2012 Cambridge Business & Economics Conference
ISBN : 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: javed.hussain@bcu.ac.uk
Samia Mahmood
Birmingham City University, City North Campus
Franchise Street, Perry Barr, Birmingham, B42 2SU, UK
Email: samiamahmood1@yahoo.com
Email: samia.mahmood@mail.bcu.ac.uk
Track: Economics and Finance
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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.
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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
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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,
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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
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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
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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
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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
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with non-clients, concluded that “most clients come from moderately poor and vulnerable nonpoor 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 intergenerational 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
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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
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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
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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.
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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
base of natural logarithms,
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the probability of Y occurring, e the
regression coefficient of variable
is:
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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
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microfinance’. The result of Pearson Chi square χ2 = 12.62ii, 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.15001Rs.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
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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).
Binary logistic regression is run to assess the impact of amount of microfinance loan on the
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
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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
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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).
Binary logistic regression is run to assess the impact of amount of microfinance loan on the
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.
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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.
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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
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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
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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 H2 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
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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
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Percentage
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Age of Women (in years)
 18-39
 40- more than 40
Education of Women
 No education,
 school education,
 college/University/profession education
Number of Children
 No children,
 1-4 children,
 5 and more
Family system
 Nuclear
 Joint
Household Head
 Women
 Husband
 Both / Any other
Business experience of Women
 Less than 1 year- 2 years
 3-5 years
 6-10 and more years
Enterprise developed by Women
 existing enterprise
 newly established enterprise
Control on decision relating to spend money on family
health and nutrition after microfinance
 no
 yes
Amount of microfinance loan (amount in Rupees)
 5000 – 15000-low
 15001-25000-medium
 25001-35000 and more-high
Increase in income after microfinance (0,1)
Increase in children’s education after microfinance (0,1)
Increase in family health and nutrition after
microfinance (0,1)
66%
33%
53%
40%
7%
14%
50%
36%
58%
42%
26%
44%
30%
23%
28%
49%
84%
16%
48%
52%
48%
34%
18%
(24%, 76%)
(45%, 55%)
(33%, 66%)
Table 2: Logistic regression estimation of increase in income of the family
Increase in income after
microfinance
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Coef.
B
Std err
Sig.
p
Odds
ratio
95% CI for odd ratio
(B)
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Exp B
Lower
Upper
Constant
Amount of loan
5000 - 15000
15001-25000
25001-35000 and more
1.937
1.676
.248
6.935
1.548*
20.937
.685
8558.061
.024
.998
4.703
1.227
.000
18.022
Age
18-39 years
More than 40
-1.479*
.717
.039
.228
.056
0.928
Education
No education
School education
College/Uni. education
-.215
-.856
.596
1.222
.876
.614
.807
.425
.251
.039
2.595
4.660
Children
No children,
1-4 children,
5 and more
-.766
-.368
1.215
1.326
.397
.077
.465
.692
.043
.051
5.033
9.317
Household Head
Women
Husband
Both / Any other
-1.249
-.093
.772
.884
.105
.916
.287
.911
.063
.161
1.301
5.149
Family system
Nuclear
Joint
.106
.574
.854
1.112
.361
3.426
Business experience of Women
Less than 1 year- 2 years
3-5 years
6-10 and more years
.130
.705
.708
.686
.854
.304
1.139
2.024
.284
.528
4.565
7.768
Enterprise developed by Women
Existing enterprise
Newly 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
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Table 3: Logistic regression estimation of increase in children’s education
Increase in children’s education
after microfinance
Constant
Amount of loan
5000 - 15000
15001-25000
25001-35000 and more
Coef.
B
Std err
Sig.
p
Odds
ratio
Exp B
95% CI for odd ratio
(B)
Lower
Upper
-3.696
1.143
.001
.025
1.484**
1.619*
.525
.675
.005
.016
4.412
5.050
1.577
1.344
12.340
18.973
Age
18-39 years
More than 40
-.123
.533
.818
.885
.311
2.515
Education
No education
School education
College/Uni. education
.437
.383
.516
1.066
.397
.719
1.548
1.466
.563
.182
4.255
11.847
Children
No children,
1-4 children,
5 and more
1.912*
1.766
.844
.916
.023
.054
6.770
5.848
1.296
.971
35.377
35.215
Household Head
Women
Husband
Both / Any other
1.481**
.788
.558
.624
.008
.207
4.397
2.200
1.474
.647
13.118
7.479
Family system
Nuclear
Joint
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
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Coef.
B
Std err
Sig.
p
Odds
ratio
95% CI for odd ratio
(B)
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Exp B
Constant
Amount of loan
5000 - 15000
15001-25000
25001-35000 and more
Lower
Upper
-2.063
1.144
.071
.127
.346
-.282
.534
.671
.517
.679
1.413
.758
.496
.203
4.023
2.823
Age
18-39 years
More than 40
.009
.583
.987
1.009
.322
3.165
Education
No education
School education
College/Uni. education
-.215
-.856
.596
1.222
.876
.614
.807
.425
.251
.039
2.595
4.660
2.232**
2.641**
.840
.961
.008
.006
9.316
14.027
1.797
2.133
48.292
92.226
Household Head
Women
Husband
Both / Any other
-.323
.282
.595
.677
.587
.677
.724
1.326
.226
.352
2.324
4.997
Family system
Nuclear
Joint
.361
.479
.451
1.435
.561
3.670
Children
No children,
1-4 children,
5 and more
Increase in Women’s health after
microfinance
No
Yes
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
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Figure 1: Phases of poverty reduction with entrepreneurship
1st Phase: Failure
2nd Phase: Improvement
3rd Phase: Success
Vision is limited due to poverty clouds
Start to have broader vision and learned from experience
Clear vision of entrepreneurial ideas
Poverty limits the entrepreneurial and
prospects ideas
Client education:
Poverty reduction by entrepreneurship



Product knowledge
Training from MFI
Budgeting and marketing
Poverty
clouds
Limited
Vision
Broad vision
Impact:


Poverty
clouds start
to lift up
Clear vision
Impact:
Good ideas are not fully exploited or
conceived
Immediate need of shelter
All 10 cases before microfinance
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


Poverty
reduction
and success
Impact:


Better health
Better children’s education
Business experience

Financial Inclusion
Business Networks
Increase in household income
20% of the cases mean two women
All 10 cases after microfinance
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Figure 2: Impact of microfinance on poverty reduction
Figure 3: Impact of microfinance on entrepreneurship attributes
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Figure 4: Impact of increase in amount of microfinance on poverty reduction and
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)
iii
Exchange 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)
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