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Role of Assets Holding of the Availing Micro Credit of Women: A Study With Reference to Madurai District of Tamil Nadu | OMICS International
ISSN: 2167-0234
Journal of Business & Financial Affairs
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Role of Assets Holding of the Availing Micro Credit of Women: A Study With Reference to Madurai District of Tamil Nadu

Selvaraj N*, Balajikumar P and Alagukanna AS

Saraswathi Narayanan College, Madurai, Tamilnadu, India

*Corresponding Author:
Dr. Selvaraj N
Assistant Professor of Commerce
Saraswathi Narayanan College
Madurai, Tamilnadu, India
Tel: +91-9843727975
E-mail: [email protected] yahoo.com

Received: May 25, 2015 Accepted: July 16, 2015 Published: July 30, 2015

Citation: Selvaraj N, Balajikumar P, Alagukanna AS (2015) Role of Assets Holding of the Availing Micro Credit of Women: A Study With Reference to Madurai District of Tamil Nadu. J Bus Fin Aff 4:144. doi:10.4172/2167-0234.1000144

Copyright: © 2015 Selvaraj N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Micro-credit is a critical antipoverty tool, a wise investment in human capital. When the poorest especially women receive credit, they become economic actors with power to improve not only their own lives, but in a widening circle of impact, the lives of their families, their communities and their relations. Microfinance helps the NGOs all over the world in alleviation of urban and rural poverty through promoting micro enterprises as a solution to the problem of unemployment and underemployment among the poor. In India also NGOs use this methodology for financing the poor. MYRADA in Karnataka and SEWA in Gujarat are the forerunners in the field in India. SEWA has promoted a cooperative bank exclusively for women and is engaged in financing income generating activities of women. Friends of WWB (FWWB) Ahmedabad, an affiliate of Women’s World Banking, New York, networks with NGOs giving financial assistance to women groups. The total value of assets includes movable and immovable assets owned by the respondents and their family members. The assets indicate the financial background of the respondents before availing credit. Assets of the respondents increased due to the additional income earned through new activities and savings.

Keywords

Micro-Credit; Assets; Agricultural; Manufacturing and trading

Introduction

Micro-credit is a critical antipoverty tool, a wise investment in human capital. When the poorest especially women receive credit, they become economic actors with power to improve not only their own lives, but in a widening circle of impact, the lives of their families, their communities and their relations.

Today in India, there are 15,000 registered NGOs and many more non-registered informal groups. These organizations have touched the lives of an estimated number of 15 million persons. According to an international estimate, 100 million persons have been helped worldwide by NGOs of one sort or another. NGOs follow different methods to reach the poor. Those who follow the ‘grassroots’ or the ‘barefoot’ approaches have attracted a good deal of admiration [1].

Microfinance helps the NGOs all over the world in alleviation of urban and rural poverty through promoting micro enterprises as a solution to the problem of unemployment and underemployment among the poor. NGOs undertake the responsibility of forming self-help groups, give training to members, and monitor business performance and promptness in repayment.

In India also NGOs use this methodology for financing the poor. MYRADA in Karnataka and SEWA in Gujarat are the forerunners in the field in India [2]. SEWA has promoted a cooperative bank exclusively for women and is engaged in financing income generating activities of women. Friends of WWB (FWWB) Ahmedabad, an affiliate of Women’s World Banking, New York, networks with NGOs giving financial assistance to women groups. The Working Women’s Forum (WWF), Chennai has organized women cooperative societies for pursuing income generating activities and facilitated empowerment of women. Attempts have also been made to replicate Bangladesh Grammen Bank model by SHARE in Andhra Pradesh and RDO in Manipur [3,4].

Objectives of the Study

To evaluate the impact of micro-credit on women empowerment with reference to Assets holding and to offer suitable suggestions based on the findings.

Period of Study

The present study is based on the primary. The primary data have been collected from the respondents directly for the period of study is impounded to one year (i.e., 2014-2015).

Analytical Framework to Test the Factors

Impact on assets

Assets holding of the respondents (Pre-credit): The total value of assets includes movable and immovable assets owned by the respondents and their family members. The assets indicate the financial background of the respondents before availing credit. In the present study, the assets of the respondents before availing credit are computed and classified as Nil, Below Rs 10000, Rs 10000 to Rs 15000, Rs 15000 to 20000, and above Rs 20000.

Table 1 presents the assets holding of the respondents before availing micro credit in various sectors.

Assets holding  of the Respondents Manufac-turing Sector Service Sector Trading Sector Total
Nil 25
[30.86]
26
[25.49]
31
[26.50]
82
[27.33]
Below Rs.10000 18
[22.22]
25
[24.51]
37
[31.62]
80
[26.67]
Rs.10000 - 15000 21
[25.93]
31
[30.39]
32
[27.35]
84
[28.00]
Rs.15000 - 20000 9
[11.11]
16
[15.69]
10
[8.55]
35
[11.67]
Above Rs.20000 8
[9.88]
4
[3.92]
7
[5.98]
19
[6.33]
Total 81
[100.0]
102
[100.0]
117
[100.0]
300
[100.0]

Table 1: Assets holding of the respondents (pre-credit).

In the study area, before availing the credit, the value of asset holding of the respondents accounts only a meager amount. About 82 respondents had no assets. Majority of the respondents that is 28.00 per cent of the respondents had assets value below Rs 10000–15000. 26.67 per cent of the respondents had assets value in the range of below Rs 10000. 11.67 per cent have assets value of Rs 15000 to Rs 20000. A very low percentage constituting 6.33 per cent of the respondents had assets value of Rs 20000 and above.

Assets holding of the respondents (Post-credit): Assets of the respondents increased due to the additional income earned through new activities and savings. The assets after availing credit are measured and classified as Nil, Below Rs 10000, Rs 10000 to Rs 15000, Rs 15000 to 20000, and above Rs 20000. Table 2 presents the assets holding of the respondents after availing credit in various sectors [5,6].

Assets holding of the Respondents Manufacturing Sector Service Sector Trading Sector Total
Nil 1
[1.23]
0
[0.0]
0
[0.0]
1
[0.33]
Below Rs.10000 5
[6.17]
12
[11.76]
3
[2.56]
20
[6.67]
Rs.10000 - 15000 18
[22.22]
19
[18.63]
26
[22.22]
63
[21.00]
Rs.15000 - 20000 14
[17.29]
18
[17.65]
30
[25.64]
62
[20.67]
Above Rs.20000 43
[53.09]
53
[51.96]
58
[49.58]
154
[51.33]
Total 81
[100.0]
102
[100.0]
117
[100.0]
300
[100.0]

Table 2: Assets holding of the respondents (post-credit).

Table 2 clearly shows that, out of 81 respondents in manufacturing sector, 1 (1.23 per cent) respondent had no assets, 5 (6.17 per cent) of them belong to the holding of assets below Rs 10000, 18 (22.22 per cent) of them belong to the assets holding of Rs 10001 to Rs 15000, 14 (17.29 per cent) of them belong to the assets holding of Rs 15001 to Rs 20000, and 43 (53.09 per cent) of them belong to the assets holding of Rs 20001 and above after availing the credit.

Out of 102 respondents in service sector, 12 (11.76 per cent) of the respondents belong to below Rs 10000, 19 (18.63 per cent) of the respondent’s family come under the value of assets holding in the range of Rs 10001 to Rs 15000, 18 (17.65 per cent) of the respondents come under assets holding of Rs 15001 to Rs 20000, and 53 (51.96 per cent) of the respondents belong to the assets holding Rs 20001 and above, after availing credit [7].

It is further inferred from the table, out of 117 respondents in trading sector, 3 (2.56 per cent) of them come under the value of assets holding below Rs 10000, 26 (22.22 per cent) of them belong to the value of assets holding of Rs 10001 to 15000, 30 (25.64 per cent) of them belong to the value of assets holding of Rs 15001 to Rs 20000, and 58 (49.58 per cent) of them belong to the value of assets holding of Rs 20001and above after availing the credit [8,9].

Thus it could be concluded from the table that after availing credit, the value of the assets holding of the respondent and her family has increased in the study area. 0.33 per cent respondent had no assets, 6.67 per cent of the respondents had the assets value of below Rs 10000, 21.00 per cent of the respondents and their family had the assets value of Rs 10001 to Rs 15000, 20.67 per cent of the respondents and their family had assets value of Rs 15001 to Rs 20000, and 51.33 per cent of the respondents and their family had the assets value of above Rs 20001 [10].

The null and alternative hypotheses are framed and tested in this study:

Null hypothesis (H0): There is no relationship between assets holding of the respondents and the various sectors.

Alternative hypothesis (H1): There is a relationship between asset holding of the respondents and the various sectors.

As the calculated value of χ2 is greater than the table value at 5 per cent level of significance, the investigator rejects the null-hypothesis (H0). Therefore, there exists a relationship between the assets holding of the respondents after joining the self-help groups and the various sectors (Table 3).

Test Value d.f Asymp.Sig.
(2-Sided)
Table Value Result
Pearson Chi-square 26.311 12 .006 21.0 Rejected

Table 3: Result of chi-square test – asset holding of the respondents and various sectors.

Mean value of asset holding: The micro-finance program might have a major impact of the activities undertaken beneficiaries. Sectorwise analysis of the mean value of asset holdings of the respondents is shown in Table 4.

Table 4 reveals that, the manufacturing sector has the highest mean value in the assets holding amounting Rs 24241.31, followed by the service sector with Rs 21433.26. Next is followed with the trading sector amounting to Rs 21411.61.

Sectors Mean Value of Assets Total Std.Deviation
Manufacturing Sector 24241.31 81 9961.25
Service Sector 21433.26 102 7621.43
Trading Sector 21411.61 117 5761.45
Total 18634.41 300 7201.22

Table 4: Mean value of asset holding of the respondents in various sectors.

Association between family size of the respondents and their assets (post-credit) in various sectors

Null hypothesis (H0): There is no association between family size of the respondents and their assets (post-credit) in various sectors.

Alternative hypothesis (H1): There exists association between family size of the respondents and their assets (post-credit) in various sectors.

In order to find out whether there is any correlation between the family size of the respondents and their assets in various sectors, chi-square test has been applied. The results of the chi-square test are furnished below.

As the calculated value of χ2 is greater than the table value at 5 per cent level of significance, the investigator rejects the null-hypothesis (H0) and accepts the alternative hypothesis (H1). Therefore there exists an association between family size of the respondents and their assets after availing credit in various sectors (Table 5) [11,12].

Family Size Assets (Post-Credit) Total
Nil Below Rs.10000 Rs.10001-Rs.15000 Rs.15001-Rs.20000 Above Rs.20001
Below 5 1
[100.00]
8
[40.00]
29
[46.03]
28
[45.16]
62
[40.26]
128
[42.67]
5 – 8 0
[00.0]
7
[35.00]
23
[36.51]
21
[33.87]
54
[35.06]
105
[35.00]
9 and above 0
[0.0]
5
[25.00]
11
[17.46]
13
[20.97]
38
[24.68]
67
[22.50]
Total 1
[100.0]
20
[100.0]
63
[100.0]
62
[100.0]
154
[100.0]
300
[100.0]

Table 5: Contingency table – family size of the respondents and their value of assets (post-credit) in various sectors.

Association between family income (post-credit) of the respondents and their assets (post-credit) in various sectors

Null Hypothesis (H0): There is no association between family income of the respondents and their assets (post-credit) in various sectors.

Alternative Hypothesis (H1): There exists association between family income of the respondents and their assets (Post-Credit) in various sectors.

In order to find out whether there is any correlation between the family income after availing the micro credit of the respondents and their assets in various sectors, chi-square test has been applied. The results of the chi-square test are furnished in Table 6.

Test Value d.f Asymp.Sig.
(2-Sided)
Table Value Result
Pearson Chi-square 122.5683 8 0.000 15.5 Rejected

Table 6: Result of chi-square test – family size of the respondents and their value of assets (post-credit) in various sectors.

As the calculated value of χ2 is greater than the table value at 5 per cent level of significance, the investigator rejects the null-hypothesis (H0) and accepts the alternative hypothesis (H1). Therefore, there exists an association between family income after availing the credit of the respondents and their assets of the respondents in various sectors (Table 7).

Family Income (Post – Credit) Assets (Post-Credit) Total
Nil Below Rs.10000 Rs.10001-Rs.15000 Rs.15001-Rs.20000 Above Rs.20001
Below Rs.25000 1
[100.0]
3
[15.00]
5
[7.94]
5
[8.06]
24
[15.58]
38
[12.67]
Rs.25001–Rs.35000 0
[0.0]
5
[25.00]
18
[28.57]
21
[33.87]
38
[24.68]
82
[27.33]
Rs.35001– Rs.45000 0
[0.0]
6
[30.00]
29
[46.03]
26
[41.94]
48
[31.17]
109
[36.33]
Above Rs.45001 0
[0.0]
6
[30.00]
11
[17.46]
10
[16.13]
44
[28.57]
71
[23.67]
Total 1
[100.0]
20
[100.0]
63
[100.0]
62
[100.0]
154
[100.0]
300
[100.0]

Table 7: Contingency table– family income of the respondents and their value of assets (post–credit) in various sectors.

Association between savings (post-credit) of the respondents and their assets (post-credit) in various sectors

Null Hypothesis (H0): There is no association between savings of the respondents and their assets (post-credit) in various sectors.

Alternative Hypothesis (H1): There exists association between savings of the respondents and their assets (post credit) in various sectors

In order to find out whether there is any correlation between the savings of the respondents and their assets in various sectors, chisquare test has been applied. The results of the chi-square test are furnished in Table 8.

Test Value d.f Asymp.Sig.
(2-Sided)
Table Value Result
Pearson Chi-square 1861.6662 12 0.000 21.0 Rejected

Table 8: Result of chi-square test – family income of the respondents and their value of assets (post-credit) in various sectors.

As the calculated value of χ2 is less than the table value at 5 per cent level of significance, the investigator accepts the null-hypothesis (H0) and rejects the alternative hypothesis (H1). Therefore there is no exists an association between savings after availing the credit of the respondents and their assets in various sectors (Table 9).


Assets (Post – Credit)
Savings (Post – Credit)  Total
Below Rs.1200 Rs.1201-Rs.2400 Rs.2401-Rs.3600 Rs.3601-Rs.4800 Above Rs.4801
Nil 1
[1.23]
0
[0.0]
0
[0.0]
0
[0.0]
0
[0.0]
1
[0.33]
Below Rs.10000 5
[6.17]
4
[6.45]
4
[5.19]
6
[9.23]
1
[6.67]
20
[6.67]
Rs.10001–Rs.15000 16
[19.75]
13
[20.97]
14
[18.18]
16
[24.62]
4
[26.66]
63
[21.00]
Rs.15001– Rs.20000 17
[20.99]
12
[19.35]
15
[19.48]
11
[16.92]
7
[46.67]
62
[20.67]
Above Rs.20001 42 
[51.85]
33
[53.23]
44
[57.15]
32
[49.23]
3
[20.00]
154
[51.33]
Total 81
[100.0]
62
[100.0]
77
[100.0]
65
[100.0]
15
[100.0]
300
[100.0]

Table 9: Contingency table– savings (post-credit) of the respondents and their value of asset holding in various sectors.

Variation in asset holding of the respondents and their family size among the various sectors

In this section, an attempt has been made to examine the variation in asset holding of the respondents among the various sectors, such as manufacturing, service and trading sectors. For this one way ANOVA is applied separately and the results are presented in Table 10.

Test Value d.f Asymp.Sig.
(2-Sided)
Table Value

Result

Pearson Chi-square 6.2534 16 0.000 26.3 Accepts

Table 10: Result of chi-square test – savings (post-credit) of the respondents and their value of asset holding in various sectors.

Table 11 revealed that there was a significant variation in the asset holding of the respondents among the various sectors and their family size at 5 per cent level of significant. Hence, it may be concluded that the asset holding of the respondents and their family size varied significantly among the various sectors such as manufacturing sector, service sector and trading sector.

Assets Variation Sum of Squares Df Mean Square F-ratio Sig. Table F0.05
Between Groups 3.32E+09 2 176231142 41.211 0.000  
Within groups 1.49E+11 297 42562341.31      
Total 1.82E+11 299        

Table 10: Result of chi-square test – savings (post-credit) of the respondents and their value of asset holding in various sectors.

Summary of Findings

While the analyzing the assets holding pre-credit, the value of assets holding of the respondents accounts only a meagre amount. About 82 respondents have no assets. Majority of the respondents, that is 26.67 per cent of the respondents, have assets value below Rs 10000, 28.00 per cent of the respondents possess assets to the value Rs 10000 to Rs 15000, and 11.67 per cent have assets about value Rs 15000 to Rs 20000. A very low percentage constituting 6.33 per cent of the respondents have assets worthy of Rs 20000 and above.

It is observed that the assets holding of the respondents and their family, post-credit member has increased in the study area. 0.33 per cent of respondents have no assets, 6.67 per cent of the respondents have the assets, below Rs 10000, 21.00 per cent of the respondents have assets, to the value of Rs 10001 to Rs 15000, 20.67 per cent of the respondents have assets in the range of Rs 15001 to Rs 20000, and 51.33 per cent of the respondents and their family have the assets above Rs 20001.

ANOVA test reveals that there is a significant variation in the assets holding of the respondents among the various sectors and their family size at 5 per cent level of significant. Hence, it may be concluded that the assets holding of the respondents and their family size varies significantly among the various sectors such as manufacturing sector, service sector and trading sector.

The paired ‘t’ test shows there is some significant difference in the assets holding of the respondents pre-credit and post-credit. The mean assets holdings of the respondent pre-credit are Rs 18611.241 and postcredit it is Rs 14321.731 and the difference Rs 14321.731 is statistically significant.

Suggestions

In the light of the above discussion and findings, the following suggestions are made:

It is suggested that the NGOs should be prevented from interfering with the SHG movement. Steps should be taken to keep them as voluntary organizations. They should not be allowed to use extraneous influences.

SHG women are more concerned with poverty and its effect on society. Since they themselves fight against poverty by being members of SHG and move upwards from below poverty line, in future, the poverty alleviation programs can be implemented through SHGs, They can monitor themselves effectively, with all enthusiasm and involvement.

Institutional credit facility must be extended to women to develop their managerial skill for prompt repayment consciousness.

Micro-Finance should be used to meet the immediate demand of the poor women – for Health, Education or consumption purposes. This will improve the quality of their life and will be ready to take active participation in economic activities.

The NGOs can provide some common services to the Self-Help Groups for procurement of raw materials, marketing etc.

Though, these groups have made a positive impact on SHG women, SHG members suffer from lack of motivation, backward and forward linkages, inadequacy of infrastructure, insufficient loan, inadequate provision of marketing, non-availability of inputs, lack of systematic monitoring and follow up activities etc,. Thus there is need to evaluate the SHGs annually to assess the progress of different income and employment generating schemes.

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