javed ghulam hussain, samia mahmood

2012 Cambridge Business & Economics ConferenceISBN : 9780974211428

Impact of Microfinance Loan on Poverty Reduction amongst Female

Entrepreneurship in Pakistan

Javed Ghulam Hussain

Birmingham City University, City North Campus

Franchise Street, Perry Barr, Birmingham, B42 2SU, UK

Email: [email protected]

Samia Mahmood

Birmingham City University, City North Campus

Franchise Street, Perry Barr, Birmingham, B42 2SU, UK



Track: Economics and Finance

Cambridge Business and Economics Conference

June 27-28, 2012Cambridge, UK 1

mailto:[email protected]




Impact of Microfinance Loan on Poverty Reduction amongst Female Entrepreneurship in

Pakistan

ABSTRACT:

The purpose of this paper is to examine the impact of microfinance loan on poverty reduction for

female entrepreneurs as perceived by fund providers and experienced by aspiring female

entrepreneurs in a developing country context. This study is based on an empirical investigation

of 123 semi structured questionnaires and case study of 10 female entrepreneurs who secured

funds for their enterprises. The study is exploratory and broadly focused. Emergent empirical

results explores the impact of access to microfinance on poverty reduction of women by

establishing microenterprise and using case study approach to assess the attributes of female

entrepreneur’s client and examines what may constitute success or failure in enterprise and

household context. The research findings suggest entrepreneurial attributes and characteristics

are critical for the success for an enterprise in general and the improvement in household of

women in particular. The study contributes to the body of literature by attempting to understand

and analyse the nature of micro clients’ success indicators, outcomes such as ability of individual

to break out of poverty, improvement in family health, educational engagement of children and

enhanced skills such as product knowledge, peer mentoring and business networks which

contribute towards the success.



INTRODUCTION

The analysis of statistics by Minnitie et al., (2005) suggests women’s economic activity is central

in promoting and enhancing growth prospects of world economies. Given such recognition it is

important for all, but more specifically emerging economies to offer conducive economic and

financial environment for females to engage in self employment. It is recognised that poverty is a

complex inter linked and complicated phenomenon that cannot be considered or measured in

terms of monetary value. According to United Nation Development Program UNDP Annual

Report (2008) “lack of access to essential resources goes beyond financial hardship to affect

people’s health, education, security and opportunities for political participation”. Poverty is

traditionally viewed as lack of income, assets and the resources but recent studies recognise that

it includes issues related to dignity and autonomy (Cagatay, 1998). Weiss, et al., (2003)

distinguish the various groups of poor in order to understand degree and range of measure of

poverty. Weiss divide poor into two groups, one that are long term or chronic poor and other that

are transitory poor, those who temporarily fall into poverty as a result of the adverse shock”. The

Chronic poor are further divided into groups of destitute “those who are either so physically or

socially disadvantaged that without welfare support they will always remain in poverty” and non

destitute “the larger group who are poor because of their lack of assets or opportunities”. The non

destitute group may be distinguished by depth of poverty with those significantly below the

poverty line termed ‘core poor’ and transitory poor in order to develop strategic policies directed

at specific cause (Weiss, et al., 2003).

Female poverty is an extremely important issue in the study of poverty alleviation due to size of

the population and the critical role they hold to ‘up skill’ and ‘empower’ future generations.

Poverty is viewed as a process in which instead of focusing on what poor lack, the focus is on



what assets they own and resources they can access. Assessing poverty is a challenge and often

qualitative approaches are used to identify participants own criteria to develop strategies for

poverty solutions (Cagatay, 1998). This approach to poverty (Cagatay, 1998) has ‘far reaching

implications for analyzing the general nature of poverty as well as the relationship between

gender inequalities and overall poverty levels’. Attempt to frame poverty and its systems

continue to evolve but the most accepted approach is one that is offered by UNDP that helps to

see the cause of poverty not only its symptoms. The UNDP measurement of human poverty

focuses on capabilities such as clean water, health services and level of literacy. Such an

approach attempts to reconcile capability approach with the absolute and relative poverty. The

concept of human poverty is helpful in clarifying the relationship between gender inequality and

poverty, as it focuses on gender differences in deprivation of education, health, life expectancy

and socially constructed constraints on the choices of various groups such as women or lower

castes (Cagatay, 1998).

Poor people face trade-offs between different dimensions of poverty, however women encounter

many more because of gender inequalities in distribution of income, access to credit, control over

property or earned income and gender biases in labour market and social exclusion in variety of

economic and political institutions (Cagatay, 1998); these factors are more prevalent in emerging

economies and to a lesser extent in developed economies. Lucy et.al., (2008), in her study in

Bangladesh reported that all citizens of the country suffer from poverty but women and children

bear most of the poverty burden as women continue to face discrimination in the area of health,

nutrition, access to education, employment and political participation. Women at large and more

specifically in emerging economies are not only at greater risk of chronic poverty but also

vulnerable to transient poverty due to familial, personal or social or economic crises,



macroeconomic policies, political and ethnic conflicts or health related crises. Women are also

time poor (time committed to raising family) and much of their work is economically

unrecognised since it is unpaid, yet such an activity is essential for the future well being and

enhancement of social care and family’s empowerment. Nevertheless, female are robust and

adopt different strategies to deal with adversities but there is a need for formulating

macroeconomic policies to eradicate poverty amongst women; it is extremely important issue to

be left to market forces.

To gain an insight into the link between poverty and economic growth, Morrison et al., (2007)

develops a framework to examine the link between poverty reduction and economic growth.

According to Morrison, a given level of male earnings leads to improvements in women’s

productivity and earnings and children wellbeing which results in poverty reduction and

economic growth both simultaneously and in future growth. On the other hand increase in female

earnings stimulates short term growth and reduces current poverty and stimulates long term

growth and reduces future poverty through higher consumption expenditures and higher savings

respectively, an exit map observed with families exiting poverty threshold. Moreover increased

female earnings lead to higher bargaining power of women in the household that directly

promotes child well being and educational access to education. Economically active women have

positive correlation with the economic development. It is reported that a value chain program

that target poor women of Pakistan benefits more than 50 percent of all participants from elevated

status in the household due to their greater economic contribution. Women engagement in

enterprise also leads to broader empowerment on a number of levels; participation in community

groups, changing family relationships, and engagement with the larger society (Jones, et

al., 2006). To capitalise and harness women efforts, the development programs explicitly



inculcate women participation to achieve the goal of economic success. More specifically access

to credit is essential (Cagatay, 1998), to enable women to gain a foothold on economic ladder to

help them to uplift their families well being.

CONCEPTUAL AND CONTEXTUAL ISSUES

The poverty is a major challenge faced by most of the developing world and in some cases in

pockets of developed countries too, this calls for strategies and development programs which

may alleviate poverty and promote self-reliance. To achieve this objective microfinance has

potential to make significant contribution towards reduction of poverty (Mawa, 2008).

This optimism about microfinance is reflected in various empirical studies. Morris and Barnes,

(2005) studied three microfinance programs in Uganda and he found reduction in financial

vulnerability through diversification of income source and accumulation of assets. Positive

impact of the microfinance program in Uganda includes addition of new products and services,

improved or expanded enterprise activities and markets, reduced cost of inventory purchases and

increase in sales volume. Many scholars refer to Bangladesh as an example to illustrate the

positive results linked with improved access to finance through microfinance, however, it is noted

that the presence of microfinance in Bangladesh remain limited to a few regions. For instance Pitt

and Khandker, (1998) study three group based programs in Bangladesh and found an increase in

annual consumption expenditures. They reported the increase of every 18 taka for every 100 taka

borrowed by women and 11 taka for every 100 taka borrowed by men. Chemin, (2008) reported

that the participants of microfinance programs in Bangladesh have 3% more income for

expenditure than the similar non participants. Moreover, he found positive impact of

microfinance on supply of labour and both for male and female school enrolment. Another study



on Bangladesh microcredit program argued that the impact of microcredit is short lived. The

improvement associated with micro credit are associated with lower objective and subjective

poverty with strong impact of it on poverty for about six years, thereafter the resulting benefit

level off (Chowdhury, et al, 2005). Khandker, (2005) using panel data from Bangladesh indicate

that microfinance contributes to annual poverty reduction for more than half of the 3 percentage

for microfinance program participants. The benefits of microfinance spill over to wider

community through local income growth that leads to increase expenditure that reduces the

average village poverty level by 1 percentage point each year in program areas.

Critiques of microfinance, through research have shown that poverty is not reduced by

microfinance it just burdened the poor with additional debt. Coleman, (1999) conducted his study

on credit program of Northeast Thailand and concluded that there is insignificant impact of credit

program on physical assets, savings, production, sales, productive expenses, labour time and the

expenditures on health care and education. In later study by Coleman, (2006), he refines the

methodology used in the previous study and concluded that credit programs are bias in favour of

better off and tend to be skewed towards wealthier than the poor. He found that the impact of

the program is positive on household welfare of the richer committee members than rank-and-file

members of microfinance institution. Similarly Duong and Izumida, (2002) conducted a study on

rural development finance in Vietnam, they also concluded that banks are rationing the credit on

the basis of reputation, collateral of the household and the amount of credit applied.

The studies on microfinance are criticized for the methodologies employed to investigate the

impact of microfinance on poverty. Such as the work of Pitt and Khandker, (1998) is criticized on

the ground that low land holding constraint of less than half an acre was not strictly enforced in

the sample (Weiss, et al, 2003). Morduch, (1998) reworked on it by simple comparisons that



takes into account the bias not controlled in previous work. But he found no impact of

microfinance program on consumption or increase in educational enrolment of children. However

Pitt, (1999) on re-examination of the work reconfirms the earlier positive findings in Pitt and

Khandker, (1998). Chemin, (2008) criticized that Pitt and Khandker, (1998) overestimate the

results by not enforcing the eligibility criteria of land holding for borrowing and Morduch, (1998)

underestimate the results as he did not account for non-random program placement. Similarly the

results of the study by Coleman, (1999) appears highly questionable on the ground that after

months of village bank membership there is no impact on the income or asset variables (Weiss, et

al, 2003). From this discourse it is found that empirical evidence on the impact of microfinance

on poverty not only differs in their conclusions showing mixed results in different countries but

also casts doubt on the ground of biases caused by different statistical techniques used. The

conflicting results suggests further research on issue of microfinance to gain an in-depth insight

of its impact on poverty reduction will lead to higher level confidence of the impact

microfinance for poverty reduction measures.

On the other hand Hermes and Lensink, (2007) argued that although microfinance has a positive

impact on economic development, it has not reached the poorest of the poor. Microfinance is a

good method to fight poverty but there is a need to target poorest borrowers first. The

microfinance institutions have to make distinction between “marginally poor” and “very poor”

(Sengupta and Aubuchon, 2008). As Weiss.et al., (2003) pointed out that the microfinance loan

officers and members of borrowers in group lending may exclude the very poor from borrowing

because of high risk of bad credit. Chemin, (2008) also in his study concluded that microfinance

is not targeting the poorest. A research on seven Micro Finance Institutions (MFIs) in four

countries that are Bolivia, Bangladesh, Uganda, and the Philippines, to compare clients of MFIs



with non-clients, concluded that “most clients come from moderately poor and vulnerable non-

poor households, with some clients from extreme-poor households also participating; programs

that explicitly target poorer segments of the population generally have a greater percentage of

clients from extreme-poor households; and destitute households are outside the reach of

microfinance programs” (Helms, 2006).

Helms, (2006) indicated that microfinance is serving limited number of clients and many

potential clients remained unserved; he points out a different view as “Microcredit is not

appropriate for the destitute and hungry who have no reliable income or means of repayment. In

many cases, small grants, infrastructure improvements, employment and training programs, and

other nonfinancial services may be more appropriate for destitute people”. Mosley and Rock,

(2004), from their study of six African microfinance institutions, suggested that the advantage

of microfinance is that it reaches vulnerable, non-poor, the working poor or entrepreneurial poor.

Microfinance operations benefits the extreme poor indirectly through labour market as poor

people enter into labour market as employees of microfinance clients, and human capital as

increased expenditures on education and health extend to poor through intra-household and inter-

generational effects and social capital externalities (enhancement of social capital for the clients

extends to poor through extension of credit groups to include poor and through stabilisation of

village income) than by direct lending.

Microfinance is often criticized on the grounds that it is administration is costly due to high

transaction and information cost and that is why most of the Microfinance programs depend on

donor subsidies. Most importantly there are few rigorously tested empirical research studies on

poverty reduction effect of micro-credit (Hermes and Lensink, 2007). Therefore, there is still

room for research in area of microfinance to find out its effect on poverty alleviation. More



specifically, it would be beneficial to investigate microfinance impact in a specific region from

the direct interaction with the borrowers and lenders of such program to identify the real impact

of it on poverty reduction in that country and towards this end this research.

Use of Microcredit to reduce poverty and enhance economic growth appears to have gained

recognition in developed and emerging economies. For example, State Bank of Pakistan

Quarterly report, (2005) offered its own model to evaluate performance of Microfinance

providers. Microfinance is considered an effective tool to fight poverty through enabling

individuals to engage in self employment. Microfinance institutions tend to target women as

poorest segment of the society which helps to enhance the women empowerment. The logic of

the proposed approach is that female participation in the economy leads to improvement in

gender equality and has a positive impact on the status of women within family decision making

that enhances social status of women that leads to lower birth rate and increase the family

wellbeing.

The studies above and the ensuing debate have provided insight into the role of Microfinance

institutions to alleviate poverty. These studies have contrasted benefits with challenges and

considered the wider social dynamics which emerge when access to finance is offered to females.

In the context of Pakistan, 6th most populated country in the world, 169.9m and 23% of its

population living below the poverty line that is $1.25 a day is a challenge for policy makers

within country and donors too. Up till 2005-06 the efforts to reduce poverty were having some

positive impact but the world economic and political crises have negated improvement in poverty

reduction (Economic Survey of Pakistan, 2009-10) and these findings are corroborated by

Multidimensional Poverty Index (MPI)i 2010 indicators. However, poverty amongst women



remains a cause for concern for government and international community at large. 55.5% women

are living below poverty line (United Nations Human Poverty Index, 1995 cited in Goheer, 1999,

p.2) and they experience greater barriers to break out from poverty trap due to market

inefficiencies which are compounded with social, religious and cultural norms. To break this

cycle of poverty microfinance is often considered to be an effective strategy to enable poor and

vulnerable females, the most marginalised sections of the population, to engage with economic

activity. This study attempts to examine how Microfinance institutions impact on the well being

of females and what are the factors contributing towards the success of women engaged with

MFIs?

RESEARCH METHODOLOGY

In an attempt to answer above questions and to determine the impact of microfinance for female

borrowers this exploratory study was carried out using a structured questionnaire by a female

researcher herself to overcome cultural and gender sensitivities. The questionnaire was designed

to gather information both qualitative and quantitative by writing a range and variety of questions

close ended, rank order, open ended and multiple questions to ascertain full range of experiences

and let the responses to be triangulated. In total 123 useable questionnaires were collected which

were completed by the women entrepreneurs who used microfinance facility. The quantitative

analysis using SPSS was conducted to analyse the impact of microfinance on poverty reduction

by examining increase in income, family health and education; the analysis used binary logistic

regression. Furthermore the open ended response to question were recorded and analysed using

the inductive analysis. To explore in detail the connection of microfinance and poverty reduction

with enterprise development, qualitative study approach was carried out by interviewing 10

women to gain in-depth understanding. These 10 women either lived below or were just above



the poverty line. The geographic scope of this study was limited to participants from mainly four

districts of the Punjab region of Pakistan. Punjab being relatively effluent region where female is

more likely to engage in self employment. There are some limitations inherent in this research

study. Firstly, it is a static study that captures “certain aspect of reality” at a particular time of the

survey (see Johnson and Loveman, 1995) which may pollute experiences of the participants

hence respondents may have over or under stated certain responses. Secondly, it is possible that

some respondents did not give their true opinion when responding to questions; this could have

been due to issue of trust or their reluctant to disclose their true experiences with an outsider.

Therefore, caution must be exercised in generalising the emergent results of this study as it

always the case with all case studies.

FINDINGS

Quantitative Analysis

After careful examination of literature the three main research questions to be tested were

formulated in form of hypothesis:

H1. The increase in amount of microfinance loan used in enterprise development by women leads

to an increase in income of the family

H2. The increase the amount of microfinance loan leads to increase in expenditures on children’s

education

H3. Higher amount of microfinance loan leads to better health and nutrition of the family.



There are wide range of definitions for entrepreneurs but for the purpose of this research the

definition of woman entrepreneur is that who takes risk to start a new income generating activity

(new enterprise) or invest in already establish income generating activity (old enterprise). The

poverty is made up of many factors such as income, consumption, asset, health and education.

But in quantitative research only income, health and education of the family is considered. It is

worth noting that the microcredit is disbursed only to one woman in a family and only those

women are studied in this research who themselves used this money to set-up a microenterprise.

However, the impact of microfinance is analysed on the basis of income, health and education of

the family instead because women is considered to be benefiting the family with the productive

use of loan (Morrison et al., 2007).

The model

The equation for simple liner regression from the equation of straight line is:

(1)

Where is the Y intercept and β is the coefficient and X is the independent variable and is a

residual term.

The logistic regression is the, “prediction of the probability of Y occurring given known values of

X’s” (Field, 2009). The logistic model equation with the probability of Y occurring, e the

base of natural logarithms, regression coefficient of variable is:



The dependent variables “increase in income/family health/children education after microfinance

loan” is taken as binary variables where income / health / education increase takes value of 1 and

the value is 0 for no increase after microfinance. Therefore the unobserved variable Y in case of

binary logistic regression is:

The independent variable for all the three hypotheses is the amount of microfinance loan with

three categories. All have the control variables of age, education, number of children of the

women understudy, family system and household head. The two more control variables ‘number

of years of business experience’ and ‘newly established or old enterprise with the use of

microfinance facility’are included in case of dependent variable “increase in income” due to

enterprise development factors. The variable of control on decision to spend money on family

health by women is included in case of dependent variables of family health. The table -1 shows

the dependent and independent variables and their respective statistics.

To check whether the predictors are not highly correlated the multicollinearity test is run between

the independent variables. The values of tolerance not less than 0.1 and VIF not greater than 10

show that there is no problem of collinearity between the predictors.

Increase in income after microfinance



In the preliminary analysis the chi square test for independence explores the relationship between

two dependent variable ‘increases in income’ with independent variable ‘Amount of

microfinance’. The result of Pearson Chi square χ2 = 12.62 ii, test is significant which indicates

that there is relationship between levels of loan amount and increase in income after

microfinance. To check whether the results will be same with number of control variables in the

model, we run binary logistic regression using SPSS (Table 2).

Binary logistic regression is run to assess the impact of amount of microfinance loan on the

likelihood that respondents reports that their income in the household increase. The model

contained eight independent variables (amount of loan, age, education, number of children,

household head, family system, business experience of women and enterprise developed by

women). The full model containing all predictors are statistically significant χ2 (8, N=114)

= 28.92, p < .01, indicating that the model is able to distinguish between the women with or

without the increase of income after microfinance. The model as a whole explained between

22.4 % (Cox & Snell R square), 34.4% (Nagelkerke R square) of the variance in increase in

income, and correctly classified in 79.8% of cases. The Table 2 shows that only two independent

variables of age and amount of loan made a statistically significant contribution in the model. The

amount of loan predictor indicate that women taking medium loan amount of Rs.15001-

Rs.25000 (£104.6 - £174.4)iii are 4 times (odd ratio of 4.703) more likely to report the increase in

income of household than those who are taking loan of less amount ranging Rs. 5000- Rs.15000

(£ 34.9 - £ 104.6), controlling for all other factors in the model. However the women taking loan

of high amount of Rs.25001- Rs.35000 (£ 174.4 - £244.1) and more shows insignificant



improvement in their results. The likelihood of women to have increase income after

microfinance, increases with the increase in the amount of loan, but have no contribution in the

model with higher amount of microfinance loan than Rs 25000 (£174.4). The odd ratio of .22 for

age is less than 1, indicating that women aged more than 40 years are .22 times less likely to

report increase in income as compared to women aged 18-39 years, controlling all the factors in

the model (Pallant, 2007; pp.177-178).

Increase in children education after microfinance

In the preliminary analysis the chi square test for independence explores the relationship between

two dependent variable ‘increases in children education’ with independent variable ‘Amount of

microfinance’. The result of Chi square χ2 = 8.430 iv, test is significant which indicates that there

is relationship between loan amount and increase in children’s education after microfinance. To

check whether the results will be same with number of control variables in the model, we run

binary logistic regression in SPSS (Table 3).


likelihood that respondents reports that their children’s education increase. The model contained

six independent variables (amount of loan, age, education, number of children, household head

and family system). The full model containing all predictors is statistically significant χ2 (6,

N=117) = 28.70, p < .01, indicating that the model is able to distinguish between the women with

or without the increase of children’s education after microfinance. The model as a whole

explained between 21.8% (Cox & Snell R square), 29.1% (Nagelkerke R square) of the variance



in increase in children’s education, and correctly classified in 69.2% of cases. Table 3 shows that

four independent variables: number of children, household head, family system and amount of

loan made a statistically significant contribution in the model. The loan predictor indicates that

women taking medium amount of loan are 4 times (odd ratio of 4.412, p<.01) and women taking

high loan amount are 5 times (odd ratio 5.050, p<.05) more likely to report the increase in

children’s education than those who are taking low loan amount, whilst controlling for all other

factors in the model. The women living with their family in joint system are 3 times (odd ratio of

2.994, p<.05) more likely to report increase in children’s education than those who are living as

nuclear family, controlling for all other factors in the model. Interestingly if household head is

husband then it is (odd ratio of 1.474, p<.01) more likely that there is increase in children’s

education than if the household head was a woman, however if household head is any one other

person then there is no probability of increase in children’s education. The likelihood of increase

in children’s education is significant at p<.05 when there is 1- 4 numbers of children (odd ratio

1.296) than if there is no children, controlling for all other factors in the model. However with the

increase in number of children from 5 the probability became insignificant means five or with

more than five children there is no significant increase in the probability of children’s education

after microfinance.

Increase in family health after microfinance

In the preliminary analysis, the reported chi square test for independence explores the

relationship between two dependent variable ‘increases in family health’ with independent

variable ‘Amount of microfinance’. The result of Chi square χ2 = .971, test is insignificant which



indicates that there is no relationship between loan amount and increase in family health after

microfinance. To check whether the results will be same with number of control variables in the

model, we run binary logistic regression using SPSS (Table 4).


likelihood that respondents reports that their children’s education increase. The model contained

seven independent variables (amount of loan, age, education, number of children, household

head, family system and control on decision to spend money on family health by woman after

microfinance). The full model containing all predictors is statistically significant χ2 (7, N=117)

=20.14, p < .05, indicating that the model is able to distinguish between the women with or

without the increase in family health after microfinance. The model as a whole explained

between 15.8% (Cox & Snell R square), 22.2% (Nagelkerke R square) of the variance in increase

in family health, and correctly classified in 75.2% of cases. The Table 4 shows that two

independent variables, number of children and control on decision to spend money on family

health by woman after microfinance, made a statistically significant contribution in the model.

The main independent variable, the amount of microfinance loan is insignificant and hence

confirms the result of chi square test of independence. The number of children predictor indicates

that women having children between 1-4 are 9 times (odd ratio of 9.316, p<.01) and women

having 5 or more children are 14 times (odd ratio 14.027, p<.01) more likely to report the

increase in family health than those who have no children, controlling for all other factors in the

model. Similarly women’s control on family health expenditures after microfinance predictor



indicated that woman with control is 3 times (odd ratio 3.684, p<.01) more significant than

woman who has no control, controlling for all other factors in the model.

Qualitative Analysis

For the study of poverty reduction and entrepreneurial success this paper divides the poverty with

entrepreneurship into three phases. The first phase is the failure phase with limited vision due to

poverty clouds, as depicted in figure 1. The women were in this phase before accessing the

microfinance facility and entrepreneurship opportunities. The second phase is improvement,

resulting from empowerment and enterprise that provides a broader vision due to access to

finance and achieving some of the attributes of entrepreneurial skills. The women whether

starting new business or running an existing family business moves toward this phase when they

use microfinance loan in their business. The microfinance institutions provide finance, trainings,

product knowledge and help them in establishing business networks and peer mentoring facilities

which ultimately propels the women towards the success phase. Three Phases are shown in figure

1.

The qualitative study used 10 case studies of the women who are either living below poverty line

or just above the poverty line to investigate impact of microfinance on their well being. The

sample of 10 women was drawn from 4 districts in Punjab, the largest state of Pakistan. The semi

structured interviews were conducted in the local language to find out women income before

microfinance, number of family members, education, business type, business experience,

membership of MFI, training, business networks, peer monitoring, product knowledge, budgeting



and finance, and better health and children’s education after microfinance which were inductively

analysed.

Microfinance and reduction in poverty

In the sample of these ten women, all lived below the poverty line of £1.25 per day before

microfinance and hence experienced extreme poverty, in seven cases they fell under the

category of as core poor (significantly below poverty line). After obtaining loan from MFI and

investing it in their enterprise their income, assets, expenditure, health, education and political

participation significantly changed positively. The figure 2 shows that after access to

microfinance for the enterprise the general condition of the women has improved. Their income,

expenditure, income, health and children’s education improved; these results validate results

reported using the quantitative results above. The results show that all the women are in the Phase

1 of failure before taking microfinance and graduated to phase 2 after access to microfinance

suggesting improvement.

The figure 2 shows that 20% of the women moved from phase 2 to phase 3, a change brought

about due to increase in all six factors, leading to poverty reduction that is estimated through

income, expenditures, assets, health, children education and political participation.

Microfinance and entrepreneurship



In the sample, these women were educated, at most, up to GCSE level and had family size in the

range of 2-12 persons, including children. With large families and few resources, they managed

to establish an enterprise or invested in already established enterprise through accessing

microfinance loan, either as a sole proprietor or as partner with their husbands’. These micro

enterprises mostly had either retail outlet, sale of clothes, electronic items, blankets and food

items or very limited level of production by manufacturing carpets, wooden decoration pieces,

car mates, hosiery. And a few were involved in livestock and services business like stitching and

sewing of cloths. These women’s business related experience ranged from one year to more than

10 years and all were members of microfinance institution for six month to 6 years. The figure 3

shows that 50% of the women avail training facilities, established business networks and have

product knowledge offered by microfinance. 80% of them had benefited from peer mentoring

because of the group lending technique of microfinance, where women have to attend a monthly

meeting to qualify for a loan.

The results reported and the discussion above has illustrated that women in the sample have

enjoyed significant enhancement in the quality of their life after accessing microfinance. This is

evidenced from the fact that 30% of the women moved from Phase 2 to 3 because access to

finance offered greater entrepreneurial opportunities of training, business networks, peer

mentoring, product knowledge and budgeting and finance that enabled 20% of the women to

move into Phase 3 of success where they experienced significant poverty reduction and greater

opportunities to enhance their entrepreneurial skills to break out of poverty cycle.

DISCUSSION AND CONCLUSION



A general conclusion that emerges from this research study is that access to finance is important

for females to unlock them from the shackles of poverty to realise their full potentials. The

statistical reported results derived using quantitative analysis suggests that all three variables:

income, education and health are significant and have a high correlation with access to finance. A

closer examination of results suggest that an increase in income of the family is positively

correlated with the size of loan up to a point but this relationship does not holds when the size of

loan reached a certain size; in this study we observe loans below (£ 174.4 - £244.1) significantly

enhanced well being of women as all stated variables were significant but within this range and

above, similar improvement was not experienced, suggesting there may be an optimal loan size

which MFIs should offer. Thus the relationship between increase in income and increase in

amount of loan has inverted U shaped. Therefore the result of H1 is not conclusive; this may be

due to use of loan amount at high level for any other purpose instead of productive use in the

enterprise. These results have implications for microfinance organisations themselves, donors and

policy makers at large. The logistic regression results show that with the increase in amount of

loan, there is probability of increase in children education, therefore we accept H 2 at p<.01 at

medium level of loan amount and at p<.05 at high level of loan amount . The third Hypothesis is

rejected as there is no probability of increase in family health with the increase in amount of loan.

The qualitative analysis reported in Figure 4, shows microfinance loans have positive impact on

poverty reduction. Access to finance leads to an increase in income, product knowledge,

especially when this is supported with peer mentoring for the new members of microfinance

institutions, especially for new members who borrow in the range of (£34.9 - £70). The increase

in product knowledge and peer mentoring help to reduce information asymmetry and the regular

monthly meetings and repayments help to build bonds, create a sense of belonging, learning



relating to business practices and in instilling business discipline. Furthermore, there is a positive

correlation with the size of loan and political participation which is measured using the right to

vote in election by women. Business engagement requires national identity cards (NIC) in

Pakistan, something women in poor segment of population do not feel the need to have one but

when they take loan from Microfinance institutions they are required to have NIC that unintended

benefit of loan is the acquisition of NIC, something MFIs assist women to complete forms and

lodge the application. Being on the voter list brings greater interaction with political parties

which gives women greater awareness and improves their social networking and flow of

information. Reported results indicate this lead to 50% increase in political participation. This

figure has further potential further rise if all MFIs only accepted the women’s own NIC instead

of their husband’s or father’s.

There is excessive focus amongst MFIs to support start-ups who may have potential to become

independent earners. Results of this study suggest there is logic in supporting established

enterprise or newly developed enterprise in phase II as they serve dual role, enabling others to

learn from their experiences, networking themselves with others and drawing upon training

opportunities and access to larger amount of loan, thereby effectively performing the role of a

mentor. Therefore experienced women entrepreneurs have a positive attitude towards enterprise

initiatives and the derive to succeed that serves as a pull factor for all the participants thereby

ensuring the MFIs have a desired positive impact on poverty reduction amongst females.

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Table 1: Dependent and Independent variables Statistics

Variables PercentageAge of Women (in years)

18-39 40- more than 40

66%33%

Education of Women No education, school education, college/University/profession education

53%40% 7%

Number of Children No children, 1-4 children, 5 and more

14%50%36%

Family system Nuclear Joint

58%42%

Household Head Women Husband Both / Any other

26%44%30%

Business experience of Women Less than 1 year- 2 years 3-5 years 6-10 and more years

23%28%49%

Enterprise developed by Women existing enterprise newly established enterprise

84%16%

Control on decision relating to spend money on family health and nutrition after microfinance

no yes

48%52%

Amount of microfinance loan (amount in Rupees) 5000 – 15000-low 15001-25000-medium 25001-35000 and more-high

48%34%18%

Increase in income after microfinance (0,1) (24%, 76%)Increase in children’s education after microfinance (0,1) (45%, 55%)Increase in family health and nutrition after microfinance (0,1) (33%, 66%)



Table 2: Logistic regression estimation of increase in income of the family

Increase in income after microfinance

Coef.B

Std err Sig.p

Odds ratio Exp B

95% CI for odd ratio (B)

Lower Upper

Constant 1.937 1.676 .248 6.935Amount of loan5000 - 1500015001-2500025001-35000 and more

-1.548*

20.937.685

8558.061.024.998

4.703 1.227.000

18.022

Age18-39 yearsMore than 40

--1.479*

.717.039 .228 .056 0.928

EducationNo educationSchool education College/Uni. education

--.215-.856

.5961.222

.876

.614.807.425

.251

.0392.5954.660

ChildrenNo children, 1-4 children, 5 and more

--.766-.368

1.2151.326

.397

.077.465.692

.043

.0515.0339.317

Household HeadWomenHusbandBoth / Any other

--1.249-.093

.772

.884.105.916

.287

.911.063.161

1.3015.149

Family systemNuclearJoint

-.106 .574 .854 1.112 .361 3.426

Business experience of Women Less than 1 year- 2 years3-5 years6-10 and more years

-.130.705

.708

.686.854.304

1.1392.024

.284

.5284.5657.768

Enterprise developed by WomenExisting enterpriseNewly established enterprise

-.274 .719 .703 1.315 .321 5.384

Notes: - indicates the reference category; number of obs. = 114; R2 = .93 (Hosmer&Lemeshow), .22 (Cox & Snell), .34 (Nagelkerke); Model χ2= 28.92, p < .01; * p<.05



Table 3: Logistic regression estimation of increase in children’s education

Increase in children’s education after microfinance

Coef.B

Std err Sig.p

Odds ratio Exp B


Lower Upper

Constant -3.696 1.143 .001 .025Amount of loan5000 - 1500015001-2500025001-35000 and more

-1.484**

1.619*.525.675

.005

.0164.4125.050

1.5771.344

12.34018.973


--.123

.533.818 .885 .311 2.515


-.437.383

.5161.066

.397

.7191.5481.466

.563

.1824.25511.847


-1.912*

1.766.844.916

.023

.0546.7705.848

1.296.971

35.37735.215


-1.481**

.788.558.624

.008

.2074.3972.200

1.474.647

13.1187.479


-1.096**

.473.020 2.994 1.185 7.563

Notes: - indicates the reference category; number of obs. = 117; R2 = .78 (Hosmer&Lemeshow), .22 (Cox & Snell), .29 (Nagelkerke); Model χ2= 28.70 p < .01; * p<.05, **p<.01



Table 4: Logistic regression estimation of increase in family health

Increase in family health after microfinance

Coef.B

Std err Sig.p

Odds ratio Exp B


Lower Upper

Constant -2.063 1.144 .071 .127Amount of loan5000 - 1500015001-2500025001-35000 and more

-.346

-.282.534.671

.517

.6791.413.758

.496

.2034.0232.823


-.009

.583.987 1.009 .322 3.165


--.215-.856

.5961.222

.876

.614.807.425

.251

.0392.5954.660


-2.232**

2.641**

.840

.961 .008.006

9.31614.027

1.7972.133

48.29292.226


--.323.282

.595

.677.587.677

.7241.326

.226

.3522.3244.997


-.361 .479 .451 1.435 .561 3.670

Increase in Women’s health after microfinanceNoYes

-1.304**

.490.008 3.684 1.411 9.620

Notes: - indicates the reference category; number of obs. = 117; R2 = .47 (Hosmer&Lemeshow), .16 (Cox & Snell), .22 (Nagelkerke); Model χ2= 20.14, p < .05; ** p<.01


Limited

Vision Broad vision Clear vision

2012 Cambridge Business & Economics Conference ISBN : 9780974211428


Poverty reduction and success

Poverty clouds start to lift up

Poverty clouds

1st Phase: Failure

Vision is limited due to poverty clouds

Poverty limits the entrepreneurial and prospects ideas

2nd Phase: Improvement

Start to have broader vision and learned from experience

Client education: Product knowledge Training from MFI Budgeting and marketing

3rd Phase: Success

Clear vision of entrepreneurial ideas

Poverty reduction by entrepreneurship

Impact:

Good ideas are not fully exploited or conceived

Immediate need of shelter

Impact:

Better health Better children’s education Business experience

Impact:

Financial Inclusion Business Networks Increase in household income

Figure 1: Phases of poverty reduction with entrepreneurship

All 10 cases before microfinance All 10 cases after microfinance 20% of the cases mean two women


Figure 2: Impact of microfinance on poverty reduction

Figure 3: Impact of microfinance on entrepreneurship attributes

i http://www.ophi.org.uk/wp-content/uploads/OPHI-MPI-Brief.pdf

ii n=120, p=.002, Cramer V=.324 (effect size medium=.30)

iiiExchange rate from http://www.xe.com/ucc/ dated on 22-02-2012

iv n=120, p=.015, Cramer V=.265 (effect size small=.01 and medium=.30)


http://www.xe.com/ucc/

http://www.ophi.org.uk/wp-content/uploads/OPHI-MPI-Brief.pdf


Figure 4: Impact of increase in amount of microfinance on poverty reduction and entrepreneurship attributes


javed ghulam hussain, samia mahmood

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