The Impact of Financial Inclusion on Income Inequality: Case Study in Europe
Manh Tien Pham
1, Vy Ha Nguyen
2, Minh Nhat Ngo
31 Faculty of Finance, Banking Academy of Vietnam, 12 Chua Boc, Dong Da, Hanoi, Vietnam e-mail: [email protected]
2 K22CLCC-Faculty of Finance, Banking Academy of Vietnam, 12 Chua Boc, Dong Da, Hanoi, Vietnam
3 K22CLCB- Faculty of Finance, Banking Academy of Vietnam, 12 Chua Boc, Dong Da, Hanoi, Vietnam
DOI: 10.51865/EITC.2022.01.04
Abstract
Financial inclusion is considered an important factor which contributes to reducing poverty and income inequality. A lot of previous researches showed the impact of some dimensions of financial inclusion on income inequality. However, financial inclusion is measured by many different dimensions; therefore, to assess the combined impact of financial inclusion on income inequality, the authors first used the principal components analysis to build a composite financial index, with the datasets from 29 high- and upper-middle-income countries in Europe in the period of 2011-2017. Next, the authors used the two- stage least squares (2SLS) regression method to estimate the impact of financial inclusion on income inequality. The research results found that the financial inclusion, the percentage of the population aged 25 and over graduating from secondary education, the economic openness had an opposite effect on income inequality. On the contrary, the employment-to-population ratio had a directional effect on income inequality. Based on the obtained estimates, the study proposed some solutions to reduce income inequality.
Key words: financial inclusion; income inequality.
JEL Classification: G21; O11; O15; O40.
Introduction
Reducing poverty and income inequality has always been one of the top goals in the economic development strategy of every country in the world. Theoretically and practically, the concept of inequality has been proposed by socio-economic researchers and scholars under a lot of different definitions. According to the World Bank (2017), income inequality is “the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution”, and is measured by the Gini coefficient. Lorenz (1905) proposed a revolutionarily graphical measure of inequality known as the Lorenz curve. Based on the Lorenz curve, in 1912, Gini proposed a parametric measure of
Corresponding author
inequality, called the Gini coefficient. Idrees & Ahmad (2017) showed that the coefficient of variation, the Kakwani index, the Gini coefficient, the generalized Gini index, the two measures of Theil, the generalized entropy index, the Atkinson index and the Ebert index were the best measures for measuring income inequality.
In addition to income inequality, Financial Inclusion (FI) is also currently a matter of concern for countries around the world because it brings a lot of socio-economic benefits to the poor in particular and to the sustainable economic growth and development of countries in general.
Concepts referring to FI are also mentioned diversely with many different criteria systems.
However, most concepts and perspectives on FI agree with the common viewpoint that FI refers to the ability of an individual or group of people to access and use available financial services at a reasonable cost (World-Bank, 2018). Moreover, the theoretical and experimental studies on FI have also introduced and used the relatively diverse concepts of FI.
To assess the impact of financial inclusion and other controlling factors on income inequality, first of all, the authors used the principal components analysis (PCA) method to build a composite financial inclusion index; Next, the authors used the two-stage least squares (2SLS) regression with the datasets from 29 high- and upper-middle-income countries in Europe in the period of 2011-2017. The research results found that the financial inclusion, the percentage of the population aged 25 and over graduating from secondary education, the economic openness had an opposite effect on income inequality. Increasing the access to and use of financial services, increasing the percentage of the population graduating from secondary education, and implementing policies to open the economy would reduce income inequality. On the contrary, the employment-to-population ratio had a directional effect on income inequality, increasing the employment-to-population ratio was one of the causes of increasing income inequality; there was no evidence on the impact of the consumer price index on income inequality.
The structure of this research paper, in addition to the introduction including literature review contains research model and data source, result and discussion, and conclusion.
Literature Review
Financial inclusion
Financial inclusion is considered as the provision of affordable financial services, ensuring access to appropriate and necessary financial products and services for disadvantaged and low- income segments of society (Chakraborty & Nandi, 2011). Financial inclusion means that all working-age adults have effective access to credit, savings, payments and insurance from formal service providers Cull et al. (2014). A financial inclusion system is the one that maximizes use and access, and minimizes involuntary financial exclusion Cámara & Tuesta (2014). These services include access to accounts at formal financial institutions, access to formal accounts, use of formal accounts, mobile payments, savings, credit, insurance, and pensions (Demirgüç- Kunt & Klapper, 2012).
Some recent studies used the Principal Components Analysis (PCA) such as Cámara & Tuesta (2014); Le et al. (2019); Tran et al. (2020); Tran & Le (2021). Cámara & Tuesta (2014) built a financial inclusion index based on three dimensions: usage, barriers, and access. The usage of financial services was measured through 3 indicators: using at least one financial service (account), savings, and loan at a formal financial institution. Barriers were assessed through four indicators: geographical distance to financial service providers, affordability, lack of necessary documents, and trust in financial intermediaries. The access to the financial systems was assessed through four indicators: the ratio of ATMs/ 100,000 adults, the ratio of commercial bank branches/ 100,000 adults, the ratio of ATMs/ 1,000 km2, and the ratio of commercial bank branches /1,000 km2. Le et al. (2019) built a financial inclusion index based on the dimension
of access, using 4 indicators: the ratio of ATMs/ 100,000 adults, the ratio of commercial bank branches/ 100,000 adults, the rate of outstanding deposits at commercial banks, the rate of outstanding loans at commercial banks.
Income inequality
According to the World Bank (2017), income inequality is “the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution”, and is measured by the Gini coefficient. Houghton
& Khandker (2009) in the World Bank’s Handbook on Poverty and Inequality 2009 stated that income inequality was a concept associated with poverty, and the matter of income inequality often concentrated can be understood as “the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution”, and is measured by the Gini coefficient. Lorenz (1905) proposed a revolutionarily graphical measure of inequality known as the Lorenz curve. Based on the Lorenz curve, in 1912, Gini proposed a parametric measure of inequality, called the Gini coefficient.
Idrees & Ahmad (2017) showed that the coefficient of variation, the Kakwani index, the Gini coefficient, the generalized Gini index, the two measures of Theil, the generalized entropy index, the Atkinson index, and the Ebert index were the best measures for measuring income inequality. Among them, if the purpose was simply to measure income inequality, the Gini coefficient was the most appropriate measure; if the purpose was to analyze it, together with the Gini coefficient, the generalized entropy measures were the best choice; and if the purpose was to incorporate value judgment, the Atkinson index was the best choice.
The impact of financial inclusion on income inequality
Beck et al. (2007) stated that financial development affected to the extent that an individual’s economic opportunity was determined by his or her skill and creativity, or parental wealth, social status, or political affiliation. Under the imperfect financial market conditions, according to the arguments of Swinnerton & Rogers (2000), or Ranjan (2000), when income shocks occurred, if the poor families were not able to withstand these shocks and could not borrow to cover their basic expenses, they would have to stop investing in their children’s education and let them do low-paying jobs. Demirgüç-Kunt & Levine (2009) developed a more general theoretical framework to explain the finance-inequality relationship. According to these two researchers, an individual’s income comes from two basic sources: salary, and income from assets. Salary is determined by human capital, and human capital is determined by two factors:
innate ability, and investment in education. Financial development has influence on social welfare through the mechanism of creating opportunities of human capital investment of the previous generation for the next generation. Galor & Moav (2004) described the finance - growth - inequality relationship with attention directed at the accumulation of human capital and physical capital, in which two basic assumptions were made: i) the marginal propensity to consume increases with income, and (ii) the return on physical capital accumulation is higher than the return on human capital in the initial stage of economic development, and this is reversed in the later stage of development. Therefore, in the initial stage of development, inequality increases due to channeling resources to individuals with a higher propensity to save.
During this period, financial development and growth promotion will also tend to increase inequality. In China, Jalil & Feridun (2011) analyzed the national data from 1978 to 2007 and concluded that financial development helped reduce income inequality. In India, Ang (2010) found that the expansion of credit to the private sector and the bank density can narrow income inequality; meanwhile, the impact of stock market development cannot yet be concluded. In Pakistan, Shahbaz & Islam (2011) showed that financial development led to more equal distribution and less financial instability. In Iran, Baligh & Pirace (2013) showed that financial development and income inequality had an opposite relationship, that is, the more financial development is, the smaller the income gap in society is. In the research in Iran, Muhammad et
al (2012) pointed out that there was evidence for the existence of both linear and nonlinear relationships, despite the conclusion that in the long run, financial development can be reduce income inequality. Chu & Le (2012) reported that financial inclusion had an opposite effect on income inequality.
Park & Mercado (2018) examined the factors that had influence on financial inclusion, and the significance of financial inclusion in reducing poverty and income inequality, focusing on 37 developing Asian economies. They found that per capita income, regulations of law, and demographic structure increased financial inclusion, while a higher age-dependency ratio significantly decreased financial inclusion. Primary education completion rate and literacy rate do not have significant effect on financial inclusion in developing countries in Asia.
Furthermore, financial inclusion significantly reduces poverty; there is also evidence that it reduces income inequality when more regression coefficients are considered. Park & Mercado (2018) also found little econometric evidence that financial inclusion reduced income inequality. In addition, the authors found an insignificant relationship between financial inclusion and income inequality. These different findings may be due to differences in measurement of financial inclusion, differences in sample sizes and time periods, and methodologies. García-Herrero & Turégano (2015) assessed the role of two dimensions of financial development (the scale of the financial system, and financial inclusion) in reducing income inequality, and found that financial inclusion contributed to a significant reduction in income inequality when the regression was controlled for factors of economic development and fiscal policy. Ho & Phan (2019) showed that there was a negative relationship between financial inclusion and income inequality in these transitional economies. Abdullah & Kazuo (2020) also showed that the expansion of financial inclusion significantly reduced poverty and income inequality in developing countries. Abdullah & Kazuo Inaba (2020) built the financial inclusion index based on the indicators reflecting the level of financial access. The research results provided strong evidence that financial inclusion significantly reduced poverty and income inequality in developing countries.
Thus, there have been a lot of studies on the impact of financial inclusion on income inequality within a country or a territory. However, these studies only measure financial inclusion in a certain dimension through one or a few certain criteria. There are relatively few studies assessing the impact of financial indicators (combining many different dimensions of financial inclusion) on income inequality.
The impact of other factors on income inequality
Le et al. (2014) estimated the elasticity between growth and poverty reduction in Vietnam and indicated that the elasticity had a marked decrease from time to time, which showed that the distribution of the results of growth tended to be more unfavorable for the poor. Theoretically, inequality in the first phase could promote investment through the marginal propensity to save, which was the income constituting the future investment capital, and in the later phase, the results of investment (growth/ income) affected the income distribution. Chu (2014) provided some empirical evidence for effect in this second phase. The results of this study showed that every 1 percentage point of private investment increased in the previous period (2 years ago) may make the Gini coefficient increase to 0.03% - 0. 05% in the current period. The research by Tran & Yabe (2011) pointed out that the difference in income between regions in Vietnam was caused by the allocation of investment capital not matching the advantages of each region.
Regarding the impact of FDI, Cao (2004) showed that the FDI sector was unlikely to have an impact on increasing salary for workers and also did not increase income inequality.
Meanwhile, Le & Booth (2010) based on living standards survey data from 1993 to 2002, studied the effect of FDI on gender income equality at the provincial level in Vietnam. The research results showed that the ratio of female workers’ salary to male workers’ salary increased from 0.78 in 1993 to 0.8 in 1998, continued to increase to 0.86 in 2002. The typical
study on the impact of trade openness on income inequality was the study of Cao (2004) using provincial data and applying the modified Cobb–Douglas production function model. Taking exports per capita as a measure of trade openness, the author initially found empirical evidence of a directional relationship between trade openness and income inequality. Previously, Jensen
& Tarp (2005) also concluded that trade integration was unlikely to have an effect on reducing poverty and inequality. In the same topic but from a micro perspective, Hội & Ngọc (2015) carried out a quantitative study on provincial data in the period of 2002-2012 by using the GMM method. The results showed that the marginal impact of domestic trade (measured by the ratio of retail sales of goods and services/GDP) on income inequality (measured by the Gini coefficient in logarithmic form) was between 0.01% and 0.013%, which meant that expansion of domestic trade increased inequality. The research by Long et al., (2019) on the industry and enterprise level data showed that trade liberalization had a small negative impact on employment and salary. However, trade liberalization helped to reduce the income gap by gender and skill among workers. Accordingly, the authors found that the income gap between male and female workers decreased during the study period.
Muszynska & Wedrowska (2018) demonstrated that human capital-related characteristics were the factor that had the greatest influence on income variability among households. They also showed that human capital-related characteristics were the most influential factor for income variation among households, in which 20% of the total inequality among households can be attributed to differences in knowledge, education.
Method, Data, and Analysis
Research models
Based on the data sources that can be collected, and the literature review of the previous studies, to assess the impact of financial inclusion and other controlling factors on income inequality, the authors used the two-stage least squares (2SLS) regression model:
(1)
with constraint:
(2)
in which: i is country, t is year; α is the regression coefficient; ci is a spatially specific characteristic; uit is the random error; GINI is a coefficient reflecting income inequality; FII the financial inclusion index built by using the principal components analysis method; EPR is the employment-to-population ratio; IUI is the ratio of Individuals using the Internet to the total population; PSE is the percentage of the population aged 25 and over graduating from secondary education; ICF is the index of economic freedom; OPEN is the economic openness, CPI is the consumer price index.
Financial inclusion is measured by a lot of different dimensions, and each dimension is measured by many different criteria. Therefore, to build a financial inclusion index, the authors used the principal components analysis (PCA) method. Although the PCA method has been widely used for explanatory data analysis, it has not been used much to build financial inclusion index. Currently, there have been only some studies that used PCA to build composite financial index such as Cámara & Tuesta (2014); Tran et al. (2020); Le et al. (2019, 2020); Tran & Le (2021).
Based on the existing data, and the literature review of the previous studies, the authors used the principal components analysis to build the financial inclusion index based on two dimensions:
Access to financial inclusion was demonstrated through 2 indicators: ATM (the ratio of ATMs/
100,000 adults), BRAN (the ratio of commercial bank branches/100,000 adults); Usage of
financial services was built through 6 indicators: FIA (the percentage of individuals aged 15 and over having a financial institution’s account), SFI (the percentage of individuals aged 15 and over having savings at a financial institution), BFI (the percentage of individuals aged 15 and over borrowing from a financial institution), BFF (the percentage of individuals aged 15 and over borrowing from family and friends), DCO (the percentage of individuals aged 15 and over having a debit card), CCO (the percentage of individuals aged 15 and over having a credit card).
The results of Table 1 showed that the KMO coefficient = 0.724 was in the range of 0.5<KMO<1, which meant that the actual data in this study fitted the principal components analysis. Bartlett’s test with a significance level Sig. = 0.000 < 0.05 showed that the observed variables had a linear correlation with the representative factor. Thus, the results of both tests showed that the use of PCA was appropriate in building the financial inclusion index.
Table 1. Results of Barlett and Kaiser-Meyer-Olkin tests
Barlett test of sphericity Kaiser-Meyer-Olkin of Sampling Adequacy Chi-square Degree of freedom p-value
FII 1074.542 28 0.000 0.724
Source: Authors’ data processing.
The results of Table 2 showed that PCA generated two factors representing the financial inclusion index, these two factors explained 70.302% of total variance of the financial inclusion index; this was a large percentage completely acceptable.
Table 2. Total variance explained
Component Eigenvalues % of Variance Cumulative Variance
FII 1 3.849 48.110 48.110
2 1.775 22.191 70.302
Source: Authors’ data processing.
With the collected data, to assess the impact of financial inclusion and other factors on income inequality, the authors selected the array data regression model. In the array data regression model, three commonly-used methods include the Pooled OLS model, the fixed effects model (FEM), and the random effects model (REM). However, one drawback of the array data regression model is that it is very difficult to deal with heteroscedasticity. In addition, if there is an endogeneity problem in the model, the REM and FEM estimations are no longer effective.
The researches on the impact of financial inclusion and factors on income inequality often have endogeneity problem. Therefore, to solve the endogeneity problem in the model, the authors used the instrumental variable regression model, especially the two-stage least squares (2SLS) regression model. When implementing the 2SLS regression model, the authors performed Durbin and Wu-Hausman test to check endogeneity, and Sargan and Basmann tests of overidentifying restrictions.
Data sources
Financial inclusion data were collected from the Global Findex Database. Data on labor were collected from ILO (2019), ILOSTAT database and World Development Indicators, World Bank. Data on education were collected from the UNESCO Institute for Statistics (2019). Data on science and technology, and trade freedom were collected from the World Development Indicators (WDI), World Bank.
Data on assessing the impact of financial inclusion and factors were collected from 29 high- and upper-middle-income countries in Europe in the period of 2011-2017.
Table 3. Defining and measuring variables
Factors Variables Measurement Unit Data sources
Financial inclusion
FIA The percentage of individuals aged 15 and over having a
financial institution’s account % Global Findex database (http://datatopics.worldbank.org/financial
inclusion/) SFI
The percentage of individuals aged 15 and over having savings
at a financial institution %
Global Findex database (http://datatopics.worldbank.org/financial
inclusion/) BFI
The percentage of individuals aged 15 and over borrowing from
a financial institution %
Global Findex database (http://datatopics.worldbank.org/financial
inclusion/) BFF
The percentage of individuals aged 15 and over borrowing from
family and friends %
Global Findex database (http://datatopics.worldbank.org/financial
inclusion/) DCO
The percentage of individuals aged 15 and over having a debit
card %
Global Findex database (http://datatopics.worldbank.org/financial
inclusion/) CCO
The percentage of individuals aged 15 and over having a credit
card %
Global Findex database (http://datatopics.worldbank.org/financial
inclusion/)
ATM The ratio of ATMs/ 100,000 adults
The number of ATMs/
100,000 adults
International Monetary Fund, Financial Access Survey.
BRAN The ratio of commercial bank branches/100,000 adults
The number of commercial
bank branches/
100,000 adults
International Monetary Fund, Financial Access Survey.
Income
inequality GINI Gini coefficient % World development indicators, World Bank
Inflation CPI Consumer price index % World development indicators, World Bank
Education PSE The percentage of the population aged 25 and over graduating from
secondary education % UNESCO Institute for Statistics (2019) Labor EPR The employment-to-population
ratio % World development indicators, World
Bank Science
and technology
IUI The ratio of Individuals using the
Internet to the total population % World Development Indicators (WDI), World Bank
Economic openness
ICF The index of economic freedom % World Development Indicators (WDI), World Bank
OPEN The ratio of exports and imports to
GDP % World Development Indicators (WDI),
World Bank Source: Authors’ data processing.
Research Results
Table 4 presents descriptive statistics of the variables used in the model. According to table 4 data, the average Gini of 29 countries which has high and upper middle income in Europe is
0,3507. The country with the lowest Gini is 0.24 and the highest one is 0.535. The countries with upper-middle income in Europe have relatively high levels of financial inclusion, the average number of ATMs/100,000 adults in a country is 71.43, the number of commercial bank branches/100,000 adults on average is 24.43. The level of financial using is also quite high: the average percentage of people aged over 15 with an account which is 68.25%; the national average rate of people aged over 15 with a debit card which is 54%; the average rate of people aged over 15with a credit card per country which is 24.45%.
Table 4. Descriptive statistics of variables used in the model Variable Obs Mean Std. Dev. Min Max
GINI 315 35.070 7.433 24 53.5 FT1 305 0.000 0.999 -1.812 2.577 EPR 315 54.990 7.565 38.03 76.06
PSE 310 79.371 19.948 38.6 100
OPEN 315 106.087 64.186 23.9 416.4 CPI 301 114.089 16.076 99.939 235.229 IUI 315 63.902 20.055 12.28 98.14
ICF 315 65.637 7.686 45.8 81.9
FIA 309 68.245 27.267 3.757 100 SFI 309 26.412 19.743 0.819 79.3
BFI 309 13.415 5.486 1.81 35
BFF 309 17.712 9.262 3.281 43.007 DCO 309 54.450 28.027 1.702 98.8 CCO 309 25.402 17.901 0.495 72.405 ATM 311 71.432 33.861 11.913 191.172 BRAN 314 24.438 16.075 0.453 85.948 Source: Authors’ data processing.
To check whether the selection of a model with an instrumental variable in assessing the impact of financial inclusion and factors on income inequality is appropriate or not, the authors use Durbin and Wu-Hausman tests. Durbin and Wu-Hausman test results in Table 5 show that P- value = 0.0000 < 0.05, the model exists endogenous phenomenon, so the selection of regression model with instrumental variable to estimate the impact of financial inclusion and factors on income inequality is completely appropriate.
Table 5. Durbin and Wu-Hausman test results Test of endogeneityH0: variables are exogenous:
Durbin (score) chi2(1) = 22.6237 (p = 0.000) Wu-Hausman F(1,279) = 23.9658 (p = 0.000) Source: Authors’ data processing.
Next, to test whether the regression model with the instrumental variable exists disadvantages or not, the authors continue to perform the Sargan and Basmann test. The test results in Table 6 show that the p-values of both tests are greater than 0.05, showing that the research model does not have limitations and the model's estimates are solid estimates. Table 6:
Table 6. Sargan and Basmann test results Test of overidentifying restrictions:
Sargan (score) chi2(1) = 1.45366 (p=0.2279) Basmann chi2(1) = 1.42532 (0=0.2325) Source: Authors’ data processing.
We are going to test whether the selected instrumental variable is suitable or not by using first- stage regression. The test results of Table 7 show that the selection of FII as an endogenous variable is completely appropriate. Exogenous variables (IUI, ICF) explained 74.72% of the variation in FII.
Table 7. Instrumental variable test results First-stage regression summary statistics
Variable R-sq. Adjusted
R-sq.
Partial
R-sq. F(2,279) Prob>F
FII 0.7525 0.7472 0.6935 315.697 0.000
Minimum eigenvalue statistic = 315.697
Critical Values # of endogenous regressors: 1 H0: Instruments are weak # of excluded instruments: 2
2SLS relative bias 5% 15% 20% 30%
(not available)
2SLS Size of nominal 5% Wald test 10% 15% 20% 25%
19.93 11.59 8.75 7.25
LIML Size of nominal 5% Wald test 8.68 5.33 4.42 3.92
Source: Authors’ data processing.
The above test results show that it is completely appropriate to choose the 2SLS model to quantify the impact of financial inclusion and other controlling factors on income inequality.
The results of the 2SLS model estimation are shown in Table 8.
Table 8. Estimating the impact of financial inclusion and other factors on income inequality Instrumental variables (2SLS) regression Number of obs = 286
Wald chi2(5) = 452.92 Prob > chi2 = 0.000 R-squared = 0.526 Root MSE = 4.612
GINI Coef. Std.Err. z P>|z| [95% Conf. Interval]
FII -1.069 .368 -2.90 0.004 -1.792 -.346
EPR .1879 .038 4.86 0.000 .112 .263
PSE -.191 .016 -11.90 0.000 -.222 -.159
OPEN -.035 .006 -5.73 0.000 -.047 -.023
CPI -.008 .018 -0.49 0.628 -.044 .026
_cons 44.566 3.395 13.12 0.000 37.911 51.221
Instrumented: FII
Instruments: EPR PSE OPEN CPI IUI ICF Source: Authors’ data processing.
The estimated results in Table 8 show financial inclusion (FI1), the proportion ofthe population aged over 25 who have graduated from secondary school (PSE); Openness of the economy (OPEN) has a negative impact on income inequality. Increasing access to and use of financial services, increasing the proportion of the population graduating from secondary school, and implementing policies to open-up the economy will reduce income inequality. In contrast, the employment-to-population ratio (EPR) has a positive impact on income inequality, increasing the ratio of employed workers to the total population is one of the most important reasons to
increase income inequality. Moreover, there is no evidence to suggest the impact of the consumer price index (CPI) on income inequality.
Discussion
The estimation results obtained in Table 4 provided further evidence that financial inclusion had an opposite effect on income inequality, development of financial inclusion will reduce income inequality. Financial inclusion will expand access to finance to a large number of low-income individuals and small-sized enterprises. Moreover, bank loans also help farmers and poor people reduce risks in life such as illness, disease, crop failure, natural disasters. The poor will avoid the vicious cycle of having to borrow from the informal sector with high interest rates, creating an increasingly-high debt payment burden, and then the poor will become poorer and even impoverished. People without bank accounts are also easily excluded from other services such as health care and insurance. Especially, in the context of the Covid-19 pandemic, the increase in unemployment and income reduction may cause a part of people to fall into poverty, leading to an increase in income inequality. The research results also showed the percentage of the population aged 25 and over graduating from secondary education (PSE), and economic openness (OPEN) had an opposite effect on income inequality. This implies that in addition to the policies of financial inclusion, policy makers need to pay attention to the policies of economic openness, maintain the policies of universal education.
Conclusion
Financial inclusion is an important factor contributing to reducing income inequality in countries. To reduce income inequality, the State policies need to be directed at encouraging and creating opportunities for the poor and the disadvantaged groups to participate in financial inclusion. Besides, there should be policies to promote development of financial inclusion, specifically:
Firstly, financial education is an important factor to promote financial inclusion. The lack of knowledge about the characteristics and conditions of using financial products/ services leads to lack of confidence, fear of access and distrust towards financial products/ services in the formal financial market. This creates a major barrier in accessing financial services in the formal market, increases the number of people who have little access to banking products, promotes the emergence of informal financial products/services (black markets), and hinders the improvement of financial inclusion in each country. Financial education can provide the people with necessary knowledge about formal financial products/ services create trust and confidence to actively access products and services available in the formal market, limit the expansion of informal financial markets, and directly promote financial inclusion in each country. Moreover, thanks to financial education, individuals/ households will tend to save and manage their budget better, which helps to increase savings resources among the people, promote investment capital for the society, and create a positive effect on investment and economic growth. Financial education and propaganda should be promoted to change people’s perception of financial inclusion. Most surveys on finance and banking in recent time have showed that number of people do not have enough knowledge to understand the financial products, and the risks related to financial products. Furthermore, the majority of individuals does not know how to budget for the future and do not effectively implement their financial management decisions. This has a negative impact on the stability of the financial system and the economy as well as each individual or household, especially those with low income.
Secondly, increase access to financial services for the poor in rural areas. The system of financial service providers needs to operate safely, efficiently and responsibly; among them, the role of microfinance institutions and non-bank credit institutions, etc. should be promoted. The
goal should be to provide basic financial services in an appropriate manner (both traditional and modern distribution channels) for the financially excluded persons. Expand bank branches and ATMs in rural areas; implement flexible savings packages based on their income stream (every week, month, season, etc.). One of the factors that make the poor become the financially excluded persons is the complicated administrative procedures and papers that make them feel doubtful and afraid, and then decide to use informal financial resources. Improvement in the direction of streamlining, use of simple and understandable language will help increase access to financial services and products in rural areas.
An important goal in the economic development of countries around the world is to constantly improve people’s living standards and narrow the gap between the rich and the poor. The State needs to focus on the policies to reduce income inequality such as: Use taxes as one of the most important tools to re-distribute income to the groups of people, whereby people with higher incomes have to pay more income tax; Increase social spending, develop the labor market, actively invest in agriculture, and improve the tax system; The government needs to have policies to support female workers, young workers, elderly workers and the vulnerable in the economy.
The State should ensure that all citizens may access the high-quality education. Attention should be paid to increasing investment in education. The education and training system should associate training with use, scientific research and technology transfer to meet social demands;
strengthen educational support for disadvantaged areas, ethnic minorities and social policy beneficiaries; develop science and education, expand and improve the effectiveness of international cooperation. Education on ethics, life skills, creativity, and practical skills needs to be focused to meet the needs of high-quality human resources, ensure social justice in education and lifelong learning opportunities for every citizen so that everyone has the opportunity to have lifelong learning.
To promote the opening of the economy, it is necessary to focus on developing potential service sectors such as tourism, air transport insurance, construction, labor export, etc., encourage the development of new services with high competitiveness; promote exploitation of potentials and advantages of each service sector; strengthen cooperation between service sectors for mutual development and competition. Promote the export of services, and in-place foreign currency collection services through tourism, finance-banking, remittances collection, direct sales, post and telecommunications, air transport and sea transport; reduce service balance deficit; conduct promotion of service industries, trade promotion, investment promotion to improve the capacity and efficiency of marketing of service needs from abroad.
In conclusion, this research provides further evidence that financial inclusion has an impact on reducing income inequality. To reduce income inequality, policymakers should implement the policies that encourage the access, availability and use of financial products for the bottom of the income pyramid. Regarding this issue, the policies should also handle the constraints to financial inclusion in order to avoid financial exclusion. In addition, it is important to expand the scope of financial products to meet customers’ specific needs, which could be the products of special savings, or health insurance, or climate risk insurance.
References
1. Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation of errorcomponent models, Journal of Econometrics, 68, pp. 29–51.
2. Baligh, N. and Piraee, K. (2013). Financial Development and Income Inequality Relationship in Iran, Middle-East Journal of Scientific Research, 13 (Special Issue of Economics), pp. 56-64.
3. Beck, T., Demirgũc-Kunt, A., and Levine, R. (2007). Finance, inequality and the poor, Journal of Economics Growth, 12, pp. 27-49.
4. Cámara, N. and Tuesta, D. A. (2014). Measuring Financial Inclusion: A Muldimensional Index’.
BBVA Research, Paper No. 14/26, Economic Research Department, Madrid.
5. Chakraborty, C. and Nandi, B. (2011). Mainline telecommunications infrastructure, levels of development and economic growth: Evidence from a panel of developing countries.
Telecommunications Policy, 35(5), 441–449. https://doi.org/10.1016/J.TELPOL.2011.03.004 6. Hoi, C. M. and Hoi, L. Q. (2012). Financial Development and Income Inequality in Vietnam: An
Empirical Analysis, Journal of Economics and Development, 14 (2), pp. 5-25.
7. Hoi, C. M. (2014). Relationship between financial access and income, Economy and Forecast Review, the issue in June, pp. 10-13.
8. Hoi, C. M. (2017). The impact of financial development on income inequality in Vietnam. Doctoral thesis. Central Institute for Economic Management, Hanoi.
9. Cull, R., Ehrbeck, T. and Holle, N. (2014). Financial inclusion and development: recent impact evidence. CGAP focus note, no.92 Washington, D.C: World Bank Group.
http://documents.worldbank.org/curated/en/269601468153288448/Financial-inclusion-and- development-recent-impact-evidence.
10. Demirgüç-Kunt, A. and & Levine, R. (2009). Finance and Inequality: Theory and Evidence, Policy Research Working Paper 4967, World Bank.
11. Dung, C. X. (2004). The Impacts of Trade Openness on Growth, Poverty, and Inequality in Vietnam: Evidence from Cross-Province Analysis. Semantic Scholar.
https://www.semanticscholar.org/paper/THE-IMPACTS-OF-TRADE-OPENNESS-ON- GROWTH%2C-POVERTY%2C-Dũng/5b53a1f75d0a8b56fd5549b3edb0b517c0ebc3e1 12. Galor, O. and Moav, O. (2004). From physical to human capital accumulation: inequality and the
process of development, Rev. Econ. Stud. 71(4):1001- 1026
13. García-Herrero, A. and Turégano, D.A. (2015). Financial inclusion, rather than size, is the key to tackling income inequality, BBVA Working Paper no. 15/05.
14. Lan, H.H. andThuong, P. T. H. (2019). Financial Inclusion and Income Inequality: Empirical Evidence from Transition Economies. Journal of Economics and Development; National Economics University; volume 21, Special Issue, pp. 23-24.
15. Tran, H. T. T. and Le, H. T. T. (2021). The impact of Financial Inclusion on Poverty Reduction.
Asian Journal Law and Economic, 12 (1): 95-119.
16. Houghton, J. and Khandker, S. R. (2009). Handbook on Poverty and Inequality. Washington, DC:
World Bank.
17. Huong Thi Thanh Tran, Quynh Thuy Nguyen, Hoa Viet Nguyen and Giang Duc Doan (2020). The Impact of Financial Inclusion on Economic Growth. Banking Science & Training Review. No. 223, pp 1-12.
18. Hội, C. M. and Ngọc, Đ. B. (2015). Tác động của phát triển thương mại đến bất bình đẳng thu nhập ở Việt Nam: Lý thuyết và thực tiễn. Nghiên Cứu Kinh Tế, 24–33(2).
19. Jalil A. and Feridun, M. (2011). Long-run relationship between income inequality and financial development in China, Journal of the Asia Pacific Economy, 16 (2), pp. 202-214.
20. Jensen, H. T. and Tarp, F. (2005). Trade liberalization and spatial inequality: A methodological innovation in a Vietnamese perspective. Review of Development Economics, 9(1), 69–86.
https://doi.org/10.1111/J.1467-9361.2005.00264.X
21. Muszynska, J and Wedrowska, E (2018). Income inequality of households in Poland: A subgroup decomposition of generalized entropy measures. Econometrics Ekonometria, Sciendo; Vol.22, No.4.
22. Le, H. T. and Booth, A. L. (2010). Inequality in Vietnamese Urban-Rural Living Standards, 1993- 2006.
23. Le, M.S, Tho. N.D. and Singh, T. (2014). Economic Growth and Poverty in Vietnam: Evidence from Elasticity Approach, Discussion paper no. 2014-01, Griffith Business School.
24. Long, P. D., Ngoc, P. T. B. and Görg, H. (2019). Trade Liberalization and Labor Market Adjustments: Does Rent Sharing Matter? 57(6), 1828–1841. Available through:
https://doi.org/10.1080/1540496X.2019.1643316
25. Lorenz, M.O. (1905). Methods of measuring the concentration of wealth. Journal of the Royal Statistical Society, 9, 209–219.
26. Muhammad, S., Tiwari, A. and Reza, S. (2012). Financial Development and Income Inequality: Is there any Financial Kuznets curve in Iran?, MPRA Paper No. 40899.
27. Omar, M. A. and Inaba, K. (2020). Does financial inclusion reduce poverty and income inequality in developing countries? Journal of Economic Structures. The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS). SpringerOpen; Number 37.
28. Park, C. Y. and Mercado, R. (2018). Financial Inclusion Poverty and Incom Inequality. Singapore
Economic Review, 63(1), 185–206. Available through: https://doi.org/10.1142/S0217590818410059 29. Ranjan, R. (2000). Child Labor, Child Schooling, and Their Interaction with Adult Labor:
Empirical Evidence for Peru and Pakistan, WB Economic Review, 14(2) pp. 347-367.
30. Raychaudhuri, A. and De, P. (2016). Trade, Infrastructure and Income Inequality in Selected Asian Countries: An Empirical Analysis. International Trade and International Finance; pp 257-278.
31. Rogers, C. and Swinnerton, K. A. (2000). Inequality, Productivity, and Child Labor: Theory and Evidence. Working Papers, Georgetown University, Department of Economics.
32. Shahbaz, M. and Islam, F. (2011). Financial development and income inequality in Pakistan: An application of ARDL approach, Journal of Economic Development, 36(1), pp. 35-58.
33. Tran, V. H. and Yabe, M. (2011). The Change of Income Inequality Dimensions in Rural Vietnam:
A Perspective From Agriculture Production, China-US Business Review, 10 (12), pp.1347-1360.
34. World Bank (2017). Governance and The Law. In World Bank Group.
35. World Bank (2018). Ending Poverty, World Bank Annual Report.
36. World Bank (2018). Financial inclusion overview. Available through:
https://www.worldbank.org/en/topic/financialinclusion/overview. [Accessed 20 April, 2020].
37. World Bank (2019). World Development Indicators database. Washington, DC. Available through:
http://data.worldbank.org. [Accessed 20 April, 2020].
38. Work Bank (2020). Financial Inclusion overview. Available through:
https://www.worldbank.org/en/topic/financialinclusion/overview#1. [Accessed 20 April, 2020].
