In the literature, both definition of financial

inclusion and index formation to define financial inclusion have been

extensively discussed. Studies of causes of financial inclusion either focused

on particular regions or covered all countries. First, index formation will be

discussed then literature looking at financial inclusion’s impact on growth,

stability and income equality will be presented.

Definition of

Financial Inclusion and Index Formation

Existing literature on financial inclusion has

different definitions of the concept and the notion of financial inclusion attracted

a mounting interest from the academia. Numerous studies define the concept in

terms of financial exclusion instead which is linked to a broader context of

social inclusion. Sinclair

(2001) indicated that the notion of financial exclusion was the

incapability to access essential financial services while Leyshon and Thrift (1995)

defined it as the processes which serve to preclude some social groups and/or persons

from accessing the formal financial system. Similarly, Carbo et al. (2005) defined financial

exclusion as the incapacity of some groups in accessing the financial system.

On the other hand, Government of India’s definition of

financial inclusion lies on the basis of creating a system that

guarantees/ensures access by exposed groups (including low income ones) to

financial services with (i) acceptable credit conditions and (ii) with an affordable

cost, in a timely manner. Rajan

(2014) signifies that financial inclusion encompasses the deepening of

financial services for those people with limited access as well as extension of

financial services to those who do not have any access. Furthermore, Amidži?,

Massara, and Mialou (2014) and Sarma (2008) directly define financial

inclusion. The former describe financial inclusion as an economic state where persons

and firms have access to basic financial services. (

Other studies have results that certainly could have

significant policy implications with regards to increasing the level of financial

inclusion. For instance, Burgess

and Panda (2005) found that the expansion of bank branches in rural

India had a significant impact on alleviating poverty. Meanwhile, Allen et al. (2013) explored

the factors behind the financial development and inclusion amongst African

countries. Particularly, Brune

et al. (2011) conducted experiments in rural Malawi examining how access

to formal financial services improves the lives of the poor, pertaining to

saving products.

Although it appears that there is a consensus on how financial inclusion is defined, there certainly is no standard way of

measuring it. Hence, existing studies offer differing measuring techniques of financial inclusion. For example, Honohan

(2007 and 2008) constructed an

indicator measuring financial

access by taking into account the overall

adult population in an economy

with access to formal financial

intermediaries. For countries with existing data on

financial access, the composite

indicator is formulated by

utilizing household survey data.

For those without household

survey, the indicator is formed using the information on bank account numbers in

combination with GDP per capita.

The data is constructed as a

cross-section series using the most

recent data as the reference year varying across economies.

However, Honohan’s (2007 and 2008) calculations only deliver a snapshot of

financial inclusion across various countries and is not appropriate for comprehending the relative trends and

changes across countries over time.

In order to overcome the aforementioned deficiencies,

Sarma (2008, 2010, and 2012) and Chakravarty and Pal (2010) suggested construction

of composite indices of financial inclusion that combine various banking sector

parameters. Importantly, these indices assign equal weights to all parameters

and dimensions, with the assumption that these dimensions have equal effect on

financial inclusion. These indices are created in order to gauge the availability

and accessibility; as well as the usage of banking services.

Sarma (2008) described financial inclusion as the

level of ease for any individual or a group to access, to reach availability

and to make use of the formal financial system. The study followed a

multidimensional approach with an index of financial inclusion (IFI). The

multi-dimensional index captured information on various dimensions of financial

inclusion under one single digit between 0 and 1. On the one extreme, 0 displayed complete financial

exclusion; while on the other side of the spectrum 1 reflected complete

financial inclusion in an economy at a given point in time. The easy to

calculate index contains information on various dimensions of an inclusive

financial system. The calculated index in this paper could be utilized to

compare different levels of financial inclusion across economies at a specific

time point. It could also be utilized for observing the advancement of policy

initiatives for financial inclusion over a time period. These two attributes

were the biggest advantage of this study. In other words, this paper filled the

gap of a comprehensive measure that can be utilized to measure the extent of

financial inclusion across economies.

The construction methodology and computation for this

index was relatively similar to the well-known development indices of the HDI,

the HPI, the GDI. Similar to these indices, the study proposed a dimension

index for each dimension of the financial inclusion. The dimension is calculated

by subtracting the minimum value from the actual value and dividing it by the

difference between the maximum and minimum values. Once each dimension are

computed, the index then was determined by the normalized inverse Euclidian

distance of the ideal point.

The IFI index took into account three fundamental dimensions

which were selected mainly due to the data availability for large number of

countries as well as the recent trends in literature.

banking penetration which is measured

by dividing number of bank accounts by the total population;availability of the banking services

which is proxied by the number of bank branches per 1000 inhabitants; and, banking system usage which is

estimated by dividing the volume of credit and deposit by the GDP of the country.

Diverging from the methodology utilized by the UNDP

for the HDI, the HPI, the GDI which is the simple arithmetic average; the IFI

index was a measurement of the distance from the ideal. Moreover, the choice of

minimum and maximum values for the dimensions was also different since the UNDP

methodology preferred pre-fixed values for the minimum and maximum values for

each dimension to calculate the dimensional index. Instead, this study took

into account the minimum and maximum values within the dataset for each

dimension. It was difficult to determine the minimum and maximum for any

dimension of financial inclusion. For several dimensions such as the literacy

rate and life expectancy, used in UNDP’s HDI, it was easy to define limits. However, this was a

dynamic index where minimum and maximum values for any dimension may alter at

different time points.

In sum, Sarma (2008) followed a different approach to

calculate the indicator. He first computed a dimension index for each financial

inclusion dimension and then aggregated each index as the normalized inverse of

Euclidean distance. The distance is calculated with respect to an ideal

reference point, and then normalized by the number of dimensions in the

composite index. The index did not impose any weights for each dimension.

The index had some limitations; it did not have country

specific information, geographical aspects and gender dimension. Due to lack of

appropriate data, Sarma was not able to combine numerous aspects of an inclusive

financial system including financial services’ affordability, timeliness and quality.

Amidrzic et al

(2014) defined financial inclusion as an economic state

where persons and firms have access to fundamental financial services based on

motivations except for efficiency criteria. They concluded that financial

inclusion played an important role in sustaining employment, economic growth,

and financial stability. However, it was

not robustly measured yet. There was no new composite index with weighting

methodology. In their paper, countries were ranked based on the new composite

index (variables are listed below on Table 1.1), providing an additional tool

which could be used for monitoring and policy purposes on a regular basis.

Table 1.1: Composite index

variables (Amidži?,

2014)

Variable

Description

Number of

ATMs per 1,000 square kilometers

Sum of all ATMs multiplied by

1,000 and divided by total area of the country in square kilometers.

Number of

branches of ODCs per 1,000 square kilometers

Sum of all branches of commercial

banks, credit unions& financial cooperatives, deposit- taking

microfinance institutions and other deposit takers multiplied by 1,000 and

divided by total area of the country in square kilometers.

Total

number of resident household depositors with ODCs per 1,000 adults

Sum of all household depositors

with commercial banks, credit unions & financial cooperatives,

deposit-taking microfinance institutions and other deposit takers multiplied

by 1,000 and divided by the adult population.

Total

number of resident household borrowers with ODCs per 1,000

Sum of all household borrowers

from commercial banks, credit unions & financial cooperatives,

deposit-taking microfinance institutions and other deposit takers multiplied

by 1,000 then divided by the total adult population.

Source:

Assessing Countries’ Financial Inclusion Standing A New Composite Index,

Amidrzic et al, February 2014.

The size of the sample was relatively small for each

year, given that few countries were reporting the data for the four variables

at once. Even with a small sample, the calculated index showed interesting

results pertaining to financial inclusion. The dataset considered in this paper

satisfied the required conditions for the use of factor analysis (FA).

For the computation of the index, the authors used a five-step

sequence. As a first step, the variables were normalized making the scale which

they were measured irrelevant similar to the UNDP’s approach. Then, using FA,

the authors introduced a statistical identification of financial inclusion

dimensions in order to ascertain whether the statistical groups obtained from

FA are similar to the theoretical dimensions. With the statistical dimensions

corresponding to the theoretical ones, the authors then used in the third step,

the statistical properties of the dataset to give weights to both individual

variables and sub-indices. Finally, unlike the UNDP’s indices which were computed

using the simple average mean, the outcomes of the second and third steps let

them choose in the fourth and fifth steps a weighted geometric average as the

functional form of the aggregator for the calculation of the dimension and

composite indices.

Aggregation over variables that were expressed with

different measurement units and have varying ranges necessitates normalization.

Normalization addresses the lack of scale invariance. There has been various proposed

normalization approaches in the literature. A comprehensive review of the

different approaches may be found in Freudenberg (2003), Jacobs et al. (2004), and OECD (2008). Practically

speaking, however, the most common methods are the standardization, the minimum-maximum,

and the distance to a reference. Of the three main techniques, Amidrzic et al. utilized

the distance to a reference in this paper. The distance to a reference measures

the relative position of a given variable with respect to its reference point.

The reference point was a target at a given time or the value of the variable

in the country of reference. The authors identified the reference point for

each variable to be the maximum value of the variable across countries. In

other words, for a given variable, the benchmark country was the group leader.

The normalized variable was between 0 and 1 where a score of 1 is given to the

leading country and the others countries are given percentage points away from

the leader. Additionally, this normalization method satisfied most of the

prerequisite technical properties.

In a nutshell, Amidži?, Massara, and Mialou (2014)

constructed a financial inclusion indicator as a composite indicator of

variables pertaining to its dimensions, outreach (geographic and demographic

penetration), usage (deposit and lending), and quality (disclosure requirement,

dispute resolution, and cost of usage).

Each measure was normalized, statistically identified for each

dimension, and then aggregated using statistical weights. The aggregation

technique followed weighted geometric mean.

A downside of this approach was that it used factor analysis method to

determine which variables are to be included for each dimension. Therefore, it

did not fully utilize all available data for each country. Furthermore, it assigned various weights for

each dimension, which implied the importance of one measure versus another.

Unlike Amidrzic et al (2014) and Sarma (2008), Honohan (2008) formed a

financial access indicator for 160 economies that combined both household

survey datasets and published financial institutions data into a composite

indicator; and assessed country characteristics that might influence financial

access. Among the variables tested, aid as percent of gross national income (GNI), age dependency

ratio, and population density significantly lowered financial access; while

mobile phone subscription and quality of institutions significantly increased

financial access. Looking at the cross-country link between poverty and

financial access, his results showed that financial access considerably reduces

poverty, but the result holds only when financial access is the sole regressor,

it loses significance when other variables are introduced as regressors.

In an earlier

version of his paper, Honohan (2007) tested the significance of his financial

access indicator in reducing income equality. His results stated that higher

financial access significantly reduced income inequality as measured by the

Gini coefficient. However, the link between the two variables depends on which

specification is used, i.e., when the access variable is included on its own

and/or includes financial depth measure, the results are significant, but the

same does not hold when the control variables including per capita income and

dummy variables are used.