Research Article |
Corresponding author: Thakur Dev Pandey ( thakurdev009@gmail.com ) Academic editor: Marina Sheresheva
© 2023 Thakur Dev Pandey.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Pandey TD (2023) Impact of Financial Inclusion on Human Development Index: Special Reference to BRICS Countries. BRICS Journal of Economics 4(2): 209-223. https://doi.org/10.3897/brics-econ.4.e96288
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The BRICS countries are frequently referred to as “emerging economies”; they account for a sizable proportion of the global population and face issues such as poverty, income inequality, slow economic growth, gender inequality, and high unemployment rates. Policy measures are currently being considered and implemented in response to these difficulties. Previous studies suggest that greater financial inclusion has a positive effect on such countries’ development, hence its importance as a tool used to deal with the socioeconomic challenges faced by emerging economies. When we talk about “financial inclusion,” we are referring to the ease with which people can access and make use of basic financial services, such as savings accounts, credit cards, and insurance. In this paper, we use data from the Global Findex Database and the World Bank Database to create the Financial Inclusion (Finclusion) Index, which provides a comparative measure of financial access for different nations. The primary purpose of this paper is to evaluate the effect of financial inclusion on HDI in 105 countries; per capita income and gender development are also compared across BRICS countries. The study found that financial inclusion had a significant impact on human development in general and a positive effect on the development of women in particular. The result is valid for the BRICS countries, where financial inclusion has considerably boosted human development and is positively correlated with women empowerment.
HDI, BRICS, Financial Inclusion, Finclusion Index, Gender Development, Human Development.
The term BRICs was coined in 2001 by the then-chairman of Goldman Sachs Asset Management, Jim O’Neill, in the work Building better global economic BRICs, to address the importance of emerging economies in Asia, Europe, and South America and their impact on the global market (J.
1.1. Brazil: The Banking Correspondents (BCs) Model, which had its origins in the 1970s but saw renewed attention in the 2000s as a result of amendments that broadened the scope of BCs’ services and eliminated various restrictions to increase the availability of banking throughout the country, had been instrumental in promoting branchless banking. Therefore, the number of people with bank accounts more than doubled, from 63.7 million in 2000 to 125 million in 2008.
1.2. Russian Federation: Nearly a third of the population is underbanked, unbanked, or unserved in the Russian Federation, according to the Alliance for Financial Inclusion (2011). This is explained by unequal access to financial services and low financial literacy. The high cost of banking services and the low profits to be made by opening branches in rural areas were the main factors of this exclusion. Long-term funding from AFI allowed the Ministry of Finance, the Central Bank of Russia, and the Russian Microfinance Center to launch a program in September 2010 in order to promote innovative regulation and monitoring of banking agents and improve access to financial services throughout the country, solving the problem of financial exclusion (
1.3. India: In the early 2000s, the Reserve Bank of India (RBI) recognized the issue of financial exclusion. The Reserve Bank of India (RBI) and the Government of India (GOI) have been working to increase banking penetration in their country and to remove obstacles to FI (Gupta, 2011; Joshi, 2011). The Reserve Bank of India has streamlined many banking-related policies to increase financial inclusion. These include Zero Balance Accounts and Overdraft Facility; Oversimplification of Know Your Customer (KYC) and Account Opening Norm; Financial Literacy; Simplification of Branch Authorization; General Credit Cards; Kisan Credit Cards (KCCs); Implementation of Business Facilitators (BFs) and Business Correspondents (BCs); and Expansion of Bancassurance.
1.4. China: A disparity in access to banking services between rural and urban areas was noted by the China Banking Regulatory Commission in 2006. While 3,302 villages in China had no bank branches at all, 8,231 villages had only one. This meant that the rural population lived in a financial desert. China’s microfinance program “has been implemented as part of the Chinese government’s initiative for financial inclusion of low-income and rural people” (
1.5. South Africa: The high cost of banking had been considered a major cause of South Africa’s high rate of financial exclusion. That is why six million Mzansi accounts were opened in the country of 32 million people after the Central Bank of Africa had urged five major banks to offer the no-frills “Mazansi Accounts” to its customers with five free transactions a month (2009 reported by the non-profit FinMark Trust).
2.1. Data Source: The present study is based on secondary data from Global Findex Database 2021, Women Business and Law Index 2021, World Bank, and reports from RBI during the period 2018-2021. Availability of data for key observations in the Global Findex Database 2021 report allowed the authors to select for the analysis a total of 105 countries including the BRICS countries.
2.1.1. Variables: For Human Development, Human Development Index (HDI) 2021 was selected; for Women’s/ Gender Development, Gender Development Index (GDI) 2021; for national income, Per Capita Gross National Income (PGNI) 2021; for financial inclusion, Finclusion Index is constructed using Global Findex Database 2021. The study also used Women Business and Law (Index 2021, total literacy rate, GDP per capita PPP (at 2017 US$), and Human Capital Index (HCI) 2021.
2.2. Objectives:
2.3. Financial Inclusion (Finclusion) Index Construction method: A Finclusion Index is constructed using Human Development Report (HDR) 2010 methodology (
Each dimension Dij contains n number of subdimension δi.j.
Di = (δ1.j , δ2.j, δ3.j, ..., δn.j)
We standardize each dimension δij so that each dimension’s value lies between 0 and 1 as:
where,
i = dimension number
The selection of Finclusion Index dimensions is determined by measuring the intensity of financial inclusion in accordance with the fundamental definition of financial inclusion, i.e. providing easy access to banking services such as saving and deposit bank accounts, credit facilities, insurance, and other credit related services to the mass population. Financial inclusion for a particular nation can be quantified using the following: percentage of the population (above 15 years of age) having active bank accounts; the frequency of using a bank account or banking services that include credit cards, debit cards, mobile banking, or any other banking-related service such as unified payment system and internet banking; and, finally, how easily one can use banking services.
2.3.1. Dimension 1:
Active Bank Account: Percentage of the population having an active bank account with a financial institution. The following observations were taken from the Global Findex Report 2021:
a. Financial institution account (% age 15+).
δ1: Having an active bank account.
D1 = δ1
2.3.2. Dimension 2:
Frequency of using Bank Accounts: Percentage of the population using banking services such as ATMs, debit cards, credit cards, and mobile banking. The following observations were taken from the Global Findex Report 2021: -
a. Used a credit card (% age 15+)
b. Used a debit card (% age 15+)
c. Used a mobile phone or the internet to make payments, buy things, send or receive money using a financial institution account (% with a financial institution account, age 15+); made a deposit (% with a financial institution account, age 15+)), (withdrew money from a financial institution account two or more times a month (% age 15+).
δ2.1: Using a credit card.
δ2.2: Using a debit card.
δ2.3: Mobile banking
δ2.4: Other banking services
2.3.3. Dimension 3:
Ease of opening a bank account: D3 measures the difficulty of opening a bank account and using banking services. The following observations were taken from the Global Findex Report 2021:
a. No account because financial institutions are too far away (% without an account, age 15+)
b. No account because financial services are too expensive (% age 15+)
c. No account because of insufficient funds (% age 15+)
d. No account because of a lack of necessary documentation (% age 15+)
e. No account because of a lack of trust in financial institutions (% age 15+); No account because of religious reasons (% age 15+); No account because someone in the family has one (% age 15+).
е 3.1: Not having a bank account because banks are far away
е 3.2: Not having a bank account because banking services are expensive
е 3.3: Not having a bank account because of no funds
е 3.4: Not having a bank account because of no documents
е 3.5: Not having a bank account because of other reasons
е 3. j is a negative subdimension relative to financial inclusion as a higher value of е 3. j shows a higher degree of financial exclusion, so we standardize the sub-variable е 3.j as
D3 = 1 – у 3
2.3.4. Finclusion Index: Finclusion Index is constructed as a geometric mean of three dimensions, namely D1 (Active bank account), D2 (Frequency of using bank account), and D3 (Ease of opening bank account). The higher value of the Finclusion Index shows a higher degree of financial inclusion, and the lower value shows a lower degree of financial inclusion in the region.
Where, D1 = Active Bank Account; D2 = Frequency using Bank Account; D3 = Ease of opening bank account
2.4. Econometric Model: To assess the impact of financial inclusion on the Human Development index a Limited Information Maximum Likelihood (LIML) Instrumental Variable Method was used (
Instrumented: I_PGNI, I_FI
Instruments: I_GDP_PPP, I_T_Lit, GDI
Instrumented: I_PGNI, I_FI
Instruments: I_GDP_PPP, I_T_Lit, HCI
Where,
HDI = Human Development Index
l_PGDP = log of Per capita Gross Domestic Product
l_FI = log of Finclusion Index
I_WBL_Index = log Women Business and Law Index
GDI = Gender Development Index
I_T_Lit = log Total Literacy Rate
I_GDP_PPP = log Per Capita Gross Domestic Product (PPP 2017 US$),
HCI = Human Capital Index
As expected, developed countries had a higher reported value of the Finclusion Index (FI), which measures the extent to which financial services are available to the population, than did less developed economies. Table
BRICS Rank | Country | GDI 2021 | HDI 2021 | HDI RANK | Finclusion Index | FI Rank | PGNI USD | T Lit | F Lit | GDPPC PPP 2017 USD | WBL Index | HCI 2021 |
1 | Russian Federation | 1.007 | 0.82 | 52 | 0.786 | 37 | 26666.9 | 99.71 | 99.73 | 25926.443 | 73.125 | 0.681 |
2 | China | 0.957 | 0.76 | 85 | 0.784 | 38 | 15970.2 | 96.35 | 95.16 | 14243.533 | 75.625 | 0.653 |
3 | Brazil | 0.993 | 0.762 | 84 | 0.717 | 47 | 14327.3 | 92.58 | 93.43 | 14524.614 | 81.875 | 0.551 |
4 | South Africa | 0.986 | 0.707 | 114 | 0.718 | 45 | 12171.2 | 94.59 | 94.53 | 13860.270 | 88.125 | 0.425 |
5 | India | 0.82 | 0.636 | 131 | 0.442 | 72 | 6516.4 | 72.22 | 65.79 | 6182.922 | 68.75 | 0.493 |
The Gender Development Index, Human Development Index, Per Capita Gross National Income, Women Business, and Law Index, Human Capital Index, and Finclusion Index rankings and values for the BRICS nations are displayed in Table
Variable | Mean | Median | S.D. | Min | Max | |||||
105 countries | BRICS | 105 countries | BRICS | 105 countries | BRICS | 105 countries | BRICS | 105 countries | BRICS | |
GDI | 0.950 | 0.953 | 0.968 | 0.986 | 0.0577 | 0.0763 | 0.745 | 0.820 | 1.04 | 1.01 |
HDI | 0.757 | 0.737 | 0.776 | 0.760 | 0.145 | 0.0692 | 0.430 | 0.636 | 0.954 | 0.820 |
Fin. I. | 0.615 | 0.689 | 0.654 | 0.718 | 0.257 | 0.142 | 0.162 | 0.442 | 0.982 | 0.786 |
PGNI | 15130 | 23308 | 14327 | 4670 | 7372 | 20136 | 6516 | 1037 | 26667 | 87404 |
WBLI. | 79.3 | 77.5 | 82.5 | 75.6 | 16.7 | 7.60 | 26.9 | 68.8 | 100. | 88.1 |
HCI | 0.595 | 0.561 | 0.599 | 0.551 | 0.139 | 0.107 | 0.318 | 0.425 | 0.879 | 0.681 |
Table
When compared to the FI, GDI, and PGNI, the correlation between HDI and their respective values was strong, while the GDI reported only a moderate correlation with all other variables. Table
GDI | HDI | FI | PGNI | WBL Index | HCI | |
1.000 | 0.626 (0.852) | 0.607 (0.932) | 0.432 (0.729) | 0.570 (0.622) | 0.590 (0.335) | GDI |
1.000 | 0.889 (0.888) | 0.865 (0.939) | 0.464 (0.127) | 0.948 (0.766) | HDI | |
1.000 | 0.824 (0.766) | 0.500 (0.459) | 0.900 (0.538) | Finclusion Index | ||
1.000 | 0.405 (-0.051) | 0.863 (0.776) | PGNI | |||
1.000 | 0.505 (-0.487) | WBL Index | ||||
1.000 | HCI |
Figure
Table
Model 1, LIML, Dependent variable: HDI. Instrumented: l_PGNI, l_FI. Instruments: const, l_GDP_PPP, l_T_Lit, l_WBL_I, GDI
Using observations 1-104 (105-Countries- Dropping 1 variable) | Using observations 1-5 (BRICS -Countries) | |||||
Coefficient | z | p-value | Coefficient | z | p-value | |
const | 0.705 (0.577) | 1.221 | 0.222 | −5.87641e+09 (2.31149e+09) | −2.542 | 0.0110 |
l_PGNI | 0.032 (0.039) | 0.814 | 0.415 | 0.108 (8126.21) | 1.340e-005 | 1.0000 |
l_FI | 0.253** (0.106) | 2.380 | 0.017 | 0.238 (9353.77) | 2.545e-005 | 1.0000 |
l_WBL_I | −0.024 (0.037) | −0.663 | 0.506 | 1.35200e+09** (6.32306e+08) | 2.138 | 0.0325 |
Chi-square(3) 1158.86 | p-value 6.2e-251 | Chi-square(3) 160.409 | p-value | 1.50e-34 | ||
Smallest eigenvalue = 1.03328 | LR over-identification test: Chi-square(1) = 3.40446 [0.00] | Smallest eigenvalue = 1.5479e+023 | LR over-identification test: Chi-square(1) = 266.982 [0.0000] | |||
Test for normality of residual - Null hypothesis: error is normally distributed. Test statistic: Chi-square(2) = 9.6481 with p-value = 0.008 | ||||||
Pesaran-Taylor test for heteroskedasticity - Null hypothesis: heteroskedasticity not present. Asymptotic test statistic: z = 3.21883 with p-value = 0.001 |
In Model 1, the test for normality and heteroskedasticity gives consistent results. The output of model 1 for 105 countries reports a significant impact of financial inclusion on human development. However, the model did not show any significant impact of the PGNI and WBL index on HDI. The model for BRICS countries reports a significant impact of the WBL Index on HDI, while the PGNI and FI did not have any significant impact on HDI.
Table
Model-2, LIML, Dependent variable: GDI. Instrumented: l_PGNI, l_FI, HCI. Instruments: const, l_GDP_PPP, l_T_Lit, HDI
using observations 1-104 (105-Countries- Dropping 1 variable) | using observations 1-5 (BRICS -Countries) | |||||
Coefficient | z | p-value | Coefficient | z | p-value | |
const | 2.529*** (0.943) | 2.680 | 0.0074 | 5.79998e+06** (2.46206e+06) | 2.356 | 0.018 |
l_PGNI | −0.081 (0.053) | −1.524 | 0.1275 | −0.120 (64.237) | −0.0018 | 0.998 |
l_FI | 0.528* (0.287) | 1.836 | 0.0663 | 0.126 (107.606) | 0.0011 | 0.999 |
HCI | −0.816 (0.688) | −1.186 | 0.2356 | −0.338 (-) | - | - |
Chi-square(3) 13.088 | p-value 0.004 | Chi-square(3) 175.57 | p-value | 7.97e-38 | ||
Smallest eigenvalue = 1 | Equation is just identified | Smallest eigenvalue = 2.35695e+022 | LR over-identification test: Chi-square(1) = 247.19 [0.0000] | |||
Test for normality of residual - Null hypothesis: error is normally distributed. Test statistic: Chi-square(2) = 7.546 with p-value = 0.022 | ||||||
Pesaran-Taylor test for heteroskedasticity - Null hypothesis: heteroskedasticity not present. Asymptotic test statistic: z = 3.07 with p-value = 0.002 |
In Model 2, the test for normality and heteroskedasticity gives consistent results. The output of model 2 for 105 countries reports a significant impact of financial inclusion on the Gender Development Index. However, the model did not reveal any significant impact of PGNI and the Human Capital index on GDI. For BRICS countries the model reported that any of the exogenous variables did not have any significant impact on GDI, which may be a limitation attributed to the small size of the sample.
Therefore, from the output of Model 1 and Model 2, we can accept that financial inclusion does have effective significance for human development but in the case of BRICS countries, women’s development has a more significant impact on human development. It is possible to conclude that financial inclusion also has a significant impact on women’s development, but there have been no relevant observations for the BRICS countries and this issue may thus provide a scope for further research.
The primary purpose of the study was to compare per capita incomes and gender development indices across the BRICS countries and to analyze the impact of financial inclusion on the Human Development Index and Gender Development Index. The research used data from the Global Findex Database 2021 to develop a Finclusion Index and ranked 105 countries showing that across the board Russia performed best among the BRICS countries, while India performed lowest. Performance on the GDI and FI were both above average for the BRICS, while HDI and PGNI were both below average (2017 PPP USD). According to the results of the econometric model fitting, financial inclusion was found to have a far greater effect on human development than either per capita income or women’s development. It was also found that financial inclusion and per capita income are positively correlated with gender development, suggesting that these metrics can be used when checking on progress toward human development since high-level human development can be achieved by prioritizing the advancement of both sexes and working to create a banking system that is accessible to all. The study also revealed the fact that India’s low results dragged down the BRICS average performance, showing that the other BRICS countries could work to help the weaker links improve in the areas of capacity building, policy recommendation, and monitoring in order to maximize the bloc’s overall performance. Also, it should be remembered that the BRICS organization has been around for nearly two decades: it may be time for the BRICS countries to expand their membership to other emerging economies, such as South Korea, Indonesia, Iran, Mexico, Saudi Arabia, and Turkey. This would be beneficial for both the current and the new members as the expanded BRICS could serve as a platform for its members, giving them the chance to maximize their economic potential.