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The objective of paper is to examine status of financial inclusion in India and study its
determinants.
Journal of Financial Economic Policy
Financial inclusion and its determinants: evidence from India
Nitin Kumar
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Nitin Kumar, (2013),"Financial inclusion and its determinants: evidence from India", J ournal of Financial
Economic Policy, Vol. 5 Iss 1 pp. 4 - 19
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Disha Bhanot, Varadraj Bapat, Sasadhar Bera, (2012),"Studying financial inclusion in
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dx.doi.org/10.1108/02652321211262221
Stephen Sinclair, (2013),"Financial inclusion and social financialisation: Britain in a European context",
International J ournal of Sociology and Social Policy, Vol. 33 Iss 11/12 pp. 658-676http://dx.doi.org/10.1108/
IJ SSP-09-2012-0080
Louis de Koker, (2011),"Aligning anti-money laundering, combating of financing of terror and financial
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Financial inclusion and its
determinants: evidence fromIndia
Nitin Kumar
Reserve Bank of India, Mumbai, India
Abstract
Purpose – The objective of paper is to examine status of ?nancial inclusion in India and study its
determinants.
Design/methodology/approach – Panel ?xed effects and dynamic panel generalized methods of
moments (GMM) methodologies have been applied to study determinants of ?nancial inclusion.
Additionally, Kendall’s index of rank concordance has been derived to test for convergence of states in
achieving ?nancial inclusion.
Findings – Branch network has unambiguous bene?cial impact on ?nancial inclusion. Both
proportion of factories and employee base turn out to be signi?cant determinants of penetration
indicators. The ?ndings reveal the importance of a region’s socio-economic and environmental setup
in shaping banking habit of masses. Using test for convergence it is found that regions tend to
maintain their respective level of banking activity, with no support for closing gap.
Originality/value – To the best of the author’s knowledge, no panel data study has been performed
for India based on data for large number of states and a reasonable time span. This study utilizes 29
major states and union territories encompassing 1995 to 2008, which helps to increase degree of
freedom and provide reliable results. The study helps us to ascertain direction and strength of various
causal factors in process offer policy makers’ strategies, for improving ?nancial inclusion.
Keywords Banks, Financial services, India, Econometrics, Personal ?nance, Financial inclusion,
Generalized methods of moments
Paper type Research paper
1. Introduction
The Indian banking industry has shown tremendous growth in volume and complexity
over the last decade or so. Despite making signi?cant improvements in all areas relating
to ?nancial viability, pro?tability and competitiveness, there are concerns that much
needed banking services have not reached underprivileged sections. In this context,
efforts are being made as ?nancial inclusion can truly lift ?nancial condition and
standards of life of the poor and disadvantaged (Leeladhar, 2006; Subbarao, 2009a;
Thorat, 2007).
A robust and ef?cient ?nancial climate lays down strong foundations for economic
growth and developmental activities. Considerable empirical literature using various
sophisticated techniques have been employed across countries that validate this point
effectively (Shaw, 1973; Obstfeld, 1994; Levine, 2002). Availability of banking
amenities and strong bank branch network are major facilitators of capital formation
and expansionary activities (Feldstein and Horioka, 1980; Ford and Poret, 1991).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – G21, C23, C26
The author is Assistant Adviser at the Reserve Bank of India, Mumbai, India. The views
expressed in the paper are those of author and not of the organization to which he belongs. All
the errors, omissions, if any, are the responsibility of the author.
Journal of Financial Economic Policy
Vol. 5 No. 1, 2013
pp. 4-19
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381311317754
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The issue of ?nancially connected systems has gained prominence even in developed
economies like UK and USA (IMF, 2009; The World Bank, 2005a, b). Devlin (2005)
undertook a study to understand determinants of range of banking ?nancial services in
UK. Results indicated that although factors vary according to kind of ?nancial service,
however certain variables portray consistent and signi?cant in?uence across an array of
?nancial services. Variables concomitantly affecting dependent variable turned out to
be employment status, household income and housing tenure.
Mihasonirina and Kangni (2011) performed a study focusing on South African
countries that found signi?cance of communicationtechnologies (ICT) like mobile phones,
?xed phones, cost of call on ?nancial inclusion. Toxopeus and Lensink (2007) attended to
the issue of remittance in?ows on?nancial inclusionfor cross-section of emergingnations.
Remittances, interms of size, are not onlymaincapital in?ows indevelopingcountries, but
also have robust positive effect on?nancial inclusionand inturn oneconomic growth. Ina
detailed exposition, Carbo et al. (2005) have highlighted how ?nancial exclusion has
emerged as major concern for both developed and developing countries.
Issue of ?nancial exclusion could be ill afforded to be ignored for satisfactory and
inclusive growth. It has been pointed out by Subbarao (2009a) that out of 600,000
habitations in India, only about 30,000 centres are covered by commercial banks. With
two-thirds of population living in rural agglomerations, rural-urban divide in terms of
?nancial access indicators (branch and automatic teller machine (ATM) density) is
clearly visible (Figures A1 and A2). A comparison of ?nancial access, depth and size in
India vis-a` -vis other emerging nations, namely, China, Malaysia, Thailand provides
alarming picture (Table I).
The study performed by Sarma (2008), attempted to construct Index of Financial
Inclusion, based on three aspects of ?nancial inclusion, namely, penetration of banking
system, its availability to users andits actual usage for a cross-sectionof countries for 2004.
The index was aggregative in nature that preserves same weight for all three components.
Financial access Financial depth and size
Countries/
groups
Number of branches per
100,000 persons
Number of ATMs per
100,000 persons
Private credit to GDP
ratio (percent)
India 6.33 1.63 33.3
China 1.33
a
3.8
a
111.8
Indonesia 3.73 4.84
b
23
Malaysia 8.26 16.44 126.6
Thailand 7.37 17.05 90.5
Asian peer
group (range) 1.33-20 3.80-17.05 23.00-126.60
Australia 24 115 109.73
Canada 28 158 75.65
Japan 45 136 97.9
UK 23 97 160.48
USA 26 134 47.84
OECD group
(range) 23-45 57-158 47.80-160.48
Notes:
a
2003 data;
b
2000 data; data relate to 2005 unless otherwise speci?ed
Source: Kiatchai and Kulathunga (2009)
Table I.
Indicators of ?nancial
inclusion across globe
Financial
inclusion
determinants
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However, countryspeci?c regulations, features andgeneral preferences leadingto ?nancial
inclusion vastly differs across nations (Kempson et al., 2004; Kendall et al., 2010;
Sinclair et al., 2009; The World Bank, 2008a). This fact leads to inconsistencies in inclusion
scores. Existing study signi?cantly differs from Sarma (2008) work in following ways. At
the outset, present analysis is in no way attempt to construct any Index of Financial
Inclusion. There is vast literature on ?nancial institutions and banks aiding economic
development and vice versa (Obstfeld, 1994; Levine, 2002). However, literature on evidence
on what determines coverage of banking is scarce, which is critical issue as bringing poor
under scope of banking services can help improve their economic well-being. India is fast
growing economy facing high inequality and skewed banking coverage. Additionally,
scarcity of literature focusing on determinants of ?nancial inclusion for India leads to void.
To bridge the gap, existing study is endeavor to examine status of Financial Inclusion
focusing in emerging nation like India and to explore its determinants. A rich panel of
29 major states from1995 to 2008 has been employed. Additionally, Kendall’s indexof rank
concordance has been derived to test for convergence of states in achieving ?nancial
inclusion. It is revealed that although both deposit and credit accounts have improved over
time, but their growth has not matched population increase. So, it signi?es howpopulation
growth and concentration are outpacing rate of banking expansion. Other results are:
?nancial development (bank presence) and industrialization (factory presence) have
bene?cial impact on ?nancial inclusion. Using test for convergence it is found that regions
tend to maintain their respective level of banking activity giving rise to policy implication
that more attention is required to be paid for usual laggards.
Rest of the article is organized as follows. Section 2 brie?y discusses scope of
?nancial inclusion, its signi?cance and consequences for emerging nation, India.
Exposition of data and key variables are provided in Section 3. Section 4 is devoted to
econometric model and methodology employed for analysis followed by Section 5,
which discusses results of empirical analysis. Section 6 concludes with overall
summary and major ?ndings of study.
2. Scope of ?nancial inclusion and its relevance for India
As per Rangarajan Committee (2008) report, Financial Inclusion is de?ned:
[. . .] as the process of ensuring access to ?nancial services and timely and adequate credit
where needed by vulnerable groups such as weaker sections and low-income groups at
affordable cost.
Broadly speaking, Financial Inclusion is delivery of banking services at affordable cost
to vast sections of disadvantaged and low-income groups. Goals of ?nancial inclusion
can be met by initiative of banking sector to cut across various strata of society,
regions, gender and income and encourage public to embrace banking habit. Also,
Reserve Bank of India (RBI), as chief regulator has intervened for success of ?nancial
inclusion by various enactments[1], ?nancial literacy drives, leveraging technology,
etc. In addition to banking system, Indian ?nancial network consists of Indian postal
department[2], insurance companies; self-help groups (SHGs), civil society
organizations (CSOs), non-banking ?nancial companies (NBFCs), non-government
organizations (NGOs), micro ?nance institutions (MFIs), which are vital ?nancial
intermediaries.
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Branch density denotes the spread of banks and level of comfort, convenience
available for public to carry out banking pursuits. Figure A1 denotes trend of branch
density over last few years. An improvement from 14.5[3] in 2009 to 14.0 thousand per
branch in 2010 is registered, albeit clear disparity exists between rural and urban
regions[4]. On-site and off-site ATMs are indispensable element of modern banking era.
Glance at ATM density in India shows improvement over past few years (Figure A2).
Cross-countrycomparisonof some ?nancial inclusionindicators are presentedinTable I.
It may be noted that although branch density in India is comparable with other Asian
nations, both ATM spread and private credit to GDP ratio are at lower levels in India.
Consequences of ?nancial exclusion vary depending on nature and extent of
services denied. Small businesses may suffer due to loss of access to middle class and
higher-income consumers, higher cash handling costs and delays in remittances of
money leading to social exclusion (Burgess and Pande, 2003). Among developed
nations, UK was one of the earliest to realize importance of ?nancial inclusion
(Kempson et al., 2004; Collard et al., 2001). Around 8 percent of households lacked any
kind of deposit account. Reasons varied from low credit scoring, mistrust by people on
margins of society, terms and condition, physical distance and others. In Australia,
prevalence of unbanked adults is much lower than in other developed nations, with
estimates of just 3 percent of adults lacking bank account.
Access to ?nancial services for people, especially poor and deprived, is critical.
Indian legislature has been conscious of this fact since early. Bank nationalization
provided ?rst vigorous impetus for mass banking. Rationale for creating Regional
Rural Banks (RRBs) was also to bestow banking services to poor. Commercial banks
and RRBs have increased from 8321 in 1969 to 84,504 branches as at end of March
2010. Number of “No frill” accounts has also registered growth over last few years
(Thorat, 2007). In view of their vast branch network, public sector banks and RRBs
have been able to scale up their efforts by merely leveraging on existing capacity.
Additionally, new branch authorization policy of RBI encourages banks to open
branches in under banked regions. New policy also places lot of emphasis on the efforts
made by RBI to achieve, inter alia, ?nancial inclusion and other policy objectives.
3. Data source and key variables
Annual data from varied sources has been utilized for analysis. The study is state-wise
unbalanced panel data analysis from 1995 to 2008. Pooled dataset, as employed in
present scenario offers host of advantages. It allows controlling for heterogeneity
across cross-sections due to their inherent characteristics’ variations. Additionally, it
discounts for time effects, which may occur due to changes in policy and other socio
macroeconomic environment in turn having impact on our parameters of interest.
Large degrees of freedom also help to derive more robust and consistent results with
meaningful policy implications. Following subsections are devoted to detailed
discussion on construction and explanations for the variables employed.
3.1 Description of endogenous variables
Sarma (2008) had proposed bank accounts per capita as indicator of penetration
of banking system. In those lines number of deposit accounts percent of population,
number of credit accounts percent of population has been constructed as
measure of penetration indicators, which constitutes our dependent variable[5].
Financial
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Separate penetration indicators based on deposit and current accounts has advantage
of avoiding aggregation problem as generally faced while constructing indices.
Additionally, both credit and deposit account are separate banking instruments with
diverse objectives. Deposit (savings and term) account may be more useful to
individuals and households earning regular income to deposit their savings that can be
withdrawn as per needs. It is expected that people staying in urban regions and
employed in formal sectors shall have deposit account(s). Acredit account on other hand
caters to requirements of entrepreneurs and households for business and personal
pursuits, respectively. For opening credit account, bank ensures sound ?nancial
position/income source of its borrower in order to reduce cases of default. Owing to
diverse features of deposit and credit instruments of banks, it is imperative to bestow
separate focus to them individually.
Branch network is used in ?nancial inclusion studies to capture banking access and
branch density (Subba Rao, 2007; Burgess and Pande, 2003; Leeladhar, 2006; The World
Bank, 2008a). Although, banks expand as per their business strategies, in India, RBI has
taken manyinitiatives not onlyto improve branchnetwork but also bankingconvenience.
Among direct measures of improving network, foremost is general permission clause,
according to which banks do not need prior permission of RBI to setup branch/mobile
branch/administrative of?ce/Central Processing Centre in centre with population
,50,000. As per one of stringent measures, RBI has made mandatory for banks to open
one-third of their total branches in under banked districts. Generally private banks have
tendency to setup branches only in metropolitan cities for greater pro?t making. To check
such instances, RBI has made mandatory for private players to ensure that 25 percent of
their branches are in rural conglomerates. Banks are encouraged to utilize business
correspondents/facilitators for greater reach in rural and isolated localities (Subbarao,
2009a, b; Reserve Bank of India, 2011). In view of this background, it is obvious that as
regulator, RBI has control on banks’ branch expansion. Hence, it is more sensible to keep
branch density (average population per branch (APPB)) as control variable rather
endogenous variable and inspect its impact on penetration indices.
Number of deposit and credit accounts[6] has been collected from Basic Statistical
Returns of Scheduled Commercial Banks in India published by RBI. Actual state wise
population ?gures are available only for census years, such as, 1981, 1991, 2001 and so on.
However, projected state wise population ?gures are available from Of?ce of Registrar
General and Census Commissioner of India, which are utilized for existing analysis.
3.2 Description of exogenous variables
At the onset, time trend has been included as independent variable to control for
various policies implemented over time. Among other explanatory variables, is
population density. Population density is population per square kilometer to capture
region-wise demographics and understand role of population concentration on
penetration of banking system.
A vital variable to examine the segment of population to which branch caters is
average population per bank branch (APPB). APPB is the ratio of population
(in thousand) to total number of branches. The bank of?ces devoted solely for
administrative purpose were excluded while deriving number of bank branches.
Information on branches has been sourced from Branch Banking Statistics published
by RBI.
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Income is measured by per capita net state domestic product (NSDP) at 1999-2000
constant prices. Logarithm of per capita NSDP has been included to understand
in?uence of states’ economic and ?nancial position on penetration of banking system.
Data on NSDP has been collected from Handbook of Statistics on Indian Economy
published by RBI.
Deposit SDP ratio is elementary indicator of level of deposit in the system. Similarly,
Credit SDP ratio portrays the level of credit utilization. Both Deposit SDP ratio and
Credit SDP ratio denote usage of basic ?nancial products in ?nancial system. High
ratios of both indicators are usually associated with higher banking and investment
activities (Beck et al., 2007; The World Bank, 2008a, b, 2009). State wise information on
both credit and deposit is available in statistical publication, Basic Statistical Returns
of Scheduled Commercial Banks in India published by RBI. State-wise GDP is obtained
from Handbook of Statistics on Indian Economy published by RBI.
Proportion of factories has been taken as proxy for the level of industrialization and
sociological modernization. Usually advanced economies with greater industrialization
are expected to have greater role for banking and ?nancial activities. Employment
proportion represents employment status of region. Those of more secure status
economically are less likely to be ?nancially excluded (Devlin, 2005). Information for
number of employees along with data on factories has been collated from various
volumes of Annual Survey of Industries (ASI) published by Central Statistical
Organisation (CSO) of India.
4. Econometric model and methodology
The modeling strategy basically rests on two methodologies, namely, ?xed/random
effects regression and dynamic panel generalized methods of moments (GMM)
technique to control for potential biases associated with simultaneity and reverse
causality.
Due to peculiarities of pooled dataset, observations for individual may not be
independent and usual ordinary least squares method may provide biased estimates.
Hence, we employ panel data estimation techniques (?xed-effects model and
random-effects model) to control for ?xed or random individual differences. Hausman
test has been applied to test for appropriateness of ?xed or random effects model
(REM). Basic functional form of regression equation is as follows:
Y
it
¼ b
0
þb
1
X
it
þa
i
þ1
it
ð1Þ
Here, Y
it
represents value of endogenous variable for ith state at tth period. b
0
stands
for intercept term and X
it
is matrix of exogenous variables. b
1
is vector of associated
parameters. a
i
is treated as random variable with speci?ed probability distribution
(usually normal, homoscedastic, and independent of all measured variables) in case of
REM, whereas set of ?xed parameters in ?xed effects model. 1
it
is usual stochastic
disturbance term following normal distribution with mean 0 and variance s
2
.
The existing study tries to understand determinants of penetration indicators.
Among explanatory variables included are certain ?nancial inclusion access and usage
indicators also, such as, APPB, Deposit SDP ratio, Credit SDP ratio. Such explanatory
variables may themselves be endogenous, giving rise to situation where one or
more regressors are correlated with error term. In such situation, usual methods of
estimation cannot consistently estimate casual effect of regressor on dependent variable.
Financial
inclusion
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So, GMMestimator developed for dynamic panel data, introduced by Arellano and Bond
(1991) and Arellano and Bover (1995) has been employed, formulated as follows:
y
i;t
¼ ay
i;t21
þb
0
X
i;t
þh
i
þ1
i;t
ð2Þ
Here, y is dependent variable. X depicts matrix of explanatory regressors, h is
unobserved state effect, 1 being usual stochastic term. Separate regressions have been
performed for deposit and credit penetration indicators. Common set of strictly
explanatory variables being time trend, population density, income level, proportion of
factories to capture industrialization, employee base as proxy for demographic status.
5. Empirical analysis
A snapshot of variables for few selected years is provided in Table II. From table it is
evident that number of branches rose by around 14,000 during period. Number of
credit accounts marked slight decline of around 57 lakhs in 1999 as compared to 1995.
However, thereafter it has consistently swelled and crossed ?gure of 10 crore in 2008.
Other variables have risen in magnitude except some minor decline shown by number
of factories and employment.
All 35 states and Union Territories of India could not be considered for carrying out
estimation due to unavailability of information on certain series. Consequently, six
states/Union Territories were dropped (Table III). The unbalanced panel was
constructed on basis of 29 states and Union Territories for time span of 14 years.
Estimation result for deposit penetration indicator is provided in Table IV.
Model 1 depicts the results of ?xed effect robust estimation[7]. Population density is
not only having inverse in?uence but also signi?cant. The outcome suggests that
although deposit accounts have improved over time, its penetration has not matched
population growth that has been witnessed for the study period. In line with intuition,
APPB is, actually having negative and signi?cant impact on deposit penetration
for models 1 and 2[8]. Deposit SDP ratio is coming out to be positively signi?cant
in determination of deposit penetration in both models. Socio demographic
variables, factory proportion and employee base are also signi?cant in Model 1.
Employee base, has positive conventional sign (Devlin, 2005). Overall, the exercise
seems to indicate that branch spread along with state level development and social
characteristics de?nitely have robust and direct impact for determination of deposit
penetration.
Table V displays regression results for credit penetration, focusing on credit side of
banking activity with credit penetration as dependent variable. Credit SDP ratio is
having strong positive impact on dependent variable in both models. Employee base is
coming out to be signi?cant with positive sign in Model 3. Similarly, factory proportion
is positively signi?cant in Model 4. Additionally, test of structural change was
performed, which indicated structural change in 2001. The shift could be due to
multiple factors, such as phased implementation of Narasimham Committee (1998)
report, which emphasized increase of branch network and encouraged private and
foreign banks’ entry, among other.
To sum up analytical ?ndings: branch density is having strong positive impact on
?nancial inclusion drive. Measures taken by RBI for relaxation of branch opening,
setting up of business correspondent model for rural masses, enhanced ATM kiosks
and other steps[9] are bearing desired results. As indicated by Carbo et al. (2005),
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Table II.
Arithmetic mean
of variables for
selected years
Financial
inclusion
determinants
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Devlin (2005) among socio-economic determinants both level of industrialization and
employee base are found to be having bene?cial impact for ?nancial inclusion.
Last but not least, natural query which arises is that whether rankings of the states
according to their level of credit or deposit penetration indicators vary signi?cantly
over years[10]. To address the issue, we compute Kendall’s index of rank
concordance[11].
Kendall’s index of rank concordance is calculated as follows:
KI
t
¼
Var
P
T
t¼1
ARðEÞ
it
h i
Var½T
*
ARðEÞ
i
?
ð3Þ
No. State/UT Incomplete information
1 Andaman and Nicobar
2 Andhra Pradesh
3 Arunachal Pradesh X
4 Assam
5 Bihar
6 Chandigarh
7 Chhattisgarh
8 Dadra and Nagar Haveli X
9 Daman and Diu X
10 Delhi
11 Goa
12 Gujarat
13 Haryana
14 Himachal Pradesh
15 Jammu and Kashmir
16 Jharkhand
17 Karnataka
18 Kerala
19 Lakshadweep X
20 Madhya Pradesh
21 Maharashtra
22 Manipur
23 Meghalaya
24 Mizoram X
25 Nagaland
26 Orissa
27 Puducherry
28 Punjab
29 Rajasthan
30 Sikkim X
31 Tamil Nadu
32 Tripura
33 Uttar Pradesh
34 Uttarakhand
35 West Bengal
Note: “X” denotes dropped region from regression analysis
Table III.
List of states/union
territories
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6
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where, AR(E)
it
depicts actual rank of ith state in year t. AR(E)
i1
is actual rank of
ith state in initial year t ¼ 1, and T is number of years for which data is used
for construction of index. The value of rank concordance index ranges from zero to
one. Closer the index value is to zero, greater is the mobility within distribution and
vice versa.
Kendall’s index for credit penetration is tabulated in Table VI. It may be seen that
null hypothesis of no association among ranks of different years is rejected decisively
for all years at 5 percent level of signi?cance. Thus, cross-sectional dispersion of credit
penetration is not diminishing over time and the laggards are not showing any
indication of improvement over the years. Similar interpretation may be deduced for
deposit penetration index (Table VII). It is clear that there exists stability in ranks
obtained by various states with regard to their level of deposit penetration. So, overall
gap among states is not showing any evidence of narrowing down.
Fixed effects robust (Model 1) Dynamic panel GMM (Model 2)
Intercept 21.267
(78.108)
L1.D 0.558
* *
(0.263)
Time 0.247 1.984
(0.413) (1.551)
Population density 20.905
*
0.484
(0.292) (1.449)
APPB 20.12
* *
21.344
* *
(0.046) (0.609)
ln (per capita NSDP) 3.434 214.224
(8.395) (27.164)
Deposit/SDP 0.145
* * *
0.170
* *
(0.085) (0.082)
Credit/SDP 20.022 20.511
(0.075) (0.348)
Factory/Popn 436.421
* * *
566.145
(213.582) (1,194.540)
Employee/Popn 9.949
* *
0.183
(3.83) (4.325)
Model statistics
Cross-section dummies Yes Yes
Time dummies No No
R
2
0.088
F-statistics 11.62
*
Wald-x
2
67.47
*
Hansen test 10.69
AR1 20.09
AR2 21.38
Number of observations 338 271
Notes: Signi?cant at:
*
1,
* *
5 and
* * *
10 percent levels; number of cross-sections: 29; number of time
periods: 14; ?gures in brackets denote robust standard errors for Models 1 and 2; L1.D denotes ?rst lag
of dependent variable; AR1 and AR2 denotes the Arellano-Bond test for AR(1) and AR(2) in ?rst
differences, respectively
Table IV.
Estimation results for
deposit penetration
Financial
inclusion
determinants
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6. Conclusion
The study provides empirical analysis of status and determinants of ?nancial inclusion
in India. It employs annual information of 29 major states from 1995 to 2008. The
empirical results indicate that supply side of inclusive efforts through branch network
expansion is having intended impact of improved banking activity as re?ected
in penetration indicators. However, demand side pressure exists in systemas penetration
indicators are unable to match pace of population growth. Both, level of industrialization
and employee base are having bene?cial in?uence on penetration indicators.
Major policy inputs emanating from study are multi pronged strategies for
enhancing employee base and industrial activity especially in backward states.
Employment generating schemes have multiple bene?ts. It not only strikes poverty
menace but also helps improve income level and ?nancial inclusion in the process.
Similarly, legislations towards industrial reforms in general and sector speci?c
schemes in speci?c aids entrepreneurship, small sector and industrial activity and
translate into inclusion, among others.
Fixed effects robust (Model 3) Dynamic panel GMM (Model 4)
Intercept 217.222
(24.59)
L1.D 20.330
(0.457)
Time 20.152 20.301
(0.127) (0.296)
Population density 20.174 1.344
(0.128) (1.264)
APPB 20.012 20.038
(0.008) (0.055)
ln (per capita NSDP) 2.259 2.919
(2.628) (5.230)
Deposit/SDP 0.013 20.049
(0.04) (0.040)
Credit/SDP 0.104
*
0.172
* * *
(0.04) (0.090)
Factory/Popn 254.259 543.258
* * *
(88.699) (302.757)
Employee/Popn 3.015
*
0.747
(1.173) (2.367)
Model statistics
Cross-section dummies Yes Yes
Time dummies No No
R
2
0.409
F-statistics 8.43
*
Wald-x
2
39.40
*
Hansen test 14.19
AR1 1.02
AR2 1.17
Number of observations 338 271
Note: All the footnotes apply here also as expressed under Table IV
Table V.
Estimation results for
credit penetration
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Notes
1. The RBI has instructed banks to make a basic banking “no-frills” account available for
low-income individuals, with either zero or low minimum balances and charges. Several
banks have since introduced such “no-frills” account with and without value-added features.
To extend hassle-free credit to bank customers in rural areas, the guidelines on general credit
card (GCC) schemes are simpli?ed to enable customers’ access credit on simpli?ed terms and
conditions, without insistence on security, purpose or end-use of credit. Also, the banks are
encouraged to increase IT infrastructure for increasing scope and coverage of ?nancial
inclusion (Mohan, 2006).
2. Analysis of ?nancial inclusion in India through its Postal Network is provided by Kumar
(2011).
Year Kendall’s index x
2
statistics
1995 1.00 22.00
1996 0.98 43.33
1997 0.98 64.67
1998 0.98 86.32
1999 0.97 106.57
2000 0.97 127.47
2001 0.95 146.15
2002 0.94 166.15
2003 0.94 186.83
2004 0.94 207.28
2005 0.94 227.62
2006 0.94 246.99
2007 0.93 266.24
2008 0.92 284.03
Note: Tabulated value of x
2
at 5 percent level of signi?cance is 33.92
Table VI.
Kendall’s index of
rank concordance for
credit penetration
Year Kendall’s index x
2
statistics
1995 1.00 22.00
1996 1.00 43.91
1997 0.99 65.62
1998 0.99 87.29
1999 0.99 108.99
2000 0.99 130.66
2001 0.99 152.19
2002 0.99 173.58
2003 0.98 194.87
2004 0.98 215.90
2005 0.98 236.77
2006 0.98 258.12
2007 0.98 279.25
2008 0.98 300.60
Note: Tabulated value of x
2
at 5 percent level of signi?cance is 33.92
Table VII.
Kendall’s index of
rank concordance for
deposit penetration
Financial
inclusion
determinants
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3. All the population data have been normalized by thousand. So, a branch density of 14.5 in
year 2009 essentially signi?es 14,500 individuals being served by a single branch. The unit
concept remains the same for the number of individuals per unit of ATM.
4. Kumar (2012) provides a detailed exposition on ?nancial inclusion, focusing on rural and
urban regions separately for India.
5. Ideally adult population ?gure should have been employed. However, due to absence of a
comprehensive state-wise adult population database for non-census years, total population
?gures have been utilized. The total deposit accounts has been utilized instead of
savings accounts as by its broad meaning ?nancial inclusion is not limited to opening
savings accounts only but availing other banking services also encompassing current and
term accounts also.
6. Credit ?gures are as per place of utilization.
7. The Hausman test was performed, which favored the ?xed effect model versus the REM. So,
REM results are not reported. However, the results are available on request. It may be noted
that robust standard errors are calculated as follows:
v
OLS
½
^
b
OLS
? ¼ s
2
ðX
0
XÞ
21
; s
2
¼
P
i
^ u
2
i
n2k
where uˆ
i
are regression residuals, s denotes the standard error. The robust standard errors
help to improve overall estimate’s small sample properties. Similarly, in case of panel
information, robust standard errors are calculated to various kinds of mis-speci?cations
(MacKinnon and White, 1985; White, 1980).
8. The Hansen test for validity of instruments does not reject null hypothesis of
over-identifying restrictions, implying validity of instruments in both Models 2 and 4.
Similarly, null hypothesis of no serial correlation is not rejected for both models.
9. See Mohan (2006), Subbarao (2009b) and RBI (2011) for various policy measures towards
greater inclusion.
10. In other words, it is attempted to test for the convergence hypothesis.
11. See Boyle and McCarthy (1997) for a detailed discussion on Kendall’s index of rank
concordance.
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Appendix
Figure A1.
Behaviour of population
group-wise branch density
over the years
Source: Report on Trend and Progress (2010)
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About the author
Nitin Kumar is presently working as Assistant Adviser in the Department of Statistics and
Information Management at the Reserve Bank of India, which is the central bank of India
conducting monetary policy. He completed his PhD in Economics, dealing with issues related to
personal and corporate taxation, from Indira Gandhi Institute of Development Research (IGIDR),
Mumbai, India in 2009. The ?ndings of the dissertation have been published in reputed journals
in abridged form. He is active in carrying out analytical studies and has contributed research
papers to various domestic and international journals. His research interests include banking,
corporate governance and applied econometrics, among others. Nitin Kumar can be contacted at:
[email protected]
Figure A2.
Behaviour of population
group-wise ATM density
over the years
Source: Report on Trend and Progress (2010)
Financial
inclusion
determinants
19
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monetary policy in SAARC countries?. Cogent Economics & Finance 4, 1127011. [CrossRef]
2. FANNY SALIGNAC, KRISTY MUIR, JADE WONG. 2015. Are you really Financially Excluded if you
Choose not to be Included? Insights from Social Exclusion, Resilience and Ecological Systems. Journal
of Social Policy 1-18. [CrossRef]
3. Madhu Sehrawat, A K Giri. 2015. Financial development and income inequality in India: an application
of ARDL approach. International Journal of Social Economics 42:1, 64-81. [Abstract] [Full Text] [PDF]
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doc_197687491.pdf
The objective of paper is to examine status of financial inclusion in India and study its
determinants.
Journal of Financial Economic Policy
Financial inclusion and its determinants: evidence from India
Nitin Kumar
Article information:
To cite this document:
Nitin Kumar, (2013),"Financial inclusion and its determinants: evidence from India", J ournal of Financial
Economic Policy, Vol. 5 Iss 1 pp. 4 - 19
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Financial inclusion and its
determinants: evidence fromIndia
Nitin Kumar
Reserve Bank of India, Mumbai, India
Abstract
Purpose – The objective of paper is to examine status of ?nancial inclusion in India and study its
determinants.
Design/methodology/approach – Panel ?xed effects and dynamic panel generalized methods of
moments (GMM) methodologies have been applied to study determinants of ?nancial inclusion.
Additionally, Kendall’s index of rank concordance has been derived to test for convergence of states in
achieving ?nancial inclusion.
Findings – Branch network has unambiguous bene?cial impact on ?nancial inclusion. Both
proportion of factories and employee base turn out to be signi?cant determinants of penetration
indicators. The ?ndings reveal the importance of a region’s socio-economic and environmental setup
in shaping banking habit of masses. Using test for convergence it is found that regions tend to
maintain their respective level of banking activity, with no support for closing gap.
Originality/value – To the best of the author’s knowledge, no panel data study has been performed
for India based on data for large number of states and a reasonable time span. This study utilizes 29
major states and union territories encompassing 1995 to 2008, which helps to increase degree of
freedom and provide reliable results. The study helps us to ascertain direction and strength of various
causal factors in process offer policy makers’ strategies, for improving ?nancial inclusion.
Keywords Banks, Financial services, India, Econometrics, Personal ?nance, Financial inclusion,
Generalized methods of moments
Paper type Research paper
1. Introduction
The Indian banking industry has shown tremendous growth in volume and complexity
over the last decade or so. Despite making signi?cant improvements in all areas relating
to ?nancial viability, pro?tability and competitiveness, there are concerns that much
needed banking services have not reached underprivileged sections. In this context,
efforts are being made as ?nancial inclusion can truly lift ?nancial condition and
standards of life of the poor and disadvantaged (Leeladhar, 2006; Subbarao, 2009a;
Thorat, 2007).
A robust and ef?cient ?nancial climate lays down strong foundations for economic
growth and developmental activities. Considerable empirical literature using various
sophisticated techniques have been employed across countries that validate this point
effectively (Shaw, 1973; Obstfeld, 1994; Levine, 2002). Availability of banking
amenities and strong bank branch network are major facilitators of capital formation
and expansionary activities (Feldstein and Horioka, 1980; Ford and Poret, 1991).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – G21, C23, C26
The author is Assistant Adviser at the Reserve Bank of India, Mumbai, India. The views
expressed in the paper are those of author and not of the organization to which he belongs. All
the errors, omissions, if any, are the responsibility of the author.
Journal of Financial Economic Policy
Vol. 5 No. 1, 2013
pp. 4-19
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381311317754
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The issue of ?nancially connected systems has gained prominence even in developed
economies like UK and USA (IMF, 2009; The World Bank, 2005a, b). Devlin (2005)
undertook a study to understand determinants of range of banking ?nancial services in
UK. Results indicated that although factors vary according to kind of ?nancial service,
however certain variables portray consistent and signi?cant in?uence across an array of
?nancial services. Variables concomitantly affecting dependent variable turned out to
be employment status, household income and housing tenure.
Mihasonirina and Kangni (2011) performed a study focusing on South African
countries that found signi?cance of communicationtechnologies (ICT) like mobile phones,
?xed phones, cost of call on ?nancial inclusion. Toxopeus and Lensink (2007) attended to
the issue of remittance in?ows on?nancial inclusionfor cross-section of emergingnations.
Remittances, interms of size, are not onlymaincapital in?ows indevelopingcountries, but
also have robust positive effect on?nancial inclusionand inturn oneconomic growth. Ina
detailed exposition, Carbo et al. (2005) have highlighted how ?nancial exclusion has
emerged as major concern for both developed and developing countries.
Issue of ?nancial exclusion could be ill afforded to be ignored for satisfactory and
inclusive growth. It has been pointed out by Subbarao (2009a) that out of 600,000
habitations in India, only about 30,000 centres are covered by commercial banks. With
two-thirds of population living in rural agglomerations, rural-urban divide in terms of
?nancial access indicators (branch and automatic teller machine (ATM) density) is
clearly visible (Figures A1 and A2). A comparison of ?nancial access, depth and size in
India vis-a` -vis other emerging nations, namely, China, Malaysia, Thailand provides
alarming picture (Table I).
The study performed by Sarma (2008), attempted to construct Index of Financial
Inclusion, based on three aspects of ?nancial inclusion, namely, penetration of banking
system, its availability to users andits actual usage for a cross-sectionof countries for 2004.
The index was aggregative in nature that preserves same weight for all three components.
Financial access Financial depth and size
Countries/
groups
Number of branches per
100,000 persons
Number of ATMs per
100,000 persons
Private credit to GDP
ratio (percent)
India 6.33 1.63 33.3
China 1.33
a
3.8
a
111.8
Indonesia 3.73 4.84
b
23
Malaysia 8.26 16.44 126.6
Thailand 7.37 17.05 90.5
Asian peer
group (range) 1.33-20 3.80-17.05 23.00-126.60
Australia 24 115 109.73
Canada 28 158 75.65
Japan 45 136 97.9
UK 23 97 160.48
USA 26 134 47.84
OECD group
(range) 23-45 57-158 47.80-160.48
Notes:
a
2003 data;
b
2000 data; data relate to 2005 unless otherwise speci?ed
Source: Kiatchai and Kulathunga (2009)
Table I.
Indicators of ?nancial
inclusion across globe
Financial
inclusion
determinants
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However, countryspeci?c regulations, features andgeneral preferences leadingto ?nancial
inclusion vastly differs across nations (Kempson et al., 2004; Kendall et al., 2010;
Sinclair et al., 2009; The World Bank, 2008a). This fact leads to inconsistencies in inclusion
scores. Existing study signi?cantly differs from Sarma (2008) work in following ways. At
the outset, present analysis is in no way attempt to construct any Index of Financial
Inclusion. There is vast literature on ?nancial institutions and banks aiding economic
development and vice versa (Obstfeld, 1994; Levine, 2002). However, literature on evidence
on what determines coverage of banking is scarce, which is critical issue as bringing poor
under scope of banking services can help improve their economic well-being. India is fast
growing economy facing high inequality and skewed banking coverage. Additionally,
scarcity of literature focusing on determinants of ?nancial inclusion for India leads to void.
To bridge the gap, existing study is endeavor to examine status of Financial Inclusion
focusing in emerging nation like India and to explore its determinants. A rich panel of
29 major states from1995 to 2008 has been employed. Additionally, Kendall’s indexof rank
concordance has been derived to test for convergence of states in achieving ?nancial
inclusion. It is revealed that although both deposit and credit accounts have improved over
time, but their growth has not matched population increase. So, it signi?es howpopulation
growth and concentration are outpacing rate of banking expansion. Other results are:
?nancial development (bank presence) and industrialization (factory presence) have
bene?cial impact on ?nancial inclusion. Using test for convergence it is found that regions
tend to maintain their respective level of banking activity giving rise to policy implication
that more attention is required to be paid for usual laggards.
Rest of the article is organized as follows. Section 2 brie?y discusses scope of
?nancial inclusion, its signi?cance and consequences for emerging nation, India.
Exposition of data and key variables are provided in Section 3. Section 4 is devoted to
econometric model and methodology employed for analysis followed by Section 5,
which discusses results of empirical analysis. Section 6 concludes with overall
summary and major ?ndings of study.
2. Scope of ?nancial inclusion and its relevance for India
As per Rangarajan Committee (2008) report, Financial Inclusion is de?ned:
[. . .] as the process of ensuring access to ?nancial services and timely and adequate credit
where needed by vulnerable groups such as weaker sections and low-income groups at
affordable cost.
Broadly speaking, Financial Inclusion is delivery of banking services at affordable cost
to vast sections of disadvantaged and low-income groups. Goals of ?nancial inclusion
can be met by initiative of banking sector to cut across various strata of society,
regions, gender and income and encourage public to embrace banking habit. Also,
Reserve Bank of India (RBI), as chief regulator has intervened for success of ?nancial
inclusion by various enactments[1], ?nancial literacy drives, leveraging technology,
etc. In addition to banking system, Indian ?nancial network consists of Indian postal
department[2], insurance companies; self-help groups (SHGs), civil society
organizations (CSOs), non-banking ?nancial companies (NBFCs), non-government
organizations (NGOs), micro ?nance institutions (MFIs), which are vital ?nancial
intermediaries.
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Branch density denotes the spread of banks and level of comfort, convenience
available for public to carry out banking pursuits. Figure A1 denotes trend of branch
density over last few years. An improvement from 14.5[3] in 2009 to 14.0 thousand per
branch in 2010 is registered, albeit clear disparity exists between rural and urban
regions[4]. On-site and off-site ATMs are indispensable element of modern banking era.
Glance at ATM density in India shows improvement over past few years (Figure A2).
Cross-countrycomparisonof some ?nancial inclusionindicators are presentedinTable I.
It may be noted that although branch density in India is comparable with other Asian
nations, both ATM spread and private credit to GDP ratio are at lower levels in India.
Consequences of ?nancial exclusion vary depending on nature and extent of
services denied. Small businesses may suffer due to loss of access to middle class and
higher-income consumers, higher cash handling costs and delays in remittances of
money leading to social exclusion (Burgess and Pande, 2003). Among developed
nations, UK was one of the earliest to realize importance of ?nancial inclusion
(Kempson et al., 2004; Collard et al., 2001). Around 8 percent of households lacked any
kind of deposit account. Reasons varied from low credit scoring, mistrust by people on
margins of society, terms and condition, physical distance and others. In Australia,
prevalence of unbanked adults is much lower than in other developed nations, with
estimates of just 3 percent of adults lacking bank account.
Access to ?nancial services for people, especially poor and deprived, is critical.
Indian legislature has been conscious of this fact since early. Bank nationalization
provided ?rst vigorous impetus for mass banking. Rationale for creating Regional
Rural Banks (RRBs) was also to bestow banking services to poor. Commercial banks
and RRBs have increased from 8321 in 1969 to 84,504 branches as at end of March
2010. Number of “No frill” accounts has also registered growth over last few years
(Thorat, 2007). In view of their vast branch network, public sector banks and RRBs
have been able to scale up their efforts by merely leveraging on existing capacity.
Additionally, new branch authorization policy of RBI encourages banks to open
branches in under banked regions. New policy also places lot of emphasis on the efforts
made by RBI to achieve, inter alia, ?nancial inclusion and other policy objectives.
3. Data source and key variables
Annual data from varied sources has been utilized for analysis. The study is state-wise
unbalanced panel data analysis from 1995 to 2008. Pooled dataset, as employed in
present scenario offers host of advantages. It allows controlling for heterogeneity
across cross-sections due to their inherent characteristics’ variations. Additionally, it
discounts for time effects, which may occur due to changes in policy and other socio
macroeconomic environment in turn having impact on our parameters of interest.
Large degrees of freedom also help to derive more robust and consistent results with
meaningful policy implications. Following subsections are devoted to detailed
discussion on construction and explanations for the variables employed.
3.1 Description of endogenous variables
Sarma (2008) had proposed bank accounts per capita as indicator of penetration
of banking system. In those lines number of deposit accounts percent of population,
number of credit accounts percent of population has been constructed as
measure of penetration indicators, which constitutes our dependent variable[5].
Financial
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Separate penetration indicators based on deposit and current accounts has advantage
of avoiding aggregation problem as generally faced while constructing indices.
Additionally, both credit and deposit account are separate banking instruments with
diverse objectives. Deposit (savings and term) account may be more useful to
individuals and households earning regular income to deposit their savings that can be
withdrawn as per needs. It is expected that people staying in urban regions and
employed in formal sectors shall have deposit account(s). Acredit account on other hand
caters to requirements of entrepreneurs and households for business and personal
pursuits, respectively. For opening credit account, bank ensures sound ?nancial
position/income source of its borrower in order to reduce cases of default. Owing to
diverse features of deposit and credit instruments of banks, it is imperative to bestow
separate focus to them individually.
Branch network is used in ?nancial inclusion studies to capture banking access and
branch density (Subba Rao, 2007; Burgess and Pande, 2003; Leeladhar, 2006; The World
Bank, 2008a). Although, banks expand as per their business strategies, in India, RBI has
taken manyinitiatives not onlyto improve branchnetwork but also bankingconvenience.
Among direct measures of improving network, foremost is general permission clause,
according to which banks do not need prior permission of RBI to setup branch/mobile
branch/administrative of?ce/Central Processing Centre in centre with population
,50,000. As per one of stringent measures, RBI has made mandatory for banks to open
one-third of their total branches in under banked districts. Generally private banks have
tendency to setup branches only in metropolitan cities for greater pro?t making. To check
such instances, RBI has made mandatory for private players to ensure that 25 percent of
their branches are in rural conglomerates. Banks are encouraged to utilize business
correspondents/facilitators for greater reach in rural and isolated localities (Subbarao,
2009a, b; Reserve Bank of India, 2011). In view of this background, it is obvious that as
regulator, RBI has control on banks’ branch expansion. Hence, it is more sensible to keep
branch density (average population per branch (APPB)) as control variable rather
endogenous variable and inspect its impact on penetration indices.
Number of deposit and credit accounts[6] has been collected from Basic Statistical
Returns of Scheduled Commercial Banks in India published by RBI. Actual state wise
population ?gures are available only for census years, such as, 1981, 1991, 2001 and so on.
However, projected state wise population ?gures are available from Of?ce of Registrar
General and Census Commissioner of India, which are utilized for existing analysis.
3.2 Description of exogenous variables
At the onset, time trend has been included as independent variable to control for
various policies implemented over time. Among other explanatory variables, is
population density. Population density is population per square kilometer to capture
region-wise demographics and understand role of population concentration on
penetration of banking system.
A vital variable to examine the segment of population to which branch caters is
average population per bank branch (APPB). APPB is the ratio of population
(in thousand) to total number of branches. The bank of?ces devoted solely for
administrative purpose were excluded while deriving number of bank branches.
Information on branches has been sourced from Branch Banking Statistics published
by RBI.
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Income is measured by per capita net state domestic product (NSDP) at 1999-2000
constant prices. Logarithm of per capita NSDP has been included to understand
in?uence of states’ economic and ?nancial position on penetration of banking system.
Data on NSDP has been collected from Handbook of Statistics on Indian Economy
published by RBI.
Deposit SDP ratio is elementary indicator of level of deposit in the system. Similarly,
Credit SDP ratio portrays the level of credit utilization. Both Deposit SDP ratio and
Credit SDP ratio denote usage of basic ?nancial products in ?nancial system. High
ratios of both indicators are usually associated with higher banking and investment
activities (Beck et al., 2007; The World Bank, 2008a, b, 2009). State wise information on
both credit and deposit is available in statistical publication, Basic Statistical Returns
of Scheduled Commercial Banks in India published by RBI. State-wise GDP is obtained
from Handbook of Statistics on Indian Economy published by RBI.
Proportion of factories has been taken as proxy for the level of industrialization and
sociological modernization. Usually advanced economies with greater industrialization
are expected to have greater role for banking and ?nancial activities. Employment
proportion represents employment status of region. Those of more secure status
economically are less likely to be ?nancially excluded (Devlin, 2005). Information for
number of employees along with data on factories has been collated from various
volumes of Annual Survey of Industries (ASI) published by Central Statistical
Organisation (CSO) of India.
4. Econometric model and methodology
The modeling strategy basically rests on two methodologies, namely, ?xed/random
effects regression and dynamic panel generalized methods of moments (GMM)
technique to control for potential biases associated with simultaneity and reverse
causality.
Due to peculiarities of pooled dataset, observations for individual may not be
independent and usual ordinary least squares method may provide biased estimates.
Hence, we employ panel data estimation techniques (?xed-effects model and
random-effects model) to control for ?xed or random individual differences. Hausman
test has been applied to test for appropriateness of ?xed or random effects model
(REM). Basic functional form of regression equation is as follows:
Y
it
¼ b
0
þb
1
X
it
þa
i
þ1
it
ð1Þ
Here, Y
it
represents value of endogenous variable for ith state at tth period. b
0
stands
for intercept term and X
it
is matrix of exogenous variables. b
1
is vector of associated
parameters. a
i
is treated as random variable with speci?ed probability distribution
(usually normal, homoscedastic, and independent of all measured variables) in case of
REM, whereas set of ?xed parameters in ?xed effects model. 1
it
is usual stochastic
disturbance term following normal distribution with mean 0 and variance s
2
.
The existing study tries to understand determinants of penetration indicators.
Among explanatory variables included are certain ?nancial inclusion access and usage
indicators also, such as, APPB, Deposit SDP ratio, Credit SDP ratio. Such explanatory
variables may themselves be endogenous, giving rise to situation where one or
more regressors are correlated with error term. In such situation, usual methods of
estimation cannot consistently estimate casual effect of regressor on dependent variable.
Financial
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So, GMMestimator developed for dynamic panel data, introduced by Arellano and Bond
(1991) and Arellano and Bover (1995) has been employed, formulated as follows:
y
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þb
0
X
i;t
þh
i
þ1
i;t
ð2Þ
Here, y is dependent variable. X depicts matrix of explanatory regressors, h is
unobserved state effect, 1 being usual stochastic term. Separate regressions have been
performed for deposit and credit penetration indicators. Common set of strictly
explanatory variables being time trend, population density, income level, proportion of
factories to capture industrialization, employee base as proxy for demographic status.
5. Empirical analysis
A snapshot of variables for few selected years is provided in Table II. From table it is
evident that number of branches rose by around 14,000 during period. Number of
credit accounts marked slight decline of around 57 lakhs in 1999 as compared to 1995.
However, thereafter it has consistently swelled and crossed ?gure of 10 crore in 2008.
Other variables have risen in magnitude except some minor decline shown by number
of factories and employment.
All 35 states and Union Territories of India could not be considered for carrying out
estimation due to unavailability of information on certain series. Consequently, six
states/Union Territories were dropped (Table III). The unbalanced panel was
constructed on basis of 29 states and Union Territories for time span of 14 years.
Estimation result for deposit penetration indicator is provided in Table IV.
Model 1 depicts the results of ?xed effect robust estimation[7]. Population density is
not only having inverse in?uence but also signi?cant. The outcome suggests that
although deposit accounts have improved over time, its penetration has not matched
population growth that has been witnessed for the study period. In line with intuition,
APPB is, actually having negative and signi?cant impact on deposit penetration
for models 1 and 2[8]. Deposit SDP ratio is coming out to be positively signi?cant
in determination of deposit penetration in both models. Socio demographic
variables, factory proportion and employee base are also signi?cant in Model 1.
Employee base, has positive conventional sign (Devlin, 2005). Overall, the exercise
seems to indicate that branch spread along with state level development and social
characteristics de?nitely have robust and direct impact for determination of deposit
penetration.
Table V displays regression results for credit penetration, focusing on credit side of
banking activity with credit penetration as dependent variable. Credit SDP ratio is
having strong positive impact on dependent variable in both models. Employee base is
coming out to be signi?cant with positive sign in Model 3. Similarly, factory proportion
is positively signi?cant in Model 4. Additionally, test of structural change was
performed, which indicated structural change in 2001. The shift could be due to
multiple factors, such as phased implementation of Narasimham Committee (1998)
report, which emphasized increase of branch network and encouraged private and
foreign banks’ entry, among other.
To sum up analytical ?ndings: branch density is having strong positive impact on
?nancial inclusion drive. Measures taken by RBI for relaxation of branch opening,
setting up of business correspondent model for rural masses, enhanced ATM kiosks
and other steps[9] are bearing desired results. As indicated by Carbo et al. (2005),
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5
Table II.
Arithmetic mean
of variables for
selected years
Financial
inclusion
determinants
11
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Y
U
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V
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I
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t
2
1
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4
5
2
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2
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(
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)
Devlin (2005) among socio-economic determinants both level of industrialization and
employee base are found to be having bene?cial impact for ?nancial inclusion.
Last but not least, natural query which arises is that whether rankings of the states
according to their level of credit or deposit penetration indicators vary signi?cantly
over years[10]. To address the issue, we compute Kendall’s index of rank
concordance[11].
Kendall’s index of rank concordance is calculated as follows:
KI
t
¼
Var
P
T
t¼1
ARðEÞ
it
h i
Var½T
*
ARðEÞ
i
?
ð3Þ
No. State/UT Incomplete information
1 Andaman and Nicobar
2 Andhra Pradesh
3 Arunachal Pradesh X
4 Assam
5 Bihar
6 Chandigarh
7 Chhattisgarh
8 Dadra and Nagar Haveli X
9 Daman and Diu X
10 Delhi
11 Goa
12 Gujarat
13 Haryana
14 Himachal Pradesh
15 Jammu and Kashmir
16 Jharkhand
17 Karnataka
18 Kerala
19 Lakshadweep X
20 Madhya Pradesh
21 Maharashtra
22 Manipur
23 Meghalaya
24 Mizoram X
25 Nagaland
26 Orissa
27 Puducherry
28 Punjab
29 Rajasthan
30 Sikkim X
31 Tamil Nadu
32 Tripura
33 Uttar Pradesh
34 Uttarakhand
35 West Bengal
Note: “X” denotes dropped region from regression analysis
Table III.
List of states/union
territories
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1
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4
5
2
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a
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2
0
1
6
(
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where, AR(E)
it
depicts actual rank of ith state in year t. AR(E)
i1
is actual rank of
ith state in initial year t ¼ 1, and T is number of years for which data is used
for construction of index. The value of rank concordance index ranges from zero to
one. Closer the index value is to zero, greater is the mobility within distribution and
vice versa.
Kendall’s index for credit penetration is tabulated in Table VI. It may be seen that
null hypothesis of no association among ranks of different years is rejected decisively
for all years at 5 percent level of signi?cance. Thus, cross-sectional dispersion of credit
penetration is not diminishing over time and the laggards are not showing any
indication of improvement over the years. Similar interpretation may be deduced for
deposit penetration index (Table VII). It is clear that there exists stability in ranks
obtained by various states with regard to their level of deposit penetration. So, overall
gap among states is not showing any evidence of narrowing down.
Fixed effects robust (Model 1) Dynamic panel GMM (Model 2)
Intercept 21.267
(78.108)
L1.D 0.558
* *
(0.263)
Time 0.247 1.984
(0.413) (1.551)
Population density 20.905
*
0.484
(0.292) (1.449)
APPB 20.12
* *
21.344
* *
(0.046) (0.609)
ln (per capita NSDP) 3.434 214.224
(8.395) (27.164)
Deposit/SDP 0.145
* * *
0.170
* *
(0.085) (0.082)
Credit/SDP 20.022 20.511
(0.075) (0.348)
Factory/Popn 436.421
* * *
566.145
(213.582) (1,194.540)
Employee/Popn 9.949
* *
0.183
(3.83) (4.325)
Model statistics
Cross-section dummies Yes Yes
Time dummies No No
R
2
0.088
F-statistics 11.62
*
Wald-x
2
67.47
*
Hansen test 10.69
AR1 20.09
AR2 21.38
Number of observations 338 271
Notes: Signi?cant at:
*
1,
* *
5 and
* * *
10 percent levels; number of cross-sections: 29; number of time
periods: 14; ?gures in brackets denote robust standard errors for Models 1 and 2; L1.D denotes ?rst lag
of dependent variable; AR1 and AR2 denotes the Arellano-Bond test for AR(1) and AR(2) in ?rst
differences, respectively
Table IV.
Estimation results for
deposit penetration
Financial
inclusion
determinants
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4
5
2
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6
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6. Conclusion
The study provides empirical analysis of status and determinants of ?nancial inclusion
in India. It employs annual information of 29 major states from 1995 to 2008. The
empirical results indicate that supply side of inclusive efforts through branch network
expansion is having intended impact of improved banking activity as re?ected
in penetration indicators. However, demand side pressure exists in systemas penetration
indicators are unable to match pace of population growth. Both, level of industrialization
and employee base are having bene?cial in?uence on penetration indicators.
Major policy inputs emanating from study are multi pronged strategies for
enhancing employee base and industrial activity especially in backward states.
Employment generating schemes have multiple bene?ts. It not only strikes poverty
menace but also helps improve income level and ?nancial inclusion in the process.
Similarly, legislations towards industrial reforms in general and sector speci?c
schemes in speci?c aids entrepreneurship, small sector and industrial activity and
translate into inclusion, among others.
Fixed effects robust (Model 3) Dynamic panel GMM (Model 4)
Intercept 217.222
(24.59)
L1.D 20.330
(0.457)
Time 20.152 20.301
(0.127) (0.296)
Population density 20.174 1.344
(0.128) (1.264)
APPB 20.012 20.038
(0.008) (0.055)
ln (per capita NSDP) 2.259 2.919
(2.628) (5.230)
Deposit/SDP 0.013 20.049
(0.04) (0.040)
Credit/SDP 0.104
*
0.172
* * *
(0.04) (0.090)
Factory/Popn 254.259 543.258
* * *
(88.699) (302.757)
Employee/Popn 3.015
*
0.747
(1.173) (2.367)
Model statistics
Cross-section dummies Yes Yes
Time dummies No No
R
2
0.409
F-statistics 8.43
*
Wald-x
2
39.40
*
Hansen test 14.19
AR1 1.02
AR2 1.17
Number of observations 338 271
Note: All the footnotes apply here also as expressed under Table IV
Table V.
Estimation results for
credit penetration
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Notes
1. The RBI has instructed banks to make a basic banking “no-frills” account available for
low-income individuals, with either zero or low minimum balances and charges. Several
banks have since introduced such “no-frills” account with and without value-added features.
To extend hassle-free credit to bank customers in rural areas, the guidelines on general credit
card (GCC) schemes are simpli?ed to enable customers’ access credit on simpli?ed terms and
conditions, without insistence on security, purpose or end-use of credit. Also, the banks are
encouraged to increase IT infrastructure for increasing scope and coverage of ?nancial
inclusion (Mohan, 2006).
2. Analysis of ?nancial inclusion in India through its Postal Network is provided by Kumar
(2011).
Year Kendall’s index x
2
statistics
1995 1.00 22.00
1996 0.98 43.33
1997 0.98 64.67
1998 0.98 86.32
1999 0.97 106.57
2000 0.97 127.47
2001 0.95 146.15
2002 0.94 166.15
2003 0.94 186.83
2004 0.94 207.28
2005 0.94 227.62
2006 0.94 246.99
2007 0.93 266.24
2008 0.92 284.03
Note: Tabulated value of x
2
at 5 percent level of signi?cance is 33.92
Table VI.
Kendall’s index of
rank concordance for
credit penetration
Year Kendall’s index x
2
statistics
1995 1.00 22.00
1996 1.00 43.91
1997 0.99 65.62
1998 0.99 87.29
1999 0.99 108.99
2000 0.99 130.66
2001 0.99 152.19
2002 0.99 173.58
2003 0.98 194.87
2004 0.98 215.90
2005 0.98 236.77
2006 0.98 258.12
2007 0.98 279.25
2008 0.98 300.60
Note: Tabulated value of x
2
at 5 percent level of signi?cance is 33.92
Table VII.
Kendall’s index of
rank concordance for
deposit penetration
Financial
inclusion
determinants
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3. All the population data have been normalized by thousand. So, a branch density of 14.5 in
year 2009 essentially signi?es 14,500 individuals being served by a single branch. The unit
concept remains the same for the number of individuals per unit of ATM.
4. Kumar (2012) provides a detailed exposition on ?nancial inclusion, focusing on rural and
urban regions separately for India.
5. Ideally adult population ?gure should have been employed. However, due to absence of a
comprehensive state-wise adult population database for non-census years, total population
?gures have been utilized. The total deposit accounts has been utilized instead of
savings accounts as by its broad meaning ?nancial inclusion is not limited to opening
savings accounts only but availing other banking services also encompassing current and
term accounts also.
6. Credit ?gures are as per place of utilization.
7. The Hausman test was performed, which favored the ?xed effect model versus the REM. So,
REM results are not reported. However, the results are available on request. It may be noted
that robust standard errors are calculated as follows:
v
OLS
½
^
b
OLS
? ¼ s
2
ðX
0
XÞ
21
; s
2
¼
P
i
^ u
2
i
n2k
where uˆ
i
are regression residuals, s denotes the standard error. The robust standard errors
help to improve overall estimate’s small sample properties. Similarly, in case of panel
information, robust standard errors are calculated to various kinds of mis-speci?cations
(MacKinnon and White, 1985; White, 1980).
8. The Hansen test for validity of instruments does not reject null hypothesis of
over-identifying restrictions, implying validity of instruments in both Models 2 and 4.
Similarly, null hypothesis of no serial correlation is not rejected for both models.
9. See Mohan (2006), Subbarao (2009b) and RBI (2011) for various policy measures towards
greater inclusion.
10. In other words, it is attempted to test for the convergence hypothesis.
11. See Boyle and McCarthy (1997) for a detailed discussion on Kendall’s index of rank
concordance.
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Appendix
Figure A1.
Behaviour of population
group-wise branch density
over the years
Source: Report on Trend and Progress (2010)
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About the author
Nitin Kumar is presently working as Assistant Adviser in the Department of Statistics and
Information Management at the Reserve Bank of India, which is the central bank of India
conducting monetary policy. He completed his PhD in Economics, dealing with issues related to
personal and corporate taxation, from Indira Gandhi Institute of Development Research (IGIDR),
Mumbai, India in 2009. The ?ndings of the dissertation have been published in reputed journals
in abridged form. He is active in carrying out analytical studies and has contributed research
papers to various domestic and international journals. His research interests include banking,
corporate governance and applied econometrics, among others. Nitin Kumar can be contacted at:
[email protected]
Figure A2.
Behaviour of population
group-wise ATM density
over the years
Source: Report on Trend and Progress (2010)
Financial
inclusion
determinants
19
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of Social Policy 1-18. [CrossRef]
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of ARDL approach. International Journal of Social Economics 42:1, 64-81. [Abstract] [Full Text] [PDF]
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