Livestock Research for Rural Development 25 (10) 2013 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Determinants and implications of rural women’s participation in microfinance programme: an analysis of dairy self-help groups in Uttarakhand State of India

S Bhoj, D Bardhan and A Kumar

Department of Veterinary and AH Extension Education, College of Veterinary and Animal Sciences
G.B. Pant University of Agriculture and Technology, Pantnagar – 263 145, Uttarakhand, India
dwaipayanbardhan@gmail.com

Abstract

The present study was carried out in Haldwani block of Nainital district in Uttarakhand to identify the factors significantly influencing rural women’s participation in dairy SHGs and assess impact of such participation on their empowerment. The study was carried out on a sample of 60 members and 30 non-member respondents, having representation from different wealth status categories on proportionate basis. Logit model was fitted to identify significant variables influencing participation in SHGs. A women empowerment index was constructed taking into account major social and economic parameters to assess the impact of SHGs.

Respondent’s age, level of education, non-farm income source, herd size and distance to market emerged as the variables significantly influencing membership in women’s dairy SHGs. Probability of SHG membership increased with increase in respondent’s age, education level, herd size and distance to market. However, non-farm income had negative influence on probability of membership in SHGs, indicating that households which are more dependent upon dairying and agriculture for their livelihood are more likely to be members of SHGs. Impact assessment revealed that SHGs had significantly contributed to women empowerment on major social and economic parameters.

Keywords: Logit analysis; Uttarakhand; Women empowerment


Introduction

Credit is a necessary input in various aspects of farm operation, as in most developing countries including India, lack of credit facilities has been regarded as the major constraint farmers face when they try to improve their economic activities and/or living conditions (Binswager et al 1993; Agbor 2004; Olaoye and Odebiyi 2011).

Several initiatives over time have been taken by various Governments in India to improve farmers’ access to institutional credit by strengthening the institutional mechanism of rural credit system, chief among them being acceptance of Rural Credit Survey Committee Report (1954), nationalization of major commercial banks (1969 & 1980), establishment of RRB’s (1975), establishment of National Bank of Agriculture and Rural Development (NABARD) (1982) and the financial sector reforms (1991 onwards), Special Agricultural Credit Plan (1994-95) and launching of Kisan Credit Cards (1998-99) (Kumar et al 2010).

However, significant share of poor and marginal sections of the rural community still remain excluded from the formal credit delivery mechanism (Satyasai 2008). In spite of considerable efforts to streamline, reinforce, expand and institutionalize the agricultural credit system, the achievements fall short of proclamations, policies and programmes.

The post-liberalization period saw a paradigm shift in the banking sector which coincided with the growth of a silent microfinance revolution in rural India. The genesis of this was a NABARD initiated pilot, the SHG-Bank linkage project in 1991, which focused on Self-Help Groups (SHGs) as a channel for delivery of microfinance (Satish and Mehrotra 2009). Microfinance (mF) through self-Help Groups (SHGs) was envisaged as a panacea for the ills associated with credit delivery system in rural India. Microfinance provides credit support in small doses along with training and other related services to people who are resource poor but are unable to undertake economic activities. The main objective is to bring about socioeconomic upliftment of rural poor by providing them income-generating assets through a mix of bank credit and governmental subsidy.

Credit delivery through SHGs has tremendous potential not only for a way out for rural development vis-à-vis poverty alleviation but also for emancipation of rural women through their social and economic empowerment. Women have been the vulnerable section of society and they constitute a sizeable segment of the poverty-struck population. Several studies have shown a positive correlation between women empowerment and SHG membership (Tankha et al 2005; Reddy et al 2009; Vimala 2009). Livestock activity, especially, dairying holds special significance for the SHG movement involving rural women as 71 per cent of work-force engaged in livestock farming are women as against a share of only 33 per cent in crop husbandry. As such, dairying has all along been promoted as a key economic activity in the SHG movement. However, the performance of SHGs, with dairying as a key activity, has been skewed in favour of only a few states and the beneficial impact of them in terms of social, economic, political and cultural empowerment of women has not been seen uniformly across the country.

In this context, better understanding of the decision-making processes of the rural women is urgently required to guide policy decisions regarding scaling up the microfinance movement. As the credit off-take depends on the willingness and ability of the person to avail loans, there is a crucial need to address the problems from demand side (Karmakar 2007). It is expected that such analyses would provide information pertaining to ways of overcoming challenges in credit accessibility. Few studies are available that have dealt with primary data from farm households in order to document the nature of demand for credit, its determinants and constraints in accessing credit both in Indian context (Pandit et al 2007; Anjugam and Ramasamy 2007; Feroze et al 2011) and in the context of other developing countries (Mpuga 2008’ Guirkinger and Boucher 2008; Balogun and Yusuf 2011; Olaoye et al 2012; Zhao and Zhang 2012). However, the results as reported from these studies may not be generalized to other, especially poor regions. Further, scant research attention has been given on analyzing the factors that influence women’s participation in dairy SHGs and the impact that such participation has had on their empowerment.

The present research aims to address this research gap and provide field level quantitative information regarding the determinants and implications of rural women’s membership in dairy SHGs so as to facilitate design and implementation of efficient pro-poor livestock credit services so that the livestock sector can be used a more effective tool in the fight against poverty.


Methodology

Study area

The study was carried out in Uttarakhand state of India. Livestock activity specially, dairy husbandry, forms a source of livelihood for almost all the households in the state, with each household possessing 1-2 animals. Over 80 per cent of all livestock species are owned by small holders (landless agricultural labourers, marginal and small farmers). Livestock is thus considered to have high prospect to enhance the level of living of the poorest of the poor in the state.

Sampling

Multistage purposive and stratified random sampling was followed in the selection of district, block, dairy SHGs and the respondents for the study. Uttarakhand has two administrative divisions, viz. Kumaon and Garhwal. Kumaon division was selected for the study on account of higher livestock density (Bardhan et al 2010). Nainital district was then selected from Kumaon division purposively as the district has the highest number of SHGs in Kumaon region of the state. In Nainital district, SHGs generally come under Swarnajayanti Gram Swarozgar Yojana (SGSY) and National Bank of Agriculture and Rural Development (NABARD) models II (SHGs formed by NGO and financed through bank) and III (SHGs financed by banks using NGOs as financial intermediaries). The SGSY model - promoted by Ministry of Rural Development, Government of India - has subsidy component and has a mandate of including at least 70-80 per cent of its members from people living below poverty line. The NABARD model does not have such specifications. The SGSY model was selected for the study so as to emphasize more on the role of SHGs in livelihoods of poor people. Out of total eight blocks in Nainital District, Haldwani block was selected on account of having the highest number of women dairy SHGs, which are linked to the Banks and are in existence for at least three years.

A list of all women dairy SHGs of Haldwani block of Nainital district was prepared and the SHGs were then stratified into low, medium and high performing groups on the basis of savings made by the groups in one year using Cumulative Square root of Frequency method. A total of 15 women dairy SHGs were selected for the study having representation from each stratum on proportionate basis. A complete enumeration of all members of selected SHGs was conducted for the purpose of developing a sampling frame. All the members were classified according to their wealth status into rich, medium and poor categories on the basis of annual household income by using cumulative square root of frequency method. Four members were selected randomly from each SHG having representation from different wealth categories on proportionate basis, thus making a sample size of 60 members. Apart from this, thirty non-members having milch animals and having similar socio-economic status as that of members were selected from the same villages so as to make the ratio of members to non-members 2:1 in the sample. Thus the study constituted a total sample size of 90 households.

The data for the study were collected through personal interview method with the help of a well-structured, comprehensive and pretested interview schedule. Respondent in this study pertains to the female member of sample household who was also the member of SHGs (categorized as member respondents) and female non-member households of SHGs who were the key informant during the survey (categorized as non-member respondents). Descriptive and tabular analysis were carried out to derive meaningful inferences about socio-economic profile and institutional support structure of member and non-member respondents.

Explaining dairy SHG membership

Binary Choice Regression model (Logit) was formulated in an attempt to explain the factors influencing participation in SHGs. Logit analysis is a mathematical modelling approach which describes the relationship of one or several explanatory variables (X’s) to a binary response variable (Y) coded to take the value of 1 or 0 for success or failure, respectively. The dependent variable in this study was dichotomous in nature (dependent variable assumes a value of 1 in case a respondent is a member of SHG and 0 if the respondent is not a member). The Logit model is of the form:


Where, Pi is the probability that the dependent variable assumes a value of 1

is the probability that the dependent variable assumes a value of 0, where

Zi = α + ∑βiXi

Odd’s Ratio (OR) = = eZi

Taking log on both sides,

= Zi = α + ∑βiXi + ei

Where Xi is a vector of independent variables and βi’s are the coefficients to be estimated. These coefficients represent change in log of odds of participation in SHGs. A positive estimated coefficient implies an increase in likelihood that the respondent will be a member of SHG with a unit increase in the concerned explanatory variable. eβ gives the Odd’s Ratio associated with change in independent variable. The Odd’s Ratio means the ratio of probability of happening of an event to probability of not happening of that event. The odds are expressed as single number to the ratio to 1. Odds of 2, for example, mean that likelihood of participation in SHG is twice that of not participating. Table 1 gives the explanatory variables included in the model.

Alternative specifications of the above model, including size of landholding, family type, extension contact and wealth status were also estimated. However, the parameter estimates associated with these variables were not statistically significant at accepted levels. Hence, these were dropped from the model. Hence, the final estimated Logit model included the variables specified in Table 1.

Table 1: Explanatory variables included in Logit analyses

Variables

Description

Nature

AGE

Age of member of ith household (years)

Continuous

 EDU

Education level of member (0-Illiterate, 1-Read & write, 2-Primary, 3-Middle, 4-High school, 5-Intermediate, 6-Graduation & above)

Continuous

NFI

Dummy for non-farm income (whether ith household has a non-farm income source: Yes=1; No=0)

Categorical

HERD SIZE

Herd size of ith household (measured as standard animal units)

Continuous

MKTDIST

Distance to market (kms)

Continuous

Measuring impact of participation in SHGs

Impact of participation in SHGs was ascertained on three major parameters, viz. empowerment status, asset possession and annual household income.

Impact on women empowerment

To ascertain the impact of SHGs on women empowerment, an empowerment index was constructed by considering relevant variables that constitute empowerment. To construct empowerment index, the weighted indices was worked out. The variables considered and the scores assigned are presented in Table 2. The empowerment index was worked out by summating the scores associated with response to each variable across all the variables and dividing this summated score with the maximum possible summated scores (i.e. the summated score a respondent would have if he chose the response with the highest score for each variable). The respondents were then categorized as having high, medium and low empowerment levels based on the magnitudes of the index using Cumulative Square root of Frequency method.

Table 2: Variables considered for measuring Empowerment Index

Variables

 Response

Scores

Education                                                    

Illiterate

Read/write to high school

Intermediate and above

0

1

2

Land ownership                                        

No land

Joint ownership

Independent ownership

0

1

2

Control over household income contributed by her to the family                                            

No control

Partial control

Full control

0

1

2

Control over the income from  dairying                                                                         

No control

Partial control

Full control

0

1

2

Savings

No savings of their earnings

Upto 25% of earnings

Upto 50% of earnings

0

1

2

Control over decision pertaining to accessing credit                                                    

No control

Partial control

Full control

0

1

2

Social participation                                   

Not a member of any organizations apart from SHG

Member

Office bearer

0

 

1

2

Freedom of mobility                                 

Cannot go out

Can go out but with permission

Can go out without permission

0

1

2

Asset possession                                        

Low asset possession

Medium asset possession

High asset possession

0

1

2

Involvement in political institutions      

No participation

Utilize their right to vote

Member of panchayat

0

1

2

Control over decision regarding sale of milk                                                             

Not involved

Jointly involved

Independently involved

0

1

2

In order to ascertain the impact of participation in SHGs on women’s empowerment, the ‘paired t-test’ was carried out, to compare the member respondents before and after joining SHGs. The data collected on empowerment indicators of members before joining SHGs was based on respondents’ recall data.

Impact on asset possession

Total value of assets, a respondent possessed, was calculated for both agricultural and non-agricultural assets using the market rates of those assets. Categorization of respondents was then done as low asset possession (if the amount of assets being possessed was less than mean-S.E.); medium asset possession (if the amount of assets being possessed was between mean-S.E. and mean+S.E.) and high asset possession (if the amount of assets being possessed was more than mean+S.E.). The scores 0, 1 and 2 were provided to low, medium and high asset possession. In order to ascertain the impact of participation in SHGs on asset possession, the ‘paired t-test’ was carried out, to compare the member respondents before and after joining SHGs.

Impact on annual household income

Data were collected regarding income from dairying and percentage share of dairying income contributing towards household income. Thus, annual household income for each respondent was calculated. All the respondents were classified into rich, medium and poor categories on the basis of annual household income by using cumulative square root of frequency method. In order to ascertain the impact of participation in SHGs on annual household income, the ‘paired t-test’ was carried out, to compare the member respondents before and after joining SHGs.


Results and discussion

Socioeconomic profile of members and non-members of dairy SHGs

Table 3 compares the socio-economic profiles of member and non-member households. Average age of member respondents (40 years) was significantly higher than that of their non-member counterparts (37 years). Education level of respondents of member group was non-significantly different from that of non-member group. On an average, member respondents were educated upto high school level while non-member respondents were educated upto middle and high school levels. Greater proportion of member respondents (67%) belonged to scheduled caste category followed by general (31%) and Scheduled tribe categories (3%). Almost similar caste wise distribution was observed in case of non-member group as 73 per cent, 23 per cent and 3 per cent of non-member respondents belonged to Scheduled caste, general and Scheduled tribe categories, respectively. Agriculture and animal husbandry were the main and subsidiary occupations respectively for majority of both member and non-member respondents.

Table 3: Comparison of Socio-economic Profiles of member and non-member respondents                     

Sr. No.

Particulars

Members

Non-members

A.

Respondent specific Characteristics

 

 

1.

Age

39.8a

37.2b

2.

Education*

4.00

3.70

3.

Caste(% of respondent)

 

 

 

General

30.7

23.3

 

SC

66.7

73.3

 

ST

2.56

3.33

 

OBC

-

-

4.

Main Occupation(% of respondent)

 

 

a.

Agri.

43.3a

23.3b

b.

Agri.+ AH

13.3

10.0

c.

Agri.+ Other

3.33

-

d.

Agri. Labour

13.3

10.0

e.

Govt. Service

1.67a

16.7b

f.

AH

21.7

20.0

g.

AH + other

3.33

10.0

h.

Pensioner

-

10.0

5.

Subsidiary Occupation(% of respondents)

 

 

a.

Agri.

7.69

10.0

b.

Agri.+ AH

-

-

c.

Agri.+ Other

7.33

3.33

d.

Agri. Labour

8.91

10.0

f.

AH

23.9

30.0

g.

AH + other

1.28

-

h.

Pensioner

6.41

3.33

B.

Household specific Characteristics

 

 

1.

Family size (Adult Equivalent)**

3.56

3.27

2.

Family type(% of respondents)

 

 

a.

Joint

27.5

23.3

b.

Nuclear

72.5

76.7

3.

% of respondent HH’s having at least one member with NFI

67.8a

93.3b

4.

Annual HH Income(INR)

1,31,599

1,25,654

C.

Farm Characteristics

 

 

1.

Operational land(acres)

0.36a

0.20b

2.

Land used for dairying(acres)

(% of operational land)

0.04

(11.1)

0.07

(30.4)

3.

Herd size(SAU)

4.68a

4.06b

* Education: Illiterate-0; Read &Write-1; Primary School-2; Middle School-3; High School-4; Intermediate-5; Graduate and above-6
** 4 children=3 adult women=2 adult men
ab Means in the same row without common letter are different at P<0.05

However, significantly higher proportion of member respondents (43.33%) pursued agriculture as their main occupation as compared to non-member respondents (23.33%). Animal husbandry was the main occupation for 22 per cent and 20 per cent of member and non-member respondents while it was subsidiary occupation for 24 per cent and 30 per cent of member and non-member respondents, respectively. Government service was main source of income for significantly higher proportion of (16.67%) non-member respondents as compared to their member counterparts (1.67%). Proportion of agricultural labourers was 13 per cent and 10 per cent, respectively for member and non-member groups. The above findings regarding occupational status revealed that member respondents had mainly agriculture as their main occupation whereas their non-member counterparts had service as main source of income along with agriculture.

Average family size was 3.56 adult equivalents for member households and 3.27 adult equivalents for non-member households. There were no significant differences in household sizes across these two groups. Greater proportion of respondents of both member and non-member groups belonged to nuclear families than joint families. In case of member group, 73 per cent of respondents belonged to nuclear families while 27 per cent belonged to joint families. In case of non-member group 77 and 23 per cent of respondents belonged to nuclear and joint families, respectively. Percentage of respondent households having at least one member with non farm income was significantly higher in case of non-member group (93%) than that of member group (68%). Average annual household income for member group (Rs. 1, 31, 599) was non-significantly different from that of non-member group (Rs. 1, 25, 654). Average size of landholding for member group was 0.36 acres which was non-significantly different from that of non-member group (0.2 acres). However proportion of operational land used for dairying was more in case of non-member group (30%) as compared to member group (11%). Member households on average owned herd size of 4.68 SAU which was significantly higher than average herd size owned by non-member households (4.06 SAU).

Institutional Support Structure

Table 4 elicits the institutional variables associated with respondent households in the study area. Average distance to market was significantly higher for member households (6.50 km) than that of their non-member counterparts (5.97 km) indicating that membership in SHGs increased with increase in distance to market. 73 per cent of both the member and non-member respondents reported that they had good road connectivity to the market. Significantly higher proportion (90%) of SHG members had insured their animals than non-members (50%). This might be due to the fact that insurance of animals is an important criterion for the members to avail the loan from bank.

  Table 4: Institutional support structures

S.N.

Particulars

          Members

Non-members

1.

Distance to market (km)

6.50a

5.97b

2.

 Percentage of respondents having good road connectivity to market

73.3

73.3

3.

 Percentage of respondents having insured their animals

90.0a

50.0b

4.

Percentage of respondents having Extension worker contact

61.7

     -

5.

Percentage of respondents having access to ICT enabled services

31.7

16.7

ab Means in the same row without common letter are different at P<0.05

Sixty seven per cent of members of SHGs reported that they had contact with extension worker while no non-member respondent had extension contact. Thirty two percent of member respondents had access to ICT enabled services while the same figure for non-member group was 17 per cent.

Factors influencing participation in dairy SHGs

The results of Logit analysis carried out to identify the factors influencing likelihood of participation of rural women in dairy SHGs are presented in Table 5. Age (P<0.01), level of education (P< 0.05), non-farm income source (P<0.1), herd size (P<0.05) and distance to market (P<0.05) emerged as the significant variables that influenced membership in dairy SHGs. The signs of regression coefficients for all these variables were positive except non-farm income which negatively influenced participation in SHGs. This implies that likelihood of participation in dairy SHGs increased with increase in age and education level of respondents, scale of milk production and distance to market. The positive association between likelihood of SHG membership with distance to market is understandable as larger is the distance to market, the higher is the transaction costs incurred by individual respondents in regard to market transactions for both inputs and outputs. Membership in SHGs in such cases probably provide them better bargaining power and economies of scale out of collective action.

Table 5: Factors influencing participation in dairy SHGs

Sl. No.

Variables

Β

Odd’s Ratio

1.

INTERCEPT

           -11.1***

             (4.13)

             -

2.

AGE (yrs.)

              0.140*

             (0.082)

             1.15

3.

EDU

              1.04**

             (0.442)

             2.83

4.

LAND (acres)

             -2.14

             (1.57)

             0.118

5.

FAM TYP( joint=1, nuclear=0)

             -0.765

             (1.03)

             0.465

6.

NFI( Y=1, N=0)

             -3.84***

             (1.48)

             0.021

7.

HERD SIZE (SAU)

              0.577**

             (0.256)

             1.78

8.

MKTDIST (km)

              0.388**

             (0.177)

             1.47

9.

WLTHSTAT

             -0.140

             (0.558)

             0.870

10.

-2LOG LIKELIHOOD

              43.0

 

11.

R2 (COX & SNELL)

               0.55

 

12.

% CORRECT PREDICTION

               91.1%

 

Figures in parentheses indicate Standard Error
Significant at ***1%, **5% and *10% level of significance

Non-farm income had negative influence on probability of membership in SHGs, indicating that households which are more dependent upon dairying and agriculture for their livelihood are more likely to be members of SHGs. The odds ratio associated with each variable indicated that the likelihood of being a member of SHG increases by 1.15 times with each one year increase in respondents’ age. The likelihood of SHG membership increased almost 3 times with increase in each unit level of education of women. The odds of participation also increased by almost 2 times and 1.5 times with increase in each SAU and each km distance to market, respectively. On the other hand, likelihood of SHG membership declined by 98 per cent when the respondent household had at least one NFI source. Thus most important factors which influenced participation in dairy SHGs were education level of respondents, herd size and non-farm income source.

Factors motivating participation in SHGs

Although the above logit analysis gave a quantitative insight into the factors significantly influencing participation in SHGs and their extent of influence, an attempt was also made to qualitatively analyze the various motivating factors which influenced the member respondents’ decision to participate in SHGs. For this, the respondents were asked to elicit the different reasons which motivated them to join SHGs. The results of this analysis are presented in Table 6.

Table 6: Distribution of members among the factors motivating them to join the group

Sl. No.

                  Particulars

Rich

Medium

Poor

Pooled

1.

          Social backwardness

1  (7.69)a

5    (19.23)

6  (28.57)b

12  (20.0)

2.

          Prior indebtedness

3    (23.1)

7     (26.9)

7  (33.3)

17  (28.3)

3.

                  Savings

7   (53.9)

16    (61.5)

9   (42.9)

32  (53.3)

4.

       Employment generation

4  (30.8)a

16    (61.5)b

14 (66.7)b

34   (56.7)

5.

       DRDA/BDO/Sarpanch                                  

5   (38.5)a

9      (34.6)a

15 (71.4)b

29  (48.3)

6.

      Loan at low interest rate

8  (61.5)

16    (61.5) 

9  (42.9)

33  (55.0)

ab Means in the same row without common letter are different at P<0.05
Figures in parentheses indicate percentage

Provision of employment opportunity’ and ‘loan at low interest rate’ (57% and 55%, respectively) were the most important reasons for joining SHGs for highest proportion of respondents for overall category. High proportion of all categories of respondents also reported ‘opportunity for savings’ (53%) and ‘persuasion by DRDA/BDO/ Panchayat personnel’ (48%) as the other motivating factors for joining SHGs. ‘Social backwardness’ and ‘prior indebtness’ were cited as reasons for joining SHGs by relatively lower proportion of respondents (20% and 28%, respectively). The findings are in contrast to the observation made by Anjugam and Ramasamy (2007) in Coimbatore and Ramanathapuram districts of Tamil Nadu. They reported that social backwardness and indebtedness of farm households have a significant positive influence on participation in SHGs.

Wealth category wise analysis revealed that significantly higher proportions (29%, 67%, 71%) of poor respondents had joined SHGs for the reasons of ‘social backwardness’, ‘opportunity for employment generation’ and ‘persuasion by DRDA/BDO/Panchayat personnel’ than their rich counterparts (7.69%, 31% and 38%) for the same reasons, respectively. These findings imply that DRDA/BDO/Panchayat personnel have been proactive in the study area in mobilizing greater membership from poorer sections of the society. The major role of ‘Government officials’ in persuading respondents to join SHGs was earlier reported by (Batra 2012).

Impact of participation in SHGs

Table 7 compares the member respondents before and after joining SHGs on important economic and social parameters, viz. empowerment index, asset possession and household income. Overall average empowerment index before joining the group was 0.32 which was significantly (P<0.05) lower than overall average empowerment index (0.49) after joining the group. Disaggregated analysis revealed that empowerment status of all wealth categories of respondents significantly increased after joining SHGs. Hence it can be concluded that SHGs had significantly contributed to empower women socially, financially and culturally.

Significant (P<0.05) difference was observed in regard to asset possession by members before and after joining SHGs for all categories of respondents. Members of all categories combined, possessed on average assets of value Rs. 3, 535 after joining SHGs as compared to asset value of Rs. 1, 922 which they possessed before joining. Bhardwaj and Gebrehiwot (2012) had also reported that participation in SHGs brought about improvement in decision making power and asset possession of rural women.

Table 7 also compares the member respondents on household income before and after joining SHGs. Average annual household income for pooled sample significantly increased to Rs. 1, 38, 272 after joining SHGs from Rs. 1, 06, 543, which was their annual income before joining. The significant increase in annual household income was observed in case of all wealth category households. Puhazhendi and Satyasai (2000) had also reported increase in household income of member after joining SHGs.

Table 7: Comparison of SHG members before and after joining SHG

A. Empowerment Index

S. N.

Wealth   categories

Index value (before)

Index value (after)

Gain in empowerment

‘t’ value

1.

 Rich

           0.332

           0.49

          0.161

   6.45*

2.

Medium

           0.321

           0.49

          0.172

  10.1*

3.

Poor

           0.321

           0.48

          0.160

   9.08*

4.

Overall

           0.321

           0.49

          0.171

  15.0*

B. Asset Possession (INR)

S. N.

Wealth categories

Av. of assets being possessed in ` (before)

Av. of assets being possessed in ` (after)

Difference

‘t’ value

1.

Rich

      2, 423

       4, 208

     1, 785

   1.80*

2.

Medium

      1, 846

       3, 581

     1, 735

   2.32*

3.

Poor

      1, 705

       3, 062

     1, 357

   1.91*

4.

Overall

      1, 922

       3, 535

     1, 613

   3.43*

C. Household income (INR)

S. N.

Wealth categories

Av. HH income   per annum ` (before)

Av. HH income per annum ` (after)

Difference

‘t’ value

1.

Rich

   1, 52, 692

   1, 86, 069

     33, 377

   9.35*

2.

Medium

   1, 11, 423

   1, 34, 663

     23, 240

  12.6*

3.

Poor

      71, 931

        94, 086

     22, 155

  10.7*

4.

Overall

   1, 06, 542

   1, 38, 272

     26, 257

  17.6*

* Significant at P<0.05


Conclusions


Policy Suggestions


References

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Received 17 March 2013; Accepted 24 September 2013; Published 1 October 2013

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