In groups (including low income ones) to

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

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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.