FINANCIAL INCLUSION BY DCBS – A DEMAND SIDE...

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FFIINNAANNCCIIAALL IINNCCLLUUSSIIOONN BBYY DDCCBBSS –– AA DDEEMMAANNDD SSIIDDEE AANNAALLYYSSIISS

6.1 Sample Profile 6.2 Factors Explaining Financial Inclusion (FI) 6.3 Conclusion

Backdrop

Banking is a key driver of Financial Inclusion. FI is construed as a

process that ensures the ease of access, availability and usage of the formal

financial system for all members of an economy. It refers to persons or

households accessing institutional credit from Commercial banks, Co-

operative banks, Regional Rural Banks (RRBs), NABARD - SHG linkage and

other NGO’s.

Kerala has the rare advantages in its FI programme, being a State with

hundred percent ‘Banking Inclusion’ (The State Level Bankers’ Committee

(SLBC) claims that Kerala has achieved 100 per cent FI in all of its

distric ts in December, 2007) and one of the largest Government-run

women-empowerment programmes in the country, called ‘Kudumbashree’.

All Kudumbashree NHGs (Neighbourhood Groups) have bank accounts

through which members of NHGs have access to savings and credit services of

banks. Besides the SHG and micro finance related initiatives, efforts have

been made by the state to ensure greater FI by banks through opening

numerous ‘no frills accounts’. However, after the evaluation of the FI schemes

by independent external agencies, RBI has contradicted the claims of SLBCs

Cont

ents

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that the actual inclusion was not 100 per cent. Most of the accounts that have

been opened as part of the FI drive have remained inoperative due to various

reasons. Various studies reveal contradictory results on FI scenario extant in

Kerala.

Compared to other states in India, Kerala has had a higher percentage of

people with bank accounts. According to 2011 census, around 74 per cent of

the total households in the State are availing banking services meaning that

around 26 per cent do not have access to financial services. In rural areas, the

percentage of penetration is 73.85 and in urban areas the percentage is 74.68.

On the surface, it may appear that, given today’s age of omnipresent

Automated Teller Machines (ATMs) and 24-Hour banking, Kerala is

adequately penetrated when it comes to at least basic banking services. But it

is not, going by the statistics.

Why is this so? The biggest factor is the lack of relevance, even when

financial services come within reach. That is because, merely providing an

account — which is what the Government and even the banks think needs to

be done — is not enough. For promoting FI, the issue of exclusion of people,

who desire the use of financial services but are denied access to it, needs to be

addressed.

In this backdrop, in order to understand the views of respondents about

FI and to assess the role of DCBs in FI in Kerala, a field survey was

conducted. This chapter is intended to explain the factors contributing to FI/FE

and to examine the extent of FI/FE among the beneficiaries of linkage banking

of DCBs by analysing the data collected from 320 respondents who are the

members of SHGs/NHGs belonging to three districts of Kerala. A multi stage

random sampling was used for the selection of samples. In the first stage, the

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state was divided into three regions, viz: Southern, Central and Northern

regions, on the basis of geographical location. From each region, one district

was selected at random - Thiruvananthapuram from South; Ernakulam from

Centre and Kannur from North. In the second stage, from each district

selected from each region, one bank branch was selected at random. Thus,

Vembayam branch from Thiruvananthapuram district, Vazhakkala branch

from Ernakulam district and Sreekandapuram branch from Kannur district

were selected. Finally the sample size of 320 beneficiaries was allocated to

these three branches, based on the proportion of number of linkage banking

beneficiaries in each branch. Of the total, 116 samples were drawn from

Trivandrum, 88 from Ernakulam and 116 from Kannur districts. The data was

collected through a pre tested questionnaire, which was translated into the

regional language of Malayalam, before being administered among the

respondents. The analysis is presented under the following two heads.

1) Profile of the sample respondents

2) Factors explaining Financial Inclusion

6.1 Sample Profile

The profile of the sample respondents explaining demographic features

is presented in the following Table.

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Table 6.1. Respondent’s Profile Categories Frequency Percentage

District

Trivandrum 116 36.3 Ernakulam 88 27.4 Kannur 116 36.3 Total 320 100.0

Gender

Female 304 95.0 Male 16 5.0 Total 320 100.0

Age group

Less than 30 50 15.6 30 to 50 213 66.6 50 and above 57 17.8 Total 320 100.0

Marital status

Married 293 91.6 Unmarried 9 2.8 Widowed 18 5.6 Total 320 100.0

Religion

Hindu 214 66.9 Christian 37 11.5 Muslim 69 21.6 Total 320 100.0

Caste

General 107 33.4 Scheduled Caste 72 22.5 Scheduled Tribe 11 3.4 OBC/OEC 130 40.7 Total 320 100.0

Education

Primary 52 16.3 Secondary 63 19.6 Higher secondary 181 56.6 Degree 24 7.5 Total 320 100.0

Occupation

Agriculture 37 11.6 Business 18 5.6 Government & Private Employee 24 7.5 Daily Worker 50 15.6 Self Employed 97 30.3 Housewife 94 29.4 Total 320 100.0

Area

Rural 211 66.0 Urban 109 34.0 Total 320 100.0

Category

BPL 203 63.43 APL 117 36.57 Total 320 100.0

Source: Survey Data


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It can be observed from Table 6.1 that out of 320 samples, 36.3 per cent

belong to Trivandrum district and an equal percentage of respondents belong

to Kannur district (36.3). Ernakulam district accounts for 27.4 per cent.

Gender wise classification discloses that 95 per cent of the respondents are

female and the male respondents constitute only 5 per cent. Majority of the

respondents (66.6 per cent) belong to the age group 30-50, followed by 17.8

per cent belong to the category of above 50 years. Similarly, married

respondents constitute 91.6 per cent of the total sample. Religion wise

classification demonstrates that the majority of the respondents are Hindus

(66.9 per cent), followed by Muslim (21.6 per cent). Among the respondents,

SC accounts for 22.5 per cent and ST accounts for 3.4 per cent. Majority in

this group belong to OBC/OEC category (40.7 per cent) and general category

constitutes 33.4 per cent of the sample. Considering the educational

qualifications, majority of the sample (56.6 per cent) are educated up to higher

secondary level. 30.3 per cent of the respondents are self employed, while 29.4

per cent (94) are house wives. 211 respondents (66 per cent) are residing in

rural Panchayats area, while 34 per cent are residing in urban areas (20.6 per

cent in Municipalities and 13.4 per cent in Corporations). Of the 320 sample

respondents, 63.43 per cent (203) constitutes BPL category and 36.57 per cent

(117) belongs to APL category.

To establish and assess the cross-relationship between the sample

districts of the respondents and the category they belong to, the following

Table has been included.

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Table 6.2. District* Category Cross Tabulation

Districts Category

Total BPL APL

Trivandrum Count 55 61 116

% within category 27.1 52.1 36.3

Ernakulam Count 73 15 88


Kannur Count 75 41 116


Total Count 203 117 320

% within category 100.0 100.0 100.0 Source: Survey Data

The sample provides lot of insights into the various categories

considered under the study. Mainly the sample respondents are classified as

BPL and APL with BPL accounting for 203 cases and APL accounts for 117

out of 320. The districts under consideration provide a clear picture of this

composition of BPL and APL. While Trivandrum accounts for more APL

category (52.1 per cent), Ernakulam accounts for more BPL category (36 per

cent). Kannur seems to have equal division of BPL and APL respondents. This

is further substantiated by the Chi- Square test and the details are shown

below. Table 6.3. Chi-Square Tests for District* Category cross tab

Value df Sig.

Pearson Chi-Square 27.367 2 .000*

Likelihood Ratio 28.656 2 .000

Linear-by-Linear Association 7.410 1 .006 Source: Survey Data *Significant at 5 per cent level of significance

Pearson Chi- Square value is seen to be 27.367 at 2 df (p < 0.05). This

implies that the distribution of APL and BPL is very much varying among the


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selected districts. To establish and assess the cross-relationship between the

sample area of the respondents and the category they belong to, the following

Table has been incorporated.

Table 6.4. Area * Category Cross tabulation

Area Category

Total BPL APL

Rural Count 147 64 211


Urban Count 56 53 109


Total Count 203 117 320

% within category 100.0 100.0 100.0 Source: Survey Data

The area under study provides a clear picture of the composition of BPL

and APL respondents. While rural area accounts for more BPL category (72.4

per cent), urban area accounts for more APL category (45.3 per cent). This is

further validated by the measure of Chi – Square.

Table 6.5. Chi-Square Tests for Area* Category cross tab

Value df Sig.

Pearson Chi-Square 10.368 1 .001*

Likelihood Ratio 10.232 1 .001

Linear-by-Linear Association 10.336 1 .001 Source: Survey Data *Significant at 5 per cent level of significance

Pearson Chi- Square value is seen to be 10.368 at 1 df (p < 0.05), which

implies that the distribution of APL and BPL is very much varying among the

selected rural and urban areas.

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6.2 Factors Explaining Financial Inclusion (FI)

This part of the analysis deals with the factors explaining FI/FE and

evaluates to what extent these factors explain the degree of FI/FE prevailing

among the respondents. From the available literature, the following factors

explaining FI/FE were identified and incorporated in this thesis for the purpose

of analysis.

1) Financial Awareness

2) Financial Necessity

3) Financial Availability

4) Financial Access

5) Access to Financial Information

6) Attitude of the People

7) Access to Informal Finance.

6.2.1 Financial Awareness

The importance of financial awareness or financial literacy is much

acknowledged in the literature on FI. Financial literacy has a positive impact

on the day to day financial management of the people. FI and financial literacy are

the two pillars of the financial system. Financial literacy stimulates the demand

side - making the people aware of what they can and should demand. FI acts from

the supply side - providing in the financial market, what people demand. Financial

awareness measures how well an individual can ‘understand’ and ‘use’ personal

finance related information (Sandra, J. Houston (2010). Financial awareness/

literacy does not refer to formal education in finance instead, it can encompass an

understanding of how to use credit responsibly, manage money and savings,

minimise financial risks and derive benefits of savings (Chakraborthy, 2010).

Lack of financial awareness can lead to over indebtedness and greater economic


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vulnerability (http://ifmr.ac.in/cmf/eomf8.). It is a determinant of ‘access’ to

finance. A financially illiterate person may not understand the importance of

formal savings and may face lack of access to a range of financial products and

services.

Given the importance of financial awareness/literacy, it is proposed to

analyse the level of financial awareness among the respondents in four

separate dimensions, identified during the initial survey, viz: (i) Awareness on

bank products, (ii) Awareness on financial services, (iii) Awareness on micro

insurance, and (iv) Awareness on no- frill accounts.

Awareness on Bank Products

The term ‘awareness on bank products’ is used in this thesis to refer to

and include: (i) Awareness on various deposit schemes that are accessible by

the beneficiaries, (ii) Awareness on interest in force on various deposit

schemes, (iii) Awareness on various loans accessible by the beneficiaries and

(iv) Awareness on interest in force on various loans.

Awareness on Financial Services

The term ‘awareness on financial services’ is used to refer to and

include: (i) Awareness on ATMs, (ii) Awareness on Credit cards,

(iii) Awareness on Cheque/ DD, (iv) Awareness on Fund transfer,

(v) Awareness on Locker, (vi) Awareness on Mutual funds, (vii) Awareness

on Mobile banking, (viii) Awareness on Internet banking and (ix) Awareness

on Money advice and credit counseling.

Awareness on Micro-Insurance

The term awareness on micro-insurance refers to the awareness in

respect of the following types of insurance that are facilitated by the banks:

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(i) Life insurance, (ii) Medical insurance, (iii) Health insurance, (iv) Accident

insurance, (v) Vehicle insurance, (vi) Property insurance, (vii) Cattle

insurance and (viii) Crop insurance.

Awareness on No- frill Accounts

Awareness on No- frill Accounts refers to the level of awareness of the

respondents about the zero balance accounts provided by the banks in Kerala.

To assess the agreement of the respondents to the identified components,

responses were collected on a five point Likert scale from ‘very high’ to ‘very

low’. Table below provides the descriptive statistics on the level of awareness

of 320 respondents under study, belonging to APL and BPL categories of

urban and rural areas from three selected districts - Trivandrum, Ernakulam

and Kannur – of the State of Kerala.


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Table 6.6. Scores on the Level of Financial Awareness of the Respondents

Components of awareness N Min Max Mean Std. Dev Awareness on Deposits Savings Bank deposits (SB) 320 1.00 5.00 3.7531 1.10499 Fixed deposits (FD) 320 1.00 5.00 3.5000 1.26689 Recurring Deposits (RD) 320 1.00 5.00 2.9250 1.36481 Awareness on Interest on deposits Interest on SB 320 1.00 5.00 3.1469 1.34152 Interest on FD 320 1.00 5.00 3.1531 1.33848 Interest on RD 320 1.00 5.00 2.5719 1.29182 Awareness on Loans Agriculture loans 320 1.00 5.00 3.5687 1.32789 Gold loans 320 1.00 5.00 3.8375 1.20025 Personal loans 320 1.00 5.00 3.3344 1.33816 Housing loans 320 1.00 5.00 3.3031 1.38916 Vehicle loans 320 1.00 5.00 2.8313 1.40408 Education loans 320 1.00 5.00 2.9156 1.38138 Business loans 320 1.00 5.00 2.7688 1.42623 Awareness on Interest on loans Interest on Agriculture loans 320 1.00 5.00 3.2563 1.36571 Interest on Gold loans 320 1.00 5.00 3.5188 1.36215 Interest on Personal loans 320 1.00 5.00 2.9656 1.35376 Interest on Housing loans 320 1.00 5.00 3.0125 1.42520 Interest on Vehicle loans 320 1.00 5.00 2.6250 1.29745 Interest on Education loans 320 1.00 5.00 2.5875 1.33620 Interest on Business loans 320 1.00 5.00 2.4219 1.30113 Awareness on Financial services ATM 320 1.00 5.00 2.9219 1.45685 Credit card 320 1.00 5.00 2.5469 1.27589 Cheque/DD 320 1.00 5.00 3.3750 1.39749 Money transfer 320 1.00 5.00 2.9937 1.40753 Locker 320 1.00 5.00 2.9187 1.39848 Mutual Fund 320 1.00 5.00 2.2250 1.16882 Mobile Banking 320 1.00 5.00 2.2063 1.19349 Internet Banking 320 1.00 5.00 2.1719 1.12474 Money advice and credit counseling 320 1.00 5.00 2.4094 1.26115 Awareness on Micro-Insurance Life insurance 320 1.00 5.00 3.5906 1.35925 Medical insurance 320 1.00 5.00 3.1188 1.34780 Health insurance 320 1.00 5.00 3.3813 1.36858 Accident insurance 320 1.00 5.00 3.1531 1.37088 Vehicle insurance 320 1.00 5.00 2.8344 1.41224 Property insurance 320 1.00 5.00 2.6937 1.34606 Cattle insurance 320 1.00 5.00 2.7375 1.34135 Crop insurance 320 1.00 5.00 2.5625 1.29231 Awareness on No- Frill account (zero balance account) 320 1.00 5.00 2.8063 1.36456

Source: Survey Data

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Considering the Table, it is observed that respondents have high

awareness on savings bank accounts (Mean score 3.7531) and fixed deposits

(Mean score 3.5). However, awareness on recurring deposits is found low

among the respondents (Mean score 2.9250). With respect to interest on

deposits, it is observed that the awareness is more on the interest on Fixed

Deposits (with mean score of 3.1531) and Savings deposits (mean score-

3.1469). Low level awareness on recurring deposits (mean score 2.5719)

indicates the limited role of the DCBs in this direction.

Examining the level of awareness on loans, it is observed that the mean

scores for gold loan (3.8375), agricultural loan (3.5687), personal loan

(3.3344) and housing loan (3.3031) are higher than the neutral value of 3,

which is an indication of the customers’ preference to such loans. It seems that

education loan (2.9156), vehicle loan (2.8313) and business loan (2.7688) are

not much popular among the customers, for, the mean scores are less than the

neutral value of 3. It is interesting to note that the customers do have a

similar level of awareness on interest on loans, with more mean value on

interest on gold loan (3.5188) and agricultural loan (3.2563). The mean

scores associated with all other loans are less than or closer to the neutral

value of 3. Hence we are led to believe that the gold loans and agricultural

loans are the most sought after loans among the beneficiaries. Similarly, the

respondents are observed to be concerned on the interest on these loans as

well. The awareness level of the customers on various loans and their

interests show that the DCBs in Kerala still confine to traditional loan

portfolio by offering gold loan and agricultural loans.

With regard to the awareness on other financial services, the only

service seems to be popular among the beneficiaries is the provision and


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use of cheque book (Mean score 3.3750). Thus the provision and use of

ATMs, credit cards, money transfer, mutual funds, internet and mobile

banking, money advice and credit counseling seem to be not popular among

the masses (with mean value less than 3). Considering the awareness on

micro-insurance, it seems that beneficiaries are more aware on life

insurance (Mean score - 3.5906), followed by health insurance (Mean-

3.3813), accident insurance (mean- 3.1531) and medical insurance (Mean-

3.1188). About cattle insurance, crop insurance and property insurance they

seem to have little awareness (with mean score less than 3). Likewise,

awareness level of the beneficiaries about the zero balance account is

observed to be very low with a mean score of 2.8063 (less than neutral

value 3). Considering the awareness level of the customers on various

financial services, insurances and zero balance accounts, it is clear that the

role of the DCBs in Kerala is very limited in this respect and even today

they have confined their financial activities to a limited conventional

sphere. It is likely that the present customers may opt for newer pastures.

Therefore, it is high time for the DCBs in Kerala to get their banking

portfolio fine tuned so as to retain the present customer base.

6.2.1.1 Correlation between Components of Awareness

Having examined the level of awareness of the respondents on

various components constituting the total awareness, it is proposed to check

the correlation between these components before undertaking further

analysis. Table below illustrates the correlation between various

components of financial awareness intended to be discussed further in this

thesis.

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Table 6.7 Correlation between Components of Awareness

Components of awareness

Depo

sits

Inte

rest

on

depo

sit

Loan

Inte

rest

on

loan

Fina

ncia

l se

rvic

es

Mic

ro-

Insu

ranc

e

No- f

rill

acco

unt

Deposits Pearson Correlation 1 .627** .641** .543** .475** .499** .300**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000

N 320 320 320 320 320 320 320

Interest on deposit

Pearson Correlation .627** 1 .590** .670** .562** .592** .317**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000

N 320 320 320 320 320 320 320

Loan Pearson Correlation .641** .590** 1 .706** .569** .643** .331**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000

N 320 320 320 320 320 320 320

Interest on loan

Pearson Correlation .543** .670** .706** 1 .657** .692** .364**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000

N 320 320 320 320 320 320 320

Financial services

Pearson Correlation .475** .562** .569** .657** 1 .695** .482**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000

N 320 320 320 320 320 320 320

Micro -Insurance

Pearson Correlation .499** .592** .643** .692** .695** 1 .422**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000

N 320 320 320 320 320 320 320

No-frill account

Pearson Correlation .300** .317** .331** .364** .482** .422** 1

Sig. (2-tailed) .000 .000 .000 .000 .000 .000

N 320 320 320 320 320 320 320

**. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data

It is observed that various components of awareness identified and

proposed to be analysed further seem to be fairly related among them. A

change in the awareness of one component results in a relative change in the

other components also. The correlation is observed significant at 1 per cent

level of significance (p <0.01).


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6.2.1. 2 Awareness on Bank Products

The awareness level of the people on the bank products may have an

impact on the level of FI. It is believed that awareness would trigger the

people demand what they need and inclusion would make available what is

needed. Ignorance of various bank deposits may give rise to increased

exposure to various threats. Keeping idle cash at home may result in loss of

money. Being uninformed, people may opt for informal sources which would

end up in the depths of despair. Lack of awareness may keep the people

excluded from formal sources and they may be preyed to unscrupulous money

lenders. Besides, those going after informal agencies are likely to lose tax

advantages as well. Hence, it is considered prudent to examine the awareness

level of the beneficiaries belonging to APL and BPL categories residing in

urban and rural areas of the three districts under study. Following table

provides the average for the three way classified data on scores of awareness

on bank products.

Table 6.8. Three - way Classified Mean Score on Awareness on Bank products

District Mean Std. Error

Trivandrum 59.526 1.651

Ernakulam 60.811 2.005

Kannur 65.899 1.691

Area

Rural 64.550 1.295

Urban 59.607 1.686

Category

BPL 58.431 1.323

APL 65.726 1.700

Grand Mean 62.078 1.074 Source: Survey Data

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Considering the mean scores of awareness on bank products as seen

among the respondents belonging to BPL and APL categories residing in rural

and urban areas of the three districts of the study, it is observed that there is

considerable variation in the mean values of the scores. It is believed that it

may be due to the variations in the characteristics of all the three districts,

level of poverty and rural urban divide. It is further proposed to analyse the

data to test for difference in mean scores among the three districts, between

APL and BPL categories and rural - urban areas using a three - way ANOVA.

The results of the analysis are discussed below.

Table 6.9. Tests of Between-Subjects Effects on Awareness on Bank Products

Source Type I Sum of Squares df Mean Square F Sig.

District 2654.057 2 1327.028 4.327 .014*

Area 962.402 1 962.402 3.138 .077

Category 3494.645 1 3494.645 11.394 .001*

Error 96609.893 315 306.698

Total 1333677.000 320 Source: Survey Data *Significant at 5 per cent level.

It may be observed from the ANOVA output that the mean variation

among the urban and rural areas is not significant with, F = 3.138 and p = 0

.077>0.05 suggesting that there is no area wise difference in the level of

awareness on bank products among the beneficiaries. The category wise

variation with F = 11.394 and p = 0.001< 0.05 indicates that the variation in

the awareness level of the respondents belonging to APL and BPL category is

significant. With a higher mean score (65.726), respondents belonging to APL

category seem to be more aware about various bank products. Further, the

district wise variation is also significant with, F = 4.327 and p = 0.014< 0.05.


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To make the difference in the mean scores among the districts clearer a Post

Hoc test was performed and the results are reported below.

Table 6.10. Post Hoc Tests for District-wise Awareness on Bank Products

(I) District (J)District Mean Difference(I-J) Std Error Sig

Trivandrum Ernakulam 1.1603 2.47571 .640

Kannur -5.4138* 2.29954 .019*

Ernakulam Trivandrum -1.1603 2.47571 .640

Kannur -6.5741* 2.47571 .008*

Kannur Trivandrum 5.4138* 2.29954 .019*

Ernakulam 6.5741* 2.47571 .008* Source: Survey Data *. The mean difference is significant at the .05 level

The Post Hoc test revealed that Trivandrum and Ernakulam districts do

not have difference in the mean scores while Kannur has a mean score which

is different from the scores of other two districts and significant (p < 0.05).

Since the mean value of Kannur (65.899) is high as compared to Ernakulam

and Trivandrum, in conclusion we are led to believe that the awareness level

of beneficiaries on bank products is at a higher level in Kannur district than

other two districts.

6.2.1.3 Awareness on Financial Services

Access to a bank account, or, having a little amount of savings with the

banks, or, availing credit from the formal financial institutions like DCBs,

amount to only a limited extent of FI. Financial institutions should also

provide some other services to the customers to make the FI meaningful.

These services include provision of ATMs, Credit/Debit Cards, Money

Transfer facilities, Mutual Funds, Locker facilities, Mobile banking, Internet

Banking, Money Advice and Credit Counseling. Therefore, having examined

and elaborated the awareness of beneficiaries on bank products, it is

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considered imperative to look into the awareness of the beneficiaries on bank

services. The following table provides the average for the three way classified

data on scores of awareness on financial services.

Table 6.11. Three - way Classified Mean Score on Awareness on Financial Services


Trivandrum 24.100 .832

Ernakulam 22.499 1.010

Kannur 24.961 .852

Area

Rural 24.738 .652

Urban 22.969 .849

Category

BPL 22.048 .667

APL 25.659 .857

Grand Mean 23.853 .541 Source: Survey Data

Looking at the Table, there seems to be considerable variation in the

mean values of the districts, area and category. It is further proposed to

analyse the data to test for difference in mean scores among the three districts,

between APL and BPL categories and rural urban areas using a three - way

ANOVA. The results of the analysis are discussed below.

Table 6.12. Tests of Between Subjects Effects on Awareness on Financial Services

Source Sum of Squares df Mean Square F Sig.

District 582.598 2 291.299 3.741 .025*

Area 92.717 1 92.717 1.191 .276

Category 856.595 1 856.595 11.000 .001*

Error 24528.978 315 77.870

Total 206846.000 320 Source: Survey Data. *Significant at 5 per cent level


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Category wise, F = 11.000 and p = 0.001< 0.05 implies that the level of

awareness among the respondents between APL and BPL category is

significant at 5 per cent level. It can be inferred that with a high mean score

(25.659), the APL category seems to be more aware on financial services than

BPL category. Further, it is observed that at 5 per cent level, the variation

between urban and rural areas is not significant, with F = 1.191 and p = 0.276

> 0.05 which implies that between the beneficiaries belonging to urban and

rural areas no significant variation may be observed with respect to their views

on financial services. Since district wise variation is significant, with F = 3.741

and p = 0.025 < 0.05 a Post Hoc test is applied and the result is reported in the

following Table.

Table 6.13. Post Hoc Tests for District - wise Awareness on Financial Services

(I)District (J)District Mean Difference(I-J) Std Error Sig

Trivandrum Ernakulam 2.8315* 1.24747 .024*

Kannur -.3448 1.15870 .766

Ernakulam Trivandrum -2.8315* 1.24747 .024*

Kannur -3.1763* 1.24747 .011*

Kannur Trivandrum .3448 1.15870 .766

Ernakulam 3.1763* 1.24747 .011* Source: Survey Data * The mean difference is significant at the .05 level.

The Post Hoc test revealed that Kannur and Trivandrum districts do not

have difference in the mean scores while Ernakulam has a mean score which is

different from the scores of other two districts and significant (p < 0.05). Since

the mean value of Ernakulam (22.499) is lower as compared to Trivandrum

and Kannur, we are led to believe that the awareness level of beneficiaries on

financial services is slightly at a lower level in Ernakulam than other two

districts.

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6.2.1.4 Awareness on Micro-Insurance

Banks should not only provide access to accounts, savings, credit and other

financial services, but also to affordable insurance as well. Micro-Insurance is fast

emerging as an important strategy for the low-income people engaged in a wide

variety of income generation activities and who remain exposed to a variety of

risks. The term Micro - insurance is generally used to refer to insurance to the low

income people. Recognising the need for providing social security to vulnerable

groups, of late, banks have started providing innovative insurance policies at

affordable cost covering life, disability, health and some other cover in association

with insurance companies. A bank account can be used by the State Governments

to provide social security services like health insurance and calamity insurance

under various schemes for the disadvantaged. In the thesis, an attempt is made to

identify the awareness level of the beneficiaries with regard to various insurance

products which are accessible. The following table provides the average for the

three way classified data on scores of awareness on insurance products provided

by the DCBs.

Table 6.14. Three - way Classified Mean Score on Awareness on Micro- insurance



Ernakulam 22.405 .918

Kannur 26.627 .774

Area

Rural 24.674 .593

Urban 24.043 .772

Category

BPL 22.281 .606

APL 26.436 .778



227

Considering the mean scores, it is observed that there is considerable

variation among the respondents belonging to BPL and APL categories

residing in rural and urban areas of the three districts of the study. It is

believed that it may be due to the variations in the characteristics of all the

three districts, level of poverty and rural urban divide. It is further proposed to

analyse the data to test for difference in mean scores among the three districts

between APL and BPL categories and rural, urban areas using a 3 way

ANOVA. The results of the analysis are discussed below.

Table 6.15. Tests of Between Subjects Effects on Awareness on Micro-insurance


District 1256.475 2 628.237 9.772 .000*

Area .671 1 .671 .010 .919

Category 1133.851 1 1133.851 17.637 .000*

Error 20250.351 315 64.287

Total 208067.000 320 Source: Survey Data *Significant at 5 per cent level

ANOVA output tells that the variation in the mean scores between rural

and urban areas is not significant at 5 per cent level of significance. Yet, mean

variation between the categories is observed significant at 5 per cent level of

significance, with F = 17.637 and p=0.000 < 0.05. Since the mean score of

APL category is higher (26.436) than BPL category (22.281), it is concluded

that APL category seems to have more awareness with regard to insurance

products. At 5 per cent level, the variation in the mean scores between districts

is found significant, with F = 9.772 and p=0.000 < 0.05. The Post Hoc test

results explain the extent of variation.

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Table 6.16.Post Hoc Tests for District-wise Awareness on Micro-insurance


Trivandrum Ernakulam 3.0964* 1.13346 .007*

Kannur -1.9052 1.05280 .071

Ernakulam Trivandrum -3.0964* 1.13346 .007*

Kannur -5.0016* 1.13346 .000*

Kannur Trivandrum 1.9052 1.05280 .071

Ernakulam 5.0016* 1.13346 .000* Source: Survey Data *. The mean difference is significant at the .05 level.

The Post Hoc test revealed that Kannur and Trivandrum districts do not

have difference in the mean scores while Ernakulam has a mean score which is

different from the scores of other two districts and significant (p < 0.05). Since

the mean value of Ernakulam (22.405) is lower as compared to Trivandrum

and Kannur, in conclusion, it seems that the awareness level of the

beneficiaries on micro-insurance is slightly at a lower level in Ernakulam than

other two districts.

6.2.1.5 Awareness on No-frill Accounts

In order to promote FI amongst the unbanked, the Reserve Bank of

India (RBI) initiated ‘no frills’ account drive which began in November

2005. RBI urged all the banks to take the initiative to see that such ‘no frill

accounts’ are opened for those excluded. It is a basic saving bank account

having special features. The holder is not required to maintain any minimum

balance requirement and also nothing is charged for opening this type of

account. KYC norms have been simplified so that everyone can have this

account. Transactions are limited to 5-10 free transactions per month. ATM

facility is provided free of cost and there is no account maintenance cost. It

was assumed that the basic no frill account would be the ‘gate way’ to FI.


229

Keeping this in view, it is proposed to examine the level of awareness of the

customers concerning the no frill accounts. The following table provides the

average for the three way classified data on scores of awareness on zero

balance account

Table 6.17. Three - way Classified Mean Score on Awareness on No-frill Accounts




Kannur 2.942 .129

Area

Rural 3.040 .099

Urban 2.580 .129

Category

BPL 2.563 .101

APL 3.057 .130


Considering the mean scores, it is observed that district wise mean

scores of the awareness level of beneficiaries on no frill account have only

slight variation whereas, area wise and category wise variation seems to be

significant. It is further proposed to analyse the data to test for difference in

mean scores among the three districts between APL and BPL categories and

rural urban areas using a Three- way ANOVA. The Hypotheses can be stated

as:

1. H0- There is no significant variation in the mean values of the scores of

awareness on no- frill accounts among the 3 districts.

H1- There is significant variation in the mean values of the scores of

awareness on no-frill accounts among the 3 districts.

Chapter 6

230


awareness on no- frill accounts among APL and BPL categories.


awareness on no-frill accounts among APL and BPL categories.


awareness on no- frill accounts among urban and rural areas.


awareness on no-frill accounts among urban and rural areas.

The ANOVA output given below provides the explanation for the mean

variation among the groups.

Table 6.18. Tests of Between Subjects Effects of Awareness on No-frill Accounts


District 6.370 2 3.185 1.786 .169

Area 9.975 1 9.975 5.595 .019*

Category 16.031 1 16.031 8.992 .003*

Error 561.611 315 1.783

Total 3114.000 320 Source: Survey Data *Significant at 5 per cent level.

With F = 1.786 and p=0.169 > 0.05 ANOVA output signifies that no

significant difference exists among the respondents belonging to three districts

and the first hypothesis stands accepted. However, other two hypotheses give

significant F values and the null hypotheses are rejected. Variation between

rural and urban areas is significant at 5 per cent level; with F = 5.595 and

p=0.019 < 0.05. With high mean score (3.040) rural area has more awareness

than urban areas (mean score-2.580). Further, with F = 8.992 and p=0.003 <

0.05, there exists significant variation among respondents belonging to APL


231

and BPL categories. With high mean score (3.057), it is observed that the

respondents belonging to APL category has more awareness with respect to

zero balance accounts.

6.2. 2 Financial Necessity

FI needs to be understood in relation to people’s individual needs, which

can vary from individual to individual and are subject to taste. Therefore, one

important component of the understanding of FI is financial necessity. It may

be considered as a wider concept entailing various aspects covering from

opening an account with a formal bank, to availing financial advice and credit

counseling. It is believed that financial necessity may guide people to decide

the sources of finance to be tapped. Lack of availability of formal finance may

compel the people to go after informal sources of finance. Therefore, it is

considered prudent to study the level of ‘financial necessity’ among the

sample respondents who were asked to indicate their level of necessity for

Deposits, Loans, Financial services and Micro-insurance. The responses were

drawn on a five point Likert scale, with 5 for ‘very high’ need and 1 for ‘very

low’ need. The mean scores associated with the responses of the beneficiaries

on the financial necessity are shown below.

Table 6.19. Table Showing the Level of Financial Necessity among the Customers

Need for N Min Max Mean Std. Deviation

Deposits 320 1.00 5.00 3.8500 .98706

Loans 320 1.00 5.00 3.8531 1.01724

Financial Services 320 1.00 5.00 3.5375 1.14955

Micro-Insurance 320 1.00 5.00 3.6438 1.13008 Source: Survey Data

From the Table, it may be observed that the mean score associated with

the need for deposits, loans, financial services and micro-insurance are above

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232

the neutral score of 3. So, there seems to be an above average demand for

these services. Considering the mean scores, it may be observed that among

the various necessities; the need for various loans seems to be more, followed

by deposits, micro-insurance and financial services.

6.2.2.1 Financial Necessity – District wise, Area wise and Category wise

Further, it is considered relevant to study the variation in the opinion of

the respondents on necessity of bank products and financial services over

different districts, categories and areas. To identify District wise, area wise

and category wise variation a Three-way ANOVA is attempted. Following

Table provides the average for the three way classified data on scores of

financial necessity among the respondents

Table 6.20. Three-way Classified Mean Scores on Financial Necessity

District Mean Std Error



Kannur 14.243 .322

Area

Rural 15.219 .247

Urban 14.763 .321

Category

BPL 14.631 .252

APL 15.351 .324 Source: Survey Data

Since the mean scores among the districts, area and categories seem to

have a modest variation, ANOVA output has been obtained to examine

whether this variation is significant or not. The results of ANOVA are reported

below.


233

Table 6.21. Tests of Between Subjects Effects on Financial Necessity of the Customers


District 120.035 2 60.017 5.388 .005*

Area 7.832 1 7.832 .703 .402

Category 34.003 1 34.003 3.053 .082

Error 3508.853 315 11.139

Total 3670.722 319 Source: Survey Data *Significant at 5 per cent level

Area wise and category wise comparison of mean scores seem to have a

variation but the ANOVA output reveals that no significant variation exists in

the mean scores of area and category on financial necessity. To conclude, it is

observed that the customers belong to APL and BPL category hailing from

both rural and urban areas do not differ in terms of their financial necessity.

However, the mean score variation over different districts is statistically

significant at 5 per cent level, with F =5.388 and p=0.005< 0.05. To explain

the variation between the districts more clearly, a Post Hoc test was applied

and the output is reported below.

Table 6.22. Post Hoc Tests for District-wise Financial Need

(I) District (J) District Mean Difference

(I-J) Std. Error Sig.

Trivandrum

Ernakulam -.9020 .47182 .057

Kannur .6466 .43824 .141

Ernakulam

Trivandrum .9020 .47182 .057

Kannur 1.5486* .47182 .001*

Kannur

Trivandrum -.6466 .43824 .141

Ernakulam -1.5486* .47182 .001* Source: survey data * The mean difference is significant at the .05 level.

Post Hoc Test shows that among the three districts, the variation

between Ernakulam and Kannur is significant (p < 0.05). Estimated Marginal

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234

Means (Table 6.20) tells us that with higher mean score (15.937) the

customers of Ernakulam district are seen to have more financial necessity and

among the remaining two districts of Trivandrum and Kannur no significant

variation may be observed.

6.2.2.2 Financial Necessity and Financial Awareness

After having a discussion about the financial awareness and financial

necessity of the respondents, it is considered decisive to examine whether

these two have any relation between each other. A Chi-square test is applied

to test the following hypothesis.

H0- There is no dependence between financial necessity and financial awareness.

H1- There is dependence between financial necessity and financial awareness.

To examine the awareness in the context of necessity, they are classified into

low, medium and high. The following table depicts the result of cross tabulation.

Table 6.23. Table showing Awareness in the Context of Necessity

Total awareness

Total Low Medium High

Necessity

Low Count 16 26 2 44

% within Need 36.4 59.1 4.5 100.0

% within Awareness 30.8 11.9 4.1 13.8

Medium Count 31 164 38 233

% within Need 13.3 70.4 16.3 100.0

% within Awareness 59.6 74.9 77.6 72.8

High Count 5 29 9 43

% within need 11.6 67.4 20.9 100.0

% within awareness 9.6 13.2 18.4 13.4

Total Count 52 219 49 320

% within need 16.3 68.4 15.3 100.0

% within awareness 100.0 100.0 100.0 100.0 Source: Survey Data


235

From the above Table, it is observed that a low level financial necessity

makes awareness also to be low. About 4.5 per cent among the low necessity

beneficiaries have high awareness, while for 36.4 per cent among the low

necessity beneficiaries have low level of awareness. This information is

reversed in the case of beneficiaries with high level of financial necessity.

About 11.6 per cent beneficiaries with high necessity report low level of

awareness, while about 20.9 per cent say they are highly aware of bank

products and financial services. This suggests that there is significant

dependence between financial necessity and financial awareness, which is

further validated by the chi-square value of 17.829 at 4 df p < 0.01 as

depicted in the Table below.

Table 6.24. Chi-Square Test for Awareness in the Context of Necessity

Value df Asymp. Sig. (2-sided) Pearson Chi-Square 17.829 4 .001*

Source: Survey Data *Significant at 5 per cent level of significance

Therefore, the null hypothesis is rejected and it is observed that there is

a significant dependence between the two variables.

6.2.3 Financial Availability (Supply of Financial Products and Services)

One of the prerequisites of FI is the availability of cheap and appropriate

financial products and services. This is viewed as a core issue, as inadequate

supply of cheap, timely and appropriate products and services by the formal

financial institutions may deter the poor from being included. It is believed

that FI would be improved with the availability of appropriate and timely

financial products and services at economical terms. Lack of availability of

formal finance may compel the people to go after informal sources of

finance which would result in ‘being excluded’. For this reason it is

Chapter 6

236

proposed to examine the level of availability of bank products and financial

services from the DCBs in Kerala based on the responses obtained from the

respondents.

Table 6.25. Level of Availability of Financial Products and Services

Products & Services N Minimum Maximum Mean Std. Deviation

Deposits 320 1.00 5.00 3.8469 1.00702

Loans 320 1.00 5.00 3.8937 1.00217

Financial services 320 1.00 5.00 2.2656 .78069

Micro - insurance 320 1.00 5.00 2.0000 .88527 Source: Survey Data

From the Table, it may be observed that the mean score of the

availability of deposits and loans is above the neutral score of 3 and it is close

to 4. Hence, the availability of these two services from the DCBs in the

districts under study seems to be high. However, in respect of the financial

services and micro-insurance, the availability seems to be low, for, the mean

scores are below the neutral value of 3. Further, it can be observed that the

mean value in respect of the availability of micro-insurance is 2 and the lowest

which indicates that the provision of micro-insurance by the DCBs in the

sample districts is very low.

While discussing the necessity of various financial products and

services, it was observed that there is a high necessity on the part of the

respondents (Table 6.19). But the availability of modern financial services and

micro-insurance from the DCBs is observed to be low (Table 6.25). Thus it

may be observed from the above that there is a mismatch between the demand

and supply of financial services and DCBs in Kerala are not able to meet all

the financial requirements of their customers, especially the modern financial

services and insurance. For this reason, it is considered relevant to study the


237

variation in the financial availability of bank products and services over

different districts, categories and areas.

6.2.3.1 Financial Availability – District wise, Area wise and Category wise

To identify District wise, area wise and category wise variation, a

Three-way ANOVA is attempted. Following table provides the average for the

three way classified data on scores of financial availability.

Table 6.26. Three-way Classified Mean Score on Financial Availability




Kannur 11.932 .206

Area

Rural 12.274 .158

Urban 11.991 .205

Category

BPL 11.563 .161


Above table reveals that there exists a modest variation in the mean

scores between districts, area and categories of respondents. Therefore, a three

- way ANOVA is proposed to examine the significance of the variation, the

output of which is reported below.

Table 6.27. Tests of Between Subjects Effects on Financial Availability


District 7.973 2 3.987 .878 .417

Area .499 1 .499 .110 .741

Category 85.032 1 85.032 18.724 .000*

Error 1430.484 315 4.541 Source: Survey Data *Significant at 5 per cent level

Chapter 6

238

ANOVA output validates that at 5 per cent level, the variation between

districts is not significant with F = 0.878 and p=0.417>0.05. Similarly, mean

variation between the rural and urban areas too, is not significant at 5 per cent

level, with F =0.110 and p=0.741>0.05.However, the variation between APL

and BPL categories seems to be significant and it is being validated by the

ANOVA output, with F = 18.724 and p=0.000< 0.05. Since the mean score of

APL (12.701) is more than BPL (11.563), it can be concluded that the

respondents belong to APL category seems to have more availability of

various financial services than BPL category. However, in respect of

availability of various services from the DCBs, no district wise or area wise

difference was observed.

6.2.3.2 Financial Availability and Financial Awareness

After examining the availability of bank products and services, it is

proposed to check whether the financial availability and financial awareness

do have any association with each other. It is expected that the level of

availability of bank products and services would improve the level of

awareness of the customers and this would result in increased demand for

more products and services on the part of the beneficiaries. The hypotheses

may be stated as:

H0: There is no dependence between the financial availability and financial

awareness of the beneficiaries of DCBs in Kerala.

H0: There is dependence between the financial availability and financial

awareness of the beneficiaries of DCBs in Kerala.


239

Table 6.28. Table Showing the Correlation between Financial Availability and Awareness

Financial Awareness Financial Availability

Financial Awareness Pearson Correlation 1 .300**

Sig. (2-tailed) .000

N 320 320 **. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data

From the correlation co-efficient Table, it is absorbed that the two

variables are having a correlation coefficient of 0.300 which is significant at 1

per cent level. Thus, the null hypotheses stands rejected and suggest that there

is dependence between financial availability of banking products and services

and financial awareness of the customers.

6.2.4 Financial Access.

An important element of FI is the financial access.From the previous

analysis and discussion on ‘financial necessity’ and ‘financial availability,’ it

is observed that there is a soaring need for various bank products and services

on the part of the beneficiaries. It is also observed that there is adequate

availability of various savings and loan products from the DCBs of Kerala.

The pertinent question now is whether the beneficiaries are able to access

these products and services; for, lack of access would lead to financial

exclusion. The term financial access is used in this thesis, to refer to the right

of an individual to use financial products and services and includes:

1) Access to basic bank account

2) Access to savings products

3) Access to appropriate credit and

4) Access to financial services including insurance.

Chapter 6

240

6.2.4.1 Access to Bank Account

Prominent among the explanations for FI is the access to a basic bank

account for everyday transactions. Generally, the process of FI starts with

opening of a bank account. A bank account may be considered as the stepping

stone towards FI which paves the way for getting into the mainstream formal

banking. Once an account is opened, it can be used as the ‘gateway’ for

availing many other services. But unfortunately people may confront with

certain barriers in this respect comprising Procedural delay, Attitude of the

bank staff, Mistrust of the banks, Minimum balance requirement, Filling forms

and Lack of savings habit. For this reason, it is considered judicious to

examine the extent of the difficulties the beneficiaries might confront with

while opening a bank account. The table below explains the scores associated

with the level of difficulties faced by the beneficiaries while opening accounts

with the DCBs.

Table 6.29. Difficulties Associated with Opening of Accounts

Difficulties N Min Max Mean Std. Deviation

Procedural delay 320 1.00 5.00 3.6656 1.17073

Unfriendly attitude of Staff 320 1.00 5.00 3.6719 1.08934

Mistrust of banks 320 1.00 5.00 2.0969 .98896

Minimum balance requirement 320 1.00 5.00 3.2750 1.27405

Filling of forms 320 1.00 5.00 3.0594 1.38495

Lack of savings habit 320 1.00 5.00 1.9094 .93079 Source: Survey Data

Considering the mean score, Table 6.29 illustrates that the beneficiaries

feel some difficulty with respect to staff attitude (Mean 3.6719), procedural

delay (Mean 3.6656), maintaining minimum balance (Mean 3.275) and filling

of forms (Mean 3.0594) respectively. It is also observed that the mean scores

with respect to mistrust of banks and lack of savings habit are less than 3


241

(neutral value), which indicates that the respondents seem to have trust in

banks and sound banking habits. Therefore it is observed that even though the

DCBs in Kerala provide conventional products of deposits and loans (Table

6.25) and the customers have sound banking habits (Table 6.29), the

respondents seem to have certain problems in accessing these products. As

seen from the Table 6.29, the major problem associated with the access is

observed to be the staff attitude, followed by procedural delay, minimum

balance requirement and filling of various forms. This is a clear indication that

there exists some degree of ‘access exclusion’ among the customers of DCBs

in Kerala, which is a major supply side constraint. For this reason, it is desired

to study the difference in the opinion of the respondents across the districts,

area and category under survey.

6.2.4.1.1 Access to Bank Account – District wise, Area wise and Category wise

A three-way ANOVA is used to study the district wise, area wise and

category wise variation in respect of access to bank account (banking

exclusion). The following table provides the average for the three way

classified data on scores of financial access.

Table 6.30. Three-way Classified Mean Score on Access to Bank Account




Kannur 17.165 .410

Area

Rural 17.873 .314

Urban 17.134 .409

Category

BPL 17.757 .321


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242

There seems to be a modest variation in the mean values of the districts,

area and category. To test for difference in mean scores, ANOVA output is

used and the results are discussed below.

Table 6.31. Tests of Between Subjects Effects on Access to Bank Account


District 17.059 2 8.529 .472 .624

Area 49.191 1 49.191 2.722 .100

Category 16.925 1 16.925 .937 .334

Error 5692.672 315 18.072 Source: Survey Data

ANOVA validates that the district wise variation is not significant, with

F = 0.472 and p=0.624>0.05. Area wise also the variation is not significant,

with F = 2.722 and p=0.100>0.05 and the variation between the categories as

well seem to be insignificant, with F = 0.937 and p=0.334>0.05. Thus, no

significant variation is observed among the respondents over the districts, area

and category under survey and we are led to believe that the customers of

DCBs experience similar access problems (access exclusion) in the form of

staff attitude, procedural hassles, maintenance of minimum amount and

inconvenience in filling of various forms.

6.2.4.2 Access to Savings

Another explanation for FI is an access to savings products such as

savings deposits, recurring deposits, fixed deposits etc. FI strategy aims to

improve individual’s wealth and financial well-being through building up

savings and assets. People excluded from savings services are more vulnerable

to theft as they are forced to keep their cash and savings at home. There are

instances where people opt for savings with money lenders and informal

financial institutions expecting more returns. Being without formal savings


243

can be problematic in two respects. First, people who save by informal means

not benefited from the interest and tax advantage that people using formal

savings methods enjoy. Second, informal saving channels are much less secure

than formal saving facilities. Holding a savings product reduces financial

exclusion to a substantial extent. Lack of deposit may be due to reasons like

lack of money to save, lack of habit to save, unwilling to deal with banks

because of some negative past experiences, affinity towards informal finance

etc. Thus improving people’s financial situation and their ability to save might

achieve better outcomes in terms of savings inclusion.

From the earlier analysis and discussion, it was observed that access

exclusion exists among the sample respondents. Therefore, it is considered

essential to examine whether the access problems create further problems

pertain to savings (savings exclusion) and credit (credit exclusion). This part

of the analysis examines the difficulties related to savings (savings exclusion)

among the respondents.

Table 6.32. Table Showing the Difficulties Related to Savings with DCBs


High cost of living 320 1.00 5.00 3.9406 1.26673

Savings with private banks 320 1.00 5.00 2.7219 1.36256

Un- friendly attitude of the bank staff 320 1.00 5.00 3.5063 1.21896 Source: Survey Data

Table 6.32 shows the degree of difficulties the beneficiaries experience

with regard to savings with DCBs in Kerala. The major difficulty identified

with savings is high cost of living, unfriendly attitude of bank staff and

deposits with informal agencies. From the Table, it is observed that the

respondents consider high cost of living (Mean - 3.9406) and unfriendly

attitude of bank staff (Mean - 3.5063), as the dominant factors affecting

Chapter 6

244

savings with DCBs, among the identified problems. Thus, it may be noted here

that along with the high cost of living, the customers of DCBs in Kerala find

fault with the unfriendly attitude of the DCB staff resulting in savings

exclusion, which is a major supply side restraint to FI. Following this, it is

proposed to study the variation among the respondents.

6.2.4.2.1 Access to Savings – District wise, Area wise and Category wise

District wise, Area wise and category wise variation has been examined

here, using a three way ANOVA. Following Table provides the average for

the three way classified data on scores of savings problems.

Table 6.33. Three-way Classified Mean Score on Access to Savings




Kannur 10.073 .241

Area

Rural 10.336 .185

Urban 9.967 .241

Category

BPL 9.929 .189


Looking at the above Table, the mean score of Ernakulam (9.940) is

found less than other two districts of Trivandrum (10.441) and Kannur

(10.073). Similarly, the mean score between the rural and urban areas also

differ moderately. Further, between APL and BPL categories as well, the

variation seems to be significant. Hence, ANOVA is used for validation.


245

Table 6.34 . Tests of Between Subjects Effects on Access to Savings

Source Sum of Squares df Mean Square F Sig. District 22.506 2 11.253 1.798 .167 Area 6.073 1 6.073 .970 .325 Category 12.972 1 12.972 2.073 .151 Error 1971.336 315 6.258

Source: Survey Data

However, as per the ANOVA output, the district wise variation is

observed not significant with, F = 1.798 and p=0.167> 0.05. With F =0.970

and p=0.325> 0.05, variation between urban and rural areas is not significant

at 5 per cent level. Likewise, variation between APL and BPL categories is not

significant at 5 per cent level with F = 2.073 and p=0.151>0.05. Thus, with

regard to savings problems the beneficiaries do not differ significantly over the

sample districts, area and category.

6.2.4.2.2 Access Exclusion and Savings Exclusion

From the preceding discussion, the customers of DCBs in Kerala seem

to have certain problems with regard to access to basic bank account (Access

exclusion) as well as access to savings (Savings exclusion). In this perspective,

it is considered appropriate to examine the existence of any dependence

between Access exclusion and Savings exclusion by using Pearson’s

Correlation co-efficient. The procedure of validation is explained below.

Table 6.35. Table Showing the Correlation between Banking Exclusion and Savings Exclusion

Bank account access Savings inclusion

Bank account access Pearson Correlation 1 .389**


N 320 320


Chapter 6

246

As per Table 6.35, it is absorbed that the two variables are having a

positive correlation coefficient of 0.389 which is significant at 1 per cent level.

6.2.4.3 Access to Credit

FI primarily implies access to a bank account backed by access to

affordable credit. The concern for the credit aspect of FI stems mainly from

the apparent exclusion of low-income households from affordable sources of

credit. People, especially the poor, may find it difficult to obtain credit from

formal banking institutions on account of various reasons. Important among

them are the Procedural hassles, Lack of collaterals, Indifferent attitude of the

staff etc. Lack of access to mainstream credit may compel people to resort to

borrow from illegal moneylenders. Lack of access to affordable credit options

is identified as a contributory factor to debt problems. It is not only the costs

of illegal credit that causes concern, but also the lending practices of some of

these lenders which lead clients into over indebtedness. Thus, low-income

consumers seek access to an appropriate source of borrowing to prevent future

financial difficulties.

Having observed the existence of banking exclusion (access exclusion)

and resulting savings exclusion, among the customers of DCBs, it is believed

imperative to study whether this would create any constraints in availing credit

(credit exclusion). To determine the level of difficulties experienced by the

customers of DCBs while availing loans, they were asked to put their opinion

on a five point scale on eight identified components as shown in the Table

6.36.The scores associated with the level of difficulty observed is shown

below.


247

Table 6.36. Difficulties in Obtaining Loan


Delay in sanctioning & disbursing 320 1.00 5.00 3.8125 1.16472

Heavy rate of interest 320 1.00 5.00 3.8531 1.11431

Lack of co-operation from bank staff 320 1.00 5.00 3.2719 1.24108

Less number of installments 320 1.00 5.00 3.4000 1.22000

Heavy amount of installments 320 1.00 5.00 3.4469 1.24580

Shorter repayment period 320 1.00 5.00 3.3875 1.17970

Restriction in the use of loan 320 1.00 5.00 3.4000 1.20968

No subsequent loan due to default 320 1.00 5.00 3.4844 1.26432 Source: Survey Data

From Table 6.36, it is observed that among the difficulties encountered

by the respondents, the prominent are the high rate of interest (Mean score -

3.8531) and the delay in sanctioning and disbursing loan amount (3.8125).

Table 6.36 depicts that other factors also act as restraints to credit inclusion

(with mean score above 3). The level of difficulty as shown by the table may

be considered as a clear symptom of credit exclusion among the customers.

Thus, it may be observed from the above that together with banking exclusion

and savings exclusion, the customers of DCBs in Kerala experience credit

exclusion as well.

Further, it is proposed to examine the variation in the mean scores

associated with the level of credit exclusion among the customers.

6.2.4.3.1 Access to Credit – District wise, Area wise and Category wise

Variation in the mean scores associated with the level of credit exclusion

over the districts, area and category under study is analysed by using a three

way ANOVA. Following Table provides the average for the three way

classified data on the scores of credit exclusion.

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Table 6.37. Three-way Classified Mean Score on Credit Exclusion




Kannur 27.614 .640

Area

Rural 28.136 .490

Urban 28.382 .638

Category

BPL 27.794 .501


Comparing the mean scores between the districts, area and category it

seems that there is a considerable variation. To test for difference and for

validating the significance, ANOVA is used and the output is reported

below.

Table 6.38. Tests of Between Subjects Effects on Credit Exclusion


District 71.675 2 35.838 .814 .444

Area 11.954 1 11.954 .272 .603

Category 56.744 1 56.744 1.290 .257

Error 13860.614 315 44.002

Total 14000.988 319 Source: Survey Data

From the ANOVA output, it is observed that the mean scores among the

respondents belonging to APL and BPL categories of rural and urban areas

from the three selected districts do not have significant variation as the ‘p’

values associated with ‘F’ are greater than 0.05. Therefore, it may be inferred

that the customers of DCBs in Kerala seem to have difficulties associated with


249

credit exclusion and no significant variation may be observed among the

respondents belonging to different demographic groups.

6.2.4.3.2 Access Exclusion and Credit Exclusion

It is believed that access exclusion may lead to credit exclusion. To

identify the dependence between these two variables, a correlation coefficient

is attempted. It is expected that the Table provided below may be useful for

further clarification.

Table 6.39. Correlation between Access Exclusion and Credit Exclusion

Bank account access Credit inclusion

Bank account access

Pearson Correlation 1 .485**



From Table 6.39, value of R is observed to be 0.485, which indicates a

positive correlation between the variables and this is observed significant at 1

per cent level of significance. Thus, it may be observed that access exclusion

and credit exclusion are correlated with each other and the access exclusion

would lead to credit exclusion as well.

6.2.4.4 Access to Financial Services

The concept of FI is not confined to ensuring an easy access of a basic

bank account and products like deposits and credits. A customer should also

be provided with an array of other facilities and financial services comprising

Cheque Book facility, Money Transfer facility, Locker facility, ATM, Debit

card, Credit card, Mutual funds, Mobile banking, Internet banking, Insurance,

Financial Advice and Counseling etc: to choose from. The need for financial

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services and the difficulty of some individuals with accessing financial

products have been increasingly recognized in the literature as a concept of

‘Financial Service Exclusion’. Supply of financial services does not imply

access; neither does access entail use of a service. Lack of availability rather

than lack of affordability may be the main barrier to financial service

exclusion. For promoting FI, the issue of exclusion of people who need the use

of financial services but are denied access to it needs to be addressed.

The concept of financial service exclusion has been increasingly

recognised in the literature in recent years. If people are excluded from using

financial products and services then there is likelihood that these people may

become socially excluded as well (Clare Louise Chambers, 2004). Financial

exclusions are risks constituting of any form of externalities that prevent the

accessibility, availability, affordability and usage of financial services and

products (Molyneux Philip, 2007).

In this part of the analysis, perception of the customers on the access to

financial services being provided by the DCBs in Kerala is measured using a

measurement instrument under Likert framework consisting of 16 statements,

identified during the pilot survey. To assess the agreement of the respondents

to the identified statements, responses were obtained on a five point Likert

scale. Table 6.40 provides the descriptive statistics on the agreement of 320

sample respondents under study, belonging to APL and BPL categories of

urban and rural areas of three selected districts of Trivandrum, Ernakulam and

Kannur.


251

Table 6.40. Descriptive Statistics of Variables Explaining Access to Financial Services

Sl. No Statements N Min Max Mean Std.

Deviation

1 I need insurance against loss of life and property

320 1.00 5.00 4.4437 .86555

2 I am not aware that, banks facilitate insurance policies

320 1.00 5.00 4.0531 1.07420

3 Insurance companies sell policies through their agents

320 1.00 5.00 3.8781 1.19593

4 I take insurance policies due to agents’ compulsion.

320 1.00 5.00 3.8844 1.21475

5 I am not using ATMs/credit cards 320 1.00 5.00 3.4719 1.33643

6 I don’t know how to operate ATM, so I don’t use it.

320 1.00 5.00 3.4187 1.38044

7 I don’t use cheque book-due to minimum balance requirement.

320 1.00 5.00 4.1281 1.14436

8 I don’t use cheque book because I have no deposits

320 1.00 5.00 3.7750 1.31759

9 I don’t transfer money through bank because it is expensive

320 1.00 5.00 3.3500 1.28263

10 Sending money through post office is convenient and less expensive.

320 1.00 5.00 3.4000 1.23278

11 I don’t use locker because I have no valuables /jewelleries

320 1.00 5.00 3.7781 1.30738

12 I don’t use locker because of heavy bank charges

320 1.00 5.00 3.5281 1.27398

13 I don’t use locker because it is not safe 320 1.00 5.00 2.6563 1.27452

14 I don’t have mutual funds 320 1.00 5.00 3.6219 1.36371

15 I use mobile phone but don’t use mobile banking

320 1.00 5.00 3.5563 1.35881

16 Mob banking/internet banking is popular in cities and towns

320 1.00 5.00 3.6281 1.36543

Source: Survey Data

From Table 6.40, it can be seen that all the statements except statement

number 13, have obtained mean scores above the neutral value of 3 which

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252

indicates the agreement of the respondents to the identified variables and it

seems that the use of various financial services among them is very low. Thus,

it may be observed from the study that financial service exclusion is

substantial among the customers of DCBs in Kerala. This may be due to the

non availability of modern financial services and insurance as observed earlier

and reported in Table 6.25. For further study, the responses on five point

scale are used with ‘Factor Analysis’ to reduce dimensions and to identify the

dominant dimensions resulting from the exercise.

Factor Analysis is a statistical technique used to reduce a set of variables

to a smaller number of variables or factors. Factor analysis attempts to identify

underlying variables or factors that explain the pattern of correlations within a

set of observed variables. Factor analysis is often used in data reduction to

identify a small number of factors that explain most of the variance that is

observed in a much larger number of manifest variables. The purpose of data

reduction is to remove redundant (highly correlated) variables from the data

file, perhaps replacing the entire data file with a smaller number of

uncorrelated variables. Factor analysis examines the pattern of inter-

correlations between the variables and determines whether there are subsets of

variables (or factors) that correlate highly with each other but that show low

correlations with other subsets (or factors). The results and the findings are

narrated below.

The factor analysis requires that there be some correlations greater than

0.30 between the variables included in the analysis. Table 6.41, given below

shows the correlation matrix.

Financial Inclusion by DCBS – A

Dem

and Side Analysis

253

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254

From the correlation matrix, the variables are observed to have

significant correlations indicating that the model is suitable for further

analysis. The output of factor analysis is examined after validating the

variables using the communalities. Communalities represent the proportion of

the variance in the original variables that is accounted for by the factor

solution. The following table provides the communalities extracted for the

factors for financial service exclusion.

Table 6.42. Communalities Extracted for the Variables Explaining Access to Financial Services

Sl. No Statements Initial Extraction

1 I need insurance against loss of life and property 1.000 .618

2 I am not aware that, banks facilitate insurance policies 1.000 .674

3 Insurance companies sell policies through their agents 1.000 .691

4 I take insurance policies due to agents’ compulsion. 1.000 .596

5 I am not using ATMs/credit cards 1.000 .729

6 I don’t know how to operate ATM, so I don’t use it. 1.000 .741

7 I don’t use cheque book-due to minimum balance requirement. 1.000 .658

8 I don’t use cheque book because I have no deposits 1.000 .768

9 I don’t transfer money through bank because it is expensive 1.000 .700

10 Sending money through post office is convenient and less expensive. 1.000 .659

11 I don’t use locker because I have no valuables /jewelleries 1.000 .677

12 I don’t use locker because of heavy bank charges 1.000 .509

13 I don’t use locker because it is not safe 1.000 .288

14 I don’t have mutual funds 1.000 .820

15 I use mobile phone but don’t use mobile banking 1.000 .836

16 Mob banking/internet banking is popular in cities and towns 1.000 .625 Source: Survey Data


255

It is observed that the communalities show sufficiently large values

suggesting that the statements are equally important for the contemplated

problem. (Communalities with values more than 0.3 may be taken as

important as a thumb rule when the sample size is sufficiently large). On

iteration 1, the communality for the statement ‘I don’t use locker because it is

not safe’ (statement-13) is 0.288. Since this is less than 0.30, this variable is

removed and the Principal Component Analysis is computed again. The

factors extracted and the related results are given below

Table 6.43. Total Variance Explained on the Variables Explaining Access to Financial Services

Component Initial Eigen values Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 5.298 33.113 33.113 5.298 33.113 33.113

2 1.732 10.823 43.936 1.732 10.823 43.936

3 1.347 8.417 52.353 1.347 8.417 52.353

4 1.158 7.240 59.593 1.158 7.240 59.593

5 1.055 6.596 66.189 1.055 6.596 66.189 Source: Survey Data

It is seen that 66.189 per cent variation in the responses on 15 variables

can be reduced to 5 different factors using the standard procedure to consider

those factors having Eigen values greater than 1. Thus 5 dominant factors are

considered and the factor loadings after rotation are reported in Table 6.44

below.

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Table 6.44. Rotated Component Matrix for the Variables Explaining Access to Financial Services

Sl. No Statements

Component

1 2 3 4 5

1 I need insurance against loss of life and property .725 .070 .294 .023 .005

2 I am not aware that, banks facilitate insurance policies .806 .048 .126 .062 -.053

3 Insurance companies sell policies through their agents .807 .124 .029 .135 .075

4 I take insurance policies due to agents’ compulsion. .666 .218 .094 .142 .275

5 I am not using ATMs/credit cards .073 -.022 .096 .169 .828

6 I don’t know how to operate ATM, so I don’t use it. .152 .251 .094 .001 .804

7 I don’t use cheque book due to minimum balance requirement.

.295 .266 .659 .211 .147

8 I don’t use cheque book because I have no deposits .128 .093 .842 .145 .115

9 I don’t transfer money through bank because it is expensive

.096 .114 .212 .792 .076

10 Sending money through post office is convenient and less expensive.

.140 .187 .021 .766 .131

11 I don’t use locker because I have no valuables /jewelleries .129 .319 .740 .102 .011

12 I don’t use locker because of heavy bank charges .249 .198 .233 .592 -.048

13 I don’t have mutual funds .113 .854 .203 .157 .111

14 I use mobile phone but don’t use mobile banking .212 .857 .141 .177 .076

15 Mobile banking/internet banking is popular in cities and towns

.071 .711 .248 .205 .107

Factor Highest loading value .807 .857 .842 .792 .828 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization Source: Survey Data

In the Table 6.44, the variables having high loadings are indicated.

These variables are collected and organised based on their loadings (first

column gives the number of the statement). Thus, the information in 15

statements can be represented by 5 factors. The factors and the supporting

statements (variables) are illustrated below.


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Table 6.45. Factor 1 (Non Availability of Micro-insurance)

Sl. No

Statement No

Supporting Statements

1 1 I need insurance against loss of life and property 2 2 I am not aware that, banks facilitate insurance policies. 3 3 Insurance Companies sell policies through their agents 4 4 I take insurance policies due to agents compulsion

Source: Survey Data

Table 6.46. Factor 2 (Non Availability of Financial Services)

Sl. No

Statement No

Supporting Statements

1 13 I don’t have mutual funds 2 14 I am using mobile phone, but don’t use mobile banking 3 15 Mobile banking & internet banking are popular in towns and cities

Source: Survey Data

Table 6.47. Factor 3 (Non Affordability of Financial Services)

Sl. No

Statement No Supporting Statements

1 7 I am not using cheque book, because I can’t maintain minimum balance in the account.

2 8 I am not using cheque book, because I have no deposits 3 11 I am not using locker because I don’t have valuables/ jewelries

Source: Survey Data

Table 6.48. Factor 4 (Non Reasonability of Financial Services)

Sl. No


1 9 I don’t like to transfer money through banks 2 10 Sending money through Post office is cheap 3 12 I am not using locker because of high charges

Source: Survey Data

Table 6.49. Factor 5 ( Non Access to Financial Services)

Sl. No


1 5 I am not using ATMs/credit cards 2 6 People in the villages do not know how to use ATMs &credit cards

Source: Survey Data

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Based on the common thread seen among the statements in each group,

appropriate names were recommended. Thus, the information contained in the

customers’ responses may imply the information contained in factors named

as:

1). Non Availability of Micro-insurance.

2). Non Availability of Financial Services.

3). Non Affordability of Financial Services

4). Non Reasonability of Financial Services

5). Non Access to Financial Services.

All the above factors clearly indicate the prevalence of financial service

exclusion among the customers of DCBs in Kerala. Thus, the analysis is

proposed to be continued further by describing scores obtained on these five

factors. It is decided to obtain the scores in the simplest manner, i.e. to get

them as sums of the observations on each variable contributing to the factor.

Thus the basic summaries are given below.

Table 6.50. Descriptive Statistics of Factors Explaining Financial Service Exclusion

Factors N Min Max Mean Std. Deviation

Non- Availability of micro-insurance 320 4 20 16.2594 3.43021

Non- Availability of financial services 320 3 15 10.8063 3.56752

Non- Affordability of financial services 320 3 15 11.6813 3.12412

Non -Reasonability of financial services 320 3 15 10.2781 2.98599

Non -Access of financial services 320 2 10 6.8906 2.34699 Source: Survey Data

The first factor ‘non-availability of micro-insurance’ is constructed by

summing four statements having a common thread. The maximum scoring

possible in a five point Likert framework for four statements is 20. Here the

maximum scoring is reported to be 20 and the minimum is 4.The mean score


259

is observed to be 16.26, which is above the neutral value of 12, with a standard

deviation of 3.43. This is an indication of the agreeability of the respondents to

the first factor. The second factor ‘non-availability of financial services’ is

constructed by summing three statements and found to have a mean score of

10.81 with a standard deviation of 3.57. Since the mean score is above the

neutral value of 9, it also indicates the agreeability of the respondents to this

factor. The third factor ‘non-affordability of financial services’ is the

combined effect of three statements with minimum value 3 and maximum

value 15 and a neutral value of 9. The mean score of 11.68 and the standard

deviation of 3.12 reveal the agreeability of the respondents towards this factor.

The fourth factor, ‘non- reasonability of financial services’ is constructed by

combining another three statements. It also indicates the respondent’s

agreeability to this factor with a mean score of 10.278 and a standard deviation

of 2.99. The fifth factor, ‘non-access to financial services’ is the combined

effect of two statements, which is found to have a mean score of 6.89 and with

a standard deviation of 2.35. This also seems to endorse the respondents’

agreeability to the financial service exclusion by way of non - access to

financial services.

Now, the perception of the respondents on financial service exclusion

may be taken together to observe whether there exists any significant variation

among them. The table given below provides the total mean score and the

mean score of Trivandrum, Ernakulam and Kannur districts associated with

the factors.

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Table 6.51. District - wise Statistics for the Factors Explaining Financial Service Exclusion

Factors

Mean Std. Deviation

Triv

andr

um

Erna

kula

m

Kann

ur

Tota

l

Triv

andr

um

Erna

kula

m

Kann

ur

Tota

l

Non-Availability of micro-insurance

15.894 17.907 15.957 16.586 .301 .459 .315 .211

Non-Availability of Financial Services

10.552 11.273 11.466 11.097 .325 .496 .340 .228

Non-Affordability of Financial services

11.665 11.653 12.027 11.782 .289 .441 .302 .202

Non-reasonability of Financial services

10.181 10.795 10.719 10.565 .274 .418 .286 .192

Non-Access to Financial services 7.261 7.278 6.622 7.054 .215 .328 .225 .151 Source: Survey Data

It is observed that the total mean score and the mean scores of the five

factors across the sample districts differ significantly. The first factor, Non-

Availability of micro-insurance shows a total mean score of 16.586, with

significant degree of variation across the sample districts. In respect of this

factor, Ernakulam district shows the highest mean score (17.907), followed by

Kannur (15.957) and Trivandrum (15.894) districts. Non-Availability of

financial services (second factor) shows a total mean score of 11.097 and in

this respect, Kannur district is observed to have the highest mean score

(11.466), followed by Ernakulam (11.273) and Trivandrum (10.552) districts.

Comparing the total mean score and the district wise mean scores of other

factors also, similar variations may be observed. .

To explain the possible variations in the mean scores of these five

factors across the three sample districts under study, a MANOVA is proposed

to be used. Here the five variables are taken together believing that the


261

variables are more meaningful if taken together than considered separately.

MANOVA is used here to consider the following hypotheses.

H0: There is no significant variation in the mean scores of set of variables

describing financial service exclusion among the districts under study.

H1: There is significant variation in the mean scores of set of variables

describing financial service exclusion among the districts under study.

The Multivariate Test Table which provides the actual result of the

MANOVA is given below.

Table 6.52. Multivariate Test for Analysing Variance in Factors Explaining Financial Service Exclusion among Districts

Effect Value F Hypothesis df Error df Sig.

District Pillai's Trace .135 4.504 10.000 622.000 .000

Wilks' Lambda .868 4.559 10.000 620.000 .000

Hotelling's Trace .149 4.614 10.000 618.000 .000

Roy's Largest Root .124 7.703 5.000 311.000 .000 Source: Survey Data

It is generally an accepted procedure to take Wilks’ Lambda for testing the

hypotheses from among the four standard statistics reported above. However,

from some recent studies it is seen that Pillai’s Trace is more powerful than the

other tests. From Table 6.52, it is clear that for the districts, all the four statistics,

especially Pillai’s Trace, provides significant F value (4.504) at 1 per cent level (p

< 0.01). Hence the null hypothesis is rejected which indicates that there is a

significant multivariate main effect of districts on the perception of beneficiaries

on financial service exclusion by the DCBs in Kerala.

Now, given the significance of overall test, the univariate main effects

are examined and reported below.

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Table 6.53. Tests of Between Subjects Effects on Factors Explaining Financial Service Exclusion

Source Dependent Variable Type I Sum of Squares

df Mean Square

F Sig.

District F1 248.580 2 124.290 11.844 .000*

F2 16.953 2 8.477 .693 .501

F3 2.608 2 1.304 .135 .874

F4 13.817 2 6.908 .796 .452

F5 24.660 2 12.330 2.299 .102 Source: Survey Data *Significant at 5 per cent level F1: Non-Availability of Micro-insurance, F2: Non-Availability of Financial Services, F3: Non-affordability of Financial Services, F4: Non-Reasonability of Financial Services, F5: Non-access to Financial services

The results of univariate analysis show that the variation in the mean

scores of the first factor (Non-Availability of micro-insurance) among the

districts of Trivandrum, Ernakulam and Kannur is significant at 5 percent level

with F = 11.844 and p=0.000<0.05, while the variation in the mean scores of

other factors is not significant as the p values are greater than 0.05. Thus it can

be observed that when five factors are taken individually the mean variation is

significant only for the first factor but when all these factors are taken together

as a bundle, the mean variation of all the five factors are observed to be

significant which shows that there is a multivariate main effect of districts on

the perception of the beneficiaries with regard to the financial services

exclusion by the DCBs in Kerala.

To make the variation in the mean scores associated with the first factor,

Non - Availability of Micro-insurance more clearly across the districts under

study, a Post Hoc Test for multiple comparisons between two districts at a

time was performed. The results are shown below.


263

Table 6.54. Post Hoc Tests for District-wise Financial Service Exclusion Using LSD

Dependent variable (I) district (J)District Mean Difference(I-J) Std. Error Sig

Non-Availability of Micro-insurance

Trivandrum Ernakulam -1.7739* .45794 .000*

Kannur .3448 .42535 .418

Ernakulam Trivandrum 1.7739* .45794 .000*

Kannur 2.1187* .45794 .000*

Kannur Trivandrum -.3448 .42535 .418

Ernakulam -2.1187* .45794 .000* Source: Survey Data *The mean difference is significant at the .05 level.

Post Hoc test revealed that Kannur and Trivandrum districts do not have

difference in the mean scores while Ernakulam has a mean score which is

different from the scores of other two districts and significant (p<0.05). Since

the mean value of Ernakulam (17.907) is high (Table 6.51) as compared to

Trivandrum and Kannur, it seems that Non-Availability of micro - insurance is

slightly at a higher level in Ernakulam district than other two districts under

survey.

From the above, it may be observed that the customers of DCBs in

Kerala experience different forms of financial service exclusion. Non-

Availability of micro-insurance is found more in Ernakulam district. However,

with regard to non-availability, non-affordability, non-reasonability and non-

access to other financial services, customers of different districts under survey

seem to have similar level of exclusion. This part of the analysis reveals that

the role played by the DCBs in Kerala, with respect to financial service

inclusion is very limited.

6.2.5 Access to Financial Information

The essence of FI is to ensure that a range of appropriate financial

services is available to every individual and enabling them to ‘understand’ and

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264

‘access’ those services. FI does not require that everyone who is eligible uses

each of these services but they should be able to choose them if they desired to

use them. To do so, the individuals and households need to have ‘knowledge

of sources of credit and an understanding of basic financial terminology.

Without being financially literate and capable, households can be locked in a

cycle of poverty and exclusion or suffer as a result of inappropriate product

choice, high cost credit or, for some, illegal money lending. The well informed

customers are the most valuable assets for the banks. Lack of access to timely

and appropriate information and not being able to take informed decisions is

Information Exclusion.

This part of the analysis looks into the level of availability of financial

information among the respondents. Eight components of financial

information identified and believed relevant in this context are incorporated in

Table 6.55. Table below illustrates the response level of the respondents on the

availability of information, concerning the identified components.

Table 6.55. Table Showing Frequency Distribution of Availability of Financial Information

Components of Information

Availability Total

Very high High Indifferent Low Very Low Day to day cash management

2(1) 13(4) 46(14) 96(30) 163(51) 320

Profitable Investment 4(1) 17(5) 66(21) 81(25) 152(48) 320 Effective use of credit 3(1) 28(9) 66(21) 106(33) 117(37) 320 Modern financial services 6(2) 18(5) 70(22) 96(30) 130(41) 320

Starting micro/small enterprises

14(4) 26(8) 83(26) 82(26) 115(36) 320

Interest rates in force 6(2) 19(6) 52(16) 92(29) 151(47) 320

Zero balance account 6(2) 14(4) 69(22) 100(31) 131(41) 320

Exploitation by money lenders 9(3) 28(9) 77(24) 86(26) 120(38) 320 Source: Survey Data * Figures in brackets are percentages to total


265

From Table 6.55 it is clearly observed that availability of financial

information from the DCBs is very low. Only 12 per cent of the sample

customers seem to have opined that the availability of information is sufficient

and for this reason it can be construed that non-availability of financial

information (information exclusion) is rampant among the customers of DCBs

in Kerala. Further, the variation across the demographic groups is analysed.

6.2.5.1 Information Exclusion - District wise, Area wise and Category wise

To examine the District wise, Area wise and Category wise response level

and variation in the mean scores, a three way ANOVA is attempted. The average

for the three way classified data on information exclusion is provided below.

Table 6.56. Three - way Classified Mean Score on Information Exclusion




Kannur 31.352 .551

Area

Rural 32.680 .422

Urban 32.864 .550

Category

BPL 31.691 .431


Evaluating the mean scores, it is seen that there is a modest variation in

the mean scores of information exclusion among the districts, area and

category. It is further proposed to analyse the data to test for difference in

mean scores among the three districts between APL and BPL categories and

rural urban areas using a Three - way ANOVA. The Hypotheses can be stated

as:

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266

1. H0- There is no variation in the mean scores of information exclusion

among three districts under study.

H1- There is variation in the mean scores of information exclusion

among three districts under study.


among APL and BPL categories.


among APL and BPL categories.


among the urban and rural areas.


among the urban and rural areas.

The ANOVA output is provided below. Table 6.57. Tests of Between Subjects Effects on Information Exclusion


District 684.732 2 342.366 10.496 .000*

Area 22.629 1 22.629 .694 .406

Category 307.023 1 307.023 9.413 .002*

Error 10274.503 315 32.617 Source: Survey Data *Significant at 5 per cent level of significance.

It can be seen from the ANOVA output (Table 6.57) that the variations

in the mean scores among the districts and category are significant. Since the

variation across the area under study is not significant, the third hypothesis is

accepted and concludes that there is no variation in the mean scores of

information exclusion across the urban - rural areas under study. With F =

9.413 and p= 0.002< 0.05, the mean variation among the APL and BPL


267

categories seems to be significant and the second hypothesis is rejected. Thus,

information exclusion seems to be more among the beneficiaries belonging to

APL category with high mean score of 33.853 (Table 6.69). The mean

variation among the districts under study is significant with F = 10.496 and

p=0.000< 0.05and the first hypothesis is rejected. For further analysis, a post

hoc test was undertaken and the output is reported below.

Table 6.58. Post Hoc Tests for District-wise Information Exclusion

(I) District (J) District Mean Difference (I-J) Std. Error Sig


Kannur .6983 .74991 .352


Kannur 3.5568* .80736 .000*

Kannur Trivandrum -.6983 .74991 .352

Ernakulam -3.5568* .80736 .000* Source: Survey Data *The mean difference is significant at the .05 level

It is observed from the Post Hoc Test results that Trivandrum and

Kannur districts do not have variation in the mean scores, while Ernakulam

has a mean score which is different from other two districts and significant

(p<0.05).Considering the means, it is observed that the mean score of

Ernakulam is much higher (35.298) than other two districts. For this reason, it

may be seen that the degree of information exclusion is more in Ernakulam

district as compared to Trivandrum and Kannur.

6.2.5.2 Information Exclusion and Credit Exclusion

To examine the association between information exclusion and credit

exclusion, a simple correlation is proposed. The details of the analysis are

discussed below.

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Table 6.59. Correlation between Information Exclusion and Credit Exclusion

Credit inclusion Information inclusion

Credit inclusion Pearson Correlation 1 .262**


N 320 320 **. Correlation is significant at the 0.01 level (2-tailed).

Source: Survey Data

Looking at the Table 6.59, it is observed that R = 0.262 which implies

that the two variables are positively correlated and the correlation is significant

at 1 per cent level.

6.2.6 Attitude of People

Even when the banking facilities are available, people tend to avert

and decide not to use the banking facilities. People may be reluctant to

access and use a basic bank account because of various reasons. The

reasons may range from inadequate income to save, to psychological

barriers resulting from mistrust of banks. Alongside the issues of access,

other forms of exclusion can influence people’s perceptions about

mainstream financial service providers. People may feel that financial

services are not for them and that banks are reluctant to do business with

them. These feelings of mistrust of mainstream financial institutions are

widespread among people who are largely excluded from the financial

system. Mistrust of banks is one of the factors which explain why

individuals might not want to access or use mainstream financial products.

This puts exclusion from the financial system into a different light, since it

suggests that some consumers prefer not to be included for one reason or

another. A degree of choice exists in deciding which type of services

people want to use (ranging from very basic services to more sophisticated

banking products) and how they want to use available products (e.g. use of


269

automated banking). Thus, even when the banking facilities are available,

some people show an aversion to this and decide not to use credit or other

financial services. This is termed as ‘Self Exclusion’ or ‘Attitudinal

Exclusion’.

In this perspective, it is considered apposite to examine the attitude of

the beneficiaries of DCBs in Kerala, towards financial services as well as

financial activities. The attitudinal behaviour of the respondents is examined in

two separate dimensions, viz: (1) Attitude in terms of Interest, and (2) Attitude

in terms of Initiative.

6.2.6.1 Attitude in Terms of Interest

Customers’ attitude is proposed to be studied here, in terms of their

interest towards financial services and transactions. The following table

presents the descriptive statistics associated with the responses of the

respondents with regard to the components such as; Opening account, Making

deposits, Taking loans, Taking insurance policies, Seeking financial advices

and Starting own business.

Table 6.60. Descriptive Statistics Associated with Attitude in Terms of Interest Level of Interest N Min Max Mean Std. Deviation

In opening account 320 1.00 5.00 4.1656 .87498

In making deposits 320 1.00 5.00 4.1406 .90397

In taking loans 320 1.00 5.000 4.0063 1.020154

In taking insurance policies 320 1.00 5.00 4.0031 1.00625

In seeking financial advice 320 1.00 5.00 3.9000 1.09830

In starting own business 320 1.00 5.00 3.8187 1.13022 Source: Survey Data

From Table 6.60, it can be seen that the mean scores associated with the

identified variables representing the level of interest of the respondents,

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exceed the neutral value of 3. This seems to be an indication that the

respondents are ‘highly interested’ (the score being close to 4) in availing

various financial products and services being offered by the DCBs and in

financial activities like starting own business. Further, it is proposed to study

the variation in the level of interest of the respondents over the districts, area

and category.

6.2.6.1.1 Attitude in Terms of Interest – District wise, Area wise and Category wise

Using a three - way ANOVA, variation in the level of interest of the

respondents over the districts, area and category is analysed. Table below

provides the average for the three way classified score on the respondents’

interest level.

Table 6.61. Three - way Classified Score on the Respondents’ Interest Level

District Mean Std. Error Trivandrum 23.907 .410 Ernakulam 26.774 .498 Kannur 22.648 .420 Area

Rural 24.099 .321 Urban 24.787 .418 Category

BPL 24.090 .328 APL 24.796 .422

Source: Survey Data

Looking at the mean scores, it seems that there is a moderate variation

among the districts, area and category. To test for difference, the ANOVA

output is reported below.


271

Table 6.62. Tests of between Subjects Effects on Attitude in Terms of Interest


District 815.005 2 407.503 21.568 .000*

Category 47.113 1 47.113 2.494 .115

Area 32.863 1 32.863 1.739 .188

Error 5951.641 315 18.894

Total 191695.000 320 Source: Survey Data

ANOVA output (Table 6.62) signifies that the difference in the mean

scores among the different categories and areas under study are not significant,

while the difference among the districts is significant with F = 21.568 and

p=0.000< 0.05. For this reason, a Post Hoc Test was carried out and the result

is reported below.

Table 6.63. Post Hoc Tests for District-wise Attitude in terms of Interest



Kannur 1.4224* .57075 .013*


Kannur 4.0180* .61448 .000*

Kannur Trivandrum -1.4224* .57075 .013*

Ernakulam -4.0180* .61448 .000* Source: Survey Data *The mean difference is significant at the .05 level

From the Post Hoc Test results, it can be observed that the variation in

the mean scores between all the three districts under study is significant at 5

per cent level of significance (p < 0.05).i.e. the level of interest of the

selected customers in availing banking products and services differ

significantly over the three sample districts. Considering the mean scores, it is

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observed that the level of interest is highest in Ernakulam district (mean

26.774) and lowest in Kannur district (mean 22.648).

6.2.6.2 Attitude In Terms of Initiative

After examining the level of interest of the customers of DCBs in

Kerala, it is believed discreet to examine the attitudinal behaviour of the

respondents in terms of their initiative to ask for various financial services and

looking for financial activities. Table below provides the descriptive statistics

associated with the level of initiative of the respondents.

Table 6.64. Descriptive Statistics Associated with the Level of Initiative

Level of Initiative N Min Max Mean Std. Deviation

In opening account 320 1.00 5.00 4.0875 .96579

In savings and deposits 320 1.00 5.00 4.0344 .98360

In taking loans 320 1.00 5.00 3.9344 1.04089

In taking insurance 320 1.00 5.00 3.8719 1.05007

In seeking financial advice 320 1.00 5.00 3.7094 1.20341

In starting own business 320 1.00 5.00 3.7406 1.15489 Source: Survey Data

Considering the mean scores associated with the level of initiative of the

respondents, it seems that the level of initiative in respect of all the identified

variables is high, with mean score closer to 4. It seems that the respondents

take more initiative in opening bank accounts and in savings and deposits

(with score above 4). It is interesting to note that the customers take least

initiative in seeking financial advice (score 3.7094).

6.2.6.2.1 Attitude In Terms of Initiative – District wise, Area wise and Category wise

To study the variation in the level of initiative among the respondents

over various demographic groups, a three way ANOVA is used. Table below


273

provides the average for the three way classified score on the level of initiative

of the respondents under study.

Table 6.65. Three Way Classified Score on the Level of Initiative




Kannur 21.987 .492

Area

Rural 23.049 .377

Urban 24.250 .490

Category

BPL 24.000 .385

APL 23.299 .494 Source: Survey data

Considering the mean scores, it seems that the mean scores among the

districts, area and category under study have a considerable variation. For

validating the observation and to test for the difference in the mean scores,

ANOVA is attempted and the output is given below.

Table 6.66. Tests of Between Subjects Effects on Level of Initiative


District 739.854 2 369.927 14.266 .000*

Category 32.266 1 32.266 1.244 .265

Area 83.187 1 83.187 3.208 .074

Error 8167.939 315 25.930 Source: Survey Data

It can be observed from the ANOVA output (Table 6.66) that, the

variation in the mean scores among the respondents belonging to APL and

BPL categories of urban and rural areas is not significant. However, the

variation in the mean scores between the districts under survey is significant

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with, F = 14.266 and p=0.000<0.05. Further, a Post Hoc Test is carried out to

make the analysis more clear.

Table 6.67. Post Hoc Tests for District-wise Level of Initiative



Kannur 1.2845 .66863 .056


Kannur 3.8197* .71986 .000*

Kannur Trivandrum -1.2845 .66863 .056

Ernakulam -3.8197* .71986 .000* Source: Survey Data * The mean difference is significant at the .05 level

Considering the Post Hoc Test results, it is observed that the Trivandrum

and Kannur districts do not have variation in the mean scores while Ernakulam

has a mean score, which is different from the scores of other two districts and

is significant (p < 0.05). Since the mean score of Ernakulam (25.642) is higher

than other two districts, the respondents of Ernakulam district seem to have

more initiative in looking for financial services and to engage in financial

activities, when compared to other two districts under survey.

6.2.6.3 Financial Availability and Attitude of the Respondents

Having observed the dependence between the financial availability and

financial awareness (Table 6.28), it is considered pertinent to observe whether

there is an association between financial availability and attitude of the

beneficiaries. It is believed that the level of attitude would improve with the

availability of bank products and services. Therefore, it is attempted to

examine the relationship between financial availability and attitudinal interest

of the respondents and financial availability and attitudinal initiative of the

respondents of DCBs.


275

6.2.6.3.1 Financial Availability and Attitudinal Interest

A correlation coefficient analysis may be proposed to examine the

dependence between level of attitudinal interest and financial availability.

Table 6.68. Correlation between Financial Availability and Attitudinal interest

Financial availability Attitude - interest

financial availability Pearson Correlation 1 .293**


N 320 320 **. Correlation is significant at the 0.01 level (2-tailed).

Source: Survey Data

Table 6.68 indicates that there is a positive correlation between the

variables (R=.293), which is significant at 1 per cent level, which implies

that a proportional change in financial availability would result in

proportional change in interest of the respondents in availing the financial

services.

6.2.6.3.2 Financial Availability and Attitudinal Initiative

Having discussed the relationship between the financial availability and

attitudinal interest, it is considered appropriate to examine the relationship

between the financial availability and attitudinal initiative. The results of

analysis, using simple Correlation are reported below.

Table 6.69. Correlation between Financial Availability and Attitudinal Initiative

Financial availability Attitude - initiative

Financial availability Pearson Correlation 1 .169**


N 320 320


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276

Table 6.69 shows that the relationship between the variables is positive

(R=.169), and significant at 1 per cent level. This implies that a proportional

change in financial availability would result in a proportional change in the

initiative of the respondents for availing the services.

6.2.6.4 Attitude of the People and Level of Awareness

From the previous studies, it is observed that financial availability would

have an impact on the attitude of the beneficiaries in availing bank products

and services. It is considered prudent to examine the association between the

attitude and awareness of the beneficiaries in this context. It is believed that a

change in the attitude of the people would make them more aware of the

financial products and services.

6.2.6.4.1 Attitudinal Interest and Level of Awareness

Pearson’s coefficient of correlation was attempted to examine the

dependence between financial awareness and the attitudinal interest.

Table 6.70 Correlation between Attitudinal interest and Financial Awareness

Attitude - interest Awareness

Attitude - initiative Pearson Correlation 1 .193**


N 320 320


Table 6.70 shows that the variables, attitude-interest and awareness are

positively correlated (R = 0.193), which is significant at 1 per cent level. It

may be implied that a proportional change in the interest of the respondents

towards the financial services would result in a proportional change in their

awareness level also which would improve their status of Financial Inclusion.


277

6.2.6.4.2 Attitudinal Initiative and Level of Awareness

To study the dependence between the initiative of the respondents and

the level of their awareness, a correlation coefficient was attempted and the

output is given below.

Table 6.71. Correlation between Attitudinal Initiative and Financial Awareness

Attitude initiative Awareness

Attitude - initiative Pearson Correlation 1 .203**


N 320 320


Table 6.71 shows that the two variables are positively correlated (R = .203)

and is significant at 1 per cent level, which indicates that a proportional change in

the initiative of the respondents would result in a proportional change in their

level of awareness which in turn would bring about improvement in FI.

6.2.7 Access to Informal Finance

Financially included as well as excluded may opt for informal finance.

The major factors responsible for this phenomenon consists of lack of

financial awareness, inadequate access to formal finance, unsuitable banking

products, mistrust of formal banks, easy access to money lenders and informal

bankers etc. Informal sector works in an environment, which is suited to the

low income people. The informal sector responds remarkably well to the short

term credit requirements of lower income people and it allows them to access

finance not available from the formal institutions. Both financier and borrower

know each other by face, and cultural affinity creates the feeling of confidence

in each other. Easy availability of money from the money lenders often

persuades the people to borrow even for wasteful expenditures. As it is a

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costly borrowing and many of the borrowers do not have regular income to

pay back, often the repayment obligation multiplies beyond their capacity,

which leads to suicides, fleeing from houses or, ends up in clashes and

physical fights. They are sometimes considered as ‘anti-social’ institutions.

In this circumstance, it is considered essential to look into the level of

access to and influence of informal financial institutions on the customers of

DCBs in Kerala. Table below illustrates the response level of the customers on

the suitability of the informal financial institutions in Kerala, with reference to

the identified variables.

Table 6.72. Suitability of Transactions with Informal Financial Agencies

Variables Suitability Total Very high High Not sure Little Very little

Interest on deposits 101(32) 138(43) 61(19) 16(5) 4(1) 320 Interest on loans 6(2) 27(8) 52(16) 116(36) 119(37) 320 Availability of loan 90(28) 126(39) 56(18) 31(10) 17(5) 320 Purpose of loan taken 107(34) 124(39) 59(18) 23(7) 7(2) 320 Time & formalities 99(31) 115(36) 65(20) 29(9) 12(4) 320 Convenience 90(28) 138(43) 55(17) 27(9) 10(3) 320 Attitude 75(23) 124(39) 63(20) 26(8) 32(10) 320

Source: Survey Data * Figures in parenthesis are percentages to column total

From Table 6.72, it is observed that the majority of the respondents find

it suitable for them to transact with the money lenders and private banks with

reference to the identified variables, except the interest on loans. The

percentage of the customers stating high/very high suitability with money

lenders and private banks may be reported as shown below.

(i) On interest on deposits – 75 per cent; (ii) On availability of loans – 67

per cent; (iii) On purpose of the loan – 73 per cent; (iv) On time and

formalities – 67 per cent; (v) On travelling to reach the agencies – 71 per cent;

(vi) On the attitude of the money lenders and private banks – 62 per cent.


279

Only 10 per cent of the respondents find the interest on loans suitable for

them which indicates that the interest levied by the money lenders and other private

banking institutions on various loans seem to be unsuitable for the customers.

From the above, it seems that the respondents find the dealings of the

money lenders and other private banks suitable for them and it may be an

indication of the influence of the informal financial agencies on the customers.

6.2.7.1 Access to Informal Finance – District wise, Are wise and Category wise

Having examined the level of influence of money lenders and other

informal financial agencies on the beneficiaries it is considered appropriate to

study the variation among the respondents belonging to APL and BPL categories,

residing in urban and rural areas of the three districts under study, using a three

way ANOVA. Following Table provides the average for the three - way classified

data on scores of influence of informal finance on the customers.

Table 6.73. Three- way Classified Scores on Influence of Informal Finance




Kannur 26.188 .459

Area

Rural 27.604 .352

Urban 26.511 .458

Category

BPL 26.503 .359


Considering the influence of informal finance as seen among the

respondents belonging to BPL and APL categories residing in rural and urban

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280

areas of the three districts of the study, it is observed that there is considerable

variation in the mean values of the scores. It is believed that, it may be due to

the variations in the characteristics of all the three districts, level of poverty

and rural urban divide. It is further proposed to analyse the data to test for

difference in mean scores among the three districts between APL and BPL

categories and rural urban areas using a Three- way ANOVA. The Hypotheses

can be stated as:

1. H0- There is no difference in the mean scores of influence of informal

finance among three districts under study.

H1- There is difference in the mean scores of influence of informal

finance among three districts under study.


finance among APL and BPL categories.


finance among APL and BPL categories.


finance among the urban and rural areas.


finance among the urban and rural areas.

The ANOVA output is provided below. Table 6.74. Tests of Between Subjects Effects on Influence of Informal Finance


District 515.275 2 257.637 11.381 .000*

Category 55.929 1 55.929 2.471 .117

Area 82.815 1 82.815 3.658 .057

Error 7130.781 315 22.637 Source: Survey Data.


281

It is seen from the ANOVA output (Table 6.74) that the variation in the

mean scores for the districts is significant, with F = 11.381 p=0.000<0.05.

The first hypothesis stands rejected and there seems to be variation in the

mean scores for the three districts under survey. However the other two

hypotheses are not giving significant F values and it will imply that there is

no difference in the mean scores between APL and BPL categories as well

as rural and urban areas. To explain the variation in the mean scores for the

three districts more clearly, a Post Hoc Test was carried out and the output

is reported below.

Table 6.75. Post Hoc Test for District-wise Influence of Informal Finance



Kannur -.2241 .62474 .720


Kannur 2.7218* .67260 .000*

Kannur Trivandrum .2241 .62474 .720

Ernakulam -2.7218* .67260 .000* Source: Survey Data. * The mean difference is significant at the .05 level.

The Post Hoc Test revealed that Kannur and Trivandrum districts do not

have difference in the mean scores, while Ernakulam has a mean score which

is different from the scores of other two districts and significant (p<0.05). As

the mean score of Ernakulam district is higher (29.146) than other two

districts, we are led to believe that the influence of informal finance is slightly

at a lower level in Trivandrum and Kannur but at a higher level in Ernakulam

District. Similarly, there is no significant difference between APL respondents

and BPL respondents who belong to rural and urban areas.

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6.2.7.2 Access Exclusion and Influence of Informal Finance

To identify the dependence between access exclusion and the influence

of informal finance, a correlation coefficient analysis was carried out. To

authenticate the result of analysis the following Tables and explanations are

provided.

Table 6.76. Correlation between Access Exclusion and Influence of Informal Finance

Access exclusion Influence of informal finance

Access exclusion Pearson Correlation 1 .191**


N 320 320


Table 6.76 shows a positive relationship between access exclusion and

influence of informal finance (R = .191), which is found significant at 1 per

cent level. It may be inferred that a change in access exclusion would bring

about a proportionate change in the influence of informal finance.

6.2.7.3 Savings Exclusion and Influence of Informal Finance.

To ascertain the relationship between savings exclusion and the

influence of informal finance on the respondents, Pearson’s correlation

coefficient was used and the results are shown below.

Table 6.77. Correlation between Savings Exclusion and Influence of Informal Finance

Savings exclusion Influence of informal finance

Savings exclusion Pearson Correlation 1 .220**


N 320 320



283

Table 6.77 tells us that a positive relationship does exist between savings

exclusion and the influence of informal finance (R = .220), which is found

significant at 1 per cent level and indicates that a change in savings exclusion

would result in a proportionate change in the influence of informal finance.

6.2.7.4 Information Exclusion and Influence of Informal Finance

This part of the analysis attempts to observe the dependence between

information exclusion and the influence of informal financing agencies, using

a correlation analysis. The results of analysis are reported below.

Table 6.78. Correlation between Information Exclusion and Influence of Informal Finance

Information exclusion Influence of informal finance

Information inclusion Pearson Correlation 1 .286**



It is evident from Table 6.78 that there exists a positive relationship

between information exclusion and the influence of informal finance

(R = .286), and the correlation is found significant at 1 per cent level. Hence, it

may be concluded that a change in the information exclusion would result in a

proportionate change in the influence of informal finance.

6.3 Conclusion

In this chapter, an analysis of the perceptions and preferences of

SHG/NHG members were made, to understand their views on various aspects

explaining FI/FE and to evaluate the level of FI/FE present among the

respondents selected for the study. The demographic profile of the members

Chapter 6

284

under study has been analysed with respect to districts, area and category of

the respondents. The district-wise analysis reveals that an equal number of

respondents were drawn from the districts of Trivandrum and Kannur.

Category-wise analysis shows that majority of the respondents were from BPL

category. The Chi Square test used for testing the statistical relationship

between districts and category proves the relationship to be significant. Area-

wise classification shows that the members of rural area are predominant in

the sample. To test the cross relationship between category and area, Chi

square test was attempted with its result turned out to be significant.

Analysis of the awareness level reveals that the respondents have high

awareness on deposit schemes except recurring deposits, on interest on various

deposits in force except on recurring deposits, on gold loans and agricultural

loans and on interest on gold loans and agricultural loans. Analysis thus

exposes that, DCBs in Kerala are still confined to traditional loan portfolio by

offering gold loans and agricultural loans. Considering the awareness level of

the customers on various financial services, micro-insurances and zero balance

accounts, it is clear that the respondents have only little awareness about all

these services. Thus, the level of awareness of the respondents under survey

clearly indicates that the beneficiaries of DCBs in Kerala were aware of the

conventional banking products and services and they were little aware about

the modern banking products and services.

For further analysis, the correlation among the components of awareness

has been checked and found fairly correlated. The correlation is observed

significant at 1 per cent level of significance (p < .01). Using a three-way

ANOVA, difference in the opinion of respondents on the awareness on bank

products, financial services, micro-insurance and no-frill account were


285

analysed across the districts, area and category under survey. It reveals that, on

bank products, area wise variation does not exist while respondents belonging

to APL category have more awareness. Among districts, respondents of

Kannur have more awareness on bank products. On financial services, APL

category respondents have more awareness than BPL and no variation exists

between urban and rural areas. Among districts, respondents of Ernakulam

have less awareness as compared to respondents from Kannur and

Trivandrum. On micro-insurance, rural – urban areas do not differ, while APL

respondents have more awareness than BPL. Among districts, Ernakulam has

less awareness level. On no-frill accounts, no variation exists among the

districts. Respondents of APL category from rural areas have more awareness

on no-frill accounts. Analysis thus reveals that, the respondents belonging to

APL category are found to have more awareness on all the four variables

constituting total awareness.

Analysis of financial necessity reveals that there is a high demand for

financial products and services on the part of the respondents. Among various

necessities; the need for various loans is more, followed by deposits, insurance

policies and financial services. Using a three-way ANOVA, difference in the

opinion of respondents on the financial necessity is analysed. Analysis

discloses that the respondents belong to APL and BPL category hailing from

both rural and urban areas do not differ in terms of financial necessity. Among

districts, respondents of Ernakulam district have more financial necessity and

among two districts of Trivandrum and Kannur no significant variation was

observed. Analysis of the dependence between financial necessity and

financial awareness, using Chi-Square test proves that there is significant

dependence between the two (p<0.01).

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286

Analysis of the financial availability indicates that various deposits and

loans have high availability, whereas, availability of financial services and

micro-insurance is very low, which suggests that there is a mismatch between

demand and supply of financial services across different districts under survey.

It can be considered as a clear signal of service exclusion rampant among the

customers of DCBs in Kerala. Analysis of the variation in the opinion of the

respondents using three-way ANOVA reveals that no district wise or area wise

difference exists among the respondents, whereas, respondents belonging to

APL category have more availability of financial services than BPL category.

Financial Access has been analysed in terms of four components, viz: 1)

Access to bank account, 2) Access to savings products, 3) Access to

appropriate credit, and (4) Access to financial services including insurance. It

shows that respondents experience certain hassles in accessing basic bank

account. Dominant problem associated with the access is the staff attitude

followed by procedural delay, minimum balance requirement and the

inconvenience of filling of various forms. This is a clear indication that there

exists some degree of ‘banking exclusion’ among the customers of DCBs in

Kerala, which is a major supply side constraint. Analysing the variation in the

respondents’ opinion, it was revealed that no significant variation exists

among the respondents over the districts, area and category under survey

which implies that the customers of DCBs experience similar access problems

(banking exclusion) in the form of staff attitude, procedural hassles,

maintenance of minimum amount and inconvenience in filling of various

forms. Analysis of access to savings product indicates that along with the high

cost of living, the customers of DCBs in Kerala find fault with the unfriendly

attitude of the DCB staff resulting in savings exclusion, which is a major

supply side restraint to FI. Analysis of the variation among the sample groups


287

indicates that with regard to savings problems the beneficiaries do not differ

significantly.

Analysis of credit inclusion reveals that among the difficulties

encountered by the respondents, the prominent are the high rate of interest and

the delay in sanctioning and disbursing loan. It suggests that together with

banking exclusion and savings exclusion, the customers of DCBs in Kerala

experience credit exclusion as well. Analysing the variation among the

customers of DCBs in Kerala, it is revealed that no significant variation exists

among the respondents belonging to different demographic groups

Analysis of access to financial services discloses that financial service

exclusion is substantial among the customers of DCBs in Kerala. The

variables related with financial service exclusion were analysed through 15

statements which were reduced to five factors through a factor analysis after

proving the correlation between the variables. The factors are: i) Non-

availability of insurance, ii) Non-availability of financial services, iii) Non-

affordability of financial services, iv) Non-reasonability of financial services,

and v) Non-access to financial services. Further, to locate the mean variations

across the three districts and two categories, a test of Multi-variate Analysis of

Variance has been undertaken. The results of multi variate test give significant

F values indicating the difference in the mean scores in the bundle of variables

associated with financial service exclusion. It may be concluded that the

factors explaining financial service exclusion vary in the three districts

surveyed. An analysis of the mean scores using multiple comparisons using

LSD suggests that the customers of DCBs in Kerala experience different forms

of financial service exclusion. Non-availability of Micro-insurance is found

more in Ernakulam district. However, with regard to non-availability, non-

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288

affordability, non-reasonability and non-access to other financial services,

customers of different districts under survey seem to have similar level of

exclusion.

An analysis of access to information reveals that non-availability of

financial information (information exclusion) is rampant among the customers

of DCBs in Kerala. Variation among the districts, category and area was

analysed and revealed that information exclusion is more among the

respondents belonging to APL category and no variation exists among the

areas. Among districts, respondents of Ernakulam find to have more

information exclusion as compared to Trivandrum and Kannur.

Attitude of the respondents was analysed in terms of two dimensions: 1)

in terms of interest, and 2) in terms of initiative. Analysis of the attitude in

terms of interest reveals that the respondents are highly interested in availing

the products and services being offered by the DCBs across different districts

of Kerala. Analysis of the variation using three-way ANOVA shows that no

difference exists among categories and areas under study, while the districts

have a significant variation. Among the districts, respondents of Ernakulam

show the highest interest and respondents of Kannur show the least interest.

Analysing the initiative of the respondents, it was disclosed that

respondents take more initiative in opening bank accounts and in savings and

deposits and take least initiative in seeking financial advice. Further, analysis

of the variation in the mean scores reveals no difference among areas and

category while districts do have significant variation. Among districts,

respondents of Ernakulam find to have highest initiative in availing various

financial product and services.


289

Analysis of access to informal finance indicates that the respondents find

the dealings of money lenders and other private banks suitable for them and it

may be an indication of the influence of the informal financial agencies on the

customers. Further, analysing the variation across the demographic groups

using three-way ANOVA, it is revealed that there is no difference in the mean

scores between APL and BPL categories as well as rural and urban areas.

However, the influence of informal finance is slightly at a lower level in

Trivandrum and Kannur but at a higher level in Ernakulam District.

Chapter 6

290

References

[1]. Houston, S. J. (2010). Measuring Financial Literacy, The Journal of Consumer Affairs, vol.44, No.2.

[2]. Chakraborthy, K. C. (2010). Welcome address, RBI-OECD workshop on delivering financial literacy, RBI bulletin, April 13.

[3]. Tiwari, A. (2008). How do MF clients understand their loans? Eye on microfinance, vol.8, October. Available at http://ifmr.ac.in/cmf/eomf8.).

[4]. Chambers, C. L. (2004). Financial Exclusion and Banking Regulation in the United Kingdom: a Template Analysis, Unpublished doctoral dissertation, Bournemouth University.

[5]. Philip, M. (2007). What are the specific Economic Gains from Improved Financial Inclusion? A Tentative Methodology for Estimating these gains. In Anderloni, L., Braga, M.D and Carluccio, E.M (Eds.), New Frontiers in Banking Services: Emerging Needs and Tailored Products for Untapped Markets, Berlin: Springer.

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FINANCIAL INCLUSION BY DCBS – A DEMAND SIDE...

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