impact of financial inclusion on economic...

IMPACT OF FINANCIAL INCLUSION ON ECONOMIC

DEVELOPMENT

THESIS

SUBMITTED TO THE UNIVERSITY OF JAMMU

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

COMMERCE

Supervisor Submitted by

Prof. Neetu Andotra Ms. Preeti Salathia

POST GRADUATE DEPARTMENT OF COMMERCE

UNIVERSITY OF JAMMU

JAMMU

2014

CONTENTS Page No.

Certificate i

Acknowledgement ii-iii

List of Tables iv-v

List of Figures vi-vii

Abbreviations viii

Preface ix-x

Chapter I An Overview of Financial Inclusion and Economic

Development

1-22

Chapter II Review of Literature 23-64

Chapter III Research Methodology 65-104

Chapter IV Financial Inclusion, Socio-economic Empowerment and

Economic Development

105-159

Chapter V Financial Inclusion and Poverty Reduction 160-186

Chapter VI Financial Inclusion and Area Development 187-209

Chapter VII Conclusion and Strategic Implications 210-228

Bibliography 229-237

Annexures 238-245

i

DEPARTMENT OF COMMERCE

UNIVERSITY OF JAMMU, JAMMU -180006

NEW CAMPUS, BABA SAHEB AMBEDKAR ROAD.

CERTIFICATE

Certified that Ms. Preeti Salathia, who was admitted for the Degree of Ph.D. in

Commerce under my supervision, has completed her work. The title of her thesis is

‘Impact of Financial Inclusion on Economic Development’. She has fulfilled all

the statutory requirements for the submission of thesis for evaluation. It is further

certified that:

i. The title of her thesis has been approved by the Board of Research Studies in

Commerce;

ii. The thesis embodies the work of the candidate herself;

iii. The candidate has worked under me for the period required under rules;

iv. The candidate has got one paper, relevant with research, published in the

national journal;

v. The candidate has put in the required attendance and delivered a seminar in

the department during the period of research; and

vi. The conduct of the scholar remained good during the period of research.

Dated: Prof. Neetu Andotra

(Supervisor)

ii

ACKNOWLEDGEMENT

First of all, I thank Almighty GOD for generating enthusiasm and granting me

spiritual strength to successfully pass through this challenge.

Then, I would like to extend my sincere thanks to my guide Prof. Neetu

Andotra for her mentorship, valuable advice and extensive discussions during my

research. This work would not have been possible without your guidance, support and

encouragement.

I gratefully acknowledge academic staff of our department namely, Prof.

Hardeep Chahal, Dr. Gurjeet Kaur, Dr. Jeevan Jyoti, Sh. Tarsem Lal, Sh. Sunil and

Dr. Bodh Raj for their valuable suggestions.

I also thank Sh. M. P. Raina, Ms. Jyoti, Ms. Pooja, Sh. Ashok besides others

who have not directly contributed in my research work but indirectly helped me out.

I take this opportunity to sincerely acknowledge staff of Rattan Tata Library,

Delhi University and MDI, Gurgaon for letting me avail their library facility to gather

secondary data which buttressed me to perform my work more comfortably.

I extend my sincere thanks to all Business Correspondents for their invaluable

help during survey. My thanks is also due to Mitu, Jagjeet, Neetu Jasrotia, Manjeet

Jamwal, Satvinder, Rajinder, Nikhil besides others for their facilitation during survey.

Without you people, it would not have been possible for me to reach such far-flung

areas for collecting primary data.

My warm appreciation is due to all the Ph.D. scholars of our department

especially, Himani, Ritika, Kamini, Purnima, Jagmeet and Sonia who shared their

knowledge regarding various techniques for testing of hypotheses.

I feel highly indebted to Dr. Kiran Bakshi and other faculty members of Govt.

College for Women, Gandhi Nagar for their constant support, generous care and

homely feeling at my work place.

This list is incomplete without acknowledging my friends specially Pariksha,

Sonal, Aashu and Sushil Choudhary for their genuine friendship and the wonderful

time together. I truly acknowledge all your friendly help that remained the source of

inspiration for me throughout this work.

I convey special thanks to Shruti Gupta. During the inevitable ups and downs

of conducting my research she often reminded me life’s true priorities. This helped

iii

me a lot to work for hours together tirelessly. I doubt that I will ever be able to convey

my appreciation fully, but I owe her my eternal gratitude.

It is hard to express my thanks to my parents Sh. Babishan Singh and Ms.

Sharda Devi in words. Your understanding, faith, advice and indescribable support to

me throughout my whole life are invaluable. I am very grateful for your care, love,

trust, constant interest and positive stimulation. All other family members also

deserve special thanks.

My special thanks is due to my niece Piya and Nephew Ekansh for their

unconditional love and smile which waive off my whole day tiredness. Last but not

least, my thanks is also due to my grandmother Ms. Durgi Devi.

If have forgotten anyone, I apologize. Lastly, my thanks is due to one and all.

Place: Jammu Ms. Preeti Salathia

Date:

iv

LIST OF TABLES

S.No.

Description Page No.

1.1 Definitional Aspects of Financial Inclusion/Exclusion 4

1.2 Financial Inclusion Plan-Summary Progress of all Banks

including RRBs

15

2.1 Review of Literature 36

3.1 Generation of Scale Items 74

3.2 Multicollinearity Analysis 77

4.1 Socio-economic Profile of Respondents 143

4.2 Results showing Factor Loadings and Variance Explained after

Scale Purification (Rotated Component Method)

144

4.3 Reliability & Validity of Latent Constructs 148

4.4 Discriminant Validity of Latent Constructs 148

4.5 Results of CFA Fit Indices 149

4.6 Fitness of the Structural Model 149

4.7 Results of Hypotheses Testing 150

4.8 Output from One-way ANOVA 151

4.9 Mean Difference in the Nature of Financial Inclusion through t-

test

152

4.10 Age-wise Output from One-way ANOVA 152

4.11 Caste-wise Output from One -way ANOVA 153

4.12 Religion-wise Output from One-way ANOVA 153

4.13 Qualification-wise Output from One-way ANOVA 154

4.14 Income-wise Output from One-way ANOVA 155

4.15 Mean Difference in the Nature of Financial Inclusion between

Male & Female through t-test

155

4.16 Mean Difference in the Nature of Financial Inclusion between

Married & Unmarried Beneficiaries through t-test

156

5.1 Trends of Poverty in India 163

5.2 Number & Percentage of Population below Poverty Line by

States (2011-12)

165


Scale Purification (Rotated Component Method) for Poverty

Reduction

178

v

5.4 Result of CFA Fit Indices, Reliability and Validity 179

5.5 Demographic Profile-wise Mean Satisfaction regarding Poverty

Eradication

180

5.6 Demographic Profile-wise Mean Satisfaction regarding Poverty

Reduction through Education

181


5.8 Result of Hypothesis Testing 182


Scale Purification (Rotated Component Method) for Area

Development

202

6.2 Result of CFA Fit Indices, Reliability and Validity 202

6.3 Demographic Profile-wise Mean Satisfaction regarding Area

Development

203


6.5 Result of Hypothesis Testing 204

6.6 Regression Model Summary (with Coefficient) of Access

Dimension as Dependent Variable

204

6.7 ANOVAb

for Measuring Regression Coefficient 204

6.8 Regression Coefficient’s showing the Effect of Barriers of

Financial Inclusion on Access Dimension

205

6.9 Regression Model Summary (with Coefficient) of Usage

Dimension as Dependent Variable

205

6.10 ANOVAb

for Measuring Regression Coefficient 205

6.11 Regression Coefficient’s showing the Effect of Barriers of

Financial Inclusion on Usage Dimension

206

6.12 District and Bank-wise Mean Satisfaction among Beneficiaries

of FID

206

vi

LIST OF FIGURES

S.No. Description Page No.

Figure 1.1 Evolution of Financial Inclusion since 1960’s 2

Figure 1.2 Financial Institutional Products & Services 5

Figure 1.3 Objectives of Financial Inclusion 6

Figure 1.4 Financial Institutions Promoting Financial Inclusion 8

Figure 1.5 Barriers of Financial Inclusion 16

Figure 2.1 Proposed Theoretical Model for Financial Inclusion and


28

Figure 3.1 Normality through Box Plot 90

Figure 3.2 Normality through Q-Q Plot 90

Figure 4.1 Pie Chart for Banks 107

Figure 4.2 Pie Chart for Gender 107

Figure 4.3 Pie Chart for Age 108

Figure 4.4 Pie Chart for Caste 108

Figure 4.5 Pie Chart for Religion 109

Figure 4.6 Pie Chart for Marital Status 109

Figure 4.7 Pie Chart for Qualification 110

Figure 4.8 Pie Chart for Monthly Income 111

Figure 4.9 Pie Chart for Districts 111

Figure 4.10 CFA Model for Access Dimension of FID 127

Figure 4.11 CFA Model for Availability Dimension of FID 128

Figure 4.12 CFA Model for Usage Dimension of FID 129

Figure 4.13 CFA Model for Social Empowerment 130

Figure 4.14 CFA Model for Economic Empowerment 131

Figure 4.15 CFA Model for Economic Development 132

Figure 4.16 Overall Structure Equation Model 136

Figure 4.17 Impact of Financial Inclusion on Economic Development 137

Figure 4.18 Impact of Financial Inclusion on Social Empowerment 137

Figure 4.19 Impact of Social Empowerment on Economic

Development

138

Figure 4.20 Impact of Financial Inclusion on Economic Development

through Social Empowerment

138

vii

Figure 4.21 Impact of Financial Inclusion on Economic

Empowerment

139

Figure 4.22 Impact of Economic Empowerment on Economic

Development

139

Figure 4.23 Impact of Financial Inclusion on Economic Development

through Economic Empowerment

140

Figure 5.1 Relationship between Financial Ecosystem and Poverty 161

Figure 5.2 Trends of Poverty in India 164

Figure 5.3 Poverty Alleviation Programmes 168

Figure 5.4 CFA Model for Poverty Reduction 174

Figure 5.5 SEM Model for Poverty Reduction 177

Figure 6.1 Components of Area Development 187

Figure 6.2 Area Development Programmes in India 189

Figure 6.3 CFA Model for Area Development 194

Figure 6.4 SEM Model for Area Development 198

viii

ABBREVIATIONS

2 Chi Square

AGFI Adjusted Goodness of Fit Index

ANOVA Analysis of Variance

ATM Automatic Teller Machine

AVE Average Variance Extracted

BC Business Correspondents

CFA Confirmatory Factor Analysis

CFI Comparative Fit Index

CR Critical Ratio

ED Economic Development

EE Economic Empowerment

FI Financial Inclusion

FID Financial Inclusion Drive

FI-ED Financial Inclusion-Economic Development

GFI Goodness of Fit Index

GOI Government of India

JKB Jammu & Kashmir Bank

JKGB Jammu & Kashmir Grameen Bank

KYC Know Your Customer

MFI Micro Finance Institutions

NABARD National Bank for Agriculture and Rural Development

NFI Normed Fitness Index

PNB Punjab National Bank

RBI Reserve Bank of India

RMR Root Mean Square Residual

RMSEA Root Mean Square Error of Approximation

RRB Regional Rural Bank

SBI State Bank of India

SE Social Empowerment

SEM Structural Equation Modeling

SRW Standardised Regression Weights

TLI Tucker-Lewis Index

ix

PREFACE

Financial inclusion is the mechanism of ensuring access to financial services along

with timely & adequate credit whenever needed by the vulnerable groups at an

affordable cost. India, a growing economy has special significance of financial

inclusion as it brings large deprived sections of the society under financial ambit. This

access to financial services generates income, decreases social-economic disparities,

creates financial assets, promotes area development and provides new work

opportunities across all sectors and sections of the economy. Thus, financial inclusion

has multiplier effect on the economy as a whole through higher savings pooled from

the vast segment of the bottom of the pyramid (BoP) population. It brings un-banked

people into financial mainstream and results in accelerating the economic

development of the country and is integral to the inclusive growth process. In India,

60.9 million of accounts have been opened under FID through bank branches and BCs

during the year 2013-14. Therefore, financial inclusion has emerged as an important

global agenda for sustainable long-term economic development.

The present research study focuses on evaluating the impact of financial inclusion on

economic development among beneficiaries of five banks namely, JKB, JKGB, SBI

and PNB belonging to five districts i.e., Jammu, Samba, Kathua, Udhampur and Reasi

of Jammu division, J&K state. This study would help RBI along with other financial

intermediaries to redesign financial strategic framework for achieving long term

vision of financial inclusion drive i.e. inclusive growth.

The main body of the present research work comprises of seven chapters along with

tables, figures, charts & annexures to support the analysis and findings of the study.

The scheme and content of each chapter is as follows:

The first chapter entitled, ‘An Overview of Financial Inclusion and Economic

Development’ presents conceptual analysis of financial inclusion, objectives,

significance of financial inclusion, role of financial institutions in promoting financial

inclusion, measures taken by RBI & Govt. of India under financial inclusion

programme, current status of financial inclusion in India and barriers of financial

inclusion.

x

Chapter second, ‘Review of Literature’ outlines the literature review pertaining to

diverse dimensions of financial inclusion and economic development and concluded

with research gap.

The third chapter, ‘Research Methodology’ covers all the relevant issues of the

quantitative approaches being followed in the study. This chapter includes nature &

scope, need, objectives of the study, hypotheses formulation, pretesting, generation of

scale items, sampling techniques, research instrument & analytical tools of

quantitative data, significance and limitations of the study.

Chapter fourth, ‘Financial Inclusion, Socio-economic Empowerment and

Economic Development’ comprises of conceptual analysis of different constructs,

profile of respondents, analysis of impact of financial inclusion on economic

development through socio-economic empowerment sub-divided into scale

purification, CFA & SEM and determination of significant difference in the nature of

financial inclusion across the socio-economic variables. Validity and reliability have

also been measured in the chapter.

Chapter fifth, ‘Financial Inclusion and Poverty Reduction’ embraces conceptual

analysis of poverty, trends of poverty in India, causes of poverty, poverty alleviation

programmes and analysis between financial inclusion & poverty reduction sub-

divided into scale purification, CFA, demographic profile-wise mean satisfaction and

relationship between financial inclusion and poverty reduction through SEM.

Next chapter, ‘Financial Inclusion and Area Development’ discusses the conceptual

analysis of area development, schemes of area development and analysis of financial

inclusion & area development sub-divided into scale purification, CFA, demographic

profile-wise mean satisfaction, relationship between financial inclusion & area

development through SEM and impact of barriers of financial inclusion on access &

usage dimensions.

The last chapter, ‘Conclusion and Strategic Implications’ summarises the overall

findings of the study and provides strategic framework to improve the impact of

financial inclusion.

`

Chapter-I An Overview of Financial Inclusion and Economic

Development

CONTENTS

S.No. Title Page No.

1.1 Introduction 1

1.2 Conceptual Analysis of Financial Inclusion 1

1.3 Objectives of Financial Inclusion 6

1.4 Significance of Financial Inclusion 7

1.5 Role of Financial Institutions in Promoting Financial

Inclusion

8

1.6 Measures Taken by RBI and Government of India

under Financial Inclusion Programmes

10

1.7 Current Status of Financial Inclusion in India 13

1.8 Barriers of Financial Inclusion 16

1.9 Conclusion 18

References 20

1

CHAPTER I

AN OVERVIEW OF FINANCIAL INCLUSION AND

ECONOMIC DEVELOPMENT

1.1 INTRODUCTION

Despite impressive economic growth over the last two decades, many countries in the

world are experiencing inequalities leading to adverse consequences for social

cohesion which in turn, could dampen growth prospects (Zhuang & Hasan, 2008). For

growth to be sustained in the long run, it should be inclusive and broad based across

all sectors and sections of the economy (George, 2011). Inclusive growth promotes

economic growth, increases standard of living, reduces poverty, decreases disparity,

promotes agricultural growth rate and provides new work opportunities (Deutscher &

Jacquet, 2009). Therefore, financial inclusion has emerged as an important topic on

the global agenda for sustainable long-term economic growth. It is a stepping stone

and is integral to the inclusive growth process & sustainable development of the

country (Sadakkadulla, 2007 and Subbarao, 2010).

1.2 CONCEPTUAL ANALYSIS OF FINANCIAL INCLUSION

The concept of financial inclusion was evolved with the initialisation of Co-operative

movement in India during 1904. It got momentum in 1969 when 14 major commercial

banks of the country were nationalised and Lead Bank Scheme was introduced shortly

thereafter in mid 1970’s. Large numbers of bank branches were opened across the

country even in those areas which were neglected earlier. Despite of various

measures, a huge segment of the population of the country was excluded from the

formal banking system (Chattopadhyay, 2011). In 2005, RBI promulgated a drive for

financial inclusion whereby formal financial system promotes the participation of

every household at the district level via. saving accounts for the ‘unbanked’ (Ramji,

2009 and Ramasubbian & Duraiswamy, 2012). Under the chairmanship of

Rangarajan, ‘Committee on Financial Inclusion’ was formulated by the Govt. of India

and it defines financial inclusion as ‘the mechanism of ensuring access to financial

services and timely & adequate credit whenever needed by the vulnerable groups such

as the weaker sections and low income groups at an affordable cost’. Recently, in

2014 to give leverage to financial inclusion drive ‘Pradhan Matri Jan Dhan Yojana’

2

has been introduced. Figure 1.1 summarises the key stages in evolution of financial

inclusion initiated by the government since 1960’s.

FIGURE 1.1: EVOLUTION OF FINANCIAL INCLUSION SINCE 1960’s*

*Source: Naik, Priya (2013). Financial inclusion-key to economic & social development. CSR

Mandate, June-July, 14-17.

Thus, the above figure depicts that India has made a massive contribution to the

economic development by finding innovative ways from time to time to empower the

poor starting with nationalisation of banks, priority sector lending by banks, lead bank

scheme, establishment of regional rural banks, service area approach, self help group-

bank linkage programme, introduction of Pradhan Mantri Jan Dhan Yojana etc.

Multiple steps have been initiated by the RBI over the years to increase credit access

to the poor sections of the society.

Sarma & Pais (2008) defines financial inclusion as, ‘the process that ensure the ease

of access, availability and usage of the formal financial system for all members of an

economy’. Broadly, it means access to finance & financial services for all in a fair,

transparent and equitable manner at an affordable cost (Sarma, 2008 and Solo, 2008).

Murari & Didwania (2010) denotes it as a, ‘delivery of financial services at an

affordable cost to the vast sections of the disadvantaged and low-income groups

including households, enterprises, SMEs, traders. The various financial services

1960s-70s

•Focus on increasing credit to the neglected economy sectors and weaker sections of society.

•Nationalisation of banks.

•Development of the rural banking ecosystem including Regional Rural Banks.

•Lead Bank Scheme launched for rural lending.

1980s-90s

•Establishment of National Bank for Agriculture and Rural Development (NABARD) to provide refinance to banks providing credit to agriculture.

•SHG bank linkage program launched by NABARD.

2000s

•The term 'Financial inclusion' introduced for the first time in RBI's Annual Policy Statement 2005-06.

•Banks asked to offer 'no frills account'.

•Know Your Customer (KYC) norms simplified.

•Banking Correspondent and Banking Facilitator concept introduced.

•100 percent financial inclusion drive launched.

•Electronic Bank Transfer Scheme introduced to transfer social benefits electronically to bank account of beneficiary.

• Introduction of Pradhan Mantri Jan Dhan Yojana.

3

include credit, savings, insurance and payments & remittance facilities’. Kuri & Laha

(2011) defined it as a, ‘process of bringing the weaker and vulnerable sections of

society within the ambit of the organised financial system. It creates conditions for

access to timely & adequate credit and other financial services by vulnerable groups,

such as weaker sections and low income groups at affordable cost’. Bagli & Dutta

(2012) refers it as a, ‘situation where people, in general, have connection with the

formal financial institutions through holding savings bank account, credit account,

insurance policy etc. It may help the person to have affordable access to financial

services like formal savings, credit, payments, insurance and remittance’. It is a

delivery of banking services to masses including privileged and disadvantaged people

at an affordable terms and conditions without any discrimination (Raman, 2012 and

Padma & Gopisetti, 2013). According to Paramasivan & Ganeshkumar (2013), it can

be defined as, ‘a way of easy access to formal financial services or systems and their

usage by all members of the economy’. Financial inclusion is a powerful tool to

achieve inclusive growth. Financial inclusion is the process of ensuring access to

appropriate financial products and services needed by vulnerable groups such as

economically & socially weaker sections and low income groups at an affordable cost

in a fair & transparent manner by formal financial institutions (Uma & Rupa, 2013;

Choithrani, 2013; Sharma & Kukreja, 2013; Srinivas & Upender, 2014; Banerjee &

Francis, 2014 and Shyni & Mavoothu, 2014). Financial inclusion means providing

financial services to all sections of the society which includes the underprivileged, the

poor and the disadvantaged people. It not only means to open savings account of

people but also includes providing financial advice, insurance and credit services

(Garg, 2014). Kalunda (2014) defined financial inclusion as the, ‘process of availing a

range of required financial services, at a fair price, at the right place, form & time,

through formal means and without any form of discrimination to the populace’. Over

the years, several definitions of financial inclusion/exclusion have evolved and are

shown in Table 1.1. The structure of various financial products or services embraced

in financial inclusion and the institutional arrangement is schematically presented in

Figure 1.2.

4

TABLE 1.1 DEFINITIONAL ASPECTS OF FINANCIAL

INCLUSION/EXCLUSION*

Institution/Author Definition Indicators

ADB (2000) Provision of a broad range of financial

services such as deposits, loans,

payment services, money transfers and

insurance to poor and low-income households and their

micro-enterprises.

Deposits, loans, payment

services, money transfer

and insurance.

Stephen P. Sinclair (2001)

Financial exclusion means the inability

to access necessary financial services

in an appropriate form. Exclusion can

come about as a result of problems

with access, conditions, prices,

marketing or self-exclusion in response

to negative experiences or perceptions.

Basic banking services for

money transmission, credit,

insurance, debt and debt

assistance, long-term

savings and financial

literacy.

Chant Link and Associates,

Australia (2004)

Financial exclusion is lack of access by

certain consumers to appropriate low

cost, fair and safe financial products

and services from mainstream providers. Financial exclusion becomes

a concern in the community when it

applies to lower income consumers

and/or those in financial hardship.

Deposit accounts, direct

investments, home loans,

credit cards, personal loans,

building insurance and home insurance.

Treasury Committee, House

of Commons, UK (2004)

Ability of individuals to access

appropriate financial products and

services.

Affordable credit and

savings for all and access to

financial advice.

Scottish Government (2005)

Access for individuals to appropriate

financial products and services. This

includes having the capacity, skills,

knowledge and understanding to make

the best use of those products and

services. Financial exclusion by

contrast, is the converse of this.

Access to products and

services, and/or capacity,

skills, knowledge and

understanding.

United Nations (2006 b)

A financial sector that provides ‘access’ to credit for all ‘bankable’

people and firms, to insurance for all

insurable people and firms and to

savings and payments services for

everyone. Inclusive finance does not

require that everyone who is eligible

use each of the services, but they

should be able to choose to use them if

desired.

Access to credit, insurance, savings, payment services.

Report of the Committee on

Financial Inclusion in India

(Chairman: C.Rangarajan) (2008)

The process of ensuring access to

financial services and timely and

adequate credit where needed by vulnerable groups such as weaker

sections and low income groups at an

affordable cost.

Access to financial services

and timely and adequate

credit.

World Bank (20) Broad access to financial services

implies an absence of price and non-

price barriers in the use of financial

services, it is difficult to define and

measure because access has many

dimensions.

Access to financial services

such as deposit, credit,

payments, insurance.

*Source: Reserve Bank of India. (2009). Financial Inclusion. Retrieved from http://rbidocs.

rbi.org.in/rdocs /Publications/PDFs/86734.pdf, 294-348. Accessed on 27-09-2014.

http://rbidocs/

5

FIGURE 1.2: FINANCIAL INSTITUTIONAL PRODUCTS & SERVICES*

*Source: Reserve Bank of India. (2009). Financial Inclusion. Retrieved from http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/86734.pdf, 294-348. Accessed on 27-09-

2014.

Insurance companies

Insurance

Financial inclusion: Access to

financial products & services

from the formal financial

system

MFIs/NGOs

Small value loans/credit

Post offices

Remittances Postal savings accounts

Payment and

remittance services

Saving accounts

Loan/credit accounts

Financial advice Commercial/Co-operative

bank/Credit unions

6

1.3 OBJECTIVES OF FINANCIAL INCLUSION

Financial inclusion has many objectives (Figure 1.3). A brief description is as under:

i. Economic objectives

Society is divided into different sections on the basis of income, occupation,

caste, etc. Financial inclusion embraces all the sections in the financial ambit.

Equitable growth in all the sections leads to reduction of disparities in terms of

income and savings. Thus, financial inclusion serves as a boom for the

underdeveloped and developing nations.

ii. Mobilisation of savings

Through financial inclusion, weaker sections are provided with the facility of

banking services. This facility mobilises the savings, normally piled up at their

households which can be effectively utilised for the capital formation and

growth of the economy.

FIGURE 1.3: OBJECTIVES OF FINANCIAL INCLUSION*

*Source: Mahajan, Sahil (2014). Financial inclusion & Indian banking sector. The International

Journal of Business & Management, 2(1), 67-73.

iii. Larger market

To serve the requirements and need of the large section of society, there is an

urgent need for the larger market for the financial system which opens up the

Objectives

Economic objectives

Mobilisation of savings

Larger market

Social objectives

Sustainable livelihood

Political ojectives

7

avenue for the new players in the financial sector and can lead to growth of

banking sector also.

iv. Social objectives

Poverty eradication is considered as an important objective of the financial

inclusion scheme since they bridge up the gap between the weaker section of

society and the sources of livelihood and the means of income which can be

generated for them if they get loans and advances.

v. Sustainable livelihood

Once the weaker section of society gets some money in the form of loan, they

start up their own business or support their education through which they can

sustain their livelihood. Thus, financial inclusion is turn out to be a blessing

for the low income households.

vi. Political objectives

There are certain other political objectives which can be achieved with the

wider inclusion of lower strata in the society and an effective direction can be

given to the government programmes.

1.4 SIGNIFICANCE OF FINANCIAL INCLUSION

In majority of the developing countries, access to finance is demanded more for the

bottom up pyramid community and considered as a public good, which is as important

and basic as access to safe water, primary education, etc. The most significant effect

of financial inclusion is that the entire national financial system is benefitted by

greater inclusion, especially when promoted in the wider context of economic

inclusion. India, a growing economy, has special significance of financial inclusion as

it brings large segment of the productive sectors of economy under formal financial

network and could unleash their creative capacities besides augmenting domestic

demand on a sustainable basis driven by income and consumption growth from such

sectors. Financial inclusion efforts do have multiplier effect on the economy as a

whole through higher savings pooled from the vast segment of the bottom of the

pyramid (BoP) population by providing access to formal savings arrangement

resulting in expansion in credit and investment by banks. Deeper engagements of the

8

BoP/under-banked population in the economy through the formal financial system

could lead to improvement of their financial conditions & living standards, alleviation

of the poverty, enabling them to create financial assets, generate income and build

resilience to meet macro-economic & livelihood shocks (Khan, 2012). It encourages

bringing un-banked customers into financial mainstream. All this, results in escalating

the economic development of the country.

1.5 ROLE OF FINANCIAL INSTITUTIONS IN PROMOTING FINANCIAL

INCLUSION

A number of financial institutions exist in financial ecosystem, which promotes

financial inclusion (Figure 1.4). Prominent among them are as under:

FIGURE 1.4: FINANCIAL INSTITUTIONS PROMOTING FINANCIAL

INCLUSION*

*Source: www.nabard.org. Accessed on 16-11-2014

i. Role of commercial banks

Banks play an important role in mobilisation and allocation of resources in any

country. They are the key pillars of India’s financial system. Besides opening

of accounts, GCC, KCC, micro insurance, bank is also doing other activities for

accelerating the growth of financial inclusion. They are encouraging interaction

between financial sector and rural development staff to ensure that financial

Financial institutions promoting

financial inclusion

Commercial banks

Regional rural banks

NABARD

Post offices

Micro finance

institutions

Self help groups

9

sector expertise is included on any rural project that has a finance component.

Besides this, they introduce financial services designed for the poor, providing

improved services to rural clients’ by introducing new technology, offer

flexible grant funding to financial institutions seeking to adapt or introduce new

financial products or to reduce delivery transaction costs or introduce more

diverse & transparent financial services for farmers.

ii. Role of regional rural banks (RRBs)

Regional rural banks came into existence in the Indian financial system since

last four decades. Its inception has improved the rural credit delivery

mechanism in India. Over the years, they are seen as the small man’s bank

taken deep roots and have become a sort of inseparable part of the rural credit

structure. As on March 31, 2013, there were 64 RRBs having a network of

17,867 branches all over the country for extending credit to rural masses. The

growth in the branch network has enabled the RRB’s to expand banking

activities in the unbanked areas and mobilise rural savings. They have emerged

as a strong intermediary for financial inclusion in rural areas by opening large

numbers of ‘No Frills’ accounts and financing under General Credit Card

(GCC). These banks have set up Financial Literacy and Credit Counselling

Centers (FLCC) to create awareness among public.

iii. Role of NABARD

NABARD has been very effective in promoting financial inclusion, capacity

building and assistance for creation of infrastructure. In order to promote

financial education, it has set up literacy centres to educate the masses on

finance, banking and insurance with the help of stakeholders. Based on the

Rangarajan committee’s recommendation, NABARD with the help of

government has set up a Financial Inclusion Promotion and Development Fund

(FIPDF) and Financial Inclusion Technology Fund (FITF) in order to foster

financial inclusion programme.

iv. Role of post offices

In order to speed up financial inclusion process, the Indian Post also plays a

vital role with its wide range network of 1,39,182 rural post office branches and

15,797 urban post office branches. Post offices are offering services like saving

10

account, recurring deposit account, monthly income scheme, public provident

fund, time deposit, senior citizen saving scheme, national saving certificate,

postal life insurance, rural postal life insurance etc. in order to cover more and

more of the rural population under the financial inclusion drive.

v. Role of micro finance institutions

Micro finance has been looked upon as an important means of financial

inclusion in India. Micro finance has to act pro-actively not just as a means to

financial inclusion but also to work towards reducing dependence of poor

borrowers on various informal sources of credit that are often known for the

onerous terms at which they offer credit. It also plays a significant role in

reducing poverty in India. Providing access to micro finance can prove to be an

effective way of reaching the poor and improving their lives. It is an enabling,

empowering and bottom-up tool to poverty alleviation that has provided

economic and non-economic externalities to low-income households in India.

vi. Role of self help group-bank linkage programme

Self help groups have emerged to be the most effective instrument for financial

inclusion. The objectives of self help group programmes are to alleviate

poverty, increase sustainability, improve capacity building and help the weaker

section to build assets. The self help group-bank linkage model is the dominant

channel where commercial banks lend directly to self help groups formed

explicitly for this purpose. This serves as a meaningful linkage between

commercial banks and self help groups. Thus, the micro finance services

provided through self help group-bank linkage programme proved to be the

most successful initiative in financial inclusion. They have been accepted as

effective tools to inclusive growth for extending various financial services to

hitherto excluded categories of poor and rural households.

1.6 MEASURES TAKEN BY RBI AND GOVERNMENT OF INDIA

UNDER FINANCIAL INCLUSION PROGRAMMES

Several measures have been initiated by both the Reserve Bank of India and the

Government to bring the financially excluded people to the fold of the formal banking

services. The important financial inclusion initiatives of RBI are as under:

11

i. Swavalamban

A co-contributory pension scheme launched on September 26, 2010 for

workers of unorganised sector. It is applicable to all citizens in the unorganised

sector who join the National Pension System (NPS) administered by the

Pension Fund Regulatory and Development Authority (PFRDA) Act, 2013.

ii. Swabhiman

In a way to achieve financial inclusion programme, the central government

launched ‘Swabhiman scheme’ on February 10, 2011. It aims to bring banking

services to large rural areas in the country. This campaign is operated by the

Ministry of Finance, Government of India and the Indian Banks' Association

(IBA) to bring banking within the reach of the masses of the Indian population.

iii. Opening of bank branches

Government had issued detailed strategy and guidelines on financial inclusion

in October, 2011. According to the framed strategy, banks were advised to

open branches in all habitations of 5,000 or more population in under-banked

districts and 10,000 or more population in other districts.

iv. No-frills account

In the mid term review of the policy (2005-06), RBI exhorted the banks, with a

view to achieving greater financial inclusion, to make available a basic banking

‘no frills’ account either with ‘NIL’ or very minimum balances as well as

charges that would make such accounts accessible to vast sections of the

population. Banks have been advised to provide small overdrafts in such

accounts.

v. Simplification of know your customer (KYC) norms

In order to ensure that persons belonging to low income group both in urban

and rural areas do not face difficulty in opening the bank accounts due to the

procedural hassles, the KYC procedure for opening accounts has been

simplified to enable those belonging to low income groups without documents

of identity and proof of residence to open banks accounts.

12

vi. Aadhaar - Unique identification authority of India (UIDAI)

The Government of India has embarked an initiative to provide an individual

identification number to every citizen of India and in 2009, it established the

UIDAI to issue these cards on behalf of the GOI. This number provided by

UIDAI serves as a proof of identity and address anywhere in India. The

Aadhaar number also enable people to have access to services such as banking,

mobile phone connections and other government and non-government services

in due course. In addition, the UIDAI has introduced a system in which the

unbanked population will be able to open an account during enrollment with

Aadhaar without going to a bank. The individual will be able to access such

bank accounts through a micro-ATM network with large geographic reach.

vii. Ensuring reasonableness of bank charges

Reserve bank receives several representations from public about unreasonable

service charges being levied by banks. The existing institutional mechanism in

this regard is not adequate and is a basic reason for people reluctance to

opening accounts. Accordingly, to ensure fair practices in banking services, the

RBI has issued instructions to banks making it obligatory for them to display

and continue updating their offices/branches and their websites regarding

various service charges in a prescribed format.

viii. Business correspondent model

With the objective of ensuring greater financial inclusion and increasing the

outreach of the banking sector, banks were permitted by RBI in 2006 to use the

services of intermediaries in providing financial and banking services through

the use of Business Facilitators (BFs) and Business Correspondents (BCs).

Business Correspondents are retail agents engaged by banks for providing

banking services at locations other than a bank branch/ATM. They represent

the bank concerned and enable a bank to expand its outreach and offer limited

range of banking services at low cost, particularly where setting up a brick and

mortar branch is not viable.

ix. Setting up of ultra small branches (UBSs)

Considering the need for close supervision and mentoring of the Business

Correspondent Agents (BCAs) by the respective banks and to ensure that a

13

range of banking services are available to the residents of such villages, Ultra

Small Branches (USBs) are being set up in all villages covered through BCAs

under financial inclusion.

x. Expansion of ATM network

In close consultation with the Department of Financial Services, the Public

Sector Banks (PSBs) worked on a model of area based deployment of

ATMs/Cash dispensers, taking benefit of the power of aggregation, with all

PSBs/RRBs clubbing their requirement and one of the PSB issuing a common

RFP on behalf of all these banks for a geographical cluster. At present, PSBs

have installed around 60,000 ATMs.

xi. General credit card

With a view to help the poor and the disadvantaged with access to easy credit,

banks have been asked to consider introduction of a general purpose credit card

facility up to `25,000 at their rural and semi-urban branches. The objective of

the scheme is to provide hassle-free credit to banks’ customers based on the

assessment of cash flow without insistence on security, purpose or end use of

the credit. This is in the nature of revolving credit entitling the holder to

withdraw up to the limit sanctioned.

xii. Pradhan mantri jan dhan yojana (PMJDY)

It is a national mission for financial inclusion to ensure access to financial

services namely, banking savings & deposit accounts, remittances, credit,

insurance and pension in an affordable manner. It focuses on coverage of

households as against the earlier plan which focussed on coverage of villages.

It focuses on rural as well as on urban areas. Earlier plan targeted only villages

above 2,000 population while under PMJDY whole country is to be covered by

extending banking facilities in each sub-service area consisting 1,000-1,500

households such that facilities are available to all within a reasonable distance.

1.7 CURRENT STATUS OF FINANCIAL INCLUSION IN INDIA

As per census (2011), out of 24.67 crore households in the country, 14.48 crore

(58.7%) households had access to banking services. Of the 16.78 crore rural

14

households, 9.14 crore (54.46%) were availing banking services. Of the 7.89 crore

urban households, 5.34 crore (67.68%) households were using banking services. In

the year 2011, banks covered 74,351 villages, with population more than 2,000 (as per

2001 census), with banking facilities under the ‘Swabhiman’ campaign through

Business Correspondents. However, the program had a very limited reach and impact.

The present banking network of the country (as on 31.03.2014) comprises of a bank

branch network of 1,15,082 and an ATM network of 1,60,055. Of these, 43,962

branches (38.2%) and 23,334 ATMs (14.58%) are in rural areas. Moreover, there are

more than 1.4 lakh Business Correspondents (BCs) of Public Sector Banks and

Regional Rural Banks in the rural areas. BCs are representatives of bank to provide

basic banking services i.e. opening of basic bank accounts, cash deposits, cash

withdrawals, transfer of funds, balance enquiries, mini statements, etc. However,

actual field level experience suggests that many of these BCs are not actually

functional (Keshavamurthy, 2014).

The Reserve bank has encouraged banks to adopt a structured and planned approach

to financial inclusion with commitment at the highest levels through preparation of

board approved FIPs. The first phase of FIPs was implemented over 2010-13. The

Reserve bank has used FIPs to gauge the performance of banks under their FI

initiatives. With the completion of the first phase, a large banking network has been

created and a large number of bank accounts have also been opened. However, it has

been observed that the accounts opened and the banking infrastructure created has not

seen substantial operations in terms of transactions. In order to continue with the

process of ensuring meaningful access to banking services to the excluded ones,

banks were advised to draw up fresh three-year FIPs for 2013-16. Banks were also

advised that the FIPs prepared by them are disaggregated and percolate down to the

branch level so as to ensure the involvement of all the stakeholders in FI efforts and

also to ensure uniformity in the reporting structure under FIPs. The focus under the

new plan is now more on the volume of transactions in the large number of accounts

opened. A brief performance of banks under FIP up to March 31, 2014 (Table 1.2) is

as under:

i. The number of banking outlets has gone up to nearly 3,84,000. Out of these,

1,15,350 banking outlets were opened during 2013-14.

15

ii. Nearly 5,300 rural branches have been opened during the last one year. Out of

these, nearly 4,600 branches fall in unbanked rural centres.

iii. Nearly 33,500 BC outlets have been opened in urban locations during the year

taking the total number of BC outlets in urban locations to 60,730 as at the end

of March 2014.

iv. More than 60 million Basic Savings Bank Deposit Accounts (BSBDAs) are

added during the last year taking the total number of BSBDAs to 243 million.

v. With the addition of 6.2 million Small Farm Sector Credits during 2013-14,

there are 40 million such accounts as on March 31, 2014.

vi. With the addition of 3.8 million Small Non-farm Sector Credits during 2013-

14, there are 7.4 million such accounts as on March 31, 2014.

vii. Nearly 328 million transactions are carried out in BC-ICT accounts during the

last year as compared to 250 million transactions during 2012-13.

TABLE 1.2: FINANCIAL INCLUSION PLAN-SUMMARY PROGRESS OF

ALL BANKS INCLUDING RRBS*

Particulars Year

ended

March 2010

Year

ended

March 2013

Year

ended

March 2014

Period

April 2013-

March 2014

Banking outlets in villages – Branches 33,378 40,837 46,126 5,289

Banking outlets in villages – Branchless

mode

34,316 2,27,617 3,37,678 1,10,061

Banking outlets in villages –Total 67,694 2,68,454 3,83,804 1,15,350

Urban locations covered through BCs 447 27,143 60,730 33,587

Basic Savings Bank Deposit A/c through

branches (no. in million)

60.2 100.8 126.0 25.2


branches (amt. in `billion)

44.3 164.7 273.3 108.6


BCs (No. in million)

13.3 81.3 116.9 35.7


BCs (Amt. in ` billion)

10.7 18.2 39.0 20.7

BSBDA total (No. in million) 73.5 182.1 243.0 60.9

BSBDA total ( Amt. in ` billion) 55.0 182.9 312.3 129.3

ICT A/cs-BC- Transaction - (No. in

million) (During the year)

26.5 250.5 328.6 328.6

ICT A/cs-BC- Transactions - (Amt. in `

billion) (During the year)

6.9 233.9 524.4 524.4

*Source: Reserve Bank of India. (2014). Credit delivery and financial inclusion. Retrieved from http://www.rbi.org.in/scripts/AnnualReportPublications.aspx?Id=1122, 64-71. Accessed on

27-09-2014.

16

1.8 BARRIERS OF FINANCIAL INCLUSION

Over a period of time several measures have been taken by the banks in India to

improve access to affordable financial services through financial education,

leveraging technology, launching of various schemes and generating awareness.

Despite this, access to formal banking system by weaker section of society in India is

affected by several barriers. The lack of awareness, low income & assets, social

exclusion, illiteracy are the barriers from demand side. The distance from bank

branch, branch timings, cumbersome banking procedure & requirements of

documents for opening bank accounts, unsuitable banking products or schemes,

language, high transaction costs and attitudes of bank officials are the barriers from

supply side. Hence, there is a need for financial inclusion to build uniform economic

development, both spatially & temporally and ushering in greater economic & social

equity. Various barriers are shown is Figure 1.5 and a brief description of them is as

under:

FIGURE 1.5: BARRIERS OF FINANCIAL INCLUSION*

*Source: http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/86734.pdf. Accessed on 16-11-2014

High cost

Non price barriers

Behavioural aspects

Geographical barrier

Financial illiteracy

Technological hindrances

Environmental and market factors

Social barriers

Psychological and cultural barriers

Lack of social security payments

Barriers of

financial inclusion

17

i. High cost

Providing and utilising financial services is not available free of cost for both the

service provider and service utiliser.

a. Cost for service provider: Setting up of branches in rural areas are

generally not advantageous due to high cost and low business

b. Cost for service utiliser: It has been observed that the poor living in rural

area are reluctant to utilise these services due to high cost e.g., minimum

balance requirements in saving account, fixed charges in credit cards and

debit cards, loan processing charges, etc.

ii. Non price barriers

Access to formal financial sources requires documents of proof regarding

person’s identity, postal address, income, staff attitude, unsuitable products, etc.

Poor people generally do not have these documents and thus are excluded from

financial services.

iii. Behavioural aspects

As per IDBI Gilts Report (2007) research in behavioral economics has shown

that many people are not comfortable using formal financial services due to

difficulty in understanding the language, reading the document and various

hidden terms & conditions. Poor people also think that financial services and

financial products are meant only for the upper strata of the society.

iv. Geographical barrier

It is concerned with geographical inaccessibility to services in general and

banking outlets in particular. It includes remoteness of residence, insurgency in

a location branch timings, restricted mobility either due to old age or disability

or lack of access to private transport or public transport services.

v. Financial illiteracy

Limited financial literacy, i.e., basic mathematics, business financial skills as

well as lack of understanding often acts as a constraint for accessing financial

services. Literacy requirements inhibits access for those with lower literacy, lack

of awareness and/or English language competency skills.

18

vi. Technological hindrances

Customers sometimes from fear or lack of familiarity hesitates to conduct their

banking activities through technological advancements. Some of those groups

affected by restricted mobility may also be vulnerable to technological

exclusion.

vii. Environmental and market factors

Environmental & market factors includes the broader socio-economic and

demographic trends such as changing market structure, political trends such as

transfer of risk & responsibilities from state and employer to individuals.

viii. Social barrier

It comprises of two major factors i.e., gender and age.

a. Gender issues: Access to credit is often limited for women who either do

not have or cannot hold title to assets such as land and property or must seek

male guarantees to borrow.

b. Age factor: Financial service providers usually target the middle of the

economically active population, often overlooking the design of appropriate

products for older or younger potential customers.

ix. Psychological and cultural barriers

The feeling that banks are not interested to look into their cause has led to self-

exclusion for many of the low income groups. However, cultural and religious

barriers to banking have also been observed in some of the countries.

x. Lack of social security payments

The countries where the social security payment system is not linked to the

banking system, banking exclusion has been higher.

1.9 CONCLUSION

To sum up, financial inclusion is considered to be critical for achieving inclusive

growth, a prerequisite for economic development. Recognising the importance of

economic development in India, efforts are being taken to make the financial system

19

more inclusive. RBI is furthering financial inclusion in a mission mode through a

combination of strategies ranging from relaxation of regulatory guidelines, provision

of innovative products, encouraging use of technology and other supportive measures

for achieving sustainable and scalable financial inclusion. Despite the laudable

achievements in the field of rural banking, issues such as slow progress in increasing

the share of institutional credit, high dependence of small & marginal farmers on non-

institutional sources, skewed nature of access to credit between developed regions &

less developed regions appear larger than ever before. Therefore, the key issue now is

to ensure that rural credit from institutional sources achieves wider coverage and

expands financial inclusion drive.

20

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`

Chapter-II Review of Literature

CONTENTS


2.1 Introduction 23

2.2 Literature Review 23

2.3 Research Gap 29

References 30

23

CHAPTER II

REVIEW OF LITERATURE

2.1 INTRODUCTION

The relevant literature has been reviewed to explore the theoretical foundation behind

financial inclusion and various aspects pertaining to it. This attempt also focus on

analysing and understanding the extent of financial inclusion throughout India,

relationship between financial inclusion & poverty reduction, financial inclusion &

rural development, impact of financial inclusion on socio-economic empowerment

and various barriers of financial inclusion. The chapter concluded with research gap

and proposed model on the basis of reviewed literature.

2.2 LITERATURE REVIEW

Financial inclusion is a timely delivery of banking services at affordable cost to vast

sections of vulnerable groups such as weaker sections, disadvantaged and low income

groups (Ramji, 2009; Murari & Didwania, 2010; Arputhamani & Prasannakumari,

2011; Mishra, 2012; Divya, 2013; Padma & Gopisetti, 2013; Sharma & Kukreja,

2013; Banerjee & Francis, 2014; Srinivas & Upender, 2014; Shyni & Mavoothu,

2014; Garg, 2014; Verma & Aggarwal, 2014; Kalunda, 2014 and Joseph, 2014). It

does not mean opening of saving accounts only but also includes providing insurance

(Bagli, 2012; Padma & Gopisetti, 2013 and Garg, 2014), credit services (Bagli, 2012;

Padma & Gopisetti, 2013 and Garg, 2014) and financial advice (Garg, 2014). Swamy

(2011) identified exclusion of large number of households from the ambit of financial

services in India. Over the years, situation has improved as in 2007, 41% of the

population had no bank account (Cnaan et al., 2011), in 2009 about 36% of the

sample remained excluded from any kind of formal or informal saving accounts

(Ramji, 2009), in 2010, only 23% were not having bank accounts (Cnaan et al., 2011)

and in 2011, merely 17% of the population were excluded from access to financial

services (Rachna, 2011). Real rate of financial inclusion in India is very low due to

lack of interest in opening bank account, reluctance in opening bank branches in rural

areas, poor connectivity, no issuance of smart card and lack of trust on business

correspondents (Choithrani, 2013; Sharma & Kukreja, 2013 and Srinivas & Upender,

24

2014). Widespread disparities exist among various regions in the spread and progress

of banking services in the country (Pokhriyal & Ghildiyal, 2011). It showed that

banking network & services have grown better in Southern, Northern & Western

regions compared to North-eastern regions where banks need to put in more

concentrated efforts for eliminating the disparities (Pokhriyal & Ghildiyal, 2011).

Region-wise, exclusion is most acute in Central, Eastern and North-eastern regions

(Kr. & Sahoo, 2011). Financial inclusion through micro finance in the country is not

uniform and unevenly distributed in six regions of the country viz. Northern, North-

eastern, Eastern, Central, Western and Southern (Swamy, 2011 and Shankar, 2013).

Southern and Western regions are characterised by widespread availability, while the

Central region has low availability of MFI & banking services (Shankar, 2013).

Further, it is revealed that Eastern and North-eastern regions showed high availability

of micro finance but not banking services, while the Northern region showed high

availability of banking but not micro finance services (Shankar, 2013). Among

different states in India, Chandigarh is at the top and Manipur is at the bottom in

terms of level of financial inclusion (Kuri & Laha, 2011). Another study by Chithra &

Selvam (2013) revealed that only two states - Maharashtra and Uttar Pradesh have

high financial inclusion, whereas other states namely, Kerala, Tamil Nadu, Punjab

and West Bengal are falling under the medium financial inclusion category and states

particularly, Karnataka, Uttarakhand, Himachal Pradesh, Andhra Pradesh, Haryana,

J&K, Gujarat, Orissa, Bihar, Assam, Madhya Pradesh & Rajasthan are forming the

group of low financial inclusion. Majority of poor are excluded from financial

services and suffer from higher incidence of poverty, unemployment and inequality in

income distribution which can be reduced through financial inclusion, micro finance

and micro-credit plus services of micro finance (Barik, 2009; Rautela et al., 2010;

Goel et al., 2011; Kr. & Sahoo, 2011; Das, 2012 and Srinivas & Upender, 2014).

Financial inclusion plays a crucial role in creating employment, improving access to

credit for consumption & productive purpose, increasing household’s expenditure,

preventing exploitation by the informal moneylenders, increasing income & asset,

developing human capital and improving standard of living which in turn can lead to

poverty reduction, rural development and social & economic development (Rautela et

al., 2010; Ellis et al., 2010; Arputhamani & Prasannakumari, 2011; Kumar & Sharma,

2011; Das, 2012; Raman, 2012; Sajeev & Thangavel, 2012; Sharma & Kukreja, 2013;

Padma & Gopisetti, 2013; Verma & Aggarwal, 2014; Banerjee & Francis, 2014;

25

Satpathy et al., 2014; Kapoor & Singh, 2014; Shyni & Mavoothu, 2014; Mutai &

Achieno, 2014 and Srinivas & Upender, 2014). Access to micro finance also brings

positive change in income which leads to socio-economic empowerment through

increasing saving habits, lessening family violence, raising capabilities to deal with

social evils, day to day problems, enhanced asset ownership, creation of employment,

improved purchasing power, buying of new clothes, boosting confidence of rural

masses, declining income inequality, greater ability to meet unforeseen circumstances,

improved standard of living and change in life style (Sathpathy, 2014; Verma &

Aggarwal, 2014; Mutai & Achieno, 2014 and Saidu et al., 2014). Micro finance

institutions play a significant role in facilitating inclusion of excluded population

(Verma & Aggarwal, 2014). MFIs break down many barriers of access to financial

inclusion (Shankar, 2013). If barriers of financial inclusion i.e., high charges,

minimum balance requirement, lack of financial literacy are reduced, then it can

stimulate household investment, thereby contributing to growth in developing

countries (Ellis et al., 2010). There is positive relationship between financial inclusion

and growth of banking system, more growth of banking system leads to more of

financial inclusion (Raman, 2012). Financial inclusion leads to inclusive development

of rural areas and bring their quality of life at par with the people of urban areas

(Padma & Gopisetti, 2013). Factors such as economic status of the household, non-

farm employment, rural & area development, social security, level of education, asset

holding, level of income, accessibility, culture and occupation status are the

significant contributors to financial inclusion (Kuri & Laha, 2011; Ghatak, 2013 and

Joseph, 2014). Ghatak (2013) revealed that accessibility to financial services is the

most important and asset possessed by a person is the least important driver of

financial inclusion. Kapoor & Singh (2014) highlighted that well functioning financial

system enables economically & socially excluded people to actively contribute to

development and protect themselves against economic shocks. Inclusion in the

financial services brings positive and significant changes in general and economic

condition of people (Uma & Rupa, 2013) and brings economic prosperity to region or

a state (Gupta et al., 2014). There is positive correlation between sustainability of

financial inclusion and rural dwellers which are the mainstream for economic growth

in any country (Nwankwo & Nwankwo, 2014). Several studies found financial

development and monetary benefits influence economic growth and are considered

critical in achieving inclusive growth (Nagadevra, 2009; Barik, 2009 and Das, 2012)

26

which ultimately leads to economic development (Srinivas & Upender, 2014).

Financial inclusion enables rural people to channelise their savings in such ways that

lead to increase in GDP of the country (Garg, 2014). Singh & Kodan (2011) identified

existence of significant relationship between financial inclusion & economic

development. Positive correlation exists between IFI & HDI i.e., higher the level of

financial inclusion higher will be the level of human development (Sarma & Pais,

2011; Arputhamani & Prasannakumari, 2011; Kuri & Laha, 2011; Banerjee &

Francis, 2014 and Gupta et al., 2014). States with high financial inclusion have high

GDP per capita and good human development index and vice versa (Gupta et al.,

2014). Sarma (2010) identified penetration, availability and usage as the indicators of

financial inclusion. Most of the states have significant and positive relationship

between deposit & credit penetration which has increased in the current decade

(Kumar, 2009). Number of households with bank accounts doubled over the duration

of the financial inclusion drive or from past five years to receive government

assistance but have low usage & awareness about the account (Ramji, 2009 and

Ramasubbian & Duraiswamy, 2012). Joseph (2014) revealed that level of financial

inclusion has increased due to rising awareness about financial products and services

among respondents. There is negative influence of population density on deposit

penetration (Kumar, 2009). Most accessible financial services are saving accounts and

loans (Cnaan et al., 2011). Access to affordable financial services, especially credit &

insurance, enlarges livelihood opportunities through adoption of different economic

activities. No significant difference exists among respondents belonging to different

age group (Dias, 2013 and Kalunda, 2014), gender (Dias, 2013 and Kalunda, 2014),

income group (Dias, 2013) and occupation (Dias, 2013) with regard to demand and

use of financial services under financial inclusion scheme. Whereas, on other

demographic variables such as, marital status, level of education, level of income

(Divya, 2013), gender (Anjuman, 2011) and caste (Swamy, 2014) significant

difference exist. The NGOs, MFIs, Cooperatives, NBFCs and the SHGs are regularly

connecting the poor with NABARD, SIDBI, RRBs and other commercial banks to

make resources available to them for their social & economic upliftment (Pandey &

Kumar, 2011). SHGs have positive impact on financial inclusion especially on access

to financial services as number of bank accounts, credit availed and repayment of

credit has increased among SHGs members (Anjuman, 2011 and Uma & Rupa, 2013).

Other studies found out SHG-bank linkage programme increased the degree of

27

financial inclusion and served only the large & medium farmers but completely

neglected or no linkage can be inferred to marginal & small farm size group

(Anjuman, 2011; Swamy, 2011 and Sajeev & Thangavel, 2012). SHG through

financial inclusion ensures social and economic empowerment of its members (Uma

& Rupa, 2013). There exists significant difference in the family income of the

respondents before and after joining the SHG (Arputhamani & Prasannakumari,

2011). Gupte et al. (2012) found that the outreach (penetration & accessibility) is

directly proportional to financial inclusion and added another dimension i.e., ease of

transaction variable which includes number of location to open deposit/loan account

& submit loan application. Kalunda (2014) revealed high level of inclusion leads to

high usage in terms of credit access. In rural and semi-urban location, people were not

satisfied with the services provided by the banks & NGOs as only nationalised banks

had been entrusted the task of implementing financial inclusion and they hesitate in

operating in rural areas for lower income-earning classes (Rachana, 2011; Pokhriyal

& Ghildiyal, 2011 and Ramassibbian & Duraiswamy, 2012). Socio-economic

variables such as, per capita GDP, high income level, literacy, ownership of house,

urbanisation, infrastructure variables such as, network of paved road, telephone &

internet subscription shows positive and significant relationship with financial

inclusion (Arputhamani & Prasannakumari, 2009; Sarma & Pais, 2011; Singh &

Kodan, 2011 and Chithra & Selvam, 2013). Whereas other socio-economic variables

such as, income inequality, rural population, unemployment, infrastructural variable

like radio, newspaper, cable T.V. & computer, sex ratio, urban population as the

percent of total population and banking variable i.e., NPA, CAR, foreign ownership

interest rate shows negative relationship with financial inclusion (Arputhamani &

Prasannakumari, 2009; Sarma & Pais, 2011; Singh & Kodan, 2011 and Chithra &

Selvam, 2013). Das (2012) identified gap exist between demand and supply side

factor responsible for low inclusion. Various studies revealed demand side factors i.e.,

lack of awareness, unsuitability of the financial products, unfriendly & unempathetic

attitude, exorbitant & non-transaction fees and age group influence financial inclusion

(Kumari, 2009; Nagadevra, 2009; Bihari, 2011; Venkataramaraju & Ramesh, 2011;

Rachna, 2011; Cnaan et al., 2011; Goel et al., 2011; Mishra, 2012 and Jain et al.,

2012). Supply-side factors which act as barriers to financial inclusion are high

transaction cost, typology (rural/urban), gender and marital status (Nagdevra, 2009;

Bihari, 2011; Gupte et al., 2012 and Jain et al., 2012). Other factors such as lower

28

level of job, legal identity, affordability, minimum amount required to open account,

number of days to process loan application, complicated procedure, psychological &

cultural barriers, spatial distribution of banking services and annual fee charged for

ATM’s have inverse relationship with financial inclusion (Rachana, 2011;

Venkataramaraju & Ramesh, 2011; Jain et al., 2012 and Gupte et al., 2012). Literacy

is a pre-requisite for creating investment awareness (Paramasivan & Ganeshkumar,

2013) as inadequate financial education results into lower financial literacy (Kalunda,

2014). But it is not always true, such as Kerala state has a very low value of usage

dimension of financial inclusion despite having high literacy rate, while, Karnataka

state has higher value of usage dimension than literacy rate. Therefore, literacy alone

cannot guarantee high financial inclusion (Paramasivan & Ganeshkumar, 2013), other

factors such as branch density (Paramasivan & Ganeshkumar, 2013), behavioural

factors etc. be emphasised rather than bringing mere improvement in literacy rate

(Gupta & Singh, 2013). Various RBI efforts such as, opening of no frill account,

issuance of general credit card, opening of rural bank branches should be properly

implemented for making financial inclusion a success and bringing prosperity to the

aspiring poor through financial inclusion (Garg, 2014).

The integrated review of literature, based on secondary literature on financial

inclusion and its determinants is summarised in tabular form (Table 2.1).

On the basis of aforesaid literature, a proposed theoretical model (Figure 2.1) is as

under:

FIGURE 2.1: PROPOSED THEORETICAL MODEL FOR FINANCIAL

INCLUSION & ECONOMIC DEVELOPMENT

Access

Availability

Usage

Economic-empowerment

Social-empowerment

Economic

development Financial

inclusion

29

2.3 RESEARCH GAP

The aforesaid reviewed literature revealed that various conceptual studies have been

conducted on financial inclusion but few empirical studies that too with limited

geographical coverage, have touched varied aspects of financial inclusion. Further,

there is paucity of empirically tested relation between financial inclusion & poverty,

financial inclusion & area development and financial inclusion & economic

development (Rautela et al., 2010; Murari & Didwania, 2010; Sharma et al., 2011;

Kumar & Sharma, 2011; Arputhamani & Prasannakumari, 2011; Singh & Kodan,

2011; Sharma et al., 2011; Das, 2012; Jain et al., 2012; Mishra, 2012; Sharma &

Kukreja, 2013; Padma & Gopisetti, 2013; Banerjee & Francis, 2014; Srinivas &

Upender, 2014; Verma & Aggarwal, 2014; Satpathy et al., 2014; Kapoor & Singh,

2014 and Garg, 2014). Impact of no-frill account on financial inclusion (Barik, 2009),

socio-economic impact (Rautela, 2010; Murari & Didwania, 2010; Cnaan et al., 2011;

Raman, 2012; Das, 2012; Sharma & Kukreja, 2013; Banerjee & Francis, 2014;

Srinivas & Upender, 2014; Verma & Aggarwal, 2014; Garg, 2014; Sathpathy, 2014

and Kapoor & Singh, 2014), urban-rural & gender aspect of financial inclusion

(Sarma, 2010), social inclusion through financial inclusion (Kumar & Sharma, 2011

and Cnaan et al., 2011) and financial inclusion role in empowerment (Kr & Sahoo,

2011; Kumar & Sharma, 2011; Uma & Rupa, 2013; Mutai & Achieno, 2014 and

Sathpathy, 2014) have been integrated into one scale i.e., access, availability, usage,

social empowerment, economic empowerment, economic development, poverty

reduction & area development and the extent of its impact on economic development

have been measured among beneficiaries residing in five districts namely, Jammu,

Samba, Kathua, Reasi & Udhampur of Jammu division of Jammu & Kashmir state.

30

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36

TABLE 2.1: REVIEW OF LITERATURE

S.

No.

Author (Year) Objectives Methodology Findings Limitation/

Future research

1

Kumar (2009) Attempted to

understand the

behaviour and

determinants of

financial inclusion

in India.

Annual data from varied

sources such as hand book

of statistics on Indian

economy were explored to

collect the data. Standard

econometric techniques

were employed for state-

wise panel data spanning

over a period of 1995-

2008.

Most of the states have significant & positive

relationship between the deposit & credit

penetration. High credit penetration has high

deposit penetration. There is continuous

improvement of credit & deposit penetration

in the current decade. There is negative

influence of population density on deposit

penetration. Level of economic condition is a

vital determinant of financial inclusion.

Primary study need

to be conducted.

2 Ramji (2009) Explored extent of

financial inclusion

and find out the

indicators of

financial depth and

financial inclusion

in India.

Survey, in-depth

interviews and in-situ

observation method were

conducted for data

collection. Structured

questionnaire was used

and administered on 999

respondents, spread over

50 villages of Gulbarga

district in Karnataka state.

Mean and percentage

were used to analyse the

data.

Number of households with bank account

doubled over the duration of the financial

inclusion drive. 36% of the sample remained

excluded from any kind of formal or semi-

formal savings accounts. Access to credit and

usage are the indicators of financial inclusion.

Bank accounts mainly opened to receive

government assistance. Usage & awareness

among accountholder’s remain low.

The study is limited

to Gulbarga district

in Karnataka and

further be

conducted in other

regions to validate

its result.

37

3 Barik (2009) Analysed the role

of financial

inclusion in

empowering rural

households in

India.

Secondary sources of

information were used to

gather the data.

Found that financially excluded population

suffer from higher incidence of poverty.

Financial inclusion is the key to

empowerment of poor, underprivileged and

low skilled rural households. It lifts the

financial condition & improves the standard

of lives of people & the disadvantages.

Access to affordable financial services,

especially credit & insurance, enlarges

livelihood opportunities through adoption of

different economic activities. Better financial

inclusion would lead to increasing economic

activities and self/wage employment

opportunities for rural households. Financial

inclusion provides monetary fuel for

economic growth and is considered critical

for achieving inclusive growth.

Impact of ‘no-frill

accounts’ on

empowerment of

rural households

need to be study in

the country.

4 Nagadevra

(2009)

Identified supply

& demand side

factors that

influences


Data were collected from

national data survey on

saving patterns. The

sample covered both rural

& urban areas of the

country.

The study found that financial development is

a major factor influencing economic growth.

Factors such as annual income, age group &

ownership of occupied house influence

financial inclusion from demand side.

Similarly, the factors that are specific to

supply side are media exposure, typology

(rural/urban), gender, exposure to radio &

marital status.

Other factors like

living standard,

qualification,

occupation etc. also

need to be explored

in future.

38

5 Rautela et al.,

(2010)

Examined the role

of micro-finance in

rural development.

Various secondary sources

such as, national &

international journals,

websites of various MFI’s

etc. were explored to

gather the relevant

information.

Micro-finance is a powerful tool for poverty

alleviation and rural development.

Socio-economic

impact of micro

finance needs to be

examined more

closely through

empirical study.

6 Sarma (2010) Measured the

extent of financial

inclusion across

different

economies and

monitored

progress with

respect to financial

inclusion over

time.


information were used to

collect the data.

Comparisons were done to

examine the data.

Identified penetration, availability and usage

as the indicators of financial inclusion. Across

49 economies, Austria leads with the highest

IFI value of 0.953 and Madagascar ranks

lowest with an IFI value of 0.009. Inspite of

low density of bank branches, the usage of the

banking system in terms of volume of credit

and deposit seems to be moderately high in

India.

Urban-rural and

gender aspect of

financial inclusion

are not covered.

Other aspects like

affordability,

timeliness and

quality of the

financial services

are also missing.

7 Ellis (2010)

To examine the

impact of access to

financial services

on household

investment.

Finscope survey data from

Kenya and Tanzania were

utilised for analysis.

The study found out that access to financial

services enable households to invest in

activities which contribute to high future

income, therefore growth. Further, it was

revealed that if barriers of financial inclusion

i.e., high charges, minimum balance

requirement, lack of financial literacy are

reduced, then it can stimulate household

investment, thereby contribute to growth and

The study need to

be extended to

other parts of the

country and in other

countries as well.

39

poverty reduction in developing countries.

8 Murari &

Didwania (2010)

To assess the

impact of financial

inclusion on

poverty.

Secondary sources were

explored for data

collection.

The study revealed that financial inclusion

along with micro finance is an effective

instrument for lifting the poor above the level

of poverty as it provides increased self

employment opportunities and make them

credit worthy.

Empirical study

need to be

conducted.

9 Cnaan et al.

(2011)

Aimed to study the

breadth & depth of

financial inclusion

in rural south India

and also validate

the claims of

financial reach &

breadth.

A schedule was used to

collect the relevant

information from residents

of four villages of south

India i.e., Karnataka,

Andhra Pradesh, Tamil

Nadu & Kerala. Mean,

Chi-square & logistic

regression, ANOVA and

scheffe test were used to

explore the data.

Majority of households have access to banks

as only 23% do not have bank account in

2011 as compare to 41% in 2007. Most

accessible financial services are saving

accounts & loans. Most poverty-stricken

villages are composed of Hindus, Schedule

castes are more financially excluded. Middle

caste people having informal loans &

households with low level of education are

more likely to be financially excluded.

Long-term

consequences such

as poverty

alleviation, social

inclusion need to be

evaluated in future.

10 Anjuman (2011) Analysed the

impact of SHG-

bank linkage

program on

promotion of

financial inclusion,

assessed

association

Multi-stage purposive

sampling technique was

followed to select

districts, taluks and

villages in Tamil Nadu.

Then, random sampling

was used to collect data

from 30 households with

SHG program had significant impact on the

access to financial services in terms of

borrowings by households. Marginal farmers

tend to avail only institutional borrowings &

shun non-institutional borrowings. There exist

inequity between men & women in terms of

access to financial services. Also found out

SHG-bank linkage program increased the


to few districts of

Tamil Nadu state

only.

40

between the degree

of financial

inclusion &

participation in

SHGs and also

determined the

gender equity in


or without SHG’s each. T-

test and correlation were

used to evaluate the data.

degree of financial inclusion among landless

household but no linkage can be inferred with

respect to marginal & small farm size groups.

The study also revealed that SHG-bank

linkage had increased the flow of institutional

credit to landless & marginal farm households

& discouraged non-institutional borrowing.

11 Sarma & Pais

(2011)

Examined the

relationship

between financial

inclusion &

development and

investigated the

factors associated

with financial

inclusion.


information were explored

to collect the data.

Regression & correlation

were applied to derive the

results.

Correlation exists between IFI & HDI.

Countries with high level of human

development also have high level of financial

inclusion. Socio-economic variables such as,

GDP per capita, higher income level, literacy,

urbanisation and infrastructure variables such

as, network of paved road, telephone &

internet subscription shows significant &

positive relation with financial inclusion.

Whereas, other socio-economic variables i.e.,

income inequality, rural population &

unemployment; infrastructural related

variable i.e., radios, newspaper, cable T.V. &

computer and banking variable i.e., NPA,

CAR, foreign ownership interest rate shows

no significant connection with financial

inclusion.

Effect of public

policy initiatives

aimed at improving

financial inclusion

need to be covered

in future.

41

12 Kodan &

Chhikara (2011)

Analysed the

status of financial

inclusion in

Haryana and also

compared it with

aggregate India.


such as, basic statistical

returns of scheduled

commercial banks in

India, reports of trends &

progress of banking in

India, annual report of

RBI, national income

statistics, centre for

monitoring Indian

economy were explored to

collect the data. Statistical

techniques i.e., average,

ACGP, kruska-lwallis test

& t-test were applied to

analyse the data.

No significant difference exist between the

number of deposit accounts & credit accounts

per 1000 population in Haryana & India but

status of deposit account in Haryana is

somewhat better as compared to India &

status of India is better than Haryana w.r.t.

credit account per 1000 population. Exist no

difference between Haryana & India

regarding availability of banking services in

terms of population per bank office, moreover

status of Haryana regarding availability of

banking services is superior to aggregate

India. Also indicated that there is no

difference between the usage ratio of Haryana

& India, but the usage ratio of Haryana is low

as compared to aggregate India. Regression

equation clearly shows there is positive &

strong association between usages ratio &

growth of NSDP/GDP.

The study covers

only limited time

i.e., between

2000/01 to 2008/09.

Covered only some

selective variables.

The study used

comparative

measure of the

status of financial

inclusion in

Haryana to India

which is not a

standard measure.

Based purely on

secondary data.

13 Rachana (2011) Studied financial

inclusion in rural

areas, determined

the reasons for low

inclusion,

satisfaction level

of the rural people

Structured questionnaire

was used to collect the

data from 200 people

residing in Ambasan,

Jotana and Khadalpur

villages of Gujarat.

Secondary sources i.e.,

83% of the sample had bank accounts & 17%

don’t have account. Lower level of jobs,

lower education qualification and lower

annual income of rural public were some of

the reasons for low inclusion. Rural people

were not strongly satisfied with the services

provided by the bank & NGOs and govt.


to only three

villages of Gujarat

state.

42

towards banking

services and

assessed the

performance of the

banks working in

rural areas.

internet, articles of RBI &

bank circulars were also

used to collect the data.

Chi-square, ANOVA and

tabulation were used to

analyse the data and for

hypotheses testing.

effort for financial inclusion. Though banks in

rural areas had good coverage but most of

them are running into losses.

14 Pandey &

Kumar (2011)

Focussed upon the

achievement of the

micro finance

services towards



explored to collect the

data. Additions and

comparisons of the current

year with previous year

were done to analyse the

data.

Micro finance institutions helps general

public to have access to all the available

financial services in the economy. The NGOs,

MFI, cooperatives, NBFCs and the SHGs are

regularly connecting the poor with NABARD,

SIDBI, RRBs and other commercial banks to

make them available the resources for their

social & economic upliftment. The retail

banking services in the micro finance sector

result in fulfilment of the financial inclusion

objective.

-

15 Kr. & Sahoo

(2011)

Examined the role

of micro finance in

empowering

people and

realisation of

financial inclusion

in India.


reports of national sample

survey organisation and

reports on status of micro

finance in India.

Percentage and sum were

used to analyse the data.

54% of farmers are financially excluded. 73%

of farmers have no access to formal sources

of credit. Exclusion is most acute in central,

eastern & north-eastern regions of India.

Marginal farmer constitutes 66% of total farm

households. Only 45% of these households

are indebted to either formal or non-formal

sources of finance. Credit plus services of

Primary data need

to be collected to

verify the results.

43

micro finance positively correlated with the

improvement in household’s expenditure,

income, assets and employment. Micro-credit

plus services of micro finance bring out the

poor from below poverty line and reduces

poverty.

16 Pokhriyal &

Ghildiyal (2011)

Critically analysed

the spread of SCBs

and progress of the

SHG-bank linkage

program.

Secondary sources

comprising of various

issues of banking statistics

review, handbook of

statistics on Indian

economy, report on trends

& progress of banking in

India, publications of

NABARD and other

published work were used

to collect the data. Sum

and percentages were

utilised to analyse the

data.

Network of commercial banks inclined more

towards metropolitan cities. Credit extention

has reduced in all areas i.e., rural, semi-urban

& urban except metropolitan. Goals of

financial inclusion are not achieved as SCBs

hesitate in operating in the rural areas & for

low income-earning classes. The study also

revealed widespread disparities exist among

various regions in the spread & progress of

banking. It showed that banking network &

services have grown better in southern,

northern & western regions and other regions

are lagging behind particularly north eastern

region where banks need to put in more

concentrated efforts for eliminating the

disparities. Rural areas & rural people are still

deprived & discriminated in the context of

financial inclusion & growth. SHG movement

has remained successful in status where an

incidence of poverty is comparatively less.

The study is based

on secondary

sources only.

44

`17 Sharma et al.

(2011)

Focussed on the

need of financial

inclusion for

poverty alleviation

and GDP growth.

Various reports such as

report of world bank,

IARI-FAO/RAP study,

UNDP human

development report and

CSO were used to gather

the information.

Correlation and

percentage were

calculated to analyse the

data.

Poverty in rural areas decreases with the

increase in farm size. Irrigation has positive

impact on poverty alleviation. Literacy has a

very high impact on poverty alleviation & on

hunger reduction. Majority of illiterate

people, whether urban or rural are the most

poor & malnourished. There exists negative

relationship between levels of poverty per

capita SDP (State Development Product)

across the 14 major states in India.

Empirical study

need to be

conducted.

18 Venkataramaraju

& Ramesh (2011)

Focussed on

various aspects of

financial inclusion

in India, UK & US

in conjunction

with an analysis of

its outcome in

India and factors

influencing it.

Secondary sources such as

report of RBI and report

of promoting financial

inclusion (UK) were used


Percentages were

calculated to analyse the

data.

80% of the population is without life, health

& non-life insurance cover. India has around

403 million mobile users, about 46% of them

did not have bank account. Only 5.2% of

India’s 650000 villages have bank branches.

Legal identity, limited literacy, level of

income, terms & conditions, complicated

procedures, psychological & cultural barriers,

place of living and lack of awareness are the

factors that affect access to financial services.

Primary data need

to be collected.

19 Goel et al.

(2011)

Aimed to show

how the banking

correspondent

model can create

financial

RBI basic statistical return

& population census,

report of world bank and

Rangarajan committee

report on financial

The study shows that lower degree of

financial inclusion is leading to higher

poverty, unemployment and inequality in

income distribution. The study on impact of

BC model indicates lack of financial literacy

Empirical research

need to be

conducted in the

future.

45

inclusivity and

help in

empowering

people. Also

evaluated the

impact of the BC

model in India so

far.

inclusion were used to

collect the data.

among the people in the rural areas is the

major hindrance in its successful

implementation.

20 Kumar &

Sharma (2011)

Studied the extent

of financial

inclusion in India

through micro

finance and

discussed the

required means for

people

empowerment &


Secondary sources such as

reports of government of

India committee on

financial inclusion, other

reports & various

published or unpublished

journals were used to

gather the relevant

information.

Access to financial services by the poor &

vulnerable groups is a prerequisite for poverty

reduction, social cohesion, people

empowerment and financial inclusion.

Empirical study

need to be

conducted in the

future.

21 Swamy (2011) Evaluated the

coverage, progress

& trends of

financial inclusion

in India.


such as, websites, reports

of world bank, NABARD,

RBI, national sample

survey organisation and

national accounts statistics

of central statistical

organisation were

Coverage through number of bank is not

adequate for the large population living in

rural areas. Banking services in six regions of

the country viz., northern, north-eastern,

eastern, central, western & southern is

unevenly distributed. Agri-credit as a ratio of

total credit is still below the level of 1970’s.

Continuous downslide in the contribution of

Empirical research

need to be

conducted in the

future.

46


data. Analysis had been

done using tables &

graphs.

agriculture to the GDP. Large number

households are excluded from financial

services. Financial inclusion among the

farmer’s households has so far been able to

save only the large & medium farmers and

has completely neglected the marginal &

small farmers.

22 Arputhamani &

Prasannakumari

(2011)

Examined the role

of financial

inclusion on rural

development

through micro

finance

beneficiaries in

Rajapalayam block

of Virudhnagar

district of Tamil

Nadu. Also to

study the

relationship

between financial

inclusion & socio-

economic

variables.

Both primary & secondary

sources of data were used

to collect the information.

Primary data were

collected from 250

members of 25 SHGs by

using simple random

sampling technique.

Various statistical tools

like, percentages, mean, z-

test, correlation,

regression, dummy

variable model and

Garett’s ranking technique

were used to analyse the

collected data.

The study found out that states having high

level of human development index will have a

high level of financial inclusion. PSNDP and

literacy rate are positively associated with

financial inclusion. Income inequality is

negatively associated with financial inclusion.

There exists significant difference in the

family income of the respondents before and

after joining the group. Financial inclusion

through micro finance laid the seeds for rural

development, because all round economic

development depends on rural development.


to Rajapalayam

block of

Virudhnagar district

of Tamil Nadu.

47

23 Bihari (2011) Introduced the

term financial

inclusion and

highlighted its

global & national

scenario. Also

presented the

supply-side &

demand-side

barriers of


Secondary sources i.e.,

RBI websites, speeches,

RBI bulletin were used to

collect the relevant

information.

The study found out demand side factor i.e.,

lack of awareness, unsuitability of the

financial products, unfriendly & un-

empathetic attitude and exorbitant & often

times non-transparent fees whereas supply

side factors i.e., high transaction costs, lack of

communication, lack of infrastructure and low

literacy levels are barriers to access of

financial services.

Empirical study

need to be

conducted.

24 Latif et al.

(2011)

Investigated the

sustainability of

micro-credit

system in Pakistan

and determined its

impact on poverty

alleviation.

Data were collected

through structured

interview method from

200 respondents who used

micro credit such as,

farmers/growers, officers

of micro credit etc. by

using simple random

sampling technique and

were analysed using SPSS

18 version.

The study shows micro credit has positive

impact on poverty alleviation. Micro credit

results in increasing productivity by creating

employment & developing human capital

which can lead to reduction of poverty.


to 200 respondents

only which cannot

represent whole of

the Pakistan, so

more of the sample

should be collected

to verify and

validate the results.

25 Singh & Kodan

(2011)

Examined the

relationship

between financial

inclusion &


secondary sources i.e.,

annual report of RBI,

NABARD, report of trend

Positive & significant relationship exists

between financial inclusion & economic

development. No significant difference

subsists among Indian states with regard to


to 15 states of India

while other states

were excluded

48

economic

development and

also determined

the factors

associated with


& progress of banking in

India, economic survey of

India, etc. Various

statistical techniques such

as, mean, standard

deviation, C.V., t-test,

multiple regression (OLS)

and IFI were used to

analyse the data.

financial inclusion. Per capita NSDP

significantly & positively predicts financial

inclusion while employment rate do not.

Among social development indicators,

urbanisation positively & significantly

explore financial inclusion while literacy rate,

urban population as per cent of total

population & sex ratio do not.

including J&K.

26 Kuri & Laha

(2011)

To measure the

inter-state

variations in the

access to finance

using a composite

index of financial

inclusion and to

identify factors

that are

responsible for

creating obstacles

in the process of

financial inclusion

in rural West

Bengal.

Secondary data were

collected from basic

statistical return,

economic survey,

economic review of West

Bengal, census, etc.

Whereas, primary data

were collected on various

indicators of financial

inclusion i.e., penetration,

availability and usage of

banking services from

beneficiaries of the three

districts namely, Birbhum,

Bankuria and North

Pargana. Regression is

used to examine the

The results revealed that among different

states of India, Chandigarh is at the top and

Manipur is at the bottom in terms of the level

of financial inclusion. District-wise variation

is not so prominent as most of the districts

belong to lower financial inclusion category.

It is also revealed that economic status of the

household, level of education, assets holding,

non-farm employment, rural development &

social security are the significant factors of



to West Bengal

only and need to be

further extended to

other states of the

country as well.

49

determinants of financial

inclusion.

27 Kuri & Laha

(2011)

To establish the

relationship

between financial

inclusion and HDI.

Secondary data on

financial inclusion

indicators (penetration,

availability & usage of

banking services) and

HDI indicators (decent

standard of living, long &

healthy life and

knowledge) were

collected from statistical

return (RBI), census

(2001), economic survey

(2009-10), national family

health survey.

The results of the study revealed that there is

positive correlation between financial

inclusion and human development i.e., higher

the level of financial inclusion higher will be

the level of HDI.

Empirical research

need to be

conducted in the

future.

28 Raman (2012) To assess the

relationship

between financial

inclusion and

growing of Indian

banking system.

Secondary data were used. The study revealed that there is positive

relationship between financial inclusion and

growth of banking system, more growth of

banking system leads to more of financial

inclusion. The study further highlighted that

financial inclusion plays a crucial role in

reducing poverty in the country. Additionally,

it exposed that financial inclusion leads to

economic growth, raising standard of living ,

equality, etc.


to secondary data

only.

50

29 Ramasubbian &

Duraiswamy

(2012)

Analysed the

issues pertaining to

implementation of

financial inclusion

in economically

down trodden

district of Tamil

Nadu.

Interview based

questionnaire method was

used to collect the data.

Graphs were used to

analyse the data.

The study revealed that no-frill saving bank

accounts & general purpose credit cards had

been issued to ensure the implementation of

financial inclusion in India but other steps

such as, granting overdraft facilities in saving

bank accounts & providing banking services

at the door step of villages through smart card

had not been implemented. The study found

that nationalised bank had been entrusted the

task of implementing financial inclusion in

rural & semi-urban locations. Gradual

increase in opening of saving accounts had

been identified from past 5 years.


to a district of

Tamil Nadu only.

30 Sajeev &

Thangavel

(2012)

Analysed the

impact of SHG

bank linkage

programme in the

promotion of

financial inclusion

in rural areas and

role of bank in the

upliftment of

landless SHG

members.

Structured questionnaire

related to socio-economic

status was used to collect

the data administered on

3500 SHG members of 9

district of Kerala on 51

attributes/parameters.

Cluster techniques such as

K-means and Fuzzy C-

means algorithm were

used to examine the data.

The study revealed that financial inclusion is

effectively working in a small group of SHG

members. Majority of SHG members (33.9%)

have savings in post office/insurance, whereas

only 18.15% members have savings in banks.

Institutional credit to landless members is

available only through SHG. The percentage

of inclusion is relatively more among the

households with SHG. Bank play important

role in the upliftment of landless SHG

members as they are availing maximum

benefits from bank.


to only one state i.e.

Kerela only, so

need to be extended

to other parts of the

country as well.

51

31 Das (2012) Analysed the

status & concerns

of access to

financial services.

Secondary sources like,

RBI website, NABARD

website, newspapers,

different issues of Journal

of Indian Institutes of

Banking & Finance were

used to gather the

information.

The study revealed that there exist gap

between demand & supply and majority of

poor are excluded from financial services.

Inclusive growth cannot happen without

ensuring banking services at affordable cost

to the weaker section of society who do not

have any access to the formal financial

system. Financial inclusion is a great step to

alleviate poverty.

Empirical study

need to be

conducted in future.

32 Gupte et al.

(2012)

Aimed to study the

determinants that

measure the extent

of financial

inclusion and

focussed on the

computation of an

index that would

comprehensively

capture the impact

of multi-

dimensional

variables with

specific reference

to India.

Secondary sources such

as, reports of CGAP the

world bank group,

NABARD and published

papers of past reviewers

of financial inclusion were


relevant data.

The study found out that outreach

(penetration & accessibility) is directly

proportional to the financial inclusion index.

Further, it was revealed that there is direct

relation between the usage & the other

variable i.e., volume of deposits & loans as a

percentage of GDP. In case of ease of

transactions, two variables i.e., number of

location to open deposits/loan account &

submit loan application are directly related to

dimension whereas other variable i.e.,

affordability, minimum amount to open

account, documents required, number of days

to process loan application are inversely

related to the dimension. High value of

variable i.e., annual fee charged for ATMs,

cost of international transfer of money

Empirical research

need to be

conducted in the

future.

52

reduces the level of financial inclusion.

33 Sharma (2012) Focussed on

concept,

importance,

measures and path

of financial

inclusion.

Secondary source i.e.,

report of NSSO was used

to collect the data. Mean

was calculated to analyse

the data.

Out of 89.3 million, only 45.9 million have

access to credit from institutional or non-

institutional sources. 32.8% borrowed from

institutional source & 67.2% from non-

institutional sources.

To measure the

efficiency and

effectiveness of

financial inclusion,

primary source

need to be

explored.

34 Mishra (2012) Determined the

level of awareness

among people

about various

financial products

& services and

studied the impact

of SHG-bank

linkage program &

low income people

on promotion of


The data were collected

from various publications

and different government

and non-government

sources.

The study found out people are not aware

about various financial products & services.

Government should encourage banks to adopt

financial inclusion by means of financial

assistance, advertisements & awareness

programmes etc. to achieve inclusive growth.

Primary data need

to be collected to


35 Jain et al. (2012) Examined the role

of financial

inclusion in

reduction of

poverty and to find

out the challenges

Various secondary data

sources such as, reports on

trends & progress of

banking sector in India,

websites of RBI &

NABARD were explored

The study found out financial inclusion is

seen as an intensification & continuation of

poverty alleviation efforts. Further, the study

revealed various challenges such as, high

cost, spatial distribution of banking services,

non-price barriers, behavioural aspect in the

To find out

relationship

between financial

inclusion & poverty

reduction, empirical

study need to be

53

in the area of

financial inclusion

in India.

to collect the relevant

information.

area of financial inclusion exits. conducted.

36 Sharma &

Kukreja (2013)

To explore the role

of financial

inclusion for

economic & social

development of

society and to

analyse current

status of financial

inclusion.

Secondary data were

collected from books,

magazines, newspapers,

research articles, research

journals, e-journals, RBI

report, report of

NABARD etc.

The study found that financial inclusion plays

a catalytic role for the economic & social

development of society. Further, it

highlighted developing countries like India

are not showing keen interest in opening bank

account and in providing basic facility of

opening of number of bank branches in the

rural areas.

Primary study need

to be conducted.

37 Shankar (2013) To analyse

whether micro

finance institutions

break down

barriers to

financial service

access in India.

Both primary and

secondary sources were

explored. Primary data

were collected through

interview administered on

103 MFI field officers of

Grama Vidiyal

microfinance limited in

state of Tamil Nadu.

It is found that MFIs break down many

barriers to access to financial inclusion. The

study revealed that micro finance penetration

in the country is not uniform. Southern and

western regions were characterised by

widespread availability of MFI & banking

services, while the central region had low

availability of both kinds of services. Further,

it was revealed that eastern and north eastern

regions showed high availability of micro

finance but not banking services, while the

northern region showed high availability of

banking but not microfinance services.


to Tamil Nadu state

only, so need to be

conducted in other

parts of the country

too.

54

38 Gupta & Singh

(2013)

To explore the

relationship

between financial

inclusion index

and literacy rate.

Secondary data were used

for analysis. Karl pearson

coefficient of correlation

was used for analysis.

The study revealed that although literacy has

positive impact on financial inclusion. But it

is not always true, for instant Kerala has a

very low value of the usage dimension of

financial inclusion despite of high literacy

rate. While, Karnataka has higher value of

usage dimension than literacy rate. It is

further revealed that behaviour factors be

emphasised along with bringing improvement

in literacy rate.

The study is

confined to

secondary data

only.

39 Ghatak (2013) To identify factors

influencing

demand of

financial inclusion

and establishes a

relationship

between various

factors and


Primary data were

collected by administering

on 500 respondents.

Sample was chosen using

simple random sampling

technique. Various

statistical tool such as,

factor analysis, multiple

regression, correlation

were used for analysing

data.

The study purported that factors i.e.,

accessibility, culture, assets, literacy and

income influences the demand of financial

inclusion. Further, it is revealed that

accessibility to financial services is the most

important factor that derives the demand for

financial inclusion and assets possessed by a

person is the least important driver of


-

40 Paramasivan &

Ganeshkumar

(2013)

To discuss the

overview of

financial inclusion

in India.

Secondary data were used. The study revealed that literacy is a pre-

requisite for creating investment awareness in

financial inclusion. Further, it is showed that

literacy alone cannot guarantee high level

financial inclusion in a state. Branch density


to secondary data

only.

55

has significant impact on financial inclusion.

It is further revealed that creating investment

awareness and improving investment

opportunities are key tool for accomplishing


41 Uma & Rupa

(2013)

To highlight the

role of SHGs in


Primary data were

collected using structured

questionnaire

administered on 300

members in Hunsur taluk

of Mysore district of

Karnataka state. Random

sampling method was

adopted to select the

sample. Percentage and

paired sample t-test were

used for checking results

of the collected data.

The results revealed that SHGs have positive

impact on financial inclusion as number of

bank accounts, credit availed and repayment

of credit has increased among SHGs

members. It is further found that SHGs

through financial inclusion enables social and

economic empowerment of its members.


to one district of

Karnataka only

which hinders its

generalisability, so

need to be

conducted in other

part of the country.

42 Choithrani

(2013)

To analyse

financial inclusion

status in India and

to find problems &

challenges to



as, RBI bulletin, referred

journals, internet and

newspaper were explored

for data collection.

The result revealed that commercial banks

are forcibly opening 25% of their branches in

rural areas and taking no interest in this

scheme resulted into non-operational

accounts, poor connectivity, no issuance of

smart card and lack of trust on BCs.

Empirical research

need to be

conducted.

56

43 Chithra &

Selvam (2013)

To measure the

index of financial

inclusion across

states in India and

to identify the

determinants of


Secondary data from RBI

annual reports, websites,

journals, books, etc. were

used.

The study revealed that only two states

(Maharashtra & Uttar Pradesh) have high

financial inclusion, states namely, Kerela,

Tamil Nadu, Punjab & West Bengal are

falling under the medium financial inclusion

category, whereas others that is, Karnataka,

Uttarakhand, Himachal Pradesh, Andhra

Pradesh, Haryana, J&K, Gujarat, Orissa,

Bihar, Assam, Madhya Pradesh & Rajasthan

are forming the group of low financial

inclusion. Further, the results showed GDP,

income, literacy, internet, phone facilities &

road deposit penetration have positive

association with financial inclusion. Whereas,

unemployment, newspaper, credit penetration,

credit-deposit ratio and investment ratio have

negative association with financial inclusion.

Primary study need

to be conducted.

44 Dias (2013) To measure the

relationship

between

demographic

variables and


Data were collected using

simple random sampling

from 70 respondents of

Kotekar area of

Mangalore district.

Correlation was used to

analyse the data.

The study revealed that no significant and

positive relationship exists between financial

inclusion and different demographic variables

i.e., age, gender, income & occupation

Other variables

such as, geography,

psychography can

be studied in future.

45 Padma &

Gopisetti (2013)

To examine the

relationship

Primary data were

collected through personal

The study revealed that there is positive



to Nizamabad

57

between financial

inclusion and rural

development.

interview administered on

beneficiaries of

Nizamabad district of

Andhra Pradesh.

rural development. Further, it is revealed that

financial inclusion leads to inclusive

development of rural areas and bring their

quality of life at par with the people of urban

areas.

district of Andhra

Pradesh which

restricts its

generalisability.

46 Uma (2013) To examine the

impact of

financial inclusion

on economic

condition of Saral

savings account

holders and to

assess change in

general conditions

of beneficiaries

before and after


Primary data were


questionnaire

administered on 100

respondents of Hunsur

taluk in Mysore district.

Random sampling method

was adopted for sample

selection. Analysis was

done using paired sample

t-test.

The study showed that there is positive

change in economic condition of financially

included people. Further, it is revealed that

positive and significant changes have occur in

general condition of Saral saving account

holders after being covered under financial

inclusion scheme.


to Mysore district

only, so need to be

extended.

47 Divya (2013) To assess the

impact of financial

inclusion on daily

wage earners and

to find whether

financial services

are reaching to low

income groups or

Questionnaire was used to

collect data from 210

daily wage earners of

Tenali town in Guntur

district of Andhra

Pradesh. Random

sampling was done to

choose the respondents.

The finding exhibited that male, married &

illiterate respondents are more interesting in

availing financial inclusion services than

female, unmarried & literate respondents

respectively. It is further revealed that daily

wagers with more income are more inclined

towards financial inclusion.

The study can

further be

conducted for other

categories of

respondents i.e.,

farmers, local

businessmen, etc.

58

not. Mean and frequency were

calculated for analysis.

48 Kalunda (2014) To assess the

current level of

financial inclusion

& credit

accessibility by

Small Scale Tea

Farmers (SSTF) in

Kenya and to find

the relationship

between gender

and age on

demand and use of

financial product.

Data were collected

through simple random

sampling using structured

questionnaire from 133

farmers. Frequencies and

Chi-square test were used

to analyse the data.

The study revealed that the level of inclusion

is high and usage in terms of credit access is

also high. It is also exhibited that inadequate

financial education results into lower financial

literacy. Further, it was found that male and

female do not differ with regard to demand &

use of financial services. There is no

difference in the demand and usage pattern of

farmers belonging to different age group.

The study should

further be

conducted with

larger sample size.

49 Gupta & Chotia

(2014)

To analyse the

extent of financial

inclusion across 28

states and 6

regions of India

and to explore the

relationship

between financial

inclusion and

human

development.

Secondary data from RBI

report, report of state

government, journals,

websites were used for

analysis.

The results showed that states with high

financial inclusion have high GDP per capita

and good human development index.

Whereas, states with low financial inclusion

have low GDP per capita, social

backwardness and slow economic progress.

Further, results revealed that there is positive

relation between financial inclusion index and

human development index. It was also

highlighted that there is positive relation of

financial inclusion with economic prosperity

Due to inadequacy

of data, present

study has not

quantified various

initiatives taken by

RBI & GOI.

Various other

parameters such as,

affordability,

timeliness &

quality of banking

59

of a region or a state. services has not

taken under

consideration.

50 Banerjee &

Francis (2014)

To investigate the

impact of financial

inclusion on the

social

development.

Secondary data from

various journal, websites,

reports, census were used

for analysis.

The results revealed that there is direct

correlation between financial inclusion and

human development index. Financial

inclusion leads to poverty reduction.

Empirical research

need to be

conducted in the

future.

51 Saidu et al.

(2014)

To examine the

change in income

of farmers due to

participation in

micro finance

schemes.

A multi-stage cluster

sampling technique was

used to collect data from

364 beneficiaries of Kano

state, Nigeria. Structured

questionnaire was used for

data collection. Paired

sample t-test was used to

analyse the data.

The study revealed that participation in micro

finance scheme brings positive change in

income and standard of living of the

beneficiaries. Further, the study showed that

after participating in micro finance scheme

average annual income of beneficiaries has

extensively increased. Micro finance scheme

leads to economic empowerment which

ultimately results into improved purchasing

power, buying of new cloths, domestic

appliances, enable them for unforeseen

circumstances, improvement in standard of

living and change in life style.


in the geographical

area of Kano state

in Nigeria.

52 Srinivas &

Upender (2014)

To investigate the

role of Indian

banking sector &

RBI in economic

development


the published reports of

RBI, NABARD, GOI,

journals and internet

search engines like google

The study highlighted that more than half of

Indian population is financially excluded.

Most of the account holders do not use their

account even once in a month. Real rate of

financial inclusion in India is very low.

Empirical study

need to be

conducted.

60

through financial

inclusion.

etc. Financial inclusion process help in bringing

people out of miserable poverty condition.

Further, it is revealed that financial inclusion

leads to the economic growth which

ultimately leads to economic development.

53 Verma &

Aggarwal (2014)

To determine the

impact of micro

finance institutions

on financial

inclusion with

special focus on

poverty alleviation

and women

empowerment.


such as, journals, articles,

various research based

websites and reports on

micro finance were used

to gather the data.

The study revealed that micro finance

institutions play a significant role in

facilitating inclusion of excluded population.

It is further revealed that micro finance is the

most effective tool for reducing poverty and

enhancing socio-economic position of women

in the society.

Empirical research

need to be

conducted in the

future.

54 Mutai &

Achieno (2014)

To investigate the

impact of micro

finance on

economic

empowerment of

MFIs women

clients belonging

to Narok town of

Kenya.

Data were collected

through questionnaire

administered on 107

women clients and 10

MFIs staff in Narok town

using non probability

snowball sampling

technique. Data so

collected were analysed

using descriptive

techniques such as,

frequencies & percentages

The study purported that access to micro

finance has positive impact on economic

empowerment of women as it has improved

their income, asset ownership and created

employment. Additionally, the study revealed

that access to micro finance leads to

improved standard of living of women.

Future research

should be

conducted in other

regions in order to

assess the nation

wide impact of

micro finance

programmes on the

economic

empowerment of

women. Also need

to be extended in

61

and results were presented

in tables, bar & pie charts.

other country’s as

well.

55 Garg (2014) To evaluate need

and find the level

of financial

inclusion

compliance with

the RBI guidelines

and to examine the

role of different

financial

institutions

towards it.


information were explored



enables rural people to channelise their

savings in such ways that leads to increase in

GDP of the country. Various RBI efforts such

as, opening of no frill account, issuance of

general credit card are properly implemented.

Different financial institutions are doing

tremendous job by increasing rural bank

branches and bringing prosperity to the

aspiring poor through financial inclusion.

Empirical research

need to be

conducted to


56 Swamy (2014) To examine the

significance of

financial inclusion

through micro

finance in the

economic

upliftment of poor

households in

Indian economy.

Both secondary and

primary data were


data. Secondary data was

collected from RBI

publications, NABARD

publication, status report

of micro finance in India,

etc. whereas, primary data

were collected using

questionnaire

administered on 1052

respondents across

The study revealed that there is significant

impact of financial inclusion on income of the

poor particularly, women. Further, study

highlighted that general category women are

having favourable impact of financial

inclusion programs because of their

awareness levels and access of instruments of

economic progress. Additionally, women

belonging to SC/ST categories have large

impact of financial inclusion on standard of

living.

Sample size need to

be increase as it is

not truly

representing the

country.

62

different regions of India.

Stratified random

sampling approach was

used for sample selection.

57 Shyni &

Mavoothu

(2014)

To explore means

towards inclusive

growth of the vast

excluded

population through




data.


assist banks in penetrating into unbanked

areas and thereby attaining profit. Further, it

is disclosed that low income & weaker section

of the society avail various financial services

through financial inclusion which ultimately

prevent their exploitation by the informal

moneylenders. Additionally, it is concluded

that financial inclusion is an important step

and pillar of inclusive growth.

Empirical research

need to be

conducted.

58 Pavithran &

Raihanath (2014)

To investigate the

role of commercial

banks in the

financial inclusion

programme.


as, reports of GOI,

journals, websites, etc.

were explored to collect

the data.

The study revealed that commercial banks

play a very crucial role in making financial

inclusion a success. Many financial inclusion

programme such as, financial literacy, credit

counselling, BC/BF model, KYC norms, no

frill accounts, branch expansion, mobile

banking, etc. have been undertaken by

commercial banks for success of financial

inclusion.

Primary sources

need to be

explored.

59 Nwankwo &

Nwankwo

(2014)

To examine the

sustainability of

financial inclusion

Primary data were

collected using well

structured and multiple

The study revealed that there is positive

correlation between sustainability of financial

inclusion and rural dwellers. Sustainability of

The study is

conducted in

Nigeria, need to be

63

to rural dwellers in

Nigeria.

choice questionnaire

administered on bank

officials and rural

dwellers. Mean and

correlation were used to

analyse the data.

financial inclusion among rural dwellers

remains the mainstream for economic growth

in any country.

validated in other

countries as well.

60 Satpathy (2014) To identify

variables of micro

finance initiatives

that contributes to

the economic

development of

rural area in

Jagatsinghpur

district of Odisha.

Secondary data were used

for the study.

The study highlighted that micro finance

initiatives have increased the household

income significantly which resulted into

reducing poverty, declining income

inequality, increasing saving habits &

borrowings, enhance employment

performance, boost confidence of rural

masses, lessen family violence, increases

capabilities to deal with social evils & day to

day problems, helps in empowering

economically & socially.

Primary study need

to be conducted to


61 Joseph (2014) To measure the

intensity of

financial inclusion

and financial

awareness among

the people.


both secondary and

primary sources.

Published books,

periodicals, journals, etc.

were explored to gather

secondary data. Whereas,

primary data were


The study revealed that level of income, level

of education and occupation status is

dependent on financial inclusion. It is further

revealed that respondents were having above

average awareness about financial products

and services of banking system.

The study has

limited coverage, so

needs to be

extended to other

parts as well.

64

questionnaire

administered on 100

respondents belonging to

different occupational

groups of Piravom

panchayat in Ernakulum

district of Kerala state.

62 Kapoor & Singh

(2014)

To evaluate &

analyse the

contributions of

well functioning of

financial system in

the economic and

social

development of the

nation.

Secondary data were

gathered from books,

magazines, newspaper,

research articles, research

journals, e-journals, RBI

report and report of

NABARD, etc.

The study revealed that well functioning

financial system enables economically &

socially excluded people to actively

contribute to development and protect

themselves against economic shocks. Access

to finance is an essential poverty alleviation

tool. Financial inclusion is playing a catalytic

role for the economic and social development

of society

Empirical research

need to be

conducted in the

future.

`

Chapter-III Research Methodology

CONTENTS


3.1 Introduction 65

3.2 Steps in Research Methodology 65

3.2.1 Nature and Scope of the Study 66

3.2.2 Need of the Study 66

3.2.3 Objectives of the Study 67

3.2.4 Hypotheses Formulation 67

3.2.5 Scale Pretesting and Purification 73

3.2.6 Significance of the Study 88

3.2.7 Limitations of the Study 88

References 91

65

CHAPTER III

RESEARCH METHODOLOGY

3.1 INTRODUCTION

Research is a scientific & systematic search for pertinent information on a specified

area and to solve the research problem. It involves gathering, recording and analysing

critically relevant facts about any problem along with logic behind them. It is the

manipulation of things, concepts or symbols for the purpose of generalising to extend,

correct, verify knowledge, whether that knowledge aids in construction of theory or in

practice of an art (Kothari, 2005). It is directed towards development of an organised

body of knowledge & discovery of new insights into unsolved problems.

3.2 STEPS IN RESEARCH METHODOLOGY

To assess the impact of financial inclusion on economic development, following

sequential steps have been followed:

3.2.1 Nature and scope of the study

3.2.2 Need of the study

3.2.3 Objectives of the study

3.2.4 Hypotheses formulation

3.2.5 Scale pretesting & purification

a. Nature & sources of information

b. Generation of scale items and data collection form

c. Pretesting

d. Sampling techniques and data collection

e. Outliers

f. Normality

g. Multicollinearity

h. Statistical tools applied

66

i. Goodness of fit indices

j. Reliability and validity

3.2.6 Significance of the study

3.2.7 Limitations of the study

A brief description of these aforesaid steps is as under:

3.2.1 Nature and Scope of the Study

The study is both descriptive and evaluative in nature. It examines the impact of

financial inclusion on economic development. The study is limited to five districts

i.e., Jammu, Samba, Kathua, Udhampur and Reasi of Jammu division of J&K state.

Data are collected from beneficiaries of financial inclusion belonging to four banks

viz., Jammu & Kashmir Bank (JKB), Jammu & Kashmir Grameen Bank (JKGB),

State Bank of India (SBI) and Punjab National Bank (PNB). The proposed study shall

provide useful insight to researchers, financial analysts, RBI & commercial bank

officials and shall assist them to assess the impact of financial inclusion on economic

development along with testing other important relationships i.e., financial inclusion

& socio-economic empowerment, financial inclusion & poverty reduction and

financial inclusion & area development.

3.2.2 Need of the Study

Since 2005, financial inclusion is considered as a fast emerging concept and a

mechanism for inclusive growth. It is an innovative notion which helps in promoting

the banking habits among people. Access to a well-functioning financial system,

enables economically and socially excluded people to integrate better and actively

contribute to development of the economy and protect themselves against economic

shocks. Therefore, financial inclusion plays a very crucial role in the process of

economic growth by channelising all resources from bottom to top. Literature which

is available so far highlights the various dimensions of financial inclusion and

theoretically establishes its relationship with economic development, poverty

reduction and area development, though empirical research on stated relationship is

scanty. Also, the mediating role played by social and economic empowerment in FI-

ED link has not been addressed anywhere in the literature. So, need arises to

empirically test the relationships, overcome the barriers to financial inclusion and

67

bringing improvement in the process of financial inclusion which is focus of the

study. This study will help the central bank to contribute substantially towards

inclusive growth.

3.2.3 Objectives of the Study

Financial inclusion has gained increasing prominence in the past few years as national

policy initiative for balanced regional & area development, policy guidelines of RBI

to banking institutions and others in the development field. Accordingly, the present

study has been undertaken with the following objectives:

i. To identify significant predictors of financial inclusion.

ii. To analyse the direct impact of financial inclusion on social empowerment,

economic empowerment and economic development.

iii. To examine the mediating relationship between financial inclusion and

economic development through socio-economic empowerment.

iv. To assess the impact of financial inclusion on poverty reduction and area

development across socio-economic profile of the respondents.

v. To unearth the barriers of financial inclusion on the access and usage

dimensions.

vi. To suggest measures to bring more people within the ambit of financial

inclusion and ensuring their empowerment & overall economic development.

3.2.4 Hypotheses Formulation

The proposed study would examine and verify the following hypotheses formulated

on the basis of review of literature:

Financial inclusion is a process that ensures the ease of access, availability and usage

of the formal financial system for all members of an economy (Sarma & Pais, 2008).

This definition underlines numerous dimensions of financial inclusion, viz.,

accessibility, availability and usage of the financial system assists in building

inclusive financial system (Commonwealth Secretariat & La Francophonie, 2011;

Arputhamani & Prasannakumari, 2011 and Rao & Bhatnagar, 2012). Alliance for

financial inclusion (2011) provided guidelines on financial inclusion measurement,

including a more robust catalogue of indicators covering the access and usage

68

dimensions of financial inclusion. Financial inclusion encompasses primary

dimensions i.e., access to a range of formal financial services and usage (Ramji,

2009). Three basic dimensions of an inclusive financial system include banking

penetration, availability of the banking services and usage of the banking system

(Chattopadhyay, 2011; Kuri & Laha, 2011; Yorulmaz, 2013; Gupta & Singh, 2013;

Padmanbhan & Sumam 2014; Chibango, 2014; Malik & Yadav, 2014 and Banerjee &

Francis, 2014). Further, Gupta (2014) identified three dimensions viz., banking

penetration to measure accessibility, availability and usage of banking services for

evaluating the extent of financial inclusion. Thus, it is hypothesised that,

H1a: Access significantly predicts the financial inclusion.

H1b: Availability significantly predicts the financial inclusion.

H1c: Usage significantly predicts the financial inclusion.

Financial institutions act as a catalyst in the economic and social growth of the

stakeholders (Banerjee & Francis, 2014). Financial inclusion through these

institutions is a tool for empowering financial users (Reyes et al., 2011). It lays impact

on achieving economic and social empowerment (Jha, 2008 and Barik, 2009).

Financial inclusion increases the economic opportunities for the poor & low income

people, which lead towards positive result in social progress, economic development,

economic empowerment and social/political/legal empowerment (Ali & Hatta, 2012

and Mishra, 2012). Financial inclusion is the key to empowerment of poor,

underprivileged and low skilled rural households (Jha, 2008; Barik, 2009 and

Ranganath & Rao, 2011). To improve the financial condition and living standard of

the poor & disadvantaged classes, efforts on financial inclusion need to be stressed

leading a thrust on empowerment of the common person and marginal income groups

in the lower strata of the society (Jha, 2008; Barik, 2009 and Reyes et al., 2011).

Providing access to financial services promotes social inclusion, builds confidence

and helps in empowering vulnerable groups (Banerjee & Francis, 2014 and

Tamilarasu, 2014). Financial inclusion is one of the building blocks of empowerment

(CYFI National Implementation Workshop, 2014). Credit facility in financial

inclusion plays a critical role in social and economic empowerment of the poor

(Savagaon, 2012; Singh & Yadav, 2012 and Shetty, 2008). Financial inclusion

through micro finance generates habits of economic independence and self reliance

69

(Singh & Yadav, 2012) and is considered as one of the best mechanisms in

empowering stakeholders (Devi et al., 2012 and Singh & Yadav, 2012). Financial

inclusion provides monetary fuel for economic development and is considered critical

for achieving inclusive growth (Klapper et al., 2004 and Barik, 2009).

Macroeconomic evidence indicates that well developed financial systems have a

strong positive impact on economic development over long time period (Thorsten,

2007 and Cull, 2012). Financial inclusion is a path way and prerequisite for

sustainable economic development of the country (Bihari, 2011; Raman, 2012;

Memdani & Rajyalakshmi, 2013; Ghatak, 2013; Chithra & Selvam, 2013; Uma &

Rupa, 2013; Gupta et al., 2014; Srinivas & Upender, 2014 and Shyni & Mavoothu,

2014). It helps in bringing people in the ambit of financial services which leads to

greater economic and social equity resulting in accelerating economic development of

the country (Bihari, 2011; Roy, 2012; Porkodi & Aravazhi, 2013; Uma, 2013 and

Srikanth, 2013). Therefore, it is hypothesised that,

H2a: Financial inclusion has direct impact on social empowerment.

H2b: Financial inclusion has direct impact on economic empowerment.

H2c: Financial inclusion has direct impact on economic development.

Economic empowerment is an essential element for the inclusive growth of an

economy (Jarrett, 2013). It emerged as an important idea for development in 1980’s

(Tucker & Ludi, 2012). Economic empowerment is a key prerequisite for pro-poor

growth (Tucker & Ludi, 2012) which accelerate the overall rate of growth and leads

to the economic development (Oxaal & Baden, 1997 and Duflo, 2012). Jarnett (2013)

emphasised on clear economic link between empowerment and economic

development. Social and economic empowerment has a significant positive impact on

economic development of the country (Kalyani & Seena, 2012 and Prakash &

Chandarsekar, 2012). Enhancing the economic empowerment among financially

excluded is a crucial mechanism of improvement, which boosts economic growth of

the country and promotes economic development (Ajani et al., 2013). Empowerment

is a key measure to foster economic development (Tertilt, 2010). Hence, it is

hypothesised that,

H3a: Social empowerment has direct impact on economic development.

H3b: Economic empowerment has direct impact on economic development.

70

Financial inclusion results in achieving social & economic empowerment which leads

to significant positive impact on economic development of the country (Barik, 2009

and Prakash & Chandarsekar, 2012). The potential for social empowerment over the

state and the economy is enhanced when concentration of economic power are

eliminated (Noya & Clarence, 2007). Economic empowerment combined with similar

advances in social empowerment make economic development much more effective.

Financial inclusion is a powerful agent which empowers individuals and their families

by providing economic opportunities for strong and inclusive growth (Largarde,

2014). Thus, the next hypothesis formulated as,

H4a: Social empowerment mediates the relationship between financial inclusion

& economic development.

H4b: Economic empowerment mediates the relationship between financial

inclusion & economic development.

Poverty alleviation has all along been the priority goal of Indian polity and financial

inclusion is considered a pre-requisite for poverty reduction (Murari & Didwania,

2010; Cnaan et al., 2011; Kumar et al., 2012 and Jain et al., 2012). Well developed

financial system can effectively alleviate poverty (Beck, 2005). Access to financial

services enables the poor to fight with various dimensions of poverty, make

improvement in their lives and provides impetus for the growth & development

(Rautela et al., 2010 and Gupte et al., 2012). Mishra (2012) identified close

connection between poverty and financial inclusion, which can lead to estrangement,

disaffection and reduced participation in society by low-income families. Banerjee &

Newman (1993) have observed that a critical factor that enables people to exit poverty

by enhancing productivity is access to finance. According to Swamy (2011) financial

inclusion has far reaching positive consequences, which can facilitate many people to

come out of the abject poverty conditions. Financial inclusion has the potential to

reduce poverty and promote pro-poor growth (Hassein & Kirkpatrick, 2005;

Setboonsarng & Parpiev, 2008 and Chibba, 2009). It is a tool for combating and

bringing people out of awful poverty condition (Nalini & Mariappan, 2012;

Krishnakumar & Vijaykumar, 2013; Kapoor & Singh, 2014; Verma & Aggarwal,

2014 and Srinivas & Upender, 2014). An inclusive financial system results into better

employment opportunities, economic upliftment and poverty alleviation of the weaker

groups of the society (Rahman, 2008 and Gupta et al., 2014). Access to safe, easy &

affordable credit and other financial services is a prerequisite for poverty reduction as

71

it helps poor and socially & economically vulnerable groups to raise their income,

acquire capital and break the chain of poverty (Beck et al., 2007; Jack & Suri, 2009;

Ranparia, 2013; Krishnakumar & Vijaykumar, 2013; Gandhi, 2013; Kapoor & Singh,

2014; Raihanath & Pavithran, 2014; Gupta et al., 2014 and Banerjee & Francis,

2014). Kuri & Laha (2011) and Satpathy et al. (2014) highlights negative correlation

exists between financial inclusion and poverty. Financial inclusion has positive

impact on the lives of rural Indians and helps in bringing them out of the clutches of

poverty (Shaffer, 2008 and Bansal, 2012). It is critical tool that has positive impact on

poverty reduction (Shaffer, 2008; Uma & Rupa, 2013 and Nwankwo & Nwankwo,

2014). Policies resulting in reduction of barriers to financial services boosts household

investment, thereby results into poverty reduction (Ellis, 2010). Financial inclusion

plays major role in assisting people in improving their lives, reducing inequalities and

thereby driving away poverty from the country (Raman, 2012; Shivani, 2013 and

Kapoor & Singh, 2014). Thus, the fourth hypothesis set as,

H5: Financial inclusion is positively related to poverty reduction.

Finance is the lubricant, which oils the wheels of development and financial inclusion

is identified as a key factor in shaping the growth process of the economy

(Arputhamani & Prasannakumari, 2011 and Christabell & Vimal, 2012). Financial

inclusion enables the downtrodden in the rural areas to become self reliant and obtain

financial independence and freedom so that they can play an active role in the process

of development. Financial inclusion through micro finance, laid the seeds for area

development because, the all round economic development depends upon area

development (Das et al., 2008). Banking the ‘unbankable’ through financial inclusion

is a valuable contribution to the development planning as it presents an alternative

way to development (Arputhamani & Prasannakumari, 2011). Kalpana (2008) pointed

that access to bank credit leads to the area development. Micro finance as a means of

financial inclusion has been widely recognised as the modern tool for achieving area

development (Sultana, 2014). Financial inclusion has great potential in area

development particularly of rural areas (Roy, 2011). Padma & Gopisetti (2013) and

Garg (2014) highlight close relationship between financial inclusion and area

development. Hence, it is hypothesised that,

H6: Financial inclusion is positively related to area development.

72

Banking industry has shown tremendous growth in volume and complexity over the

last few decades (Anjugam, 2011). But despite making significant improvements in

all areas relating to financial viability, profitability & competitiveness, there are

serious concern that banks have been unable to include vast segments of the

population into the fold of basic banking services, especially the under-privileged

section of society (Thorat, 2007). The literature on financial inclusion has identified

financial exclusion as reflection of a broader problem of ‘social exclusion’. Several

studies have shown that the exclusion from the financial system occur to person who

belong to low income group, the ethnic minorities, immigrants and the aged

(Kempson & Whyley, 1998; Connolly & Hajaj, 2001 and Barr, 2004). Geographical

factors such as people living in rural areas and in location that is remote from urban

financial centres are more likely to be financially excluded (Leyshon & Thrift, 1995

and Kempson & Whyley, 1998). Low level of income inequalities tends to have

relatively high level of financial inclusion (Kempson & Whyley, 1998; Buckland et

al., 2005 and Kempson, 2006). Another factor that has been associated with financial

inclusion is employment (Goodwin et al., 2000). The unemployed or those with

irregular and insecure employment are less likely to participate in the financial system

(Sarma & Pais, 2011). Thus, it is hypothesised that,

H7: Nature of financial inclusion differs across socio-economic profile of

respondents.

Barriers limit the participation by certain section of the society to access financial

services and remain fragmented & incomplete (Reyes et al., 2010). Barriers like

geographical barriers, cultural barriers, trust issues or inadequate products & services

for a specific environment prevent customers accessing existing financial institutions

but would enable its access and usage in case such barriers are lifted (Reyes et al.,

2010). Poor education or lack of literacy represents a significant barrier to accessing

and properly using formal financial services (Ellis et al., 2010). Qualifying

requirements such as minimum account balance, availability of collateral or

guarantor, proper documentation, fees and others are main barriers to abstain the

reach of poor people to financial services (Ellis et al., 2010). Agrawal (2008)

underpinned low income, ignorance, low levels of financial literacy, cultural &

psychological barriers such as, language, perceived/actual racism, suspicion or fear of

73

financial institutions result in lack of access and result in financial exclusion. Hence,

it is hypothesised that,

H8: Barriers to financial inclusion have significant impact on the access and

usage dimensions.

3.2.5 Scale Pretesting and Purification

The present study tries to explore the impact of financial inclusion on economic

development as well as on social empowerment & economic empowerment,

association between financial inclusion and area development & poverty reduction.

The following steps are taken to make it more effective and accurate.

a. Nature and sources of information

The study used both primary and secondary sources for collecting required

information pertaining to research problem. Primary data are obtained personally

from the beneficiaries of financial inclusion drive of RBI belonging to five districts

i.e., Jammu, Samba, Kathua, Udhampur and Reasi of Jammu division through self

developed schedule. Questions are put in Dogri dialect from beneficiaries belonging

to four banks i.e., Jammu & Kashmir Bank Ltd., Jammu & Kashmir Grameen Bank

Ltd., State Bank of India and Punjab National Bank. Secondary information has been

collected from journals viz., Asian Economic Review, International Journal of

Advanced Research and Innovations, International Journal of Economics and Finance,

International Journal of Innovative Research & Development, International Research

Journal of Commerce & Behaviour Science, IOSR Journal of Economics and Finance,

Journal of Accounting and Finance, Journal of Economics, Business and

Management, Journal of Global Business and Economics, Journal of International

Business and Economics, Journal of International Development, Journal of Rural

Development, Journal of Social Policy etc., published information from internet, RBI

reports and magazines.

b. Generation of scale items and data collection form

Extensive relevant literature has been reviewed to generate items pertaining to

different dimensions of financial inclusion, social & economic empowerment,

economic development, poverty reduction and area development. Since no paper has

been found with well established scale, so the research papers mentioned in Table 3.1

are reviewed to get an idea to frame a self developed schedule.

74

TABLE 3.1: GENERATION OF SCALE ITEMS

Dimensions No. of

items

Sources

Access 17 Sarma & Pais, 2008; Kumar, 2011 and Gupte

et al., 2012

Availability 18 Sarma & Pais, 2008; Kumar, 2011 and Gupte

et al., 2012

Usage 9 Sarma & Pais, 2008; Kumar, 2011 and Gupte

et al., 2012

Social empowerment 25 Barik, 2009; Kumar & Sharma, 2011;

Arputhamani & Prasannakumari, 2011 and

Cnaan et al., 2011

Economic empowerment 11 Barik, 2009; Kumar & Sharma, 2011;

Arputhamani & Prasannakumari, 2011 and

Cnaan et al., 2011

Economic development 11 Agrawal, 2007 and Das, 2011

Poverty reduction 10 Rautela et al., 2010; Latif et al., 2011 and

Mishra, 2012

Area development 8 Rautela et al., 2010 and Arputhamani &

Prasannakumari, 2011

The scale items are finalised after reviewing the above mentioned literature, detailed

discussions with the subject experts and academicians. Schedule is thereafter used for

collecting the requisite information from the financial inclusion beneficiaries.

Schedule consisted of two sections, one general and other to elicit information about

eight dimensions of financial inclusion namely, access, availability, usage, social

empowerment, economic empowerment, economic development, poverty reduction

and area development. Schedule comprised of total 127 items, out of which 11 p

ertained to general information, 44 items related to financial inclusion (17 of access,

18 of availability & 9 of usage), 25 items of social empowerment, 11 items of

economic empowerment, 11 items of economic development, 10 items of poverty

reduction, 8 items of area development and remaining 7 items pertained to reasons for

not having a bank account. The data are collected on five point Likert scale (5<----1>)

where 5 denotes strongly agree and 1 denotes strongly disagree. Suggestions are kept

in open ended form.

c. Pretesting

The initial schedule was prepared in the year 2012. To assess its comprehension

among beneficiaries and calculate final sample size, pretesting is done on 100

75

beneficiaries covered under the financial inclusion drive of RBI. The respondents are

selected on judgement basis, selecting two beneficiaries from ten villages each of five

districts of Jammu division i.e., Jammu, Samba, Kathua, Reasi and Udhampur. The

schedule comprised questions in dichotomous form, open ended and ordinal form of 5

point Likert scale, where rank ‘5’ denotes ‘strongly agree’ and rank ‘1’ denotes

‘strongly disagree’. The initial schedule contained 141 items on various dimensions

i.e., access, availability, usage, social empowerment, economic empowerment,

economic development, poverty reduction and area development. In order to collect

more clear and satisfactory responses from beneficiaries, some items are modified &

few items deleted and ultimately 127 items are retained for final survey. Due to the

non-availability of authentic records with BCs, the final sample size is arrived at 884

using following formula (Malhotra, 2002).

n = σ

2 * z

2/D

2

The final sample size is round off to 900. Thereafter, the subsequent chapters on

access, availability, usage, social empowerment, economic empowerment, economic

development, poverty reduction and area development are completed with such a

refined schedule.

d. Sampling techniques and data collection

The study is confined to villages belonging to five districts namely, Jammu, Samba,

Kathua, Reasi and Udhampur of Jammu division. Business correspondents of all

eighty three villages are contacted, out of which twenty eight either out rightly

rejected to cooperate or refused by saying nothing has been done on financial

inclusion till date. Of the remaining fifty five villages, primary data are collected from

523 beneficiaries on judgement sampling, criteria adopted is availability and

willingness to respond. The survey is carried on during February-July, 2013 and the

effective response rate came out to be 58.11%.

e. Outliers

An outlier is an observation which is numerically away from rest of the data (Barnett

& Lewis, 1994). According to Grubbs, ‘an outlying observation is one which appears

deviated from the other members of the sample’. There are number of methods

provided in the statistics for identifying and deleting outliers. Box plot is considered

as the most objective and quantitative approach to look out outliers (Mendenhall et

76

al., 1993). In the present study, outliers are identified through box plot by calculating

Z-scores of all the dimensions individually with the help of SPSS (17.0 version). The

outlier observations which are occurring for 3 or 4 times are deleted. Thereafter,

overall Z score of all dimensions is calculated. Again outliers are identified and

deleted with the help of box plot. In box plot, those points which are outside the end

of the whiskers are outliers. There are 23 outlier observations, which are deleted from

the data sheet (Figure 3.1). Further to check normalcy, Kolmogorov-Smirnov and

Shapiro-Wilk test are performed which came out to be insignificant and proved that

data is normal.

f. Normality

Normality is a test which is used to determine whether a set of data is well defined by

normal distribution or not. As we all know that assessment of normality is prerequisite

before applying any parametric statistical tests. Normality can be assessed by two

ways: Graphically and Numerically (Park, 2008).

Graphically method

The output of Q-Q plot (quantile-quantile plot) is used to determine normality.

In Q-Q plot, if the data are normally distributed then the data points fall

approximately on a diagonal straight line (Figure 3.2), which indicates high

correlation and if the data points strayed away from the diagonal line then data

are not normally distributed (Field, 2009). All the data points are closer to the

straight diagonal line and no point is strayed outside, which indicates that data

are normally distributed.

Numeric method

While testing normality numerically in SPSS, Skewness and Kurtosis are

some of the easiest tests (Mardia, 1970) and as per rule of thumb, the data

become normal when its Skewness and Kurtosis have value between -1 and +1

or closer to zero (Gao et al., 2008).

With the help of SPSS (17.0 version), Skewness and Kurtosis tests are

performed and the value of Skewness is .270 and Kurtosis is -.241, which is as

per rule of thumb between -1 to +1. This shows that data is normally

distributed.

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g. Multicollinearity analysis

Multicollinearity is also checked between all the latent constructs viz., financial

inclusion, social empowerment, economic empowerment, economic development,

poverty reduction and area development to eliminate high inter-construct correlation.

Table 3.2 predicted that tolerance is greater than .10 and the variance inflation factor

(VIF) is less than 10 in all the cases, suggesting that multicollinearity is not an issue.

TABLE 3.2: MULTICOLLINEARITY ANALYSIS

Dependent variables Independent variables Collinearity statistics

Tolerance VIF

Financial inclusion

Social empowerment .353 2.834

Economic empowerment .727 1.375

Economic development .330 3.030

Poverty reduction .350 2.859

Area development .690 1.449

Social empowerment Economic empowerment .716 1.397




Financial inclusion .468 2.138

Economic

empowerment






Economic development






Poverty reduction Area development .687 1.456





Area development Financial inclusion .413 2.422





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h. Statistical tool and techniques applied

The data collected with the help of survey method have been analysed in order to

bring out relevant results with the help of appropriate statistical tools. The descriptive

analysis of access, availability, usage, social & economic empowerment, economic

development, poverty reduction and area development have been carried out with the

help of mean and standard deviation. Mean has been used in order to know the value

of each observation. Further, to know the amount of deviation in the respondents’

view, standard deviation has been analysed (Beri, 2005). Multivariate techniques used

in the study are discussed as under:

Exploratory factor analysis

Exploratory factor analysis is used to summarises the data for further analysis.

The multivariate data reduction techniques of factor analysis has been used

with the help of 17.0 version of SPSS which is the most appropriate for the

present study as it involves the examination of interrelationships among

variables and reduces large number of variables into few manageable sets

(Stewart, 1981). EFA has two primary functions i.e., data summarisation and

data reduction. In data summarisation, factor analysis derives underlying

dimensions that when interpreted and understood, describes the data in much

smaller numbers of concepts than the original individual variables. Data

reduction can be achieved by calculating scores for each underlying dimension

and substituting them for original variables. This is done through factor score.

The study used principal component analysis with a varimax rotation (Kakati

& Dhar, 2002), as it is the best and the most commonly used orthogonal

rotation procedure (Gorsuch, 1974; Stewart, 1981 and Malhotra, 2002).

Therefore, the present study focuses on high communalities and on high

reliabilities to reduce the number of variables (Fabrigar et al., 1995). The

Eigen value equal to or more than one criterion has been used to determine the

number of components to be extracted for further analysis (Stewart, 1981 and

Alfansi & Sergeant, 2000). KMO measure of sampling adequacy has been

used to verify the appropriateness of a factor loading, where the value greater

than .50 is acceptable, values between .50 & .70 are mediocre, .70 & .80 are

good, .80 & .90 great and above .90 superb (Malhotra, 2002). Further, Bartlett

test of spherecity, which is also called zero identity matrix, has also been used

79

to determine correlation among the variables (Hair et al., 1995 and Field,

2000).

The statement with factor loading less than .50 are ignored for subsequent

analysis (Hair et al., 2007). The data reduction is performed in four steps, first

inter-item correlation is checked, the items with value less than 3 are removed,

in the second step, anti-image correlation matrix, the items with value less

than .50 on the diagonal axis are deleted. In the third step, the extracted

communalities are checked and items with values less than .50 are ignored for

further analysis. In the fourth & the final step, rotated component matrix,

statement with cross or multiple loading and values below .50 are deleted.

Regression

Regression term is first used by Francis Galton in the end of nineteenth

century. Regression analysis is a powerful technique for analysing the

associative relationship between a dependent variable and one or more

independent variables (Malhotra, 2002). It is a measure of the average

relationship between two or more variables in terms of the original units of the

data. The objective of regression analysis is to use the independent variable,

whose values are known to predict the single dependent variable. When the

problem involves two or more independent variables, it is termed as multiple

regression.

One way ANOVA and t-test

Analysis of variance (abbreviated One Way ANOVA) is a statistical technique

used to determine whether samples from two or more groups come from

population with equal means (i.e., Do the group means differ significantly?). It

is used to compare means of two or more samples. ANOVA examines one

dependent variable. It is used to test the differences among at least three

groups.

The term ‘t-test’ is introduced by William Sealy Gosset in 1908. T-test

assesses the statistical significance of the difference between means of two

samples for a single dependent variable. The t-test is a special case of

ANOVA for two groups or levels of treatment variables.

80

Confirmatory factor analysis

CFA is used to provide a confirmatory test to the measurement theory. It is a

way of testing how well measured variables represent a construct. To assess

fitness, reliability and validity of latent constructs, CFA is performed. In CFA,

there is no distinction between exogenous and endogenous constructs, hence it

is an interdependence techniques. CFA is different from EFA as in EFA all

measured variables are related to every factor by a factor loading estimate,

whereas in CFA researcher has to assign variables to each factor on the basis

of preconceived theory. Thus, CFA statistics tells us how our specification of

the factor matches the reality i.e., the actual data (Hair et al., 2009). In CFA,

measurement model can be of two types viz., reflective or formative factor

models. Reflective measurement theory is based on the idea that latent

constructs is reflected through the measured variables and that the error results

is an inability to fully explain these measure. The reflective model are more

common in social science studies (Hair et al., 2007).

Structural equation modeling

Structural equation modeling (SEM) often involves both a measurement

theory and a structural theory. SEM has become one of the most widely

applied data analysis techniques in the business research. The reason being its

ability to assess simultaneously the fitness of the measurement models and the

structural model, where measurement models tests relationship (i.e. paths)

between the measured (manifest) variables and the construct, i.e., latent

variables, structural model specifies relationships between latent variables of

interest (composite measures). Maximum likelihood estimation is used in

estimating the structural model. It is most commonly and widely used

approach. Researchers compared maximum likelihood estimation (MLE) with

other technique and found that it produce reliable results under many

circumstances (Marsh et al., 1998). One of the major benefits of using SEM

techniques is that it allows for concurrent assignment of both reliability and

validity by applying CFA. Moreover, it can handle various kind of relationship

became a dependent model in other relationship. Thus, it can be concluded

that SEM is a more appropriate technique as compared to multiple regression.

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i. Goodness of fit indices

Goodness of fit refers to the ability of a model to reproduce the data (i.e., usually the

variance-covariance matrix). A good fitting model is one that is reasonably consistent

with the data and so does not require re-specification. In addition, a good fit

measurement model is required before interpreting the casual paths of the structural

model.

Parameter estimates must be carefully examined to determine if one has a good model

estimates, it should have good fitting model too. Also, it is important to rely that one

might obtain a good fitting model, yet it is still possible to improve the model and

remove specification error. Measurement model’s validity depends on the goodness of

fit (GOF) of the model. GOF indicates how well the specified model reproduces the

covariance matrix among the indicators items (i.e., similarity of observed and

estimated covariance matrix). The model fit compares theory to reality as represented

by the data. If the proposed theory is perfect, the estimated covariance matrix would

be same, thus the closer the values of these two matrices, the better the model is said

to fit. GOF can be measured in following ways:

Absolute fit indices

Absolute fit indices determine how well a priori model fits the sample data

(McDonald & Ho, 2002) and demonstrate which proposed model has the most

superior fit. These measures provide the most fundamental indication of how

well the proposed theory fits the data. Unlike incremental fit indices, their

calculation does not rely on comparison with a baseline model but is instead a

measure of how well the model fits in comparison to no model at all (Joreskog

& Sorbom, 1993). Included in this category are the Chi-square test, GFI,

AGFI, RMR, SRMR and RMSEA.

Chi-square statistics (χ2)

The Chi-square value is the traditional measure for evaluating overall model

fit and assesses the magnitude of discrepancy between the sample and fitted

co-variances matrices (Hu & Bentler, 1999). A good model fit would provide

an insignificant result at a 0.05 threshold (Barrett, 2007), thus the Chi-square

statistic is often referred to as either a ‘badness of fit’ (Kline, 2005) or a ‘lack

of fit’ (Mulaik et al., 1989) measure. Chi-square statistics is the fundamental

82

measure used in SEM to quantify the differences between the observed and the

estimated covariance matrices. A large value of Chi-square relative to the

degree of freedom signifies that the observed and estimated matrices differ

considerably. Statistical significance level indicates the probability that these

differences are solely due to sampling variations. Thus, the p-value of Chi-

square test should be large, indicating no statistical difference between the

matrices. While the Chi-square statistics retains its popularity as a fit statistic,

there exist a number of severe limitations in its use. Firstly, this test assumes

multivariate normality and severe deviations from normality may result in

model rejections even when the model is properly specified (McIntosh, 2006).

Second, because the Chi-square statistic is in essence a statistical significance

test it is sensitive to sample size which means that the Chi-square statistic

nearly always rejects the model when large samples are used (Bentler &

Bonnet, 1980 and Joreskog & Sorbom, 1993). On the other hand, where small

samples are used, the Chi-square statistic lacks power and because of this may

not discriminate between good fitting models and poor fitting models (Kenny

& McCoach, 2003). Now a days, researchers are using Chi-square and degree

of freedom ratio, a value less than 5 is deemed appropriate for model to be fit.

Goodness-of-fit statistics (GFI)

The Goodness-of-fit statistic (GFI) is created by Joreskog & Sorbom in 1993

as an alternative to the Chi-square test and calculates the proportion of

variance that is accounted for by the estimated population covariance

(Tabachnick & Fidell, 2007). By looking at the variances and co-variances

accounted for by the model, it shows how closely the model comes to

replicating the observed covariance matrix (Diamantopoulos & Siguaw, 2000).

This statistic ranges from 0 to 1. When there are a large number of degrees of

freedom in comparison to sample size, the GFI has a downward bias (Sharma

et al., 2005). In addition, it has also been found that the GFI increases as the

number of parameters increases (MacCallum & Hong, 1997) and also has an

upward bias with large samples (Bollen, 1990 and Miles & Shevlin, 1998).

Traditionally, an omnibus cut-off point of 0.90 has been recommended for the

GFI. However, simulation studies have shown that when factor loadings and

83

sample sizes are low a higher cut-off of 0.95 is more appropriate (Miles &

Shevlin, 1998).

Adjusted GFI (AGFI)

Related to the GFI is the AGFI which adjusts the GFI based upon degrees of

freedom, with more saturated models reducing fit (Tabachnick & Fidell,

2007). In addition to this, AGFI tends to increase with sample size. As with

the GFI, values for the AGFI also range between 0 & 1 and it is generally

accepted that values of 0.90 or greater indicate well fitting models.

Root mean square residual (RMR) and standardised root mean square

residual (SRMR)

The RMR and the SRMR are the square root of the difference between the

residuals of the sample covariance matrix and the hypothesised covariance

model. The range of the RMR is calculated based upon the scales of each

indicator, therefore, if a questionnaire contains items with varying levels

(some items may range from 1-5 while others range from 1-7), the RMR

becomes difficult to interpret (Kline, 2005). The standardised RMR (SRMR)

resolves this problem and is therefore much more meaningful to interpret.

Values for the SRMR range from zero to 1.0 with well fitting models

obtaining values less than .05 (Byrne, 1998 and Diamantopoulos & Siguaw,

2000), however values as high as 0.08 are deemed acceptable (Hu and Bentler,

1999). A SRMR of 0 indicates perfect fit but it must be noted that SRMR will

be lower when there are high number of parameters in the model and models

are based on large sample sizes.

Root mean square error of approximation (RMSEA)

The RMSEA is first developed by Steiger in 1990. The RMSEA tells us how

well the model with unknown but optimally chosen parameter estimates would

fit the populations’ covariance matrix (Byrne, 1998). In recent years, it is

regarded as ‘one of the most informative fit indices’ (Diamantopoulos &

Siguaw, 2000). Recommendations for RMSEA cut-off points have been

reduced considerably in the last fifteen years. Up till the early nineties, a

RMSEA in the range of 0.05 to 0.10 is considered an indication of fair fit and

values above 0.10 indicated poor fit (MacCallum et al., 1999). It is then

84

thought that an RMSEA of between 0.08 to 0.10 provides a mediocre fit and

below 0.08 shows a good fit (MacCallum et al., 1999). However, more

recently, a cut-off value close to .06 (Hu & Bentler, 1999) or a stringent upper

limit of 0.07 (Steiger, 2007) seems to be the general consensus amongst

authorities in this area. One of the greatest advantages of the RMSEA is its

ability for a confidence interval to be calculated around its value (MacCallum

et al, 1999). This is possible due to the known distribution values of the

statistic and subsequently allows for the null hypothesis (poor fit) to be tested

more precisely (McQuitty, 2004). In a well-fitting model, the lower limit is

close to 0 while the upper limit should be less than 0.08.

Incremental fit indices

Incremental fit indices, also known as comparative fit indices (Miles &

Shevlin, 2007) or relative fit indices (McDonald and Ho, 2002). These are a

group of indices that do not use the Chi-square in its raw form but compare the

Chi-square value to a baseline model.

Normed fit index (NFI)

NFI statistic assesses the model by comparing the χ2 value of the model to the

χ2 of the null model. The null/independence model is the worst case scenario

as it specifies that all measured variables are uncorrelated. Values for this

statistic range between 0 and 1 with Bentler & Bonnet (1980) recommending

values greater than 0.90 indicating a good fit. More recent suggestions state

that the cut-off criteria should be NFI ≥ .95 (Hu & Bentler, 1999). A major

drawback to this index is that it is sensitive to sample size, underestimating fit

for samples less than 200 (Mulaik et al., 1989 and Bentler, 1990) and is thus

not recommended to be solely relied on (Kline, 2005). This problem is

rectified by the Non Normed Fit Index (NNFI, also known as the Tucker-

Lewis index), an index that prefers simpler models. However in situations

where small samples are used, the value of the NNFI can indicate poor fit

despite other statistics pointing towards good fit (Bentler, 1990; Kline, 2005

and Tabachnick & Fidell, 2007). However, Hu & Bentler (1999) have

suggested NNFI ≥ 0.95 as the threshold.

85

Comparative fit index (CFI)

The Comparative fit index (Bentler, 1990) is a revised form of the NFI, which

takes into account sample size (Byrne, 1998) that performs well even when

sample size is small (Tabachnick & Fidell, 2007). This index is first

introduced by Bentler in 1990. Like the NFI, this statistic assumes that all

latent variables are uncorrelated (null/independent model) and compares the

sample covariance matrix with this null model. As with the NFI, values for

this statistic range between 0.0 and 1.0 with values closer to 1.0 indicating

good fit. A cut-off criterion of CFI ≥ 0.90 is initially advanced. However,

recent studies have shown that a value greater than 0.90 is needed in order to

ensure that mis-specified models are not accepted (Hu & Bentler, 1999). From

this, a value of CFI ≥ 0.95 is presently recognised as indicative of good fit (Hu

and Bentler, 1999). Today this index is included in all SEM programs and is

one of the most popularly reported fit indices due to being one of the measures

least effected by sample size (Fan et al., 1999).

j. Reliability and validity

Reliability

Reliability is defined as the extent to which a questionnaire, test, observation or any

measurement procedure produces the same results on repeated trials. In short, it is the

stability or consistency of scores over time or across raters. Without the agreement of

independent observers able to replicate research procedures, or the ability to use

research tools and procedures that yield consistent measurements, researchers would

be unable to satisfactory draw conclusions, formulate theories, or make claims about

the generalisability of their research. In addition to its important role in research,

reliability is critical for many parts of our lives, including manufacturing, medicine

and sports. Joppe (2000) defined reliability as, ‘the extent to which results are

consistent over time and an accurate representation of the total population under study

is referred to as reliability and if the results of a study can be reproduced under a

similar methodology, then the research instrument is considered to be reliable’. It is

measured in following ways:

86

i. Cronbach’s alpha

It refers to the extent to which items inter-correlate with one another. Internal

consistency implies that multiple items measure the same construct and inter-

correlate with one another. In contrast, low inter-item correlation indicates that

some items are not drawn from the appropriate domain and are unreliable

(Churchill, 1979). The commonly accepted measure of internal consistency is

Cronbach’s alpha. The value of an alpha is .70 is the minimum acceptable

standard for demonstrating internal consistency (Kennedy et al., 2002).

ii. Construct/Composite reliability

It is the measurement of reliability and internal consistency of the measured

variables representing latent construct. It is easily computed from the squared

sum of factor loadings for constructs and the sum of the error terms for a

construct (Hair et al., 2001).

CR = (Sum of standardised loading)2 / (Sum of standardised loading)

2 + Sum

of error terms

The rule of thumb for composite reliability is 0.70 or higher (Fornell &

Larcker, 1981).

Validity

Validity refers to the degree to which a study accurately reflects or assesses the

specific concept that the researcher is attempting to measure. While reliability is

concerned with the accuracy of the actual measuring instrument or procedure, validity

is concerned with the study’s success in measuring, what the researchers set out to

measure. Validity determines whether the research truly measures, which it is

intended to measure or how truthful are the research results. There are different types

of validity criteria namely, content, construct and divergent validity.

i. Content validity

It is the extent to which the content of the items is consistent with the

construct definition (Hinkin, 1995). It can be established through existing

literature on the subject or discussions with subject experts.

87

ii. Construct validity

It is the extent to which a set of measured items actually reflects the theoretical

latent construct. It deals with the accuracy of measurement (Joppe, 2000). It

can be established through convergent, discriminant and nomological validity.

Convergent validity

Convergent validity tests the extent to which the covariance between the

two measures is uniquely explained by the trait factor. Thus, items that are

indicators of a specific construct should converge or share a high

proportion of variance in common. It involves the extent to which a

measure correlates highly with other measures designed to measure the

same construct.

It can be established in following ways:

a) Factor loading: High factor loading i.e., above 0.50 or ideally 0.70 or

higher indicate level of convergence.

b) Average variance extracted: In CFA, the average percentage of

variance extracted (VE) is a summary indicator of convergence. AVE

is calculated by using standardised loadings, which is as under:

AVE = Sum of squared standardised factor loadings / Number of items

If AVE is above 0.50, convergent validity gets established.

Discriminant validity

Discriminant validity refers to the extent to which the measure differs

from other measures designed to measure different concepts. It can be

examined through the evaluation of the average variance extracted (AVE).

Fornell & Larcker (1981) highlighted the importance of evaluating the

discriminant validity of the construct used in the research. They suggested

that average variance extracted for each construct should be greater than

the squared correlation between constructs.

Nomological validity

It is a type of validity that assesses the relationship between theoretical

constructs. It seeks to confirm significant correlations between the

88

constructs as predicted by theory. It is a form of construct validity. A

nomological net is built in which several construct are systematically

interrelated (Hair et al., 2007). It gets established by proving the already

existing theoretical relations.

3.2.6 Significance of the Study

Heading fast towards the status of ‘Economic Super Power’, the Indian economy still

bears the stigma of financial exclusion, as 50% of its population lives in poverty &

69% of its masses is disadvantaged & deprived of any sort of financial access.

Financial inclusion needs to reach the unreached section of people and to bring them

to the mainstream economy. In fact, an access to savings and credit can initiate or

strengthen a series of interlinked and mutually reinforcing ‘virtuous spirals’ of

empowerment in society. In the era of financial globalisation, financial inclusion has

been considered as a major requirement which protects against risks and shocks by

using finance facility. Thus, leading to increase in income earning opportunities. The

significance of the study lies in finding predictors of financial inclusion, its

relationship with social & economic empowerment, economic development, poverty

reduction and area development. The study ends with certain suggestions and if the

global & national policy makers adhere to those suggestions, it will actually proof to

be a boon for the country in particular and world in general.

3.2.7 Limitations of the Study

All feasible efforts are made to make the study more reliable, valid and exhaustive,

yet certain limitations could not be ruled out and are required to keep in the mind

whenever its findings are considered for implementation. These limitations are as

under:

i. The scope of the study is limited to five districts of Jammu division only because

of restricted resources and time availability. Comparison of the extent of

financial inclusion between districts, divisions and states can be undertaken in

future.

ii. The study is based on cross-sectional data and further be extended on

longitudinal data.

iii. The information obtained from the respondents may not be free from

subjectivity.

89

iv. The study is limited to financial inclusion beneficiaries’ perception only and

could be carried further on the perception of other stakeholders such as bank

officials, business correspondents and Sarpanchs’.

v. The study has covered financial inclusion beneficiaries through commercial

banks only. Other institutes like, post office, cooperative banks, regional rural

banks, cooperative societies, SHG’s besides other are excluded from the study.

vi. Comparative study between those who availed the financial inclusion scheme

and those who have not availed, has not been done.

90

FIGURE 3.1: NORMALITY THROUGH BOX PLOT

FIGURE 3.2: NORMALITY THROUGH Q-Q PLOT

91

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`

Chapter-IV Financial Inclusion, Socio-economic Empowerment and


CONTENTS


4.1 Introduction 105

4.2 Dimensions of Financial Inclusion and Economic

Development

105

4.3 Profile of Respondents 107

4.4 Financial Inclusion and Economic Development

through Socio-economic Empowerment

112

4.4.1 Scale Purification 112

4.4.2 Confirmatory Factor Analysis 123

4.4.2.1 CFA and Construct Validity 123

4.4.2.2 Fitness of CFA Models 125

4.4.2.3 CFA Models 126

4.4.3 Structural Equation Modeling 132

4.4.3.1 Structural Model 133

4.4.3.2 Hypotheses Testing 134

4.4.3.3 Mediation of SE in FI-ED Link 136

4.4.3.4 Mediation of EE in FI-ED Link 139

4.4.4 Output from One-way ANOVA 140

References

157

105

CHAPTER IV

FINANCIAL INCLUSION, SOCIO-ECONOMIC

EMPOWERMENT AND ECONOMIC DEVELOPMENT

4.1 INTRODUCTION

Financial inclusion is the process of ensuring access to financial services by

vulnerable groups such as the weaker sections and low income groups at an affordable

cost (Rangarajan Committee Report, 2008). It provides access to various banking

products & services like deposit account, credit products, micro insurance, transfer &

payment of money which helps in getting the facilities like secured savings,

unexploited credit, safe transfer of money and getting direct benefits from government

(Uma et al., 2013). Access to financial services is provided by the financial

institutions which act as the catalyst in the economic & social growth of an individual

and progress of an economy (Banerjee & Francis, 2014). This access helps in gaining

financial empowerment, promoting social inclusion, building self-confidence and thus

leading to social & economic empowerment of rural folk especially women which

ultimately leads to economic development (Kelkar, 2010; Uma et al., 2013;

Paramasivan & Ganeshkumar, 2013 and Banerjee & Francis, 2014). Financial

inclusion is a key driver for economic empowerment at an individual level and

economic development at national level (Lewis, 2007). Therefore, financial inclusion

is a fundamental cornerstone of economic & social empowerment and both cause &

effect of economic development (Uma & Rupa, 2013).

4.2 DIMENSIONS OF FINANCIAL INCLUSION AND ECONOMIC

DEVELOPMENT

Financial inclusion is a broader concept which embraces three key dimensions

namely, access, availability and usage. Other development aspects undertaken in the

study are social empowerment, economic empowerment and economic development.

A brief overview of these dimensions is as under:

i. Access

It refers to the ability to use available financial products & services from formal

106

institutions. It provides an insight and analysis of potential barriers to opening &

using of bank account such as cost, physical proximity of bank branches, etc.

ii. Availability

It is described as services which bank is providing. It includes services such as loan,

overdraft, insurance, pass book, debit card, etc.

iii. Usage

It is related to regularity, frequency and length of time used. It focuses on the depth

and extent of financial service or product.

iv. Social empowerment

Empowerment in general refers to the process of change that gives an individual the

ability to gain access to the resources they need while also gaining the ability to

influence the wider policy, regulatory and institutional environment that shapes their

livelihoods and lives. Social empowerment in particular means an improvement of the

social status and living standard of the beneficiaries. It reflects the income security,

housing security, improved housing conditions & hygiene, security of health and

social reputation.

v. Economic empowerment

It refers to the sustained, concerted efforts of policy makers & community to promote

the standard of living & economic health in a specific area. It is the process which

increases beneficiaries’ real power over economic decisions that influence their lives

and priorities in society.

vi. Economic development

Economic development is a continuous process which is extended over a long period

of time so as to break the vicious circle of poverty and lead a country to a stage of

self-sustaining growth or to self-generating economy. In the words of Meier &

Baldwin, ‘economic development is a process whereby an economy's real national

income increases over a long period of time’. According to Okun & Richard, ‘it is a

sustained secular improvement in well being, which may be considered to be reflected

in an increasing flow of goods &services’. Baran refers it as, ‘as an increase over time

in per capital output of material goods’. Clark defined as, ‘an improvement in

economic welfare’.

107

4.3 PROFILE OF RESPONDENTS

A brief profile of respondents (Table 4.1) contacted during the study is as under:

i. Name of the bank

Table 4.1 connotes that out of 500 respondents, 47.2% respondents are covered by

JKB, 25% belongs to JKGB, 18% belongs to SBI and 9.8% belongs to PNB. Thus, it

is found that JKB has the largest share and PNB has the smallest share (Figure 4.1).

FIGURE 4.1: PIE CHART FOR BANKS*

*Source: Survey

ii. Gender

Figure 4.2 shows 87.6% respondents are male and 12.4% are female. It is found that

male are more interested in opening bank accounts and female are still reluctant to

open an account in bank even if it is a no frill account.

FIGURE 4.2: PIE CHART FOR GENDER*

*Source: Survey

JKB

JKGB

SBI

PNB

Male

Female

108

iii. Age

Figure 4.3 reveals age-wise distribution of respondents. It shows that majority of the

respondents are in the age group of 40-50 years which constitute 36.4% of the total

respondents. Respondents in the age group of upto 30 years consist of only 10.2% and

35.8% i.e., 179 belongs to 30-40 years age group. The age group above 50 years

comprises of 88 respondents that contributes 17.6% to the total respondents. Thus, it

reveals that middle age people are more interested in opening accounts and availing

the benefits of government schemes.

FIGURE 4.3: PIE CHART FOR AGE*

*Source: Survey

iv. Caste

As far as caste is concerned, it is found out that 57% respondents belongs to general

caste, 35% belongs to SC, 2.6% belongs to ST and 5.4% belongs to OBC (Figure 4.4).

Thus, it can be inferred that general caste people are more aware about the

government programmes and lower castes & tribes are still unaware.

FIGURE 4.4: PIE CHART FOR CASTE*

*Source: Survey

Above 50 Years

40-50 Years

30-40 Years

Upto 30 Years

General

SC

ST

OBC

109

v. Religion

Religion-wise, respondents are Hindu (97.2%), Muslims (2.2%) and Sikhs (0.6). As

evident from Figure 4.5, Hindu respondents are majorly covered under the financial

inclusion drive in the five districts i.e., Jammu, Udhampur, Reasi, Samba and Kathua.

FIGURE 4.5: PIE CHART FOR RELIGION*

*Source: Survey

vi. Marital status

It is found that 90% (450) respondents are married and 10% (50) respondents are

unmarried (Figure 4.6). Thus, it can be concluded that married respondents are more

family & saving oriented and unmarried want to spend money.

FIGURE 4.6: PIE CHART FOR MARITAL STATUS*

*Source: Survey

Hindu

Muslim

Sikhs

Married

Unmarried

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vii. Qualification

Figure 4.7 highlights that 5.8% respondents are illiterate, whereas 2.8% respondents

are having qualification below primary. Another group of respondents who are

qualified upto primary & middle are 13% & 31% respectively. 172 respondents are

just secondary pass constituting 34.4% of the total respondents. 11.4% respondents

are having higher secondary qualification and 1.4% are graduates. Thus, it becomes

clear that financial inclusion drive is covering everyone whether they are illiterate or

highly qualified.

FIGURE 4.7: PIE CHART FOR QUALIFICATION*

*Source: Survey

viii. Monthly income

As far as income of respondents is concerned, it is found that 51.6% of the

respondents are having income upto `5,000. 39.8% respondents are having monthly

income of `5,000-10,000. Respondents with `10,000-20,000 income per month are

7.8% or 39 in number. Whereas only 0.8% respondents are having income above

`20,000 (Figure 4.8). Thus, it is found that maximum respondents are having income

upto `5,000.

Illiterate

Below primary

Upto Primary

Upto Middle

Upto Secondary

Upto Higher Secondary

Upto Graduate

Anyother

111

FIGURE 4.8: PIE CHART FOR MONTHLY INCOME*

*Source: Survey

ix. District

District-wise analysis (Figure 4.9) shows 204 respondents are from Jammu district

which comprises of 40.8% of the total respondents. Kathua district has 32.6%

contribution in the total respondents’ percentage. Only 3% respondents belong to

Reasi district. 7.8% and 15.8% respondents contacted are from Samba and Udhampur

district respectively. Thus, it is found out that Jammu is the largest district among all

and has majority of respondents covered under FID.

FIGURE 4.9: PIE CHART FOR DISTRICTS*

*Source: Survey

Income upto Rs 5,000

Rs 5,000-10,000

Rs 10,000-20,000

Above Rs 20,000

Jammu

Kathua

Reasi

Samba

Udhampur

112

4.4 FINANCIAL INCLUSION AND ECONOMIC DEVELOPMENT

THROUGH SOCIO-ECONOMIC EMPOWERMENT

The relationship between financial inclusion and economic development through

socio-economic empowerment is examined under the following sub-heads:

4.4.1 Scale purification

4.4.2 Confirmatory factor analysis

4.4.2.1 CFA and construct validity

4.4.2.2 Fitness of CFA models

4.4.2.3 CFA models

4.4.3 Structural equation modeling

4.4.3.1 Structural model

4.4.3.2 Hypotheses testing

4.4.3.3 Mediation of SE in FI-ED link

4.4.3.4 Mediation of EE in FI-ED link

4.4.4 Output from One-way ANOVA

A brief description of each aforesaid sub heads is as under:

4.4.1 Scale Purification

Purification of constructs administered on beneficiaries of financial inclusion drive of

RBI is separately carried using SPSS (version 17.00) and the emergent results are

shown in Table 4.2. Constructs under consideration are access, availability, usage,

social empowerment, economic empowerment and economic development.

Beneficiaries’ perception regarding access

The suitability of raw data for factor analysis obtained from bank customers is

examined through KMO value, Bartlett test of sphercity and p-value = 0.000,

indicating sufficient common variance and correlation matrix (Dess et al., 1997 and

Field, 2000). The process of R-mode principal component analysis (PCA) with

Varimax rotation brought the construct to the level of 12 statements out of 17

statements originally kept in the domain of access. The KMO value (0.906) and

Bartlett test of sphercity (2986.617) indicates acceptable and significant values.

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Therefore, factor loading in the final factorial design are consistent with conservative

criteria, thereby resulting into three-factor solution using Kaiser criteria (i.e. eigen

value ≥ 1) with 67.60% of the total variance explained. The communality for 12 items

ranges from 0.558 to 0.806 indicating moderate to high degree of linear association

among the variables. The factor loading ranges from 0.633 to 0.845 and the

cumulative variance extracted ranges from 35.17 to 67.60 percent. The communalities

and percentage of variance explained by each factor is displayed in the Table 4.2. A

brief description of factors emerged are as under:

Factor 1 (Information accessibility)

This factor consists of seven items namely, ‘The bank is conveniently located’, ‘The

employees are easily accessible when needed’, ‘Banking institution or its substitute is

easily approachable’, ‘Bank is easily approachable in case of emergencies’, ‘You

have easy access to the information that is useful’, ‘Employees’ of bank are

cooperative, friendly and knowledgeable’ and ‘Employees’ possess sufficient banking

information’. The mean values varied between 3.44 - 3.84, factor loading between

.650 - .816 and communalities from .569 - .806. This factor highlights that

accessibility of bank representatives & officials, information, cooperative behaviour is

must for success of financial inclusion.

Factor 2 (Physical accessibility)

This factor envisages three items i.e., ‘Bank have sufficient staff to meet its

customers’ requirements’, ‘The bank manager promptly redress your problems’ and

‘Banking officials respond well’. The mean values for the aforesaid items ranges

between 2.42 - 3.34. The factor loading ranges between .633 - .742 and communality

from .558 - .721. This factor connotes that access in terms of location is must for


Factor 3 (Approachability)

The items, ‘This is the only bank in your area’ and ‘As compared to other banks, this

bank is nearest to you’ are taken into consideration by this factor which supports the

items with significant mean values 4.12 & 4.29, high factor loading values .845 &

.811 and communalities with values .741 & .710 respectively. On the whole, all items

significantly contribute towards this factor.

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Reliability

Three factors are obtained after scale purification falling within the domain of access

dimension in financial inclusion. As evident from the Table 4.2, the Cronbach’s

reliability coefficients for all 12 scale items underlying three factor ranges from .580 -

.896. The alpha reliability coefficients for F1 i.e., Information accessibility (.896) is

higher than the criteria of .77 obtained by Gordon & Narayanan (1984) indicating

high consistency. F2 namely, Physical accessibility (.700) and F3 i.e., approachability

(.580) are also at minimum acceptable level of 0.50 as recommended by Brown et al.

(2001) and Kakati & Dhar (2002) thereby obtaining satisfactory internal consistency.

However, overall alpha reliability score for all factors is very much satisfactory at

0.877. Adequacy and reliability of sample size to yield distinct and reliable factors is

further demonstrated through Kaiser-Meyer-Olkin measure of sampling adequacy that

is 0.906 and all factor loadings are greater than .50.

Validity

The three factors obtained alpha reliability higher or equal to 0.50 and satisfactory

KMO value at .906, indicating significant construct validity of the construct (Hair et

al., 1995).

Beneficiaries’ perception regarding availability


examined through Anti-image, KMO value, Bartlett test of sphercity and p-value =

0.000, indicating sufficient variance and correlation matrix (Dess et al., 1997 and

Feild, 2000). The process of R-mode principal component analysis (PCA) with

Varimax rotation extracted 9 statements out of 18 statements which are actually kept

in the construct of availability. The KMO value (.770) and Bartlett test of sphercity

(2170.576) indicates highly acceptable and significant values. Therefore, factor

loadings in the final factorial design are consistent with conservative criteria, thereby

resulting into three-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with

72.56% of the total variance explained. The communalities for 9 statements range

from .546 to .918, indicating high degree of linear association among the variables.

The factor loading ranges from .616 to 0.944 and the cumulative variance extracted

ranges from 29.516 to 72.562 percent. The communalities and percentage of variance

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explained by each factor is displayed in the Table 4.2. A brief description of factors

emerged are as under:

Factor 1 (Loan availability)

It contained three items, ‘Loan is easily available’, ‘Loan is available within time

limit’ and ‘Procedure involved in getting loan is easy’. The mean values for all the

items ranges from 2.37 to 2.42, factor loadings from .885 to .944 and communalities

from .826 to .918. The statement ‘Loan is easily available’ adjudged to be most

important and strongest among all with highest factor loading (.944) & communality

(.918). The beneficiaries perceives that easy procedure for availing loan that too

within time frame is must for achieving efficiency.

Factor 2 (Support & assistance)

This factor comprised of four items specifically, ‘Help desk/assisting staff is available

for filling withdrawal/deposit form’, ‘Fieldworkers promotes various schemes of

bank’, ‘Infrastructure is as per the requirements of the customers’ and ‘Bank follows

quick problem solving approach’. The mean values for the items fluctuate between

2.87 to 3.52 representing moderate position. The factor loadings ranges between .616

- .757 and communalities from .546 - .589. The factor depicts that bank should not

only focus on making various products & services available but due consideration be

given for lending helping hand to the customers.

Factor 3 (Promotion)

The two items falling under this factor consisted of ‘Employee’s are helpful in

making information available regarding new schemes’ and ‘New bank schemes are

advertised frequently’. The two variables factor loading values are .890 & .880 and

communalities .812 & .792 respectively which reveals that the variables significantly

and positively contributes to the factor. Beneficiaries strongly perceive that

information about the new schemes be advertised frequently.

Reliability

Three factors are obtained after scale purification falling within the domain of

availability in financial inclusion. As evident from the Table 4.2, the Cronbach’s

reliability for all 9 scale items underlying in three factors ranges from .700 to .933.

The alpha reliability coefficients for F1 (Loan availability) & F3 (Promotion) is .933

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& .782 respectively which is higher than the criteria of .77 obtained by Gordon &

Narayanan (1984) indicating high internal consistency. For F2 namely, Support &

assistance, calculated alpha value arrived at .700 which is also at a minimum

acceptable level of 0.50 as recommended by Brown et al. (2001) and Kakati & Dhar

(2002) thereby obtaining satisfactory internal consistency. However, overall alpha

reliability score for all factors is very much satisfactory at 0.806. Adequacy and

reliability of sample size to yield distinct and reliable factors is further demonstrated

through Kaiser-Meyer-Olkin measure of sampling adequacy that is 0.770 and all

factor loadings are greater than .50.

Validity

The three factors obtained alpha reliability higher or equal to 0.50 and satisfactory


al., 1995).

Beneficiaries’ perception regarding usage


examined through Anti-image, KMO value, Bartlett test of sphercity and (p-value =

0.000), indicating sufficient variance and correlation matrix (Dess et al., 1997 and


Varimax rotation extracted 6 statements out of 9 statements which are actually kept in

the construct of usage. The KMO value (.677) and Bartlett test of sphercity (708.037)

indicates acceptable and significant values. Therefore, factor loadings in the final

factorial design are consistent with conservative criteria, thereby resulting into two-

factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with 67.78% of the total

variance explained. The communalities for 6 statements range from .522 to .739,

indicating high degree of linear association among the variables. The factor loading

ranges from .652 to .856 and the cumulative variance extracted ranges from 35.96 to

65.78 percent. The communalities and percentage of variance explained by each

factor is displayed in the Table 4.2. A brief description of factors emerged are as

under:

Factor 1 (General usage)

This factor envisages three items focussing upon ‘You frequently use credit facilities

of the bank’, ‘Advance schemes of bank are frequently used by you’ and ‘You are

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using bank for the repayment of loan’. The mean values for the aforesaid items ranges

between 1.66 - 2.03. The factor loadings and communalities exhibited significant

values (Table 4.2). This factor emphasises on not merely opening of account but on its

usage as well. Accounts opening under this scheme must be operated, used for

availing credit facilities, repayment of loans, etc.

Factor 2 (Specific usage)

The three variables included in this factor are ‘You are using bank for depositing

money’, ‘You are using banking services, because interest charged by the bank on

advance is economical than charged by the moneylender’ and ‘You are a regular

visitor of the bank’ signifying mean values between (3.46 - 3.99), factor loadings

(.652 - .831) and communalities (.522 - .692). Beneficiaries recognise that frequent

visits are necessary for effective implementation of financial inclusion scheme.

Reliability

Two factors are obtained after scale purification falling within the domain of usage in

financial inclusion. As evident from the Table 4.2, the Cronbach’s reliability for all 6

scale items underlying two factors ranges from .601 to .780. The alpha reliability

coefficient for F1 i.e., General usage (.780) is higher than the criteria of .77 obtained

by Gordon & Narayanan (1984) indicating high internal consistency. F2 namely,

Specific usage (.601) is also at a minimum acceptable level of 0.50 as recommended

by Brown et al. (2001) and Kakati & Dhar (2002) thereby obtaining satisfactory

internal consistency. However, overall alpha reliability score for all factors is very

much satisfactory at 0.662. Adequacy and reliability of sample size to yield distinct

and reliable factors is further demonstrated through Kaiser-Meyer-Olkin measure of

sampling adequacy that is 0.677 and all factor loadings are greater than .50.

Validity

The two factors obtained alpha reliability higher or equal to 0.50 and satisfactory


al., 1995).

Beneficiaries’ perception regarding social empowerment



118




in the construct of social empowerment. The KMO value (.803) and Bartlett test of

sphercity (3406.412) indicates acceptable and significant values. Therefore, factor


resulting into four-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with







Factor 1 (Creditworthiness)

It contained seven variables namely, ‘FI has changed your personality & life style’,

‘You are socially more developed after being covered under FI drive’, ‘FI has made

you socially more reputed’, ‘FI has led to improved hygiene & education’, ‘FI

improved your health & household hygiene’, ‘FI has increased your confidence level’

and ‘FI enhanced your confidence level, business relation & reduced family crisis &

social violence’ represents average mean values fluctuating between 3.29 - 3.76 but

identifies significant factor loadings (.602 - .837) and communalities (.526 - .730).

The factor accentuates that positive change in personality & lifestyle, individual

development, improved hygiene and building confidence is necessary for social

empowerment.

Factor 2 (Liberation)

This factor encompasses of only two items namely, ‘You are free to move to any

NGO or any other for any kind of help & support’ and ‘You are free to move to any

SHG or any other for any kind of help & support’ with low mean values 1.54 & 1.62,

high factor loadings .925 & .922 and communalities .881 & .870 respectively. This

factor stresses on free movement of beneficiaries to anywhere for availing any sort of

financial help.

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Factor 3 (Awareness)

This factor includes two items that is, ‘You avail those special schemes that are

offered by the government’ and ‘You are aware about all special schemes that are

offered by the government’ which exhibits mean values 2.06 & 2.95, factor loadings

.849 & .800 and communalities .816 & .784 respectively. This factor believes that

awareness about various available schemes is imperative for social empowerment.

Factor 4 (Grievances)

The final factor envisages three items, ‘You made complaints to authorities regarding

the delivery of financial services’, ‘Your response and feedback is always appreciated

regarding any financial issue’ and ‘You can bring any change in the society easily’

with low mean values (2.37 - 3.37), factor loadings (.509 - .866) and communalities

(.535 - .802). This factor underlines that all items moderately to significantly

contribute to the construct.

Reliability

Four factors are obtained after scale purification falling within the domain of social

empowerment. As evident from the Table 4.2, the Cronbach’s reliability for all 14

scale items underlying four factors ranges from .637 to .930. The alpha reliability

coefficient for creditworthiness (.770) and liberation (.930) is higher than the criteria

of .77 obtained by Gordon & Narayanan (1984) indicating high internal consistency.

Alpha value for awareness (.709) and grievances (.637) is also at a minimum

acceptable level of 0.50 as recommended by Brown et al. (2001) and Kakati & Dhar

(2002) thereby obtaining satisfactory internal consistency. However, overall alpha

reliability score for all factors is very much satisfactory at 0.830. Adequacy and

reliability of sample size to yield distinct and reliable factors is further demonstrated

through Kaiser-Meyer-Olkin measure of sampling adequacy that is 0.803 and all

factor loadings are greater than .50.

Validity

The four factors obtained alpha reliability higher or equal to 0.50 and satisfactory


al., 1995).

120

Beneficiaries’ perception regarding economic empowerment






in the construct of economic empowerment. The KMO value (.901) and Bartlett test

of sphercity (2534.429) indicates acceptable and significant values. Therefore, factor


resulting into two-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with


from .529 to .779, indicating moderate degree of linear association among the

variables. The factor loading ranges from .639 to 0.846 and the cumulative variance

extracted ranges from 46.768 to 67.873 percent. The communalities and percentage of

variance explained by each factor is displayed in the Table 4.2. A brief description of

factors emerged are as under:

Factor 1 (Stability)

It comprises of seven items, ‘FI has prepared you for emergencies’, ‘FI has increased

your purchasing power’, ‘You have enough savings to meet any contingent situation’,

‘FI has raised your living standard’, ‘FI enabled your children to get better education’,

‘FI has reduced your need to borrow money or goods’ and ‘FI enhanced your source

of income’. The items attained mean values between 3.24 - 3.79, significant factor

loadings .639 - .846 and communalities .529 - .779. This factor indicates that

preparedness for emergencies, enhanced purchasing power, raised living standard,

ability to face contingent situation are main components of economic empowerment.

Factor 2 (Employability)

Two items included in this factor are ‘FI created new employment opportunities’ and

‘FI directly effects capital formations & technological investment’. The mean values

identified are 2.63 & 2.27, factor loadings as .830 & .751 and communalities .702 &

.598 respectively. This factor believes that new employment opportunities and

increased technological investment make beneficiaries financially sound and

empowered.

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Reliability

Two factors are obtained after scale purification falling within the domain of

economic empowerment. As evident from the Table 4.2, the Cronbach’s reliability for

all 9 scale items underlying two factors ranges from .568 to .912. The alpha reliability

coefficient for stability (.912) is higher than the criteria of .77 obtained by Gordon &

Narayanan (1984) indicating high internal consistency whereas for employability

(.568) is also at a minimum acceptable level of 0.50 as recommended by Brown et al.

(2001) and Kakati & Dhar (2002) thereby obtaining satisfactory internal consistency.





Validity



al., 1995).

Beneficiaries’ perception regarding economic development






in the construct of economic development. The KMO value (.875) and Bartlett test of





from .591 to .811, indicating moderate to high degree of linear association among the

variables. The factor loading ranges from .546 to .875 and the cumulative variance




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Factor 1 (Growth)

This factor contained five items namely, ‘FI has increased per capita income of your

family’, ‘FI has increased life expectancy of your family members’, ‘FI has increased

access to education of the society’, ‘FI has led to increase in value of your assets’ and

‘FI has reduced level of stress in your life’. The mean values for the factor ranges

between 3.22 - 3.70, factor loadings from .546 - .846 and communalities between .591

- .759. This factor acknowledges the importance of increased per capita income,

enhanced life expectancy, improved access to education, enlargement in value of

assets and reduces stress level for economic development.

Factor 2 (Development)

Three items included in this factor are ‘FI has led to progress of the village’, ‘FI has

made the village sustainable for further progress’ and ‘FI has empowered the

members of the society’. The mean scores identified are 3.83, 3.62 & 3.68, factor

loadings as .875, .854 & .601 and communalities as .811, .761 & .678 respectively.

This factor underlines continuous progress is must for development.

Reliability

Two factors are obtained after scale purification falling within the domain of

economic development. As evident from the Table 4.2, the Cronbach’s reliability for

all 8 scale items underlying two factors ranges from .864 to .803. The alpha reliability

coefficient for growth (.864) and development (.803) is higher than the criteria of .77

obtained by Gordon & Narayanan (1984) indicating high internal consistency.





Validity



al., 1995).

123

4.4.2 Confirmatory Factor Analysis

CFA is a statistical tool that enables researchers to either confirm or reject

preconceived theory. It is a deductive approach and multivariate statistical technique

that is used to test how well the measured variables represent the construct and model

building. To perform CFA, it is essential to specify both the number of factors that

fall within a set of variables and which factor of each variable will load highly on

before results can be computed. CFA is of great use in improving quantitative

measurement in social sciences. It is generally based on a strong theoretical and

empirical foundation that allows the analyst to specify an accurate factor structure in

advance.

CFA is conducted with the objective of verifying the fitness of each latent construct.

In the present study, it is performed to assess the fitness, reliability and validity of five

measured constructs, viz., financial inclusion (FI) consists of three main dimensions

i.e., access, availability & usage; social empowerment (SE); economic empowerment

(EE) and economic development (ED). CFA is a way of testing how well measured

variables represent a smaller number of constructs. Once baseline models are

identified and measures are validated for discriminant and convergent validity

(Fornell & Larchel, 1981), reliability is assessed through the computation of

Cronbach’s alpha, composite reliability and average variance extracted (Hair et al.,

2009).

CFA is carried out construct-wise to restrict the number of indicators. During CFA,

19 items from the latent constructs having SRW below .50 got deleted (Hair et al.,

2009). All the CFA models fulfilled the necessary condition of identification,

according to which there must be at least three manifest variables for each construct

so that it can have enough degrees of freedom to estimate all free parameters. The

constructs have been found to be both uni-dimensional as well as multi-dimensional.

Most of the indices like GFI, AGFI, NFI, TLI and CFI are above .90 whereas badness

of fit indices i.e., RMSEA of all the constructs is below .08 and Chi-square statistics

(CMIN/DF) is less than recommended 5.0 level (Bagazzi & Yi, 1988).

4.4.2.1 CFA and construct validity

CFA is a unique type of factor analysis and is the first component of a comprehensive

test of a structure model. It is applied to analyse construct validity of each construct,

124

as construct validity has immediate influence on the substantive models under testing

(Bagozzi & Edwards, 1998). One of the biggest merits of CFA/SEM is its potential to

assess the construct validity of a proposed model (Hair et al., 2009) and also to enable

the concurrent assessment of both reliability and validity (Landis et al., 2009).

Construct validity is the extent to which a set of manifest variables actually represent

the theoretical latent construct that is intended to measure. Thus, it is concerned with

the accuracy of the measurement. Proof of the construct validity helps in ensuring that

manifest variables have been taken from a sample representing the actual true score

that exists in the population. Construct validity comprises of two components, i.e.,

convergent validity and discriminant validity and the present study also examines

construct validity through convergent and discriminant validity.

i. Convergent validity

It is the extent to which the manifest variables of a specific construct share a high

proportion of variance in common. Thus, it is the test in which the covariance

between the two measures is uniquely explained by trait factor (Lim & Ployhart,

2006). It can be measured as:

(a) Factor loading/SRW

The size of the factor loading/SRW is one of the important considerations.

High factor loading, i.e., above .50 or ideally .70 or higher indicates level of

convergence. Convergent validity gets established in the present study as

majority of standardised loading are .50.

(b) Average variance extracted (AVE)

It is used to ascertain average percentage of variation explained among the

manifest variables. By using standardised loadings, we calculated variance

extracted. It is computed as the total of all squared standardised factor

loadings divided by the number of items. AVE should be 0.50 or greater to

suggest adequate convergent validity. In the present study, the value of AVE

for all the constructs came above 0.50 (Table 4.3).

ii. Reliability

Reliability is concerned with testing the consistency of the measures and is also an

indicator of convergent validity. Cronbach’s alpha co-efficient is one of the most

125

prominent estimate of reliability and in the present study, overall value of Cronbach’s

alpha stands above .70 for all the constructs. In this regard, Hatcher (1994) suggested

that reliability reaching 0.70 or exceeding 0.70 satisfy the general requirement of

reliability for the study. Alternative way of testing reliability is through composite

reliability (CR), which is generally computed through structural model. It is computed

as the square of sum of standardised loadings divided by square of sum of

standardised loadings plus sum of error variance term. The rule of thumb for

composite reliability is .70 or higher (Fornell & Larchel, 1981). In the present study,

the value of composite reliability of all the latent constructs is above .90, which

indicates internal consistency of the data (Table 4.3).

iii. Discriminant validity

It is the extent to which a construct is truly distinct from other constructs. It has been

assessed by comparing AVE with the squared correlation between constructs (Fornell

& Larcker, 1981). The squared correlation between pair of constructs came out to be

less than AVE in almost all the cases (Table 4.4), thereby suggesting discriminant

validity (Ok et al.,2005).

4.4.2.2 Fitness of CFA models

A good fitting model is one that is reasonably consistent with the data. The model fit

compares the theory to the reality as represented by the data. In case the proposed

theory is perfect, the estimated covariance matrix and the actual observed covariance

matrix would be the same. Thus, closer the value of these two matrices, better the

model is said to be fit. CFA model validity depends on the goodness-of-fit (GOF) and

its measure can be classified into three groups.

i. Absolute fit measures/indices

It is a direct measure of how well the model specified by the research reproduces the

observed data (Kenny & McCoach, 2003). Some of the prominent absolute fit indices

used in the present study are (a) Chi-square statistics (b) Goodness-of-fit (GFI) (c)

Adjusted GFI (AGFI) (d) Root measure squared error of approximation (RMSEA).

ii. Incremental fit indices

It studies to what extent a specified model fits relative to some alternative baseline

model. The most common baseline model is called a null model, on that presumes all

126

observed variables are uncorrelated. Some of the important incremental fit indices

used in the study are (a) Normed fit index (NFI) (b) Tucker-lewis index (TLI) and (c)

Comparative fit index (CFI).

iii. Parsimony fit indices

It provides information about which model among a set of competing models is best,

considering its fit relative to its complexity. Since these indices are somewhat

controversial, they have not been considered in the present study.

4.4.2.3 CFA models

CFA is applied to assess the fitness, reliability and validity of six constructs, viz.,

financial inclusion (FI) consists of three main dimensions i.e., access, availability &

usage; social empowerment (SE); economic empowerment (EE) and economic

development (ED). The various resulting models are as under:

CFA model for access

First order CFA (Figure 4.10) is performed on access dimension, which constituted of

twelve items. Among twelve items, three items got deleted as they are not meeting the

criteria i.e. SRW’s > .50. After deleting, CFA produced good fit as CMIN/DF =

4.735, GFI = .955, AGFI = .912, NFI = .960, TLI = .950, CFI = .968 and RMSEA =

.087 (Table 4.5).The model has been found to be valid and reliable. The alpha value is

.884 whereas composite reliability came out to be .991 thereby indicating that all

items are reliable. Model has been proved to valid, as AVE came out to be .533

(Table 4.3). The construct validity also stands established as all the indicators have

factor loading above .50. Out of the twelve items, ‘Employee’s are helpful in making

information available regarding new schemes’ emerged to be strongest contributor

towards access dimension, as its regression weight is .90 .

127

FIGURE 4.10: CFA MODEL FOR ACCESS DIMENSION OF FID

AC1= The bank is conveniently located; AC2 = The employees are easily accessible when needed;

AC5 = Banking institution or its substitute is easily approachable; AC6 = Banking institutes response

well; AC8 = The bank manager promptly redress your problem; AC13 = Bank is easily approachable

in case of emergencies; AC15 = You have easy access to the information that is useful; AC16 =

Employees’ of bank are cooperative, friendly and knowledgeable and AC17 = Employees’ possess

sufficient banking information.

CFA model for availability

First order CFA (Figure 4.11) is executed on the latent construct i.e. availability

which consisted of nine items. The different fit indices evaluated the fitness of the

model and the results shows that the model fits the data well as CMIN/DF = 1.852,

GFI = .967, AGFI = .941, NFI = .963, TLI = .965, CFI = .975 and RMSEA = .065

(Table 4.5). Four items got deleted as their regression weights are below .50.

Access

AC17 e1

.76

AC16 e2

.81

AC15 e3 .90

AC13 e4 .62

AC8 e5 .56

AC6 e6

.77

AC5 e7

.77

AC2 e8

.81

AC1 e9

.51

128

Remaining five items are having above .50 regression weights, thus it becomes clear

that all remaining measured items are the significant contributors of this constructs.

In addition to the model fit, reliability and validity are also examined. The scale

exceeded the recommended cut off value of .70 as composite reliability = .981. So, it

is reasonable to conclude that the scale is reliable. Reliability is also confirmed

through Cronbach’s alpha value which is measured to be .806. As far as AVE is

concerned, the value is greater than 0.50 (AVE = .602). Also, each of the item loading

is greater than .50 (Table 4.3), which provides empirical support for the convergent

validity of construct. Among five items, ‘Loan is made available within time limit’

contributes highest to the main constructs with SRW .95.

FIGURE 4.11: CFA MODEL FOR AVAILABILITY DIMENSION OF FID

AV1 = Loan is easily available; AV9 = Loan is made available within time limit; AV11 = Procedure

involved in getting loan is quite lengthy; AV13 = New bank schemes are advertised frequently and

AV16 = Help desk/assisting staff is available for filling withdrawal/deposit form

CFA model for usage

First order CFA (Figure 4.12) is performed on usage dimension. It constituted of six

items. The model has been found valid and reliable after deleting three items having

regression weights below .50. The result of CFA shows the model fully fits the data,

CMIN/DF = 4.563, GFI = .988, AGFI = .965, NFI = .983, TLI = .979, CFI = .975 and

RMSEA = .085 (Table 4.5). The model found to be valid and reliable which is

Availability

AV1 e1 .93

AV9 e2 .95

AV11 e3 .82

AV13 e4

.52

AV16 e5

.53

129

confirmed through Cronbach’s alpha (.628), composite reliability (.965) and AVE =

.546 (Table 4.3). Out of five items, item ‘You frequently use credit facilities of the

bank’ contributes highest with regression weight .84.

FIGURE 4.12: CFA MODEL FOR USAGE DIMENSION OF FID

US3 = You frequently use credit facilities of the bank; US6 = You are using bank for repayment of

loan and US8 = Advance schemes of bank are frequently used by you

CFA model for social empowerment

Figure 4.13 depicts first order CFA is performed on social empowerment construct

which consisted of fourteen indicators. Each indicator of the construct is measured

using five point scale. While running CFA, seven items got deleted as they are not

meeting the criteria. The results reveals that the model fit statistics are within

recommended levels i.e., CMIN/DF = 3.286, GFI = .984, AGFI = .950, NFI = .979,

TLI = .965, CFI = .985 and RMSEA = .068 (Table 4.5). Additionally, this model has

been found to be valid and reliable, as AVE is .608, composite reliability equals to

.989. The value of Cronbach’s alpha is .806 and all items loading above .50 (Table

4.3). Thus, validity and reliability got established. Among seven items, ‘FI has

changed your personality & life style’ has the highest factor loading of .84, thus

contributes maximum to social empowerment.

Usage

US3 e1 .84

US6 e2

.67

US8 e3

.70

130

FIGURE 4.13: CFA MODEL FOR SOCIAL EMPOWERMENT

Social-emp = Social empowerment; SE2 = FI has led to improved hygiene and education; SE3 = FI has

changed your personality & life style; SE5 = FI has increased your confidence level; SE10 = FI enhanced

your confidence level, business relations and reduced family crisis & social violence; SE11 = FI improved

your health and household hygiene; SE16 = You can bring any change in the society easily and SE26 =

You are socially more developed after being covered under FI drive.

CFA model for economic empowerment

Figure 4.14 reveals first order CFA is performed on economic empowerment

construct which consisted of nine items. Responses are measured using five point

Likert scale. As evident from Table 4.5, CFA model yields good model fit results,

CMIN/DF =2.597, GFI = .981, AGFI = .959, NFI = .985, TLI = .985, CFI = .984 and

RMSEA = .057. The model has been found to be valid and reliable after deleting two

items. Reliability is also examined by calculating composite reliability (.992) (Table

4.3). AVE reflects the overall variance in the indicators accounted for by the latent

constructs. In this model, AVE exceeds the critical level of .50 (.595). This establishes

the reliability and convergent validity of measurement scale in the study. Also all

regression weights are above .50, thus it becomes clear that all measured variables are

Social-

emp

SE2 e1

.75

SE3 e2 .84

SE5 e3 .62

SE10 e4

.55

SE11 e5

.69

SE16 e6

.50

SE26 e7

.71

131

significant contributor of this construct. Scale reliability is established through

Cronbach’s alpha (.890). Among seven items, ‘FI has increased your purchasing

power’ adjudged as the strongest contributor as it is having highest SRW.

FIGURE 4.14: CFA MODEL FOR ECONOMIC EMPOWERMENT

Eco-emp = Economic empowerment; EE1 = FI has reduced your need to borrow money or goods; EE2 = FI has raised your living standard; EE3 = FI has prepared you for emergencies; EE4 = You have

enough savings to meet any contingent situation; EE7 = FI has increased your purchasing power; EE8 =

FI enabled your children to get better education and EE11 = FI enhanced your source of income.

CFA model for economic development

Figure 4.15 portrays first order CFA is performed on economic development

construct, which comprised of eight items. The results of CFA indicated that model fit

the data, CMIN/DF = 4.601, GFI = .966, AGFI = .919, NFI = .968, TLI = .952, CFI =

.974 and RMSEA = .085 (Table 4.5). In addition to model fit data, composite

reliability and validity are examined. The overall composite reliability and validity

came out to be .992. Scale reliability is established through Cronbach’s alpha .888.

The value of AVE is greater than .50. (.512). Furthermore, it is found that each factor

Eco-emp

EE11 e1

.75

EE8 e2 .83

EE7 e3 .88

EE4 e4 .69

EE3 e5

.74

EE2 e6

.86

EE1 e7

.67

132

loading is greater than .50 (Table 4.3), which provides empirical evidence for the

convergent validity of the construct. Item ‘FI has increased access to education of the

society’ emerged as the strongest contributor (.79) of the main construct.

FIGURE 4.15: CFA MODEL FOR ECONOMIC DEVELOPMENT

Eco-dvt = Economic development; ED1 = FI has increased life expectancy of your family members;

ED2 = FI has increased access to education of the society; ED3 = FI has empowered the members of the

society; ED4 = FI has led to progress of the village; ED5 = FI has made the village sustainable for

further progress; ED8 = FI has reduced level of stress in your life; ED10 = FI has increased per capita

income of your family and ED11 = FI has led to increase in value of your assets

.

4.4.3 Structural Equation Modeling

Structural equation modeling (SEM) is introduced to overcome the main weakness of

mostly used research techniques, viz., multiple regression, factor analysis,

multivariate analysis of variance and discriminant analysis that examine only a single

relationship at a time. It allows researcher to test theoretical propositions regarding

Eco-dvt

ED1 e1

.74

ED2 e2 .79

ED3 e3 .78

ED4 e4 .63

ED5 e5

.59

ED8 e6

.71

ED10 e7

.77

ED11 e8

.68

133

how constructs are theoretically linked and the direction of the significant

relationships. In other words, it is a multivariate technique combining aspects of

factor analysis and multiple regression that enables the researcher to simultaneously

examine a series of interrelated dependence relationships among the manifest

variables and latent constructs (Hair et al., 2009).

SEM represents the theory with a set of structural equations and is usually depicted

through a visual diagram. It is a procedure for accommodating measurement error

directly in the estimation of a series of dependence relationships. SEM is the best

multivariate procedure for testing both construct validity and theoretical relationships

among a set of concepts represented by multiple measured variables. It is the only

multivariate technique that allows the simultaneous estimation of multiple equations.

These equations represent the way constructs relate to manifest variables as well as

the way constructs are related to one other. Thus, when SEM techniques are used to

test a structural theory, it is equivalent to performing factor analysis and regression

analysis in one step (Hair et al., 2009)

4.4.3.1 Structural model

After applying CFA and checking for reliability and validity, SEM is conducted by

using AMOS (version 16.00) to assess fitness of structural model. The SEM technique

is used as the main statistical tool to test the main hypotheses proposed in the study.

As suggested by Hair et al. (2009), the proposed theoretical model is modeled in a

recursive manner to avoid problems associated with statistical identification. There

are a total of 25 indicators contained in the final structural model. Each indicator is

connected to the underlying theoretical construct in a reflective manner. The structural

relationship between latent constructs represented by single headed straight arrows are

specified according to the hypotheses established. In summary, the present structural

model includes (a) path from FI to SE (b) path from FI to EE (c) path from FI to ED

(d) path from SE to ED and (e) path from EE to ED. The proposed model strived to

identify the financial inclusion impact on economic development. By using reliable

and validated four constructs measures, the proposed original model is tested and

assessed in this section to identify the best fitted model.

Prior to model testing, the standardised loading estimates for the proposed structural

model are examined to ensure problems associated with interpretational confounding

134

are not existed (Hair et al. 2009). It is found that all loading estimates for the

structural model did not change substantially compared to loading estimates of the

final modified model. This further support the validity of modified model specified.

Full SEM model including all indicators are tested. The fit indices for the proposed

model are presented in the Table 4.6. The proposed model fitness results revealed that

the proposed model has not met the best-fitting model criteria.

The initial proposed structural model fit (CMIN/DF = 9.963, GFI = .874, AGFI =

.811, TLI = .827, NFI =.849, CFI = .862, RMSEA = .134) demonstrated inadequate

fit with the observed data, indicating the model could be further improved by

modifications. This involves adjusting the estimated model by freeing (estimating) or

setting (not estimating) parameters. Several modifications are made to the

hypothesised model based on the modification index (Byrne 2001). In this case, error

terms for items of same construct are allowed to covary. The reviews of the MIs for

the regression weights revealed four parameters with relatively large scores. (a) a2 &

a3, (b) d3 & d4, (d) c4 & c5 and (d) availability & access. The modifications are

given within the same constructs.

After incorporating these modifications to the model, the modified model

demonstrated a better model fit and is used as the final model (Figure 4.16) for

hypothesis testing (CMIN/DF= 4.924, GFI = .937, AGFI = .896, NFI = .932, TLI =

.924, CFI =.945, RMSEA = .089) (Table 4.6)

4.4.3.2 Hypotheses testing

On the basis of SEM results, the framed hypotheses have been tested (Table 4.7) and

the results are as under:

H1a: Access significantly predicts the financial inclusion.

H1b: Availability significantly predicts the financial inclusion.

H1c: Usage significantly predicts the financial inclusion.

It becomes evident from the SEM results (Figure 4.16 & Table 4.7) that Access (ß =

.75, p = .000), Availability (ß = .46, p = .000) and Usage (ß = .54, p = .000)

significantly predicts financial inclusion. Thus, hypotheses H1a, H1b and H1c stands

accepted.

135

H2a: Financial inclusion has direct impact on social empowerment.

H2b: Financial inclusion has direct impact on economic empowerment.

H2c: Financial inclusion has direct impact on economic development.

SEM results indicate that financial inclusion has positive significant and direct impact

on social empowerment (ß = .74, p = .000), economic empowerment (ß = .79, p =

.000) and economic development (ß = .47, p = .000). Therefore, hypotheses H2a, H2b

and H2c are accepted.

H3a: Social empowerment has direct impact on economic development.

H3b: Economic empowerment has direct impact on economic development.

It is inferred from SEM results that social empowerment has direct impact on

economic development (ß = .42, p = .029) and economic empowerment also has

significant impact on economic development (ß = .68, p = .020). Hence, H3a & H3b

stands accepted.

136

FIGURE 4.16: OVERALL STRUCTURE EQUATION MODEL

AC= Access; AV= Availability; US= Usage; FI= Financial inclusion; EE= Economic empowerment;

SE= Social empowerment; ED= Economic development; SE2= FI has led to improved hygiene and

education; SE3= FI has changed your personality & life style; SE5= FI has increased your confidence

level; SE10= FI enhanced your confidence level, business relations and reduced family crisis & social

violence; SE11= FI improved your health and household hygiene; SE16= You can bring any change in the

society easily; SE26= You are socially more developed after being covered under FI drive; EE1= FI has

reduced your need to borrow money or goods; EE2= FI has raised your living standard; EE3= FI has prepared you for emergencies; EE4= You have enough savings to meet any contingent situation; EE7= FI

has increased your purchasing power; EE8= FI enabled your children to get better education; EE11= FI

enhanced your source of income; ED1= FI has increased life expectancy of your family members; ED2=

FI has increased access to education of the society; ED3= FI has empowered the members of the society;

ED4= FI has led to progress of the village; ED5= FI has made the village sustainable for further progress;

ED8= FI has reduced level of stress in your life; ED10= FI has increased per capita income of your family

and ED11= FI has led to increase in value of your assets.

4.4.3.3 Mediation of SE in FI-ED link

Structural equation modeling (SEM) has been used to check various propsed relations,

it is a multivariate technique that seeks to explain the relationship among multiple

variables (Kaplan, 2000). In the present study, the relationship between financial

inclusion, social empowerment and economic development have been assessed. In

order to test the mediating effect, all the conditions described by Baron & Kenny

(1986) are first satisfied. These conditions are (a) the relationship between

137

independent variable and dependent variable should be significant (b) the relationship

between independent and third variable i.e. mediator should be significant (c) the

relationship between the mediator or third variable and outcome should also be

significant (d) when the mediator is entered into the equation, relationship between

independent and dependent variables becomes insignificant.

We used four step procedure through structural analysis in which we first assessed the

impact of financial inclusion on economic development (Figure 4.17), which is

significant (SRW= 0.52, p<0.01). Hence, satisfying first condition for mediation.

FIGURE 4.17: IMPACT OF FINANCIAL INCLUSION ON ECONOMIC

DEVELOPMENT

In the second step, we analysed the impact of financial inclusion on social

empowerment (Figure 4.18), which is significant (SRW= 0.56, p<0.01). Therefore,

second condition for mediation fulfilled.

FIGURE 4.18: IMPACT OF FINANCIAL INCLUSION ON SOCIAL

EMPOWERMENT

In the next step, we assessed the impact of social empowerment on economic

development (Figure 4.19), which is significant (SRW= 0.48, p<0.01). So, the first

three conditions of mediation given by Baron & Kenny (1986) are satisfied.

138

FIGURE 4.19: IMPACT OF SOCIAL EMPOWERMENT ON ECONOMIC

DEVELOPMENT

In order to test the mediating effect, in the last step we added the mediating variable

i.e., social empowerment between financial inclusion and economic development. The

result revealed that when the mediator i.e. social empowerment is entered into the

equation of financial inclusion and economic development, the relationship between

financial inclusion and economic development became insignificant (SRW= 0.37,

p>0.05) and relationship between financial inclusion & social empowerment and

social empowerment & economic development remained significant (p<.05), (Figure

4.20) which satisfies the conditions of mediation effect as suggested by Baron &

Kenny (1986). This indicates that hypothesis, ‘Social empowerment mediates the

relationship between financial inclusion and economic development’ stands

accepted.


DEVELOPMENT THROUGH SOCIAL EMPOWERMENT

139

4.4.3.4 Mediation of EE in FI-ED link

In order to test the mediating effect all the conditions described by Baron & Kenny

(1986) are first satisfied. We used four step procedure through structure equation

modeling in which we first assessed the impact of predictor i.e. financial inclusion on

dependent variable i.e. economic development, which is significant (SRW= 0.52, P <

.01). Hence, first condition of mediation is accepted as financial inclusion is

positively affecting economic development (Figure 4.17).

In the second step, we analysed the impact of financial inclusion on economic

empowerment. The results (Figure 4.21) revealed that financial inclusion is a

significant predictor of economic empowerment (SRW= 0.52, P< .01). Hence, second

condition of mediation is also fulfilled.


EMPOWERMENT

In the next step, we evaluated the impact of economic empowerment (mediator) on

economic development (criterion). The results (Figure 4.22) revealed that economic

empowerment is significantly affecting economic development. Hence third condition

of mediation also gets fulfilled (SRW= 0.75, P< .01).

FIGURE 4.22: IMPACT OF ECONOMIC EMPOWERMENT ON ECONOMIC

DEVELOPMENT

140

In order to test the mediating effect, in the last step the mediating variable i.e.

economic empowerment between financial inclusion and economic development is

added. It is found that with the introduction of economic empowerment as mediator in

financial inclusion and economic development equation, the relationship between

financial inclusion and economic development become insignificant (SRW=0.40,

p>0.05) and relationship between financial inclusion & economic empowerment and

economic empowerment & economic development remained significant (p< .05)

Figure 4.23 satisfy the conditions of mediation effects as suggested by Baron &

Kenny, 1986. This shows that hypothesis, ‘Economic empowerment mediates the

relationship between financial inclusion and economic development’ stands

accepted.


DEVELOPMENT THROUGH ECONOMIC EMPOWERMENT

4.4.4 Output from One-way ANOVA

Table 4.8 shows output from One-way ANOVA using different socio-economic

variables subdivided into age, caste, religion, qualification and income on nature of

financial inclusion. Socio-economic variable wise, variance of groups is not same as

the value of p is less than 0.05, indicating significant mean difference exist in the

nature of financial inclusion with regard to religion, qualification and income whereas

for age and caste, p value is more than 0.05 indicating no significant different exists.

Table 4.9 depicts the output from independent t-test measuring significance of mean

difference on the basis gender and marital status. As evident from the table, there exist

141

no significant difference between male & female and married & unmarried

respondents, as value of p>0.05 level of significance.

So, on the basis of Table 4.8 & 4.9, we can say that the hypothesis ‘Nature of

financial inclusion differs across the socio-economic variable’ is accepted for

religion, qualification & income and rejected for age, caste, gender & marital status.

Table 4.10 depicts age-wise output from One-way ANOVA using different

dimensions of financial inclusion subdivided into access, availability and usage. In

case of access, variance of group is same as the value of p is more than 0.05,

indicating insignificant mean difference exist among respondents of different age

groups. Whereas in case of availability and usage, variance of group is not same as

the value of p is less than 0.05, indicating significant mean difference exist among

different age groups. With regard to availability & usage dimensions of financial

inclusion, beneficiaries belonging to above 50 years of age (2.81 & 2.93) are highly

satisfied followed by 40-50 years (2.70 & 2.88), 30-40 years (2.61 & 2.76) and upto

30 years (2.44 & 2.67).

Table 4.11 shows caste-wise output from One-way ANOVA using different

dimensions of financial inclusion i.e. access, availability and usage. For access &

usage dimensions, variance of group is not same as the value of p is less than 0.05

indicating significant mean difference exist among respondents belonging to different

caste. Whereas no significant mean difference exist among respondents of different

caste with respect to availability dimension as variance of group is same as the value

of p is more than 0.05. Caste-wise analysis shows that with regard to access

dimension general caste (3.54) beneficiaries are highly contended followed by SC

(3.53), ST (3.31) and OBC (3.19) beneficiaries. As far as usage dimension is

concerned, general caste (2.88) beneficiaries are more satisfied than SC (2.78), OBC

(2.64) and ST (2.46) beneficiaries.

Table 4.12 indicates religion-wise output from One-way ANOVA using different

dimensions of financial inclusion i.e. access, availability and usage. Insignificant

mean difference exists among respondents belonging to different caste as value of p is

more than 0.05 in case of access and availability dimensions. As far as usage

dimension is concerned, variance of group is not same as the value of p is less than

0.05 indicating significant mean difference exist among respondents belonging to

142

different castes. Religion-wise analysis depicts that Sikh respondents (3.83) are more

satisfied with usage dimension followed by Hindu (2.83) and Muslim (2.39)

beneficiaries.

Table 4.13 depicts qualification-wise output from One-way ANOVA for different

dimensions of financial inclusion. For all the dimensions of financial inclusion i.e.

access, availability and usage variance of group is not same as the value of p is less

than 0.05, indicating significant mean difference exist among beneficiaries under

different qualification categories. For all the dimensions i.e., access, availability &

usage, beneficiaries with upto higher secondary qualification are maximally satisfied

with mean value (3.79, 2.85 & 3.00) and beneficiaries with below primary

qualification are minimally satisfied with mean value (2.89, 2.10 & 2.33)

respectively.

Table 4.14 shows income-wise output from One-way ANOVA. Variance of group is

not same as the value of p is less than 0.05, indicating significant mean difference

exist among respondents belonging to different monthly income categories with

regard to different dimensions of financial inclusion i.e., access, availability and

usage. Beneficiaries with above `20,000 income are highly satisfied with regard to

access & availability dimensions followed by beneficiaries with `10,000-20,000;

`5,000-10,000 and upto `5,000 monthly income. On the dimension of usage,

beneficiaries with income between `10,000-20,000 are more satisfied than

beneficiaries with `5,000-10,000, above `20,000 and upto `5,000 monthly income.

Table 4.15 shows output from independent t-test measuring significance of mean

difference among male & female. As evident from the table, significant difference

exists with regard to usage as value of p is less than 0.05. Whereas no significant

mean difference exist between male & female with regard to access & availability as

the p value is greater than 0.05. With regard to usage dimension, male are found to be

more contended with mean score 2.84 than female with mean value of 2.70.

Table 4.16 reveals output from independent t-test measuring significance of mean

difference among married & unmarried respondents. As evident from the table, value

of p is less than 0.05 indicating significant mean difference exists between married &

unmarried with regard to availability & usage. But insignificant mean difference

exists on the dimension of access as p value is more than 0.05. Married beneficiaries

143

are more satisfied (2.69 & 2.84) than unmarried beneficiaries (2.41 & 2.66) with

regard to availability & usage dimension of financial inclusion respectively.

TABLE 4.1: SOCIO-ECONOMIC PROFILE OF RESPONDENTS*

S.No. Variable Classification Number Percentage 1 Name of bank JKB 236 47.20

JKGB 125 25.00

SBI 90 18.00

PNB 49 9.80

Sub Total 500 100

2 Gender Male 438 87.60

Female 62 12.40

Sub Total 500 100

3 Age Upto 30 years 51 10.20

30-40 years 179 35.80

40-50 years 182 36.40 Above 50 years 88 17.60

Sub Total 500 100

4 Caste General 285 57.00

SC 175 35.00 ST 13 2.60

OBC 27 5.40

Sub Total 500 100

5 Religion Hindu 486 97.20 Muslim 11 2.20

Sikh 3 0.60

Sub Total 500 100

6 Marital Status Married 450 90.00 Unmarried 50 10.00

Sub Total 500 100

7 Qualification Literate but below Primary 14 2.80

Upto primary 65 13.00 Upto middle 155 31.00

Upto secondary 172 34.40

Upto higher secondary 57 11.40

Upto graduate or higher 7 1.40 Any other 1 0.20

Illiterate 29 5.80

Sub Total 500 100

8 Income Upto ` 5,000 258 51.60

(Monthly) ` 5,000-10,000 199 39.80

` 10,000-20,000 39 7.80

Above ` 20,000 4 0.80

Sub Total 500 100

9 District Jammu 204 40.80

Kathua 163 32.60

Reasi 15 3.00 Samba 39 7.80

Udhampur 79 15.80

Sub Total 500 100

*Source: Survey

144

TABLE 4.2: RESULTS SHOWING FACTOR LOADINGS AND VARIANCE EXPLAINED AFTER SCALE PURIFICATION

(ROTATED COMPONENT METHOD)*

Factor-wise dimension Mean Standard

deviation

Factor

loading

Eigen

value

Variance

explained

%

Cumulative

explained

%

Communality Alpha

(α)

ACCESS

Factor 1: Information accessibility 5.386 40.764 40.764 .904

You have easy access to the information which is useful 3.44 1.05 .857 .810

Banking officials respond well 3.34 1.24 .848 .719

Employees’ of bank are cooperative, friendly and

knowledgeable

3.50 1.02 .816 .699

Banking institution or its substitute is easily approachable 3.57 1.06 .785 .650

Employees’ possess sufficient banking information 3.62 .89 .771 .643

The employees are easily accessible when needed 3.66 1.02 .753 .721

The bank manager promptly redress your problems 2.70 1.41 .733 .628

Factor 2: Physical accessibility 1.507 14.585 55.348 .593

Bank is easily approachable in case of emergencies 3.54 1.10 .547 .604

ATM service is nearby from your place 2.51 1.48 .802 .651

The bank is conveniently located 3.83 1.13 .639 .589

Factor 3: Approachability 1.244 12.456 67.805 .580

This is the only bank in your area 4.12 .55 .835 .713

As compared to other banks, this bank is nearest to you 4.29 .51 .822 .712

AVAILABILITY

Factor 1: Loan availability 3.562 29.516 29.516 .933

Loan is easily available 2.42 1.53 .944 .918

Loan is available within time limit 2.37 1.50 .934 .903

Procedure involved in getting loan is easy 2.42 1.46 .885 .826

145

Factor 2: Support and assistance 1.765 22.743 52.260 .700

Help desk/assisting staff is available for filling withdrawal/

deposit form

3.52 1.02 .757 .589

Field workers promotes various schemes of bank 3.24 1.26 .699 .546

Infrastructure is as per the requirements of the customers 3.49 .99 .704 .557

Bank follows quick problem solving approach 2.87 1.32 .616 .585

Factor 3: Promotion 1.203 20.302 72.562 .782

Employees’ are helpful in making information available

regarding new schemes

1.94 1.25 .890 .812

New bank schemes are advertised frequently 1.69 1.03 .880 .794

USAGE

Factor 1: General usage 2.246 35.956 35.956 .780

You frequently use credit facilities of the bank 1.92 1.20 .856 .739

Advance schemes of bank are frequently used by you 2.03 1.26 .845 .716

You are using bank for the repayment of loan 1.66 1.20 .778 .605

Factor 2: Specific usage 1.701 29.821 65.777 .601

You are using bank for depositing money 3.87 .75 .831 .692

You are using banking services, because interest charged

by the bank on advance is economical than charged by the

moneylender

3.99 .49 .815 .672

You are a regular visitor of the bank 3.46 1.01 .652 .522

SOCIAL EMPOWERMENT

Factor 1: Creditworthiness 4.972 25.346 25.346 .864

FI has changed your personality & life style 3.45 .99 .834 .725

FI has made you socially more reputed 3.52 .94 .723 .696

FI has led to improved hygiene & education 3.29 1.02 .744 .614

You are socially more developed after being covered under

FI drive

3.76 .58 .764 .619

FI improved your health & household hygiene 3.45 .93 .731 .580

146

FI has increased your confidence level 3.54 .95 .673 .524

FI enhanced your confidence level, business relations &

reduced family crisis & social violence

3.56 .86 .607 .546

Factor 2: Liberation 2.683 15.896 41.242 .860

You are free to move to any NGO or any other for any

kind of help or support

1.54 .92 .922 .868

You are free to move to any SHG or any other for any kind

of help or support

1.62 .99 .888 .809

Without FI, you feel difficult in socialising with people of

different social groups

1.96 1.04 .786 .629

Factor 3: Awareness 1.446 8.893 50.135 .637

You avail those special schemes that are offered by the

govt.

2.06 1.21 .844 .805

You are aware about all special schemes that are offered

by the govt.

2.95 1.32 .789 .785

Factor 4: Grievances 1.159 8.662 58.797 .503

You made complaints to the authorities regarding the

delivery of financial services

2.59 1.32 .759 .687

You can bring any change in the society easily 3.37 1.02 .703 .696

ECONOMIC EMPOWERMENT

Factor 1: Stability 4.993 46.768 46.768 .912

FI has prepared you for emergencies 3.51 .97 .846 .719

FI has increased your purchasing power 3.33 1.06 .824 .779

You have enough savings to meet any contingent situation 3.28 1.08 .812 .660

FI has raised your living standard 3.46 .99 .808 .736

FI enabled your children to get better education 3.27 1.05 .762 .703

FI has reduced your need to borrow money or goods 3.79 .81 .683 .529

FI enhanced your source of income 3.24 1.04 .639 .683

Factor 2: Employability 1.116 21.106 67.873 .568

FI created new employment opportunities 2.63 1.13 .830 .702

147

FI directly effects capital formation & investment in

technology

2.27 1.08 .751 .598

ECONOMIC DEVELOPMENT

Factor 1: Growth 4.558 40.266 40.266 .864

FI has increased per capita income of your family 3.23 1.05 .846 .759

FI has increased life expectancy of your family members 3.33 .94 .798 .683

FI has increased access to education of the society 3.47 .89 .768 .693

FI has led to increase in value of your assets 3.22 1.05 .765 .620

FI has reduced level of stress in your life 3.70 .82 .546 .591

Factor 2: Development 1.039 29.694 69.960 .803

FI has led to progress of the village 3.83 .69 .875 .811

FI has made the village sustainable for further progress 3.62 .88 .854 .761

FI has empowered the members of the society 3.68 .78 .601 .678

*Source: Survey

148

TABLE 4.3: RELIABILITY & VALIDITY OF LATENT CONSTRUCTS*

Constructs AVE Composite

reliability

Cronbach’s alpha

(α)

Access .533 .991 .884

Availability .602 .981 .806

Usage .546 .965 .628

Social empowerment .608 .989 .806

Economic empowerment .595 .992 .890

Economic development .512 .992 .888

*Source: Survey

TABLE 4.4: DISCRIMINANT VALIDITY OF LATENT CONSTRUCTS*

AC AV US SE EE ED POV AD

AC (.533)

AV .28 (.602)

US .19 .48 (.546)

SE .38 .28 .15 (.608)

EE .36 .30 .21 .50 (.595)

ED .38 .25 .22 .48 .51 (.512)

POV .32 .23 .13 .44 .42 .52 (.634)

AD .22 .08 .07 .12 .11 .17 .09 (.491)

AC = Access, AV = Availability, US = Usage, SE = Social empowerment, EE = Economic empowerment, ED

= Economic development, POV = Poverty and AD = Area Development

*Source: Survey

149

TABLE 4.5: RESULTS OF CFA FIT INDICES*

Constructs CMIN/DF GFI AGFI CFI NFI TLI RMSEA

Access 4.735 .955 .912 .968 .960 .950 .087

Availability 1.852 .967 .941 .963 .965 .975 .065

Usage 4.563 .988 .965 .983 .979 .975 .085

Social empowerment 3.286 .984 .950 .985 .979 .965 .068

Economic empowerment 2.597 .981 .959 .984 .985 .985 .057

Economic development 4.601 .966 .919 .974 .968 .952 .085

*Source: Survey

TABLE 4.6: FITNESS OF THE STRUCTURAL MODEL*

Model CMIN/DF GFI AGFI CFI NFI TLI RMSEA

Modified model 4.924 .937 .896 .945 .932 .924 .089

Proposed model 9.963 .874 .811 .862 .849 .827 .134

*Source: Survey

150

TABLE 4.7: RESULTS OF HYPOTHESES TESTING*

Hypotheses CR SRW P-

value

Accepted/

Rejected

H1a Access significantly predicts the financial

inclusion.

10.966 .75 .000 Accepted

H1b Availability significantly predicts the


14.997 .46 .000 Accepted

H1c Usage significantly predicts the financial

inclusion.

17.868 .54 .000 Accepted

H2a Financial inclusion has direct impact on

social empowerment.

12.463 .74 .000 Accepted

H2b Financial inclusion has direct impact on

economic empowerment.

8.702 .79 .000 Accepted

H2c Financial inclusion has direct impact on

economic development.

3.187 .47 .000 Accepted

H3a Social empowerment has direct impact on

economic development

2.273 .42 .029 Accepted

H3b Economic empowerment has direct impact

on economic development

2.333 .68 .020 Accepted

H4a Social empowerment mediates the



Accepted

H4b Economic empowerment mediates the



Accepted

H7 Nature of financial inclusion differs across

the socio-economic variables

Partially

accepted

*Source: Survey

151

TABLE 4.8: OUTPUT FROM ONE-WAY ANOVA*

Particular Description of variable Mean Nature of

variable

Sum of

square

Df Mean

square

F Sig. Remarks

Financial inclusion Age Upto 30 years 2.95 Between group 2.273 3 .758 2.139 .094 Insignificant

30-40 years 3.03 Within group 175.729 496 .353

40-50 years 3.10 Total 178.003 499

Above 50 years 3.18

Financial inclusion Caste General 3.11 Between group 2.670 3 .890 2.517 .057 Insignificant

SC 3.07 Within group 175.333 496 .353

ST 2.87 Total 178.003 499

OBC 2.82

Financial inclusion Religion Hindu 3.08 Between group 2.731 2 1.366 3.872 .021 Significant

Muslim 2.80 Within group 175.272 497 .353

Sikh 3.88 Total 178.003 499

Financial inclusion Qualification Below primary 2.51 Between group 15.708 7 2.244 6.803 .000 Significant

Upto primary 2.82 Within group 162.295 492 .330

Upto middle 3.01 Total 178.003 499

Upto secondary 3.22

Upto higher

secondary

3.28

Upto graduate or

higher

3.08

Any other 3.19

Illiterate 2.98

Financial inclusion Income Upto ` 5,000 2.89 Between group 19.713 3 6.571 20.590 .000 Significant

(Monthly) ` 5,000-10,000 3.25 Within group 158.290 496 .319

` 10,000-20,000 3.39 Total 178.003 499

Above ` 20,000 3.56

*Source: Survey

152

TABLE 4.9: MEAN DIFFERENCE IN THE NATURE OF FINANCIAL INCLUSION THROUGH T-TEST*

Particular Nature of variable Mean Standard deviation t-value Df Sig. Remarks

Financial inclusion Gender Male 3.08 .61 .501 91.597 .617 Insignificant

Female 3.04 .48

Financial inclusion Marital status Married 3.09 .60 1.719 498 .086 Insignificant

Unmarried 2.94 .52

*Source: Survey

TABLE 4.10: AGE-WISE OUTPUT FROM ONE-WAY ANOVA*

Dimensions of

financial inclusion

Description of

variables

Mean Nature of

variable

Sum of

square

Df Mean

square

F Sig. Remarks

Access Upto 30 years 3.47 Between group .697 3 .232 .518 .670 Insignificant


40-50 years 3.51 Total 223.094 499

Above 50 years 3.59

Availability Upto 30 years 2.44 Between group 5.198 3 1.733 2.739 .043 Significant


40-50 years 2.70 Total 318.924 499

Above 50 years 2.81

Usage Upto 30 years 2.67 Between group 3.642 3 1.214 3.152 .025 Significant


40-50 years 2.88 Total 194.641 499

Above 50 years 2.93

*Source: Survey

153

TABLE 4.11: CASTE-WISE OUTPUT FROM ONE-WAY ANOVA*

Dimensions of

financial inclusion

Description

of variables

Mean Nature of

variable

Sum of

square

DF Mean

square

F Sig. Remarks

Access General 3.54 Between group 3.497 3 1.166 2.633 .049 Significant

SC 3.53 Within group 219.597 496 .443

ST 3.31 Total 223.094 499

OBC 3.19

Availability General 2.70 Between group 1.824 3 .608 .951 .416 Insignificant

SC 2.64 Within group 317.100 496 .639

ST 2.55 Total 318.924 499

OBC 2.46

Usage General 2.88 Between group 3.948 3 1.316 3.423 .017 Significant

SC 2.78 Within group 190.693 496 .384

ST 2.46 Total 194.641 499

OBC 2.64

*Source: Survey

TABLE 4.12: RELIGION-WISE OUTPUT FROM ONE-WAY ANOVA*

Dimensions

of financial

inclusion

Description of

variables

Mean Nature of

variable

Sum of

square

Df Mean

square

F Sig. Remarks

Access Hindu 3.51 Between group 1.704 2 .852 1.912 .149 Insignificant


Sikh 4.00 Total 223.094 499

Availability Hindu 2.66 Between group 3.677 2 1.839 2.898 .056 Insignificant


Sikh 3.74 Total 318.924 499

Usage Hindu 2.83 Between group 5.093 2 2.547 6.677 .001 Significant


Sikh 3.83 Total 194.641 499

*Source: Survey

154

TABLE 4.13: QUALIFICATION-WISE OUTPUT FROM ONE-WAY ANOVA*

Dimensions of

financial inclusion

Description of variables Mean Nature of

variable

Sum of

square

Df Mean

square

F Sig. Remarks

Access Below primary 2.89 Between group 20.354 7 2.908 7.056 .000 Significant



Upto secondary 3.65

Upto higher secondary 3.79

Upto graduate or higher 3.40

Any other 3.75

Illiterate 3.41

Availability Below primary 2.10 Between group 15.055 7 2.151 3.482 .001 Significant



Upto secondary 2.81



Any other 2.55

Illiterate 2.64

Usage Below primary 2.33 Between group 11.122 7 1.589 4.259 .000 Significant



Upto secondary 2.98



Any other 3.00

Illiterate 2.65

*Source: Survey

155

TABLE 4.14: INCOME-WISE OUTPUT FROM ONE-WAY ANOVA*

Dimensions of

financial inclusion

Description of

variables

Mean Nature of

variable

Sum of

square

Df Mean

square

F Sig. Remarks

Access Upto ` 5,000 3.36 Between group 13.606 3 4.535 10.738 .000 Significant

` 5,000-10,000 3.64 Within group 209.489 496 .422

` 10,000-20,000 3.82 Total 223.094 499

Above ` 20,000 3.92

Availability Upto ` 5,000 2.43 Between group 29.330 3 9.777 16.745 .000 Significant

` 5,000-10,000 2.88 Within group 289.594 496 .584

` 10,000-20,000 2.96 Total 318.924 499

Above ` 20,000 3.44

Usage Upto ` 5,000 2.63 Between group 21.472 3 7.157 20.500 .000 Significant

` 5,000-10,000 3.01 Within group 173.169 496 .349

` 10,000-20,000 3.16 Total 194.641 499

Above ` 20,000 3.00

*Source: Survey

TABLE 4.15: MEAN DIFFERENCE IN THE NATURE OF FINANCIAL INCLUSION BETWEEN MALE & FEMALE THROUGH

T-TEST*

Dimensions of

financial inclusion

Nature of

variable

Mean Standard

deviation

t-value Df Level of

significance

Remarks

Access Male 3.50 .69 -1.259 95.863 .211 Insignificant

Female 3.59 .51

Availability Male 2.68 .81 1.227 498 .220 Insignificant

Female 2.54 .75

Usage Male 2.84 .65 2.139 103.399 .035 Significant

Female 2.70 .44

*Source: Survey

156

TABLE 4.16: MEAN DIFFERENCE IN THE NATURE OF FINANCIAL INCLUSION BETWEEN MARRIED & UNMARRIED

BENEFICIARIES THROUGH T-TEST*

Dimensions of

financial

inclusion

Nature of

variable

Mean Std. dev. t-value Df Level of

significance

Remarks

Access Married 3.51 .68 .466 498 .641 Insignificant

Unmarried 3.47 .60

Availability Married 2.69 .80 2.322 498 .021 Significant

Unmarried 2.41 .72

Usage Married 2.84 .62 1.95 498 .052 Significant

Unmarried 2.66 .65

*Source: Survey

157

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`

Chapter-V Financial Inclusion and

Poverty Reduction

CONTENTS



5.2 Conceptual Analysis of Poverty & Poverty

Reduction

161

5.3 Trends of Poverty in India 162

5.4 Causes of Poverty in India 166

5.5 Poverty Alleviation Programmes 167

5.6 Poverty Reduction through Financial Inclusion 171



5.6.3 Demographic Profile-wise Mean Satisfaction

regarding Poverty Reduction

175

5.6.4 Relationship between Financial Inclusion and

Poverty Reduction

177

References 183

160

CHAPTER V

FINANCIAL INCLUSION AND POVERTY REDUCTION

5.1 INTRODUCTION

Financial inclusion is an important tool for combating the multi-dimensional aspects

of poverty (Nalini & Mariappan, 2012). It enhances financial access which leads to

several benefits such as overcoming problem of poverty, unemployment, inequality

and deteriorating welfare (Nirmala & Yepthomi, 2014). Access to basic financial

services such as savings, loans, insurance, credit etc. through financial inclusion has

generated a positive impact on the lives of the poor (Caskey et al., 2006 and Dupas &

Robinson, 2009) and help them to come out of the clutches of poverty (Bhandari,

2009 and Bansal, 2012). Financial inclusion provides access to a well-functioning

financial system which limits the risks, enables economically & socially excluded

people to integrate and actively contributes in the development of the economy and

shields themselves from shocks of drought, illness and death (Swamy &

Vijayalakshmi, 2009; Collins et al., 2009; Napier et al., 2013; Chibango, 2014 and

Shyni & Mavoothu, 2014). There is a bidirectional cause and effect relationship

between poverty and financial inclusion (Bhandari, 2009). Access to financial

services through financial inclusion provides opportunities to save, avail credit, make

investment and meet emergency situations and enables the poor to smoothen

consumption pattern, properly manage risk, increase assets gradually, enhance earning

capacity and providing good standard of living. Financial inclusion is a win-win

opportunity for the poor, for the banks and for the nation (Subbarao, 2009). It

contributes in employment generation, income generation, proper utilisation of

resources, mobilisation of savings etc. which help in poverty alleviation and thus lead

to GDP growth in any economy (Shyni & Mavoothu, 2014). The linkage between a

developed financial system or ecosystem and improved wealth of the poor is depicted

in Figure 5.1 (Selvarajah et al., 2012).

161

FIGURE 5.1: RELATIONSHIP BETWEEN FINANCIAL ECOSYSTEM AND

POVERTY*

*Source: Selvarajah, Jeyanthi, Nair, Mahendhiran, Vaithilingam, Santha, & Ng, Jason (2012).

Financial inclusion and the blueprint financial ecosystem in poverty reduction: an

exploratory analysis. Retrieved from www.ibrarian.net/navon/mismachKarov.jsp?Ppid=

21309790. Accessed on 27-09-2014. Accessed on 27-09-2014.

5.2 CONCEPTUAL ANALYSIS OF POVERTY & POVERTY

REDUCTION

The word ‘poverty’ derives from French word ‘pauvre’ meaning poor (Khanna,

2012). Poverty means not having enough money for basic needs such as food, water,

shelter or toilets. It means the state of lacking material possessions of having little or

no means to support oneself.

India is the first country in the world to define poverty as the total per-capita

expenditure of the lowest expenditure class. Rowntree (1910) defines poverty as a

‘situation where the total earnings of a family are insufficient to obtain the minimum

162

necessities for the maintenance of merely physical efficiency’. The World Bank

(1990) refers poverty as, ‘the inability to attain a minimal standard of living’. Further,

‘Poverty is hunger. Poverty is lack of shelter. Poverty is being sick and not being able

to see a doctor. Poverty is not having access to school and not knowing how to read.

Poverty is not having a job, is fear for the future, living one day at a time. Poverty is

losing a child to illness brought about by unclean water. Poverty is powerlessness,

lack of representation and freedom’ (World Bank, 1990). Amartya Sen (2000) states

that poverty refers to, ‘insufficient income along with absence of wide range of

capabilities, including security and ability to participate in economic and political

systems’. According to World Bank (2001), poverty refers to, ‘deprivation of well-

being related to lack of material income or consumption, low levels of education &

health, vulnerability & exposure to risk, no opportunity to be heard and

powerlessness’. Poverty is lack of or the inability to achieve a socially acceptable

standard of living (Bellu, 2005). European Commission (2007) defines poverty as,

‘when resources of people, families and groups of persons are so limited and they are

unable to achieve minimum acceptable way of life in the state to which they belong’.

Selvarajah et al., (2012) & Mel et al., (2009) describe poverty as, ‘lacking of basic

needs such as food, shelter, employment, healthcare and education’. Murari &

Didwania (2010) defined poverty as ‘when a person faces difficulties in meeting the

minimum requirement of acceptable living standards’. Poverty refers to deprivation

from income, basic needs and human capabilities (Chibango, 2014). According to

Government of India reports, a person who consumes 2400 kcal/day in rural and 2100

kcal/day in urban areas are not poor or below the poverty line category (Sharma et al.,

2011). But recent report claims that those spending `32 and `47 in towns and cities

respectively should not be considered as poor (Singh, 2014). Poverty reduction is

defined as successfully lessening deprivation of well-being (Sunderlin, 2004). Poverty

reduction refers to reduction of number or percentage of people living in poverty or

the severity of the impact of poverty on the lives of poor people (Kraai, 2012).

5.3 TRENDS OF POVERTY IN INDIA

According to Planning Commission of India, poverty has shown a declining trend as

evident from Table 5.1 and Figure 5.2. In 1993-94, about 403.7 million of people

(45.3 percent of the total population) lived below poverty line. In 2004-05, as many as

163

407.1 million of people (37.2 percent of population) were living below poverty line.

During 2009-10, the level of poverty has come down to 29.8 percent. Whereas,

poverty has further declined to a record of 22 percent in 2011-12. The total number

of poor estimated at 269.3 million, out of which 216.5 million reside in rural India.

For the year 2013-14, estimated value highlights further decline in poverty ratio to

18.7. However, total estimated number of poor arrived at 230.8 million, where 183.6

million belongs to rural India.

TABLE 5.1: TRENDS OF POVERTY IN INDIA*

Year Poverty ratio (%) Number of poor (million)

Rural Urban Total Trend Rural Urban Total Trend

1993-94 50.1 31.8 45.3 - 328.6 74.5 403.7 -

2004-05 41.8 25.7 37.2 326.3 80.8 407.1

2009-10 33.8 20.9 29.8 278.2 76.5 354.7

2011-12 25.7 13.7 21.9 216.7 53.1 269.8

2013-14 20.6** 11.3** 18.7** 183.6** 47.2** 230.8**

*Source: Planning Commission, Government of India. (2014). Review of methodology for

measurement of poverty. Retrieved from planningcommission.gov.in/reports/genrep/pov_rep

0707.pdf. Accessed on 5-10-2014.

**Estimated figures

164

FIGURE 5.2: TRENDS OF POVERTY IN INDIA*

*Source: Retrieved from http://www.livemint.com/r/LiveMint/Period1/2013/07/25/ Photos/g-

charticle (poverty).jpg. Accessed on 5-10-2014.

Table 5.2 shows state-wise number and percentage of below poverty line population

for the year 2011-12. The percentage of population below poverty line is different in

different states of India. Overall, Chhattisgarh (39.9%), Jharkhand (37.0%) and

Manipur (36.9) are reported to have largest population below poverty line. Whereas in

Goa, only 5.1% of population is below poverty line, which is lowest among all states.

Likewise in Kerala, 7.1% of the population is living below the poverty line. Urban

area-wise, Manipur (32.6) & Bihar (31.2) are considered to have largest below

poverty line population and Sikkim (3.7), Goa (4.1), Himachal Pradesh (4.3) & Kerala

165

(5.0) are regarded as states with least below poverty population. Rural area-wise,

poverty is accounted to be largest in Chhattisgarh (44.6%) & Jharkhand (40.8) and

least measured in Goa (6.8%), Punjab (7.7%) & Himachal Pradesh (8.5%).

TABLE 5.2: NUMBER & PERCENTAGE OF POPULATION BELOW

POVERTY LINE BY STATES 2011-12*

S.No. States Rural Urban Total

No. of

persons

(lakhs)

%age of

persons

No. of

persons

(lakhs)

%age

of

persons

No. of

persons

(lakhs)

%age

of

persons

1 Andhra Pradesh 61.8 11.0 17.0 5.8 78.8 9.2

2 Arunachal Pradesh 4.2 38.9 0.7 20.3 4.9 34.7

3 Assam 92.1 33.9 9.2 20.5 101.3 32.0

4 Bihar 320.4 34.1 37.8 31.2 358.2 33.7

5 Chhattisgarh 88.9 44.6 15.2 24.8 104.1 39.9

6 Delhi 0.5 12.9 16.5 9.8 17.0 9.9

7 Goa 0.4 6.8 0.4 4.1 0.8 5.1

8 Gujarat 75.4 21.5 26.9 10.1 102.2 16.6

9 Haryana 19.4 11.6 9.4 10.3 28.8 11.2

10 Himachal Pradesh 5.3 8.5 0.3 4.3 5.6 8.1

11 Jammu & Kashmir 10.7 11.5 2.5 7.2 13.3 10.3

12 Jharkhand 104.1 40.8 20.2 24.8 124.3 37.0

13 Karnataka 92.8 24.5 37.0 15.3 129.8 20.9

14 Kerala 15.5 9.1 8.5 5.0 23.9 7.1

15 Madhya Pradesh 191.0 35.7 43.1 21.0 234.1 31.6

16 Maharashtra 150.6 24.2 47.4 9.1 197.9 17.4

17 Manipur 7.4 38.8 2.8 32.6 10.2 36.9

18 Meghalaya 3.0 12.5 0.6 9.3 3.6 11.9

19 Mizoram 1.9 35.4 0.4 6.4 2.3 20.4

20 Nagaland 2.8 19.9 1.0 16.5 3.8 18.9

21 Orissa 126.1 35.7 12.4 17.3 138.5 32.6

22 Punjab 13.4 7.7 9.8 9.2 23.2 8.3

23 Rajasthan 84.2 16.1 18.7 10.7 102.9 14.7

24 Sikkim 0.4 9.9 0.1 3.7 0.5 8.2

25 Tamil Nadu 59.2 15.8 23.4 6.5 82.6 11.3

26 Tripura 4.5 16.5 0.8 7.4 5.2 14.0

27 Uttar Pradesh 479.4 30.4 118.8 26.1 598.2 29.4

28 Uttarakhand 8.2 11.6 3.4 10.5 11.6 11.3

29 West Bengal 141.1 22.5 43.8 14.7 185.0 20.0

Population with high below poverty line population

Population with low below poverty line population

*Source: Planning Commission, Government of India. (2014). Review of methodology for measurement

of poverty. Retrieved from planningcommission.gov.in/reports/genrep/pov_rep0707.pdf. Accessed on 5-10-2014.

166

5.4 CAUSES OF POVERTY IN INDIA

There are many reasons for poverty in India. Some of them are as follows:

i. Rapidly rising population

Population has been rising in India at a rapid pace. On an average 17 million people

are added every year to its population which raises the demand for consumption

goods considerably. This rise is mainly due to fall in the death rate and more or stable

birth rate, for the last many decades. Heavy pressure of population adds to

dependency burden, implying greater poverty over time.

ii. Low productivity in agriculture

Low level of agricultural productivity due to subdivided & fragmented holdings, lack

of capital, use of traditional methods of cultivation, underutilisation of human

resources, illiteracy etc. is the main cause of poverty in the country. This brings them

down in their standard of living.

iii. Low rate of growth

Rate of growth of economy has been quite low during five year plans in India. During

the period of planning, growth rate of GDP has been nearly 4 percent. But owing to

nearly 2 percent growth rate of population, per capita income grew by 2.4 percent

only. Low growth rate of per capita income has tended to sustain poverty.

iv. Inflationary pressures

Due to low rate of production and high rate of population growth, less developed

economies like India are often caught in the cobweb of inflationary pressures.

Inflation has persisted almost as a permanent feature of the Indian economy. It means

a situation of continual rise in prises. This continuous and steep rise in prices has

added to the miseries of poor.

v. Chronic unemployment and underemployment

India is a country sustaining chronic unemployment and underemployment. Number

of job seekers is increasing at higher rate than the expansion in employment

opportunities. This expanding problem of unemployment is another cause of poverty.

167

vi. Paucity of able and efficiency entrepreneurship

In the initial stages of industrial development of a country, there is a need for efficient

and skilled entrepreneurs possessing initiative, imagination and risk-taking ability.

Unfortunately, there is a great shortage of such entrepreneurs in India. Consequently,

production activity has remained at a very low level. Low level of production implies

low level of employment and high level of poverty.

vii. Outdated social institutions

The social structure of our country is full of outdated traditions and institutions like

caste system, joint family system, laws of inheritance & succession, etc. All these

obstruct dynamic changes in the economy. This backwardness is not conducive to

faster development. Due to this, growth rate is hampered and have aggravate the

problem of poverty.

viii. Lack of infrastructure

Energy, transport and communication - the vital components of economic

infrastructure as well as education, health and housing services - the principal

components of social infrastructure are in a very bad shape. These serve as the

foundation of any programme of growth and development. But unfortunately, this

foundation is still in its infancy stage despite more than 63 years of planning. Slow

multiplication of growth trends and persistence of poverty are the obvious

consequences.

5.5 POVERTY ALLEVIATION PROGRAMMES

Many poverty reduction programmes have been initiated in most developing

countries. Since the inception of planning in India, poverty reduction has become

important goal of developmental programmes. These programmes have been broadly

categorised into self-employment & wage employment programmes, food safety

programme, social security programmes and housing programmes (Figure 5.3).

168

FIGURE 5.3: POVERTY ALLEVIATION PROGRAMMES*

*Source: http://planningcommission.nic.in, Accessed on 16-11-2014

A. Self-employment and Wage Programme

i. Swarnajayanti gram swarozgar yojana (SGSY)

SGSY came into existence in April, 1999. It replaces earlier poverty eradication

programmes like integrated rural development programme (IRDP), training for

rural youth for self-employment (TRYSEM), etc. It is a holistic programme for

developing micro enterprises in rural areas. The micro enterprises are organised

as individual enterprises as well as on collective basis as self help groups

(SHGs). The SHG may consist of 10 to 20 members. It laid focus on lending

credit, organising the rural poor into self-help groups, capacity-building,

training, participatory approach to planning of self-employment ventures,

infrastructure support, technology and credit & marketing linkages. Thus, SGSY

is a credit-cum-subsidy programme, where credit is the major component and

Self-employment and wage programmes

• Swarnjayanti gram swarozgaryojana

• Mgnrega

• Wage emplyment programme

• Jwahar rozgar yojana

• Sampoorna gramin rozgar yojana

Food safety programmes

• Annapurna

• National food for work programme

Social security programmes

• National old age pension scheme

• National family benefit scheme

• National maternity benefit scheme

Housing programmes

• Indira awaas yojana

• Valmiki ambedkar awaas yojana

169

subsidy is the minor component (Yesudian, 2007 and Murari & Didwania,

2010).

ii. Mahatama Gandhi national rural employment guarantee act (MGNREGA)

The NREGA bill was notified in 2005 and came into force in 2006. It was

further modified as Mahatma Gandhi national rural employment guarantee act

(MGNREGA) in 2008. Under this scheme, 100 days of paid work is guaranteed

to people in the rural areas. Now these 100 days work is raised to 150 days

work.

iii. Wage employment programme

Wage employment programme is an important component of the anti-poverty

strategy. In this programme, employment opportunities are provided during lean

agricultural seasons, in times of floods, droughts and other natural calamities.

They create rural infrastructure which supports further economic activity. These

programmes also put an upward pressure on market wage rates by attracting

people to public works programmes, thereby reducing labour supply and

pushing up demand for labour. While public works programmes to provide

employment in times of distress have a long history, major thrust to wage

employment programmes in the country was provided only after the attainment

of self-sufficiency in food grains in the 1970’s.

iv. Jawahar rozgar yojana (JRY)

The JRY came into existence to generate employment opportunities for

unemployed and underemployed people in rural areas through creating

economic infrastructure and community & social assets.

v. Sampoorna gramin rozgar yojana (SGRY)

SGRY was launched on September 1, 2001. The basic objectives of this scheme

are to generate employment opportunities to the surplus workers, development

of regional economic & social condition, establish durable economic

infrastructure in rural areas and provision of food & nutrition security to the

poor. Work taken under this programme is labour-intensive and the workers are

paid the minimum wages notified by the states. Workers are paid partly in cash

and partly in kind.

170

B. Food Safety Programmes

i. Annapurna

Government of India started this scheme in 1999-2000. The main aim of this

scheme is to provide food to senior citizens who cannot take care of themselves

and are not covered under the national old age pension scheme (NOAPS) and

who have no one to take care of them in their village.

ii. National food for work programme (NFFWP)

To generate additional supplementary wage employment with food security,

NFFWP was launched in November 2004 in the 150 most backward districts.

Free of cost food grains were received by states under NFFWP. The programme

focuses on works relating to water conservation, drought proofing (including

aforestation), land development, flood-control/protection (including drainage in

waterlogged areas) and rural connectivity in terms of all weather roads.

C. Social Security Programmes

i. National old age pension scheme

Under this scheme, pension is provided to old people who were above the age of

65 years (now reduced to 60 years) who could neither feed themselves nor have

any means of subsistence. This pension is given by the central government.

According to Budget (2011-12), the amount of old age pension is `200 & `500

per month for applicants aged 60-79 years & above 80 years respectively.

ii. National family benefit scheme

Government of India started this scheme in August 1995. This scheme is

sponsored by the state government under community & rural development

department. It was transferred to the state sector scheme after 2002-03. A sum of

`10,000 is given to a person who becomes the head of the family after the death

of its primary breadwinner. The breadwinner is defined as a person who is

above 18 who earns the most for the family and on whose earnings the family

survives. It is for families below the poverty line.

171

iii. National maternity benefit scheme (NMBS)

Under this scheme, a sum of `500 is given to a pregnant mother for the first two

live births. The women should be older than 19 years of age. It is given normally

12-8 weeks before the birth and in case of the death of the child the women can

still avail it. The NMBS is implemented by states and union territories with the

help of panchayats and municipalities.

D. Housing Programmes

i. Indira awaas yojana (IAY)

The scheme came into existence since 1985-86. The main aim of this scheme is

providing dwelling units free of cost to below poverty line (BPL) families in

rural areas. In 1996, this scheme gains a boost when central government

identified ‘Housing’ as one of the seven components under the basic minimum

services (BMS) agenda to provide housing to the shelterless poor in rural areas

in a time bound manner.

ii. Valmiki Ambedkar awaas yojana (VAMBAY)

VAMBAY was launched in December, 2001 to facilitate the construction and

up-gradation of dwelling units for the slum dwellers and to provide a healthy &

enabling urban environment through community toilets under Nirmal Bharat

Abhiyan. The Central government provides a subsidy of 50 per cent, with the

balance provided by the state government.

5.6 POVERTY REDUCTION THROUGH FINANCIAL INCLUSION

The relationship between poverty reduction and financial inclusion is examined under

following sub-heads:



5.6.3 Demographic profile-wise mean satisfaction regarding poverty reduction

5.6.4 Relationship between financial inclusion and poverty reduction

172



Purification of construct administered on beneficiaries of financial inclusion drive of

RBI is separately carried using SPSS (version 17.00) and the results are evident from

the Table 5.3.

Beneficiaries’ perception regarding poverty reduction






in the construct of poverty reduction. The KMO value (.904) and Bartlett test of










Factor 1 (Poverty eradication)

Six items included in this factor are, ‘Family crisis are reduced through better living

standards’, ‘Health has improved by having qualitative food’, ‘Your consumption

level has increased’, ‘You consume more qualitative food than before’, ‘Your

expenditure on clothing has increased’ and ‘Your expenditure on luxuries has

increased’. The mean values for all items lie between 3.13 - 3.52 exhibiting moderate

scores, but significant factor loading between .768 - .908 and communalities .663 -

.872. The factor acknowledges the importance of financial inclusion which assists

beneficiaries in raising living standard, obtaining quality food, increasing

consumption level and acquiring cloths & luxuries.

173

Factor 2 (Poverty reduction through education)

Two items identified by this factor are, ‘Head of the family is educated enough to

guide other members to move on right track’ and ‘Most of the members are educated

in your family’. The items have high factor loading of .881 & .845 and significant

communalities as .803 & .767 respectively. This factor clearly demonstrates that

proper guidance and education level of family member help in proper implementation

of financial inclusion scheme.

Reliability

Two factors are obtained after scale purification falling within the domain of poverty

reduction. As evident from the Table 5.3, the Cronbach’s alpha for all 8 scale items

underlying two factors ranges from .720 to .946. The alpha reliability coefficient for

F1 i.e., poverty eradication (.946) is higher than the criteria of .77 obtained by Gordon

and Narayanan (1984) indicating high internal consistency. F2 i.e., poverty reduction

through education (.720) is also at a minimum acceptable level of 0.50 as

recommended by Brown et al. (2001) and Kakati & Dhar (2002) thereby obtaining

satisfactory internal consistency. However, overall alpha reliability score for all

factors is very much satisfactory at 0.904. Adequacy and reliability of sample size to

yield distinct and reliable factors is further demonstrated through Kaiser-Meyer-Olkin

measure of sampling adequacy that is 0.904 and all factor loadings are greater than

.50.

Validity

The two factors obtained alpha reliability higher & equal to 0.50 and satisfactory


al., 1995).


CFA is applied to assess the fitness, reliability and validity of poverty reduction

dimension using AMOS (version 16.00).

Poverty reduction dimension comprises of total 8 items. First order CFA (Figure 5.4)

is performed to assess the model fitness. After applying CFA, two items namely,

‘Head of the family is educated enough to guide other members to move on right

track’ and ‘Most of the members are educated in your family’ got deleted as their

174

standard regression weight (SRW) is below the acceptable criteria of .50. Rest 6 items

have regression weight above .50, thus it become clear that all remaining measured

variables are the significant contributors of the construct. This model has been found

to have a good fit (CMIN/DF = 3.927, GFI = .982, AGFI = .947, CFI = .993, NFI =

.991, TLI = .985 and RMSEA = .077) (Table 5.4). Convergent validity also got

established as AVE arrived at .745 and Cronbach’s alpha is .946 and composite

reliability equals to .996 (Table 5.4). Thus, the model has been proved to be valid and

reliable. In our study, variable ‘Family crisis are reduced through better living

standard’ contributes highest towards poverty reduction, as its regression weight is

.943 (Table 5.4). Financial inclusion enables rural population to avail the financial

services and increases their income earning capabilities which results into raising of

standard of living and reducing family crisis. Los Cobos declaration on financial

inclusion (2012) also recognised that financial inclusion is a key component in the

development of healthy, vibrant and stable financial systems which contribute to

sustainable economic growth. Access to safe, secure and reliable financial system is

important for improving standard of living.

FIGURE 5.4: CFA MODEL FOR POVERTY REDUCTION

POVRED = Poverty Reduction; P5 = Your expenditure on clothing has increased; P6 = You consume more qualitative food than before; P7 = Your expenditure on luxuries has increased; P8 = Health has

improved by having qualitative food; P9 = Your consumption level has increased and P10 = Family

crisis are reduced through better living standard.

Pov-red

P5 e1

.84

P6 e2 .86

P7 e3 .74

P8 e4

.88

P9 e5

.90

P10 e6

.94

175

5.6.3 Demographic Profile-wise Mean Satisfaction regarding Poverty

Reduction

Table 5.5 shows demographic profile-wise mean satisfaction regarding poverty

eradication. Demographic profile is sub-categorised into age, gender, district, caste,

religion, marital status and qualification.

Age-wise analysis depicts that age is sub-divided into four categories i.e., upto 30

years, 30-40 years, 40-50 years and above 50 years. Respondents are highly satisfied

with the statement ‘Health has improved by having qualitative food’ with mean

response of 3.53 and least satisfied with the statement ‘Your consumption level has

increased’ with mean response of 3.15. Age-wise, respondents belonging to 30-40

years (3.45) category are maximally satisfied followed by above 50 years (3.41), upto

30 years (3.34) and 40-50 years (3.32) age category.

On the basis of gender, statement-wise maximum mean level of satisfaction is

observed for the statement ‘Health has improved by having qualitative food’ at 3.58

and least at 3.21 for the statement ‘Your consumption level has increased’. Gender-

wise, female respondents are found to be more satisfied with mean value of 3.55

compared to male with mean value of 3.36.

On the basis of districts, statement-wise mean satisfaction varies from lowest 3.13 for

‘Your consumption level has increased’ to highest upto 3.52 for ‘Health has improved

by having qualitative food’. On the whole, district-wise mean satisfaction among

beneficiaries in descending order is found to be Reasi (3.61), Jammu (3.58),

Udhampur (3.26), Samba (3.24) and Kathua (3.21).

Caste-wise, maximum mean score is found to be at 3.39 for ‘Health has improved by

having qualitative food’ and minimum at 2.98 for ‘Your consumption level has

increased’. Caste-wise, mean level of satisfaction in ascending order is OBC (3.06),

ST (3.07), SC (3.24) and general (3.52).

On religion-wise analysis, statement ‘Your consumption level has increased’ has

lowest mean satisfaction at 3.57 and statement ‘Health has improved by having

qualitative food’ has highest mean satisfaction at 3.29. Religion-wise, mean level of

satisfaction in descending order is Sikh (4.00), Hindu (3.39) and Muslim (2.96).

Marital status-wise analysis depicts that maximum mean satisfaction is noticed for the

statement ‘Health has improved by having qualitative food’ at 3.48 and minimum for

176

the statement ‘Your consumption level has increased’ at 3.15. Marital status-wise,

married respondents are more satisfied (3.40) in contrast to unmarried beneficiaries

(3.29).

Qualification-wise analysis shows that statement ‘Health has improved by having

qualitative food’ has highest mean value at 3.38 and lowest at 3.09 for the statement

‘Your consumption level has increased’. Qualification-wise, mean satisfaction in

increasing order is upto primary (2.61), literate below primary (2.72), illiterate (2.90),

upto graduate or higher (3.05), upto middle (3.41), upto secondary (3.63), upto higher

secondary (3.84) and any other qualification (4.00).

Table 5.6 shows demographic-wise mean satisfaction regarding poverty reduction

through education.

Statement-wise analysis across the demographic profile i.e., age, gender, district,

caste, religion, marital status & qualification depicts that beneficiaries are maximally

satisfied with the same statement ‘Health has improved by having qualitative food’

with mean value 3.54, 3.52, 3.46, 3.32, 3.62, 3.51 & 3.52 and minimally satisfied with

the same statement ‘Your consumption level has increased’ with mean response of

3.28, 3.31, 3.26, 3.06, 3.46, 3.29 & 3.36 respectively.

Age-wise analysis shows that beneficiaries under the age group of above 50 years

(3.46) are highly satisfied followed by upto 30 years (3.55), 30-40 years (3.33) and

40-50 years (3.29).

Gender-wise analysis depicts that female beneficiaries (3.49) are more satisfied in

comparison to male beneficiaries (3.34).

District-wise analysis exhibits satisfaction among beneficiaries of different districts in

ascending order, Kathua (2.96), Udhampur (3.13), Samba (3.43), Reasi (3.54) and

Jammu (3.73).

Caste-wise, mean satisfaction in descending order is found to be at 3.53 (General),

3.39 (ST), 3.19 (SC) and 2.67 (OBC).

Religion-wise, maximum mean satisfaction is found to be 4.00 (Sikh), followed by

3.35 (Hindu) and least 3.27 (Muslim).

Marital status-wise, unmarried beneficiaries are more satisfied (3.45) than married

beneficiaries (3.35).

177

Qualification-wise, mean satisfaction in descending order is found to be 5.00 (Any

other qualification), 4.09 (upto higher secondary), 3.93 (upto graduate or higher), 3.64

(upto secondary), 3.25 (upto middle), 2.83 (Illiterate), 2.58 (upto primary) and 2.36

(Literate below primary).

5.6.4 Relationship between Financial Inclusion and Poverty Reduction

Figure 5.5 highlights the relationship between financial inclusion and poverty

reduction. The results indicated that model fit the data excellently (CMIN/DF = 4.899,

GFI = .946, AGFI = .907, CFI = .972, NFI = .965, TLI = .961 and RMSEA = .088)

(Table 5.7). SEM results in Table 5.8 indicates that financial inclusion has positive

and significant relation with poverty reduction (β = .553, p = .000). Therefore,

hypothesis ‘Financial inclusion is positively related to poverty reduction’ stands

accepted. The result of the study is in line with the previous study Murari &

Didwania (2010) which has the same observation and found that scheme of financial

inclusion is an effective instrument which can lift poor above the level of poverty by

providing them increased self employment opportunities and making them credit

worthy. Another study by Rahman (2013) highlights financial inclusion combat

poverty by making advanced opportunities available for the disadvantaged poor,

thereby promoting social inclusion and inclusive socio-economic growth.

FIGURE 5.5: SEM MODEL FOR POVERTY REDUCTION*

AC = Access; AV = Availability; US = Usage; P5 = Your expenditure on clothing has increased; P6 =

You consume more qualitative food than before; P7 = Your expenditure on luxuries has increased; P8

= Health has improved by having qualitative food; P9 = Your consumption level has increased and

P10 = Family crisis are reduced through better living standard.

*Source: Survey

Poverty

reduction

P5 e5 .85

P6 e6 .87

P7 e7 .76

P8 e8 .89

P9 e9

.89

P10 e10

.93

Financial

Inclusion .76

US e4

AV e3 .90

AC e2 .61

.55

e1

178


(ROTATED COMPONENT METHOD) FOR POVERTY REDUCTION*


deviation

Factor

loading

Eigen

value

Variance

explained

%

Cumulative

explained

%

Communality Alpha

(α)

POVERTY REDUCTION

Factor 1: Poverty eradication 5.162 57.523 57.523 .946

Family crisis are reduced through better living

standard

3.45 .90 .908 .872

Health has improved by having qualitative

food

3.43 .91 .897 .830

Your consumption level has increased 3.44 .91 .888 .819

You consume more qualitative food than

before

3.52 .89 .885 .804

Your expenditure on clothing has increased 3.34 .98 .856 .793

Your expenditure on luxuries has increased 3.13 1.03 .768 .663

Factor 2: Poverty reduction through education 1.190 21.875 79.399 .720

Head of the family is educated enough to guide

other members to move on right track

3.22 1.26 .881 .803

Most of the members are educated in your

family

3.49 1.04 .845 .767

*Source: Survey

179

TABLE 5.4: RESULT OF CFA FIT INDICES, RELIABILITY AND VALIDITY*

Construct Statements SRW Fit indices Validity Reliability

Poverty

reduction

P5 Your expenditure on clothing

has increased

.843 CMIN/DF 3.927

AVE = .7455

Cronbach’s alpha = .946

Composite reliability = .996 P6 You consume more qualitative

food than before

.858 GFI .982

P7 Your expenditure on luxuries

has increased

.742 AGFI .947

P8 Health has improved by having

qualitative food

.880 CFI .993

P9 Your consumption level has

increased

.902 NFI .991

P10 Family crisis are reduced

through better living standard

.943 TLI .985

RMSEA .077

*Source: Survey

180

TABLE 5.5: DEMOGRAPHIC PROFILE-WISE MEAN SATISFACTION

REGARDING POVERTY ERADICATION*

Demographic variables Statements** Overall

mean 1 2 3 4 5 6

Age Upto 30 years 3.33 3.49 3.22 3.25 3.33 3.39 3.34

30-40 years 3.38 3.55 3.19 3.51 3.54 3.50 3.45

40-50 years 3.31 3.46 3.01 3.40 3.35 3.40 3.32

Above 50 years 3.34 3.63 3.18 3.34 3.50 3.47 3.41

Overall Mean 3.34 3.53 3.15 3.38 3.43 3.44

Gender Male 3.31 3.51 3.10 3.41 3.2 3.43 3.36

Female 3.55 3.65 3.32 3.60 3.61 3.57 3.55

Overall Mean 3.43 3.58 3.21 3.51 3.52 3.50

District Jammu 3.55 3.69 3.39 3.65 3.62 3.60 3.58

Kathua 3.15 3.38 2.89 3.25 3.28 3.31 3.21

Reasi 3.47 3.73 3.47 3.53 3.67 3.80 3.61

Samba 3.13 3.36 2.95 3.31 3.38 3.33 3.24

Udhampur 3.28 3.43 2.94 3.32 3.30 3.33 3.26

Overall Mean 3.32 3.52 3.13 3.41 3.45 3.47

Caste General 3.50 3.65 3.26 3.56 3.57 3.57 3.52

SC 3.14 3.39 2.98 3.31 3.33 3.31 3.24

ST 2.92 3.31 2.85 2.92 3.08 3.31 3.07

OBC 3.15 3.19 2.81 3.07 3.00 3.11 3.06

Overall Mean 3.18 3.39 2.98 3.22 3.25 3.33

Religion Hindu 3.35 3.53 3.13 3.44 3.45 3.45 3.39

Muslim 2.82 3.18 2.73 2.82 3.00 3.18 2.96

Sikh 4.00 4.00 4.00 4.00 4.00 4.00 4.00

Overall Mean 3.39 3.57 3.29 3.42 3.48 3.54

Marital

status

Married 3.35 3.54 3.12 3.46 3.46 3.46 3.40

Unmarried 3.28 3.42 3.18 3.24 3.32 3.33 3.29

Overall Mean 3.32 3.48 3.15 3.35 3.39 3.40

Qualification Literate below

primary

2.43 2.93 2.57 2.86 2.79 2.71 2.72

Upto primary 2.63 2.86 2.32 2.75 2.77 2.78 2.69

Upto middle 3.32 3.61 3.12 3.48 3.45 3.46 3.41

Upto secondary 3.65 3.73 3.37 3.65 3.67 3.70 3.63

Upto higher

secondary

3.84 3.86 3.75 3.84 3.88 3.88 3.84

Upto graduate

or higher

3.00 3.00 3.00 3.00 3.14 3.14 3.05

Anyother 4.00 4.00 4.00 4.00 4.00 4.00 4.00

Illiterate 2.76 3.03 2.59 2.96 3.07 2.97 2.90

Overall Mean 3.20 3.38 3.09 3.32 3.35 3.33

*Source: Survey ** 1. stands for Family crisis are reduced through better living standard; 2. Health has improved by

having qualitative food; 3. Your consumption level has increased; 4. You consume more qualitative

food than before ; 5. Your expenditure on clothing has increased and 6. Your expenditure on

luxuries has increased.

181


REGARDING POVERTY REDUCTION THROUGH EDUCATION*


mean 1 2

Age Upto 30 years 3.65 3.45 3.55

30-40 years 3.45 3.20 3.33

40-50 years 3.45 3.13 3.29

Above 50 years 3.59 3.32 3.46

Overall Mean 3.54 3.28

Gender Male 3.48 3.19 3.34

Female 3.56 3.42 3.49


District Jammu 3.79 3.67 3.73

Kathua 3.24 2.68 2.96

Reasi 3.60 3.47 3.54

Samba 3.36 3.49 3.43

Udhampur 3.29 2.97 3.13


Caste General 3.73 3.32 3.53

SC 3.21 3.16 3.19

ST 3.46 3.31 3.39

OBC 2.89 2.44 2.67


Religion Hindu 3.49 3.21 3.35

Muslim 3.36 3.18 3.27

Sikh 4.00 4.00 4.00


Marital

Status

Married 3.49 3.20 3.35

Unmarried 3.52 3.38 3.45



primary

2.50 2.21 2.36

Upto primary 2.75 2.40 2.58

Upto middle 3.44 3.06 3.25

Upto secondary 3.81 3.47 3.64

Upto higher

secondary

4.11 4.07 4.09

Upto graduate or

higher

4.29 3.57 3.93

Anyother 5.00 5.00 5.00

Illiterate 2.59 3.07 2.83


*Source: Survey

**1 stands for Head of the family is educated enough to guide other members to move on right track and

2. Most of the members are educated in your family.

182



Final

model

4.899 .946 .907 .972 .965 .961 .088

*Source: Survey

TABLE 5.8: RESULT OF HYPOTHESIS TESTING*

Hypothesis CR SRW P-value Accepted/

Rejected

Financial inclusion is positively

related to poverty reduction

9.777 .553 .000 Accepted

*Source: Survey

183

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`

Chapter-VI Financial Inclusion and

Area Development

CONTENTS



6.2 Conceptual Analysis of Area Development 188

6.3 Schemes for Area Development 189

6.4 Area Development through Financial Inclusion

192



6.4.3 Demographic Profile-wise Mean Satisfaction

regarding Area Development

195

6.4.4 Relationship Between Financial Inclusion

and Area Development

197

6.5 Barriers of Financial Inclusion 198

6.6 Impact of Barriers on Access and Usage Dimension

of Financial Inclusion

199

6.6.1 Impact of Barriers to Financial Inclusion on

Access Dimension

199

6.6.2 Impact of Barriers to Financial Inclusion on

Usage Dimension

200

6.7 Comparative Analysis of Banks 201

References 207

187

Threats

Opportunities

Commitment

Basics

Programmes

Technology

Cooperation

Contents

Benefits Cooperation

Purchasing

Production

Marketing

Selling

New work

cultures

Enthusiasm

Networking

Implementation

Skills

Will

Modernisation

Networking

CHAPTER VI

FINANCIAL INCLUSION AND AREA DEVELOPMENT

6.1 INTRODUCTION

In today’s decade of liberalisation, privatisation and globalisation, the term ‘inclusive

growth of an economy’ has become very popular all over the globe. It implies

participation and sharing benefits from the growth process. The Indian economy is

considered as the fastest growing economy of the world. Demographic dividend,

greater domestic & international competition, enhancement in factor productivity,

increase in entrepreneurial activities and globalisation are the major drivers of the

growth acceleration (Kelkar, 2010). This is the bright side of the coin. But, the other

part is dark - where we find increasing inequalities in distribution of income and

wealth. Vast disparity exists between urban and rural areas as urban regions are

growing rapidly but the growth of rural regions is stagnant (Saruparia, 2010). Finance

acts as the lubricant, which oils the wheels of development (Christabell & Vimal,

2012). Whereas, financial inclusion assists millions of poor households who are out

of reach of financial services through micro credit, micro savings, money transfers,

micro insurance, etc. (Levine, 1997). These services have become fulcrum for

development initiatives in the third world countries. Financial inclusion is important

for area development. Financial intermediation in the rural areas will help to lubricate

rural economic activities for the stimulation of area development (Matunhu & Mago,

2013). Components of area development are shown in Figure 6.1.

FIGURE 6.1: COMPONENTS OF AREA DEVELOPMENT*

*Source: http://www.google.co.in. Accessed on 5-10-2014.

188

6.2 CONCEPTUAL ANALYSIS OF AREA DEVELOPMENT

Area development generally refers to the process of improving the quality of life and

economic well-being of people living in relatively isolated and sparsely populated

areas. Area development refers to, ‘the process by which efforts of the people and

governmental authorities are united together to improve the economic, social and

cultural conditions in the life of the nation and to relate them to contribute fully to

national programme (Government of India, 1966). According to World Bank (1975),

‘it is a well designed strategy to improve the socio-economic life of rural poor by

extending the benefits of development to poorest in rural areas i.e. small farmers,

tenants, landless and other disadvantage group’. Crops (1972) defined area

development as, ‘a process which aims at improving the well being and self-

realisation of people living outside the urbanised area through collective efforts’. Area

development is, ‘a process of increasing the level of per capita income in the rural

areas along with the improvement in the quality of life of the rural population

measured by food & nutritional level, health, education, recreation and security

(Diejemaoh, 1973). Mishra & Sunderam (1979) refer area development as,

‘development of rural areas along with the development of quality of life of the rural

masses into self-reliant and sustaining modern little communities. It is, therefore,

development of rural areas in such a way that each component of rural life changes in

a desired direction’. Area development is the process whereby those who are living in

rural areas have a good standard of living (Lele, 1975). Chambers (1983) defined area

development as, ‘a strategy of enabling a specific group of people, poor rural women

and men to gain for themselves and their children more of what they want and need’.

Area development refers to ‘a process of developing and utilising natural & human

resources, technologies, infrastructural activities, institutions, organisations &

government policies & programmes to encourage & speed up economic growth in

rural areas, to provide jobs and to improve the quality of rural life towards self-

sustenance (Singh, 1986)’. According to Agarwal (1989), ‘area development is a

strategy designed to improve the economic and social life of rural poor’. Area

development refers to, ‘overall sustainable development of rural areas with a view to

improve the quality of life of rural people’ (Singh, 1999). Acharya (2008) defined

area development as, ‘a multidisciplinary process of development which seeks

transformation of the society from traditional to modern nature’. Further he connotes

189

that, ‘area development is a process of development and change to improve rural

social life by developing infrastructure, commercialising agriculture, proper utilisation

& mobilisation of resources and inclusive social development’. The United Nations

defines ‘area development as a process of change in which the efforts of people are

united with those of Government authorities to improve their economic, social and

cultural conditions and to enable them to contribute fully to national programme. It is

a process of bringing change among rural community from the traditional way of

living to progressive way of living’.

6.3 SCHEMES FOR AREA DEVELOPMENT

Schemes undertaken by centre and state government for rural development are also

the schemes for area development. Some of them are as follows (Figure 6.2).

i. Pradhan mantri gram sadak yojana (PMGSY)

Pradhan mantri gram sadak yojana (PMGSY) was launched on December 25, 2000 as

a fully funded centrally sponsored scheme. The basic objective of PMGSY is to

provide all weather road connectivity to rural areas of the country.

FIGURE 6.2: AREA DEVELOPMENT PROGRAMMES IN INDIA*

*Source: rural.nic.in, Accessed on 16-11-2014

Area development programmes

Pradhan mantri gram

sadak yojana

Council for advancement of people's action &

rural technology

Aajeevika : national rural

livelihood missin

Provision for urban

amenities in rural areas

Member of parliament local area

development scheme

Rural sanitation:

total sanitation campaign

Drought prone area programme

Desert developmen

t programme

Rural drinking

water suppply

programme

190

ii. Council for advancement of people’s action & rural technology

(CAPART)

Government of India set up CAPART in September, 1986. It is a registered society.

The basic objective of CAPART is to strengthen & promote voluntary efforts in rural

development by injecting new technological inputs, to encourage, promote & assist

voluntary action for the implementation of projects intending enhancement of rural

prosperity and to act as a catalyst for the development of technology appropriate for

rural areas.

iii. Aajeevika national rural livelihoods mission (NRLM)

Govt. of India has launched NRLM in June, 2011. The basic aim of NRLM is to

create efficient and effective institutional platforms of the rural poor enabling them to

increase household income through sustainable livelihood enhancements and

improved access to financial services.

iv. Provision for urban amenities in rural areas (PURA)

The mission of the PURA scheme is holistic and accelerated development of compact

areas around a potential growth centre in a gram panchayat through public private

partnership (PPP) framework for providing urban amenities and livelihood

opportunities to improve the quality of life in rural areas. The scheme aims to provide

urban amenities and livelihood opportunities in rural areas to bridge the rural urban

divide.

v. Member of parliament local area development scheme (MPLADS)

MPLADS was launched in 1993 and allocated 1 crore rupees per annum to take up

developmental works in the constituency. In response to the programme, the

allocation of funds was gradually enhanced to `2 crores per annum. The physical

achievements registered under this programme were not satisfactory due to some

regional problems in execution of works. To make the program a success, the hurdles

have to be identified and policies amended suitably in an environmental perspective.

vi. Rural sanitation- total sanitation campaign (TSC)

The centrally sponsored scheme of Central Rural Sanitation Programme (CRSP),

remodeled as the Total Sanitation Campaign (TSC). It has the main objectives of

bringing improvement in the general quality of life in rural areas, accelerate sanitation

191

coverage, generate demand through awareness & health education, cover all schools

& Anganwadi’s in rural areas with sanitation facilities, promote hygiene behaviour

among students & teachers, encourage cost effective & appropriate technology

development and contribute to reduce water & sanitation related diseases.

vii. Drought prone area programme (DPAP)

This national programme was launched in 1973-74 in some selected drought prone

areas of the country. The main objective of this plan was to re-establish the

environmental balance in these areas by promoting the balanced development of land,

water and other natural resources. This programme is being carried on by the rural

development department.

viii. Desert development programme (DDP)

The desert development programme was started in 1977-78 in some selected districts

to check the formation of deserts, to end the drought effects in the deserts, to re-

establish the ecological balance in the affected areas and to increase the land

productivity and water resources in these areas. This programme is being

implemented totally on the basis of union support but the division of the funds in the

hot arid areas is done between the union and the states.

ix. Rural drinking water supply programme

National drinking water mission was launched in 1986, which subsequently was

renamed Rajiv Gandhi National Drinking Water Mission (RGNDWM) in 1991. The

main objective of this programme is providing safe drinking water to all villages,

assisting local communities to maintain sources of safe drinking water in good

conditions and giving special attention for water supply to schedule caste and

schedule tribe.

x. Sansad adarsh gram yojana (SAGY)

This scheme was launched on October 11, 2014 on the birth anniversary of Jai

Prakash Narayan. Under this scheme, each Member of Parliament needs to choose

one village from the constituency that they represent, fix parameters such as piped

drinking water, connectivity to the main road, electricity supply to all households,

library, telecom & broadband connectivity, etc. and make it a model village by 2016.

192

The main purpose of adoption is to remove poverty from the adopted villages and

thus, ultimately promotes area development.

6.4 AREA DEVELOPMENT THROUGH FINANCIAL INCLUSION

The relationship between area development and financial inclusion is examined under

the following sub-heads:



6.4.3 Demographic profile-wise mean satisfaction regarding area development

6.4.4 Relationship between financial inclusion and area development




RBI is separately carried using SPSS (version 17.00) and the results are evident from

the Table 6.1.

Beneficiaries’ perception regarding area development





Varimax rotation extracted 5 statements out of 8 statements which are actually kept in

the construct of area development. The KMO value (.598) and Bartlett test of



resulting into two factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with


from .623 to .950, indicating moderate to high degree of linear association among the

variables. The factor loading ranges from .787 to 0.975 and the cumulative variance


193



Factor 1 (Balanced development)

The variables contributing towards this factor include, ‘Your area is regionally

balanced’, ‘Income is equally distributed’ and ‘Good road connectivity exists’. The

variable ‘Your area is regionally balanced’ contributes significantly to this factor with

highest factor loading (.906) and communality (.830). The other variables ‘Income is

equally distributed’ and ‘Good road connectivity exists’ also have significant factor

loadings (.882 & .787) and communalities (.801 & .623) respectively which states

their significant contribution in this factor. This factor reveals that financial inclusion

scheme is helping in reducing income gaps, providing good road connectivity and

equal regional balance.

Factor 2 (Health indicators)

Two variables identified by this factor are ‘Good health services are available’ and ‘A

regular doctor (govt./private) visits in the village’. The mean scores are normal (3.51

& 3.40). The two variables scored factor loadings .975 & .969 and communalities

.950 & .950 respectively which discloses that the variables significantly and

positively contributes to this factor. This factor perceives that good medical facilities

and availability of doctors are important components for area development.

Reliability

Two factors are obtained after scale purification falling within the domain of area

development. As evident from the Table 6.1, the Cronbach’s reliability for all 5 scale

items underlying two factors ranges from .824 to .948. The alpha reliability

coefficient for F1 i.e. balanced development (.824) and F2 i.e. health indicators (.948)

is higher than the criteria of .77 obtained by Gordon & Narayanan (1984) indicating

high internal consistency. However, overall alpha reliability score for all factors is

very much satisfactory at 0.707. Adequacy and reliability of sample size to yield

distinct and reliable factors is further demonstrated through Kaiser-Meyer-Olkin

measure of sampling adequacy that is 0.598 and all factor loadings are greater than

.50.

194

Validity

The two factors obtained alpha reliability higher & equal to 0.50 and satisfactory


al., 1995).


CFA is applied to assess the fitness, reliability and validity of area development

dimension using AMOS (version 16.00).

Area development dimension comprises of total 5 items. First order CFA (Figure 6.3)

is performed to assess the model fitness. After applying CFA, two items namely,

‘Good health services are available’ and ‘A regular doctor (Govt./private) visits in the

village’ got deleted as their standard regression weight (SRW) is below than the

acceptable criteria of .50. Rest 3 items have regression weight above .50, thus it

become clear that all measured variables are the significant contributors of the

construct. This model has been found to have a good fit (CMIN/DF = 4.671, GFI =

.988, AGFI = .965, CFI = .989, NFI = .986, TLI = .986 and RMSEA = .086) (Table

6.2). Convergent validity also got established as AVE arrived at .64 and Cronbach

alpha at .824 and composite reliability equals to .954 (Table 6.2). Thus, the model has

been proved to be valid and reliable. In our study, variable ‘Your area is regionally

balanced’ contributes highest towards area development, as its regression weight is

.963 (Table 6.2).

FIGURE 6.3: CFA MODEL FOR AREA DEVELOPMENT

Area-dvt = Area development; AD2 = Your area is regionally balanced; AD3 = Income is

equally distributed and AD8 = Good road connectivity exists.

Area-dvt

AD2 e1 .96

AD3 e2 .79

AD4 e3

.61

195

6.4.3 Demographic Profile-wise Mean Satisfaction regarding Area

Development

On the basis of demographic profile, mean satisfaction is shown in Table 6.3.

Demographic profile is sub-categorised into age, gender, district, caste, religion,

marital status and qualification.

Age-wise mean satisfaction reveals that beneficiaries under the age group of upto 30

years & above 50 years are highly satisfied with the statement ‘Your area is regionally

balanced’. Whereas, beneficiaries under the age group of 30-40 years and 40-50 years

are highly satisfied with the statement ‘Good road connectivity exists’. Beneficiaries

of all age group are least satisfied with the same statement ‘A regular doctor

(Govt./private) visits in the village’. On the whole, statement-wise satisfaction ranges

between 3.70 for ‘Your area is regionally balanced’ to 3.36 for ‘A regular doctor

(Govt./private) visits in the village’. Beneficiaries under the age group of above 50

years are highly satisfied with mean value (3.67), followed by 40-50 years with mean

value (3.57) and least satisfied beneficiaries belong to upto 30 years age group with

mean value (3.47).

On the basis of gender, mean satisfaction among male fluctuates between lowest from

3.40 for the statement ‘A regular doctor (Govt./private) visits in the village’ to the

highest upto 3.74 for ‘Good road connectivity exists’. In case of female beneficiaries,

maximum mean satisfaction value at 3.60 is observed for the statement ‘Your area is

regionally balanced’ and minimum at 3.32 for ‘Income is equally distributed’. On an

average, highest mean level of satisfaction is observed for the statement ‘Your area is

regionally balanced’ at 3.64 and lowest at 3.40 for the statement ‘A regular doctor

(Govt./private) visits in the village’. Gender-wise, male respondents are found to be

more satisfied with mean value of 3.57 compared to female with mean value of 3.46.

District-wise, highest mean satisfaction value among beneficiaries of Jammu &

Kathua is observed for the same statement ‘Good road connectivity exists’ at 3.73 &

3.70 respectively. But lowest mean satisfaction value at 3.19 is computed for ‘A

regular doctor (Govt./private) visits in the village’ by beneficiaries of Jammu district

& at 3.27 is observed for the statement ‘Income is equally distributed’ by respondents

of Kathua district. Among the beneficiaries of Reasi district, maximum mean

satisfaction computed at 4.20 for the statement ‘A regular doctor (Govt./private) visits

in the village’ and minimum for ‘Income is equally distributed’ at 3.99. In case of

196

Samba district, the highest satisfaction arrived at 3.69 for ‘Your area is regionally

balanced’ and lowest at 3.23 for ‘A regular doctor (Govt./private) visits in the

village’. Beneficiaries of Udhampur opines that the statement ‘Good health services

are available’ has maximum mean satisfaction value at 4.07 as compared to ‘Good

road connectivity exists’ with minimum mean satisfaction at 3.67. Overall,

respondents have highest mean satisfaction (3.77) for the statement ‘Your area is

regionally balanced’ and lowest mean satisfaction (3.59). District-wise, beneficiaries

of Reasi are highly satisfied (4.08), followed by Udhampur (3.89), Samba (3.52),

Jammu (3.50) and Kathua (3.44).

On the basis of caste, beneficiaries of general caste are highly contended with the

statement ‘Your area is regionally balanced’ with the mean value 3.68 and least

contended with ‘A regular doctor (Govt./private) visits in the village’ with mean value

3.41. Mean level satisfaction of SCs & OBCs fluctuates minimum from 3.39 & 3.22

for the same statement ‘A regular doctor (Govt./private) visits in the village’ to 3.78

& 3.81 for the statement ‘Good road connectivity exists’ respectively. Beneficiaries

belonging to ST category found highest mean value (3.85) for ‘Good health services

are available’ and lowest mean value (3.06) for ‘Income is equally distributed’. In

general, maximum mean score arrived at 3.59 for three statements ‘Your area is

regionally balanced’, ‘Good health services are available’ & ‘Good road connectivity

exists’ and minimum at 3.37 for ‘A regular doctor (Govt./private) visits in the

village’. Caste-wise, mean perception about satisfaction in descending order is SC

(3.58), OBC (3.57), general (3.55) and ST (3.34).

On the basis of religion, mean satisfaction found to be maximum at 3.72 for ‘Good

road connectivity exists’ and minimum at 3.42 for ‘A regular doctor (Govt./private)

visits in the village’ among Hindu beneficiaries. In case of Muslim beneficiaries,

highest mean value computed is 3.73 for the statement ‘Good health services are

available’ and lowest at 2.91 for ‘Good road connectivity exists’. The mean

satisfaction among Sikh beneficiaries score maximum at 4.00 for two statements

‘Your area is regionally balanced’ & ‘Income is equally distributed’ and minimum at

1.00 for two statements i.e., ‘Good health services are available’ & ‘A regular doctor

(Govt./private) visits in the village’. In nutshell, statement ‘Your area is regionally

balanced’ gives highest mean satisfaction (3.59) whereas statement ‘A regular doctor

(Govt./private) visits in the village’ gives least mean satisfaction (2.53). On the other

197

hand, religion-wise maximum mean satisfaction is found to be 3.57 (Hindu), followed

by 3.18 (Muslim) & 2.73 (Sikh).

Marital status-wise, maximum mean satisfaction among married & unmarried

beneficiaries is measured at 3.71 & 3.72 for the statement ‘Good road connectivity

exists’ & ‘Your area is regionally balanced’ respectively. Whereas, both married &

unmarried beneficiaries found to be least satisfied with the same statement ‘A regular

doctor (Govt./private) visits in the village’ with mean value 3.44 & 2.98 respectively.

Overall, the statement ‘Good road connectivity exists’ has highest mean score of 3.71

and the statement ‘A regular doctor (Govt./private) visits in the village’ has lowest

mean score of 3.21. On an average, unmarried beneficiaries are more satisfied in

contrast to married beneficiaries with mean value of 3.57 & 3.46 respectively.

Qualification-wise, mean score highlights that beneficiaries who are literate below

primary & having graduation degree are having highest level of satisfaction for the

same statement ‘Good road connectivity exists’ with mean value 3.64 & 4.00 and

lowest satisfaction for the same statement ‘Income is equally distributed’ with mean

value 3.00 & 3.14 respectively. The statement ‘Your area is regionally balanced’ has

maximum mean score (3.48) and the statement ‘Income is equally distributed’ has

minimum mean score (3.17) among the beneficiaries who are having only primary

qualification. Beneficiaries with middle & secondary qualification have utmost mean

score (3.75 & 3.79) for the same statement ‘Good road connectivity exists’ and least

mean score (3.40 & 3.44) for the same statement ‘A regular doctor (Govt./private)

visits in the village’. Beneficiaries who are having qualification till higher secondary

and who are illiterate are more contended with the statement ‘Your area is regionally

balanced’ with mean score 3.81 & 3.59 and least satisfied with ‘A regular doctor

(Govt./private) visits in the village’ with mean score 3.51 & 2.97 respectively.

Overall, beneficiaries are highly satisfied with ‘Good road connectivity exists’ with

mean score computed (3.73) and least satisfied with ‘Income is equally distributed’

with mean score 3.44.

6.4.4 Relationship between Financial Inclusion and Area Development

Figure 6.4 underlines the relationship between financial inclusion and area

development. The results indicated that model fit the data outstandingly (CMIN/DF =

4.291, GFI = .981, AGFI = .982, CFI = .982, NFI = .976, TLI = .961 and RMSEA =

198

.081) (Table 5.7). SEM results in Table 6.5 indicates that financial inclusion has

positive and significant relation with area development (β = .392, p = .000).

Therefore, hypothesis ‘Financial inclusion is positively related to area development’

stands accepted. A study by Padma & Gopisetti (2013) also stated that there is close

relationship between financial inclusion & area development and identified that 100%

financial inclusion is important for overall development including rural areas for

bringing their quality of life at par with the people of urban areas.

FIGURE 6.4: SEM MODEL FOR AREA DEVELOPMENT

AC = Access; AV = Availability; US = Usage; AD2 = Your area is regionally balanced; AD3 =

Income is equally distributed and AD8 = Good road connectivity exists.

6.5 BARRIERS OF FINANCIAL INCLUSION

Banking for the poor is a viable option in India as major chunk of the population is at

the bottom of the pyramid. If financial institutions successfully tap this potential, then

it would be a ‘win-win’ situation for financial institutions, people and country as well.

But this ‘win-win’ situation cannot be achieved because of certain barriers of financial

inclusion. These barriers are divided into two categories i.e. demand-side and supply-

side barriers. Demand-side barriers include lack of awareness regarding financial

services & products, limited literacy especially financial literacy, financial capability,

self-exclusion, low income/assets, psychological & cultural barriers which stem from

mistrust of banks due to negative experiences or negative perceptions and social

exclusion. Whereas, supply-side barriers consist of geographical barriers, branch

timings, cumbersome documentation & procedures, marketing exclusion, unsuitable

products, negative staff attitudes, product design, language barriers, high transaction

costs, lack of communication, lack of a proper framework or infrastructure, terms &

conditions of banks are not suitable and easy availability of informal credits

Area-dvt

AD2 e1 .90

AD3 e2 .87

AD4 e3

.60

Financial

inclusion .78

US e4

AV e5 .87

AC e6 .61

.39

e7

199

(Kempson, 2006; Thorat, 2007; Mitton, 2008; Isern & De Koker, 2009 and Goel &

Nayak, 2012).

6.6 IMPACT OF BARRIERS ON ACCESS AND USAGE DIMENSION OF

FINANCIAL INCLUSION

Financial inclusion comprises of three basic dimensions i.e., access, availability and

usage. But impact of barriers to financial inclusion is examined only on access and

usage dimension. Availability dimension remain out of this ambit because availability

is judged from bank’s point of view. The impact of barriers to financial inclusion is

examined under following sub-head:

6.6.1 Impact of barriers to financial inclusion on access dimension

6.6.2 Impact of barriers to financial inclusion on usage dimension

6.6.1 Impact of Barriers to Financial Inclusion on Access Dimension

Table 6.6 shows output from multiple regression analysis using 7 barriers of financial

inclusion to know their impact on dependent variable i.e. ‘access’. The result of linear

regression enticed four independent barriers have significant impact on the dependent

variable. These are ‘Don’t like dealing with bank’, ‘High fees & service charges’,

‘Inconvenient hours/location’ and ‘Don’t need an account’. The correlation between

predictor and outcome is .368, which signifies positive but low correlation exists

between predictor and the outcome. In model summary, R is .368 which indicates

37% association exists between dependent and independent variables. R-square for

this model is .136 which means 14% of variation in access can be explained from the

four independent variables. Further, adjusted R-square for the model is .123 which

indicates that if anytime another independent variable is added to model, the R-square

will increase by 12%.

The regression model in the ANOVA (Table 6.7) exhibits that the overall model is

significantly different. Since, the calculated value of ‘F’ is 11.000 (p = .000), it

indicates that there is significant difference between four barriers of financial

inclusion. Hence, it can be concluded that four barriers are not equally significant

towards ‘access’.

Further, information provided in coefficient table is examined to determine which

independent variable has significant impact on access (Table 6.8). The standardised

200

coefficient beta value reveals that ‘Don’t like dealing with bank’, ‘Inconvenient

hours/location’ and ‘Don’t need account’ have beta coefficient of -.117, -.116 and -

.127 (t = -2.085, -1.996 and -2.453) respectively whereas p = .038, .047 and .014

respectively which shows they have significant and negative but mediocre impact on

access. Whereas, another barrier ‘High fees & service charges’ has beta value of .102

(t= 2.358, p =.019) which indicates that it also has significant and positive but

middling impact. To assess multi-collinearity, tolerance and VIF values were

examined. Tolerance level between four barriers is above .517 and the VIF is below

1.934. Hence, it can be safely concluded that multi-collinearity among the

independent variable is not a problem.

Therefore, on the basis of above analysis, we can conclude that the hypothesis,

‘Barriers to financial inclusion have significant impact on the access dimension’ is

accepted for four barriers namely, ‘Don’t like dealing with bank’, ‘High fees &

service charges’, ‘Inconvenient hours/location’ & ‘Don’t need account’ and rejected

for three barriers i.e., ‘High minimum balance’, ‘Insufficient money’ and ‘No bank

open account’.

6.6.2 Impact of Barriers to Financial Inclusion on Usage Dimension

Table 6.9 shows output from multiple regression analysis using 7 barriers of financial

inclusion to know their impact on dependent variable i.e. ‘usage’. The result of linear

regression enticed two independent barriers has significant impact on the dependent

variable. These are ‘Insufficient money’ and ‘Don’t need account’. The correlation

between predictor and outcome is .185, which signifies positive but low correlation

exists between predictor and the outcome. In model summary, R is .185 which

indicates 19% association exists between dependent and independent variables. R-

square for this model is .034 which means 3% of variation in usage can be explained

from the two independent variables. Further, adjusted R-square for the model is .20

which indicates that if anytime another independent variable is added to model, the R-

square will increase by 20%.

The regression model in the ANOVA (Table 6.10) reflects that the overall model is

significantly different. Since, the calculated value of ‘F’ is 2.474 (p = .017), it

indicates that there is significant difference between two barriers of financial

201

inclusion. Hence, it can be concluded that two barriers are not equally significant

towards ‘usage’.

Further, information provided in coefficient table is examined to determine which

independent variable has significant impact on usage (Table 6.11). The standardised

coefficient beta value reveals that ‘Insufficient money’ has beta value of .124 (t =

2.529, p = .012) which reveals positive and significant but average impact on usage.

Whereas, ‘Don’t need account’ has beta value -.172 (t = -3.156, p = .002) which

reveals negative and significant but average impact on usage. To assess multi-

collinearity, tolerance and VIF values were examined. Tolerance level between two

barriers is above .662 and the VIF is below 1.510. Hence, it can be safely concluded

that multi-collinearity among the independent variable is not a problem.

Therefore, on the basis of above analysis, we can conclude that the hypothesis,

‘Barriers to financial inclusion have significant impact on the usage dimension’ is

accepted for two barriers i.e., ‘Insufficient money’ & ‘Don’t need account’ and

rejected for five barriers ‘Don’t like dealing with bank’, ‘High fees & service

charges’, ‘Inconvenient hours/location’, ‘High minimum balance’ and ‘No bank open

account’.

6.7 COMPARATIVE ANALYSIS OF DISTRICTS AND BANKS

District and bank-wise mean level of satisfaction among beneficiaries is shown in

Table 6.12. District-wise analysis shows that beneficiaries of Jammu district are

highly satisfied with the services of SBI (3.36) followed by JKGB (3.34), JKB (3.24)

and PNB (2.91). In district Samba, mean satisfaction is maximum among

beneficiaries of SBI (3.31) and least among beneficiaries of JKB (2.96). For district

Reasi, JKB adjudged as best with mean score of 3.26 among beneficiaries. In case of

district Kathua, JKB is performing better with high mean values of 2.95 among

beneficiaries followed by SBI (2.78), JKGB (2.47) and PNB (2.32). In Udhampur

district, beneficiaries are more satisfied with mean score of 3.48 with JKB than SBI

with mean score of 2.74. Overall, mean values highlights that JKGB is performing

better among all banks with highest mean score of 3.14 followed by JKB (3.09), SBI

(2.77) and PNB (2.49).

202


(ROTATED COMPONENT METHOD) FOR AREA DEVELOPMENT*


deviation

Factor

loading

Eigen

value

Variance

explained

%

Cumulative

explained

%

Communality Alpha

(α)

AREA DEVELOPMENT

Factor 1: Balanced development 2.409 44.577 44.577 .824

Your area is regionally balanced 3.68 .78 .906 .830

Income is equally distributed 3.51 .89 .882 .801

Good road connectivity exists 3.71 .80 .787 .623

Factor 2: Health indicators 1.745 38.515 83.092 .948

Good health services are available 3.51 1.27 .975 .950

A regular doctor (Govt./private) visits in the

village

3.40 1.34 .969 .950

*Source: Survey

TABLE 6.2: RESULT OF CFA FIT INDICES, RELIABILITY AND VALIDITY*

Construct Statements SRW Fit indices Validity Reliability

Area

development

AD2 Your area is regionally

balanced

.963 CMIN/df 4.671

AVE = .640

Cronbach’s alpha = .824

Composite reliability = .954 AD3 Income is equally distributed .788 GFI .988

AD8 Good road connectivity exists .611 AGFI .965

CFI .989

NFI .986

TLI .986

RMSEA .086

*Source: Survey

203


REGARDING AREA DEVELOPMENT*


mean 1 2 3 4 5

Age Upto 30 years 3.69 3.53 3.41 3.04 3.67 3.47

30-40 years 3.64 3.46 3.43 3.37 3.69 3.52

40-50 years 3.62 3.49 3.54 3.45 3.75 3.57

Above 50 years 3.85 3.63 3.67 3.56 3.66 3.67

Overall Mean 3.70 3.52 3.51 3.36 3.69

Gender Male 3.69 3.53 3.51 3.40 3.74 3.57

Female 3.60 3.32 3.53 3.40 3.47 3.46

Overall Mean 3.64 3.43 3.52 3.40 3.61

District Jammu 3.67 3.58 3.32 3.19 3.73 3.50

Kathua 3.55 3.27 3.38 3.29 3.70 3.44

Reasi 4.07 3.99 4.13 4.20 4.00 4.08

Samba 3.69 3.44 3.67 3.23 3.59 3.52

Udhampur 3.87 3.76 4.07 4.06 3.67 3.89

Overall Mean 3.77 3.61 3.71 3.59 3.74

Caste General 3.68 3.51 3.49 3.41 3.68 3.55

SC 3.70 3.53 3.51 3.39 3.78 3.58

ST 3.23 3.06 3.85 3.46 3.08 3.34

OBC 3.74 3.54 3.52 3.22 3.81 3.57

Overall Mean 3.59 3.41 3.59 3.37 3.59

Religion Hindu 3.69 3.52 3.52 3.42 3.72 3.57

Muslim 3.09 3.00 3.73 3.18 2.91 3.18

Sikh 4.00 4.00 1.00 1.00 3.67 2.73

Overall Mean 3.59 3.51 2.75 2.53 3.43

Marital

Status

Married 3.67 3.50 3.53 3.44 3.71 3.57

Unmarried 3.72 3.54 3.36 2.98 3.70 3.46

Overall Mean 3.70 3.52 3.45 3.21 3.71


primary

3.43 3.00 3.50 3.50 3.64 3.41

Upto primary 3.48 3.17 3.46 3.31 3.42 3.37

Upto middle 3.63 3.50 3.48 3.40 3.75 3.55

Upto secondary 3.78 3.66 3.53 3.44 3.79 3.64

Upto higher

secondary

3.81 3.67 3.63 3.51 3.74 3.67

Upto graduate

or higher

3.86 3.14 3.71 3.71 4.00 3.68

Anyother 4.00 4.00 4.00 4.00 4.00 4.00

Illiterate 3.59 3.41 3.34 2.97 3.52 3.37

Overall Mean 3.70 3.44 3.58 3.48 3.73

*Source: Survey

**1 stands for ‘Your area is regionally balanced’, 2 Income is equally distributed’, 3 Good road

connectivity exists’, 4 Good health services are available’, 5 A regular doctor (Govt./private)

visits in the village.

204



Final

model

4.291 .981 .942 .982 .976 .961 .081

*Source: Survey

TABLE 6.5: RESULT OF HYPOTHESIS TESTING*

Hypothesis CR SRW P-value Accepted/

Rejected

Financial inclusion is positively

related to area development

7.559 .392 .000 Accepted

*Source: Survey

TABLE 6.6: REGRESSION MODEL SUMMARY (WITH COEFFICIENT) OF

ACCESS DIMENSION AS DEPENDENT VARIABLE*

Model R R-square Adjusted R

square

Standard error of

the estimate

1 .368

a .136 .123 .62625

a. Predictors: (Constant), Don’t need an account; High minimum balance; High fees & service

charges; Insufficient money; Inconvenient hours/location; Don’t like dealing with banks, No bank open account.

b. Dependent variable: Access

*Source: Survey

TABLE 6.7: ANOVAb

FOR MEASURING REGRESSION COEFFICIENT*

Model Sum of

squares

DF Mean

square

F Significance

1. Regression 30.199 7 4.314 11.000 .000

a

Residual 192.566 491 .392

Total 222.765 498


charges; Insufficient money; Inconvenient hours/location; Don’t like dealing with banks, No bank

open account.

b. Dependent variable: Access

*Source: Survey

205

TABLE 6.8: REGRESSION COEFFICIENT’S SHOWING THE EFFECT OF

BARRIERS OF FINANCIAL INCLUSION ON ACCESS DIMENSION*

Model Standardised coefficient Sig. Collinearity statistics

Beta t Tolerance VIF

1. (Constant) 9.697 .000

Don’t like dealing

with banks

-.117 -2.085 .038 .555 1.803

High fees & service

charges

.102 2.358 .019 .945 1.058

Inconvenient hours/

location

-.116 -1.996 .047 .517 1.934

Don’t need an

account

-.127 -2.453 .014 .662 1.510

*Source: Survey

TABLE 6.9: REGRESSION MODEL SUMMARY (WITH COEFFICIENT) OF

USAGE DIMENSION AS DEPENDENT VARIABLE*

Model R R-square Adjusted R

square

Standard error

of the estimate

1 .185

a .034 .020 .61767

a. Predictors: (Constant), Don’t need an account; High minimum balance; High fees & service charges; Insufficient money; Inconvenient hours/location; Don’t like dealing with banks, No bank

open account.

b. Dependent variable: Usage

*Source: Survey

TABLE 6.10: ANOVAb

FOR MEASURING REGRESSION COEFFICIENT *

Model Sum of

squares

DF Mean

square

F Significance

1. Regression 6.606 7 .944 2.474 .017a

Residual 187.322 491 .382

Total 193.928 498


charges; Insufficient money; Inconvenient hours/location; Don’t like dealing with banks, No bank

open account.

b. Dependent variable: Usage

*Source: Survey

206

TABLE 6.11: REGRESSION COEFFICIENT’S SHOWING THE EFFECT OF

BARRIERS OF FINANCIAL INCLUSION ON USAGE DIMENSION*

Model Standardised coefficient Sig. Collinearity statistics

Beta t Tolerance VIF

1. (Constant) 5.896 .000

Insufficient money .124 2.529 .012 .816 1.225

Don’t need an

account

-.172 -3.156 .002 .662 1.510

*Source: Survey

TABLE 6.12: DISTRICT AND BANK-WISE MEAN SATISFACTION AMONG

BENEFICIARIES OF FID*

District Name of the bank Mean value

Jammu JKB 3.24

JKGB 3.34

SBI 3.36

PNB 2.91

Samba JKB 2.96

JKGB -

SBI 3.31

PNB -

Reasi JKB 3.26

JKGB -

SBI -

PNB -

Kathua JKB 2.95

JKGB 2.47

SBI 2.78

PNB 2.32

Udhampur JKB 3.48

JKGB -

SBI 2.74

PNB -

Overall mean

sarifaction

JKB 3.09

JKGB 3.14

SBI 2.77

PNB 2.49

*Source: Survey

207

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`

Chapter-VII Conclusion and Strategic

Implications

CONTENTS


7.1 Financial Inclusion and Economic Development 210

7.2 Research Methodology 210

7.3 Data Analysis and Interpretation 212

7.3.1 Financial Inclusion, Socio-economic

Empowerment and Economic Development

212

7.3.2 Financial Inclusion and Poverty Reduction 219

7.3.3 Financial Inclusion and Area Development 221

7.4 Strategic Implications 224

7.5 Conclusion 228

210

CHAPTER VII

CONCLUSION AND STRATEGIC IMPLICATIONS

7.1 FINANCIAL INCLUSION AND ECONOMIC DEVELOPMENT

Financial inclusion is the mechanism of ensuring access to financial services and

timely & adequate credit whenever needed by the vulnerable groups at an affordable

cost. This access to financial services generates income, increases standard of living,

reduces poverty, decreases disparity, creates financial assets, promotes area

development, escalates agricultural growth rate and provide new work opportunities

across all sectors & sections of the economy. This shows financial inclusion has

multiplier effect on the economy as a whole through higher savings pooled from the

vast segment of the bottom of the pyramid (BoP) population. It brings un-banked

people into financial mainstream and results in escalating the economic development

of the country. Thus, it is a stepping stone and is integral to the inclusive growth

process & sustainable development of the country.

7.2 RESEARCH METHODOLOGY

The present study assesses the impact of financial inclusion on the beneficiaries of

four banks namely, Jammu & Kashmir Bank (JKB), Jammu & Kashmir Grameen

Bank (JKGB), State Bank of India (SBI) and Punjab National Bank (PNB) belonging

to five districts i.e., Jammu, Samba, Kathua, Udhampur and Reasi of Jammu division

of J&K State. Data were collected from both primary and secondary sources. Primary

data for the study were gathered personally in Dogri dialect through self-developed

schedule consisted of two sections, one general and other to elicit information about

eight dimensions of financial inclusion namely, access, availability, usage, social

empowerment, economic empowerment, economic development, poverty reduction

and area development. Information relating to these dimensions were collected on five

point Likert scale (5<----1>) where 5 denotes strongly agree and 1 denotes strongly

disagree. Suggestions were kept in open ended form to get specific response. On the

basis of pretesting on a sample of 10 beneficiaries belonging to 10 villages, selecting

2 from each of five districts of Jammu division, the final sample 884 arrived at was

rounded off to 900 respondents. Judgment sampling was followed for collecting data

211

from 900 beneficiaries, criteria adopted was availability and willingness to respond.

Business correspondents of all eighty three villages were contacted, out of which

twenty eight either out rightly rejected to cooperate or refused by saying that nothing

has been done on financial inclusion till date. Of the remaining fifty five villages,

primary data were collected from 523 beneficiaries only, representing a response rate

of 58.11%. The items of the construct consisted of access dimension incorporating 17

items (Sarma & Pais, 2008; Kumar, 2011 and Gupte et al., 2012), availability

containing 18 items (Sarma & Pais, 2008; Kumar, 2011 and Gupte et al., 2012),

usage including 9 items (Sarma & Pais, 2008; Kumar, 2011 and Gupte et al., 2012),

social empowerment encompassing 25 items (Barik, 2009; Kumar & Sharma, 2011;

Arputhamani & Prasannakumari, 2011 and Cnaan et al., 2011), economic

empowerment comprising 11 items (Barik, 2009; Kumar & Sharma, 2011;

Arputhamani & Prasannakumari, 2011 and Cnaan et al., 2011), economic

development consisting 11 items (Agrawal, 2007 and Das, 2011), poverty reduction

embracing 10 items (Rautela et al., 2010; Latif et al., 2011 and Mishra, 2012) and area

development including 8 items (Rautela et al., 2010 and Arputhamani &

Prasannakumari, 2011). The survey was carried on during February-July, 2013. After

pretesting, normalcy of the data was checked which lead to deletion of 23 outlier

observations. Subsequently, the data so collected was purified with the help of factor

analysis using SPSS package (17.0 version). Thereafter, various statistical tools such

as mean, standard deviation, multiple regression, one-way ANOVA, t-test etc., were

used to derive meaningful results. Confirmatory factor analysis and SEM were also

used through AMOS (16.0 version) for further analysis. Secondary information was

collected from various journals viz., Asian Economic Review, International Journal of

Advanced Research and Innovations, International Journal of Economics and Finance,

International Journal of Innovative Research & Development, International Research

Journal of Commerce & Behaviour Science, IOSR Journal of Economics and Finance,

Journal of Accounting and Finance, Journal of Economics, Business and

Management, Journal of Global Business and Economics, Journal of International

Business and Economics, Journal of International Development, Journal of Rural

Development, Journal of Social Policy etc., published information from internet, RBI

reports and magazines.

212

7.3 DATA ANALYSIS AND INTERPRETATION

The data for the study was analysed under following three sub-heads:

7.3.1 Financial inclusion, socio-economic empowerment and economic

development

7.3.2 Financial inclusion and poverty reduction

7.3.3 Financial inclusion and area development

A brief description of each sub-head is as under:

7.3.1 Financial Inclusion, Socio-economic Empowerment and Economic

Development

Financial inclusion is the process of ensuring access to financial services by

vulnerable groups such as the weaker sections and low income groups at an affordable

cost. It provides access to various banking products & services which act as the

catalyst in the economic & social growth of an individual and progress of an

economy. This access helps in gaining financial empowerment, promoting social

inclusion, building self-confidence and thus leading to social & economic

empowerment of rural folk. Therefore, financial inclusion is a key driver for social &

economic empowerment at an individual level and economic development at national

level. Perception about various aspects of financial inclusion, socio-economic

empowerment and economic development among the beneficiaries of four banks

belonging to five districts was examined under the following sub-heads:

a. Scale purification

b. Confirmatory factor analysis

c. Structural equation modeling

d. Mediation between financial inclusion and economic development through socio-

economic empowerment

e. One-way ANOVA

f. t-test

g. Demographic profile-wise analysis

213

Purification of constructs administered on beneficiaries’ of financial inclusion drive of

RBI was separately carried using SPSS (17.0 version). Beneficiaries’ perception

regarding access dimension was analysed through factor analysis and the process of

R-mode principal component analysis (PCA) with Varimax rotation brought the

construct to the level of 12 statements out of 17 statements. The KMO value (0.906)

and Bartlett test of sphercity (2986.617) indicated acceptable and significant values,

resulted into three-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with

67.60% of the total variance explained. The communality for 12 items ranged from

0.558 to 0.806 which indicated moderate to high degree of linear association among

the variables. The factor loading ranged from 0.633 to 0.845 and the cumulative

variance extracted varied from 35.17 to 67.60 percent.

Beneficiaries’ perception regarding availability dimension of financial inclusion

divulged that the process of R-mode principal component analysis (PCA) with

Varimax rotation extracted 9 statements out of 18 statements which were actually kept

in the construct. The KMO value (.770) and Bartlett test of sphercity (2170.576)

indicated highly acceptable and significant values. Factor loadings in the final

factorial design were consistent with conservative criteria, thereby resulted into three-

factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with 72.56% of the total

variance explained. The communalities for 9 statements ranged from .546 to .918,

indicated high degree of linear association among the variables. The factor loading

ranged from .616 to 0.944 and the cumulative variance extracted from 29.516 to

72.562 percent.

The scale measuring beneficiaries’ perception regarding usage dimension of financial

inclusion revealed the KMO value of .677 and Bartlett test of sphercity at 708.037

which indicated high acceptable & significant values and resulted into two-factor

solution with 67.78% total variance explained. The communality for 6 retained items

ranged from .522 to .739, factor loadings from .652 to .856 and the cumulative

variance extracted from 35.96 to 65.78 percent.

The scale measuring beneficiaries’ perception regarding social empowerment

revealed the KMO value of .803 and Bartlett test of Sphercity 3406.412 (p-value =

0.000). 14 statements extracted out of 25 statements which were actually kept in the

construct of social empowerment, resulted into four-factor solution with 69.54% of

the total variance explained. The communalities for 14 statements ranged from .526 to

214

.870, indicated high degree of linear association among the variables. The factor

loading ranged from .509 to 0.925 and the cumulative variance extracted ranged from

29.425 to 69.539 percent.

Beneficiaries’ perception regarding economic empowerment was analysed through

factor analysis and the process of R-mode principal component analysis (PCA) with

Varimax rotation brought the construct to the level of 9 statements out of 11

statements of economic empowerment. The KMO value (.901) and Bartlett test of

sphercity (2534.429) indicated acceptable and significant values, thereby resulted into

two-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with 67.87% of the total

variance explained. The communalities for 9 statements ranged from .529 to .779,

factor loading from .639 to 0.846 and the cumulative variance from 46.768 to 67.873

percent.

The scale measuring beneficiaries’ perception regarding economic development

revealed that KMO value of .875 and Bartlett test of sphercity at 2109.914 which

indicated high acceptable & significant values and resulted into two-factor with

69.96% of the total variance explained. The process of R-mode principal component

analysis (PCA) with Varimax rotation extracted 8 statements out of 11 statements

which were actually kept in the construct of economic development. The

communalities for 8 statements ranged from .591 to .811, indicated moderate to high

degree of linear association among the variables. The factor loading ranged from .546

to .875 and the cumulative variance extracted from 40.266 to 69.96 percent.

CFA was conducted with the objective of verifying the fitness of each latent

construct. In the present study, it was performed to assess the fitness, reliability and

validity of six measured constructs, viz., financial inclusion (FI) consists of three

main dimensions i.e., access, availability & usage; social empowerment (SE);

economic empowerment (EE) and economic development (ED).

First order CFA was performed on access dimension, which constituted of twelve

items. Among twelve items, three items got deleted as they were not meeting the

criteria i.e., SRW’s > .50. After deleting, CFA produced good fit as CMIN/DF =

4.735, GFI = .955, AGFI = .912, NFI = .960, TLI = .950, CFI = .968 and RMSEA =

.087. The model had been found to be valid and reliable. The alpha value was arrived

at .884 whereas composite reliability came out to be .991, which indicated that all

215

items were reliable. Model was proved to be valid, as AVE came out to be .533. The

construct validity also stands established as all the indicators had factor loading above

.50. Out of the twelve items, ‘Employee’s are helpful in making information available

regarding new schemes’ emerged to be strongest contributor towards access

dimension, as its regression weight was .90 .

First order CFA was executed on the latent construct ‘availability’ which consisted of

nine items. The different fit indices evaluated the fitness of the model and the results

showed that the model fitted the data well as CMIN/DF = 1.852, GFI = .967, AGFI =

.941, NFI = .963, TLI = .965, CFI = .975 and RMSEA = .065. Four items got deleted

as their regression weights were below .50. In addition to the model fitness, reliability

and validity were also examined. The scale exceeded the recommended cut off value

of .70 as composite reliability = .981. So, it was reasonable to conclude that the scale

was reliable. Reliability was also confirmed through Cronbach’s alpha value which

was measured to be .806. As far as AVE was concerned, the value was greater than

0.50 (AVE = .602). Also, each of the item loading was greater than .50, which

provided empirical support for the convergent validity of construct. Among five

items, ‘Loan is made available within time limit’ contributed highest to the main

construct with SRW .95.

First order CFA was performed on usage dimension. It constituted of six items. After

deleting three items having regression weights below .50, the result of CFA showed

the model fully fits the data, CMIN/DF = 4.563, GFI = .988, AGFI = .965, NFI =

.983, TLI = .979, CFI = .975 and RMSEA = .085. The model found to be valid and

reliable which was confirmed through Cronbach’s alpha (.628), composite reliability

(.965) and AVE = .546. Out of five items, item ‘You frequently use credit facilities of

the bank’ contributed highest with regression weight .84.

First order CFA was performed on social empowerment construct which consisted of

fourteen indicators. While running CFA, seven items got deleted as they were not

meeting the criteria. The result revealed that the model fit statistics were within

recommended levels i.e., CMIN/DF = 3.286, GFI = .984, AGFI = .950, NFI = .979,

TLI = .965, CFI = .985 and RMSEA = .068. Additionally, this model had also been

found to be valid and reliable, as AVE was .608, composite reliability equals to .989.

The value of Cronbach’s alpha was .806 and all items loading above .50. Thus,

validity and reliability got established. Among seven items, ‘FI has changed your

216

personality & life style’ had the highest factor loading of .84, thus contributed

maximum to social empowerment.

First order CFA was performed on economic empowerment construct which consisted

of nine items. CFA model yielded good model fit results, CMIN/DF =2.597, GFI =

.981, AGFI = .959, NFI = .985, TLI = .985, CFI = .984 and RMSEA = .057. The

model found to be valid and reliable after deleting two items. Reliability was also

examined by calculating composite reliability (.992). In this model, AVE exceeds the

critical level of .50 (.595). This established the reliability and convergent validity of

measurement scale in the study. All regression weights were also above .50 which

indicated that all measured variables were significant contributors of this construct.

Scale reliability was established through Cronbach’s alpha (.890). Among seven

items, ‘FI has increased your purchasing power’ was adjudged as the strongest

contributor having highest SRW.

First order CFA was performed on economic development construct, which

comprised of eight items. The result of CFA indicated that model fit the data,

CMIN/DF = 4.601, GFI = .966, AGFI = .919, NFI = .968, TLI = .952, CFI = .974 and

RMSEA = .085. The composite reliability came out to be .992. Scale reliability was

established through Cronbach’s alpha .888. The value of AVE was greater than .50.

(.512). Furthermore, it was found that each factor loading was greater than .50, which

provides empirical evidence for the convergent validity of the construct. Item ‘FI has

increased access to education of the society’ emerged as the strongest contributor

(.79) of the main construct.

After applying CFA and checking reliability & validity, SEM was conducted by using

AMOS (version 16.0) to assess fitness of structural model. The SEM technique was

used to test the main hypotheses proposed in the study. There were a total of 25

indicators in the final structural model. The proposed model strived to identify the

financial inclusion impact on economic development. Final SEM model including all

indicators were tested. The final model fitness results revealed CMIN/DF= 4.924, GFI

= .937, AGFI = .896, NFI = .932, TLI = .924, CFI =.945 and RMSEA = .089. The

result of SEM depicted that access, availability and usage significantly predicts

financial inclusion where access emerged as the strongest predictor with highest beta

value (.75). Therefore, the hypothesis ‘Access, availability and usage significantly

predicts the financial inclusion’ holds true. It was also inferred that financial

217

inclusion had direct impact on social empowerment, economic empowerment and

economic development leading to the acceptance of next hypothesis i.e., ‘Financial

inclusion has direct impact on social empowerment, economic empowerment and

economic development’. SEM results also revealed that social empowerment and

economic empowerment has direct impact on economic development. Thus, the next

hypothesis ‘Social empowerment and economic empowerment has direct impact on

economic development’ stands accepted.

Mediation between financial inclusion and economic development through social &

economic empowerment was also assessed. To test the mediation effect, all the

conditions described by Baron & Kenny (1986) were satisfied first. Then mediator

i.e., social empowerment and economic empowerment were entered separately into

the equation of financial inclusion and economic development, the relationship

between financial inclusion & economic development became insignificant and

relationship between other variables i.e., financial inclusion-social empowerment &

social empowerment-economic development and financial inclusion-economic

empowerment & economic empowerment-economic development remained

significant. Therefore, the aforesaid analysis supported the hypothesis ‘Social

empowerment and economic empowerment mediates the relationship between

financial inclusion and economic development’.

The result of One-way ANOVA depicted that among five socio-economic variables

i.e., age, caste, religion, qualification & income, significant mean difference exist in

the nature of financial inclusion for all the variables except for age (.094) and caste

(.057) variables.

Output from independent t-test measuring significance of mean difference on the

basis of gender and marital status divulged that no significant difference exist between

male & female and married & unmarried beneficiaries with regard to nature of


The findings from One-way ANOVA and t-test supported the hypothesis ‘Nature of

financial inclusion differs across socio-economic profile of respondents’ for three

and rejected for four variables.

Demographic profile-wise analysis disclosed that JKB had the largest share and PNB

had the smallest share under FID. It was also found that male were more interested in

218

opening bank accounts and female were still reluctant to open an account in bank

even if it was a no frill account. It revealed that middle age people were more

interested in opening accounts and availing the benefits of government schemes.

Caste-wise, general category beneficiaries were more awared about the FID compared

to lower castes & tribes. Hindu beneficiaries were majorly covered under the financial

inclusion drive in the five districts i.e., Jammu, Samba, Kathua, Udhampur and Reasi.

It was found that married respondents were more family oriented and saving

conscious whereas unmarried want to spend money. Financial inclusion drive

uniformly covered illiterate or highly qualified. It was found that maximum

respondents were having monthly income upto `5,000 revealing that lower income

people were also covered under this drive. Among all districts, district Jammu topped

in the list of beneficiaries of financial inclusion drive. With regard to availability &

usage dimensions of financial inclusion, beneficiaries belonging to above 50 years of

age (2.81 & 2.93) were highly satisfied followed by 40-50 years (2.70 & 2.88), 30-40

years (2.61 & 2.76) and upto 30 years (2.44 & 2.67). Caste-wise analysis showed that

with regard to access dimension, general caste (3.54) beneficiaries were highly

contended followed by SC (3.53), ST (3.31) and OBC (3.19) beneficiaries. As far as

usage dimension was concerned, general caste (2.88) beneficiaries were more

satisfied than SC (2.78), OBC (2.64) and ST (2.46) beneficiaries. Religion-wise

analysis depicted that Sikh respondents (3.83) were more satisfied with usage

dimension followed by Hindu (2.83) and Muslim (2.39) beneficiaries. For all the

dimensions i.e., access, availability & usage, beneficiaries with upto higher secondary

qualification were maximally satisfied with mean value (3.79, 2.85 & 3.00) and

minimally satisfied with mean value (2.89, 2.10 & 2.33) respectively. Beneficiaries

with above `20,000 monthly income were highly satisfied with regard to access &

availability dimensions followed by beneficiaries with `10,000-20,000; `5,000-

10,000 and upto `5,000 income. On usage dimension, beneficiaries with income

between `10,000-20,000 were more satisfied than beneficiaries with `5,000-10,000,

above `20,000 and upto `5,000 income. With regard to usage dimension, male were

found to be more contended with mean score 2.84 than female with mean value of

2.70. Married beneficiaries were more satisfied (2.69) than unmarried beneficiaries

(2.41) with regard to availability dimension of financial inclusion.

219

7.3.2 Financial Inclusion and Poverty Reduction

Financial inclusion is an important tool for combating the multi-dimensional aspects

of poverty. It enhances financial access which leads to several benefits such as

overcoming problem of poverty, unemployment, inequality, deteriorating welfare etc.

and has generated a positive impact on the lives of the poor. There is a bi-directional

cause and effect relationship between poverty and financial inclusion. It is a win-win

opportunity for the poor, for the banks and for the nation. It contributes in

employment generation, income generation, proper utilisation of resources,

mobilisation of savings etc. which help in poverty alleviation and thus lead to GDP

growth in any economy. Perception about financial inclusion and poverty reduction

among the beneficiaries of four banks belonging to five districts was examined under

the following sub-heads:



c. Demographic profile-wise mean satisfaction regarding poverty eradication

d. Demographic profile-wise mean satisfaction regarding poverty reduction through

education

e. Relationship between financial inclusion and poverty reduction


RBI was separately carried using SPSS (version 17.00). The scale measuring

beneficiaries’ perception regarding poverty reduction revealed the KMO value of .904

and Bartlett test of sphercity of 3225.782 which indicated acceptable and significant

values. The process of R-mode principal component analysis (PCA) with Varimax

rotation brought the construct to the level of 8 statements out of 10 statements thereby

resulted into two-factor with 79.40% of the total variance explained. The

communalities for 8 statements ranged from .663 to .872, indicated high degree of

linear association among the variables. The factor loading varied from .768 to .908

and the cumulative variance extracted ranged from 57.52 to 79.40 percent.

First order CFA was performed to assess the model fitness. While applying CFA, two

items got deleted as their standard regression weight (SRW) was below the acceptable

criteria of .50. Rest 6 items had regression weight above .50. This model had good fit

220

(CMIN/DF = 3.927, GFI = .982, AGFI = .947, CFI = .993, NFI = .991, TLI = .985

and RMSEA = .077). Convergent validity also got established as AVE arrived at .745

and Cronbach’s alpha was .946 and composite reliability equals to .996. Thus, the

model had proved to be valid and reliable. Variable ‘Family crisis are reduced through

better living standard’ contributed highest towards poverty reduction, as its regression

weight was .943. Financial inclusion enabled rural population to avail the financial

services and increases their income earning capabilities which resulted into raising of

standard of living and reducing family crisis. Los Cobos declaration on financial

inclusion (2012) also recognised that financial inclusion is a key component in the

development of healthy, vibrant and stable financial systems which contribute to

sustainable economic growth. Access to safe, secure and reliable financial system is

important for improving standard of living.

Demographic profile-wise mean satisfaction analysis regarding poverty eradication

factor revealed that age-wise, respondents belonging to 30-40 years (3.45) category

were maximally satisfied followed by above 50 years (3.41), upto 30 years (3.34) and

40-50 years (3.32) age category. Gender-wise, female respondents were found to be

more satisfied with mean value of 3.55 compared to male with mean value of 3.36.

District-wise mean satisfaction among beneficiaries in descending order was found to

be Reasi (3.61), Jammu (3.58), Udhampur (3.26), Samba (3.24) and Kathua (3.21).

Caste-wise, mean level of satisfaction in ascending order was OBC (3.06), ST (3.07),

SC (3.24) and general (3.52). Religion-wise, mean level of satisfaction in descending

order was Sikh (4.00), Hindu (3.39) and Muslim (2.96). Marital status-wise, married

respondents were more satisfied (3.40) in contrast to unmarried beneficiaries (3.29).

Qualification-wise, mean satisfaction in increasing order was upto primary (2.61),

literate below primary (2.72), illiterate (2.90), upto graduate or higher (3.05), upto

middle (3.41), upto secondary (3.63), upto higher secondary (3.84) and any other

qualification (4.00).

Demographic profile-wise mean satisfaction analysis regarding poverty reduction

through education disclosed age-wise beneficiaries under the age group of above 50

years (3.46) were highly satisfied followed by upto 30 years (3.55), 30-40 years (3.33)

and 40-50 years (3.29). Gender-wise analysis depicted that female beneficiaries (3.49)

were more satisfied in comparison to male beneficiaries (3.34). District-wise analysis

exhibited satisfaction among beneficiaries of different districts in ascending order,

221

Kathua (2.96), Udhampur (3.13), Samba (3.43), Reasi (3.54) and Jammu (3.73).

Caste-wise, mean satisfaction in descending order was found to be at 3.53 (General),

3.39 (ST), 3.19 (SC) and 2.67 (OBC). Religion-wise, maximum mean satisfaction was

found to be 4.00 (Sikh), followed by 3.35 (Hindu) and least 3.27 (Muslim). Marital

status-wise, unmarried beneficiaries were more satisfied (3.45) than married

beneficiaries (3.35). Qualification-wise, mean satisfaction in descending order was

found to be 5.00 (Any other qualification), 4.09 (upto higher secondary), 3.93 (upto

graduate or higher), 3.64 (upto secondary), 3.25 (upto middle), 2.83 (Illiterate), 2.58

(upto primary) and 2.36 (Literate below primary).

Relationship between financial inclusion and poverty reduction was checked through

SEM (AMOS, version 16.0). The results showed that model fit the data excellently

(CMIN/DF = 4.899, GFI = .946, AGFI = .907, CFI = .972, NFI = .965, TLI = .961

and RMSEA = .088). SEM results indicated financial inclusion has positive and

significant relation with poverty reduction (β = .553, p = .000). Therefore, the

hypothesis ‘Financial inclusion is positively related to poverty reduction’ stands

accepted. The result of the study was in line with the previous study Murari &

Didwania (2010) which had the same observation and found that scheme of financial

inclusion is an effective instrument which can lift poor above the level of poverty by

providing them increased self employment opportunities and making them credit

worthy. Another study by Rahman (2013) highlighted financial inclusion combat

poverty by making advanced opportunities available for the disadvantaged poor,

thereby promoting social inclusion and inclusive socio-economic growth.

7.3.3 Financial Inclusion and Area Development

In today’s decade of liberalisation, privatisation and globalisation, the term ‘inclusive

growth of an economy’ has become very popular all over the globe. It implies

participation and sharing benefits from the growth process. The Indian economy is

considered as the fastest growing economy of the world. Finance acts as the lubricant,

which oils the wheels of development. Whereas, financial inclusion assists millions

of poor households who are out of reach of financial services through micro credit,

micro savings, money transfers, micro insurance, etc. These services have become

fulcrum for development initiatives in the third world countries. Financial inclusion is

important for area development. Financial intermediation in the rural areas will help

to lubricate rural economic activities for the stimulation of area development.

222

Perception about financial inclusion and area development among the beneficiaries of

four banks belonging to five districts was examined under the following sub-heads:



c. Demographic profile-wise mean satisfaction regarding area development

d. Relationship between financial inclusion and area development

e. Impact of financial inclusion on access & usage dimension through multiple

regression

f. Comparative analysis of banks


RBI was separately carried using SPSS (version 17.0). The process of R-mode

principal component analysis (PCA) with Varimax rotation extracted 5 statements out

of 8 statements which were actually kept in the construct of area development. The

KMO value (.598) and Bartlett test of sphercity (1534.307) indicated acceptable and

significant values. Therefore, factor loadings in the final factorial design were

consistent with conservative criteria, thereby resulting into two factor solution using

Kaiser criteria (i.e. eigen value ≥ 1) with 83.09% of the total variance explained. The

communalities for 5 statements ranged from .623 to .950 and thus indicated moderate

to high degree of linear association among the variables. The factor loading ranged

from .787 to 0.975 and the cumulative variance ranged from 44.58 to 83.09 percent.

First order CFA was performed to assess the model fitness. After applying CFA, two

items got deleted as their standard regression weight (SRW) was below than the

acceptable criteria of .50. This model had been found to have a good fit (CMIN/DF =

4.671, GFI = .988, AGFI = .965, CFI = .989, NFI = .986, TLI = .986 and RMSEA =

.086). Convergent validity also got established as AVE arrived at .64 and Cronbach

alpha at .824 and composite reliability equals to .954. Thus, the model proved to be

valid and reliable. In the study, variable ‘Your area is regionally balanced’ contributed

highest towards area development, as its regression weight was .963.

Demographic profile-wise analysis exhibited age-wise beneficiaries under the age

group of above 50 years were highly satisfied with mean value (3.67), followed by

40-50 years with mean value (3.57) and upto 30 years age group with mean value

223

(3.47). District-wise, beneficiaries of Reasi were highly satisfied (4.08), followed by

Udhampur (3.89), Samba (3.52), Jammu (3.50) and Kathua (3.44). Caste-wise mean

perception regarding area development in descending order was SC (3.58), OBC

(3.57), general (3.55) and ST (3.34). Religion-wise, maximum mean satisfaction was

found to be 3.57 (Hindu), followed by 3.18 (Muslim) and 2.73 (Sikh). Marital status-

wise, unmarried beneficiaries were more satisfied in contrast to married beneficiaries

with the mean value of 3.57 & 3.46 respectively. Gender-wise, male respondents were

found to be more contended with mean value of 3.57 compared to female with mean

value of 3.46.

The relationship between financial inclusion and area development was checked

through SEM (AMOS, version 16.0). The results indicated that model fit the data

outstandingly (CMIN/DF = 4.291, GFI = .981, AGFI = .982, CFI = .982, NFI = .976,

TLI = .961 and RMSEA = .081). SEM results indicated that financial inclusion has

positive and significant relation with area development (β = .392, p = .000).

Therefore, hypothesis ‘Financial inclusion is positively related to area development’

holds true. A study by Padma & Gopisetti (2013) also stated that there is close

relationship between financial inclusion & area development and identified that 100%

financial inclusion is important for overall development including rural areas for

bringing their quality of life at par with the people of urban areas.

Multiple regression was used to elicit the impact of barriers of financial inclusion on

access dimension. The result of analysis enticed that four independent factors i.e.,

‘Don’t like dealing with bank’, ‘High fees & service charges’, ‘Inconvenient

hours/location’ and ‘Don’t need an account’ were significant in predicting the

dependent variable from the beneficiaries perspective whereas other three barriers i.e.,

‘High minimum balance’, ‘Insufficient money’ and ‘No bank open account’ were not

predicting the dependent variable. Therefore, the aforesaid finding supported the

hypothesis ‘Barriers to financial inclusion have significant impact on the access

dimension’ for four barriers and rejected for three barriers.

Multiple regression was also used to bring out the impact of barriers of financial

inclusion on usage dimension. The result highlighted two independent barriers

namely, ‘Insufficient money’ has positive and ‘Don’t need account’ had negative but

significant impact on the dependent variable i.e., usage. Other barriers such as, ‘Don’t

like dealing with bank’, ‘High fees & service charges’, ‘Inconvenient hours/location’,

224

‘High minimum balance’ and ‘No bank open account’ had no impact on usage

dimension. Therefore, the finding supported the hypothesis ‘Barriers to financial

inclusion have significant impact on the usage dimension’ for two barriers and

rejected for five barriers.

Comparative mean satisfaction of beneficiaries belonging to four banks i.e., JKGB,

JKB, SBI and PNB. On the basis of mean values, it is concluded that JKGB was

performing better among all banks with highest mean score of 3.14 followed by JKB

(3.09), SBI (2.77) and PNB (2.49.)

7.4 STRATEGIC IMPLICATIONS

Strategic implications of the study are divided into two categories, one study specific

implications and other general implications based on the observation and existing

literature. To enhance the level of financial inclusion and its impact, implications are

as under:

Specific Implications

i. In the present study, it was found that there exists a communication gap between

bank managers and customers which is manifested from the mean score given to

the statement ‘The bank manager promptly redress your problem’. So, it is

suggested that a suitable mechanism must exist for receiving and redressing

customer grievances courteously, promptly and satisfactorily. In order to collect

grievances promptly from the customers, banks should implement a mechanism

like Centralised Complaint Management System, where all the complaints

received from various channels particularly from business correspondents are

processed and solved immediately.

ii. The mean response was low for ‘ATM service is nearby from your place’. Thus,

banks should enhance their ATM network in rural & unbanked areas to serve

poor villagers. While doing so, adequate care should be taken regarding

safety/security issues. Further, to enable beneficiaries to withdraw cash from

ATM anywhere in the country, banks need to convert FINO card to electronic

credit card. In addition to this, banks should explore the possibility of issuing

multi-purpose cards which could function as debit cards, KCCs, GCCs as per

the requirement of the rural masses.

225

iii. The beneficiaries have responded low for ‘Loan is not easily & timely

available’, which leads to the conclusion that there is cumbersome process of

getting or obtaining loan. Thus, policy makers should simplify the procedure of

granting credit to the customers. Moreover, banks must ensure adequate and

timely credit to the vulnerable section of the society. Extending timely credit or

loan would alleviate poverty and thus contribute towards economic development

which is an outcome of financial inclusion.

iv. The mean score for the item ‘Employees are helpful in making information

available regarding new schemes’ was arrived low, so it could be concluded that

BC’s themselves are not equipped with adequate information which is needed

by the customers. Thus, there is a need to establish better standards and capacity

of BC’s. Further, common programmes should be instituted by the regulators or

banks frequently for providing minimum required knowledge and skills to BCs.

Web portal be maintained and upgraded with new schemes and its access should

be provided to BCs in order to acquaint themselves with latest information.

v. Below average response was scored for the statement ‘New bank schemes are

advertised frequently’. The most important aspect of financial inclusion is

financial literacy but it was found that there is lack of awareness among rural

masses about various schemes of financial inclusion. To increase awareness and

interest in financial products offered under various schemes of financial

inclusion, it is recommended to enhance promotion through electronic or print

media in local language with local icons and artists as brand ambassador of the

campaign.

vi. The statement, ‘You frequently use credit facilities of the bank’ scored very low

which deduced that customers were not using credit facilities because of hidden

terms & conditions and high rate of interest along with hidden transaction costs.

So, it is suggested that terms & conditions for availing loan facility should be

mentioned in clear and lucid language (preferably in Hindi or in local language).

Beside this, the most important terms & conditions termed as standard set of

conditions should be highlighted and sent separately to the prospective

customers at all the stages so that customer do not remain in doubt.

226

vii. Beneficiaries gave below average score to a statement, ‘You are free to move to

any SHG or NGO for any kind of help or support’ which revealed that they were

reluctant to move to any NGO’s, SHG’s for any financial help & support. But

after probing into the reason, it was found that no such social groups were

existing in their respective villages. Thus, it was concluded that no serious

attempts were made to leverage the SHG/NGO-bank linkage programmes to

achieve the financial inclusion goals. Therefore, it is suggested that SHG’s

should exist in each and every village as they seek to reach out to the extended

category of population from banking systems.

viii. Since independence, the government has launched various social schemes to

make the villages socially empowered. But the fundamental drawback of these

social schemes is that people are unaware of the programmes designed. The

same affirmation holds true for the present study as well which is manifested

from the lower mean score to the factor ‘Awareness’. It was found that

beneficiaries were not fully availing the benefits of social schemes because of

lack of awareness among them. The probable reason could be that recipients are

never consulted or there is lack of people’s participation in the social

development schemes. The developmental programmes are sometimes thrusted

on the people even when it is not needed by family or the community. Some

awareness generation projects should be implemented under which the camps

should be organised. The voluntary organisation should come forward and

activities like role play, puppet shows, road shows, nukad nataks,

documentaries, drama skills should be included in the camp for creating

awareness among the rural masses. Furthermore, media can play an important

role in changing lives of the people through disseminating relevant information

regarding various social schemes in order to make them socially empowered.

ix. The study revealed that beneficiaries feel reluctant in complaining to the

authorities regarding delivery of financial services. Though RBI has advised

banks to constitute grievance redressal mechanism within the bank for redressal

of complaints about the services rendered by BCs. But still beneficiaries are

unaware of this redressal mechanism. Beneficiaries of BCs services are

generally illiterate and are prone to misguidance. Many a times customers tend

to perceive the BCs as bank staff & not as agents functioning on behalf of the

227

banks. Thus, the reputation and standing of bank, in the eyes of beneficiaries

will be at stake if BCs either not functioning as per the guidelines or not been

extending banking services expected of him. Thus, banks should give wide

publicity to the grievances redressal mechanism through electronic & print

media. Moreover, direct feedback system should be established whereby, bank

employees should personally visit the beneficiaries in order to look into the

quality & adequacy of customer services being extended by BCs appointed in

financially included villages. This would make beneficiaries to lodge their

complaints freely and without any hassles.

x. To ensure further employability, it is suggested that banks should move beyond

deposit products and should introduce credit products, insurance, mutual funds,

pension plans to people living in financial seclusion. Establishment of rural

infrastructure is a pre-requisite for financial inclusion and all stakeholders

including banks have to contribute towards setting up connectivity and ensuring

power supply among other things. Bank should also endeavour to bond with the

rural community by initiating programmes to adopt schools, conduct vocational

courses for the rural youth and so on.

General Implications

i. There should not be any limit on depositing and withdrawal amount.

ii. To address the issue of credibility of BCs, banks should provide banking

counters through wide network of post offices and fair price shops.

iii. Even multi-language ATMs with audio-video services could be considered.

iv. To address the high attrition among BCs because of low commission/earnings,

banks should nominate housewives, owners of fair price shops, retired people

and people with limited disabilities to become BCs to supplement their regular

income. To attract and retain people in the business of BCs, provision for higher

commission by routing more financial services through BCs can be considered

as well as providing them with respectable designation and identification cards,

with incentivised structured benefits in terms of bonus or promotion or

absorption in mainstream banking.

228

v. The RBI and commercial banks should plan a coordinated campaign in

partnership with the trainers and professional to educate customers about the

basic financial products, services and offerings.

vi. For the success of financial inclusion program, the govt. should make

subscription to financial services mandatory. At the same time, they should also

realise that simplification of procedures will encourage more people to use

banking services.

vii. There should be proper implementation of national programme of ‘Pradhan

Mantri Jan Dhan Yojana’ for wider coverage.

viii. Under financial literacy programme, RBI is issuing a guide which answers basic

questions related to managing money. Responses to certain basic queries such as

why to save, how to save, why save in banks, when to borrow, from whom to

borrow, etc. have been provided in very simple and lucid language, through

pictorial representation. Therefore, to educate, motivate and encourage people

this guide should be widely distributed.

7.5 CONCLUSION

In the wake of inclusive growth for the overall development of the economy, central

bank along with other financial intermediaries must realise the importance of financial

inclusion in promoting the banking habits among people. For enhancing financial

inclusion, suitable mechanism must exist for receiving & redressing customer

grievances courteously, promptly & satisfactorily; ATM network in rural & unbanked

areas be enhanced to serve poor villagers; FINO card be converted into electronic

credit card; single card with multi usage be issued; procedure for granting adequate &

timely credit to the customers be simplified by using clear & lucid language; for

generating awareness some common promotional programmes be instituted by the

regulators or banks through electronic & print media and direct feedback system be

established. In addition to these, the existing literature also enticed some strategies to

enhance the level of financial inclusion such as, extension to banking counters

through wide network of post offices & fair price shops be provided; multi-language

ATMs with audio-video services could be considered; higher commission provision to

BCs be proposed; subscription to financial services be made mandatory and national

programme of ‘Pradhan Mantri Jan Dhan Yojana’ be properly implemented .

`

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229

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http://www.cifor.org

234

http://www.cigionline.org

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http://www.icrier.org

http://www.ifpri.org

http://www.igidr.ac.in

http://www.indiana.edu

http://www.internationalseminar.org

http://www.jrf.org

http://www.microfinancegateway.org

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http://www.nda.org

http://www.oecd.org

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www.ibrarian.net

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235

BOOKS

Access to current accounts, London: British Bankers’Association

Attacking poverty, Oxford: Oxford University Press

Development as freedom, New York: Anchor Books

Discovering statistics using SPSS for windows, London: Sage Publications

Factor analysis, Philaphia: W.B. Saunders Co

Financial inclusion in India: emerging profitable models, Chennai: Indian Banks’

Association

Introducing LISREL, London: Sage Publications

Marketing research, New Delhi: Tata MC Graw-Hill

Marketing research: an applied orientation, New Delhi: Mac Milan Publishing

Company

Marketing research: with a changing information environment, New Delhi: Tata

McGraw Hill

Multivariate data analysis, London: Prentice-Hall Pearson Education

Multivariate data analysis, New Jersey: Prentice Hall Inc

Portfolios of the poor, Princeton University Press

Poverty: a study of town life, London: Macmillan

Principles and practice of structural equation modeling, New York: The Guilford

Press

Role of emerging economies going forward and key policy challenges,

Washington DC: IMF

Rural area development, New Delhi: Sterling Publication

Rural development in Nigeria: the role of physical policy, Nigeria: Ibadan

University Press

Rural development, New York: John Wiley & Sons, Inc

236

Rural development: principles policies and management, New Delhi: Sage

Publications

Structural equation modeling with AMOS: basic concepts, applications and

programming, New Jersy: Lawrence Erlbaum Associates

Structural equation modeling with LISREL, PRELIS and SIMPLIS: basic

concepts, applications and programming, New Jersey: Lawrence. Erlbaum

Associates

The design of rural development, London: John Hopkins University Press

Using multivariate statistics, New York: Allyn and Bacon

PRESENTED PAPERS

Annual Conference of the Human Development and Capability Association, New

Delhi

World Bank, Washington

World Business, Economics and Finance Conference, Thailand

DISCUSSION AND WORKING PAPERS

Debt, money management and access to financial services: evidence from the

1999 PSE survey of Britain. Working Paper 8, Centre for Research in Social

Policy: Loughborough University.

Saving constraints and microenterprise development: evidence from a field

experiment in Kenya. (NBER Working Paper 14693). Cambridge: National

Bureau of Economic Research.

Setboonsarng, S., & Parpiev, Z. (2008). Microfinance and the millennium

development goals in Pakistan: impact assessment using propensity score

matching (Discussion Paper No. 104, 1-25). Tokyo: ADB Institute.

The urban unbanked in Mexico and the United States, policy research. (Working

Paper 3835). Washington DC: World Bank.

237

The vulnerability of “self help”: women and microfinance in South India (IDS

Working Paper 303). Brighton, UK: Institute of Development Studies, British

Library, University of Sussex.

REPORTS

European Commission (2007), Social inclusion, pensions, healthcare and long

term care. (Joint report on social protection and social inclusion). Luxembourg:

Office for Official Publications of the European Communities

Gender and empowerment: definitions, approaches and implications for policy.

(Report No. 40, 1-35). Brighton, UK: Institute of Development Studies

Govt. of India (1966). Report of Rural Urban Relationship Committee, New

Delhi: Govt. of India Publication

`

Annexures

238

ANNEXURE – I

SCHEDULE FOR COLLECTING INFORMATION PERTAINING TO

‘IMPACT OF FINANCIAL INCLUSION ON ECONOMIC DEVELOPMENT’

Note: You are requested to notify the right information as per your knowledge. The information so

collected would be kept secret and used only for research purpose.

Research Scholar:

Preeti Salathia

Phd. Scholar

University of Jammu

General Information

1. Ordinal Information Instruction: Please share correct information on 5-point likert scale ranging from 1 to 5 on the basis of

your knowledge regarding Access, Availability, Usage, Social empowerment, Economic

empowerment, Economic development, Poverty reduction and Area development (Where, 1 stands

for strongly disagree, 2 stands for disagree, 3 stands for neither agree nor disagree, 4 for agree

and 5 stands for strongly agree)

Name

Age Upto 30 Yrs

30-40 Yrs

40-50 Yrs

Above 50 Yrs

Gender Male

Female

District Tehsil Block Village

Caste General

SC

ST

OBC

Religion

Marital Status Married

Unmarried

Qualification

Literate Literate but below

primary

Primary

Middle

Secondary

Higher secondary

Graduate or higher

Any other

Illiterate

Occupation

Income (Monthly) Upto `5,000

`5,000-10,000

`10,000-20,000

Above `20,000

Name of Prime Bank

239

1 Access to Banking Services

1.1 The bank is conveniently located 1 2 3 4 5

1.2 The employees are easily accessible when needed 1 2 3 4 5

1.3 ATM service is nearby from your place 1 2 3 4 5

1.4 Mobile ATM Van visits frequently 1 2 3 4 5

1.5 Banking institution or its substitute is easily approachable 1 2 3 4 5

1.6 Banking officials respond well 1 2 3 4 5

1.7 Financial services are accessible to disabled customers 1 2 3 4 5

1.8 The bank manager promptly redress your problems 1 2 3 4 5

1.9 Transaction timings are convenient 1 2 3 4 5

1.10 Account opening formalities are easy 1 2 3 4 5

1.11 This is the only bank in your area 1 2 3 4 5

1.12 As compared to other banks, this bank is nearest to you 1 2 3 4 5

1.13 Bank is easily approachable in case of emergencies 1 2 3 4 5

1.14 Bank have sufficient staff to meet its customers’

requirements 1 2 3 4 5

1.15 You have easy access to the information which is useful 1 2 3 4 5

1.16 Employees’ of bank are cooperative, friendly and

knowledgeable 1 2 3 4 5

1.17 Employees’ possess sufficient banking information 1 2 3 4 5

1.18 Overall, you are satisfied with your access to banking services

1 2 3 4 5

2 Availability of Banking Services

2.1 Loan is easily available 1 2 3 4 5

2.2 Attractive saving schemes are available 1 2 3 4 5

2.3 Bank provide overdraft facility 1 2 3 4 5

2.4 Bank provide insurance services 1 2 3 4 5

2.5 Debit card facility is available 1 2 3 4 5

2.6 Locker facility is available 1 2 3 4 5

2.7 For any service, hidden charges are collected 1 2 3 4 5

2.8 No frill account service is available 1 2 3 4 5

2.9 Loan is available within time limit 1 2 3 4 5

2.10 New cheque or passbook are issued as & when asked for 1 2 3 4 5

2.11 Procedure involved in getting loan is easy 1 2 3 4 5

2.12 Information regarding new interest rates is provided in time 1 2 3 4 5

2.13 New bank schemes are advertised frequently 1 2 3 4 5

2.14 Fieldworkers promotes various schemes of bank 1 2 3 4 5

2.15 Employee’s are helpful in making information available regarding new schemes

1 2 3 4 5

2.16 Help desk/assisting staff is available for filling

withdrawal/deposit form 1 2 3 4 5

2.17 Bank follows quick problem solving approach 1 2 3 4 5

2.18 Infrastructure is as per the requirements of the customers 1 2 3 4 5

2.19 Overall, you are satisfied with the banking services made

available to you 1 2 3 4 5

3 Usage of Banking Services

3.1 You save money frequently 1 2 3 4 5

3.2 You withdraw money frequently 1 2 3 4 5

3.3 You frequently use credit facilities of the bank 1 2 3 4 5

3.4 You are a regular visitor of the bank 1 2 3 4 5

3.5 You are using bank for the payment of insurance premium 1 2 3 4 5

240

3.6 You are using bank for the repayment of loan 1 2 3 4 5

3.7 You are using bank for depositing money 1 2 3 4 5

3.8 Advance schemes of bank are frequently used by you 1 2 3 4 5

3.9 You are using banking services, because interest charged

by the bank on advance is economical than charged by the

money lenders

1 2 3 4 5

3.10 Overall, you visit regularly to bank for saving, withdrawing, borrowing etc.

1 2 3 4 5

4 Social Empowerment

4.1 FI has made you independent in decision making 1 2 3 4 5

4.2 FI has led to improved hygiene and education 1 2 3 4 5

4.3 FI has changed your personality & life style 1 2 3 4 5

4.4 FI has made you socially more reputed 1 2 3 4 5

4.5 FI has increased your confidence level 1 2 3 4 5

4.6 FI has improved your technical skills 1 2 3 4 5

4.7 Your family supports your business decisions 1 2 3 4 5

4.8 FI positively affects your social status (purchase of car,

land, tour, travels, T.V., refrigerator, A.C.) 1 2 3 4 5

4.9 New house constructed after covering under FI drive 1 2 3 4 5

4.10 FI enhanced your confidence level, business relations and

reduced family crisis & social violence 1 2 3 4 5

4.11 FI improved your health and household hygiene 1 2 3 4 5

4.12 You often meet with & talked to people from other social groups outside your home regarding financial inclusion

1 2 3 4 5

4.13 Without FI, you feel difficult in socialising with people of

different social groups 1 2 3 4 5

4.14 You like to change your lifestyle 1 2 3 4 5

4.15 You participate in any community activity 1 2 3 4 5

4.16 You can bring any change in the society easily 1 2 3 4 5

4.17 You actively participate in the general assembly voting 1 2 3 4 5

4.18 You are influenced by others when choosing candidate to

support in election 1 2 3 4 5

4.19 You made complaints to the authorities regarding the

delivery of financial services 1 2 3 4 5

4.20 FI has empowered you to move to all public places freely 1 2 3 4 5

4.21 You are aware about all special schemes that are offered by the govt.

1 2 3 4 5

4.22 You avail those special schemes that are offered by the

govt. 1 2 3 4 5

4.23 You are free to move to any SHG or any other for any kind of help or support

1 2 3 4 5

4.24 You are free to move to any NGO or any other for any

kind of help or support 1 2 3 4 5

4.25 Your response and feedback is always appreciated regarding any financial issue

1 2 3 4 5

4.26 You are socially more developed after being covered under

FI drive 1 2 3 4 5

5 Economic Empowerment

5.1 FI has reduced your need to borrow money or goods 1 2 3 4 5

5.2 FI has raised your living standard 1 2 3 4 5

5.3 FI has prepared you for emergencies 1 2 3 4 5

241

5.4 You have enough savings to meet any contingent situation 1 2 3 4 5

5.5 You are assisted while deciding where savings are to be

used 1 2 3 4 5

5.6 FI has made you independent regarding spending of your

savings 1 2 3 4 5

5.7 FI has increased your purchasing power 1 2 3 4 5

5.8 FI enabled your children to get better education 1 2 3 4 5

5.9 FI created new employment opportunities 1 2 3 4 5

5.10 FI directly effects capital formation & investment in

technology 1 2 3 4 5

5.11 FI enhanced your source of income 1 2 3 4 5

5.12 Overall, FI enables you & your family to enjoy better

economic status 1 2 3 4 5

6 Economic Development

6.1 FI has increased life expectancy of your family members 1 2 3 4 5

6.2 FI has increased access to education of the society 1 2 3 4 5

6.3 FI has empowered the members of the society 1 2 3 4 5

6.4 FI has led to progress of the village 1 2 3 4 5

6.5 FI has made the village sustainable for further progress 1 2 3 4 5

6.6 FI has led to increase in the production of goods & services

in the area 1 2 3 4 5

6.7 FI has increased economic activities in each sector 1 2 3 4 5

6.8 FI has reduced level of stress in your life 1 2 3 4 5

6.9 FI has increased productivity in agriculture sector 1 2 3 4 5

6.10 FI has increased per capita income of your family 1 2 3 4 5

6.11 FI has led to increase in value of your assets 1 2 3 4 5

6.12 Overall, FI has led to the economic development 1 2 3 4 5

7 Poverty

7.1 Most of the members are educated in your family 1 2 3 4 5

7.2 Most of the members are wage laborers or earners 1 2 3 4 5

7.3 Head of the family is educated enough to guide other

members to move on right track 1 2 3 4 5

7.4 FI has increased your value of dwelling 1 2 3 4 5

7.5 Your expenditure on clothing has increased 1 2 3 4 5

7.6 You consume more qualitative food than before 1 2 3 4 5

7.7 Your expenditure on luxuries has increased 1 2 3 4 5

7.8 Health has improved by having qualitative food 1 2 3 4 5

7.9 Your consumption level has increased 1 2 3 4 5

7.10 Family crisis are reduced through better living standard 1 2 3 4 5

7.11 Overall, FI has reduced the level of poverty 1 2 3 4 5

8 Area Development

8.1 New jobs are created in your area 1 2 3 4 5

8.2 Your area is regionally balanced 1 2 3 4 5

8.3 Income is equally distributed 1 2 3 4 5

8.4 Good health services are available 1 2 3 4 5

8.5 A regular doctor (govt./pvt.) visits in the village 1 2 3 4 5

8.6 Accredited social health activists like ASHA is available 1 2 3 4 5

8.7 A school like facility for educating the adults in the village

is available 1 2 3 4 5

8.8 Good road connectivity exists 1 2 3 4 5

242

8.9 Overall, you are satisfied with the development in your area 1 2 3 4 5

9 Reason for Not Having Bank Account

9.1 You don’t like dealing with banks Yes No

9.2 The fees and service charges are too high Yes No

9.3 No bank will open an account Yes No

9.4 The minimum balance is too high Yes No

9.5 No bank has convenient hours or location Yes No

9.6 Do not have enough money Yes No

9.7 Do not need/want an account Yes No

SUGGESTIONS:

_____________________________________________________________________

_____________________________________________________________________

_____________________________________________________________________

_____________________________________________________________________

_____________________________________________________________________

_____________________________________________________________________

_____________________________________________________________________

243

ANNEXURE – II

SAMPLE POPULATION FOR THE STUDY

S. No. District Tehsil Block Name of

the

bank

Village Population

1. Jammu Akhnoor Akhnoor SBI Sungal Lower 6148

SBI Palwan 2393

PNB Ambaran 3048

JKGB - -

JKB Sungal Lower 6148

JKB Gurah Jagir 6097

Khour SBI Bakore 3713

PNB - -

JKGB Samwan 3805

JKGB Bhalwal Mullo 3525

JKB Pallanwal Lower 3821

JKB Bakore 3713

Jammu Marh SBI Gango Chak 2011

PNB - -

JKGB Akalpur 2158

JKGB Kanah Chak 2005

JKB - -

Satwari SBI Rakh Gadigarh 2066

SBI Rattno Chak 4861

PNB - -

JKGB Hakal 3618

JKGB Bhour (East) 2829

JKB - -

Bhalwal SBI Kote 9022

PNB Jandiyal 5284

PNB Bhagani 3216

JKGB Baran 5272

JKGB Amb 4277

JKB - -

Dansal SBI Badsoo 3208

SBI Katal Batal 2308

PNB Jagti 3239

PNB Bhatyari 2519

JKG Kanyalla 3278

JKGB Tarah 2940

JKB - -

R.S.Pura R.S.Pura SBI - -

PNB Kharian 2595

JKGB - -

JKB Chohalla 3800

JKB B.Qazian 2802

Bishnah Bishnah SBI - -

PNB Harsa Dabber 2597

PNB Ratnal 2074

JKGB Laswara 2966

JKGB Sehoura 2334

244

JKB Raipur 5200

JKB Allah 3240

2. Samba Samba Samba SBI Chilla Danga 2142

PNB - -

JKGB - -

JKB - -

Vijaypur SBI - -

PNB - -

JKGB - -

JKB Palie 8274

Ghagwal SBI - -

PNB - -

JKGB - -

JKB Sarara 3097

Parmandal SBI - -

PNB - -

JKB Patti 1536

JKB Jhak 1604

3. Kathua Basohli Basohli SBI Plakh 2461

PNB Dhar Jhankar 2347

JKGB Danbrah 2532

JKGB Draman 2521

JKB Hati 854

Billawar Billawar SBI Makwal 2145

PNB - -

JKGB Tehr 2704

JKGB Dungara 2629

JKB Dhar Duggan 3085

JKB Kahuag 2814

Barnoti Barnoti SBI Muthi Hardu 2280

PNB Forlain 5389

PNB Bhortian 2140

JKGB - -

JKB Falote 2793

JKB Barnoti 1351

Kathua Hiranagar SBI Chelak 3763

SBI Mangloor 3045

PNB - -

JKGB - -

JKB Kharote 3146

JKB Chak Desa

Singh

2226

Banni SBI Surjan 2569

PNB - -

JKGB - -

JKB - -

Lohai Malhar

SBI - -

PNB - -

JKGB Lahari 2167

JKB - -

4. Udhampur Udhampur Udhampur SBI - -

245

PNB - -

JKGB - -

JKB Chapper 2778

JKB Hartarya 2688

Pancheri SBI Ladda 4250

SBI Chulna 2757

PNB - -

JKGB - -

JKB - -

Chennani Chennani SBI Mada 3786

SBI Balli 2748

PNB Satyalta 2335

PNB Ladha 2098

JKGB - -

JKB Nagulta 2533

JKB Sira 2482

Ramnagar Ghordi SBI - -

PNB Jandrore 2959

PNB Nalaghorian 2661

JKGB - -

JKB - -

Ramnagar SBI Marta 2827

PNB - -

JKGB - -

JKB Kirnoo 2270

JKB Bhatyari 2252

Majalta Majalta SBI - -

PNB - -

JKGB - -

JKB Thalora 2439

JKB S.K.Bair 2059

5 Reasi Reasi Reasi SBI - -

PNB - -

JKGB - -

JKB Kotli

Manotrian

2396

JKB Bahaga 2070

Arnas SBI - -

PNB - -

JKGB - -

JKB Salalkote 2844

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