research papers on financial inclusion

The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

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• Published: 28 April 2020

Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis

• Md Abdullah Omar   ORCID: orcid.org/0000-0002-1676-5564 1 &
• Kazuo Inaba 2  

Journal of Economic Structures volume  9 , Article number:  37 ( 2020 ) Cite this article

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Financial inclusion is a key element of social inclusion, particularly useful in combating poverty and income inequality by opening blocked advancement opportunities for disadvantaged segments of the population. This study intends to investigate the impact of financial inclusion on reducing poverty and income inequality, and the determinants and conditional effects thereof in 116 developing countries. The analysis is carried out using an unbalanced annual panel data for the period of 2004–2016. For this purpose, we construct a novel index of financial inclusion using a broad set of financial sector outreach indicators, finding that per capita income, ratio of internet users, age dependency ratio, inflation, and income inequality significantly influence the level of financial inclusion in developing countries. Furthermore, the results provide robust evidence that financial inclusion significantly reduces poverty rates and income inequality in developing countries. The findings are in favor of further promoting access to and usage of formal financial services by marginalized segments of the population in order to maximize society’s overall welfare.

1 Introduction

Financial inclusion connotes all initiatives that make formal financial services accessible and affordable, primarily to low-income people. In recent years, financial inclusion has been perceived as a dynamic tool for attaining multidimensional macroeconomic stability, sustainable and inclusive economic growth, employment generation, poverty reduction, and income equality for advanced and developing countries Footnote 1 alike. Moreover, financial inclusion seems an incremental and complementary approach to meeting the United Nations’ Millennium Development Goals (Chibba 2009 ). The emergence of financial inclusion promotes social inclusion through convenient access, availability, and usage of rules-based formal financial services by the “newly banked”. These are generally underprivileged population segments, vulnerable groups such as rural dwellers, women, and low-income families who benefit enormously from basic financial services like savings, borrowings, payment, and insurance (World Bank 2014 ). Due to insufficient income levels and market discrimination in developing regions, there are still millions of people involuntarily excluded from the financial system, which creates potential loss of savings, investable funds, and accumulation of wealth. Financial inclusion helps to fill these gaps and provide households and firms greater access to resources needed for finance consumption and investment and thereby raise the level of economic activity. In addition, financial inclusion makes growth inclusive: access to finance can enable economic agents to take part in long-term participatory investment activities, facilitate efficient allocation of productive resources and thus reduce the cost of capital, cope with unexpected short-term shocks, significantly improve day-to-day management of finances, and reduce usually exploitative informal sources of credit (Demirgüç-Kunt et al. 2015 , 2018 ).

Despite decades of rapid progress in reducing poverty and boosting prosperity, a large portion of the world’s poorer population still struggles to attain a minimum standard of living across developing regions, especially in Asia, Africa, and Latin America and the Caribbean. Progress in reducing extreme poverty seems uneven in these regions because of geographical and country-specific factors. The World Bank ( 2016 ) reports that more than half of the world’s extreme poor (50.7%) live in sub-Saharan Africa. Asia contains 42.7% of the world’s poor, though the whole region has a strong historical performance in reducing overall poverty by virtue of massive growth in emerging large economies. Latin America and the Caribbean contain the next-highest portion of the world’s poor (4.4%).

Poverty reduction in developing regions is slowing because of the prevailing nature of extreme income inequality, which is considered a powerful threat to economic progress. To this end, the World Bank set goals to end extreme poverty by 2030 and raise the shared prosperity of the bottom 40% of people in each country through reducing income inequality.

Therefore, financial inclusion has moved up the global reform agenda and gained great interest for its potential to break the vicious cycle of poverty and lower income inequality. Real-world financial systems are far from inclusive, so more emphasis is being placed on financial inclusion, which reflects its potentially transformative power to accelerate inclusive development. Given its multifaceted implications, financial inclusion represents a core topic for the World Bank ( 2014 ). The United Nations member countries have included financial inclusion as a formal target and a key objective in their development agenda (Sahay et al. 2015 ). Despite progress in this direction, evidence on the macroeconomic effects of financial inclusion is limited due to inconsistent macro-level data across countries. Many studies have investigated the determinants of financial inclusion, appropriate measures of financial inclusion at the individual and country level, and effective types of financial services on the user level. There is also evidence on financial inclusion’s effects on economic growth, financial stability, female empowerment, poverty alleviation, and income inequality, which has laid the foundation for this field of research. However, these studies are not enough to understand the broader macroeconomic implications of financial inclusion. This study seeks to take another step in the existing literature by examining the relationship between financial inclusion, poverty, and income inequality, sampling entire developing countries, focusing on Asia, Africa, and Latin America and the Caribbean region, whose level of voluntary as well as involuntary financial exclusion is relatively higher than that of other countries. Thus, this study addresses the following questions: first, what are the crucial factors that affect the level of financial inclusion in developing countries? Second, does financial inclusion reduce poverty and income inequality in developing countries? Third, are there any conditions under which financial inclusion can play a more effective role in reducing poverty and income inequality in developing countries?

This study contributes to the following existent financial inclusion related literature. First, it constructs a novel index of financial inclusion using a broad set of financial sector outreach indicators with an extensive panel data set of 2004–2016, following Sarma’s multidimensional approach ( 2012 ). Second, it identifies the determinants of financial inclusion and analyzes the impact of financial inclusion on reducing poverty and income inequality, focusing on entire developing countries in Asia, Africa, and Latin America and the Caribbean region. Third, it assesses conditional relationships between financial inclusion and other micro- or macroeconomic factors under which financial inclusion mitigates poverty and income inequality in developing countries. To our knowledge, there are no empirical studies that broadly examine the indirect provisions through which financial inclusion reduces poverty and income inequality. Fourth, it analyzes all major relationships between variables using a panel data set and fixed effect model to properly process endogeneity associated with financial inclusion.

This study finds that per capita real GDP and ratio of internet users positively influence the level of financial inclusion in developing countries, while age dependency ratio, inflation, and income inequality have a detrimental effect. Our results show robust evidence that economies with higher financial inclusion significantly reduce poverty rates and income inequality in developing countries. Moreover, the interaction terms of financial inclusion with GDP growth and secondary school enrollment ratio are statistically significant for poverty, whereas the interaction terms of financial inclusion with GDP growth and rule of law are statistically significant for income inequality. This suggests that the effectiveness of financial inclusion depends not only on itself, but also on other conditions in reducing poverty and income inequality.

The remainder of the paper is organized as follows. Chapter 2 reviews existing literature related to the topic. Chapter 3 reveals the empirical methodology, data measurement, and construction of the composite financial inclusion index for testing impacts through regression analysis. Chapter 4 presents the empirical results and discussions, and Chapter 5 concludes with a summary of our findings and policy implications.

2 Empirical literature review

This chapter initially reviews the concepts and measurement issues of financial inclusion, and the factors which significantly affect the level thereof; then it reviews the available evidence on financial inclusion’s impact on poverty and income inequality.

2.1 Conceptual issues and measurement of financial inclusion

The concept of financial inclusion has been explained in diverse ways in the existing literature, but all seem to have analogous information content in terms of conclusions. The World Bank ( 2014 ) has defined financial inclusion as the share of households and firms who use financial services. Amidžić et al. ( 2014 ) defined financial inclusion as an economic state where nobody is denied access to primary financial services based on motivations other than efficiency criteria. Demirgüç-Kunt et al. ( 2013 ) conceptualized financial inclusion as the use of formal financial services among different groups that benefit the welfare of many individuals. Sahay et al. ( 2015 ) said that financial inclusion is the access, usage, and delivery of financial services at affordable costs to vulnerable segments of society, while Sarma ( 2012 ) gave a comprehensive definition of financial inclusion based on several dimensions including accessibility, availability, and usage of the formal financial system for all members of an economy.

Although there is consensus on the concept of financial inclusion, existing literature lacks a standard method by which financial inclusion can be measured across economies. Honohan ( 2007 , 2008 ) constructed a financial access indicator by combining bank and MFI account numbers from household survey cross-sectional data in a limited number of countries. Amidžić et al. ( 2014 ) constructed a composite financial inclusion index by including outreach dimension (geographic and demographic penetration) and usage dimension (depositors and borrowers). They normalized each variable, statistically identified for each dimension using factor analysis, assigned weights to variables and sub-indices, and then aggregated the data through weighted geometric average. Cámara and Tuesta ( 2014 ) constructed a composite financial inclusion index by estimating three sub-indices covering usage dimension, access dimension, and barriers dimension (obstacles causing involuntary exclusion); dimension weights were estimated endogenously by employing a two-stage principal component analysis. Sarma ( 2012 ) proposed a multidimensional index of financial inclusion by combining accessibility, availability, and usage dimension, which satisfies some vital mathematical properties and is comparable across countries and over time. He computed a dimension index for each dimension, aggregated each index based on normalized Euclidian distance of achievement points from a worst and an ideal situation, and then took a simple average. This study uses Sarma’s approach.

2.2 Empirical evidence on determinants of financial inclusion

There have been a number of empirical studies concentrating on the factors that affect a country’s level of financial inclusion, but the results show no consensus. Sarma and Pais ( 2008 , 2011 ) examined country-specific factors associated with the level of financial inclusion by using a classical OLS method for the sample year of 2004. Among possible variables, income measured by per capita GDP, adult literacy, rural population, income inequality, physical connectivity indicated by road network, electronic connectivity indicated by phone subscriptions, information availability indicated by internet usage, bank soundness measured by non-performing assets and capital asset ratio, and foreign ownership in the banking sector were significantly associated with the level of financial inclusion.

Evans and Adeoye ( 2016 ) evaluated the determinants of financial inclusion in Africa by using a dynamic panel data approach for 15 countries over the period of 2005–2014. The results show that lagged financial inclusion (implies a “catch-up effect”), GDP per capita, money supply as a percentage of GDP, adult literacy rate, internet access, and Islamic banking activities have great significance in explaining the level of financial inclusion in Africa. Allen et al. ( 2014 ) found that population density and GDP per capita are strongly positively linked, whereas natural resources are strongly negatively linked to both financial development and financial inclusion in sub-Saharan Africa than elsewhere in the world.

Rojas-Suarez and Amado ( 2014 ) analyzed the relevant factors explaining Latin America’s financial inclusion gap relative to comparable countries and found that the core obstacles were socio-economic factors (represented as income inequality) and institutional deficiencies (measured as rule of law), while macroeconomic weaknesses (represented as inflation volatility) and financial sector inefficiencies (measured as overhead cost and bank concentration) were relatively less important factors in Latin America’s low level of financial inclusion.

2.3 Empirical evidence on financial inclusion, poverty, and income inequality

Due to the limited time span of available data and a large number of missing data regarding financial inclusion, the literature’s empirical impact analysis seems to cover this topic only partially. Only a few studies have investigated the link between financial inclusion, poverty, and income inequality, with mixed results. Park and Mercado ( 2015 ) tested the factors influencing financial inclusion and the significance of financial inclusion in reducing poverty and lowering income inequality, focusing on 37 developing Asian economies. They found that per capita income, rule of law, and demographic structure increased financial inclusion, while a higher age-dependency ratio significantly reduced financial inclusion. Primary education completion and literacy rates have no significant effect on the level of financial inclusion in developing Asia. Moreover, financial inclusion significantly reduces poverty; there is also evidence that it lowers income inequality when more regressors are considered.

In the latest version of their paper, Park and Mercado ( 2018 ) assessed the cross-country impact of financial inclusion on poverty and income inequality across country income groups by introducing a new financial inclusion index for 151 economies, using principal component analysis and a cross-sectional approach. The results indicate that higher financial inclusion significantly co-varies with higher economic growth and lower poverty rates, but only for high and middle-high-income economies, not those that are middle-low and low-income. However, they did not find significant effect of financial inclusion on income inequality in any income group.

Honohan ( 2007 , 2008 ) examined the fraction of the adult population using formal financial intermediaries for 162 economies and its relationship with poverty and inequality. The composite financial access indicator was constructed by using a cross-sectional series that combined both household survey data sets and published data. The results show that financial access significantly reduced poverty on its own, but not when other control variables were included as regressors, such as per capita income, private credit as a percentage of GDP, inflation, institutions (KKZ index), institutions (freedom house bank), population size, and a sub-Saharan Africa dummy. Furthermore, there was evidence that financial access significantly reduced income inequality on its own and also when financial depth measure (private credit as a percentage of GDP and inflation) was included, but the result did not hold when per capita income and a sub-Saharan Africa dummy were included.

Jabir et al. ( 2017 ) analyzed the effect of financial inclusion on reducing poverty among the low-income household level for 35 countries in sub-Saharan Africa. Taking cross-sectional data of 2011, they found that financial inclusion significantly reduced the level of poverty in sub-Saharan Africa through providing net wealth and larger welfare benefits to the poor.

Swamy ( 2014 ) found that gender dimension, particularly poor women’s participation in financial inclusion programs in general, had a strong impact on increasing household income and improving family well-being in India. Burgess and Pande ( 2005 ) revealed that state-led bank branch expansions into rural unbanked locations significantly reduced rural poverty in India through access to formal sector credit provision and saving opportunities. Brune et al. ( 2011 ) determined that increased financial access by offering commitment savings accounts to poor smallholder cash-crop farmers in Malawi had a substantial impact on their well-being, as it provided access to funds for agricultural input.

García-Herrer and Turégano ( 2015 ) assessed the role of both dimensions of financial development (size of the financial sector and financial inclusion) in reducing income inequality. They found that financial inclusion contributed to reducing income inequality when the regression was controlled for key relevant factors, especially economic development and fiscal policy. Interestingly, financial deepening (size of the financial system) did not appreciably contribute to a more equal income distribution. Dabla-Norris et al. ( 2015 ) stated that reducing financial participation and monitoring costs and relaxing collateral constraints helped to encourage growth and lower inequality in Latin America and the Caribbean, though trade-offs were likely.

Salazar-Cantú et al. ( 2015 ) investigated the effect of financial inclusion on inequality in income distribution based on regional information in Mexico. The results indicated that higher financial inclusion would initially lead to greater income inequality, but later reduce inequality significantly as financial inclusion continued to grow within Mexican municipalities.

Although all of these studies suggest links between financial inclusion, poverty, and income inequality, they lack a comprehensive understanding of their relationship due to their lack of panel data study and a limited set of variables for constructing a financial inclusion index. This study tries to expand on existing literature regarding impact analysis of financial inclusion on poverty and income inequality with a broad set of variables for financial inclusion index, and a panel data set consisting of a large number of developing countries in Asia, Africa, and Latin America and the Caribbean.

3 Methodology

Based on previous studies, this chapter specifies an econometric model to analyze crucial factors that influence the level of financial inclusion, impact of financial inclusion on reducing poverty and income inequality, and conditional relationships of financial inclusion in reducing poverty and income inequality in developing countries. We then describe key measurements issues and compilation of data from different sources. Moreover, we explain the derivation of the three dimensions of financial inclusion by incorporating proxy variables and the construction of a composite financial inclusion index for using that index in different regression models.

3.1 Model specification

This study follows a dynamic panel regression framework and uses a fixed effect estimation method for empirical analysis. The Hausman test also supports the fixed effect model over the random effect model, as it rejects the null hypothesis at 1% significance level. For the econometric analysis, this study uses a one-way error component fixed effect model and robust standard errors to address heteroskedasticity. The explanatory variables in different regression equations mostly follow previous studies by Honohan ( 2007 , 2008 ), Sarma and Pais ( 2008 , 2011 ), Allen et al. ( 2014 ), Rojas-Suarez and Amado ( 2014 ), Swamy ( 2014 ), Alter and Yontcheva ( 2015 ), García-Herrer and Turégano ( 2015 ), Park and Mercado ( 2015 , 2018 ), Evans and Adeoye ( 2016 ), Schmied and Marr ( 2016 ), Aslan et al. ( 2017 ), and Jabir et al. ( 2017 ).

For determining the crucial factors that influence the level of financial inclusion in developing countries, the following regression equation is specified:

where cfii  = a composite financial inclusion index, lngdppc  = log of per capita real GDP, rule  = rule of law, lnpopu  = log of total population, lnagedep  = log of age dependency ratio, lninflation  = log of inflation rate, lngini  = log of Gini coefficient to measure income inequality, lnssenroll  = log of secondary school enrollment ratio, lninternet  = log of ratio of internet users, i  = 1,2,3,… n country, t  = 1,2,3,… 13 time period, α i  = the unobserved effects for i th country observation, and u i,t  = the idiosyncratic error term for i th country on the t th year. Here, per capita income, rule of law, population size, secondary school enrollment ratio, and ratio of internet users are expected to be positively associated with financial inclusion, whereas age dependency ratio, inflation rate, and income inequality are expected to have a negative relationship with financial inclusion.

In order to analyze the relationship between financial inclusion and poverty in developing countries, the following regression equation is employed:

where lnpovhead  = log of poverty headcount ratio, gdpgr  = GDP growth rate, lnpcredit  = log of credit to the private sector by banks, lngovtexp  = log of government expenditure, lntradeopen  = log of trade openness, and the other specifications are similar to Eq. ( 1 ). Here, financial inclusion is expected to be negatively associated with poverty rates because higher access to financial services by lower-income people generally helps to reduce poverty by facilitating consumption and engaging in economically productive activities.

To analyze the relationship between financial inclusion and income inequality in developing countries, the following regression equation is employed:

where lnict  = log of ICT service exports, lnmobile  = log of mobile cellular users, and the other specifications are similar to Eqs. ( 1 ) and ( 2 ). Here, financial inclusion is expected to be negatively associated with income inequality because higher access to financial services by lower- and irregular-income people allows them to save and build assets for the future, which helps to reduce unequal income distribution.

For analyzing the conditional effects of financial inclusion on poverty in developing countries, the following regression equation is used:

where cfii*lnZ  = the interaction between a composite financial inclusion index and other specific control variables ( lnZ ) that can affect the outcome of financial inclusion in reducing poverty. The other specifications are similar to the above equations.

To analyze the conditional effects of financial inclusion on income inequality in developing countries, the following regression equation is used:

where cfii*lnZ  = the interaction between a composite financial inclusion index and other specific control variables ( lnZ ) that can affect the outcome of financial inclusion in reducing income inequality. The other specifications are similar to the above equations.

3.2 Measurement and sources of data

The analysis uses 13 years of unbalanced annual panel data for the period of 2004–2016. By excluding developed countries, 116 developing countries are taken in total from three regions: 36 countries from Asia, 53 countries from Africa, and 27 countries from Latin America and the Caribbean ( Appendix A ). Most of the variables are chosen from empirical literature, with some additional variables and modifications. Due to excessive fluctuations of the data among economies, almost all of the variables (except for financial inclusion index, GDP growth rate, and rule of law) are expressed in logarithm scale in order to improve the robustness of empirical analysis. The data set is compiled from the Financial Access Survey (FAS) database of the International Monetary Fund (IMF), Standardized World Income Inequality Database (SWIID), World Development Indicator (WDI), and World Governance Indicator. A detailed description of variables and their sources is presented in Appendix B .

3.3 Construction of composite financial inclusion index (CFII)

Constructing a composite financial inclusion index (CFII) is the study’s preliminary and principal task before testing the significance of financial inclusion with other variables. Such a comprehensive measure of financial inclusion is needed to check the extent of financial inclusion across economies, standardize the measure for a large number of developing economies, monitor progress in reaching national financial inclusion targets, and make cross-country comparisons. This study considers a variety of financial sector outreach indicators under three basic dimensions of an inclusive financial system such as penetration, availability, and usage of financial services, with relevant and consistent macro-level data for a large number of developing economies. The data are collected from the Financial Access Survey (FAS) database of the International Monetary Fund (IMF). Footnote 2 All of the data for computing each dimension are a panel spanning the period of 2004–2016.

3.3.1 Penetration dimension

This reflects the maximum number of users entered into the formal financial system. Here, penetration of financial services is indicated by the number of deposit accounts with financial institutions per 1000 adults (Honohan 2007 ; Sarma 2012 ; Cámara et al. 2014 ; Rojas-Suarez and Amado 2014 ; García-Herrer and Turégano 2015 ) and the number of depositors with financial institutions per 1000 adults (Honohan 2007 ; Amidžić et al. 2014 ; Park and Mercado 2015 ; Evans and Adeoye 2016 ). Then a weighted average of these two indices is considered, using 0.70 weight for the deposit account index and 0.30 weight for the depositor index. As deposit accounts index is an imperative indicator to identify the size of the banked population and a measure of more consolidated stages of financial system, we assign a weighted average of 0.70 for this index. Furthermore, the depositor index gets less weight of 0.30, as all depositors who have deposit accounts are not active in the financial system. Finally, as penetration in the financial system is the primary measure of financial inclusion and data in determining whether an individual has penetrated in the financial system are also available, we assign an overall weight of 1 to the penetration dimension for calculating CFII.

3.3.2 Availability dimension

This indicates the depth of geographic or demographic penetration of financial services in the form of financial institutions’ outlets, such as offices, branches, and ATMs. Here, availability of financial services is indicated by two indicators: the number of financial institution’s branches per 100,000 adults (Sarma 2012 ; Cámara and Tuesta 2014 ; Rojas-Suarez and Amado 2014 ; Park and Mercado 2015 ) and the number of automated teller machines per 100,000 adults (Sarma 2012 ; Cámara and Tuesta 2014 ; Rojas-Suarez and Amado 2014 ; Park and Mercado 2015 ). Then a weighted average of these two indexes are considered for this dimension, using 0.70 weight for the financial institution’s branch index and 0.30 weight for the automated teller machine index. Footnote 3 Although traditional financial services are shifting towards an electronic base (internet banking, mobile banking, etc.) in many countries, the lack of consistent data creates a hurdle in using these indicators to quantify availability dimension. Thus, because of the difficulty of considering some significant indicators, this study assigns a lesser weight of 0.60 to this dimension for calculating CFII.

3.3.3 Usage dimension

This measures how regularly and adequately clients utilize financial services in different forms, such as savings, borrowings, making payments, remittances, transfers, etc. This dimension represents the efficiency of a financial system, as greater access is not enough in itself for an inclusive financial system. However, because of the unavailability of cross-country comparable data on payments, remittances, and transfers, the usage dimension only uses two indicators, the number of loan accounts with financial institutions per 1000 adults (Cámara and Tuesta 2014 ) and the number of borrowers from financial institutions per 1000 adults (Amidžić et al. 2014 ; Park and Mercado 2015 ). Then a weighted average of these two indices are considered in determining this dimension, using 0.50 weight for the loan account index and 0.50 weight for the borrower index. As the loan account index represents a stage of greater financial inclusion, since most people who have a loan account already have another financial product, such as a bank account or payroll account, we assign a weighted average of 0.50 for this index. Besides, the borrower index gets equal weight of 0.50, as majority of borrowers who have loan accounts are active in the financial system. Finally, we assign a relatively lesser overall weight of 0.50 to this dimension due to data unavailability of many significant indicators for calculating CFII.

As assigning weights to each indicator and dimension of any index is a complex task, this study assigns weights for calculating the CFII, which is consistent with Sarma and Pais ( 2008 ), Sarma ( 2012 ), Cámara and Tuesta ( 2014 ), and Amidžić et al. ( 2014 ). Though there is a computational difference of the CFII in the empirical studies, the weights are assigned or derived based on the relevance and availability of information to measure each indicator and dimension of financial inclusion index. However, an accurate estimate of CFII is not possible due to lack of adequate and appropriate data, such as unavailability of new forms of banking data, geographical aspects of financial inclusion (rural or urban divide), gender related aspects, etc. Appropriate methods and corresponding weights for incorporating these data into the CFII could be devised when data become available. Table  1 summarizes the indicators used to compute the financial inclusion index.

This study follows the main methodology of Sarma ( 2012 ) for constructing a multidimensional index of financial inclusion. The index is constructed similarly to that used by UNDP for computation of the well-known human development index (HDI), human poverty index (HPI), gender development index (GDI), etc. However, the composite financial inclusion index is methodologically improved, as it follows the distance-based approach, unlike the UNDP’s methodology of using an average of dimension indices. The index used in this study is based on a notion of distance from both the worst and ideal points with a little variation to the “method of displaced ideal” of Zeleny ( 1974 ), where only the displacement from the ideal point is considered. The distance-based approach is suitable because it satisfies essential mathematical properties like boundedness, unit-free measure, homogeneity, and monotonicity. UNDP’s methodology does not satisfy all the properties due to ‘perfect substitutability’ across dimensions, i.e., an increase in one dimension can be compensated for by a decrease of equal (in case of arithmetic average) or proportional (in case of geometric average) magnitude in another dimension. This is not a relevant assumption in the particular case like financial inclusion, as all dimensions are assumed to be equally important for the overall index value (Desai 1991 ). Moreover, while the UNDP’s methodology uses pre-fixed minimum and maximum values for each indicator to compute the dimensional index, this study uses empirically observed minimum and maximum values for a particular indicator of financial inclusion, as the values are not straight forward in that case. In computing the CFII, the initial step is to compute indices for each dimension of financial inclusion (penetration, availability, and usage) by using the following formula:

where d i = the index/indicator value for the dimension i ; w i  = weight attached to a certain indicator for the dimension i ; A i  = actual value of a certain indicator for dimension i for an economy k on the year t ; m i = lower limit of a certain indicator for dimension i , fixed by assigning 0; and M i  = upper limit of a certain indicator for dimension i , fixed by taking the 90th percentile value (the upper limit is fixed here to remove excessively high benchmarks and smooth the value of the index).

From Eq. ( 6 ), the value of d i is the normalized value of any indicator for any specific dimension where the higher value of d i indicates higher achievement of an economy therein. The last step is to compute the CFII for an economy i by using the following formulae, based on a notion of distance of achievement point ( X  =  d 1 , d 2 , d 3 ) from a worst ( O  =  0 , 0 , 0 , 0 ) and an ideal situation ( W  =  w 1 , w 2 , w 3 ):

The equation for X 1 ( 7 ) provides normalized Euclidian distance between the achievement position X and the worst position O on the nth-dimensional space. The equation for X 2 ( 8 ) represents the normalized inverse Euclidian distance between the achievement position X and the ideal situation W . Both these distances are normalized to position them between 0 and 1. Finally, the CFII (Eq.  9 ) is computed by taking a simple average of Eqs. ( 7 ) and ( 8 ). Here, a larger distance between X and O would indicate higher financial inclusion, and a smaller distance between X and W would indicate higher financial inclusion. Thus, the CFII is a number that lies between 0 and 1 (meaning that the index has well-defined bounds) and is monotonically increasing (meaning that a higher value of the index indicates a higher level of financial inclusion).

4 Empirical results and discussion

This chapter shows the empirical results and discussions in different sections. First, it presents the findings on the crucial factors that influence the level of financial inclusion in developing countries. Then it reports our findings on the impact of financial inclusion on poverty and income inequality, and the findings on the conditional relationships of financial inclusion on poverty and income inequality.

4.1 Findings on the determinants of financial inclusion

Table  2 presents our empirical findings on the crucial factors that influence the level of financial inclusion in developing countries. Different macroeconomic variables and models are included to check the robustness of the regression results. Model (1) includes per capita real GDP with a reduced number of control variables, while model (2) excludes per capita real GDP, as it is strongly correlated with age dependency ratio (the pairwise correlation is − 0.7733), secondary school enrollment ratio (0.7748), and ratio of internet users (0.8026). Model (3) includes all the control variables in order to see the combined outcome.

The fixed effect estimates show that per capita real GDP, age dependency ratio, inflation rate, ratio of internet users, and income inequality significantly influence the level of financial inclusion in developing countries. In particular, per capita real GDP and ratio of internet users positively influence the level of financial inclusion, while age dependency ratio, inflation rate, and income inequality have a negative influence.

The coefficient for per capita real GDP is positive and highly significant, suggesting that countries with higher per capita income experience higher financial inclusion. This finding is consistent with Sarma and Pais ( 2011 ), Chithra and Selvam ( 2013 ), Allen et al. ( 2014 ), Cámara and Tuesta ( 2014 ), Cámara et al. ( 2014 ), Rojas-Suarez and Amado ( 2014 ), Park and Mercado ( 2015 ), Tuesta et al. ( 2015 ), and Evans and Adeoye ( 2016 ).

Ratio of internet users is also positive and significantly associated with financial inclusion, meaning that connectivity and access to information through internet subscriptions enhance financial inclusion by facilitating easy mobility of financial services, a finding similar to Sarma and Pais ( 2011 ) and Evans and Adeoye ( 2016 ). However, the evidence is mild in the sense that the ratio of internet users loses its significance when per capita income is considered in the model.

On the other hand, age dependency ratio is negative and highly significant, indicating that economies with a high dependency ratio in the form of a rapidly aging population or too young-aged population have lower access to financial services. This result supports the empirical finding of Park and Mercado ( 2015 ).

Inflation has a negative and highly significant impact on the level of financial inclusion, suggesting that countries with high inflation volatility experience low financial inclusion, as the value of savings decreases in the financial system. This finding is similar to Allen et al. ( 2014 ) and Rojas-Suarez and Amado ( 2014 ), but contradicts Evans and Adeoye ( 2016 ), who find an insignificant impact of inflation on the level of financial inclusion.

Income inequality as measured by Gini coefficient is also negative and significantly associated with financial inclusion, indicating that countries with a highly skewed distribution of income lead to worsening household financial inclusion as they block or manipulate financial reforms so as to maintain upper-income benefits. This finding is consistent with Sarma and Pais ( 2011 ) and Rojas-Suarez and Amado ( 2014 ).

Interestingly, there is no evidence of a significant effect of rule of law , population size , and secondary school enrollment ratio on the level of financial inclusion in developing countries. As expected, good governance and high institutional quality through strengthening the rule of law is more likely to reduce involuntary financial exclusion. Although our result is positive in this respect, it is insignificant, which disagrees with Honohan ( 2008 ), Allen et al. ( 2014 ), Rojas-Suarez and Amado ( 2014 ), and Park and Mercado ( 2015 ). Economies with large population sizes are expected to have greater access to financial services due to convenient networking effects. This result is not positive and significant, which contrast with the findings of Chithra and Selvam ( 2013 ), Allen et al. ( 2014 ), and Park and Mercado ( 2015 ). Education in the form of a higher secondary school enrollment ratio is also expected to raise financial inclusion. This result is not significant, which is consistent with Honohan ( 2008 ), Allen et al. ( 2014 ), and Park and Mercado ( 2015 ).

4.2 Findings on the impact of financial inclusion on poverty

Table  3 presents our empirical findings on the impact of financial inclusion on poverty in developing countries. This analysis starts from a parsimonious model that considers only one variable and gradually considers additional control variables. It should be noted that considering additional control variables across models significantly reduces the number of observations, as some countries are dropping out from the sample due to data unavailability.

The fixed effect estimates show that there is a highly significant negative association between financial inclusion and poverty across the models. This implies that economies with higher financial inclusion have strongly lower poverty rates in developing countries. This result is highly significant with expected negative signs, even after controlling for many control variables. The finding for the main variable of interest of this study is consistent with Burgess and Pande ( 2005 ), Brune et al. ( 2011 ), Swamy ( 2014 ), Park and Mercado ( 2015 , 2018 ), and Jabir et al. ( 2017 ), who also found a significant effect of financial inclusion on reducing poverty. But when per capita income is added to the specification, the effect of financial inclusion on reducing poverty seems insignificant across the models, though the relationship remains negative. This finding is suggestive that financial inclusion is strongly correlated with per capita income (the pairwise correlation is 0.7269), and per capita income is highly correlated with poverty rates (− 0.6249). Also, at any given income level, the percentages of access to financial services vary widely. This finding is similar to Honohan ( 2007 , 2008 ), who found an insignificant effect of financial inclusion on reducing poverty when per capita income is included as a regressor. Park and Mercado ( 2015 , 2018 ) did not control for per capita income in their specification, probably because financial inclusion becomes insignificant in the regression in the presence of per capita income.

The robustness of the above findings is checked by conducting a 3-year average panel data analysis and cross-sectional analysis. For a 3-year average panel analysis, data for the initial year of 2004 are dropped because of data scarcity and the necessity of matching whole sample periods into similar groups (i.e., by excluding 2004 data, the remaining 12 sample periods turn into 4 groups). The 3-year average fixed effect estimates show that financial inclusion is highly significant in reducing poverty rates in developing countries, and the results are robust across the models. By taking the period average values of cross-sectional data and applying OLS regression method, the results are also robust, suggesting that financial inclusion will be effective in reducing poverty in developing countries in the long term. As the main variable of interest could possibly be endogenous in the form of reverse-causality or omitted variable bias, it may lead to a biased estimation of coefficients. To deal with this issue, a two-stage least squares (2SLS) estimation is applied by using latitude and ethnic fractionalization index as instrumental variables, but the Stock and Yogo test (2005) indicated that both of these instrumental variables are implausible. Alternatively, the system generalized method of moments (GMM) is inconsistent because huge missing variables of the poverty headcount ratio in the sample periods render the lagged independent variable ineffective as a tool variable. However, Honohan ( 2007 ) mentioned that potential endogeneity is not a serious problem in explaining poverty or inequality, as it would be in explaining growth or income levels.

Among other control variables, secondary school enrollment ratio is significantly related to poverty with expected negative signs, meaning that a higher level of education increases the knowledge, skills, and productivity of poor households, and enhances their income level, which helps to reduce poverty rates. Contrary to expectations, inflation is negatively significant, suggesting that higher inflation reduces poverty in developing countries. The probable reason is that as inflation depreciates higher income people’s value of cash holdings, inflation encourages the rich to invest their idle cash holdings into real capital expenditures, which in turn employs more unemployed low-income people and thereby reduces poverty rates. On the other hand, income inequality as measured by Gini coefficient is significantly related to poverty with expected positive signs, indicating that income inequality is detrimental to reduced poverty rates because countries with high initial levels of inequality favor the non-poor. GDP growth is positively significant, indicating that higher GDP growth increases poverty, but it loses significance when more control variables are considered. Other variables, such as rule of law and trade openness , show insignificant positive signs, while credit to private sector by banks and government expenditure show insignificant negative effects on poverty rates.

4.3 Findings on the impact of financial inclusion on income inequality

Table  4 presents our empirical findings on the impact of financial inclusion on income inequality in developing countries.

The fixed effect estimates show that there is a highly significant negative association between financial inclusion and income inequality across the models. This implies that higher financial inclusion is effective in reducing income inequality in developing countries. This result is highly significant with expected negative signs, even after controlling for many control variables. The finding for the main variable of interest of this study is consistent with Dabla-Norris et al. ( 2015 ), García-Herrer and Turégano ( 2015 ), and Salazar-Cantú et al. ( 2015 ), but differs significantly from Honohan ( 2007 , 2008 ) and Park and Mercado ( 2015 ), who found little econometric evidence that financial inclusion lowers income inequality. Moreover, Park and Mercado ( 2018 ) found an insignificant relationship between financial inclusion and income inequality. These different findings may be due to differences in measuring financial inclusion, differences in sample sizes and time periods, different methodology, etc.

The robustness of the above findings is checked by conducting a 3-year average panel data analysis and cross-sectional analysis. The 3-year average fixed effect estimates show that financial inclusion is highly significant in reducing income inequality in developing countries, and the results are robust across the models. The cross-sectional OLS regression estimates show an insignificant relationship, though the negative sign holds, suggesting that financial inclusion might not be effective in the long term in reducing income inequality in developing countries. As the main variable of interest could in principle be endogenous in the form of reverse-causality or omitted variable bias, it may lead to a biased estimation of coefficients. To deal with this issue, the system GMM is applied, and the estimates show that financial inclusion significantly reduces income inequality in developing countries. The Hansen test indicates that the instruments are valid and financial inclusion is exogenous, but the second-order serial correlation test AR (2) rejects the null hypothesis in favor of the presence of serial correlation. Alternatively, a two-stage least squares (2SLS) estimation is not considered due to the absence of plausible instrument variables.

Among other control variables, rule of law is positive and significantly related to income inequality, meaning that improving the rule of law tends to worsen income distribution in developing countries. A possible explanation is that institutional reforms render the informal economy ineffective, which generates higher additional costs for the poor at the early stages of development while benefiting those in the formal sector, resulting in higher income inequality. Nevertheless, better institutional quality eventually leads to improving the efficiency of the overall economy and thereby reduces income inequality. This finding is consistent with Chong and Calderón ( 2000 ), who found a positive relationship between institutional quality and income inequality. No other control variable is significant, likely due to the annual nature of the unbalanced panel study.

4.4 Findings on the conditional effects of financial inclusion on poverty

Assessing the conditional effects between financial inclusion and other micro- or macroeconomic factors is worthwhile, as the basic models of impact study only provide evidence on whether financial inclusion itself is a sufficient factor in reducing poverty and income inequality. The basic models also do not provide evidence on the factors, scenarios, and conditions under which financial inclusion is effective in influencing poverty and income inequality in an economy. Beck et al. ( 2009 ) suggested that financial access might reduce poverty and income inequality, not through direct provisions of financial services to the poor so much as indirect effects, such as more efficient products and labor. Thus, this study broadly examines which factors are favorable and which are not by employing interaction terms between financial inclusion and other control variables.

Table  5 presents our empirical findings on the conditional effects of financial inclusion on poverty in developing countries. Here, control variables and their interactions with financial inclusion are considered separately in order to determine the independent effect of those specific variables on poverty. The fixed effect estimates show that the interaction term of financial inclusion with GDP growth and secondary school enrollment ratio are statistically significant, while the interaction terms of financial inclusion with income inequality as measured by Gini coefficient, per capita income, rule of law, and inflation rate are not statistically significant for poverty in developing countries.

The interaction term between financial inclusion and GDP growth shows a highly significant negative effect on poverty, indicating that higher GDP growth increases the marginal effect of financial inclusion in lowering poverty rates. The result is consistent in the sense that strong economic growth creates demand for labor, raises real wages for low-skilled jobs, improves general living standards, and generates positive cycles of prosperity and opportunity in developing countries. This leads to develop an efficient and inclusive financial system that promotes participatory investment and financial risk management from poor households and ultimately helps reduce poverty. Thus, both the pace and pattern of economic growth matter for increasing financial inclusion and lowering poverty.

The interaction term between financial inclusion and secondary school enrollment ratio is highly significant and negatively associated with poverty, implying that the marginal effect of financial inclusion in reducing poverty rates increases with a higher secondary school enrollment ratio. The result is consistent in the sense that the higher the level of education in poor households, the lower the poverty rates will be, as education imparts the knowledge and general workforce skills, generates higher productivity, and raises income levels (Park and Mercado 2015 ). Education also indirectly influences poverty with respect to “human poverty” through the fulfillment of basic needs and raising living standards, which leads to a decrease in human poverty (Awan et al. 2011 ). In addition, better human development and literacy levels raise awareness and involve a large section of the lower-income population in the financial system in utilizing financial services towards reducing poverty rates in the developing countries (Atkinson and Messy 2013 ). Moreover, Arora ( 2012 ) suggested that measures on improving educational variables should be taken contemporaneously for increasing financial inclusion. Thus, countries with a higher education level and subsequent higher financial inclusion reduce poverty rates more quickly.

4.5 Findings on the conditional effects of financial inclusion on income inequality

Table  6 presents our empirical findings on the conditional effects of financial inclusion on income inequality in developing countries. The fixed effect estimates show that the interaction term of financial inclusion with GDP growth and rule of law are statistically significant, while other interaction terms are not.

The interaction term between financial inclusion and GDP growth is highly significant and positively associated with income inequality, suggesting that higher GDP growth lowers the marginal effect of financial inclusion in reducing income inequality. The most plausible explanation is that strong economic growth creates job opportunities and provides some income to the unemployed, which reduces the level of poverty but does not reduce the level of income inequality due to the temporary nature of jobs with minimum wages (Niyimbanira 2017 ). Moreover, only the non-poor reap the benefits of the early stages of economic growth with a broader and persistent income gap between rich and poor. Thus, higher economic growth reduces overall incentives and benefits of poor households’ access to financial services in a country with highly unequal income. However, sustained economic growth will reverse the marginal effect of financial inclusion in reducing income inequality in the long run by improving human capital and general skills level, correcting labor market policies, and better utilizing financial services.

The interaction term between financial inclusion and rule of law shows a highly significant positive effect on income inequality, indicating that better rule of law decreases the marginal effect of financial inclusion in reducing income inequality. The probable reason is that institutional quality improvements generate high additional costs to the poor belonging to the informal or underground sector and render the informal economy ineffective in the early stages of development, while such improvements simultaneously benefit those in the formal sector, resulting in higher income inequality in developing countries (Chong and Calderón 2000 ). At this stage of institutional reform, financial inclusion further widens income inequality as the non-poor population benefits much more from improved financial services. However, high institutional quality eventually leads to improvement in the efficiency of the overall economy in which poor households better utilize financial services in productive investment activities and thereby help to reduce income inequality.

5 Conclusions

As it is believed that financial inclusion contributes to faster and more equitable macro-economic growth, reduces poverty, and promotes income equality in developing countries by providing access to formal financial services, this study empirically examines relationships therein by taking a large sample of developing countries, focusing on Asia, Africa, and Latin America and the Caribbean. For this purpose, a one-way error component fixed effect model is used with an unbalanced annual panel data of 13 years. Moreover, this study constructs a new composite financial inclusion index using penetration, availability, and usage dimension of financial inclusion by following Sarma’s distance-based multidimensional approach ( 2012 ).

Using the financial inclusion index, this study initially investigates the crucial factors that influence the level of financial inclusion in developing countries. The results show that per capita real GDP and ratio of internet users positively influence the level of financial inclusion, while age dependency ratio, inflation, and income inequality negatively influence this level. There is no evidence of a significant effect of rule of law, population size, and secondary school enrollment ratio on the level of financial inclusion.

This study then assesses the impact of financial inclusion on reducing poverty and income inequality in developing countries. The fixed effect estimates present robust evidence that higher financial inclusion significantly reduces poverty rates and income inequality in developing countries.

This study also examines some important conditions under which financial inclusion is effective in influencing poverty and income inequality in developing countries. The fixed effect estimates show that the interaction term of financial inclusion with GDP growth and secondary school enrollment ratio are statistically significant on poverty, and the interaction terms of financial inclusion with GDP growth and rule of law are statistically significant on income inequality. This finding provides evidence that financial inclusion is not a sufficient factor in itself that can affect the real economy in a similar magnitude; rather, the effectiveness of financial inclusion depends on different economic factors, scenarios, and conditions.

The findings of this study suggest important policy implications for the developing countries. First, financial institutions should cater innovative and need based formal financial services suited to financially excluded segments of the population as the demand for financial services varies due to differences in culture, customs, beliefs, and income levels. Second, governments, central banks, financial institutions, and development partners should cooperate mutually to develop the financial services infrastructure and upgrade the financial services network in rural and urban areas. Third, a concrete time action bound targeted policy on increasing financial literacy in the rural and remote areas is necessary to raise financial awareness and change financial behavior among low-income people. Fourth, efforts should be supplemented by supportive policies like transfer of government subsidy to accountholders for effective use of dormant accounts, as higher rates of inactive accounts are not expanding financial inclusion in a true sense. Fifth, economies in developing countries must continue to improve per capita income and access to information in order to minimize involuntary financial exclusion of large segments of the population. Finally, policies should initiate necessary actions regarding specific socio-economic constraints, macroeconomic volatility, institutional inefficiencies, and financial system inefficiencies at country level to promote a more inclusive financial system.

Even though this study reveals a significant relationship between financial inclusion, poverty, and income inequality in developing countries, this is far from understanding the same relationship in individual countries. The disparity among developing countries in Asia, Africa, and Latin America and the Caribbean in terms of literacy rates, religion status, gender inequality, human rights, natural resources, road networks, etc., is not considered, though these could influence the level of financial inclusion in each country. The index of financial inclusion does not include micro-finance institutions, financial cooperatives, credit unions, SMEs, and mobile financial services measures, which also enhances access to financial services for excluded individuals in the present days. Moreover, this index does not include micro-level or demand-side data, which helps to understand users’ financial needs, socio-economic and demographic characteristics, and barriers encountered to avail financial services. The panel nature of data with a short time span and lots of missing observations basically on the main variables of interest, prevent this study from doing some statistical diagnostic tests and using sophisticated econometric models. For this reason, admittedly it is not possible to fully control for the potential endogeneity associated with financial inclusion and it may cause the empirical findings weaker.

Availability of data and materials

All data generated or analyzed during this study can be obtained from the corresponding author upon request.

This study defines developing countries or regions following World Bank-classified income groups of economies including low-income economies, lower-middle income economies, and upper-middle income economies in Asia, Africa, and Latin America and the Caribbean region depending on gross national income per capita of US$12,375 or less, calculated using the Atlas method.

The FAS database is the primary source of supply-side cross-country data surveys on financial services gathered from financial regulators. It provides insights on the access, availability, and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99% of the world’s adult population. This database contains 152 series, resulting in 47 basic indicators that are expressed as the ratios to GDP, geographic outreach, and adult population.

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Acknowledgements

The authors are very grateful to professor Kang-Kook Lee, Ritsumeikan University, Japan, for his valuable knowledge sharing, precious guidance, and constructive comments in this study. The authors are also thankful to the editors and anonymous reviewers for their useful suggestions and comments to improve the quality of the article.

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1.1 Appendix A: List of countries included in the sample

1.1.1 asia (comprising east asia, central asia, south asia, and the middle east).

Afghanistan, Armenia, Azerbaijan, Bangladesh, Bhutan, Cambodia, China, Georgia, India, Indonesia, Iran, Islamic Rep., Iraq, Jordan, Kazakhstan, Korea, Dem. People’s Rep., Kyrgyz Republic, Lao PDR, Lebanon, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Pakistan, Philippines, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Turkey, Turkmenistan, Uzbekistan, Vietnam, West Bank and Gaza, Yemen, Rep.

1.1.2 Africa (comprising North Africa and sub-Saharan Africa)

Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Cote d’Ivoire, Djibouti, Egypt, Arab Rep., Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, The, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe.

1.1.3 Latin America and the Caribbean

Argentina, Belize, Bolivia, Brazil, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, St. Lucia, St. Vincent and the Grenadines, Suriname, Turks and Caicos Islands, Venezuela, RB.

1.2 Appendix B

See Table  7 .

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Omar, M.A., Inaba, K. Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis. Economic Structures 9 , 37 (2020). https://doi.org/10.1186/s40008-020-00214-4

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• Financial inclusion
• Income inequality
• Developing countries

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Financial Inclusion Research Around the World: A Review

Forum for Social Economics, 2020

24 Pages Posted: 31 Jan 2020

Peterson K Ozili

Central Bank of Nigeria

Date Written: January 1, 2020

This paper provides a comprehensive review of the recent evidence on financial inclusion from all regions of the World. It identifies the emerging themes in the financial inclusion literature as well as some controversy in policy circles regarding financial inclusion. In particular, I draw attention to some issues such as optimal financial inclusion, extreme financial inclusion, how financial inclusion can transmit systemic risk to the formal financial sector, and whether financial inclusion and exclusion are pro-cyclical with changes in the economic cycle. The key findings in this review indicate that financial inclusion affects, and is influenced by, the level of financial innovation, poverty levels, the stability of the financial sector, the state of the economy, financial literacy, and regulatory frameworks which differ across countries. Finally, the issues discussed in this paper opens up several avenues for future research.

Keywords: Financial Inclusion, Financial Technology, Digital Finance, Poverty Reduction, Financial Stability, Financial Institutions, Economic Cycle, Systemic Risk, Controversy, Fintech

JEL Classification: D14, G02, G28.

Suggested Citation: Suggested Citation

Peterson K Ozili (Contact Author)

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Abuja Abuja, 09 Nigeria

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Financial inclusion and its ripple effects on socio-economic development: a comprehensive review.

1. Introduction

2. research methods, 2.1. journal selection criteria, 2.2. article selection criteria, 3. theoretical perspective, 3.1. socioeconomic development theory, 3.2. theory of planned behavior: understanding behavioral predictions, 3.3. technology acceptance model: understanding consumer adoption of technology, 4. discussion, 4.1. financial inclusion, fintech, and artificial intelligence, 4.2. financial and digital literacy of consumers and financial inclusion, 4.3. shgs, women empowerment and financial inclusion, 4.4. intensifying access to financial services to consumers, 4.5. financial inclusion and economic growth, 4.6. consumers’ financial inclusion and financial capability, 4.7. socioeconomic determinants of financial inclusion.

• Socioeconomic Determinants of Financial Inclusion
• Income Levels and Employment Status
• Education and Financial Literacy
• Demographic Factors: Age, Gender, and Ethnicity
• Technological Factors and Geographic Location (Urban vs. Rural): Enhancing Financial Inclusion
• Cultural and Social Norms
• Government Policies and Regulation

5. Conclusions

5.1. financial inclusion, fintech, and artificial intelligence, 5.2. financial literacy of consumers, financial capacity, and financial inclusion, 5.3. shgs, women empowerment, gender issues, and financial inclusion, 5.4. intensifying and inclusivity of access to financial services for consumers.

• Policy Implications and Frameworks for Financial Inclusion

6. Sustainable Financial Inclusion Theory and Future Research Agenda

6.1. sustainable financial inclusion theory, 6.2. future research agenda, author contributions, data availability statement, conflicts of interest.

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Mishra, D.; Kandpal, V.; Agarwal, N.; Srivastava, B. Financial Inclusion and Its Ripple Effects on Socio-Economic Development: A Comprehensive Review. J. Risk Financial Manag. 2024 , 17 , 105. https://doi.org/10.3390/jrfm17030105

Mishra D, Kandpal V, Agarwal N, Srivastava B. Financial Inclusion and Its Ripple Effects on Socio-Economic Development: A Comprehensive Review. Journal of Risk and Financial Management . 2024; 17(3):105. https://doi.org/10.3390/jrfm17030105

Mishra, Deepak, Vinay Kandpal, Naveen Agarwal, and Barun Srivastava. 2024. "Financial Inclusion and Its Ripple Effects on Socio-Economic Development: A Comprehensive Review" Journal of Risk and Financial Management 17, no. 3: 105. https://doi.org/10.3390/jrfm17030105

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Digital financial inclusion: next frontiers—challenges and opportunities

• Original Research
• Published: 18 August 2021
• Volume 9 , pages 127–134, ( 2021 )

Cite this article

• Chandra Mohan Malladi   ORCID: orcid.org/0000-0002-3377-4673 1 ,
• Rupesh K. Soni 1 &
• Sanjay Srinivasan 1  

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India’s Financial Inclusion journey has been phenomenal in the last decade and expressly promoted by the Government of India through their Digital India Movement & Pradhan Mantri Jan Dhan Yojana. Reduction of poverty and addressing the challenges of ensuring sustainable income could become a key factor to achieve an inclusive society. Information and Communication Technology are providing access to unbanked population progressively and helping to bring them into the banking segment. Digital Technologies are driving usage and making a positive impact on livelihood of citizens. In this paper we are discussing on what is achieved in Financial Inclusion so far and what next and how do we leverage and harness digital technologies to achieve an inclusive society. This paper enlists various challenges that continue to prevail in achieving an inclusive society. We have put forth recommendations on addressing the key challenges and qualified the importance of collaboration and transparency between all the key stakeholders to achieve an inclusive ecosystem.

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1 Hypotheses Development

How do we enhance the process of Digital Financial Inclusion? How can Information and Communication Technology help in providing the citizens with a sustainable livelihood and inclusive growth? How can we safeguard the people who are included in the FI Framework and guarantee that they will not be excluded again?

To achieve a financially inclusive society that is sustainable and promotes inclusive growth for all, we need to provide the citizens of the country with access to education, basic financial services, affordable healthcare & suitable way for upskilling and improving their talent. They should be brought inside the legal framework where they can sustain and thrive.

The high amount of disparity and digital divide between Urban & Rural areas in India must be eliminated and people must be educated financially and included socially. There must be a cohesive ecosystem where Financial, Social & Health Inclusion that can work in tandem to accomplish a sustainable inclusive society. India’s digital payment and rural infrastructure must be improved to ensure zero disruption and continual access to telecom networks. Access to line of credit must be provided with adequate last mile services to ensure service delivery.

2 Deployment Approach and Methodology

This research study used quantitative and qualitative data from various official sources such as websites of Reserve Bank of India, Niti Aayog, Direct Benefit Transfer, PM Jan Dhan Yojana and other information published officially by the Govt. of India and Ministry of Finance. The data points and facts visibly showcase the impact of Financial Inclusion so far in India and helps us understand the gaps that must be filled to improve growth and ensure sustainability.

Comprehensive secondary research from published journal articles and expert committee opinions were considered to understand Digital Financial Inclusion initiatives and details of the best practices followed in various geographies. By leveraging this information, we have identified the key problems preventing us to realize an inclusive society and we have provided with qualified recommendations to tackle this.

Inclusive society is well-defined in terms of Financial Inclusion, Social Inclusion & Health Inclusion. Consistent with this approach, we define our key dependent variables ‘finance’ —as the access to a line of credit, availability and usage of basic financial services, ‘social’ —access to education and literacy, improvement of skills, & ‘health’— Personal & Societal Wellness. From this, we can understand that we are deploying a self-reporting measure to evaluate our research findings and we have substantiated our recommendations with real world examples from other studies conducted in similar functions. Prior research also acknowledges the subjectivity of these self-reported measures of FI [ 24 ].

3 Introduction

Financial Inclusion (FI) means delivering basic financial services to the marginalised and excluded members of society. It is the process in which we ensure adequate line of credit accessible by the weaker section of the society at a reasonable cost. Financial inclusion helps in developing a culture of savings among semi urban and rural population by bringing low income groups within the formal framework of banking and insurance sector which is significant for national economic development. It came into prominence around 2008 when it became clear to the government that it needs to be the key driver for economic growth of the country. Vision for Financial Inclusion in India is to induce inclusive financial growth by including the unbanked and unsupported individuals and MSMEs by formal financial institutions by providing them convenient access to basic financial products including bank accounts, remittances, bill payments, government supported insurance, pension products and formal credit at reasonable costs. There has been a growing evidence on how financial inclusion has a multiplier effect in boosting overall economic output, reducing poverty and income inequality at the national level.

With the advent of “ Digital India Movement ” and telecom penetration to deep rural areas, sincere efforts are made to bring widespread formal banking channels and innovative financial technology together to create a viable and vibrant ecosystem to drive accessibility of formal financial products to unbanked and deprived segments of Indian society. We at TCS, started this journey very early for some of our partner banks, with the services related to opening of no-frills accounts, delivering smart cards containing balance and biometric information to registered on the card. There was no active network connectivity during initial stages of FI. Last mile agents used to visit the bank, withdraw money & beneficiary list, go to each beneficiary, authenticate with biometrics, and deliver the services. Post which, they go back to the bank to reconcile. Out of 650,000 villages in India, around 150,000 was identified by the govt. initially to service through BC Model [ 15 ].

Fast-forward now, there is far more online connectivity in the remotest areas, smart card is replaced by real time Aadhaar based authentication, beneficiary enrolment & transactions can be done in real-time in field. Last mile channels like micro ATMs, Kiosks, PoS machines, Tablets, Mobile Phones are utilised for service delivery. Basic requirement of UPI based FI transaction is that the beneficiary account should be opened through PM JDY scheme and it is linked to the customer’s mobile number. Banks started channelling UPI based transactions on opened bank accounts. As of 2019, little over 470 Mn (~ 34.47%) is urban population of India. However, more than 65% are in semi urban and rural areas where access to digital services is lesser than major cities [ 13 ].

There is a great need for inclusive growth. By leveraging digital technologies, this is a great opportunity for Govts. and market leaders to improve digital penetration, ease of use of digital products, contextualised and personalised offerings to citizens increase availability, drive down costs, enhance security and trust. There is a need for sustainable cooperation between govts., businesses and unbanked population [ 15 ].

National leadership and policy making institutions like RBI and Niti Aayog have brought in some strong initiatives for inclusive growth which culminated in National Mission for Financial Inclusion namely Pradhan Mantri Jan Dhan Yojana PM JDY leveraging banking network and technology innovations. It enabled access to financial services and coverage of banking to excluded population.

Till date over 344.3 Mn plus new accounts have been opened and a bunch of social and financial security products are offered to the account holders like entrepreneurial credit, financial advice, mortgage, loans and insurance, overdraft of ₹10,000, Accidental Death cum Disability Insurance (PMSBY), Term Life Cover under PMJJBY, Old Age Pension (APY scheme), PM Kisan, Educational Scholarships to students etc. [ 6 ].

With over 95% of Indian population having Unique Identification through Aadhaar, India achieved 80% of adult population having bank account by 2017. 77% Indian Women have bank accounts. In the outbreak of Covid-19 pandemic, this back bone of bank account has been instrumental to provide help of ₹500 per month for 3 months to over 200 Mn woman beneficiaries, transfer of ₹6000/- in 3 instalments per year (currently citizens are receiving their 8th instalment) of PM Kisan Samman Nidhi Yojana to farmers through direct benefit transfer schemes [ 10 ]. Under PM JDY, 423.7 Mn total no of Beneficiaries of which 279.5 Mn Semi Urban/ Rural Beneficiaries [ 24 ]. Rapid digital penetration along with enhancing the financial literacy of people has started. We are moving from assisted to self-service model for multiple services.

For almost all the public and private sector banks, TCS has provided its Financial Inclusion Solution Suite enabling end to end integration with their core banking systems (CBS) through its Branchless Banking Solution. With a wide range of services catalogue, TCS is delivering last mil services to over 150 + thousand locations. TCS has been the technology service provider (TSP) to DBT for various stake holders in which we are running a Heterogenous Technology System for money transfer. TCS is co-ordinating with multiple stakeholders in the DBT value chain such as Central & State Govts., banks & financial institutions in the country, RBI. DBT has proven to be critical in arresting leakage of govt. funds (~ ₹1700 Bn), eliminate involvement of middlemen in transactions, has capacity to cover a variety of areas, increase the number of beneficiaries (~ 770 Mn) and transactions and lower the distribution cost per transaction (Over 6 Mn Trnxs/day) [ 11 ].

Multiple technological solutions such as FI Platform, Beneficiary Registration Application, TCS BaNCS Enterprise Payments Hub, APBS (Aadhaar Payment Bridge System) Adaptor, TCS BaNCS CBS, Aadhaar Data Vault Solution were implemented by TCS and leveraged for end-end service delivery. RBI has played the guiding role which helped banks in achieving various objectives such as the introduction of MICR based cheque processing, Implementation of the electronic payment system such as RTGS (Real Time Gross Settlement), Electronic Clearing Service (ECS), Electronic Funds Transfer (NEFT), Cheque Truncation System (CTS), Mobile Banking System etc. [ 8 ].

Further to that, under the Digital India Movement, various digital payment methods—UPI, BBPS, IMPS, NETC, AePS, etc. were launched by NPCI, in coordination with RBI, some of which was run by TCS on behalf of RBI & NPCI. RBI’s working group reviewed the BC Model and suggested that BC agents or BF agents (Business Facilitator) can deliver last mile services in semi urban & rural areas. Individuals working as PCO Operators, Retired Teachers, Petrol Pump Owners, Grocery, Chemist & Fir Price Shop owners, NGOs, MFIs, SHGs linked to banked were authorised to act as last mile agents and provided a commission based on amount of services rendered [ 9 ].

There is a huge market for several financial institutions & other ecosystem players in the bottom of the pyramid by creating right products & services and ensures it reaches the intended customer [ 15 ]. TCS has been chosen as primary service provider for kiosk banking solution for many of the Customer Service points established by various Public Sector Banks (PSBs).

4 Current Challenges and Observations

People included in the Financial inclusive ecosystem end up getting excluded and are not able to sustain within the framework due to various reasons general or health related causes [ 1 ].

There is a clear demarcation of digital divide among—some are tech savvy people and delivering the services and making them understand is not difficult, where as some in semi-urban and majority of people in rural areas find difficult to understand and utilise technology efficiently [ 16 ].

Lack of financial literacy and awareness on financial cybercrimes has resulted in general mistrust among rural population which leads to reduced digital penetration [ 27 ].

There is a burden on running sustainable last mile delivery model, particularly in rural areas & last mile level service delivery. Multiple Govt. & Business agencies are trying to reach the same location for various reasons related to FI, Social or Healthcare inclusion and these are disjoint efforts and driving higher costs.

Different data elements available with govt. such as healthcare schemes data, social inclusion data, COVID data, vaccination data, etc. are not leveraged to full extent as there is clear lack of coherence between these data elements.

Last mile technological systems and artefacts are vulnerable to exposure and exploitations. It is loosely handled by BC or BF agents as adequate security measures to control it are not put in place. This has resulted in lot of frauds happening on the ground. About 22% BC agents faced fraud in 2017, a noteworthy increase from 2% in 2015 [ 17 ]. Business model of Last mile & BC Agent network must be looked at again from privacy security & safety angle.

Data Privacy is still a major concern as a lot of captured data is easily available to various stakeholders as PII norms are not completely followed. KYC Data & mobile numbers are available everywhere.

Biometric data is captured duplicitously by some BC Agents in clay who will replicate it later for fraudulent reasons.

Another way is when they give a manual receipt instead of computerised one during transactions.

SMS messages for transactions in an account are not reaching the customer due to lack of mobile device (More than 310 Mn people still do not possess a basic feature phone or a smart phone) or financial institutions are not sending these messages for low value transactions. This has led to increased dependency on local agents.

Access to credit is still a concern as small time lenders charging high rate of interest are prevalent in rural areas. Govt. schemes have not penetrated fully and need more rural outreach to enhance credit access [ 15 ]. Lack of avenues for digital lending and online loans from credible financial institutions is missing.

Recommendations for individuals based on their requirement is not provided and leveraging the personalised data of the person, by performing analytics using AI & ML, banks can offer loans, insurance and other services based on analytics & credit score [ 15 ].

5 National Strategy for Financial Inclusion

In the year 2020, RBI came up with national strategy for Financial Inclusion with focus on creating an outreach of financial services outlets to provide banking access to every household within 5KM radius. All eligible adults must have access to basic financial services such as Bank Account, line of credit, both life and other insurance, pension scheme and suitable investment product. Now, the next paradigm for financial inclusion program (2020–2024) is focused addressing inherent behavioural and practical aspects [ 22 ].

A strong financial transaction grievance redressal system to address concerns of arguably less technology savvy citizens.

Increasing digital penetration as still the smartphone usage for financial transactions are limited to urban and semi urban population predominantly.

Bank account opening for the remaining population of the country as still the PMJDY penetration is about 80% of the population.

Ensuring the privacy of data and information of citizens and prevention of fraudulent transactions and demographic data.

Easy and affordable digital payment options to suit the needs of small businesses and unstructured sector workers.

Providing access to basic and most essential financial products such as transactional accounts, digital payments, basic term insurance, basic medical insurance, and pension options to the population specially in the agricultural and unorganized MSME sector workers.

Acc. to a World Bank report, globally achieving Universal Financial Access by 2020 [ 3 ] has been one of the key developmental agenda of the World Bank which aims to provide adults who currently aren’t part of the formal financial system, with access to a transaction account to store money, send and receive payments to manage their financial lives. Our National Strategy is also aligned to these broad virtues suggested by World Bank. On key parameters, India is quite ahead and continuously progressing:

Leadership in India is having singular focus on technology enabled financial inclusion. It is evident through steps like DBT, PM Kisan, financial assistance to woman and poor during the recent Covid-19 pandemic etc.

Target based approach for specific sectors & regions including “National Mission for Capacity Building” by bankers for MSME sector, Certified Credit Counsellor Scheme for MSME to join them with the formal Financial Channels and informed financial credit decisions.

Regulatory Framework in Banking to protect customers, promote fair business processes and prevent unhealthy practices by market players. Initiatives like exclusive “Financial Inclusion Fund” (with initial corpus Rs.2000 Crore), issuances of differentiated banking license—Small Finance Banks, Payments Banks etc., launch of BC Registry with Indian Banks Association (IBA) etc. are steps towards the same.

Market Development initiatives like Branch Authorization Guidelines (2017) etc. to ensure accurate targeting of the beneficiaries, de-duplication and reduction of fraud and leakage have been taken. Linking all financial assistance schemes to DBT is a strong footprint in this direction

Strengthening Payments Structure through digital retail payments systems like AEPS, NACH, UPI, CTS, IMPS etc. operated by NPCI are significant steps. Aadhaar linked direct benefit transfer has changed the scenario for public funds distribution in India.

Last mile delivery to bridge the gap for remote connectivity and doorstep financial services is key to success. ICT based solution like business correspondents/ facilitators and IPPB are landmark steps in this area, launch of UPI on features phones will be a big game changer also enabling ecosystem for NFC based touch less payments.

Financial Literacy and Awareness is a primary bottleneck in progressive financial inclusion in India. Launch of financial literacy program in 2013 helped in addressing this to some extent.

6 Sustainability through Comprehensive Inclusion

An inclusive society helps sustain socioeconomic development and understanding the correlation between Financial Inclusion, Social Inclusion & Health inclusion helps sustainability. Having taken the right initiatives to ensure wider coverage of Financial Inclusion, it is now time to look at the rationalization of the inclusive society by leveraging iterative technology and other two key aspects—Social Inclusion (Education, Literacy, Skill), Health Inclusion (Personal & Societal Wellness).

FI implemented in a standalone ecosystem may not be enough to achieve. FI must be complemented by Social and Health Inclusion through improving skillset, education, physical and mental well-being to ensure a sustained livelihood [ 12 ]. Current model has a major short-coming. If there is a functionality lapse in any single inclusion, someone may fall out of the inclusive ecosystem.

We need to ensure that whoever is excluded financially is brought into the fold again and has all the necessary tools to sustain within the ecosystem.

To handle this, we need to focus on increasing the digital penetration and continue the account opening process for all citizens. FI Ecosystem must aim to work in tandem with Healthcare Inclusion & Education Inclusion ecosystem to ensure well-being of people and educate the citizens financially. The technological initiatives under ‘JAM Trinity’, that is, PM Jan Dhan Yojana, Aadhaar and increased Mobile Phone & internet usage had led to 355 Mn accounts opened in the last 5 years (Figs. 1 , 2 ).

Current State in India

Desired State in India

Technology should drive the recommendations to every individual to ensure social, health and fin. Inclusion. Cross Leveraging of existing and new citizen databases to provide strategic Analytics & insights to the deciding authorities.

Blockchain could prove to be a gamechanger in enhancing the value chain securely without any duplications of efforts [ 23 ]. There following measures could be taken to improve the living standard of citizens using ICT technology to deliver last mile services [ 19 ]. They are,

Fix technological breakdowns and connectivity issues and ensure wider coverage in remote areas

Facilitate a hassle-free digital experience for new users

Enhance digital security standards to improve the confidence of citizens to make digital transactions

Finetuning limits on daily transactions and commissions on low value withdrawals & deposits

Make changes to the minimum balance criteria in SB Accounts

Delivery services at BC points through Controlled devices for better safety and security for end users

People should be educated and learn to protect themselves against financial cybercrimes. Provide profile wise recommendations and better offers for people by leveraging Analytics, AI & ML using the data gathered from available official databases. For ex., through COVID Patient DB, recommendation for vaccination, availability of various health insurance schemes, access to medical loans could be provided. Building information sharing systems leveraging multiple public databases is crucial for success.

One such example may be leveraging the vaccination database to provide profile wise vaccination recommendations, information regarding availability of loan and credit lines, developing a ‘fraud repository’, and ensuring that online digital commerce platforms carry warnings to alert consumers to the risk of frauds etc. can play a game changer role in FI endeavours. RBI has guided banks to introduce a General-Purpose Credit Card (GCC) facility. It is revolving credit which entitles the card holder to make transactions of Rs 25,000 above the credit limit. This is completely based on customer's credit assessment and the limits are sanctioned without any security or collateral. Rate of Interest on revolving credit is deregulated. Under PM SVANidhi Scheme, micro lending amount of up to Rs 10,000 is provided to street vendors as working capital. Under PM MUDRA scheme, credit is provided to non-corporate, non-farm SMEs up to Rs 1 million. Microfinance institutions can help widen the coverage of reach by offering their services in remote areas using analytics & basic credit risk assessment [ 20 ].

7 Observations and Recommendations for accelerating Financial Inclusion

Innovations in the field of technology & communications strongly complements the FI ecosystem which results in inclusive socio-economic growth. This improves transparency and competitive efficiency, has the potential to reduce cost of service delivery and strengthens the back-end administrative processes [ 21 ].

The objective of providing a basic bouquet of financial services can be achieved through designing and developing customized financial products by banks and ensuring efficient delivery of the same through leveraging of FinTech and BC networks [ 18 ]. Some of the constructive recommendations are following-

Combining Financial inclusion with health inclusion and Social Inclusion to make the it more inclusive for citizens in the lower strata of the society. PM SBY, PM JJBY, RWBCIS, Ayushman Bharat (PM JAY) must be promoted. Till date, 158 Million + Ayushman Cards issued which requires expedite efforts to reach the full population of eligible citizens.

By analysing the impact of COVID-19, we can leverage FI and drive vaccination programs and other welfare schemes such as access to Medical Insurance & Loans for the needy. Comprehensive coverage of Health & Wellness through various initiatives to drive Health & Social Inclusion to achieve a sustainable growth [ 7 ].

Crucial aspect of FI is Financial Literacy [ 27 ]. Promote Financial Literacy and educate people on features such as Phone Banking, UPI & NFC enabled feature phones can be made available at low cost, enhance touchless payment (NFC & QR) framework. Common features such as Bill Payments, Ticket Booking are already interoperable through Bharat QR.

Strengthening the payment infrastructure to promote a level playing field for (NBFCs) and banks. Digitizing registration and compliance processes and diversifying credit sources to enable growth opportunities for MSMEs is an essential step for comprehensive inclusion [ 2 ].

Enabling agricultural NBFCs to access low-cost capital and deploy a ‘physical’ (physical + digital) model suggested by Niti Aayog for achieving better long-term digital outcomes is a crucial step. Digitizing land records will also provide a major boost to the sector.

Tech should aim to reduce cost per transaction and continue to drive the recommendation to every individual to ensure, social, health and financial inclusion and ensure that the money has been reaching the last mile beneficiary at low costs [ 26 ].

By combining digital education tools & digital financial tools, and slight changes to tax regulations, underbanked & unbanked people can break the chain of poverty and sustain successfully in a cash lite economy [ 5 ].

Geospatial technology could be used to analyse the population density of target service areas so that there is a clear understanding of required amount of work force for a particular area and can also be used to identify gaps in current services [ 25 ].

8 Summary of Key Problems we identified

Summary of problems identified & possible solutions in brief is displayed below in Table 1 .

In conclusion, we can say that a technological, multi-faceted & dynamic approach centred around enhancing financial literacy, social & education inclusion, improved cybersecurity & stricter laws, enhanced digital infrastructure is mandatory for wider coverage of next wave of financial inclusion in the country.

Data availability

All the data used in this paper for research purposes are properly cited with references to Source.

Abbreviations

Point of Sale

Micro, Small and Medium Enterprises

Reserve Bank of India

Pradhan Mantri Jan Dhan Yojana

Under PM Jeevan Jyoti Bima Yojana

Atal Pension Yojana

Pradhan Mantri Suraksha Bima Yojana

Aadhaar Payments Bridge System

Core banking system

PM Kisan Samman Nidhi Yojana

PM Street Vendors' Atmanirbhar Nidhi

Pradhan Mantri MUDRA

Bharat Interface for Money

Unified Payments Interface

National Automated Clearing House

Business correspondent network

Direct benefit transfer

Aadhaar enabled payments system

Cheque truncation system

National Payments Corporation of India

• Information and Communication Technology

Indian Post Payment Bank

PM Jan Arogya Yojana

Restructured Weather Based Crop Insurance Scheme

Near Field Communication

Non-Banking Financial Company

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Malladi, C.M., Soni, R.K. & Srinivasan, S. Digital financial inclusion: next frontiers—challenges and opportunities. CSIT 9 , 127–134 (2021). https://doi.org/10.1007/s40012-021-00328-5

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DOI : https://doi.org/10.1007/s40012-021-00328-5

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On Fintech and Financial Inclusion

The cost of financial intermediation has declined in recent years thanks to technological progress and increased competition. I document this fact and I analyze two features of new financial technologies that have stirred controversy: returns to scale, and the use of big data and machine learning. I argue that the nature of fixed versus variable costs in robo-advising is likely to democratize access to financial services. Big data is likely to reduce the impact of negative prejudice in the credit market but it could reduce the effectiveness of existing policies aimed at protecting minorities.

This paper was prepared for the 2019 BIS Annual Research Conference. I am grateful to my discussants Manju Puri and David Dorn, to Hyun Shin, Marina Niessner, and participants at the 2019 BIS Annual Research Conference. I thank Marcos Sonnervig for outstanding research assistance. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

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