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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
% within category 36.0 12.8 27.5
Kannur Count 75 41 116
% within category 36.9 35.0 36.3
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
% within category 72.4 54.7 65.9
Urban Count 56 53 109
% within category 27.6 45.3 34.1
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
District Mean Std. Error
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
District Mean Std. Error
Trivandrum 24.044 .756
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
Grand Mean 24.359 .492 Source: Survey Data
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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
Source Sum of Squares df Mean Square F Sig.
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
(I) District (J)District Mean Difference(I-J) Std Error Sig
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.
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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
District Mean Std. Error
Trivandrum 2.557 .126
Ernakulam 2.931 .153
Kannur 2.942 .129
Area
Rural 3.040 .099
Urban 2.580 .129
Category
BPL 2.563 .101
APL 3.057 .130
Grand Mean 2.810 .082 Source: Survey Data
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.
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2. H0- There is no significant variation in the mean values of the scores of
awareness on no- frill accounts among APL and BPL categories.
H1- There is significant variation in the mean values of the scores of
awareness on no-frill accounts among APL and BPL categories.
3. H0- There is no significant variation in the mean values of the scores of
awareness on no- frill accounts among urban and rural areas.
H1- There is significant variation in the mean values of the scores of
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
Source Sum of Squares df Mean Square F Sig.
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
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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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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
Trivandrum 14.793 .315
Ernakulam 15.937 .382
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.
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233
Table 6.21. Tests of Between Subjects Effects on Financial Necessity of the Customers
Source Sum of Squares df Mean Square F Sig.
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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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
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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
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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
Financial Inclusion by DCBS – A Demand Side Analysis
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
District Mean Std. Error
Trivandrum 12.124 .201
Ernakulam 12.341 .244
Kannur 11.932 .206
Area
Rural 12.274 .158
Urban 11.991 .205
Category
BPL 11.563 .161
APL 12.701 .207 Source: Survey Data
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
Source Sum of Squares df Mean Square F Sig.
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
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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.
Financial Inclusion by DCBS – A Demand Side Analysis
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.
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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
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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
District Mean Std. Error
Trivandrum 17.711 .401
Ernakulam 17.635 .487
Kannur 17.165 .410
Area
Rural 17.873 .314
Urban 17.134 .409
Category
BPL 17.757 .321
APL 17.250 .413 Source: Survey Data
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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
Source Sum of Squares df Mean Square F Sig.
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
Financial Inclusion by DCBS – A Demand Side Analysis
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
Difficulties N Min Max Mean Std. Deviation
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
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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
District Mean Std. Error
Trivandrum 10.441 .236
Ernakulam 9.940 .286
Kannur 10.073 .241
Area
Rural 10.336 .185
Urban 9.967 .241
Category
BPL 9.929 .189
APL 10.373 .243 Source: Survey Data
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.
Financial Inclusion by DCBS – A Demand Side Analysis
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**
Sig. (2-tailed) .000
N 320 320
**. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data
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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.
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247
Table 6.36. Difficulties in Obtaining Loan
Difficulties N Min Max Mean Std. Deviation
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
District Mean Std. Error
Trivandrum 28.465 .625
Ernakulam 28.698 .759
Kannur 27.614 .640
Area
Rural 28.136 .490
Urban 28.382 .638
Category
BPL 27.794 .501
APL 28.723 .644 Source: Survey Data
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
Source Sum of Squares df Mean Square F Sig.
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
Financial Inclusion by DCBS – A Demand Side Analysis
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**
Sig. (2-tailed) .000
N 320 320 **. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data
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.
Financial Inclusion by DCBS – A Demand Side Analysis
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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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
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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
Financial Inclusion by DCBS – A Demand Side Analysis
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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256
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.
Financial Inclusion by DCBS – A Demand Side Analysis
257
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
Statement No Supporting Statements
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
Statement No Supporting Statements
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
Financial Inclusion by DCBS – A Demand Side Analysis
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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260
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
Financial Inclusion by DCBS – A Demand Side Analysis
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.
Financial Inclusion by DCBS – A Demand Side Analysis
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
Financial Inclusion by DCBS – A Demand Side Analysis
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
District Mean Std. Error
Trivandrum 31.666 .538
Ernakulam 35.298 .654
Kannur 31.352 .551
Area
Rural 32.680 .422
Urban 32.864 .550
Category
BPL 31.691 .431
APL 33.853 .555 Source: Survey Data
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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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.
2. H0- There is no variation in the mean scores of information exclusion
among APL and BPL categories.
H1- There is variation in the mean scores of information exclusion
among APL and BPL categories.
3. H0- There is no variation in the mean scores of information exclusion
among the urban and rural areas.
H1- There is variation in the mean scores of information exclusion
among the urban and rural areas.
The ANOVA output is provided below. Table 6.57. Tests of Between Subjects Effects on Information Exclusion
Source Sum of Squares df Mean Square F Sig.
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
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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
Trivandrum Ernakulam -2.8585* .80736 .000*
Kannur .6983 .74991 .352
Ernakulam Trivandrum 2.8585* .80736 .000*
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**
Sig. (2-tailed) .000
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
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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.
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271
Table 6.62. Tests of between Subjects Effects on Attitude in Terms of Interest
Source Sum of Squares df Mean Square F Sig.
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
(I) District (J)District Mean Difference(I-J) Std Error Sig
Trivandrum Ernakulam -2.5956* .61448 .000*
Kannur 1.4224* .57075 .013*
Ernakulam Trivandrum 2.5956* .61448 .000*
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
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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
District Mean Std. Error
Trivandrum 23.320 .480
Ernakulam 25.642 .583
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
Source Sum of Squares df Mean Square F Sig.
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
(I) District (J)District Mean Difference(I-J) Std Error Sig
Trivandrum Ernakulam -2.5353* .71986 .000*
Kannur 1.2845 .66863 .056
Ernakulam Trivandrum 2.5353* .71986 .000*
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.
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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**
Sig. (2-tailed) .000
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**
Sig. (2-tailed) .002
N 320 320
**. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data
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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**
Sig. (2-tailed) .001
N 320 320
**. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data
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.
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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**
Sig. (2-tailed) .000
N 320 320
**. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data
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.
Financial Inclusion by DCBS – A Demand Side Analysis
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
District Mean Std. Error
Trivandrum 25.839 .449
Ernakulam 29.146 .545
Kannur 26.188 .459
Area
Rural 27.604 .352
Urban 26.511 .458
Category
BPL 26.503 .359
APL 27.613 .462 Source: Survey Data
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.
2. H0- There is no difference in the mean scores of influence of informal
finance among APL and BPL categories.
H1- There is difference in the mean scores of influence of informal
finance among APL and BPL categories.
3. H0- There is no difference in the mean scores of influence of informal
finance among the urban and rural areas.
H1- There is difference in the mean scores of influence of informal
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
Source Sum of Squares df Mean Square F Sig.
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.
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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
(I) District (J)District Mean Difference(I-J) Std Error Sig
Trivandrum Ernakulam -2.9459* .67260 .000*
Kannur -.2241 .62474 .720
Ernakulam Trivandrum 2.9459* .67260 .000*
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**
Sig. (2-tailed) .001
N 320 320
**. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data
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**
Sig. (2-tailed) .000
N 320 320
**. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data
Financial Inclusion by DCBS – A Demand Side Analysis
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**
Sig. (2-tailed) .000
N 320 320 **. Correlation is significant at the 0.01 level (2-tailed). Source: Survey Data
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
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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
Financial Inclusion by DCBS – A Demand Side Analysis
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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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
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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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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.
Financial Inclusion by DCBS – A Demand Side Analysis
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.
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References
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[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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