An Empirical Analysis of FI Across Population in India

8/12/2019 An Empirical Analysis of FI Across Population in India

1/16

An Empirical Analysis of Financial Inclusion Across Population Groups in India 97

An Empirical Analysis

of Financial Inclusion Across

Population Groups in IndiaNitin Kumar*

* Research Officer, Reserve Bank of India, Mumbai, India. E-mail: [email protected]. The usual disclaimerapplies.

2012 IUP. All Rights Reserved.

The financial inclusion mission has gained tremendous relevance in an emerging economy

like India. Financial exclusion seems to be more severe in rural and backward locations.

In this respect, the current analysis is an attempt to explore the behavior of inclusion/

exclusion across varied population groups. The pooled dataset spanning over the period

from 1990 to 2008 for rural and urban regions separate ly has been employed.

A set of control variables have been included to disentangle the role of various demographic

and institutional factors. Bank group size, as captured by assets, has a direct influence

on the number of operating branches. Ownership effect also plays a key role in determining

the number of branches operating. Test of convergence has been carried out to examine

if lesser branched regions are catching up with their counterparts with higher branch

network. Evidence of conditional convergence has been found. Finally, structural change

has also been observed in terms of the number of functioning branches. The result is a

testimony to the fact that inclusion policies are actually translating into significant

improvement of branch density in India.

IntroductionThe importance of efficient financial institutions is well established in the literature (King and

Ross, 1993; Beck et al., 2000 and 2004; and Klapper et al., 2006). A well-organized financial

system is the key towards growth, progress and various expansionary activities, more so in the

case of emerging markets. However, the potential benefit cannot be harnessed till the societies

are well connected with the formal system, especially the rural poor and deprived masses.

Globally, the financial inclusion drive has gained momentum lately. This is reflected in the

various number of projects, reports, and research being conducted by the international

organizations such as the World Bank and IMF (Becket al., 2007; World Bank, 2008a and

2008b; and World Bank, 2009) at a cross-country level. In fact, regular reports with a focus on

the South Asian nations are being published by the World Bank (Kiatchai and Kulathunga,

2009). The report contains in detail the various indicators, relevant information pertaining tofinancial inclusion and comparative analysis on the basis of various parameters for the South

Asian countries. Financial access surveys are also being conducted by the CGAP and World

Bank (2010). Building on the existing database, Financial Access 2010 reviews the survey

responses from 142 economies, updates statistics on the use of financial services, and analyzes

changes that took place in 2009a turbulent year for the financial sector in most economies.


2/16

The IUP Journal of Bank Management, Vol. XI, No. 1, 201298

In addition, Financial Access 2010 expands on the previous years work by reviewing three

policy areas relevant to the current financial access debate: financial inclusion mandates,

consumer protection in financial services, and access to finance by Small and Medium Enterprises

(SMEs). Nearly 60% of the economies experienced a contraction in real per capita income in

2009 due to deepening of the global financial crisis.

In India too, financial inclusion efforts have gained reasonable prominence. The Report of

the Committee on Financial Inclusion (Rangarajan Committee, 2008) has not only recognized

the importance of access of finance to the poor and vulnerable groups as a prerequisite for

poverty reduction and social cohesion, but also provided policy suggestions for the improvement

of the same. It defined inclusion in the Indian context and discussed key statistics in this

background. The Eleventh Five Year Plan (2007-12) envisions inclusive growth as the key

objective. The inclusive growth implies an equitable allocation of resources with benefits accruing

to every section of the society. It is aimed at poverty reduction, human development, healthand provides opportunity to work and be creative. Also, various initiatives have been undertaken

by the Reserve Bank of India towards fulfilling the objectives of the financial inclusion agenda

(Leeladhar, 2006; Mohan, 2006; Thorat, 2007; and Subbarao, 2009a and 2009b). Prominent

among them are the relaxation of branch licensing policy in rural agglomerations; availability

of no-frills accounts for poor and underprivileged; and easing the Know Your Customer (KYC)

norms to keep the procedural hassles involved in opening a bank account to the minimum.

In the Indian economy, still around 70% of the population dwells in rural regions with

agriculture and other small-scale entrepreneurship as their main pursuit. Although there exists

supply side deficiency, weak demand may also be a reason for low banking penetration in such

cases (Rangarajan Committee, 2008; Goyal, 2010; and Kamath et al., 2010). As per the

Rangarajan Committee Report (2008), marginal farmer households constitute 66% of the total

farm households. Only 45% of these households are indebted to either formal or non-formal

sources of finance. Overall, 73% of farmer households have no access to formal sources of

credit. Indian household financial savings in shares and debentures prior to reforms were above

20%, but post-reform it reached a low of below 5%. Technology, actually, raised entry costs,

for example, depository charges. Mutual funds through which retail investors were supposed to

enter are more interested in servicing corporates and a few high net worth individuals through

high-cost structured products. The sub-brokers that households trusted disappeared from the

markets (Goyal, 2010).

In this context, it is useful to examine if there really exists a significant difference between

the branches operated in rural and urban agglomerations. To this end, the present analysis isan attempt to explore how the relationship between the operating branches varies according to

population categories, bank groups and income. The pooled dataset has been employed to test

the hypothesis spanning over the period from 1990 to 2008 for rural and urban regions separately.

The regression has been carried out utilizing both the number of branches opened and functioning

branches as dependent variables. Certain interesting findings include: the quantum of operating

branches in more populous regions is lesser after controlling other banking and demographic

factors; both time and income level are turning out to be insignificant in the determination of


3/16


operating branches; bank group size, as captured by assets, has a direct influence on the

number of operating branches; and last but not the least, ownership effect plays a significant

role in determining the number of operating branches. The test of convergence has been carried

out to examine the trend of the functioning branches across the urban and rural population

groups over time. A negative and significant coefficient of initial branches is an evidence of

conditional convergence. The real expenditure has a direct and significant effect on the growth

rate of functioning branches. The result implies that higher income regions are experiencing a

higher branch growth. Finally, it is imperative to test if the number of branches has encountered

any marked shift from its mean level. The three series tested for structural change are: functioning

branches for rural and urban sectors, and total functioning branches. Each one of the series

indicates the presence of structural shift in the number of functioning branches, which may be

due to the impact of policy changes towards financial inclusion.

The present study is structured as follows: the next section is devoted to a brief discussionon the literature pertaining to financial inclusion, followed by an outline of the data chosen and

variables utilized. The empirical methodology is described in the next section, with results in the

subsequent section. The last section concludes with a summary of the major findings and

policy implications.

Inclusion Developments and Relevant Literature

Financial inclusion has become the buzzword in last few years. Apex organizations, including

World Bank, International Monetary Fund (IMF), G-20 nations and others, have undertaken

financial inclusion as the key agenda item. Updated online database of financial access has

been launched by World Bank and IMF. This resource is an important tool for increasing

financial inclusion. With such international data comparisons, policy makers and researcherscan set forth agendas for improving access to financial services. Policy makers can use the data

for monitoring and evaluation of pro-access policies. Researchers will use it to assess varying

policy approaches to learn what works. In fact, the steps have resulted in improved responses

with more comprehensive coverage over time (Errico and Musalem, 1999; and IMF, 2009).

An estimated 2.7 billion people in developing countries do not have access to basic formal

financial services (CGAP and World Bank, 2010). However, financial inclusion has been in

existence in a disguised form without the same nomenclature in the Indian economy (Subbarao,

2009a). The Eleventh Five Year Plan (2007-12) envisions inclusive growth as a key objective.

The inclusive growth implies an equitable allocation of resources, with benefits accruing to

every section of the society.

It is surprising to note that financial exclusion is a matter of concern in the developedeconomies also. For instance, Sinclair et al. (2009) documents that the number of households

without bank accounts in the UK has fallen in recent years, but access to banking services

remains a problem for a significant proportion of lower-income households and those living in

deprived locations. In 2001, 9.1% of American households were without transaction accounts,

a drop of only half a percent since the data was collected in 1998 (Aizcorbe et al., 2003). Of

the families in the lowest income quintile in 2001, only 62.5% had a transaction account

(Federal Reserve, 2001).


4/16


To measure financial inclusion, a multidimensional Index of Financial Inclusion (IFI) has

been proposed by Sarma (2008). The IFI is an index that captures information on various

dimensions of financial inclusion in one single digit lying between 0 and 1. It captures the

penetration of the banking system, its availability to users and its actual usage. Chakravarty

and Pal (2010) employ the axiomatic measurement approach for the measurement of financial

inclusion. It improves upon the IFI proposed by Sarma (2008) such that the index can be

utilized to determine the percentage contributions by the various factors. Kendall et al.(2010)

carried out a cross-country analysis. In developed countries, they estimate 3.2 accounts per

adult and 81% of adults banked. By contrast, in developing countries, they estimate only 0.9

accounts per adult and 28% banked. In regression analysis, they find that measures of

development and physical infrastructure are positively associated with the indicators of deposit

account, loan, and branch penetration. A state-wise dynamic panel data analysis of determinants

of financial inclusion has been performed by Kumar (2011) in the context of India. The results

disclose income to be having an unambiguously beneficial impact on the financial inclusion

indicators. The findings reveal the importance of the regions socioeconomic and environmental

setup in shaping the banking habit among the masses.

Data Strategy

This section provides in detail the discussion on the data utilized for the study. As the focus of

the analysis is to examine the banking outreach in rural vis--vis urban agglomerations, an

attempt has been made to collate variables depicting the characteristics of these two distinct

population groups. The study spans over the period from 1990 to 2008. The pooled dataset

consists of various variables for rural and urban regions separately. Among the most prominent

variables pertaining to banking outreach are the branches opened and branches functioning ina region. The branches opened depict the aggressiveness with which the bank is moving towards

greater penetration. The factors for greater aggressiveness can be attributed to various demand

and supply factors, such as relaxation of branch authorization by RBI and greater demand for

banking services.

The functioning branches are the net of branches opened and branches closed, which

portrays the actual number of operating branches at a particular instance. The information on

both these variables is obtained from theBranch Banking Statistics published by the Reserve

Bank of India.

Among the socio-demographic factors, population is a vital variable which directly affects

the demand for banking services. A large population is expected to exhibit greater banking

requirements compared to sparsely populated segmentsper se. The rural and urban population

figures are available only for the census years. To overcome this limitation, population distribution

ratio as available for the census years 1991 and 2001 has been employed to interpolate the

rural and urban population distribution for the study period.

The income level has been established as a significant determinant of the inclusion

effort (Devlin, 2005; and Kumar, 2011). Although state-wise Net State Domestic Product

(NSDP) per capita is available, population group-wise NSDP per capita is not available.


5/16


The Monthly Per Capita Expenditure (MPCE) survey was conducted by National Sample Survey

Organization (NSSO) that is the closest available proxy for income levels of rural and urbaninhabitants. The National Sample Surveys in India are integrated household surveys carried

out every year on an all-India basis with the exception of some border areas. The MPCE is

defined as the household consumption expenditure over a period of 30 days divided by the

household size. The reports present national and state level estimates of various socioeconomic

indicators and distribution of households and persons over different socioeconomic categories

in both rural and urban areas.

To control for the size of varied bank groups, their assets have been included. The information

on bank group-wise assets is obtained from Statistical Tables Relating to Banks in Indiapublished

by Reserve Bank of India. All the major bank groups active in the Indian scenario have been

included in the study, viz., State Bank and its associates (SBI), Nationalized Bank Group,

foreign banks and private banks.

Empirical Methodology

This section describes in detail the econometric methods employed to assess the rural-urban

financial inclusion disparity. Initially, a simple ordinary least squares framework was utilized

over the study period of 1990 to 2008 in an attempt to understand the determinants of branch

expansion. The basic regression takes the following form:

Y= + X+ Z+ ...(1)

Here, Yrepresents the vector of endogenous variable; the associated parameters of the

exogenous variables are represented by ;Xdepicts the matrix of explanatory variables;Zstands for the matrix of control variables to disentangle their effects; and being the vector of

stochastic term having standard distribution assumptions. The branches opened and number

of functioning branches constitute the endogenous variable. Separate regressions have been

carried out for each of them with a common set of independent variables. Among the exogenous

variables are: time trend, monthly per capita expenditure, population and fixed assets. Population

dummies and bank group dummies have been taken as the control variables to control for the

population group effect and bank group effect, respectively.

Although, urban regions may be enjoying a higher branch density compared to rural sections,

over the years, the growth of branches opened in rural areas is catching up with the urban

areas. The notion of convergence (also referred to as the catch-up effect) has been widely usedin economic growth literature (Barro et al., 1992; Mankiw et al., 1992; and Evans and Karras,

1996). Similar analogy has been implemented to test the convergence in the growth of branches

over time across the population segments.

To test if there exists any evidence of convergence of branches in low network areas vis--vis

high density agglomerations, the following regression setup has been utilized:

BRAN_GR= + *BRAN_INI + Z+ ...(2)


6/16


BRAN_GRdenotes the compounded growth rate of branches compared to the branches in

the initial year, which is represented byBRAN_INI.Zsignifies the matrix of control variables

such as time trend, population and income.

The previous couple of years have seen numerous steps being undertaken towards greater

financial inclusion. Not only has the state given financial inclusion high importance, but it has

also been undertaken in a mission mode by the Reserve Bank of India (Leeladhar, 2006;

Mohan, 2006; Thorat, 2007; Rangarajan Committee, 2008; and Subbarao, 2009a and 2009b).

Numerous outreach programs to enhance the financial awareness, relaxation of branch expansion

regulations in rural inhabitations, introduction of no-frills accounts for low income individuals,

and easing of the KYC norms to keep the procedural hassles involved in opening a bank

account to the minimum are some of the chief measures that have been initiated towards this

goal. In this context, it becomes vital to examine the stability of the estimated parameters over

the time period of the study. A structural break can render the estimated coefficients misleadingin the presence of shift. The Chow test (Chow, 1960) is most commonly used in time series

analysis to detect structural break. However, a weakness of the test is that the breakpoint has

to be supplied exogenously. As various steps towards financial inclusion are spread over the

years and their relative significance in quantitative measures is also not known, a Bai-Perron

test of structural change is applied (Bai and Perron, 2003). The test addresses the problem of

estimation of the break dates and presents an efficient algorithm to obtain global minimizers of

the sum of squared residuals. The algorithm is based on the principle of dynamic programming.

To investigate the stability of the parameters, the following null is tested

H0:

i=

0...(3)

against the alternative that at least one coefficient varies over time. A sequence ofF-statistics

is computed as follows:

kniuiu

iuiuuuF

T

TT

i2/

...(4)

Here iu denotes the residuals obtained from the segmented regression, whereas residualsobtained from unsegmented regression are depicted by u . TheF-statistics are then computed,

andH0is rejected if the value is too large. Hansen (1997) has developed an algorithm for

computing approximate asymptoticp-values of the test above. Bai and Perron (2003) extend

this approach toF-tests for 0 vs. lbreaks and lvs. l+ 1 breaks, respectively, with arbitrary but

fixed l.

Empirical Findings

Table 1 provides the trend of major variables over the selected years. As observed, the number

of branches opened seems to be accelerating lately with nearly 4,000 branches being added to

the already growing family of Scheduled Commercial Banks. There has been consistent and

continuous improvement in the number of functioning branches, which implies an enhancement


7/16


of the web of branches over time despite closures. The real MPCE has increased for most of the

years. It reflects the higher spending power of the masses for both rural and urban zones

separately. Last but not the least, the swelling population figures are also tabulated, whose

coverage is a challenge for financial inclusion strategies.

Table 1: Totals of Variables for Selected Years

Year Branches Branches MPCE MPCE Population

Opened Functioning Rural Urban (million)

1990 2,429 61,082 531.28 835.65 862.2

1994 715 62,495 523.73 852.40 935.0

1998 872 65,006 516.43 924.70 1,007.0

2002 620 66,554 602.09 1,146.39 1,078.1

2006 1,455 70,262 596.86 1,118.28 1,147.8

2008 3,997 76,611 661.92 1,262.10 1,181.4

Note: All monetary variables have been normalized by GDP deflator; and the banks consist ofScheduled Commercial Banks in India.

To understand the rural-urban disparity in terms of financial inclusion status, it is pertinent

to have a glance of the branch density across these two population groups. The Average

Population Per Branch (APPB) is presented in Table 2. The figures indicate that the density had

deteriorated over the years. However, since 2004, the ratio has improved favorably, analogous

to the popular belief that the urban regions are actually enjoying a better branch densitycompared to the rural areas. In 2008, a single branch catered to about 12,190 individuals in

urban areas as against 17,250 in rural areas.

Table 2: Trend of Branch Density Across Rural and Urban Population Groups

Year APPB Rural APPB Urban APPB Total

1990 14.47 13.17 14.11

1991 14.59 13.54 14.31

1992 14.81 13.76 14.52

1993 14.98 13.85 14.67

1994 15.25 14.21 14.96

1995 15.47 14.32 15.14

1996 15.65 14.28 15.25

1997 15.86 14.25 15.39

1998 16.06 14.15 15.49


8/16


Table 2 (Cont.)

Year APPB Rural APPB Urban APPB Total

1999 16.27 14.01 15.58

2000 16.50 14.05 15.75

2001 16.81 14.26 16.01

2002 17.05 14.36 16.20

2003 17.29 14.39 16.36

2004 17.50 14.26 16.45

2005 17.65 13.91 16.40

2006 17.78 13.57 16.34

2007 17.66 13.01 16.03

2008 17.25 12.19 15.42

Note: APPB is in thousands.

The basic data description according to major bank groups is illustrated in Table 3. The

Nationalized Bank Group is the frontrunner both in terms of largest number of average

functioning branches and highest number of average branches opened. The Nationalized Bank

Group is followed by Regional Rural Banks (RRBs) in respect of average functioning branches

and private sector banks in terms of average branches opened during the study period. Foreign

banks constitute the smallest group with average functioning branches of 130 and average

branches opened at about 7. Nationalized banks have displayed the highest stability as depicted

by the least coefficient of variation for both the functioning and opened branches. The figures

for RRBs have been the most volatile, which signifies that although growth of branches has

taken place, it has been uneven across the banks and over time.

Mean SD CV Mean SD CV

SBI and its Associates 6,617.05 2,500.73 37.79 102.16 132.65 129.85

Nationalized Banks 16,225.79 3,733.33 23.01 272.82 279.22 102.35

Foreign Banks 129.84 110.79 85.32 5.68 7.73 135.91

Private Sector Banks 2,780.74 696.90 25.06 147.26 158.91 107.91

Regional Rural Banks 7,248.13 6,861.93 94.67 39.03 66.45 170.26

Table 3: Bank Group-Wise Data Description

Bank Group

Number of Functioning

Branches

Number of Branches

Opened


9/16


The regression result with common set

of explanatory variables and functioningbranches as the dependent variable is

presented in Table 4. The dependent

variable has been normalized relative to

SBI branches. Likewise, theassetvariable

has been defined relative to SBI assets.

Over a period of time, apart from opening

of new branches, banks perform diverse

pursuits, such as closures, mergers,

amalgamations and change of status,

which not only lead to changes in the

number of functioning branches but also

variations in the status with respect to

ownership and population group

categorization. With this motivation, it

becomes useful to assess the determinants

of functioning branches disentangling the

operating and demographic factors, which

may confound the outcome. The time

trend has an insignificant impact on the

dependent variable. The finding depicts

that on controlling for various othercharacteristics, the number of branches

has not significantly improved over the

time period of the study. The bank group

size, as captured by real assets, has a

positive effect, which implies that larger

bank groups are aggressive in carrying out

branch expansionary activities. Among

the ownership dummies, both foreign and

private banks seem to possess significantly lesser number of functioning branches as compared

to the SBI group, whereas the Nationalized Group is owning significantly more number of

branches vis--vis SBI and associates.

As discussed in the empirical methodology, it is vital to examine the convergence of the

functioning branches across the urban and rural population groups over time. The convergence

results are displayed in Table 5. Model 1 is the benchmark regression excluding the RRBs and

Model 2 is the consolidated dataset including RRBs. Qualitatively, the outcome of both the

models is very similar. A negative and significant coefficient of initial branches is an evidence of

conditional convergence. The real expenditure has a direct and significant effect on the growth

Dependent variable: Number of functioning branches

Explanatory Variables Model 1

Intercept 133.8

(158.81)

Time 0.82

(2.6)

MPCE 0.01

(0.05)

Population 0.12

(0.22)

Assets 0.29*

(0.11)

Pop_gr_dum 29.29

(85.98)

Nationalized_dum 139.68*

(8.63)

Foreign_dum 75.57*

(8.62)

Private_dum 36.05*

(6.97)

R-square 0.96

Table 4: Regression Result

(Functioning Branches)

Note: Period of the study: 1990 to 2008; Figures in bracketdenote the robust standard errors; * Significantat 1%.


10/16


Dependent variable: Growth rate of functioning branches

Explanatory Variables Model 1 Model 2

Intercept 1.7 2.4

(1.7) (1.46)

Time 0.04 4.45E03

(0.04) (3.55E02)

Initial branches 4.10E05* 3.90E05*

(1.18E05) (1.08E05)

Population 1.60E04 1.04E03(1.52E03) (1.31E03)

MPCE 3.09E03*** 4.25E03*

(1.68E03) (1.42E03)

R-square 0.53 0.55

Table 5: Test of Convergence

Note: Period of the study: 1990 to 2008; Figures in bracket denote the robust standard errors; * Significantat 1%; and *** Significant at 10%.

rate of the functioning branches. The result implies that higher income regions are experiencing

a higher branch growth.

Finally, it is imperative to test if the number of branches has encountered any marked shiftfrom its mean level. It shall help to determine if any policy factor does have a role in strategizing

the branch expansion plan of the banks, and if so, the lag with which it is implemented. The

three series tested for structural change are: functioning branches for rural and urban sectors,

and total functioning branches. To examine if at all there is an indication of breakpoint(s), the

F-statistics procedure as tabulated in Equation (4) has been employed.1The plot for functioning

branches in rural areas is displayed in Figure 1. As observed from the graph, the series is

increasing slightly till around 1996. Thereafter it is stable for a few years, before a sharp

upward trajectory. The plots for urban and all India total are portrayed in Figures 2 and 3,

respectively. The figures are quite similar. Both are displaying an increasing trend till 1999, after

which there is a deceleration. A sharp reversal of curve with declining trend is observed around

2004 for both the series. On the basis of graphical outcomes, there seems to be a strong case

for structural break in the number of functioning branches. To empirically test and pinpoint the

year of shift, the Bai and Perron (2003) methodology has been utilized. The result of the

breakpoint years is available in Table 6. In addition to the single-segment model, two-segment

model has also been used primarily due to the double humped figures as obtained for urban

and total number of functioning branches. The year 2006 turns out to be the structural change

1 The OLS-based CUSUM procedure has also been used with very similar results.


11/16


Figure 2:F-Statistics for Functioning Branches in Urban Region

25

20

15

5

1995 2000 2005

Time

F-Statistics

30

35

10

0

Figure 1:F-Statistics for Functioning Branches in Rural Region

40

30

20

10

0

1995 2000 2005

Time

F-Statistics


12/16


Population Group/Model Single-Segment Model Two-Segment Model

Rural 2006 1995, 2006

Urban 2003 1997, 2005

Total 2004 1997, 2006

Table 6: Structural Change Breakpoints

year, assuming a single shift year for rural functioning branches. In the case of urban and total

number of branches, the break years are 2003 and 2004, respectively, on the basis of single-

segment model.

Conclusion

The financial inclusion agenda has gained tremendous relevance in an emerging economy like

India. The issue of financial exclusion seems to be more severe for rural and less populous

regions compared to urban and developed areas. In this respect, the current analysis is an

attempt to explore the behavior of inclusion/exclusion across the population groups.

The pooled dataset has been employed to test the hypothesis spanning over the period from

1990 to 2008 for rural and urban regions separately. A set of control variables have also been

employed in the regression analysis to understand the role of demographic and institutional

Figure 3:F-Statistics for Functioning Branches in All India

20

15

10

5

0

1995 2000 2005

Time

F-Statistics

25

30


13/16


factors. Bank group size, as captured by assets, has a direct influence on the number of

operating branches. Ownership effect also plays a significant role in the determination of branches

operating. Both time and income level turn out to be insignificant in the determination of

operating branches. The test of convergence has been carried out to examine the trend of the

functioning branches across the urban and rural population groups over time. Evidence of

conditional convergence has been found, which implies higher growth of branches in regions

having less density of branches initially. The real expenditure has a direct and significant effect

on the growth rate of functioning branches. The result implies that higher income regions are

experiencing a higher branch growth. Finally, test of structural change has been carried out to

investigate the possible shift of functioning branches over time. Each one of the series (functioning

branches for rural and urban sectors, and total functioning branches) is indicating the presence

of structural shift in the number of functioning branches. The finding is an evidence of the

positive impact of varied inclusion policies that have been implemented lately.

References

1. Aizcorbe A M, Kennickell A B and Moore K B (2003), Recent Changes in US Family

Finances: Evidence from the 1998 and 2001 Survey of Consumer Finances,Federal

Reserve Bulletin,January, pp. 1-32.

2. Bai J and Perron P (2003), Computation and Analysis of Multiple Structural Change

Models,Journal of Applied Econometrics, Vol. 18, pp. 1-22.

3. Barro R J and Sala-i-Martin X X (1992), Convergence,Journal of Political Economy,

Vol. 100, pp. 223-251.

4. Beck Thorsten, Demirg-Kunt A and Ross Levine (2004), Finance, Inequality and

Poverty: Cross-Country Evidence, Policy Research Working Paper 3338, World Bank,

Washington DC.

5. Beck Thorsten, Demirg-Kunt A and Martinez-Peria S (2007), Reaching Out: Access

to and Use of Banking Services Across Countries,Journal of Financial Economics, Vol.

85, No. 1, pp. 234-266.

6. Beck Thorsten, Ross Levine and Norman Loayza (2000), Finance and the Sources of

Growth,Journal of Financial Economics,Vol. 58, pp. 261-300.

7. CGAP and World Bank (2010), Financial Access 2010: The State of Financial InclusionThrough the Crisis, Report of the CGAP and World Bank Group.

8. Chakravarty S R and Pal R (2010), Measuring Financial Inclusion: An Axiomatic

Approach, Indira Gandhi Institute of Development Research, Mumbai Working

Papers 2010-03.

9. Chow G C (1960). Tests of Equality Between Sets of Coefficients in Two Linear

Regressions,Econometrica,Vol. 28, No. 3, pp. 591-605.


14/16


10. Devlin J F (2005), A Detailed Study of Financial Exclusion in the UK,Journal of

Consumer Policy,Vol. 28, pp. 75-108.11. Errico Luca and Alberto Musalem (1999), Offshore Banking: An Analysis of Micro-

and Macro-Prudential Issues, IMF Working Paper No. WP/99/5, International Monetary

Fund, Washington DC.

12. Evans P and Karras G (1996), Do Economies Converge? Evidence from a Panel of US

States, The Review of Economics and Statistics,Vol. 78, No. 3, pp. 384-388.

13. Federal Reserve (2001), Survey of Consumer FinancesBefore-Tax Family Income,

Percentage of Families that Saved, and Distribution of Families, by Selected Characteristics

of Families1992, 1995, 1998, and 2001 Surveys.

14. Goyal A (2010), Regulatory Structure for Financial Stability and Development,Economic and Political Weekly,Vol. 45, No. 39, pp. 51-61.

15. Hansen B E (1997), Approximate Asymptoticp-Values for Structural-Change Tests,

Journal of Business & Economic Statistics, Vol. 15, pp. 60-67.

16. IMF (2009), Access to Finance Project, International Monetary Fund, Washington DC,

available at http://fas.imf.org

17. Kamath R, Mukherji A and Sandstrom M (2010), Accessing Institutional Finance:

A Demand Side Story for Rural India,Economic and Political Weekly,Vol. 45, No. 37,

pp. 56-62.

18. Kendall J, Mylenko N and Ponce A (2010), Measuring Financial Access Around theWorld, Policy Research Working Paper No. 5253, World Bank.

19. Kiatchai S and Kulathunga A (2009), Getting Finance in South Asia 2009: Indicators

and Analysis of the Commercial Banking Sector, World Bank.

20. King Robert and Ross Levine (1993), Finance, Entrepreneurship and Growth: Theory

and Evidence,Journal of Monetary Economics,Vol. 32, pp. 513-542.

21. Klapper Leora, Luc Laeven and Raghuram Rajan (2006), Entry Regulation as a Barrier

to Entrepreneurship,Journal of Financial Economics,Vol. 82, pp. 591-629.

22. Kumar N (2011), Financial Inclusion and its Determinants: Evidence from State Level

Empirical Analysis in India, Proceedings of the 13th

Annual Conference on Money andFinance in the Indian Economy, Indira Gandhi Institute of Development Research,

Mumbai, India.

23. Leeladhar V (2006), Taking Banking Services to the Common Man: Financial Inclusion,

Reserve Bank of India Bulletin.

24. Mankiw N G, Romer D H and Weil D N (1992), A Contribution to the Empirics of

Economic Growth, Quarterly Journal of Economics,Vol. 107, pp. 407-437.


15/16


25. Mohan R (2006), Economic Growth, Financial Deepening and Financial Inclusion,

Reserve Bank of India Bulletin.26. Rangarajan Committee (2008), Report of the Committee on Financial Inclusion.

27. Reserve Bank of India, Various Volumes,Branch Banking Statistics, Mumbai.

28. Reserve Bank of India, Various Volumes, Statistical Tables Relating to Banks in India,

Mumbai.

29. Sarma M (2008), Index of Financial Inclusion, Working Paper No. 215, Indian Council

for Research on International Economic Relations.

30. Sinclair S, McHardy F, Dobbie L et al.(2009), Understanding Financial Inclusion Using

Action Research and a Knowledge Exchange Review to Establish What is Agreed, and

What Remains Contested, Friends Provident Foundation, International Gateway forFinancial Education.

31. Subbarao D (2009a), Financial Inclusion Challenges and Opportunities, Speech

Delivered by Shri Duvvuri Subbarao, Governor, RBI, at the Bankers Club in Kolkata on

December 9, 2009.

32. Subbarao D (2009b), Should Banking be Made Boring? An Indian Perspective, Keynote

Address by Dr. Duvvuri Subbarao, Governor, RBI, at the International Finance and

Banking Conference organized by the Indian Merchants Chamber on Banking Crisis

and Beyond on November 25, 2009 in Mumbai.

33. Thorat U (2007), Financial Inclusion: The Indian Experience,Reserve Bank of IndiaBulletin.

34. World Bank (2008a),Banking the Poor: Measuring Banking Access in 54 Economies,

World Bank, Washington, DC.

35. World Bank (2008b),Payment Systems Worldwide: A Snapshot. Outcomes of the Global

Payment Systems Survey 2008, World Bank, Washington, DC.

36. World Bank (2009),Doing Business 2009, World Bank, Washington, DC.

Reference # 10J-2012-02-05-01


16/16

Copyright of IUP Journal of Bank Management is the property of IUP Publications and its content may not be

copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written

permission. However, users may print, download, or email articles for individual use.

Date post:	03-Jun-2018
Category:	Documents
Upload:	durva-shastri
View:	220 times
Download:	0 times

An Empirical Analysis of FI Across Population in India

Documents