMSATS University, Pakistan Institute of Development Economics) ;
  • WADOOD, Misbah (Department of Business Administration, Faculty of Management Sciences, International Islamic University) ;
  • KHAN, Usman Shaukat (Department of Management Science, COMSATS University)
  • Received : 2021.08.15
  • Accepted : 2021.11.01
  • Published : 2021.12.30


This study aims to measure financial inclusion and examine its impact on income inequality in a panel of 18 Asian countries over the period 1997-2017. Two alternative approaches for developing financial inclusion index are used: one approach following the methodology of Sarma (2008), while the other is the Dynamic Factor Model (DFM)-based index. The impact of individual indicators and index of financial inclusion on inequality in income is analyzed. The Generalized Method of Moment (GMM) approach is used for empirical analysis. The results indicate that micro-level financial inclusion has a weak negative and statistically significant impact on income inequality. Macro-level index and all individual indicators of financial inclusion do not affect income inequality in the selected sample of economies. The income inequality issues have different natures and cannot be fixed by financial inclusion only. It needs holistic structural reforms to enable fair distribution of income and make an equitable financial system. Financial inclusion is a relatively less important intervention tool regarding fixing the issue of income inequality. This is one of the first studies that used the DFM method for financial inclusion indices construction.


1. Introduction

Financial inclusion refers to a financial system that makes financial services available to all stakeholders, such as households and enterprises, at a reasonable cost, regardless of their size or market worth. Financial inclusion has received a lot of attention on the global development agenda, with international financial organizations like the Asian Development Bank and the World Bank seeing it as critical for long-term growth. By promoting economic activity, access to finance has the potential to play a critical role in economic development and enhancing people’s well-being. Several other empirical evidence such as Beck et al. (2007), World Bank (2008), Kim et al. (2018), and Ali and Khan (2020), showed that financial access can significantly improve the well-being of the people.

Financial inclusion does not always have a similar impact on poverty and income distribution in different parts of the world (Roodman, 2012). This work adds to the body of knowledge in several ways. It examines the influence of financial inclusion on income inequality using a form of the Cobb-Douglas production function, as proposed by Yorulmaz (2018) and Kim et al. (2018) while controlling for trade openness, government spending, and employment. Existing research combines multiple indicators of financial inclusion into a single equation, resulting in a multicollinearity problem. To address this issue, we created a micro-level financial inclusion index and a macro-level financial inclusion index based on Pina’s work (2018).

Second, we looked at the impact of financial inclusion on income distribution in selected economies after evaluating the amount of financial inclusion. We also looked at the influence of financial inclusion indices and financial inclusion indicators. It allows us to have a better understanding of how financial inclusion affects income disparity. Third, this study’s modeling technique is more complete since it distinguishes between micro- and macro financial inclusion and provides a detailed examination of the influence of micro- and macro-financial inclusion on income inequality.

We used DFM and methodology of Sarma (2008) to create financial inclusion indices to test the robustness of results in terms of micro- and macro-level analyses of financial inclusion. We used the system GMM estimator to control for the possibility of endogeneity. We can learn a lot about many facets of financial inclusion and how it affects income distribution using these tools.

2. Literature Review

Financial inclusion and its impact have been discussed in various studies from different perspectives over the past three decades. Greenwood and Jovanovic (1990) found a nonlinear relationship between financial access and income disparity, in which the distributional influence of the financial sector is dependent on economic progress. Due to the high cost of joining and the need for some financial assets in the early phases of financial sector development, the rich receive comparatively large benefits of financial services, resulting in a widening of income disparity.

Private credit expansion, according to Rajan and Zingales (2003) and Beck et al. (2007), acts as a stimulus to increase the income of the poorest quintiles and reduce level income disparities. According to Beck et al. (2007), 60% of financial development benefits the poorest lowest quintile, but it also reduces income inequality by 40%. The effect of private credit on the rise of the Gini coefficient was negative. Access to finance reduces income disparity by boosting the income of the poor, hence reducing poverty.

Some studies emphasize the importance of financial institutions and financial sector changes. Financial reforms deepen the financial system rather than broadening and easing access, and as a result, a small fraction of the elites reap the majority of the advantages. Liberalization of the financial system was intended to enhance access, but in fact it has a tendency to raise inequality, fragility, and political opposition to economic changes. As a result, if financial reforms are combined with supervision mechanisms, they can be effective and successful. According to Nguyen et al. (2021), institutional quality plays an important role in financial inclusion, which can lead to a reduction in economic inequality in ASEAN.

De Haan and Sturm (2017) discovered the threshold effect while researching factors that affect income disparity in an economy. The effects of liberalization, financial development, and banking sector crises on income distribution are examined using data from 121 economies from 1975–2005. Financial liberalization, financial development, and banking sector crises all raise income inequality, according to the study. The influence of liberalization on income disparities was determined by the degree of financial sector development and the quality of institutions. Bolarinwa et al. (2021) found that financial development leads to improvement in social wellbeing when it achieves a certain threshold.

However, some evidence suggests that unequal financial accessibility adds to income inequality by allowing certain people to benefit from financial services while others are left behind. A selective increase in the availability of financial services, according to Demirguc-Kunt et al. (2008), could exacerbate economic inequality. According to Kappel (2010), the financial sector’s development in industrialized countries helps to close the income gap. Kappel (2010) discovered contradictory findings, suggesting that financial sector development improves disparities in developed nations but does not benefit emerging economies.

The relationship between financial development and wealth disparities, according to Bahmani-Oskoee and Zhang (2015), has conflicting evidence. However, Jauch and Watzka (2016) found that the financial sector’s development promotes income inequality. In an aggregate sample of 177 economies, Park and Mercado (2015) found that access to finance is associated with a reduction in poverty and a decrease in income disparity, but the linkages between access to finance and income inequality were missing in the Asian sample.

Zhang and Naceur (2019) revealed that four financial development indicators (depth, access to finance, efficiency, and stability) can decrease poverty and income inequality. However, the impact of financial liberalization on poverty and income disparity was statistically insignifi- cant. Ratnawati (2020) showed that financial inclusion positively affecting income inequality, but all variables do not have a significant effect on income inequality. Gravina and Lanzafame (2021) argued that in emerging economies financial development leads to an increase in income inequalities. The research on the finance-income inequality nexus shows mixed results; one group of studies found actual evidence that access to finance reduces income disparity, while others found the opposite and found that financial accessibility had a negative impact on income inequality. Some research shows that the impact of financial inclusion is based on institutional structure and financial reforms, while others suggest that the impact of financial inclusion is based on institutional structure and financial reforms. Therefore, there is a need to investigate finance and inequality nexus in-depth due to contradicting evidence and the importance of the Asia area.

3. Model and Methodology

In panel data, researchers most of the time facing the problem of unobserved heterogeneity. The issue of unobserved heterogeneity can be fixed by converting the static panel models to dynamic panel models. In our dynamic panel approach, we have closely followed Arellano and Bond (1991) and Arellano and Bover (1995). The dynamic GMM estimator is used for controlling country-specific and simultaneity arise which arise as a result of endogeneity among independent variables.

We have used the following theoretical model for analyzing the impact of financial inclusion on poverty;

GINI = + β1X1+ β2X2 + β3X3 + β4X4 ………………… + βnXn + ε       (1)

Where, GINI is the dependent variable which represents poverty headcount, while X1, X2, X3, X4 shows independent variable and β1, β2, β3, β4 indicate their parameters, ε reflects error term.

To examine the impact of both micro-level and macro level financial inclusion on income inequality and to deal with unobserved variation and heterogeneity of socioeconomic variables in the selected sample, we used the Dynamic Panel Generalize Method of Movement (GMM) methodology.

3.1. Econometric Specifications

In the present study, the Gini coefficient is used for showing inequality following Park and Mercado (2015). We have specified the following empirical model to examine the impact of micro-level and macro-level financial inclusion with some control variables on inequality;

GINIit = α + β1FIIit + β2GDPit + β3GSYit + β4INFit + β5Eit + β6EMPit + β7GEit + vi + εit       (2)

Where Gini coefficient (GINIit) is used as the dependent variable representing income distribution.

We have used a two-step process: in the first step, we have investigated impact of financial inclusion indices on inequality. We use micro-level and macro-level financial inclusion indices in investigating their impact on income distribution. In the second step, we examine impact of individual components (indicators) of financial inclusion on income inequality.

This study, taking lead from Park and Mercado (2015), specified dynamic model which is as follow:

GINIit = α + β1GINIit-1 + β2FIIit + β3GDPit + β4GSYit + β5INFit + β6Eit + β7EMPit + β8GEit + vi + wit       (3)

To examine the relationship and impact of each indicator of financial inclusion on income inequality, we used the following dynamic model;

\(\begin{aligned} \mathrm{GINI}_{i t}=& \alpha+\beta_{1} \mathrm{GINI}_{i t-1}+\beta_{2} \mathrm{FII}_{i t}+\beta_{3} \mathrm{GDP}_{i t}+\beta_{4} \mathrm{GSY}_{i t} \\ &+\beta_{5} \mathrm{INF}_{i t}+\beta_{6} \mathrm{E}_{i t}+\beta_{7} \mathrm{EMP}_{i t}+\beta_{8} \mathrm{GE}_{i t} \\ &+\sum_{i=1}^{6} \gamma_{i}\left\{\mathrm{LBN}_{i t}, \mathrm{LDA}_{i t}, \mathrm{LBA}_{i t}, \mathrm{LIP}_{i t}, \mathrm{LFSD}, \mathrm{LPC}_{i t}\right\} \\ &+v_{i}+\omega_{i t} \end{aligned}\)       (4)

The variables used in this paper along with their definition as well as data sources are given in Table 1.

Table 1: Definition and Data Source of Variables

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3.2. Data

Data for different sets of variables are extracted from, International Monetary Fund’s International Financial Statistics (IFS), World Bank’s World Development Indicators (WDI), World Bank’s World Governance Indicator, United Nation Educational, Scientific and Cultural Organization (UNESCO), United Nations Development Program (UNDP), World Database of Oxford University, Central Banks and Statistical Departments of respective countries. The sampled countries are taken from four regions of Asia.1 The countries under consideration were selected based on data availability from1997 to 2017.

3.3. Sampling

Based on the availability of data from 1997 to 2017, we chose 18 economies. Because the economies of China and Japan were causing outliers in the sample, they were removed from the study. Another reason could be that, in comparison to the other sampled countries, the dynamics of financial inclusion in China and Japan are considerably different. For example, the Chinese government discourages the opening of several bank accounts by a single person, whereas the Japanese government encourages the opening of multiple bank accounts by people. As a result, the number of bank accounts in Japan is excessively high, while it is comparatively modest in China. After analyzing the trend of different variables, we have excluded China and Japan from the analysis.

4. Results

4.1. Construction of Micro and Macro-Financial Inclusion Indices

Bandiera et al. (2000) established the financial liberalization index by including different variables of financial liberalization and found that the components of financial inclusion are associated with each other and have the issue of multi-dimensionality. Their findings laid the groundwork for the creation of a financial inclusion index. Khan and Qayyum (2007) developed a financial development index based on four financial metrics. Sarma (2008) proposed building a financial inclusion index by combining important components and displaying it as a signal measure.

This study divides financial inclusion into two different categories following Pina (2018) and Ali and Khan (2020). Two different approaches have been used for micro-level and macro-level financial inclusion indices development following Sarma (2008) as well as Stock and Watson (2002).

4.1.1. Index Based on Methodology of Sarma (2008)

Following Sarma (2008), the micro- and macro-level financial inclusion indexes scale the level of financial inclusion (FI) from 0 to 1. The lowest levels of financial inclusion are represented by a score of 0, while the highest degree of financial inclusion is represented by a score of 1.

Figures 1 and 2 show the rankings of micro-and macro level financial inclusion indices, respectively.

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Figure 1: Ranking of Micro-Financial Inclusion Index

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Figure 2: Ranking of Macro-Level Financial Inclusion Index

Considering Figure 1, the micro-level financial inclusion reflects the relative position of financial services accessibility in each country. It can be seen from Figure 1 that South Korea secured the highest level of financial accessibility, while the Kyrgyz Republic has the lowest level.

The ranking of macro-level financial inclusion in Figure 2 reflects the financial development in selected countries with respect to the size of the economy. The graph shows that based on macro-level financial inclusion, South Korea has the highest score, while Pakistan, Azerbaijan, Tajikistan, and Uzbekistan are in the 15th Position. The relative lower financial inclusion is because of an underdeveloped financial system, weak demand from customers, illiteracy, and lack of awareness.

The ranking along with the score of micro-level and macro-level of financial inclusion is presented in Table 2.

Table 2: Micro and Macro-Financial Inclusion

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Note: FII denotes financial inclusion index.

4.1.2. Dynamic Factor Model (DFM) Based Indices

Alternatively, the micro-level and macro-level financial inclusion indices based on the DFM approach are depicted in Figure 3.

OTGHEU_2021_v8n12_19_f0003.png 이미지

Figure 3: Trends of Macro-Level and Micro-Level Financial Inclusion Indices Based on the Dfm Approach

The DFM-based micro-level financial inclusion index presented in Figure 4.3 shows higher variation across sampled countries. This suggests that economies with high financial inclusion have higher scores. However, macro level financial inclusion indicates relatively less variation as compared to the fluctuation associated with micro-level financial inclusion.

After preliminary analysis, we may conclude that the financial inclusion index of Sarma (2008) performs better in accessing the levels of financial inclusion. Therefore, we consider the financial inclusion index based on Sarma (2008), while results on the DFM-based financial inclusion index c.

4.2. Impact of Micro and Macro-Financial Inclusion and Income Inequality

We examine the effect of Micro-FII and Macro-FII on income inequality and Table 3 presents the estimation results.

Table 3: Micro and Macro-Financial Inclusion and Income Inequality

OTGHEU_2021_v8n12_19_t0003.png 이미지

Note: Values in parentheses are t-stat, *, **, *** respectively indicate significance at 90, 95, and 99% level of significance. The values above parentheses show coefficients.

In Table 3, we have presented estimation results of eight models by considering the GINI coefficient as the dependent variable. In each model, the impact of Micro- FII and Macro-FII and individual indicators of financial inclusion is analyzed. Positive signs of the coefficients of explanatory variables imply that these variables cause to increase in income inequality, while the negative sign reveals that these variables decrease the level of income inequality. Model (A) of Table 4.10 presented the impact of Micro-FII on income inequality. The coefficient Micro-FIIit is –3.0 which is significant at a 10% level of significance. It indicates that that Micro-FIIit has a significant impact on the distribution of income in selected economies. It means that Micro-FIIit may help in the improvement of income distribution in selected Asian economies, but its impact is weak. The weak impact of Micro-FII on the distribution of income may be due to uneven affordability and availability of financial services. It is a fact that the rich and poor cannot get equal benefits from financial access. Cost of financial services, lack of collateral assets, and information asymmetry are the reasons for uneven utilization and benefits of financial access. The effect of financial inclusion on income distribution depends on the characteristics of a financial structure that how effective it is designed to deals with the issue of income inequality. Our results are similar to the findings of Jauch and Watzka (2016) who advocated that financial inclusion does not always ensure fair income distribution.

In Table 3, model (B) reports the impact of Macro- FII on income inequality. The coefficient of Macro-FII is 0.35 but is statistically insignificant. The main reason for the insignificance of the development-side of financial inclusion may be that high-income groups get more benefits from financial development as compared to poor social class, which in turn further deteriorate income distribution. Jauch Micro-FII and Watzka (2016) highlighted the fact that financial sector development does not always improve income distribution. The cost of financial services, lack of collateral assets, and information asymmetry are the reasons for uneven benefits of financial accessibility. The effect of financial inclusion on income inequality depends on the characteristics of a financial system that how effective it is designed to deals with income distribution.

The results reported in Table 3 (model C) indicate the effect of bank branches (LBNit) on income inequality. The coefficient of LBNit is 0.12 and is significant at the 5% level of significance. This indicates that expansion in bank branches causes to increase in income inequality. The reason may be that bank branches provide loans based on the financial position of individuals rather than based on principles for equality.

In model(s) (D) to (H), the impact of deposit accounts (LDA), borrower accounts (LBA), private credit (LPC), total deposit with financial system (LFSD), and insurance premium (LIP) is analyzed. The results show that all these five indicators of financial inclusion do not have a significant effect on income inequality. It implies that for improving equitable income distribution, there is a need for comprehensive government policy. The financial accessibility and financial services cannot deal with complex issues of the income distribution. It is the government’s responsibility to make the socio-economic structure more equitable and design policies for fair income distribution. This indicates that for overcoming income inequality in selected countries, a comprehensive strategic intervention is needed rather than access to banks, deposits, and borrower facilities. The services of financial institutions are available to everyone and it is evident that the rich peoples get more benefits as compared to poor people’s due to information availability, security, and better financial literacy.

The role of quality of financial institutions is important for decreasing income inequality rather than just financial services accessibility. These findings are in line with Beck et al. (2007). Additionally, the lagged GINI coefficient (GINIit–1) has a positive and statistically significant impact on the current GINI coefficient. This implies that the past period’s income inequality has a strong positive impact on the current income inequality.

With respect to the control variables, the result indicates real GDP (LGDPit) exerts a positive impact on the GINI coefficient when compared to the other control variables. However, this variable has a negative impact on income inequality. The fundamental explanation for this could be that when economies grow, income distribution gaps widen because the privileged class benefits from market opportunities far more than the less developed social classes.

The government spending (LGSYit) reveals a negative and significant effect on income distribution in all the models. The coefficients of LGSYit range from –0.32 to –0.57, which implies that an increase in government spending would improve income distribution. Thus, government plays a vital role in income distribution by adopting strategies, effective management, and appropriate allocation of funds. These findings are consistent with Kappel (2010) and Anderson et al. (2017) who highlighted the importance of government spending for equitable income distribution. (LEMPit),

In terms of the role of employment this variable is statistically significant only in model C. According to Mehic (2018), employment increases lower quantile income and narrows the income distribution gap. However, because of low salaries and unskilled labor, it may not be an effective technique in some economies.

The findings also suggested that in all models, school enrollment (LEit) has no effect on income disparity. This shows that enrollment in education has no bearing on income distribution. In truth, education has little impact on economic inequality. According to Abdullah et al. (2015), secondary school enrollment has a greater impact on income disparity than primary school enrollment.

The importance of government effectiveness is demonstrated by the results, which show that it is significant across all models except model (C). It has a coefficient of –0.02 to –0.71. Our findings are consistent with Anderson et al. (2017), who emphasized the importance of government effectiveness in addressing the issue of income disparity.

The results show that there is no issue with serial correlation, as evidenced by the insignificance of AR p-values (2). The Hansen statistic indicates that GMM estimation devices are reliable. At the 99 percent level of significance, the F-statistics suggests that the model fits well. Year dummies were also used to achieve efficient results.

5. Conclusion

The impact analysis of financial inclusion on income inequality shows that the impact of micro-level financial inclusion is (Micro–FIIit) on index income distribution significant. Although, the effect of Micro-FII on income inequality is weak but significant at the 90% level. It indicates that micro-level financial inclusion has a negative impact on income distribution. It implies that micro-level financial inclusion decreases income disparities and shrinks the income inequality gap in Asia. The impact of the macro level financial inclusion income index (Macro–FIIit) on distribution is statistically insignificant. This means that macro-level financial inclusion will not be able to address concerns of the unequal income distribution. Individual micro-level financial inclusion components such as bank branches, deposit accounts, and borrower accounts have a statistically insignificant impact on income inequality. It Micro–FIIit implies that financial inclusion is ineffective as a method for reducing income inequality.

The impact of individual indicators of macro-level financial inclusion indicates private sector credit relative to GDP, total deposits in the financial system relative to GDP, and insurance premium relative to GDP all have an insignificant impact on income inequality. Previous research looked at the impact of financial development on economic growth using private sector credit as a metric of financial sector development.

Individual macro-level financial inclusion indicators show that private sector credit as a percentage of GDP, total deposits in the financial system as a percentage of GDP, and insurance premiums as a percentage of GDP have no impact on income inequality.

5.1. Policy Implication

Empirical results of the study provide some new insights to policymakers for designing appropriate financial products and policies that can potentially reduce income disparity in the Asia region. Based on the finding we have offered the following policy recommendations.

.  The level of financial inclusion is represented by the  financial inclusion index based on the methodology of Sarma (2008), whereas DFM reflects financial inclusion growth. The DFM approach is beneficial for predicting future financial inclusion trends. Therefore, policymakers can use the approach of Sarma (2008) to assess financial inclusion.

.  The result implies that for just and equitable income  distribution, there should be some effective government strategy. Financial inclusion does not work as an effective tool for income distribution. The role of the government in designing effective financial policy for equitable access to financial services to every citizen regardless of their socio-economic status, is very important.

.  Based on the findings of the study,  we propose that all Asian  economies need to develop effective and comprehensive solutions. Financial inclusion will help to stimulate economic growth in the least developed countries, while it will increase the financial depth and make the economy more expensive in developed countries. Small loans at subsidized rates to underprivileged and poor people in remote areas must be provided, enabling them to participate actively in economic activity. Financial institutions should also simplify their products make them more relevant, relax the need for collateral and undertake advertising campaigns.

5.2. Future Research Directions

The impact of financial inclusion on income inequality was also insignificant, insurance premium relative to GDP is the only variable that significantly promotes equitable income inequality. Based on the finding of this study, we recommend the following potential research areas for future research consideration.

In future research, it would be fascinating to look at the effects of financial inclusion on a country-by-country basis using primary data. The studies that are currently accessible are based on secondary data; however, there is a need to study country-specific primary data to get new insights on the extent and impact of financial inclusion in various countries. We will be able to study the cross-country variation, issues, and challenges that financial inclusion faces in different countries using country-specific primary data analysis.

It would be interesting to examine the influence of DFM-based indices in various economies around the world. It will also look at the robustness of DFM-based indices and Sarma’s indices in other fields of economics. Furthermore, the discussion over having a perfect model for measuring financial inclusion is still ongoing; different models for accurate financial inclusion measurement must be researched further to arrive at the best possible model for measuring financial inclusion.

Mobile money accounts are a new phenomenon; future research should explore this including electronic money, or mobile money, as it represents a new enabling and driving force for financial inclusion around the world. Mobile money plays an important role in today’s technology based economies, as it promotes financial inclusion by making mobile money outlets easily available and simple to use. We were unable to evaluate data from a relatively short time period due to the unique nature of dynamic factor model-based indices. Therefore, we were unable to consider the mobile money side because data was only accessible for a short period of time and a few economies.



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