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Research Grant Proposal
Malaria in Mali: Does Combining Microfinance and Health Education Help?
Shivani Kuckreja, Akanksha Mehta, Rebecca Zhang
December 8th, 2015
Abstract
This proposal seeks to evaluate the effects of a randomized microcredit lending and health
education program on malaria preventative behaviour in Mali (measured by the uptake of insecticide
treated bednets). This program will be offered by Medicine for Mali (MFM), a microfinance organization
lending to 70% women and also providing health education in the villages it enters. (The number of
villages will be determined by our statistician) In some villages both microfinance and health education
will be offered, while in others either only microfinance or only health education will be offered. We
hope to isolate and evaluate individually the impact of providing microfinance and health education on
insecticide treated bednets. By the simultaneous provision of microfinance and health education through
an NGO that focuses on lending to women, we hope to influence not only household income and malaria
preventative knowledge but also intra household decision making patterns. Therefore, we expect to find a
higher uptake of insecticide treated bednets in the group of villages where Medicine for Mali offers both
microfinance and health education over the period of a year.
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1. Introduction
Every minute, one child dies from malaria, making malaria one of the deadliest
diseases.1In 2012, over 200 million people visited hospitals to be treated for malaria, and almost
630,000 people died from malaria.2 In 2013, the number of malaria-related deaths experienced a
relatively small decrease to just under 600,000.3 Today, over 3.2 billion people suffer from the
disease.4
Malaria is spread by the transfer of a parasite called Plasmodium into the red blood cells,
which enters the human body through an infected mosquito bite. While malaria symptoms can be
as non-threatening as fevers or headaches, malaria can also cause death if left untreated.5 What is
most astonishing to us is that malaria is both preventable and treatable, yet almost one million
people die from the disease every year.6 Malaria has for decades been one of the leading causes
of death in sub-Saharan Africa with 89% of malaria cases and 91% of malaria-related deaths
concentrated in this region.7 However, in many malaria infested regions in Africa, there has been
a significant reduction in malaria prevalence and incidence rates over the past 15 years.
However, Mali has seen the slowest decline in malaria prevalence rates from 50%-41% between
2000-15. Therefore, we choose to focus on Mali to test the maximum effectiveness of our
microfinance and health intervention.
1 “Malaria,” UNICEF, http://www.unicef.org/health/index_malaria.html, (Accessed December 1, 2015) 2 “Impact of Malaria,” Centers for Disease Control and Prevention, http://www.cdc.gov/malaria/malaria_worldwide/impact.html, (Accessed December 1, 2015) 3 “Malaria: Fact sheet on the World Malaria Report 2014,” World Health Organization, http://www.who.int/malaria/media/world_malaria_report_2014/en/, (Accessed December 1, 2015) 4 “Media Centre: Malaria,” World Health Organization, http://www.who.int/mediacentre/factsheets/fs094/en/, (Accessed December 1, 2015) 5 “Health Topics: Malaria,” World Health Organization, http://www.who.int/topics/malaria/en/, (Accessed December 1, 2015) 6 “The Reality of Malaria,” UNICEF, http://www.unicef.org/health/files/health_africamalaria.pdf, (Accessed December 1, 2015) 7 “Media Centre: Malaria,” World Health Organization, http://www.who.int/mediacentre/factsheets/fs094/en/, (Accessed December 1, 2015)
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Our outcome variable is insecticide-treated mosquito nets which have proven to
significantly decrease malaria rates. According to UNICEF, these nets could cut malaria rates in
half, but fewer than 5% of African children take advantage of bed nets.8 Furthermore,
insecticide-treated bed nets are seen to have such great potential in decreasing malaria incidence
rates in Mali that Mali’s national malaria control strategy considers these bed nets as one of four
primary ways in which malaria can be prevented and treated.9
There may be two primary reasons as to why families in Mali are not using insecticide-
treated bed nets: lack of financial resources and lack of appropriate health education to initiate
preventative measures. Our intervention looks at addressing both these concerns simultaneously
by having a microfinance organisation called Medicine for Mali provide both microfinance and
health education to villages in order to influence their malaria preventative behaviour.
1.1 The Intersection of Microfinance and Health Education
Access to financial services plays an important role in the health choices made by poor
families. It provides them access to greater funds to improve their uptake of various healthcare
initiatives. It also increases their future disposable income putting them in a better position to
bear their own burden of financial costs of illness. With 65% of Mali living below the poverty
line10 and using their income to meet their basic needs of food and water, improving access to
microfinance might encourage them to spend a proportion of their now increased annual income
on health costs if they are already aware of the benefits of doing so. Further, it might better
8 “The Reality of Malaria,” UNICEF, http://www.unicef.org/health/files/health_africamalaria.pdf, (Accessed December 1, 2015) 9 DHS Further Analysis Reports: Malaria Prevention and Treatment for Children Under Five in Mali: Further Analysis of the 2012-2013 Demographic and Health Survey,” DHS, http://dhsprogram.com/pubs/pdf/FA93/FA93.pdf, (Accessed December 1, 2015) 10Presidents Malaria Innitiative, “Fighting Malaria and Saving Lives: Mali,” “http://www.pmi.gov/docs/default-source/default-document-library/country-profiles/mali_profile.pdf?sfvrsn=18, (Accessed December 1, 2015)
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enable families to provide education to their children such that they are better able to make more
informed health decisions in the future.
However, there has been much debate as to how increased income does not lead to
smarter decisions even for people living beneath the poverty line. Also since the outcome
variable that we are looking at is insecticide treated bed nets which are provided at relatively low
costs, lack of income might not be the only problem that has led to the reluctance to use bednets.
Therefore, we have reason to believe that it is more of an information problem.
Education rates in Mali are extremely low with almost half of individuals between the
ages of 15-24 illiterate. Their primary schools also suffer from extremely low attendance rates.
Low literacy and attendance rates might indicate that general unawareness of the benefits of
malaria preventative behaviour might be the cause of the low use of insecticide treated bednets.
Further it could be that despite having awareness of the benefits of using insecticide treated
bednets, the lack of uptake could be attributed to barriers of cost (if no provision at low cost
through the government), geographic access, cultural beliefs and lack of trust in health providers.
Having a microfinance institution provide health education simultaneously, addresses
many of the concerns discussed above. It provides people living beneath the poverty line access
to funds, imparts basic health education to those that are unaware, serves as a reliable and
trustworthy source of information to people that are their clients and at many times also succeeds
in providing low cost access to disease preventative measures through partnerships with the
government. Therefore, for our intervention we have decided to partner up with Medicine for
Mali, an organization offering microfinance, health education and providing access to basic
health amenities through tie-ups with the government.
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2. Literature Review
The effect of microfinance on health has been studied in a variety of contexts. These
include interventions that offer microcredit, health insurance, and combined finance and
education approaches. Three studies that examine the effect of microfinance on malaria
preventative behaviour are Tarozzi et al. (2011)11, Blanchard-Horan (2007)12, and De La Cruz et
al. (2009)13.
Tarozzi et al. (2011) examine how microcredit increases the ownership and use of
insecticide treated bed nets compared to that of free distribution, after 1 and 1.5 years. All trial
groups received a brief information session on malaria prevention. They found that 52% of the
microloan treatment group purchased at least one bed net. However, over the span of their study,
they found little evidence of health improvement measured through blood test data. Additionally,
while microfinance had a statistically higher uptake and usage of ITNs relative to control, it was
less effective than free distribution in changing bed net use.
However, since continuous free distribution of bednets on a large scale is not always
feasible, in our study through the combination of health education and microfinance we are
hoping to show long-run sustainable effects that can guide policy. Further, the reliability of the
result that microfinance greatly increases ITN ownership, in the Tarozzi et al (2011) study, is
questionable because the study examined pre-existing MFI clients who may be more swayed by
recommendations of the MFI institution either because of established trusting relationships or
11 Tarozzi, Alessandro, Aprajit Mahajan, Brian Blackburn, Daniel Kopf, Lakshmi Krishnan, and Joanne Yoong. "Micro-loans, Insecticide-Treated Bednets and Malaria: Evidence from a randomized controlled trial in Orissa (India)." Economic Research Initiatives at Duke (ERID) Working Paper 104 (2011). 12 Blanchard-Horan, Christina. "Health microinsurance in Uganda: Affecting malaria treatment seeking behavior." International Journal of Public Administration 30, no. 8-9 (2007): 765-789. 13 De La Cruz, Natalie, Benjamin Crookston, Bobbi Gray, Steve Alder, and Kirk Dearden. "Microfinance against malaria: impact of Freedom from Hunger's malaria education when delivered by rural banks in Ghana." Transactions of the royal society of tropical medicine and hygiene 103, no. 12 (2009): 1229-1236.
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hopes that compliance could lead to better future loans. To reduce these confounding effects, in
our study, we are offering the health education and microfinance to new clients. Further, the
Tarozzi et al (2011) study found no difference in malaria health improvement which could be
attributed to the short time span of the study and insufficient coverage. The use of ITNs still
serve as one of the leading preventative steps against malaria.
Two studies that specifically study effects on malaria prevention in sub-Saharan Africa
are Blanchard-Horan (2007), and De La Cruz et al. (2009). Blanchard-Horan (2007) studied the
effect of health microinsurance (HMI) on participant malaria knowledge and delays on seeking
malaria care in a multisite qualitative case comparison study in Uganda. The HMI was offered
through three degrees of MFI involvement—directly through the MFI to members, through
credit and savings programs, and with no MFI presence. The results showed no difference in
malaria knowledge, though HMI participants did not wait as long as the control group to seek
malaria treatment. The lack of observed effect here could be attributed to the self-selective nature
of people enrolling in health microinsurance itself. Our study lessens this self-selection bias of
offering health insurance by focusing on health education interventions instead.
De la Cruz et al. (2009)13 studied how a specific health education program, Freedom from
Hunger, delivered through rural banks in Ghana could affect the malaria preventative knowledge
and practice of childbearing aged women with children under age 5. Three randomized subject
groups were studied: microfinance clients receiving malaria education during bank meetings (n=
213), clients receiving diarrhea education during bank meetings (n= 223), and non-client controls
(n=268). The malaria education group had significantly better knowledge of malaria than the
other groups and improved understanding of pregnancy/child specific malaria risk. This group
had a higher increase in bed net ownership/use in general and for pregnant women/young
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children specifically. The study also found cost to be the greatest barrier towards buying and
using a bed net at follow up. Our study focuses on providing not only malaria education, but also
low cost access to bednets through tie-ups with the government.
The De la Cruz study, while seemed to show that malaria education in a microfinance
setting can improve prevention, has a number of limitations that affect its internal validity. The
baseline characteristics of the treatment and control groups were significantly different. To avoid
this in our study, we stratify villages based on similar characteristics and then randomly assign
them to the treatment and control groups.14 Also, their study is unable to show the individual
effects of health education and microfinance since their treatment involves a combination of
both, an issue our study addresses. Further, their method of selecting control group participants
from the same villages as the treatment groups, while reducing costs faces, results in
contamination problems biasing their results. Our study addresses this problem of contamination
by randomizing different villages to treatment and control groups instead of within villages.
The literature available on the effect of microfinance interventions on malaria serves as a
precedent to believe that a combined microfinance-health education intervention could improve
malaria prevention outcomes. However, these studies are limited by issues of comparability
between control and baseline groups, strong self biases, and self reported group assignment. Our
study addresses many of these problems through stratified random village group assignment and
participant follow up. This study expands the scope of existing literature by comparing the
effects of a combined microfinance-health education approach with separate microfinance and
health education interventions.
14 The method which assigned education treatment randomly to 82 existing credit associations and treatment participants randomly from credit association lists without stratification resulted in fundamental differences between these groups, a selection bias. Clients in the malaria education group were older, which could affect decision-making and eagerness to learn. Water source, sanitation, and food security information hinted that the malaria education group may have had socioeconomic advantage.
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3. Experimental Design
3.1 The Product
Medicine for Mali is a small, non-profit organization bringing medical services, clean
water, improved educational opportunities and economic development to the people of Mali,
through a team of volunteers. From its birthplace in Ohio, it has expanded to include volunteers
from all over the country. They operate in over 10 villages, that are relatively small in size with a
population between 200 - 1000 people. They make an average of 30-40 loans per village each
year, providing around 350-390 loans in total each year. These villages are also chosen based on
whether they have transportation to access bigger villages or urban areas.
Their basic microfinance product is a 5% “fee-based” (their equivalent of interest rate)
loan15, repayable within a year given to people interested in starting a small enterprise. The loan
is decided by a committee of 5-6 members consisting of residents of that particular village,
elected by the chief and elders of the village. There are 2-3 seats reserved for women on average
on this committees.
The loan or “seed money” is given based on the credibility of the loan applicants and the
ideas presented for business endeavours by the applicants. Loans typically range from $20-$100.
It is a rule that 70% of the loan receivers have to be women, as the focus of this organization is to
provide opportunities to women outside their households. The average age of women receiving
the loan ranges from 20-30 years and those of the men is slightly higher between 25-35 years.
Medicine for Mali has a strict payback policy in the villages that they operate in. They
send volunteers to the village 2-3 times a year to make collections on the loans given out. Their
strict payback policy states that if 100% of the loans within a village are not paid back, they
15 Use the term “fee” instead of interest because a majority of the borrowers are Muslim and view the word “interest” as having negative connotations (interview with Dave, Medicine for Mali)
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withdraw operations from that village. This has resulted in a 100% repayment rate since
inception 9 years ago. The members of the villages take it upon themselves to conduct thorough
diligence before giving the loan and ensure its repayment. There is also another incentive offered
for quick repayment. Villages with better repayment times have their future loan amounts
increased.
Additionally Medicine for Mali, also trains 2-3 volunteers in the village where they offer
basic health education to all residents (includes malaria prevention, immunization, malnutrition
information). There are also monthly visits by a university trained midwife who holds sessions
with the residents and offer prenatal care. The volunteers are also in charge to organizing
immunization and other health offerings using funds provided by Medicine for Mali at the local
health clinic.
3.2 Village Level Information
Medicine for Mali currently offers microfinance to 10 villages in Mali out of which
health education is provided at 8 of those villages. Our intervention studies the entrance of
Medicine for Mali into small villages with a population of 200-1000 people. It is also a necessary
condition that these villages have access to larger villages or urban areas. This is because access
to markets where loan borrowers can sell their products is an important prerequisite for Medicine
for Mali in deciding which villages to enter.
At the onset, we will collect a list of villages from the Demographic and Health Surveys
website to get an idea of the larger sample size that we are dealing with. Villages that already
have an existing microfinance institution open are excluded because the uptake of loans from
Medicine for Mali (interventional MFI) might not be large enough to find a significant effect.
Additionally, pre-existence of an MFI branch could cause the problem of misalignment of
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incentives for the interventional MFI to agree to open a branch. This might be because the
additional uptake from the interventional MFI might be too small for them to incur the costs of
expanding into a new village. Therefore, eligible villages were only those that did not have a
microfinance institution already present.
Furthermore, we are not too concerned that villages with existing MFIs are
characteristically different from our sample of eligible villages. This is attributed to the relatively
recent popularity of MFIs in Mali, therefore, there are relatively few villages with existing MFI
branches. Also, in order to reduce differences between villages, we do not limit our choice to
only those within high malaria incidence areas but randomize all across Mali as shown in Figure
1(Appendix). Through this we are trying to reduce exogenous variables affecting our results
through an unbiased sample selection.
3.3 Randomization
Our interventional study takes the form of a randomized control trial with four different
groups.
Group 1 → villages receive both microfinance and health education through MFM
Group 2 → villages receive only access to microfinance through MFM for the next 1
year, after which they will receive health education as well
Group 3 → villages receive only health education for 1 next year through MFM, after
which they will receive access to microfinance as well
Group 4 → is the control and receives neither microfinance nor health education through
MFM for the next 1 year, after which they receive both.
We then stratify villages to ensure even distribution of characteristics in each group. We
collect demographic information on each village through either already conducted demographic
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surveys and/or talking to the village head. Villages are clustered into groups of four based on
similar characteristics such as population, poverty rates, malaria incidence and prevalence rates,
geography, employment, healthcare access etc. (A full list of these demographic characteristics
used can be found in Table 1 in the Appendix) Villages from each group are then randomly
assigned to each of the four RCT groups. Our objective is to show that microfinance provision
and health education individually and together have different effects on malaria prevention
behavior of households.
3.4 Sample Size
In order to determine our sample size, there are certain things that we must take into
consideration. For instance, since Medicine for Mali is currently a relatively small microfinance
institution, we are limited by certain budgetary and manpower constraints. However, in choosing
a sample size, we want to ensure that our sample is big enough to ensure statistically significant
results. In order to do so, we want our calculated t-statistic to be greater than 1.96. Therefore, we
need to make assumptions about the estimated effect of the combined microfinance and health
education program on insecticide treated bednets uptake, the estimated effect for each individual
group and the relative variance of the error term to the variance amongst observations of our
estimated effect. In our calculations we will assume this to be 1.
Further, to be conservative in our calculations, we will look at past literature and
determine the size of estimated effects that they found in their studies estimating a possible range
of effect size. Tarozzi et al (2011) find a 52% uptake in the number of ITNs purchased in treated
households compared to control households. (citations) Additionally, in the De La Cruz the
malaria education group experience a 9% in ITN ownership compared to the diarrhea group.16
16 Natalie De La Cruz et al, Microfinance against malaria: impact of Freedom from Hunger's malaria education when delivered by rural banks in Ghana
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Since the effects of offering health education has much smaller effects on the ITN uptake than
that of microfinance, Group 3 will have to be considerably larger than Group 2 for us to see
statistically significant results. With different studies finding different percentage values of
effects, we normalize these with respect to the variance of X to make them comparable. We then
estimate a range of these normalized effects and calculate a range of sample sizes for each group.
We will then pick a sample size that is most feasible given our budgetary constraints and such
that we observe a statistical significant effect.
3.5 Experimental survey design
We then conduct baseline surveys in all the trial villages. Due to budgetary and time
constraints, to conduct our baseline survey, we randomly select households from each of the 4
trial groups. We then collect survey information on the background of the loan taker, his/her
family, educational attainment, detailed previous/current employment, number of children,
savings, health decision information, malaria prevention behaviour etc. The endline survey after
1 year is conducted on the same population of individuals that were in the baseline survey. (The
baseline and endline survey questionnaire can be found in Table 2 in the Appendix)
We compare the 4 groups at baseline to ensure that stratification at the village level has
led to uniform distribution of characteristics across the trial groups. In addition to the first
random sample, we oversample individuals in Groups 1 & 2 from amongst those who selectively
take up microfinance loans. We then compare this oversampled population between Groups 1 &
2 to ensure relatively similar characteristics amongst people who have taken up the microfinance
loans. The endline survey for Groups 1 & 2 only contains information on the oversampled
http://www.sciencedirect.com/science/article/pii/S0035920309001187
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individuals.17 The baseline survey consists of many parts and collects information on the
background of the loan taker, his/her family, educational attainment, detailed previous/current
employment, number of children, savings, health decision information, malaria prevention
behaviour etc. The endline survey after 1 year is conducted on the same individuals that were
sampled in the baseline survey. (The baseline and endline survey questionnaire can be found in
Table 2 in the Appendix)
We then compare the baseline information collected through surveys across the 4
different trial groups to ensure that there are no significant differences in characteristics across
these groups that might skew our results and affect internal validity. Since we stratified based on
demographic village data, we do not expect to find considerable differences here.
3.6 Method and Hypothesis
We will run regressions to measure the effect of health education and microfinance
provision on malaria preventive behavior. The variable we have chosen to measure malaria
preventative behavior is insecticide treated bednets (ITNs). In our first regression, our variable
that measures health education and microfinance provision is simply a dummy variable for
whether the village receives either. We also include an interaction term, to capture the effect of
providing both.
(1) number of ITNs = ß0 + ß1 (microfinance provision) + ß2 (health education
provision) + ß3 (microfinance provision)*(health education provision) + error
term
17 We do this because we are only interested in the effects of taking up microfinance on the uptake of insecticide treated bednets.
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(a) ß0 → the expected number of ITNs used by households in the control
group that received neither microfinance or health education at the end of
the first year
(b) ß1 → the expected difference in the number of ITNs used by households
where an individual is receiving a microfinance loan relative to
households in the control group that aren’t
(c) ß2 → the expected difference in the number of ITNs used by households
where individuals are receiving health education relative to households in
the control group that aren’t
(d) ß3 → the additional expected difference in number of ITNs used by
households that receive microfinance relative to those that do not, when
they are also receiving health education
Through the above regression, we hope to isolate the effects of providing microfinance
and health education on malaria prevention behaviour. Through ß3, as indicated above, we also
hope to measure the combined effect, which we expect to be greater than provision of health
education or microfinance individually.
We hypothesize to find a larger combined impact because changes in malaria prevention
behavior or any health expenditure depends on affecting three different mechanisms. The first
one is through higher household income or profits which increases the ability of households to
increase their health care expenditures. However, as we have seen through Abhijeet Banerjee’s
and Esther Duflo’s analysis in Poor Economics, lacking ability to spend might not be the major
problem in such households. The second mechanism is having the appropriate knowledge on
prevention techniques to effect a change in expenditure behaviors. However, as shown through
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past literature, health education does not have very large effects on ITN uptake. Through our
combination of both microfinance and health education focusing mainly on women, we are
trying to influence a third mechanism, that of intra-household decision making.
In our preliminary interactions with Medicine for Mali, they stressed that women are the
members of the households making family health decisions. The pareto-efficient bargaining
model states that the allocation of consumption depends on the individual's relative bargaining
power in the household, determined by the proportion of income that each individual is bringing
into the family. If we assume this to be true for Mali, then providing both microfinance and
health education to women in Mali will play a vital role in shifting this bargaining power in favor
of them enabling them to make better, informed health decisions for their family. Our provision
of microfinance to mainly women in villages, (unlike a one time shock like a cash transfer which
might not change behaviours because a change in behaviour is based on long-run change in
wealth, not a one-time shock), acts a sustained increased stream of income. With this sustained
increase in income, hopefully increased bargaining power and increased health education, we
hope to see some change in expenditure patterns, shifted more towards health spending.
(Specifically malaria preventative purchase of ITNs)
3.7 Heterogeneous Effects
To further test our theory of intra-household decision making, we will run an additional
regression to test whether the gender of the person receiving the loan has any effect on the
outcome of the number of ITNs used. We measure this heterogeneous effect by including an
interaction term between a dummy variable for whether the person receives a microfinance loan
and a dummy variable for whether the person receiving the loan is female or male.
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(2) number of ITNs = ß0 + ß1 (microfinance provision) + ß2 (health education
provision) + ß3 (microfinance provision)*(health education provision) + ß4
(microfinance provision)*(female) + error term
(a) ß4 → the expected additional difference in the number of ITNs used by
households in which a female is receiving the microfinance loan compared
to those in which a male is receiving the microfinance loan.
We expect ß4 to have a positive and significant coefficient, that will provide some
evidence for our intrahousehold decision making theory. However, our results might be
confounded by the fact that the women receiving the loans are relatively young, therefore, they
might not have a big influence on the decision-making about the health which could be
controlled by the more senior women in the household.
In Banerjee et al.’s study ‘The Miracle of Microfinance’, they find heterogeneous effects
of microcredit based on whether individuals taking out the loans came from households with
existing businesses, households with a high propensity to become new business owners or
households with a low propensity to become new business owners. Since, Medicine for Mali is
more focused on providing “seed money” to individuals interested in undertaking small
entrepreneurial activities, this would not be very applicable in the context of our study. However,
this inspired us to test whether there are any heterogeneous effects based on what sector the
“seed money” is used to enter and if this differs for agriculture, cattle and crafts/clothing. To test
this we further add interaction terms to measure the effects of each where the variable related to
the specific sector is a dummy variable.
(3) Number of ITNs = ß0 + ß1 (microfinance provision) + ß2 (health education
provision) + ß3 (microfinance provision)*(health education provision) + ß4
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(microfinance provision)*(female) + ß5 (microfinance provision)*(agriculture) +
ß6 (microfinance provision)*(cattle)
We expect to find heterogeneous effects based on which sector the “seed money” is used
to enter because of certain differences in the continuity and scale of the income received by
operating in these different sectors. For instance, we expect a larger income impact for those
undertaking agriculture. This is due to the fact that while providing the seed money to those
entering this sector, they are also informed that they should store their grain crop and sell it at
higher prices after harvesting season is over. As a result, they benefit from a higher disposable
income, which might have some impact on their malaria preventative behavior.
3.8 Instrumental Variable
In our intervention, participation in the microfinance program is endogenous. Therefore,
despite stratifying and then randomizing, there might be some unobserved characteristics in the
people who choose to take up the loan relative to those who do not (eg: ambition). While we
have reduced omitted variable bias through randomization, it is difficult to completely eliminate
it. We can measure this by comparing outcomes of those who decide to take up the loan to those
who do not. This is also synonymous to our treated effect.
(4) number of ITNs = ß0 + ß1 (number of people who take microfinance loans) + ß2
(controls) + error
In the above regression we are likely to find a correlation between ß1 and the error term. This is
because the treated have some characteristics that should be in the error term but are being
picked up by ß1, giving us a bias. (figure out whether positive or negative). We will try to
eliminate this effect by running a 2 least squares regression. We chose our instrumental variable
to be the intent to treat effect i.e. the group we offered microfinance loans too as we expect that
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to be uncorrelated with the error term i.e. the other characteristics of the people who take up the
loan. Our 2SLS is below:
(5) number of people who take microfinance loans = ß0 + ß1 (assignment to treatment
group i.e. number of people in treatment villages where microfinance is offered) +
ß2 (controls) + error
(6) number of ITNs = ß0 + ß1 (predicted number of people who take microfinance loans) + ß2 (controls) + error
4. Limitations
Even though our design addresses many of the concerns presented in previous studies,
there are many limitations associated with conducting such randomized control trials. The
biggest one being that even though we are randomizing villages that do not have a microfinance
institution at baseline, we cannot stop other microfinance institutions from entering these
villages. This might skew our results due to spillover and contamination effects of having other
institutions provide microfinance in a village where we are not offering it through Medicine For
Mali. Further we might observe similar spillover effects in villages where health education is
being provided through other institutions.
Additionally, there are also certain ethical concerns related to providing microfinance and
health education selectively to certain villages. We address this concern by limiting the time span
of our study to only one year after which Medicine For Mali will provide both to all the trial
villages. However, awareness of such information can itself cause changes in the behaviour of
the villagers which might skew our results.
The major limiting factors in our study are those that influence the external validity or
generalizability of our results. We try and increase external validity by stratifying and then
randomly assigning villages to each of the 4 trial groups to reduce demographic differences.
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However, we randomize villages the do not have microfinance institutions at baseline. There
could be concern that such villages are fundamentally different from those that have existing
microfinance institutions. However, by choosing to conduct our randomized control trial in Mali
where overall penetration of microfinance is low, we reduce this concern. This increases the
external validity of our results than if the villages in our study were characteristically different
from those where one would expect to find microfinance institutions.
Further external validity is affected by the fact that the women receiving microfinance
loans through Medicine for Mali are relatively young while those in charge of the household
health decisions are the older women in the family. Therefore, these younger women receiving
microfinance loans might act differently than the older women receiving such loans.
Additionally, overtime we might find positive effects of providing microfinance on
uptake of bednets due to the efficient monitoring and incentive provisions in place by Medicine
for Mali, where they provide higher loans for better repayment times, eventually influencing
long-term income. Other microfinance institutions that are unwilling to incur these monitoring
costs and provide such incentives might not be as effective in influencing long-term household
health decisions. This too affects the external validity of our results.
Despite, the random assignment of villages to the four trial groups, our study will suffer
from some level of endogeneity. This results from the fact that those choosing to take out
microfinance loans are a self-selecting population. While, there is nothing that can be done to
address this issue, we attempt to avoid self-selection in the health education component of our
study by offering it to everyone in the village. Even though, there could arguably be some self-
selection here because of the people who choose to attend the health education seminars, we are
not very concerned about this problem.
20
5. Conclusion
In our study, we expect to find a higher combined impact on malaria preventative
behaviour of offering microfinance to women and health education through influencing various
mechanisms that govern household health decisions. Our major focus of influence is intra-
household decision making and the structure of household consumption. With more disposable
income, better health knowledge and relatively greater bargaining power, we expect to see a shift
away from temptation goods to more durable goods and greater investments in health. We expect
that these results will result in a new, informed way of thinking about microfinance and health
education. If we can help pinpoint how malaria incidence rates can decrease, our findings could
have serious implications for future decision-making on local, regional, national, and
international levels.
Such a convergence of microfinance institutions and health providers achieves alignment
of interests of microfinance providers with those of health care providers. The self-delivery
system of microfinance enables the distribution of health education to an entirely new type of
poor population worldwide. Microfinance institutions concerned with lacking ability to payback
due to the poverty trap are now solving the client’s health problems through provision of health
education and better access to healthcare. This not only reduces lack of losses from lack of
recovering microfinance loans, but also reduces future costs associated with monitoring and
getting new clients to replace old ones.
21
Appendix
Figure 1
Table 1 1. Population 2. % under poverty line 3. Malaria incidence rates 4. Malaria prevalence rates 5. Geography 6. Climate 7. Employment [gov jobs, self-employed, jobless] 8. % that own agricultural land 9. Distribution of health services/access to health care 10. Number of health care clinics 11. Water quality 12. Number of secondary schools available 13. Number of open water bodies 14. Family size [for income reasons] 15. Rainfall
22
Table 2
Tell Us About Yourself…
23
Section 1: Respondent's Background
Name
Age
Gender
Date of Birth
Ethnicity
Religion
Number of People Within the Household
Do You/Does Your Family Pay Rent for the House? Do You
Have Mortgage on the House? Do You Own The House (No
Mortgage)?
Number of Children Within the Household
When was the last time you gave birth?
Number of Toilets Within the Household
Who Makes the Majority of Health Care Decisions Within
the Household
Number of People Employed Within the Household
Highest Level of Education Attained Within the Household
24
Section 2: Household Information
ID
Name Sex (M/F)
Date of Birth (Day, Month, Year)
Age Health 1=Excellent Health 2=Good Health 3=Poor Health
Currently Attending School? (Y/N)
1 1 2 3
2 1 2 3
3 1 2 3
4 1 2 3
5 1 2 3
6 1 2 3
7 1 2 3
8 1 2 3
9 1 2 3
10 1 2 3
11 1 2 3
12 1 2 3
13 1 2 3
14 1 2 3
15 1 2 3
Section 3: Employment
Are you employed or have you been employed in the last year? (Y/N)
Do You Have a Government Job? Are You Self-Employed? Not Employed?
25
How Much Do You Spend on Running Your Own Business If You Are Self-
Employed? (If You Are Not Self-Employed, Please Write “N/A”)
Are You Involved in the Agricultural Industry, Cattle Industry, or Craft/Clothing Industry?
(Y/N)
Number of Different Jobs you Have Had In the Past Year from June to
October:
Maximum Number of Different Jobs You Have Held at the Same Time:
Have You Ever Been Injured/Gotten Sick Due To Your Job?
Section 4: Household Assets
Does Your Household Have Any Cash Savings? (Y/N) If “Yes”, How Much?
Do You Have Any Loans Out? (Y/N)
26
If You Have Loans Out:
ID Amount of
Loan
Lender Reason For Taking Out
the Loan
How Long Have You Had the
Loan?
Monthly
Interest Rate
on Loan
Is Any Part of the
Loan Related to Health or Paying for Healthcare
? (Y/N)
If You Are To Take Out a Microfinance Loan, What Would
You Use it For?
1
2
3
4
5
27
6
7
8
9
10
Section 5: Respondent’s Health
How Often Do You Visit Health Clinics?
Do You Have Health Insurance? (Y/N)
If You Have Health Insurance, Which Company Do You Have Health Insurance With? (If You Do Not Have Health Insurance, Write “N/A”)
28
Section 6: Child Health
Child ID
Time of Birth (Day/Month/Year)
Number of Pre-Natal Check Ups
Number of Pediatric Visits Per Child:
Does this Child Sleep Under a Bed Net? (Y/N)
If Child Sleeps Under Bed Net, What Was The Cost of The Bed Net?
1
2
3
4
5
6
7
8
9
10
Thank You! Costs to Consider:
1. Travel of volunteers to Mali 2. Cost of signing on with MFM (Would like have to pay for cost of MFM’s time, and cost
of MFM’s distributing insecticide-treated nets) a. Cost of educating teachers for malaria education b. Cost of paying these teachers to go around to teach
3. Surveys will cost time and money a. Many volunteers in each village b. Travel costs to Mali once at time of baseline survey and endline survey
4. Cost of getting the bednets from the government and providing it to the villages
