Chapter 5 Impact of Microfinance on Poverty and...

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Chapter 5

Impact of Microfinance on Poverty and Employment

In this chapter, the impact of microfinance programme on poverty and

employment has been studied on the basis of empirical data. The chapter is divided into

two sections. The first section relates to the impact of microfinance programme on

poverty, and the second with the impact of microfinance on employment.

Section-I

Microfinance and Poverty

Poverty is the lack of basic minimum necessities such as food, clothing, water,

and shelter needed for proper living. It indicates a condition in which a person fails to

maintain a living standard adequate for his physical and mental efficiency. According to

Adam Smith, “Man is rich or poor, according to the degree in which he can afford to

enjoy the necessaries, the conveniences and the amusements of human life” [1]. Mollie

Orshansky explains poverty as, “To be poor is to be deprived of those goods, services

and pleasures which others around us take for granted” [2]. Generally, poverty is

measured through monetary indicators such as income and consumption. The focus of

this section is to study the impact of microfinance programme on the poverty. The

poverty is explained through individual income, family income and through financial

vulnerability of the participant households. The study also takes into consideration the

impact of microfinance programme on income inequalities. An attempt has been made to

prepare a composite poverty index. The determinants of poverty have also been

discussed.

5.1 Poverty - Its Measurement

It is very complex and difficult to measure poverty. It is mainly due to the fact

that necessities of life are not absolute but a relative concept, and these differ from one

situation/place to another. Despite of it, an effort has been made to measure poverty by

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having a common denominator so that international or national comparisons could be

made. Let us mention some of the recently discussed criteria in this field.

The international standard for extreme poverty which was incorporated in the

first of the Millennium Development Goals (MDGs) was an income/consumption of

$1.08 per capita per day. This is often described as “$1 in a day” adjusted for purchasing

power parity (PPP). But, in 2008, World Bank has replaced the $1.08 per day poverty

line with $1.25 per day on the basis of 2005 prices adjusted for PPP for different

countries. On the basis of this international threshold limit, the poverty line for extreme

poor for India is calculated to be Rs. 429 per capita per month for rural areas and Rs. 645

for urban areas. This gives an estimate that 41.6 per cent of the population of India was

extreme poor in the year 2005. According to the World Bank if the daily income is less

than $2 per head, then the family is described as poor. On the basis of $2 income a day,

75.6 per cent of the Indian population is poor (World Bank, 2008).

Asian Development Bank (ADB) has defined the poverty line for the Asian

countries at $1.35 per capita per day adjusted for PPP estimates for the year 2005.

According to this definition, the poverty line is estimated to be Rs. 549 per capita per

month both for rural and urban areas. This poverty line implies that 54.8 per cent of total

population of India is below poverty line (BPL). The results of ADB poverty estimates

show that India is second poorest Asian country which is next only to Nepal (Himanshu,

2009).

A Task Force constituted by the Planning Commission of India recommended a

national level official poverty line for the base year 1973-74. This line was on the basis

of minimum nutritional requirement per person for healthy living, which was

recommended as 2,400 kcal/day in rural areas and 2,100 kcal/day in urban areas. To

satisfy these caloric norms, per capita monthly consumption expenditure of Rs. 49 in

rural areas and Rs. 57 in urban areas was fixed in that year. Since then the Planning

Commission is estimating poverty line by adjusting it for inflation. However, this

method has been criticised by many authors because the figures coming after adjusting

the effect of inflation are not sufficient even to fulfil the minimum calorie requirements.

For example, after adjusting the inflation, the poverty line for the year 2004-05 was Rs.

356 per person per month for rural areas and Rs. 539 for urban areas (Agrawal, 2009).

But this amount of expenditure have permitted both the rural and urban people to

consume just 1,820 kcal, whereas to consume the desired norms of 2,400/2,100 kcal the

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cutoff line for determining BPL status should have been around Rs. 700 in rural areas

and Rs. 1,000 in urban areas (Government of India, 2009). In their study, Deaton and

Drèze (2008) found that in the year 2004-05 more than 75 per cent of the population of

India was living below the above specified per capita calorie intake. Moreover, the

existing calorie based poverty line is bare minimum to fulfil the food requirement only

and does not include the cost of other basic needs for a civilised living like availability of

education, health care, housing, water, sanitation, employment and other non-food items.

According to this calorie-based definition around 301.72 million people of India, i.e.,

27.5 per cent of total population was below poverty line in 2004-05.

Planning Commission has also given the state specific poverty lines adjusted for

the price variations in different states. According to its 2004-05 estimation of BPL for

Punjab, poverty line is Rs. 410.38 per capita per month in rural areas and Rs. 466.16 for

urban areas. This estimate shows that 8.4 per cent of the total population of Punjab is

BPL but this definition has been criticised as it can provide only 1,962 kcal/per person in

rural areas and 1,670 kcal/person in urban areas, which is much below the minimum

calorie requirement (Government of India, 2009).

The Tenth Plan (2002-07) BPL estimate that is based on 13 indicators of well-

being was carried out in rural areas of Punjab. According to the estimate, 11.99 per cent

of the total rural population of Punjab is BPL.

In 2007, a detailed survey of poor families was carried out in Punjab for the

purpose of providing subsidised wheat and pulses to the poor families under Atta-Dal

scheme. In this survey, poverty line was fixed at the household income of Rs. 30,000 per

annum. This poverty line is equivalent to income of Rs. 500 per capita per month

considering a family unit of five persons. According to this survey 38.9 per cent of the

total households in Punjab live below poverty line. This poverty line is slightly less than

the World Bank defined poverty line of $1.25 per capita per day adjusted for PPP, which

comes to be Rs. 35,100 per annum where $ 1.25 adjustment for PPP is equivalent to Rs.

19.5 for the year 2005 (Himanshu, 2009).

For the purpose of studying poverty, in this work, the poverty line fixed during

the survey for Atta-Dal scheme has been used to find out the impact of microfinance

programme on the poor households.

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5.2 Microfinance and Poverty – Some Studies

The collateral based formal banking institutions make the poor difficult to

procure loans. In addition, the bureaucratic cumbersome procedures discourage the poor

to get loan for self-employment. That is why there is always urban and rich bias in

providing loan by the formal financial institutions. However, microfinance programme

helps poor people for getting loans from conventional formal financial institutions. As

discussed earlier, it provides opportunities to the resource-less people to establish their

own enterprises and strengthen their financial position. Generally, most of the

beneficiaries of this programme are women, they contribute to enhance family income

and become productive members of the economy. This also helps in increasing

consumption standard and the education standard of the family. In this sense

microfinance may be considered as one effective tool amongst many others for poverty

alleviation. However, according to Sadegh (2006), “The equation between microfinance

and poverty alleviation is not straightforward, because poverty is a complex phenomenon

and many constraints that the poor in general have to cope with.”

A number of studies regarding the microfinance and poverty reduction have been

mentioned in the review of literature. Here the results of a few studies are mentioned. An

Asian Development Bank study conducted by Khandker (2001) in Bangladesh shows

that ‘microfinance participants do better than non-participants in both 1991/92 and

1998/99 in per capita income, per capita expenditure and household net worth. The

incidence of poverty among participating households is lower in 1998/99 than in 1991/92

and lower than among non- participating households in both periods.’ A case study of

Asian countries conducted by Remenyi and Quinones (2000) concluded that household

income of the beneficiaries of microfinance has risen significantly higher than non-

beneficiaries. According to this study, in Indonesia, the annual average income of the

borrowers increased by 12.9 per cent while only 3 per cent rise was reported by non-

borrowers. In Bangladesh, a 29.3 per cent annual average rise in income was recorded

against 22 per cent annual average rise in the income of non-borrowers. Sri Lanka

indicated an increase in income by 15.6 per cent by borrowers and only 9 per cent by

non-borrowers. In case of India, 46 per cent annual average rise in the income was

reported among borrowers with 24 per cent increase by non-borrowers. The study shows

that the effects on income were higher for those just below the poverty line. In a study of

Zimbabwe conducted by Barnes and Erica (1999), it was observed that the repeat clients

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of microfinance have shown almost double of the income as compared to non-member

clients.

5.3 Impact of Microfinance Programme on Income Poverty

Income is considered to be a very important determinant of poverty. The

improved financial position of a person automatically leads to increased consumption

and education expenditures. Microfinance programme economically empowers the

beneficiaries of the programme by helping them to own productive assets that lead to

generation of additional income and employment. Increased level of income helps the

beneficiaries to come out of poverty and raise their standard of living by accessing the

basic requirements of life.

An attempt has been made here to discuss the change in individual and household

income of the participants and the income inequalities among the respondent households.

The sample households may not be necessarily below the poverty line, but they are poor,

since there are no specific guidelines/criteria given by NABARD for selecting only the

people below poverty line. The SHG members are selected by the aanganwari workers.

They select the group members according to their own judgment keeping in view the

socio-economic position of the member and no particular BPL criterion is followed. But,

generally the members of SHGs are poor.

5.3.1 Impact of Microfinance on Individual Income of the Participants

The participants of the programme are supposed to utilise micro-loans to start

productive activities, which raise their level of income. Two methods have been used to

determine the change in income: (i) The income of the participant of the programme is

compared before and after joining the programme; (ii) The income of participants is

compared with the non-participants.

(i) Change in Income of the Participants after Getting Microfinance

The microfinance programme has improved the level of income of the

participants. A perusal of Table 5.1 provides that the average income of the beneficiaries

is Rs. 1,725 per month in post-SHG as compared to only Rs. 718 per month in pre-SHG

situation, i.e., 2.5 times increase in income in post-SHG over the pre-SHG situation. This

increase in income is found to be 148 per cent, 135 per cent and 133 per cent per month

for the participants of Hoshiarpur, Jalandhar and Bathinda districts respectively. It is

evident from the table that the increase in income is the highest in district Hoshiarpur. A

paired sample t-test is used to measure the significance of difference between the mean

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incomes of the participants. The test shows that the difference between the mean incomes

of the participants of the programme in the pre- and post-SHG situation is significantly

different at one per cent level in all the districts. The studies by World Bank (1999),

Puhazhendhi and Satyasai (2000), Manimekalai (2001), Dunn & Arbuckle (2001), and

Mishra et al. (2001) also show the similar results of increase in income of the programme

participants in the post-SHG situation as compared to their income in the pre-SHG

situation.

Table 5.1: Income of the Participants (pre- and post-SHG) per month

Average Income of Participants (in Rs.) District Pre-SHG Post-SHG Increment Value of ‘t’

Jalandhar 657 1,546 889 (135) 7.340*

Hoshiarpur 772 1,915 1,143 (148) 4.794*

Bathinda 773 1,804 1,031 (133) 6.046*

Punjab 718 1,725 1,007 (140) 9.037* * Significant at 1 per cent level of significance. Source: Field survey 2008. Note: The figures given in parentheses indicate percentage increase in income.

(ii) Change in Income of the Participants and Non-participants

The income of the participants has been significantly higher as compared to non-

participants. It is evident from Table 5.2 that the average income of non-participants is

just Rs. 638 per month as compared to Rs. 1,725 per month for the participants. It shows

that the income of the participants has increased substantially. The average income of the

participants is 2.7 times more than the average income of non-participants. The

percentage increase in the income of the participants over the income of non-participants

is the highest for Hoshiarpur district, i.e., 234 per cent followed by Jalandhar and

Bathinda districts respectively. The significance of difference between the mean incomes

of the participants and non-participants is measured with t-test. This test shows that the

differences are significant at 1 per cent level of significance. Thus, microfinance

programme has helped its participants to increase their contribution to the household

income. The studies by Hossain (1988), Todd (2001) and Chen and Donald (2001) have

also concluded that the incomes of programme participants are significantly higher than

the incomes of non-participants.

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Table 5.2: Income of the Participants and Non-Participants per month

Average Income of Participants and Non-participants (in Rs.) District

Non-participants Participants Increment

Value of

‘t’

Jalandhar 646 1,546 900 (139) 4.889*

Hoshiarpur 573 1,915 1,342 (234) 4.587*

Bathinda 799 1,804 1,005 (131) 2.905*

Punjab 638 1,725 1,087 (170) 7.197* * Significant at 1 per cent level of significance. Source: Field survey 2008. Note: The figures given in parentheses indicate percentage increase in income.

5.3.2 Impact of Group Maturity on Income

Self-help groups get new loans after the successful repayment of the previous

loans. Therefore, as the group becomes old, more number of loans are availed by its

members for their development and acquisition of productive assets. In this way,

maturity of a group plays a considerable role in increasing the income earned by the

group members. In order to measure the impact of maturity of the group on the income

of participants, the SHGs are divided into three categories based on the age of the group.

These three categories are named as young groups (less than 3 years old), middle age

groups (3 to 6 years old) and mature groups (more than 6 years old). Table 5.3 presents

the income earned by the participants of Jalandhar, Hoshiarpur and Bathinda districts

according to the maturity of the group.

The average increase in income in post-SHG as compared to pre-SHG is found to

be the highest for participants in mature group followed by middle age and young group

participants. The addition in income over the pre-SHGs situation for the young, middle

age and mature group participants is Rs. 523, 936 and 1,642 per month for Jalandhar

district; Rs. 631, 867 and 1,842 for Hoshiarpur district; and Rs. 879, 1,063 and 1,500 per

month for Bathinda district participants respectively. The table also shows that the

average addition in income of young group participants in Punjab due to programme

participation is Rs. 625 per month. For the middle age group participants the addition in

income is found to be Rs. 924 per month, and for the mature group participants it is Rs.

1,745 per month. Thus, it is found that as the group gets older, the addition in income

grows.

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Table 5.3: Impact of Maturity of the Group on Income of the Participants

(Income in Rs. per month)

District

Group Maturity

Total No. of Participants

Average Income in Pre-SHG

Average Income in Post-SHG

Average Addition in

Income Young Group (Less than 3 years) 36 (40) 461 984 523

Middle age Group (3 to 6 years) 39 (43) 708 1,644 936

Mature Group (More than 6 years) 15 (17) 995 2,637 1,642

Jalandhar

Total 90 (100) 657 1,545 888 Young Group (Less than 3 years) 21 (29) 224 855 631


Mature Group (More than 6 years) 26 (35) 1,190 3,032 1,842

Hoshiarpur

Total 74 (100) 772 1,915 1,143 Young Group (Less than 3 years) 14 (54) 500 1,379 879

Middle age Group (3 to 6 years) 08 (31) 888 1,951 1,063


Bathinda

Total 26 (100) 773 1,804 1,031 Young Group (Less than 3 years) 71 (37) 399 1,024 625



Punjab

Total 190 (100) 718 1,725 1,007 Source: Field survey 2008. Note: The figures given in parentheses indicate percentage of participants. For Punjab, value of F =15.353,

which is significant at 1 per cent level of significance. The degree of freedom between the samples is 2 and the degree of freedom within the samples is 187.

Analysis of variance technique is applied to test the differences in the mean

incomes of participants in their post-SHG situation over the different group ages for

Punjab. The results show that the F-value is significant at 1 per cent level of significance.

This is mainly due to the fact that the members belonging to mature groups establish

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themselves in the income generating activities by availing more and more loans. Similar

results of positive impact of group maturity on the income of programme participants are

drawn in the impact assessment studies by Banu et al. (2001), MYRADA (2002) and

Chowdhury et al. (2005).

5.3.3 Impact of the Programme on the Household Income of the Participants

Household income is the sum of money received in the previous calendar year by

household members from all sources. The microfinance programme increases the

individual income, which subsequently enhance the total household income. In many

cases, microfinance activities are the sole source of household income. Self-help group

members invest the group loans to start new income generating activities or to expand

their existing small business. This leads to generation of income. This increase in income

enables the participants to support their families in a better way.

The household monthly income of participants and non-participants is shown in

Table 5.4. The table reflects that increase in household income is the highest in Bathinda

district (41 per cent) followed by Hoshiarpur (16 per cent) and Jalandhar (9 per cent)

districts. The average household income of participants for Punjab is Rs. 6,912 per

month which is higher than that of non-participants by Rs. 1,031, i.e., 18 per cent. The

studies undertaken by Dunn & Arbuckle (2001) and Singh (2001) have also produced

similar results showing the impact of microfinance programme on the household income.

Table 5.4: Household Income of the Participants and Non-Participants

Household Income (Rs. per month) District

Non-participants Participants Increment

Jalandhar 5,847 6,488 641 (09)

Hoshiarpur 5,725 6,661 936 (16)

Bathinda 6,445 9,094 2,649 (41)

Punjab 5,881 6,912 1,031 (18) Source: Field survey 2008.

Note: The figures given in parentheses indicate percentage increase in household income.

The microfinance programme enables its beneficiaries to contribute towards their

household income in a more effective manner. Table 5.5 carries the data showing the

level of income of both participant and non-participant households. The table reveals that

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majority of the participant households, i.e., 31 per cent belong to the income group of Rs.

4,000-6,000 per month, whereas majority of the non-participants, i.e., 38 per cent appear

in the income group of Rs. 2,000-4,000 per month. Only four per cent of the participants

and 12 per cent of the non-participants have household income below Rs. 2,000 per

month. It has also been observed that 35 per cent of the participant households have

income above Rs. 6,000 per month as compared to 27 per cent of the non-participant

households. Thus, the household income level of the participants is higher than that of

the non-participant.

Table 5.5: Level of Household Income

(Income in Rs. per month) Participants Non-participants Household

Income Level Jal. Hsp. Bti. Pun. Jal. Hsp. Bti. Pun.

Less than 2000 04 (04)

04 (05) - 08

(04) 10

(11) 10

(14) 03

(12) 23

(12)

2000-4000 25 (28)

25 (34)

07 (27)

57 (30)

37 (41)

29 (39)

07 (27)

73 (38)

4000-6000 35 (39)

17 (23)

07 (27)

59 (31)

17 (19)

17 (23)

09 (35)

43 (23)

6000-8000 08 (09)

10 (14)

03 (11)

21 (11)

07 (08)

04 (05)

03 (11)

14 (07)

8000-10000 06 (07)

08 (11) - 14

(08) 10

(11) 03

(04) - 13 (07)

Above 10000 12 (13)

10 (13)

09 (35)

31 (16)

09 (10)

11 (15)

04 (15)

24 (13)

Total 90 (100)

74 (100)

26 (100)

190 (100)

90 (100)

74 (100)

26 (100)

190 (100)

Source: Field survey 2008. Note: The figures given in parentheses indicate percentage of participants and non-participants.

5.3.4 Impact of Microfinance Programme on Income Inequality

Income inequality has been measured with the help of household income

distribution. The Lorenz curve and Gini coefficient methods have been used to find out

the impact of microfinance programme on the distribution of household income.

The Lorenz curve is a graphical representation of the proportionality of a

distribution. It represents a probability distribution of statistical values and is often

associated with income distribution calculations and commonly used in the analysis of

inequality. Here, it has been used for the analysis of income inequality. In the Lorenz

curve graph, a straight line representing same income for every person is called the line

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of perfect equality. While another curved line showing the actual income distribution is

known as Lorenz curve. The difference detween the line of perfect equality and the

Lorenz curve shows the inequality in the income distribution.

The Gini coefficient is a measure of statistical dispersion.The Gini coefficient is

the quantitative measurement of income inequality from the Lorenz curve. It is the ratio

of the area that lies between the line of equality and the Lorenz curve over the total area

under the line of equality. The Gini coefficient can range from 0 to 1. A low Gini

coefficient indicates a more equal distribution, with 0 corresponding to perfect equality,

while higher Gini coefficients indicate more unequal distribution, with 1 corresponding

to perfect inequality.The results for these methods, i.e., Lorenz curve and Gini

coefficient are discussed below:

(i) Distribution of Income in Jalandhar District

Table 5.6 shows the distribution of income of the Jalandhar district participants in

their pre- and post-SHG situation as well as the income distribution of non-participant

households.

Table 5.6: Distribution of Income for Participants and Non-participants of Jalandhar District

Percentage of Income Cumulative Percentage of Income

Participants Participants

Deciles (Respondent)

Pre-SHG

Post-SHG

Non-participants

Cumulative Percentage

of Respondents Pre-

SHGPost-SHG

Non-participants

1st Decile 2.93 2.89 2.82 10 2.93 2.89 2.82 2nd Decile 4.44 4.85 4.25 20 7.36 7.75 7.07 3rd Decile 5.36 5.74 5.02 30 12.72 13.49 12.09 4th Decile 6.08 6.52 5.73 40 18.80 20.01 17.82 5th Decile 6.91 7.03 6.24 50 25.71 27.04 21.24 6th Decile 7.86 7.80 7.87 60 33.57 34.84 31.93 7th Decile 8.81 8.87 9.44 70 42.38 43.71 41.37 8th Decile 10.35 11.11 11.93 80 52.73 54.83 53.30 9th Decile 15.86 15.06 16.08 90 68.58 69.89 69.38

Last Decile 31.42 30.11 30.62 100 100 100.00 100.00 Gini

Coefficient 0.3704 0.3511 0.3860

Source: Field survey 2008.

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A perusal of the table shows that the bottom 10 per cent (1st decile) of the

programme participants share 2.93 and 2.89 per cent of the total income in the pre- and

post-SHG situation respectively. The 2nd decile of the programme participants share 4.44

and 4.85 per cent of the total income in the pre- and post-SHG situation respectively. But

the top 10 per cent (last decile) of the participants share 31.42 and 30.11 per cent of the

total income of the participants in pre- and post-SHG respectively. Among the non-

participant households the first and last deciles share 2.82 per cent and 30.62 per cent of

the total non-participant income respectively. The table also represents the calculated

values of Gini coefficient which are 0.3704 and 0.3511 for the participant households in

their pre- and post-SHG situation respectively. However, for non-participant households

the value of Gini coefficient is 0.3860.

The above income distribution is plotted graphically in Figure 5.1. This figure

and the values of Gini coefficient show that the distribution of household income in

Jalandhar district is more unequal for non-participant households as compared to

participant households. The reduction in value of Gini coefficient in post-SHG situation

represents that the inequality in income distribution is reduced among the participant

households after joining the microfinance programme.

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100

Cumulative Percentage of Respondents

Cum

ulat

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Perc

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Participants Post-SHG

Participants Pre-SHG

Non-participants

Line of Equality

Figure 5.1: Lorenz Curve for Jalandhar District

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(ii) Distribution of Income in Hoshiarpur District

Table 5.7 shows the distribution of income and values of Gini coefficient of the

Hoshiarpur district participants in their pre- and post-SHG situation as well as the

income distribution of the non-participant households. A perusal of the table depicts that

the bottom 10 per cent of the participants share 2.45 and 2.85 per cent of the total income

in the pre- and post-SHG situation respectively. The share of top 10 per cent of the

participants is 28.25 and 28.50 per cent of the total income of the participants in pre- and

post-SHG respectively. Similarly, the bottom 10 per cent of non-participants have just

2.60 per cent of the total income as compared to 32.80 per cent of the income possessed

by top 10 per cent of the non-participants. The table represents the calculated values of

Gini coefficient also. The value of Gini coefficient for the participant households in their

pre-SHG situation was 0.4017. This value reduced to 0.3848 after getting the benefits of

microfinance programme. The value of Gini coefficient for the non-participant

households is 0.4403. This reduction in value of Gini coefficient represents that

microfinance has reduced the inequality in income distribution.

Table 5.7: Distribution of Income for Participants and Non-participants of Hoshiarpur District




Pre-SHG

Post-SHG

Non-participants


of Respondents Pre-

SHGPost-SHG

Non-participants


Last Decile 28.25 28.50 32.80 100 100 100 100 Gini

Coefficient 0.4017 0.3848 0.4403


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The income distribution is presented in Figure 5.2. The figure and the Gini

coefficient show that the distribution of household income in Hoshiarpur district is more

unequal for non-participant households as compared to participant households. It is also

found that among the group participants the distribution of income improves in the post-

SHG situation as compared to their pre-SHG situation.

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100Cumulative Percentage of Respondents

Cum

ulat

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Perc

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Non-participants

Line of Equality

Figure 5.2: Lorenz Curve for Hoshiarpur District

(iii) Distribution of Income in Bathinda District

Table 5.8 shows the distribution of income and values of Gini coefficient of the

programme participants as well as the non-participants. Similar to the income

distribution of Jalandhar and Hoshiarpur district respondents, in Bathinda district also the

income distribution is unequal. In Bathinda district, top 10 per cent of the participant and

non-participant households share 29.85 and 32.40 per cent of the total income, and the

bottom 10 per cent have just 2.90 and 2.63 per cent of the total income respectively. The

table also represents the calculated values of Gini coefficient which are 0.3859 and

0.3623 for the participant households in their pre- and post-SHG situation respectively.

However, for non-participant households the value of Gini coefficient is 0.3961.

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Table 5.8: Distribution of Income for Participants and Non-participants of Bathinda District




Pre-SHG

Post-SHG

Non-participants


of Respondents Pre-

SHGPost-SHG

Non-participants


Last Decile 31.00 29.85 32.40 100 100 100 100 Gini Coeff. 0.3859 0.3623 0.3961


The values of this income distribution are graphically presented in Figure 5.3.

The reduction in value of Gini coefficient in post-SHG situation indicates that the

inequality in income distribution is reduced among the participant households after

joining the microfinance programme.

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100

Cumulative Percentage of Respondents

Cum

ulat

ive

Perc

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Inco

me



Non-participants

Line of Equality

Figure 5.3: Lorenz Curve for Bathinda District

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(iv) Distribution of Income in Punjab

Table 5.9 shows the income distribution and values of Gini coefficient for all the

participants and non-participants surveyed in this study. A perusal of the table reveals

that the poorest 10 per cent of the programme participants have just 2.63 per cent of the

total income of the participants in the pre-SHG situation which increases to 2.89 per cent

in the post-SHG. While the richest 10 per cent of the participants have 30.96 and 29.84

per cent of the share in total income in pre- and post-SHG respectively. Similarly, the

poorest and richest 10 per cent of the non-participant households share 2.63 per cent and

32.37 per cent of the total income respectively. The average value of Gini coefficient for

the participant households is 0.3860 in their pre-SHG situation. In the post-SHG

situation, this value has reduced to 0.3622. Whereas, the value of Gini coefficient for

non-participant households is 0.3956.

Table 5.9: Distribution of Income for the Participants and Non-participants of Punjab




Pre-SHG

Post-SHG

Non-participants


of Respondents Pre-

SHGPost-SHG

Non-participants


Last Decile 30.96 29.84 32.37 100 100 100 100.00 Gini

Coefficient 0.3860 0.3622 0.3956


The values given in Table 5.9 are plotted graphically in Figure 5.4. The figure

and values of Gini coefficient show that the distribution of household income among the

146

non-participants is more unequal as compared to the participant households. The

reduction in value of Gini coefficient in post-SHG situation indicates that the programme

participation has led to reduction in the inequality in income distribution. In this way, it

can be concluded that microfinance programme contributes not only in raising the level

of income of the participant households, but also helps in bridging the gap in income

distribution.

In sum, it can be said that there are inequalities in income distribution among

participant and non-participant households and also among the participant households in

pre- and post-SHG. However, with microfinance programme the inequalities in income

have marginally declined.

0102030405060708090

100

0 10 20 30 40 50 60 70 80 90 100Cumulative Percentage of

Respondents

Cum

ulat

ive

Perc

enta

ge o

f Hou

seho

ldIn

com

e



Non-participants

Line of Equality

Figure 5.4: Lorenz Curve for Punjab

5.4 Impact of Microfinance Programme on Vulnerability

Vulnerability is defined as the risk of being in poverty or of falling into deeper poverty in

the future. It is not necessarily unexpected, it may be predictable. Poor people are very

much vulnerable to the economic shocks faced by them. These people hardly earn

income to fulfil their basic necessities. The poor people generally fail to meet the

unforeseen expenses arising due to the death of bread-winner of their family, health

problems etc. The repair/construction of a house and the expenditure incurred on social

147

ceremonies like marriage push them in greater economic crisis. A small disturbance is

likely to have a substantial impact on their ability to meet their basic needs. A timely

support can help them to come out of these crises. Microfinance programme helps the

participants to raise their level of thrift, which enables them to utilise their savings at the

time of emergencies. There is flexibility in utilising the micro-loan. The participants can

use the loan for productive activities or for meeting other household needs. In this way,

microfinance programme helps the participants to come out of their economic crisis. The

impact of microfinance programme in meeting the emergency needs of the participants

has been explained as follows:

5.4.1 Nature and Extent of Economic Shocks faced by Respondents

In case of any sudden and urgent need of money, the poor people have no other

option than to get loans from money-lenders at very high interest rates. But, these poor

borrowers fail to pay the high interest rates and ultimately caught in the debt trap. There

may be different reasons of economic crisis and borrowing money. Table 5.10 presents

the various types of economic shocks faced by the respondent households.

Table 5.10: Nature of Economic Shock

Participants Non-participants Nature of Economic Shock Jal. Hsp. Bti. Pun. Jal. Hsp. Bti. Pun.

Health Treatment 16 (47)

13 (35)

03 (43)

32 (41)

09 (23)

10 (29)

01 (12)

20 (24)

Marriage of daughter

06 (18)

17 (46)

01 (14)

24 (31)

14 (35)

10 (29)

02 (25)

26 (31)

Repair of house 09 (26)

02 (05)

03 (43)

14 (18)

08 (20)

06 (17)

03 (38)

17 (21)

Death of earning member - 03

(08) - 03 (04)

03 (07)

03 (08) - 06

(07) Debt for going abroad

02 (06) - - 02

(03) 03

(08) - - 03 (04)

Business failure 01 (03)

01 (03) - 02

(02) - - - -

Property loss - 01 (03) - 01

(01) - 01 (03) - 01

(01) Routine Household needs - - - - 03

(07) 05

(14) 02

(25) 10

(12)

Total 34 (100)

37 (100)

07 (100)

78 (100)

40 (100)

35 (100)

08 (100)

83 (100)


148

A perusal of the table provides that major reasons of the economic crisis are

illness, marriage of daughter, repair of house etc. Forty-one per cent of the participants

and 24 per cent of the non-participants have borrowed money for the medical treatment

of their own or other members of the family who face health related serious problems.

Thirty-one per cent each of the participants and non-participants borrowed money to

perform marriage of their daughter. Eighteen per cent of the participants and 21 per cent

of the non-participants borrowed money for the urgently required repair and renovation

of their house. Twelve per cent of the non-participants borrowed money for routine

household needs, while none of the participants borrowed money for this purpose. Four

per cent of the participants and seven per cent of the non-participants faced economic

crisis due to the death of an earning family member. Two per cent of the participants

faced business failure and one per cent of both the participants and non-participants

faced such crisis due to loss of property. However, three per cent of the participant and

four per cent of the non-participant households also borrowed money to send a member

of their family abroad.

Table 5.11 highlights the percentage of participant and non-participant

households which experienced economic shocks due to one or another reason during the

two years before the time of survey. It is found that 41 per cent of the participants and 44

per cent of the non-participants faced economic shock. The table also shows the average

amount spent by the respondent households in meeting such crises. It is found that the

average amount spent by the participants is Rs. 88,385 and for non-participants this

amount is Rs. 81,373 for Punjab.

Table 5.11: Number of Respondents and Amount Spent to face Economic Shocks

Number of Respondents who Faced Economic Shock

Average Amount Spent to cope up (in Rs.) District

Number of Participants

and Non-participants Participants Non-

participants Participants Non-participants

Jalandhar 90 34 (38) 40 (44) 1,17,882 1,01,175

Hoshiarpur 74 37 (50) 35 (47) 73,270 60,314

Bathinda 26 07 (27) 08 (31) 25,000 74,500

Punjab 190 78 (41) 83 (44) 88,385 81,373 Source: Field survey 2008. Note: The figures given in parentheses indicate percentage of participants and non-participants.

149

5.4.2 Sources of Finance to cope up Economic Shocks

Rural poor people have to borrow money to meet their economic exigencies

arising due to the various household needs and problems. An attempt was made to know

about the different sources from where the respondents borrowed money to meet these

various economic shocks. These different sources of finance are shown in Table 5.12. It

is found that more than 50 per cent of the non-participants and only 27 per cent of the

programme participants borrow from money-lenders at exorbitant rates of interest

ranging from 36 to 120 per cent per annum. Twenty-three per cent of the participants

borrow from the SHGs to meet their exigencies. But this option is not available to the

non-participants. Studies by Singh (2001), Raghavendra (2001), and Kabeer & Noponen

(2005) have also reported that the simple and quick delivery of credit under the

microfinance programme has reduced the dependence of programme participants on the

money-lenders.

Table 5.12: Sources of Finance to cope up Economic Shocks

Participants Non-participants Sources of Finance Jal. Hsp. Bti. Pun. Jal. Hsp. Bti. Pun. Money-lenders

15 (44)

02 (05)

04 (58)

21 (27)

19 (48)

18 (51)

05 (63)

42 (51)

Friends and Relatives

05 (15)

09 (25)

01 (14)

15 (19)

15 (37)

09 (26)

01 (12)

25 (30)

Own Savings 10 (29)

09 (24)

01 (14)

20 (26)

02 (05)

01 (03) - 03

(04) Self-help Group

04 (12)

13 (35)

01 (14)

18 (23) - - - -

Cooperative Society - 03

(08) - 03 (04)

04 (10)

07 (20)

01 (13)

12 (14)

Banks - 01 (03) - 01

(01) - - 01 (12)

01 (01)

Total 34 (100)

37 (100)

07 (100)

78 (100)

40 (100)

35 (100)

08 (100)

83 (100)

Source: Field survey 2008. Note: The figures given in parentheses indicate percentage of participants and non-participants who

borrowed money from different sources of finance to face economic shocks.

An SHG programme not only provides loans but also develops the habit of saving

among the programme participants. It is found that 26 per cent of the participants utilise

their own savings to meet the economic shocks as compared to just 4 per cent of the non-

150

participants. Hoque (2008) in his study of microfinance programme in Bangladesh

brought out that the programme participants are more able to meet the financial crisis

from their income and savings as compared to the non-participants. The above table

reflects that nineteen per cent of the participants and 30 per cent of the non-participants

arrange money from their friends and relatives. Further, only one per cent each of the

participants and non-participants borrow money directly from the banks. However, four

per cent of the participants and 14 per cent of the non-participants borrow money from

village co-operative societies.

The impact of microfinance can be seen from the fact that participant households

have higher level of savings and lower incidence of indebtedness to money-lenders to

cope up their economic crises. Morduch (1998), and Develtere & Huybrechts (2002) in

their studies found that microfinance programme has resulted in reducing household

vulnerability and had prevented them from falling further in poverty.

5.5 Impact of Microfinance on Below Poverty Line Households

Below poverty line (BPL) households are the main target group of the

microfinance scheme. Therefore, the impact of this programme has been assessed

separately for the BPL households. The BPL families among the sample households are

selected with the help of an absolute poverty line. For this purpose, the poverty line of

Rs. 2,500 per month per household as defined by the Government of Punjab for

identifying poor under Atta-Dal scheme is used. On the basis of this poverty line, the

impact of microfinance has been estimated on incidence of poverty, depth of poverty,

and severity of poverty.

5.5.1 Microfinance and Incidence of Poverty

The Head Count Index (HCI) is the most commonly used method for estimating

the incidence of poverty. It measures the proportion of population that is poor. This is the

share of the population whose income is below the absolutely defined poverty line,

which in the present study is Rs. 2500 per month. Table 5.13 shows the status of BPL

families of participant and non-participant households. A perusal of the table provides

that all the participants were not BPL before joining the microfinance programme. Most

of the SHG members selected for getting the benefit of the microfinance programme may

be poor, but not necessarily be below the poverty line. The sample study shows that

number of BPL families provided with microfinance in the study area are just 18, 22 and

19 per cent in Jalandhar, Hoshiarpur and Bathinda districts, respectively. Since this

151

programme is target driven, therefore, implementing agencies of the government are

including general poor people also. Moreover, it is not mandatory to include only BPL

families in the programme. Another reason for including less poor people may be to

avoid the failure of the group in case of non-repayment of bank loans by the extreme

poor. Another hindrance in the selection of very poor people as group member is their

inability to contribute for monthly savings. In a study by Navajase et al. (2000), it is

found that MFIs prefer to lend to the people who are above poverty line.

Table 5.13: Number of BPL Households based on HCI

Number of BPL Households Reduction in BPL Households

Participants District


and Non-participants Pre-

SHG Post-SHG

Non-participants

Pre- and Post-SHG

Analysis

Participant and Non-

participant Analysis

Jalandhar 90 16 (18) 09 (10) 16 (18) 07 [44] 07 [44]

Hoshiarpur 74 16 (22) 08 (11) 18 (24) 08 [50] 10 [56]

Bathinda 26 05 (19) - 04 (15) 05 [100] 04 [100]

Punjab 190 37 (19) 17 (09) 38 (20) 20 [54] 21 [55] Source: Field survey 2008. Note: (i) The figures given in parentheses indicate percentages of participant and non-participant BPL

households. (ii) The figures given in square brackets indicate percentage reduction in the number of BPL

households.

The pre- and post-SHG analysis of programme participants shows that 19 per

cent of the participant households were BPL in Punjab before joining the microfinance

programme but after getting the benefits of the scheme their financial position improved

and the number of BPL households was reduced to 9 per cent. So, on an average there is

54 per cent reduction in the number of BPL households. After joining their programme,

44 per cent and 50 percent of the BPL participant households from Jalandhar and

Hoshiarpur districts crossed the poverty line respectively, whereas this figure in the case

of Bathinda district was 100 per cent.

The table presents a comparison between BPL participant and non-participant

households. It is found that 20 per cent of the non-participant and just 9 per cent of the

participant households are BPL. The number of BPL families of non-participants in the

Jalandhar, Hoshiarpur and Bathinda districts are more than the BPL families of

152

participants by 44, 56 and 100 per cent respectively. It can be said that microfinance

programme has resulted in reducing the incidence of poverty among the programme

participants.

An attempt has been made to measure the impact of microfinance programme for

both the below poverty line (BPL) and above poverty line (APL) sample households

separately. The benefits provided under the programme have shown an increase in the

household income. As a result, some of the BPL households have been able to cross the

poverty line and shifted to the APL category. It is also found that large number of

programme participants were APL before joining the programme and their household

income has further increased. In this way, programme participation has led to changes in

the poverty situation of the beneficiaries as shown in Figure 5.5.

Pre-SHG Post-SHG

Fig 5.5: Change in Poverty Status from Pre- to Post-SHG

Table 5.14 reflects a change in the household incomes of BPL and APL

households separately. The table shows the impact of microfinance programme on the

level of income of participant households in pre- and post-SHG situation. It is found that

ten per cent BPL households in Jalandhar district, eleven per cent in Hoshiarpur district

and nine per cent of the total households surveyed in Punjab remained BPL even after

getting the benefits of the microfinance programme. It is also found that these BPL

families were relatively poorer at the time of joining the programme and their household

BPL 19%

APL 81%

BPL9%

APL91%

153

incomes were just Rs. 1,822, 1,563 and Rs. 1,700 per month for Jalandhar, Hoshiarpur

and Punjab in their pre-SHG situation respectively. These people who were extremely

poor could not cross the poverty line. It may be due to the fact that such people utilise the

group loans for non-productive purposes. Table 5.15 indicates that these households

utilised only 12 per cent of the total loans for productive purposes. So, there was a minor

increase in their income in the post-SHG. But in Bathinda district, most of the BPL

participants were close to the poverty line as their income was more than Rs. 2,000 per

month. Therefore, all these BPL households crossed the poverty line in post-SHG.

Table 5.14: Impact of Microfinance Programme on BPL

Change in Poverty Status from Pre- to Post-SHG


Household Income inPre-SHG

Household Income in Post-SHG

Difference in Income

Percentage Increase in

Income

Jalandhar BPL → BPL 09 (10) 1,822 1,878 56 03 BPL → APL 07 (08) 2,164 3,725 1,561 72 APL → APL 74 (82) 6,383 7,310 927 15

Total 90 (100) 5,599 6,488 889 16 Hoshiarpur

BPL → BPL 08 (11) 1,563 1,963 400 26 BPL → APL 08 (11) 2,238 3,638 1,400 63 APL → APL 58 (78) 6,517 7,727 1,210 19

Total 74 (100) 5,518 6,661 1,143 21 Bathinda

BPL → BPL - - - - - BPL → APL 05 (19) 2,280 3,500 1,220 54 APL → APL 21 (81) 9,440 10,426 986 10

Total 26 (100) 8,063 9,094 1,031 13 Punjab

BPL → BPL 17 (09) 1,700 1,918 218 13 BPL → APL 20 (10) 2,223 3,634 1,411 63 APL → APL 153 (81) 6,854 7,895 1,041 15

Total 190 (100) 5,905 6,912 1,007 17 Source: Field survey 2008. Note: The figures given in parentheses indicate percentage of participants.

Some of the BPL households crossed the poverty line in their post-SHG situation.

Eight per cent participants of Jalandhar district, 11 per cent of Hoshiarpur district, 19 per

cent of Bathinda district, and 10 per cent of Punjab crossed the poverty line in post-SHG.

154

The income level of these BPL households shows that they were close to the poverty line

when they joined the programme. The income of these BPL households was Rs. 2,164,

2,238, 2,280 and Rs. 2,223 per month for Jalandhar, Hoshiarpur, Bathinda and Punjab

respectively. Microfinance programme led to a significant increase in their income and

shifted them above the poverty line. The increase in their income is 72 per cent, 63 per

cent, 54 per cent and 63 per cent for Jalandhar, Hoshiarpur, Bathinda and Punjab

respectively. These poor households are the largest beneficiaries of the microfinance

programme. Table 5.15 shows that these BPL households utilised 66 per cent of the

loans for productive purposes.

A glance at Table 5.14 provides that large number of the programme participants

were APL at the time of joining the programme. The percentage of these APL

participants is 82, 78 and 81 in Jalandhar, Hoshiarpur and Bathinda districts with the

monthly income of Rs. 6,383, 6,517, 9,440 and Rs. 6,854 in pre-SHG respectively.

However, after joining the microfinance programme there was an increase in their

income of 15, 19 and 10 per cent respectively. Thus, in Punjab the average number of

APL participant households are 81 per cent. The monthly income of these APL

participant households is Rs. 6,854 which increased by 15 per cent to reach at Rs. 7,895

after getting the benefits of microfinance programme. These APL participants used 44

per cent of the bank loans for productive purposes as shown in Table 5.15. But this

utilisation of loan for productive purposes is less than that of the participants of previous

category. Its effect is evident when we compare the percentage increase in income of

both the categories.

Table 5.15: Percentage of Loans utilised by the Participants for Different Purposes Purpose of Loan Utilisation (in per cent) Change in

Poverty Status from Pre- to

Post-SHG Productive Purposes Consumption Household

Durables Construction Marriage Education Repayment of Previous

Loan BPL → BPL 12 23 25 24 08 06 02 BPL → APL 66 23 - 02 03 - 06 APL → APL 44 29 05 11 08 01 02

Source: Field survey 2008. 5.5.2 Microfinance and Depth of Poverty (Poverty Gap Index)

Headcount index is simple to measure and understand but it does not consider the

intensity of poverty. The Poverty Gap is a method for measuring the depth of poverty.

155

This provides information regarding how far-off households are from the poverty line.

This measure captures the aggregate income or consumption shortfall relative to the

poverty line across the whole population. In other words, it gives the total resources

needed to bring all the poor to the level of the poverty line. In this study, the values of

poverty gap are calculated for the participant and non-participant households.

Table 5.16 shows that value of poverty gap is Rs. 9,900 for the participant

households in post-SHG as compared to Rs. 19,150 before joining the microfinance

programme. The value of poverty gap is Rs. 20,775 for the non-participant households.

This shows that microfinance programme has resulted in reducing the depth of poverty

among the participant households.

Table 5.16: Value of Poverty Gap (in Rs.)

Value of Poverty Gap (in Rs.) Reduction in Poverty Gap Participants District Pre-

SHG Post- SHG

Non-participants

Pre- and Post-SHG Analysis

Participants Non-participants

Analysis Jalandhar 8,450 5,600 8,150 2,850 2,550

Hoshiarpur 9,600 4,300 9,300 5,300 5,000

Bathinda 1,100 - 3,325 1,100 3,325

Punjab 19,150 9,900 20,775 9,250 10,875 Source: Field survey 2008.

The poverty gap index measures the mean aggregate income shortfall relative to

the poverty line across the whole population. It is obtained by adding up all the shortfalls

of the poor (considering the non-poor have a zero shortfall) and dividing the total by the

population. In the study, poverty gap index is calculated for the participant and non-

participant households and the values are given in Table 5.17. The table depicts that

among the participant households the value of poverty gap was 0.040 in their pre-SHG

situation as compared to 0.021 in the post-SHG situation. Therefore, the microfinance

programme participation led to the reduction in the value of poverty gap index. The

difference in the values of poverty gap index in pre- and post-SHG situation is 0.013,

0.023 and 0.017 for the participants of Jalandhar, Hoshiarpur and Bathinda districts

respectively. The table also reveals the difference in the value of poverty gap index

between participant and non-participant households. The difference is 0.011, 0.027 and

156

0.051 for Jalandhar, Hoshiarpur and Bathinda districts respectively. Therefore,

microfinance programme reduces both the incidence as well as depth of poverty among

the programme beneficiaries.

Table 5.17: Value of Poverty Gap Index

Value of Poverty Gap Index Reduction in Poverty Gap Index Participants District Pre-

SHG Post- SHG

Non-participants



Analysis Jalandhar 0.038 0.025 0.036 0.013 0.011 Hoshiarpur 0.046 0.023 0.050 0.023 0.027 Bathinda 0.017 - 0.051 0.017 0.051 Punjab 0.040 0.021 0.044 0.019 0.023

Source: Own calculation from field survey data 2008.

5.5.3 Microfinance and Severity of Poverty (Squared Poverty Gap Index)

Squared poverty gap index takes into account not only the distance separating the

poor from the poverty line (the poverty gap), but also the inequality among the poor.

This is defined as the average of the weighted-sum of the individual poverty gaps where

the weights are proportionate poverty gaps themselves. The households falling quite

below the poverty line as compared to those standing close to this line have been given

higher weightage.

Table 5.18 gives the value of squared poverty gap index. It shows that the

severity of poverty is high among the participant households in their pre-SHG situation

as compared to the post-SHG situation.

Table 5.18: Value of Squared Poverty Gap Index

Value of Squared Poverty Gap Index

Reduction in Squared Poverty Gap Index

Participants District Pre-SHG

Post- SHG

Non-participants



Analysis Jalandhar 0.018 0.016 0.013 0.002 -0.003 Hoshiarpur 0.032 0.015 0.021 0.017 0.006 Bathinda 0.006 - 0.030 0.006 0.030 Punjab 0.022 0.014 0.019 0.008 0.005

Source: Own calculation from field survey data 2008.

157

The difference in the values of poverty severity among participants in their pre-

and post-SHG situation is 0.002, 0.017 and 0.006 for the participants of Jalandhar,

Hoshiarpur and Bathinda districts respectively. The table also shows that the problem of

poverty is more severe among the non-participant households. However, it is found that

the poverty severity is high among the Jalandhar district participants as compared to the

non-participant households.

5.6 Composite Poverty Index

The overall impact of microfinance programme on various dimensions of poverty

has also been measured by preparing a Composite Poverty Index (CPI). For this purpose,

10 score based socio-economic parameters have been identified. These indicators are

almost similar to the 13 indicators, recommended by the Expert Group constituted by the

Ministry of Rural Development for BPL census for 10th Five-Year Plan. However, some

of these 13 indicators have been omitted/modified in the present study according to the

state/field conditions. These ten indicators include: per capita household income per

month, major source of household income, per capita consumption expenditure per

month, highest education level of the household, condition of house, source of drinking

water, cooking fuel used, basic household amenities, ownership of consumer durables

and value of land owned. These indicators are assigned arbitrary scores between 0-4 as

shown in Appendix-1. Thus, the sum of scores of these 10 indicators ranges between 0-

40. The participants and non-participants who scored between 0-10 are classified as

extreme poor. Similarly, the scores between 11-20, 21-30 and 31-40 are classified as

moderate poor, threshold non-poor and non-poor respectively. The results of this poverty

index are shown in Table 5.19.

Table 5.19: Scores of Composite Poverty Index

Category Score Number of Participants

Number of Non-participants

Extreme Poor 0-10 01 (<1) 02 (01)

Moderate Poor 11-20 63 (33) 86 (45)

Threshold Non-poor 21-30 89 (47) 80 (42)

Non-poor 31-40 37 (19) 22 (12) Total 190 (100) 190 (100)


158

A perusal of the table shows that a negligible percentage of both participant and

non-participant households is in the extreme poor category. Thirty-three per cent of the

participants and 45 per cent of the non-participants are moderate poor and 47 per cent of

the participants and 42 per cent of the non-participants are threshold non-poor. Nineteen

per cent of the participants and 12 per cent of the non-participants are non-poor. Thus, it

may be said that microfinance programme has benefited the moderate poor and they have

shifted to the non-poor categories. This result shows that the medium poor people are the

actual beneficiaries of the microfinance programme.

This result is similar to the one given by various other studies. Hulme and Mosley

(1996) in their study concluded that the extreme poor people borrow essentially for

protection purposes because of their very low and irregular nature of income. This group

is also very risk averse to borrow for promotional (investment) purposes, and therefore,

is only a very limited beneficiary of microfinance programme. Develtere and Huybrechts

(2002) in a study of Bangladesh microfinance programme found that most of the bottom

poor people are not able to take part in the microfinance programme due to various client

related and programme related barriers. Morduch (2000) and Amin et al. (2003) stated

that vulnerable poor are too poor to benefit from the market-oriented approaches and

recommended some charity based welfare programmes for alleviating their poverty.

5.7 Determinants of Poverty (Regression Analysis)

In order to determine the factors affecting the poverty level of participant

households, simple linear regression equation is fitted to the field data. The independent

variables selected for this purpose are group maturity, amount of group loans used for

productive purposes, household members, household income and highest level of

education in the family. Poverty index is taken as a dependent variable. The coefficients

of poverty determinants are calculated with the help of the following linear equation:

CPI = b0 + b1 G_AGE + b2 LOAN_PROD +b3 HH_MEM + b4 HH_INCOM + b5 HL_EDU

Where:

CPI = Composite Poverty index

G_AGE = Group age to know the maturity of the group in years

LOAN_PROD = Amount of loan used for productive purposes in Rs.

HH_MEM = Total number of household members

159

HH_INCOM = Total household income in Rs.

HL_EDU = Highest level of education in the family.

The results of regression equation are shown in Table 5.20. A perusal of the table

shows that the maturity of group leads to lower levels of poverty. It is already found in

the previous discussion that as the group matures, the participants become more

economically empowered. The value of this coefficient is significant. The variable of

loan amount used for productive purposes is positively influencing the poverty index but

this is not very significant.

Table 5.20: Results of Regression Analysis

Standardised Coefficients Variables Jalandhar Hoshiarpur Bathinda Punjab

(Constant) (7.209) (5.747) (5.053) (9.751)

Group age 0.129 (1.896)***

0.013 (0.151)

0.100 (0.884)

0.090 (1.892)***

Group loans used for productive purposes

0.010 (0.138)

0.028 (0.333)

-0.085 (-0.907)

0.000 (0.011)

Number of household members

-0.276 (-4.038)*

-0.196 (-2.411)**

-0.452 (-3.992)*

-0.234 (-4.974)*

Household income 0.504 (7.385)*

0.569 (6.286)*

0.886 (6.414)*

0.575 (11.482)*

Highest level of education in the household

0.499 (6.929)*

0.296 (3.372)*

0.265 (2.288)**

0.374 (7.442)*

R2 0.862 0.841 0.876 0.628 * Significant at 1 per cent level. ** Significant at 5 per cent level. *** Significant at 6 per cent level. Source: Own calculation from field survey data 2008. Note: The figures given in parentheses indicate t-values.

The table shows that the coefficient of household members is negatively related

with the value of poverty index. This explains that higher number of family members

reduce the score of poverty index, which indicated a greater incidence of poverty. The

levels of education and household income are very significantly influencing the poverty

level of the participants. This shows that higher level of education leads to lower level of

poverty, and the participants with higher level of household income can afford better

living standards.

160

The coefficient of determination (R2) shows the goodness of fit. It represents the

proportion of variance in the dependent variable explained by the linear combination of

the independent variables in the model. The magnitude of R² is 0.628 for Punjab, which

shows that the regression equation explains about 63 per cent of the total variations.

Section-II

Microfinance and Employment

Employment is considered to be one of the most important determinants of

generating income, mitigating poverty and use of labour force both as wage labour and

self-employment. The era of globalization has increased unemployment among the poor

people of the society. Therefore, it becomes imperative to introduce such programmes

with which employment can be enhanced. The labour force in India is growing at a rate

of 2.5 per cent annually, but employment is growing at only 2.3 per cent. Thus, the

country is facing the challenge of not only absorbing new entrants to the job market

(approximately seven million people every year), but also clearing the backlog [3]. Most

of the labour force in India is working in the informal sector and they are not earning

sufficient income to bring their families above the poverty line. India’s total labour force

consists of 45.9 crore workers, out of these, 43.3 crore (94 per cent) are in the

unorganised sector and the remaining 2.6 crore (6 per cent) are in the organised sector

[4]. In unorganised sector, the Minimum Wages Act is either not followed or only

marginally implemented with very poor quality of employment. This sector does not

provide the social security and other benefits of employment.

Microfinance programme generates self-employment opportunities in rural areas.

In this programme, credit support is made available to rural entrepreneurs through the

SHGs in the form of micro-loans, who otherwise are often considered non-bankable by

the financial sector. The programmes which generate wage employment may not bring

the BPL households out of poverty on sustained basis. In the words of Yunus (1994),

“Unless designed properly, wage employment may mean being condemned to a life in

squalid city slums or working for two meals a day for one’s life. Wage employment is

not a happy road to the reduction of poverty. Removal or reduction of poverty must be a

161

continuous process of creation of assets, so that the asset-base of poor person becomes

stronger at each economic cycle, enabling him or her to earn more and more.” This

perception is shared by many of the rural poor. Rahman (1996) citing Hirashima and

Muqtada, 1986 notes that “in the rural areas among female workers in particular and

among all workers in general, self-employment is considered to be more prestigious

compared to wage employment.”

The financial help provided under microfinance programme gives impetus in the

form of entrepreneurship development to change the lives of rural women. The income

generating activities help women to become financially independent and create an

environment for women to come into the mainstream of development. Various empirical

studies such as Borbora and Mahanta (1995), Gaonkar (2001), Dunn & Arbuckle (2001),

Mishra & Hossain (2001) etc. have shown that the microfinance programme is helpful

for increasing employment, alleviation of poverty and empowerment of rural poor,

especially women.

In this section, the impact of microfinance has been assessed on employment.

However, there are some methodological challenges to establish the impact of

microfinance on employment. The following observations may be kept in mind while

going through the results of this section:

(i) It is the wisdom of the participants of the programme to make use of the loan

which they get from the bank. If the loan is invested in an enterprise and the

participants become entrepreneur, there may be generation of self-employment.

Otherwise,

(ii) Sometimes, SHG members of microfinance programme do not invest the loan

money for a productive activity and spend it for either consumption purposes or

repaying old debts. In that case, no additional employment may be generated.

5.8 Impact of Microfinance on Employment

5.8.1 Employment Status of the Participants and Non-participants

The employment status of both the participants and non-participants is presented

in Table 5.21. A perusal of the table provides that microfinance programme has helped

participants to increase their level of employment. It is found that before joining the

microfinance programme 49 per cent of the total participants were employed and 51 per

cent were unemployed. But microfinance programme changed this scenario. The

participants started utilising the loan to adopt economic activities. As a result, 80 per cent

162

of the participants are employed in post-SHG situation. Hence, 31 per cent of the

participants who were unemployed in pre-SHG situation gained employment.

Table 5.21: Employment Status of the Participants and Non-Participants

Participants Pre-SHG Post-SHG

Non-participants Status of Employment

Jal. Hsp. Bti. Pun. Jal. Hsp. Bti. Pun. Jal. Hsp. Bti. Pun.

Employed 38 (42)

44 (59)

12 (46)

94 (49)

70 (78)

61 (81)

21 (81)

152 (80)

44 (49)

31 (42)

17 (65)

92 (48)

Unemployed 52 (58)

30 (41)

14 (54)

96 (51)

20 (22)

13 (19)

05 (19)

38 (20)

46 (51)

43 (58)

09 (35)

98 (52)

Total 90 (100)

74 (100)

26 (100)

190(100)

90 (100)

74 (100)

26 (100)

190 (100)

90 (100)

74 (100)

26 (100)

190(100)

Source: Field survey 2008. Note: The figures given in parentheses indicate percentages of participants and non-participants. The value

of Chi-square (χ2) is 20.72 between participants and non-participants. The table values at 5 per cent and 1 per cent with 1 degree of freedom are 3.84 and 6.63 respectively.

In order to measure the impact of programme, participants are also compared

with the non-participants. The employment status of non-participants is almost similar to

the employment status of the participants in their pre-SHG situation. The table shows

that 48 per cent of the non-participants are employed and 52 per cent are unemployed.

Chi-square (χ2) test shows the significant difference among the participants and non-

participants regarding their level of employment.

5.8.2 Average Employment Generated in Person Days

Table 5.22 shows the employment of the respondents measured in person days

per annum. It is found that participants were employed for 80 person days when they did

not join the microfinance programme. But after receiving the benefits of the programme

the participants are employed for an average 160 days. Thus, microfinance programme

has generated 80 additional days of employment per annum for the programme

participants. This addition in employment is the highest in Bathinda district followed by

Jalandhar and Hoshiarpur districts. In Bathinda district, the additional employment

generated is 93 days as compared to 84 and 70 days of additional employment for the

participants of Jalandhar and Hoshiarpur districts respectively. The table also shows the

comparison of participants with the non-participants in terms of their employment status.

163

It is found that the non-participants are employed for just 78 days per annum as

compared to 160 days for the participants. The table reveals that there is 100 per cent

increase in employment for the programme participants in their post-SHG situation over

the pre-SHG situation, while there is 105 per cent increase in employment days for the

programme participants as compared to the non-participants. In this way, microfinance

programme not only increases the number of persons employed but also provides the

employment for more number of days.

Table 5.22: Employment Generated in Person Days per Annum

Employment Status Increase in Employment Days Participants District Pre-SHG

Post- SHG

Non-participants



Analysis Jalandhar 79 163 85 84 (106) 78 (92) Hoshiarpur 83 153 61 70 (84) 92 (151) Bathinda 73 166 99 93 (127) 67 (68) Punjab 80 160 78 80 (100) 82 (105)

Source: Field survey 2008. Note: The figures given in parentheses indicate percentage increase in employment.

5.8.3 Nature of Economic Activities Undertaken

Microfinance programme has helped the participants to adopt various economic

activities. With the help of micro-loans, the programme participants have started small

self-employment activities such as stitching and embroidery, rearing milch animals, rope

and garland making, soap, surf, jam, chalk and candle making, petty shops, STD/PCOs

etc. Table 5.23 shows the nature of economic activities undertaken by microfinance

programme participants.

A perusal of the table provides that rearing of milch animals is a popular activity

among the sample households. Twenty-three per cent of the participants and 11 per cent

of the non-participants are engaged in rearing milch animals and they generate income

by selling milk. The main reason of choosing this activity may be that most of the

women are already engaged in this activity and at the same time it does not require any

special skill.

Stitching and embroidery is the second popular activity that is preferred by

participants. The table shows that 12 per cent of the participants are doing stitching and

164

embroidery work. Some of the participants are involved in stitching of the school bags,

travelling bags, bed covers, etc. which has a good market in the village itself. The

discussion with the participants shows that the reason of adopting and expanding these

traditional and less profitable activities is the lack of marketing support to sell other non-

traditional products.

Table 5.23: Nature of Economic Activities Undertaken by Participants and Non-

Participants

Participants Non-participants S. No. Activity Jal. Hsp. Bti. Pun. Jal. Hsp. Bti. Pun.

1. Milch animals 13 (14)

20 (27)

09 (34)

42 (23)

05 (06)

08 (11)

09 (35)

22 (11)

2. Stitching & Embroidery

09 (10)

14 (19) - 23

(12) 01

(01) 05

(07) - 06 (03)

3. Petty shop 10 (11)

06 (08)

06 (23)

22 (12)

01 (01)

01 (01) 02

(01)

4. Soap/ Surf Making

12 (14) - - 12

(06) 01

(01) - - 01 (01)

5. Labour/ Domestic Servant

05 (06)

05 (06)

02 (08)

12 (06)

09 (10)

11 (15)

03 (11)

23 (12)

6. Football Sewing 12 (14) - - 12

(06) 21

(23) - - 21 (11)

7. Rope making/ Garland making

01 (01)

08 (11)

02 (08)

11 (06)

01 (01)

01 (01)

01 (04)

03 (01)

8. Service 04 (04)

02 (03) - 06

(03) 05

(06) 03

(04) 03

(11) 11

(06)

9. Dairy/ STD/PCOs

02 (02)

02 (03)

02 (08)

06 (03) - - - -

10. Agriculture 02 (02)

02 (03) - 04

(02) - - - -

11. Others - 02 (01) - 02

(01) - 02 (03)

01 (04)

03 (02)

Total (I) 70 (78)

61 (81)

21 (81)

152 (80)

44 (49)

31 (42)

17 (65)

92 (48)

12. Not involved in any activity (II)

20 (22)

13 (19)

05 (19)

38 (20)

46 (51)

43 (58)

09 (35)

98 (52)

Total (I+II) 90 (100)

74 (100)

26 (100)

190 (100)

90 (100)

74 (100)

26 (100)

190 (100)

Source: Field survey 2008. Note: The figures given in parentheses indicate percentages of participants and non-participants.

It is also observed that 27 per cent of the participants are engaged in

manufacturing and small business activities like petty shops, dairy, garland making, rope

165

making, surf making, running STD/PCOs etc. as compared to just 3 per cent by non-

participants. This shows that microfinance programme participants are attracted towards

these non-traditional activities due to some skill training and motivation provided to

them under this scheme. But discussions with these participants also show that they are

not provided any type of marketing facilities so they have limited their production to

meet the local demands only. The table also shows that none of the non-participants and

just 2 per cent of the participants are involved in agriculture. It may be because of the

fact that most of the participants and non-participants are landless.

The impact of microfinance programme on the nature of employment generated

can be observed from the comparison of occupational difference of participants and the

non-participants. It is observed during the field survey that the football-sewing is an

activity where workers are paid very low wage against their hard work. Eleven per cent

of the non-participants are involved in this occupation as compared to 6 per cent of the

participants.

It is also found that only 6 per cent participants are engaged in domestic-aid as

compared to 12 per cent of non-participants. The job of a domestic servant gets scant

respect in the society and participants with the availability of micro-loans want to get rid

of it. With the help of loans they have started micro-enterprises of their own. This has

given them economic independence. However, 20 per cent of the participants and 52 per

cent of the non-participants are not involved in any sort of economic activity.

5.8.4 Impact of Occupational Training on Employment

Under the microfinance programme, occupational training is also provided to the

group members in order to encourage them to start their own income generation

activities. The various activities for which training is generally provided are: soap/surf

making, stitching and embroidery, dairy-farming, jam, squash, sauce, chalk, candle,

football, garland making, bee-keeping, vermi-compost and mushroom cultivation.

Training has a positive impact in generation of employment.

(i) Number of Participants Imparted with Training

Table 5.24 details the number of participants imparted occupational training

under the microfinance programme. A perusal of the table reveals that just 29 per cent of

the participants are provided training. The remaining 71 per cent of the participants are

untrained. District-wise, it is found that 34 per cent participants from Jalandhar, 26 per

166

cent participants from Hoshiarpur and 23 per cent participants from Bathinda are

provided training to start income generating activities.

Table 5.24: Number of Participants Imparted with Training

Districts Participants with training

Participants without Formal Training

Total Number of Participants

Jalandhar 31 (34) 59 (66) 90 (100) Hoshiarpur 19 (26) 55 (74) 74 (100) Bathinda 06 (23) 20 (77) 26 (100) Punjab 56 (29) 134 (71) 190 (100)

Source: Field survey 2008. Note: The figures given in parentheses indicate percentages of participants.

(ii) Training Imparted for Different Occupations

Table 5.25 shows various occupations for which training is generally provided to

the SHG members.

Table 5.25: Training Imparted for Different Occupations

Occupation Jalandhar Hoshiarpur Bathinda Punjab

Soap/Surf making 15 (17)

01 (01)

01 (04)

17 (09)

Stitching and embroidery

05 (06)

08 (11)

01 (04)

14 (07)

Dairy farming 04 (04)

03 (04) - 07

(04) Papad/squash/sauce/jam making - 02

(03) 02

(07) 04

(02) Chalk and Garland making - 05

(07) 02

(08) 07

(04)

Football Making 04 (04) - - 04

(02)

Mushroom growing 01 (01) - - 01

(00)

Bee- keeping 02 (02) - - 02

(01)

Total 31 (34)

19 (26)

06 (23)

56 (29)

Participants without any occupational training

59 (66)

55 (74)

20 (77)

134 (71)

Total 90 (100)

74 (100)

26 (100)

190 (100)


167

The table shows that nine per cent of the participants are imparted training related

to the soap and surf making. The table also shows that six per cent of the participants in

Jalandhar, eleven per cent of the participants in Hoshiarpur and four per cent of the

participants in Bathinda are provided training relating to stitching of clothes, school

bags, bed covers and embroidery and patch work. Four per cent of the participants are

provided training related to dairy farming in both Jalandhar and Hoshiarpur districts.

Four per cent of the participants belonging to Jalandhar district are provided training

related to football making and two per cent are given bee-keeping training. Three per

cent of the participants from Hoshiarpur district and seven per cent participants from

Bathinda district are given training related to the manufacturing of daily household

consumables such as jam, papad, squash and sauce making. But none of the participants

took this as an income generating activity, because the members complained that the cost

of their manufacturing is higher and they face tough market competition to sell their

product even though the quality of their products is better than those available in the

market. Therefore, participants prefer to undertake only those income-generating

activities which are easily marketable within the village itself. Similar results have been

given by Mishra et al. (2001) and Manimekalai & Rajeswari (2001) regarding marketing

difficulties faced by the programme participants.

(iii) Employment Status of Trained and Untrained Participants

The employment status of trained and untrained participants is compared in Table

5.26 in order to measure the impact of training on the level of employment. A perusal of

the table shows that 90, 95 and 83 per cent of the trained participants from Jalandhar,

Hoshiarpur and Bathinda districts are employed as compared to 71, 78 and 80 per cent of

the untrained participants respectively. Table 5.26: Employment Status of Trained and Untrained Participants

Number of Participants Number of Participants District Trained Employed Untrained Employed Jalandhar 31 28(90) 59 42(71)

Hoshiarpur 19 18(95) 55 43(78)

Bathinda 06 05(83) 20 16(80)

Punjab 56 51(91) 134 101(75) Source: Field survey 2008. Note: The figures given in parentheses indicate percentages of employed participants.

168

The table also reveals that only 9 per cent of the trained participants are

unemployed as compared to 25 per cent of the untrained participants. It shows that skill

development training plays a positive role in employment generation. Therefore, more

and more participants should be imparted training.

(iv) Impact of Training on Employment Generation in Person Days

Table 5.27 shows the impact of training on the employment generation in person

days. A glance at the table provides that the trained participants get employment for 193

days as compared to 145 days in the case of untrained participants. Thus, microfinance

programme creates 48 days of additional employment for trained participants. The

district-wise data exhibits that the trained participants of microfinance programme in

Jalandhar, Hoshiarpur and Bathinda districts are employed for 197, 172 and 234 days per

annum respectively. Similarly, the untrained microfinance programme participants of

these districts are employed for 144, 146 and 145 days respectively. So, skill training

programmes have not only increased the number of participants employed but also the

number of employment days per annum.

Table 5.27: Employment Generated in Person Days per Annum

Employment Days per Annum District Trained

Participants Untrained

Participants Increment

Jalandhar 197 144 53 (37)

Hoshiarpur 172 146 26 (18)

Bathinda 234 145 89 (61)

Punjab 193 145 48 (33) Source: Field survey 2008. Note: The figures given in parentheses indicate percentage increase in employment.

5.8.5 Impact of Self-Help Group Maturity on Employment

It will be interesting to know whether the maturity of the group influences the

level of employment of the programme participants. The meaning and concept of group

maturity has already been explained in Section 5.3.2. The impact of group maturity on

employment is measured by considering the following two factors:

(i) Number of persons employed according to the group maturity.

(ii) Employment generated in person days according to the group maturity.

169

(i) Number of Persons Employed according to the Group Maturity

The employment status of participants according to their group maturity is given

in Table 5.28. A perusal of the table reveals that in the sample taken for this study, 71

participants represent the groups which are less than or equal to three years old, hence,

called young groups; 74 participants fall in the groups which are three to six years old,

called middle-age groups; and 45 participants belong to mature groups, i.e., those

established for a period of more than six years.

The employment status shows that there has been 100 per cent increase in the

number of participants employed in post-SHG over the pre-SHG situation in the young

groups. However, in the middle-age and mature groups this addition is 68 and 29 per

cent respectively. The reason for this less increase is due to the fact that in these groups

large number of participants were employed in their pre-SHG, i.e., 38 out of 74 (51 per

cent) in middle-age groups and 34 out of 45 (76 per cent) in the mature groups as

compared to 22 out of 71 (31 per cent) in the young groups. So, there was a limited

scope for addition in the number of participants to be employed in middle-age and

mature groups.

The table also shows the total number of participants employed in post-SHG

situation. It is found that 62 per cent of the participants in the young groups, 86 per cent

in the middle-age groups and 98 per cent of the participants in mature groups are

employed in post-SHG. This shows that as the group advances in years, it enables more

number of group members to engage themselves in some self-employment activities by

utilising the loans. The district-wise details show that in Jalandhar district 58, 87 and 100

per cent of the participants of young, middle-age and mature groups are employed

respectively. In Hoshiarpur district, 62, 85 and 96 per cent of the participants; and in

Bathinda district, 71, 88 and 100 per cent of the participants from young, middle-age and

mature groups are employed respectively.

In this way, it is clear from the table that as the group attains maturity almost all

the participants get employment. In the initial stages of group formation, group loans are

not generally utilised for income and employment generation purposes but as the

members stabilise in the groups, they start utilising these loans for income generating

purposes and get employment too. Thus, to generate income and employment on

sustainable basis, the sustainability of the groups is very essential.

170

Table 5.28: Number of Participants Employed according to the Group Maturity

Number of Participants Employed Young Groups Middle-Age Groups Mature Groups District

No. of Participants

Pre-SHG

Post-SHG

%age Change*

%age Employed#

No. of Participants

Pre-SHG

Post-SHG

%age Change*

%age Employed#

No. of Participants

Pre-SHG

Post-SHG

%age Change*

%age Employed#

Jalandhar 36 12 21 75 58 39 18 34 89 87 15 08 15 88 100

Hoshiarpur 21 06 13 117 62 27 15 23 53 85 26 23 25 09 96

Bathinda 14 04 10 150 71 08 05 07 40 88 04 03 04 33 100

Punjab 71 22 44 100 62 74 38 64 68 86 45 34 44 29 98 Source: Field survey 2008. *Percentage change in employment in pre- and post-SHG. # Percentage of total participants employed in post-SHG.

Table 5.29: Impact of Group Maturity on Employment in Person Days per Annum

Employment of Participants in Person Days per Annum Young Groups Middle-age Groups Mature Groups District No. of


Post-SHG

Increment No. of Participants

Pre-SHG

Post-SHG

Increment No. of Participants

Pre-SHG

Post-SHG

Increment

Jalandhar 36 57 104 47 (82) 39 84 183 99 (118) 15 115 250 135 (117)

Hoshiarpur 21 26 74 48 (185) 27 80 151 71 (89) 26 132 217 85 (64)

Bathinda 14 46 137 91 (198) 08 92 181 90 (98) 04 130 232 102 (78)

Punjab 71 46 102 56 (122) 74 83 171 88 (106) 45 126 229 103 (82) Source: Field survey 2008. Note: The figures given in parentheses indicate percentage increase in employment days.

171

(ii) Employment Generated in Person Days according to the Group Maturity

The impact of group maturity on the employment generated in person days for the

programme participants is shown in Table 5.29. A perusal of the table provides that on

an average the participants of young groups get employment for 102 days in their post-

SHG situation. The participants of middle-age and mature groups are employed for 171

days and 229 days respectively. The addition in employment generated for the

participants in the post-SHG situation over their pre-SHG situation is 56, 88 and 103

days for young, middle age and old groups respectively.

The district-wise details show that in Jalandhar there is 47, 99 and 135 days of

additional employment generated among the young, middle-age and mature group

participants respectively. This addition in employment is 48, 71 and 85 days for

Hoshiarpur district; and 91, 90 and 102 days for Bathinda district. Hence, it is clear that

the group maturity not only leads to the employment of large number of programme

participants but also those falling in mature groups get employment throughout the year.

5.8.6 Employment Status of Different Types of Participants

It is found that 49 per cent of the group participants were employed and 51 per

cent were unemployed at the time of joining the microfinance programme as discussed in

section 5.8.1. The survey shows that all these participants receive bank loans but utilise

them for different purposes. They may not necessarily utilise these group loans for

productive purposes. However, some of the participants who were employed at the time

of joining the microfinance programme utilised the group loans to expand or diversify

their existing economic activities. They may be termed as the participants who expanded

business in post-SHG situation. But some of these participants did not utilise loans for

productive purposes. Their level of employment and income remains the same as in their

pre-SHG situation. They may be termed as the participants who failed to expand their

business.

There are still other participants who were unemployed at the time of joining the

microfinance programme and they invested the group loans for self-employment. These

participants may be termed as newly employed participants. But some of these

participants did not invest the amount of loan for starting income generating activities.

They remained unemployed even after joining the microfinance programme.

172

The following factors have been taken into account to study the impact of

microfinance programme in generating employment and income for these different types

of participants:

(i) Number of participants employed

(ii) Average employment generated

(iii) Average income generated.

(i) Number of Participants Employed in Post-SHG Period

The data showing the employment status of these different types of microfinance

programme participants is given in Table 5.30. A perusal of the table provides that 80 per

cent of the participants are employed and 20 per cent are unemployed. Out of 80 per cent

of the employed participants 30.5 per cent are newly employed, 30.5 per cent of the

participants have expanded their business and the remaining 19 per cent of the

participants have failed to expand their business even after receiving loans.

Table 5.30: Employment Status of Participants under the Microfinance Programme

Participants Employed in Post-SHG District Total No. of

Participants Newly Employed

Expanded Business

Not Expanded

Total Employed

Unemployed

(1) (2) (3) (4) (2+3+4=5) (1-5=6)

Jalandhar 90 32 (36)

15 (17)

23 (25)

70 (78)

20 (22)

Hoshiarpur 74 17 (23)

33 (44)

11 (15)

61 (82)

13 (18)

Bathinda 26 09 (35)

10 (38)

02 (08)

21 (81)

05 (19)

Punjab 190 (100)

58 (30.5)

58 (30.5)

36 (19.0)

152 (80.0)

38 (20.0)


The employment status of different types of programme beneficiaries shows that

78 per cent participants of Jalandhar district are employed in post-SHG. Out of these, 36

per cent of the participants are newly employed after joining the microfinance

programme. Seventeen per cent of the employed participants are those who are also

employed in their pre-SHG situation and expanded/diversified their previous existing

business by utilising the group loans. Twenty-five per cent of the participants have failed

173

to expand their previous business. Out of 82 per cent of the employed participants

belonging to Hoshiarpur district 23 per cent of the participants are newly employed, 44

per cent have expanded their business, and 15 per cent have not expanded their business

in the post-SHG situation. Out of 81 per cent of the employed participants from Bathinda

district 35 per cent participants are newly employed, 38 per cent have expanded their

business, and 8 per cent have not expanded their business.

(ii) Average Employment Generated for Different Types of Participants

Table 5.31 shows the employment generated in person days for different

beneficiaries of microfinance programme.

Table 5.31: Average Employment Generated in Pre-SHG and Post-SHG

(in person days)

District Type of Participant No. of Participants

No. of Days

Employed in Pre-SHG

No. of Days

Employed in Post-

SHG

Incremental Employment

Generated

Newly employed 32 - 189 189

With expansion in business 15 179 280 101

Jalandhar

Without any expansion in business 23 190 190 -


With expansion in business 33 134 218 84 Hoshiarpur



With expansion in business 10 165 283 118 Bathinda



With expansion in business 58 151 246 95 Punjab



174

The table reveals that the participants who are employed in pre-SHG and have

also expanded or diversified their business in post-SHG are employed for 246 days in

their post-SHG as compared to 151 days in pre-SHG situation. In this way, there is an

addition of 95 days per annum in employment. The participants who are unemployed in

the pre-SHG situation and start new business after joining microfinance programme are

employed for 166 days per annum. The participants who are employed in pre-SHG but

have not expanded their business in post-SHG are employed for 176 days and there is no

increase in their employment as a result of microfinance programme.

The table also shows the employment generated in person days for different

beneficiaries of microfinance programme in Jalandhar, Hoshiarpur and Bathinda

districts. The participants belonging to Jalandhar district who expanded their business

with the help of group loans are employed for 280 days in post-SHG as compared to 179

days before joining the SHG. In this way, the addition in employment is 101 days per

annum. The participants who have started new business after joining the programme are

employed for 189 days per annum. The participants who have not expanded their

business are employed for 190 days both in their pre and post-SHG situation. There is no

addition in their level of employment.

The participants belonging to Hoshiarpur district who have expanded their

business with the help of microfinance programme are employed for 84 days more per

annum in post-SHG as compared to pre-SHG. The participants who have started new

business are employed for 139 days per annum. The participants who have not expanded

their business after joining microfinance programme are employed for 156 days per

annum.

The participants of Bathinda district having expanded business are employed for

283 days in post-SHG. The addition in their employment is 118 days per annum over

their pre-SHG. The participants who are not employed in their pre-SHG but start new

business after joining the microfinance programme are employed for 137 days per

annum. The participants who have not expanded their business after joining microfinance

programme are employed for 125 days per annum. Thus, it can be said that the

participants who have expanded their business are employed for greater number of days

as compared to other participants. But the addition in employment generated is the

highest for newly employed participants.

175

(iii) Average Income Generated for Different Types of Participants

Table 5.32 shows the average income generated per month for these different

microfinance beneficiaries. A perusal of table provides that the average income of the

participants who have expanded their business is Rs. 3,300 per month in post-SHG

situation as compared to Rs. 1,505 in pre-SHG situation. Hence, the addition in their

income is Rs. 1,795 per month. The average income of participants who are newly

employed as a result of microfinance programme is Rs. 1,503 per month. The whole

income is generated through the benefits of microfinance programme. There are still

other participants who have not expanded their business and their income is Rs. 1,365

per month. There is no addition in their income as a result of microfinance programme

participation.

Table 5.32: Average Income Generated in Pre- and Post-SHG (in Rs. per month)

District Type of Participant No. of Participants

Average Income in Pre-SHG

Average Income in Post-

SHG

Incremental Income

Generated

Newly employed 32 - 1,583 1,583

With expansion in business 15 1,695 3,649 1,954

Jalandhar

Without any expansion in business 23 1,467 1,467 -


With expansion in business 33 1,341 3,132 1,791 Hoshiarpur



With expansion in business 10 1,760 3,330 1,570 Bathinda


Newly employed 58 - 1,503 1,503 With expansion in business 58 1,505 3,300 1,795 Punjab Without any expansion in business 36 1,365 1,365 -


176

The table also depicts the average income of different types of microfinance

beneficiaries in Jalandhar, Hoshiarpur and Bathinda districts. The average income of the

participants of Jalandhar district who have expanded their business with the help of

group loans is Rs. 3,649 per month in the post-SHG situation as compared to Rs. 1,695

per month before joining the SHG. The addition in their income is Rs. 1,954 per month.

The income of the participants who are newly employed after joining SHG is Rs. 1,583

per month. The income of the participants who have not expanded their business is Rs.

1,467 per month both in pre and post-SHG situation. The average income of participants

of Hoshiarpur district who have expanded their business is Rs. 3,132 per month in post-

SHG situation as compared to Rs. 1,341 in pre-SHG situation. The addition in income is

Rs. 1,791 per month. The average income of participants who are newly employed as a

result of microfinance programme is Rs. 1,497 per month. The average income of the

participants who have not expanded their business is Rs. 1,173 per month both in the pre

and post-SHG situation.

The income level of different microfinance participants in Bathinda district

shows that the average income of participants who have expanded their business is Rs.

3,330 per month in post-SHG situation as compared to Rs. 1,760 in pre-SHG situation.

The addition in income is Rs. 1,570 per month. The average income of participants who

are newly employed as a result of microfinance programme is Rs. 1,233 per month. The

average income of the participants who have not expanded their business is Rs. 1,250 per

month both in the pre and post-SHG situation. Therefore, it is evident that addition in

income is higher for the participants who expanded their business using the microfinance

loans as compared to the income of the newly employed participants. But the analysis of

Table 5.31 shows that the addition in employment was larger for the newly employed

participants as compared to the participants who expanded their existing business. Thus,

comparing the results of both these tables it can be said that the participants who

expanded their business were already employed before joining the microfinance

programme. So, they are more experienced and are able to earn more even by working

for lesser number of hours.

5.9 Determinants of Employment (Regression Analysis)

Simple linear regression equation is fitted to the field data in order to determine the

factors affecting the employment level of the participants. The independent variables

selected for this purpose are: age of the participant, education level of the participant, age

177

of the SHG, number of group loans received, amount of group loans used for productive

purposes, employment days of the participants in pre-SHG situation, and the level of

household income. Employment days in post-SHG situation are taken as dependent

variable.

The age of participants should not be confused with their experience. The age

reflects the rigour and vigour of a person for doing the work. Education may be a very

important variable for wage employment, but it will be interesting to see its relationship

with self-employment. Level of group maturity plays an important role in increasing the

self-employment. It is mainly because of the fact that in the initial years loans may be

used for consumption purposes but the subsequent loans are used as investment in certain

economic activities. More number of loans means the successful repayment of the loans.

Amount of loan used for productive purposes is a very important determinant in

generating self-employment. The employment in pre-SHG and the total household

income is also taken into consideration.

The coefficients of employment determinants are calculated with the help of

following linear equation:

EMP = b0 + b1 AGE + b2 EDU + b3 GAGE + b4 NUMLOAN + b5 ALONPROD +

b6 EMPBSHG + b7 TINCOM

Where:

EMP = Employment in post-SHG in person days

AGE = Age of the participant in years

EDU = Education level of the participant

GAGE = Self-help group age to know the maturity of the group

NUMLOAN = Number of times loan taken

ALONPROD = Amount of loan used for productive purpose in Rs.

EMPBSHG = Employment in Pre-SHG in person days

TINCOM = Total household income in Rs.

The results of this regression equation are shown in Table 5.33. A perusal of the

table provides that the coefficients of age and education level of the participants appear

in the negative. It may be due to the fact that as the age of participants advances, they

178

spend less time for work and more time for other jobs or leisure. Similarly, increase in

education level of the participants has led to limited involvement in self-employment

activity. However, both these variables are not statistically significant. Maturity of the

group, i.e., group age is considered to be very important variable for increasing

employment. The regression coefficient of group age is high and significant which shows

that maturity of an SHG leads to significant addition in the employment. The coefficient

of number of group loans received shows that the large number of group loans leads to

high employment level but this coefficient is also not statistically significant.

Table 5.33: Regression Analysis

Standardised Coefficients Variables Jalandhar Hoshiarpur Bathinda Punjab

Constant (0.373) (0.197) (1.339) (1.405)

Age of the participants -0.098 (-1.330)

-0.012 (-0.159)

0.017 (0.170)

-0.041 (-0.839)

Education level of the participants

-0.063 (-0.810)

0.013 (0.181)

-0.070 (-0.645)

-0.041 (-0.808)

Maturity of the Group 0.317 (4.382)*

0.116 (1.603)

0.147 (1.273)

0.198 (4.038)*

Number of times loan taken

0.120 (1.567)

0.001 (0.011)

-0.176 (-1.658)

0.002 (0.040)

Amount of loan used for productive purposes

0.294 (3.888)*

0.324 (4.964)*

0.392 (4.109)*

0.337 (7.212)*

Employment in pre-SHG

0.543 (7.611)*

0.707 (9.775)*

0.788 (6.936)*

0.597 (12.80)*

Total household Income 0.097 (1.375)

0.183 (2.488)**

-0.195 (-1.524)

0.105 (2.188)**

R2 0.656 0.751 0.862 0.647 * Significant at 1 per cent level. ** Significant at 5 per cent level. Source: Own calculation from field survey data 2008. Note: The figures given in parentheses indicate t-values.

The amount of loans utilised for productive purposes is significantly influencing

the employment of the participants. The amount of loans utilised for starting new

ventures or expanding the existing business contribute positively towards the

employment generation. The employment of participants in the pre-microfinance is also

taken as a variable. The coefficient for this variable is positive, very high and statistically

significant showing that the participants who are already employed in pre-SHG situation

179

are getting more benefits of microfinance regarding employment. In other words, it may

be interpreted that well-established participants have more benefits of microfinance than

those who are new entrants in any economic activity. Family income of the participants

is also positively related to the level of employment. The value of this coefficient is

positive and significant which shows that the sound financial position of the family

contributes to increase the level of employment. The coefficient of determination (R2)

shows the goodness of fit. It represents the proportion of variance in the dependent

variable explained by the linear combination of the independent variables in the model.

The magnitude of R² is 0.647 in Punjab which shows that the regression equation

explains about 65 per cent of the variation.

5.10 Concluding Observations

This chapter studies the impact of microfinance programme on poverty and

employment. Impact on poverty is measured through changes in individual and

household incomes, income inequalities and household vulnerability. The analysis of

primary data showed that microfinance programme has increased the income of the

programme participants. It is also found that the programme has reduced the inequalities

in the distribution of household income. The study shows that the programme

participants are less vulnerable to the economic shocks faced by them as compared to the

non-participants. They are able to manage the financial crisis out of their savings and

borrowings from their group. However, the non-participants mainly depend on the

exploitative money-lenders. The study also shows that the programme is not specifically

targeting the BPL households. The poor people marginally above the poverty line are the

main entrants of the programme. It is found that just 19 per cent of the participants were

BPL when they joined the SHGs. The measurement of poverty among the sample

households and the results of composite poverty index show that extremely poor people

are not the actual beneficiaries of the programme. It has been seen that the impact of

microfinance programme is maximum on the moderate poor people.

Impact of microfinance programme on employment shows that more number of

programme participants are engaged in income generating activities as compared to the

non-participants. Participants have also started non-traditional income generating

activities. The study shows that participants of mature groups are better-off in terms of

their employment and income as compared to the young group participants. The poverty

180

and employment regression also show that the group maturity in significantly

contributing in increasing the employment and reducing poverty.

Under microfinance programme, occupational training is provided to the

programme participants, which helps them to start non-traditional manufacturing

activities. The survey results further reveal that the trained participants are employed for

more number of days as compared to the untrained participants. But only a limited

number of participants, i.e., 29 per cent are provided occupational training. Moreover,

lack of product marketing facilities is limiting the effect of training related to the

commercial products. Therefore, most of the programme participants are engaged in

traditional business activities. The study also brings out that among the programme

participants the addition in employment is higher for the participants who are newly

employed with the help of micro-credit but the addition in income is greater for the

participants who were employed in pre-SHG situation also and expanded their business

with the financial support provided under the microfinance programme. Therefore, it can

be said that the already working participants are more benefited as their work experience

helps them to earn more by working for the same or less hours as compared to the

participants who have started their new business.

Notes

[1] Poverty in India, Azad India Foundation, Available at: http://www.azadindia.org/social-

issues/poverty-in-india.html [Accessed on: 21.09.2009]

[2] Poverty, Available at: http://www.angelfire.com/planet/worldoneglobe/Poverty.htm

[3] Employment Scenario in India, Press Information Bureau, Government of India,

Available at: http://pib.nic.in/release/rel_print_page1.asp?relid=34349 [Accessed on:

02.01.2008].

[4] Issues concerning Unorganised Workers’ Social Security Act, 2008, Available at: http://

labour.nic.in/lc/44Slc/SLC-conference-Agenda.pdf [Accessed on: 02.01.2008].

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