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.
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
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
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.
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.
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.
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.
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
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.