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Factors Discriminating the Problem Perception Women Micro Entrepreneurs in Madurai, Ramnad and Dindigul Districts of Tamil Nadu: A Study

Selvaraj Narayanan*

Department of Commerce, Saraswathi Narayanan College, Madurai, Tamilnadu, India

*Corresponding Author:
Selvaraj N
Assistant Professor of Commerce
Saraswathi Narayanan College Madurai
Tamilnadu, India
Tel:
09843727975 E-mail: [email protected]

Received Date: February 13, 2016; Accepted Date: July 19, 2016; Published Date: July 27, 2016

Citation: Narayanan S (2016) Factors Discriminating the Problem Perception Women Micro Entrepreneurs in Madurai, Ramnad and Dindigul Districts of Tamil Nadu: A Study. Intel Prop Rights. 4:157. doi:10.4172/2375-4516.1000157

Copyright: © 2016 Narayanan S. 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

Socioeconomic developments as well as macroeconomic and financial sector stability are important components in ensuring an enabling environment for continued growth of overall economy as a whole and the micro finance industry in specific. Supporting micro enterprise is therefore necessary policy – socially & economically. When the Self Help Group includes women members is named as Women Self Help Group. These social and economic barriers can be overcome, and the participation of women in the society and economy as equal partners is possible. This has been seen in the Tamilnadu Women’s Development Project, a fore-runner in the empowerment of women in the country through women SHGs. The groups, by their strong commitment, have successfully eradicated the arrack menace in many villages.

They have successfully campaigned against female infanticide, child marriages, dowry menace and other atrocities against women. The groups have successfully overcome the problem of the money-lenders’ usurious practices. Wherever SHGs are strong, moneylenders have even had to reduce their lending rates. The present study is undertaken to analyses the of profile variables on enterprise involvement of women entrepreneurs for Madurai, Ramnad and Dindigul districts of Tamil Nadu. The field survey was conducted from September 2014 to March 2015 for the collection of primary data. The Fisher’s Discriminant Function Analysis test was applied to analyse the interest of the independent variables which discriminate the two groups’ namely good performers vs. poor performers in the present study.

Keywords

Factors; Performers; Investment; Ratio and self-help group

Introduction

Whatever profession a woman takes up the Indian society looks at her in a strange attitude. If they can take up self-employment, they can manage small business, staying at home, rendering justice both to the home and the business activities contributing at the same time to the nation’s economy by becoming self-supportive.

Non-financial services are equally important to design, develop and market product or services. Micro enterprise has edge over the large enterprises in various products; traditional knowledge and better control over business are unique strength.

Ignoring substantial population suffering from poverty may be costly for any economy and it is not prudent not to utilize strengths vested in such poor lot. Socioeconomic developments as well as macroeconomic and financial sector stability are important components in ensuring an enabling environment for continued growth of overall economy as a whole and the micro finance industry in specific. Supporting micro enterprise is therefore necessary policy – socially and economically [1].

Self Help group is formed with the member belonging to the age group of 18-60 years old, not exceeding 10-20 members in each group and especially belonging to the classes below poverty line. The member in the group is united and concentrates in their Capacity building, Social and Economic empowerment. When the Self Help Group includes women members is named as Women Self Help Group. Similarly, men form a cluster to form Men Self Help Group. There can be a possibility of both the sex forming a self-help group.

Priority is given to the destitute, widows, handicapped, scheduled caste/Scheduled Tribes in forming the Self Help Groups. The cluster is formed based on their thoughts, thinking culture, work place and occupation. The nature of forming occupational wise may help to have an in-depth developmental activity of particular occupation in that group. For instance, educated self-help group youth members may find an opportunity to base micro based enterprises and industries. Rural cottage industries members can find a channel to motivate them by organizing non-agro training programmes [2].

These social and economic barriers can be overcome, and the participation of women in the society and economy as equal partners is possible. This has been seen in the Tamilnadu Women’s Development Project, a fore-runner in the empowerment of women in the country through women SHGs [3].

The project has, simultaneously created and provided a means of continuing livelihood on a sustainable basis to women’s group members and their families, through increasing their access to credit and expanding their incomes and asset base. The groups have successfully overcome the problem of the money-lenders’ usurious practices. Wherever SHGs are strong, moneylenders have even had to reduce their lending rates. Unorthodox micro enterprises like label making, photography, videography, herbal medicines etc. have been successfully taken up. Economic empowerment and capacity building have been the real result.

The groups, by their strong commitment, have successfully eradicated the arrack menace in many villages. They have successfully campaigned against female infanticide, child marriages, dowry menace and other atrocities against women. Slowly and steadily, the living conditions have improved. Husbands and inlaws respect lady members better and they have a greater say in household affairs. Electricity, better roads, bus services, civic amenities etc., have been extended to their villages through group efforts. It has been experienced that group members are more likely to volunteer time and effort in community development activities like in digging wells, widening roads, campaigning for better bus transport to their areas, removing unwanted hedges along the pathways, deepening water tanks, maintaining the balwadis, and community centers etc., Social mobilization and improvement of status of women has been the other major achievement [4].

Interestingly, the Self Help Groups Linkage Programme has been showing faster progress as well as high rate of success. Self Help Groups form the social capital, which facilitate financial linkage of poor borrowers with formal financial institutions (FFI’s) in India. The basic principles on which the SHGs function are:

• The SHG is a network of members who fulfill locational criteria.

• They are resident in the area and are homogeneous.

• They have rules/norms regarding their functioning.

• Savings first, credit thereafter.

• Personalized services suiting the requirements of the members are ensured.

• SHG’s hold regular meetings to ensure participation of members in the activities of the group.

• SHG’s maintain accounts.

• Group leaders are elected by members and rotated periodically.

• Transparency in operations of the group and participatory decision making ensure that the benefits to members are evenly distributed.

• Market rates of interest on savings and credit are charged.

• Group liability and peer pressure act as substitutes for traditional collateral for loans.

Besides on the availability of NGOs, the ability, integrity, experience and finally their acceptability by the FIs are the other factors, which help the SHGs in getting credit. Whereas in the Western and Southern regions the average number of SHGs per NGO having availed bank credit is 92 and 66 respectively, in other regions these figures are below 30. Among the states, Andhra Pradesh, Gujarat, Maharashtra, Karnataka, Bihar, Tamil Nadu and Uttar Pradesh fare better with 145, 109, 76, 62, 59, 55 and 53 credit-linked SHGs per NGO respectively.

Among these, Bihar, Maharashtra, Uttar Pradesh and Gujarat have 8, 18,23 and 25 NGOs, have been accepted by the FIs. Interestingly, as on January 2002, regional spread of SHGs linked shows that southern states have a lead over northern states. Out of the SHGs credit linked, 71.14 percent SHGs were reported to be functional in southern region while central region accounted for just 10.94 percent. Again, out of total bank loan, Southern region accounted for 81.98 percent share while the Central region accounted 5.35 share. Most of the SHGs are linked with Commercial banks for credit purposes. There were 318 participatory banks in SHG bank linkage programme in 2008 and most of the banks were RRBs (166). About 750 NGO’s are engaged in SHGs promotion. Out of total SHGs, 70 percent SHGs were formed and strengthened by NGOs and other formal agencies.

SHG as an inexorable way to empower women-rationale

One has to believe that the progress of any nation is inevitably linked with social and economic plight of women in that particular country. For concrete results, we have to assert and act with our full might and what is needed most. Empowerment by way of participation in SHG can bring enviable changes and enhancement in the living conditions of women in poor and developing nations [5].

Statement of the problem

A self-help group (SHG) is a voluntary association of people with common goal. The concept of ‘Self Help Group’ appears to be a good alternative strategy to involve people in the development process. In these circumstances it is felt important and necessary to study theof Factors Discriminating the Good and Poor Performers of women entrepreneurs.

Hence, the present study is undertaken to analyze the Factors Discriminating the Good and Poor Performers of women entrepreneurs for Madurai, Ramnad and Din Digul districts of Tamil Nadu.

Period of study

The field survey was conducted from September 2014 to March 2015 for the collection of primary data. The reference period of the survey was 2014-2015.

Objectives of the Study

The basic objectives of the study have Factors Discriminating the Good and Poor Performers of women entrepreneurs.

Sampling design

For the purpose of primary data collection, three districts namely Madurai, Ramnad and Dindigul have been selected the main reason for selecting these districts is that the former are a developed district, the middle is backward one and the last is developing district. Out of the total of 450 sample women Micro entrepreneurs each 150 from the districts were randomly selected from SHGs functioning under NGOs and Mahilar Thittam Schemes in each block of three districts by using proportionate sample method.

Discriminant function analysis

The Fisher’s Discriminant Function Analysis test was applied to analyze the interest of the independent variables which discriminate the two groups’ namely good performers vs. poor performers in the present study.

image

Where,

Z=Total Discriminant score

X1 … Xn=Discriminant variables

L1….Ln=Linear Discriminant coefficient

N=Number of variables

Mahalenobis measures of distance between two groups is F

Where,

D2=IiDi

P=Number of Discriminant variables

N1=Number in group I

N2=Number in group II

The value of ‘F’ to be tested at P and (N1 + N2 - 1) degrees of freedom.

Factors discriminating the good and poor performers

The entrepreneurs are classified as good performers and poor performers on the basis of their mean return on investment. Fourteen profile variables, including the enterprise involvement are taken into account to identify the most important factor discriminating the good and poor performers among the entrepreneurs. The scale values of the discriminating factors are taken for the discriminant analysis [6]. Fischer’s discriminate function analysis test was applied. The meahalenbis D2 statistics was calculated to measure the distance between two groups of entrepreneurs. ‘F’ statistics was used if the two groups where different from each other the resultant discriminant function coefficient, the mean difference of the discriminant variables and their relative importance in discriminant function are computed and shown in Table 1.

Sl.No. Variables Mean Difference di Discriminant function coefficient li di x li Percentage to total
1. Enterprise involvement index 3.87*(5.3515) 1.1121 8.4761 41.21
2. Personality traits 2.21(8.171) 1.3211 3.2211 10.51
3. Education 2.09(8.172) 0.6631 2.2215 3.19
4. Earning members per family 3.19*(6.1638) 1.1121 2.0959 2.99
5. Occupational background 1.87(8.114) 0.8737 1.7961 11.21
6. Family income 1.81(6.3215) 6.4961 0.5515 5.15
7. Material possession 1.39*(0.8211) 0.0091 0.0133 0.07
8. Monthly income 1.19(5.322) 0.005 1.005 6.94
9. Type of family 0.34(1.721) -0.0981 -0.0476 -3.36
10. Martial status 1.19(3.1821) -0.2431 -0.3362 -3.48
11. Caste 1.60*(4.8511) -0.2216 -0.4314 -4.09
12. Family size 2.42*(7.319) 0.1761 0.5761 -4.95
13. Age 1.99*(5.171) -0.3961 -1.0062 -6.21
D2=3.8311, F=17.6591; *Significant at 5 per cent level. Figures in parenthesis are ‘t’ values. Source: Primary data.

Table 1: Factors discriminating the problem perception in Madurai district.

It has been inferred from the Table 1 that the values of D2 and F ratio calculated were 3.8311 and 17.6591 respectively. The F ratio was found to be significant at 5 per cent level. Hence the distance between good and poor performer was significant. This implied that thirteen variables together were useful in discriminating good and poor performer. Among the mean differences obtained in fourteen variables, the significant differences is found in the case of eleven variables. The planning of percentage of distance measured by the important variables reveals that the first three ranks comprising enterprise involvement index, personality traits, and education constituted 41.21, 10.51 and 3.19 per cent respectively. The individual contribution of the above said three variables are more than average distance in terms of discrimination as compared to other variables in discriminating good and poor performers. The calculated discriminant scores z1 and z2 for good and poor performer were 2.8216 and 1.1432 respectively. The critical value of discriminant score (z) for these two groups were 2.0134.Based on these scores, the discriminant function can be used to predict whether the entrepreneur is a poor or good performer. If the value of discriminant score of a given entrepreneur was more than 2.0134 it would indicate a tendency to poor performer [7]. Table 2 shows the factors discriminating good and poor performers in Ramnad district.

Sl.No. Variables Mean Difference di Discriminant function coefficient li di x li Percentage to total
1. Enterprise involvement index 3.94(6.3415) 2.2715 8.6891 61.21
2. Personality traits 2.28(9.262) 1.5211 3.7861 1.45
3. Education 3.19(8.311) 0.8611 2.4215 1.12
4. Earning members per family 2.18(6.532) 1.1121 2.2261 23.15
5. Occupational background 1.98(8.191) 0.9967 1.9961 21.45
6. Family income 1.78(6.515) 0.4911 0.7511 14.91
7. Material possession 1.78(6.515) 0.1111 0.1121 1.88
8. Monthly income 1.59(0.1931) 0.1080 1.124 25.92
9. Type of family 1.39(5.5214) -0.1921 -0.1415 28.16
10. Martial status 0.59(1.7911) -0.4411 -0.4211 -12.49
11. Caste 1.39*(3.6311) -0.4211 0.6411 13.08
12. Family size 1.81*(9.9996) 0.3711 0.7711 13.46
13. Age 2.19(5.456) -0.5953 -1.1152 15.91
D2=2.9121 F=19.1541. *Significant at 5 per cent level. Figures in parenthesis are ‘t’ values. Source: Primary data.

Table 2: Factors discriminating the problem perception in Ramnad district.

According to Table 2 that the values of D2 and F ratio calculated were 2.9121 and 19.1514 respectively. The F ratio was found to be significant at 5 per cent level. Hence the distance between good and poor performer was significant. This implied that thirteen variables together were useful in discriminating good and poor performer. Among the mean differences obtained in fourteen variables, the significant difference is found in the case of eleven variables [8].

The planning of percentage of distance measured by the important variables reveals that the first three ranks comprising enterprise involvement index, types of family, and monthly income constituted 61.21, 31.45 and 24.62 per cent respectively. The individual contribution of the above said three variables are more than average distance in terms of discrimination as compared to other variables in discriminating good and poor performers.

The calculated discriminant scores z1 and z2 for good and poor performer were 3.1512 and 1.2915. The critical value of discriminant score (z) for those two groups were 2.2361. Based on these scores, the discriminant function can be used to predict whether the entrepreneur is a poor or good performer. If the value of discriminant score of a given entrepreneur was more than 2.2365 it would predicate that they would be good performer and if less than 2.2361 it would indicate a tendency to be poor performer [9].

It is clearly evident from Table 3 that the values of D2 and F ratio calculated were 2.7161 and 18.8471 respectively. The F ratio was found to be significant at 5 per cent level. Hence the distance between good and poor performer was significant. It indicates that thirteen variables together were useful in discriminating good and poor performer. Among the mean differences obtained in fourteen variables, eleven variables were found to be significant difference eleven variables [9]. The analysis of percentage of distance reveals that the first three ranks comprising enterprise involvement index, personality traits, and earning members constituted 60.74, 30.66 and 24.61 per cent respectively. Three variables are more individual contribution than average distance in terms of discrimination as compared to other variables in discriminating good and poor performers.

Sl.No. Variables Mean Difference di Discriminant function coefficient li di x li Percentage to total
1. Enterprise involvement index 3.89(6.3453) 2.2713 8.6792 60.74
2. Personality traits 2.29(9.271) 1.5215 3.7415 30.66
3. Education 3.19(8.392) 0.8611 2.4261 4.61
4. Earning members per family 2.18(6.5354) 1.1121 2.2141 22.93
5. Occupational background 1.99(8.191) 0.9968 1.9781 20.94
6. Family income 1.79(6.5211) 0.4962 0.7515 14.15
7. Material possession 1.79(6.5222) 0.1121 0.1154 1.85
8. Monthly income 1.59(0.1921) 0.1182 1.1126 15.94
9. Type of family 1.39(5.5211) -0.1921 -0.1341 -12.26
10. Martial status 0.59(1.7911) -0.4421 -0.5311 -12.48
11. Caste 1.39*(3.6314) -0.4261 -0.6411 -13.09
12. Family size 1.81*(9.9921) 0.3771 -0.7711 -13.85
13. Age 2.19(5.471) -0.5911 -1.1153 -15.82
D2=2.7161 F=18.8471. * Significant at 5 per cent level. Figures in parenthesis are ‘t’ values. Source: Primary data.

Table 3: Factors discriminating the problem perception in Dindigul district.

The computed discriminant scores z1 and z2 for good and poor performer were 3.1572 and 1.2679. The critical value of discriminant score (z) for those two groups were 2.2017. The discriminant function can be used to predict whether the entrepreneur is a poor or good performer based on their scores. If the value of discriminant score of a given entrepreneur was more than 2.2017 it would predicate that they would be good performer and if less than 2.2017 it would indicate a tendency to be poor performer.

Summary of Findings

One has to believe that the progress of any nation is inevitably linked with social and economical plight of women in that particular country. For concrete results, we have to assert and act with our full might and what is needed most. Fischer’s discriminate function analysis test was applied. The meahalenbis D2 statistics was calculated to measure the distance between two groups of entrepreneurs. ‘F’ statistics was used if the two groups where different from each other the resultant discriminant function coefficient, the mean difference of the discriminant variables and their relative importance in discriminant function. This implied that thirteen variables together were useful in discriminating good and poor performer. Among the mean differences obtained in fourteen variables, the significant difference is found in the case of eleven variables. The planning of percentage of distance measured by the important variables reveals that the first three ranks comprising enterprise involvement index, personality traits, and education constituted 41.21, 10.51 and 3.19 per cent respectively. The planning of percentage of distance measured by the important variables reveals that the first three ranks comprising enterprise involvement index, types of family, and monthly income constituted 61.21, 31.45 and 24.62 per cent respectively. The values of D2 and F ratio calculated were 2.7161 and 18.8471 respectively. The F ratio was found to be significant at 5 per cent level. Hence the distance between good and poor performer was significant. It indicates that thirteen variables together were useful in discriminating good and poor performer.

Suggestions

It is observed that while analyzing their strength and weakness and personality traits, most of the women entrepreneurs are lacking in entrepreneurial skills. So the entrepreneurial development programmes must select those women with actual entrepreneurship aptitude and inculcate their skills for setting up and opening the business units and also train and motivate them to be capable of perceiving and exploiting new business opportunities.

From the analysis, it is indicated that education was found to be an important influential factor for entrepreneurship. So the Government should create awareness of its importance to the women, through programmes that insist eradication of illiteracy and bringing back their school dropout children to the formal education system with the help of voluntary agencies. The NGOs and the SHGs shall involve seriously in eradicating not only the illiteracy but also the main cause of this, the poverty.

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