5 assessing the impact of our microfinance services 1

Opportunity International: Assessing Impact in Malawi Ryan Mohling

2010

Table of Contents Table of Contents .......................................................................................................................................... 2 Executive Summary ...................................................................................................................................... 3 Introduction ................................................................................................................................................... 3 Background and Context............................................................................................................................... 4 Methodology ................................................................................................................................................. 5 Basic Analysis of Percentages and Averages ............................................................................................... 9 Logit Analysis of Perception of Change Variables ..................................................................................... 11 Linear Analysis of Income .......................................................................................................................... 15 Case Studies of Spillover Effect ................................................................................................................. 16 Conclusions and Limitations....................................................................................................................... 17 Acknowledgements ..................................................................................................................................... 19 Author Biography ....................................................................................................................................... 21 Appendix 1: Client/Comparison Survey ..................................................................................................... 22 Appendix 2: Data Cleaning Log ................................................................................................................. 24 Appendix 3: Survey Control Sheets ............................................................................................................ 25 Appendix 4: Logit Regressions of Perception of Change Variables ........................................................... 27 Appendix 5: Linear Analysis of Income ..................................................................................................... 46 Appendix 6: Summary of Findings on Gender ........................................................................................... 48 Appendix 7: Spill Over Case Studies .......................................................................................................... 49

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Executive Summary Clients of Opportunity International Bank of Malawi (Opportunity Malawi) who receive microfinance business loans are more likely to report improvement over the last 12 months compared to a similar comparison group of non-clients in various business and personal areas such as sales income (24% more likely), net income (20%), money saved (22%), number of customers (17%), ability to pay suppliers (17%), overall quality of life (15%), comfort of one’s home (17%), amount of assets (18%), and children’s education (17%). These differences are seen even when controlling for other possible explanatory factors such as current income, age, gender, industry type, business age, and education and are statistically significant at very high levels of confidence often in excess of 99%.

Introduction This study was commissioned by Opportunity International (Opportunity) and was made possible by funds provided through a grant made by the Caterpillar Corporation in 2009. Opportunity is a global network working in more than 20 countries whose mission is “to provide opportunities for people in chronic poverty to transform their lives”.1 Opportunity Malawi was selected for this study by Opportunity senior leadership because it (i) is a regulated financial institution that offers a full range of financial services including loans, savings and insurance products; (ii) has been self-sustaining since its founding in 2003; (iii) has a relatively large footprint in the country.

The purpose of the study is to determine what effect, if any, loans made by Opportunity Malawi to poor micro-entrepreneurs had in improving the business performance, financial standing, standards of living and overall quality of life of clients receiving micro-loans in both group and individual formats.

This study is the second phase of an analysis conducted by a team of MBA students2 at the Kellogg School of Management at Northwestern University in the spring of 2010 for the Microfinance course offering. Students attempted to assess social impact of Opportunity Malawi’s loans using a Client Development Survey (CDS) that had been administered in Malawi in July of 2009 and also designed a revised survey that was conducted in Malawi during June-July of 2010. This 2010 survey tested specifically for client impact and included a comparison group of micro-entrepreneurs not receiving loans from any formal financial institution.

The survey’s main focus was on changes in the last 12 months in the respondents’ business activities and personal wellbeing. The business section focused on changes in average weekly total sales 1 2

Opportunity International. http://www.opportunity.net/About/. Accessed August 20, 2010.

Group members included Josh Engel, Jimena Garcia, Ryan Mohling and Patrick Rios.

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income, net income, amount spent on food, family and household items excluding business (also referred to in this paper as family expenditures), amount of money saved, number of customers, ability to pay suppliers, and number of paid employees. The personal wellbeing section included questions on the change in the overall quality of life, comfort of respondents’ homes, amount of assets, children’s education, the respondents’ health, the overall health of their family, the amount and nutrition of meals, the availability to respondents and their families of reliable electrical supplies, clean water to their house and medical supplies, and the strength of respondents’ religious beliefs.

In addition to the survey, a final part of the study investigated “spillover effects”, cases in which Opportunity loans seemed to have a larger effect than on just the clients. Three case study examples are presented which show spillover in terms of clients recruiting, training and mentoring other clients, client groups that use their business incomes to care for orphans, and larger micro-businesses that provide employment in the community and inputs for other micro-businesses.

Background and Context According to the United Nations Development Programme (UNDP), 65.3% or almost 10 million of Malawi’s 15.3 million people live below the national poverty line. 73.9% or 11.3 million live on less than US$1.25/day and 90.4% or 13.8 million live on less than US2.00/day. Malawi ranks 160 on the Human Development Index out of a total of 182 countries in the world.3 The adult HIV/AIDS prevalence rate is 11.9%, which corresponds to 1.8 million people.4 The following table summarizes trends in some of the other key social and economic indicators from 2001 until 2009.

Population (in millions) GDP (US$ billions) GDP average annual growth Domestic consumer prices (% change) Life expectancy at birth (years) Literacy (% of pop. Age 15+) Total Exports (US$ millions) Total debt outstanding and disbursed

Malawi Social and Economic Indicators5 2001 2002 2003 2004 2005 12.2 12.6 12.9 13.3 13.7 1.7 1.9

1.9 2.1 ‐4.2 1.8

7.1 2.6 27.2 14.9

11.4 15.4

39

40

62 63

64 441 421

511 578 2737 2773

3418

2006 14.0

2007 14.4 3.6 8.6 7.9 52 72 726 836

2008 14.8 4.3 9.7 8.7 53 72 878 963

2009 15.3 5.0 5.9

912 1091

In addition, access to Malawi’s financial sector by the poor is very limited; it is estimated that only one percent of the economically active poor have access to credit and only three percent have access

3 United Nations Development Programme (UNDP). Human Development Reports. Accessed May 28, 2010. http://hdrstats.undp.org/en/countries/data_sheets/cty_ds_MWI.html. Poverty percentage is for 2000-2006.

4 2007 est. CIA World Factbook. https://www.cia.gov/library/publications/the-world-factbook/geos/mi.html. Accessed Aug. 18, 2010.

5 Source: data compiled from the World Bank Country Assistance Strategies of 2003 and 2007, World Bank Development Data 2008, the World Databank, the CIA World Factbook and the United Nations Development Programme Human Development Reports.

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to savings.6 There are currently nine microfinance institutions (MFIs) in Malawi which serve 197,207 active borrowers with an aggregate gross loan portfolio of US $55.3 million and an average of US $104.70 per borrower. There are also $26.2 million in deposits, and 505,539 deposits. Of those totals, Opportunity Malawi, accounts for $24 million of gross loan portfolio among 33,835 active borrowers with an average loan size of $713.10, as well as $25 million in deposits from 195,007 depositors.7 (The higher average loan for Opportunity Malawi is likely skewed due to the inclusion of loans made to small and medium size enterprises not considered in this study). Most MFIs have limited outreach in the country; access and availability of financial services is especially lacking in rural areas.8

Methodology For the client/comparison group part of the study, a total of 1026 surveys were gathered over the course of two weeks in late June/early July, 2010. During the data entry process, data was checked for feasibility9 and surveys with responses that fell outside normal ranges10 were removed along with any other survey that included a no response. A total of 907 surveys were used for the majority of the analysis, with 458 client surveys and 449 comparison group respondents. A complete log of the data cleaning process can be found in the appendices. The only special case was the analysis of impact on children’s education; for this section, all surveys of respondents without children had to be removed, which left 426 client surveys and 341 comparison surveys for a total of 767.

Surveys were done in the Northern and Southern regions of Malawi, based out of the Opportunity Malawi branches of Kasungu and Limbe. Surveys were translated into Chichewa, the national language of Malawi, using a group of four bilingual marketing researchers and Opportunity Malawi employees. A team of ten independent native Malawian bilingual enumerators were hired to administer the surveys.

Enumerators used the Chichewa version survey when speaking with respondents and then filled in respondent answers on an English version.

Client names were obtained from the Opportunity Malawi main office and were filtered for those who had been borrowing with Opportunity Malawi for at least two years, since it was believed that impact would require some time to become evident. From the group of clients who had been borrowing for more

6 United Nations Development Programme. Microfinance: Financial Inclusion in Malawi. http://www.undp.org/partners/business/resources/11microfinance.pdf. Accessed Aug. 18, 2010.

7 Mix Market http://www.mixmarket.org/mfi/country/Malawi/flatstore_mfi_mfdb_data.mix_diamonds__c%2Cbalance_sheet_usd.gross_loan_portfolio%2Cpro ducts_and_clients.total_borrowers/2009/. Accessed Aug. 18, 2010.

8 United Nations Development Programme. Microfinance: Financial Inclusion in Malawi.

9 Eight surveys were removed where the number of school aged children in the house was greater than 0 but then N/A was selected for the number that attend school (instead of 0 or another number).

10 Nine surveys were removed for falling outside of normal ranges. Affected questions/normal ranges were: amount of weekly savings/0 to 1,000,000 MKW (0 to $6,500 USD); age/16 to 85; business age in months/0 to 480; years of education/0 to 20; number of loans/0 to 20.

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than two years, names of individual and Premium Trust Banking (PTB) groups were then randomly selected for surveying. In the event that a client or PTB was no longer borrowing, another client or group of the same economic income level from the same town or village was substituted. In some rural areas, small villages were selected to survey instead of individual clients or groups and then all the groups (typically 5 or less) of that village were interviewed. In all cases, when a PTB group was selected for surveying, no more than half of the group’s members were interviewed in order to increase the reach of the study and to insure against bias from any one particular group.

It should be noted that restricting the client sample to longer term borrowers can potentially introduce some bias into the data. Tedeschi and Karlan (2009) found that there was an upward bias in improvement indicators when dropouts were not included in a study of a Peruvian microfinance institution.11 In this case, improvement would be overestimated since less successful clients were more likely to drop out leaving a higher percentage of successful clients to be surveyed. They also conclude, however, that it would be possible for this bias to be either negligible (in the case that dropout rates are low) or negative (in the case that successful clients “graduate� from microfinance programs or that drop outs are better off than average). Related to this, one phenomenon noted during case study interviews with Opportunity Malawi clients was the propensity of some clients to continue borrowing even when additional capital was not needed. When questioned about this, many clients described the social benefits that came with being part of the PTB group, such as respect from the community and friendships within the group. The implication is that these non-financial benefits might lead some clients who are not benefitting from the loan itself to continue borrowing when otherwise they would have dropped out.

However, without more detailed data on former Opportunity Malawi clients, it is hard to say with certainty to what extent, if any, effects from this bias might be at play.

The same survey was administered to both the client and the comparison groups and all survey responses were self-reported. One issue with this is that neither client and comparison group responses are verifiable; it is impossible to know if answers are accurate since respondents might be motivated to either overstate their life situation or please the interviewer. Clients in particular could be susceptible to this if they erroneously believed that their responses might influence future lending. To minimize these factors as much as possible, several steps were taken. First, while clients knew that the surveys were sponsored by Opportunity Malawi, all surveys were anonymous and enumerators informed respondents of this at the beginning of the survey. Second, an intentional effort was made to remove Opportunity Malawi staff from the survey process as much as possible. In each survey area, a loan officer would 11 Tedeschi, Gwendolyn and Karlan, Dean. 2009. Cross Sectional Impact Analysis: Bias from Dropouts. http://karlan.yale.edu/p/MicrofinanceDropouts-Aug2009.pdf. Accessed October 12, 2010.

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introduce an enumerator to the PTB cluster leader and then would move on to the next survey area. The cluster leader then directed the enumerator to the selected individual clients and trust groups. When interviewing trust groups, enumerators were instructed to ask cluster leaders for a representative sample of well performing, poorly performing as well as average clients. All interviews were conducted individually and without the presence of either Opportunity Malawi staff or cluster leaders.

The comparison group was obtained in two ways. In some cases, enumerators asked client respondents to refer them to another micro-entrepreneur who was very similar to the client – of the same economic level and in the same village or urban area, perhaps in the same industry – but who had never borrowed from a formal financial institution. Formal financial institutions were defined to include any traditional bank, regulated or unregulated microfinance organization, or similar lending body (such as the government lending agency). Enumerators were trained to be able to identify the different local lending organizations that would fall into this category and were instructed to screen potential comparison group respondents using this criteria. Borrowing from a local money lender and/or from friends/relatives was not considered a formal financial transaction and did not disqualify a respondent from being included in the comparison group. In other cases, enumerators sought out other non-borrowing micro-entrepreneurs of similar economic status as the clients being interviewed in that particular area. For example, if a referral was not possible, enumerators would simply survey other shop owners selling similar wares in the same market as the client respondents. Enumerators were also instructed to interview an equal number of client and comparison respondents each day in each location so as to insure against having a client sample predominantly from one area and the comparison sample from another.

It should also be noted that due to the necessity of comparing a group of clients that have taken out a loan and a similar group of entrepreneurs who have not, there is inherently a potential omitted variable bias that may be introduced into the study. Observable measures such as income, gender, age, type of business, etc., can all be controlled for in the analysis, but there also could be other unobservable characteristics that could potentially influence business performance and hence a respondent’s answers.

If these traits happen to be correlated with taking a loan from Opportunity, then the survey results could be skewed. For example, if clients were on average more ambitious, better positioned in the community, better able to navigate bureaucracy involved in obtaining a loan, or better suited to business, etc., then client businesses might have performed better even without a loan. In addition, positive effects that correspond to taking a loan could in part be attributable to the unobserved characteristic.

At the same time, however, some traits of Opportunity Malawi clients and the ways in which they become clients could mitigate the effect of some of these unobservable characteristics. First, considering the powerful role men often play in traditional societies like Malawi, the fact that the Opportunity clients

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are disproportionately female due to Opportunity’s special focus on women would suggest that if anything, clients on the whole will be more poorly connected and at more of a disadvantage in the business space than a random draw of business owners. Additionally, women will often need to care for children as well as tend to their business – clients on average also had more children than non-clients – which could further depress business performance. Second, because the vast majority of Opportunity loans are uncollateralized group loans that have been designed for extremely easy access to the working poor, it is believed that special skills needed to obtain a loan are negligible. Finally, for new client outreach, it became apparent during case study interviews that a large number of clients come to Opportunity through the personal recruitment of friends and family. This process is significantly different from a motivated entrepreneur independently resolving to apply for a loan and could also reduce the role that ambition plays in differentiating between the client and non-client groups.

In order to obtain the average values of weekly sales income, net income, food, family and household expenditures, and savings, a two step process was used. The general Malawian economy is characterized by a boom period followed by a lean period every year that corresponds to the seasons for planting and harvesting the main cash crop, tobacco. During the harvest, famers have excess income to spend which circulates through the whole economy. After planting, economic activity shrinks in almost every sector until the harvest. As such, enumerators recorded average weekly values during the boom and lean periods. All responses were taken during the same single interview and all interviews were performed during the same season. The reported average values in the data are a midpoint of these two values.

For the data analysis, the key dependent variables all focused on change aspects in respondents’ lives. A typical question might ask how the respondent’s net income had changed compared to 12 months ago with possible responses being much worse, worse, stayed the same, better, and much better.

The vast majority of respondents answered in two of the five possible answers; for simplicity, data was therefore collapsed into a binary form so that these change questions could be read as either “life is getting better” or “life is not better”. In the case that a respondent answered that a particular aspect was either better or much better, the data was given a value of 1; if not, it was given a value of 0.

With the data coded in this manner, three different types of investigation were performed. First, a preliminary analysis was carried out comparing the percentages of surveyed clients and comparison group that report improvements in their lives. Keeping in mind that unobservable characteristics may play some role, there is reasonable basis for interpreting a good part of the difference found between the two as the effect of participation in Opportunity Malawi’s services. Secondly, Logit statistical regressions were run on these improvement variables which results in the probability that a respondent with an average value

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for all characteristics will respond that their net income had improved. From this base probability (always a percentage between 0 and 100), estimates can be gathered about the marginal impact of changing a particular independent variable, holding all other key measurement variables constant. Finally, a linear analysis was performed on the absolute income values in order to estimate the impact of the independent variables on income. By including the client/comparison binary variable, there should be reasonable basis for determining if Opportunity Malawi services play a role in determining a respondent’s income while holding all other variables constant.

Basic Analysis of Percentages and Averages The first table summarizes the differences in perceived improvement in the last 12 months in the business activity and personal life variables (survey questions 1 and 2) between the client and comparison groups. Figures are the percentage of each group that reported improvement (much higher or higher /

Change Religious Belief

Change Medical Assistance

Change Clean Water

Change Electricity

Change Meals

Change Family Health

Change Your Health

Change Children’s Education

Change Assets

Change Home Comfort

Change Quality of Life

Change # Employees

Change Can Pay Suppliers

Change # Customers

Change Savings

Change Family Spending

Change Net Income

Improvement compared to 12 months ago

Change Sales Income

much better or better).

Clients

78% 72% 56% 54% 77% 56% 20% 89% 87% 73% 80% 77% 77% 73% 35% 41% 64% 71%

Comparison

54% 50% 47% 33% 57% 37% 13% 71% 67% 54% 61% 67% 68% 62% 30% 38% 61% 59%

Difference

25% 22%

9%

22% 20% 19%

8%

18% 21% 19% 19% 10% 10% 11%

5%

2%

2%

For each of these 18 variables, a greater percentage of clients reported improvement than the comparison group. The largest differences are seen in the variables which are more within the control of the respondents and depend on their businesses: sales income, net income, savings, home comfort, number of customers, ability to pay suppliers, assets, and children’s education. Smaller differences in improvement were measured in the variables for which change rests further from the control of the respondent: availability to the respondent and their family of reliable electricity, clean water to their house, and medical assistance. These results are reasonable since the latter three variables are much more likely to depend on the government; often, either electricity, clean water and health clinics are provided in a particular area or they are not. Consequently, it should be expected that differences in improvement between the clients and the comparison group would be less pronounced in these three variables than others. Furthermore, the fact that the results show this contrast tends to support the notion that

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12%

respondents thoughtfully responded in kind to each survey question. In contrast, if there had been constant large differences across all variables even when the effect would be expected to be small, this would suggest that clients might be answering questions without thinking: they received a loan, the study asks about impact related to that loan, and so a client will respond that it has improved life in every regard. This could be likened to a placebo effect in a medical trial. The fact that this pattern is not present in the data suggests that clients are systematically considering every question and responding in kind, resulting in more credible answers.

It should also be noted that the client percentages are consistent with reported values from previous years of surveys conducted with a separate set of clients with Opportunity Malawi. For example, a Client Development Survey done in July of 2009 found that 81% reported higher sales income, 81% higher net income, and 95% higher overall quality of life. These figures fall very close to the same questions asked in this survey, with 78%, 72%, 89% of clients reporting improvement respectively, suggesting that this year’s survey falls in line with previous research.

The next table summarizes the differences between the absolute values measured in the survey. It can be seen that on each of the absolute business performance indicators – average weekly sales income, net income, family expenditures, and savings – the client group reported higher amounts than the comparison group. Beyond that, however, most of the other indicators are relatively equal across the two groups with the exception of electricity, televisions and refrigerators. The ratio of children in school, number of days families eat meat, total household size, indoor sanitation, separate sleeping rooms, security from theft, house wall, floor and roof construction materials, and transport (owning a truck, car,

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0.90 0.4% 0.20 0.52

2%

1%

6%

4%

13%

5%

7%

17% 13%

The final table summarizes and compares the demographic data of the client and comparison groups. It can be seen that the clients were on average 4 years older than the comparison group, were

10

9%

Transport

7

Cell Phone

36

Refrigerator

16% 98% 80% 80% 54% 87% 81% 47% 30% 85% 48%

Television

18% 99% 87% 84% 67% 92% 88% 64% 42% 95% 49%

5.1

Concrete Floor

5.6

2.8

Metal Roof

3.0

92%

Electricity

People in House

93%

1.9

Brick House

Day Eat Meat

2.8

28

Security From Theft

Ratio Children In School

130

Separate Sleeping Room

School Age Children

40

22

Indoor Toilet

Savings ($)

29

78

359 114

Comparison 230 Difference

Family Spending ($)

Clients

Net Income ($)

Absolute Values

Sales Income ($)

motorcycle or bicycle) were all very close across the two groups.

1%

75% women compared to about 41%, and 73% owned their house compared to 55%. Client businesses were on average almost a year and half older. In terms of industry, both groups had very similar breakdowns between manufacturing, service, retail and agriculture. Both groups had nearly the same number of paid employees as well as the same amount of formal education. Clients had had on average

Age

Male

Own

Manufacturing

Service

Retail

Agriculture

Business Age (Months)

Employees

Education (Years)

Loans

Borrowing Time (Months)

four loans and had been borrowing for an average of almost three years.

Client

37

25%

73%

13%

18%

53%

16%

81

0.9

9.3

4

35

Comparison

33

59%

55%

8%

17%

61%

13%

64

0.7

9.0

N/A

N/A

Difference

4.27

‐34%

18%

5%

1%

‐9%

3%

17

0.2

0.3

N/A

N/A

Demographic Data

Logit Analysis of Perception of Change Variables In this next section of the analysis, Stata statistical software was used to perform Logit regressions on the 18 perception of change questions included in questions 1 and 2 of the survey. The next table summarizes the results of the regressions of variables related to business activities in survey question 1. Perceptions of change (the dependent variables) were regressed against a number of explanatory or independent variables including demographic data, current net income and savings levels, industry type, the geographic region (Limbe or Kasungu) as well as the client binary variable. A plus signifies that the independent and the dependent variables are positively correlated; a negative sign signifies a negative correlation. Any independent variable was considered significant at the 90% confidence level.

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Manufacturing

Service

Retail

Δ Average Weekly Net Income

+

+

Δ Family Spending

+

Dependent Variables

Δ Average Savings

+

Client

Own House

Limbe

Male

+

Years of Education

Age

Business Age

Savings

Δ Average Weekly Sales Income

Direction of 90% Significant Business Activity Variables

Agriculture

Net Income

Independent Variables

‐

+

‐

+

+

+

Δ Number of Customers

+

‐

+

Δ Ability to Pay Suppliers

+

‐

+

Δ Paid Employees

+

+

‐

The results show that on all seven dependent variables, the client binary variable was significant and positively correlated, often at well above the 90% confidence level (>99.7% for sales income, net income, number of customers, ability to pay suppliers, and number of employees). Compared with 12 months ago, all other variables held constant, clients were 24% more likely to report higher sales income, 20% more likely to report higher net income, 7% more likely to report higher amounts spent on family, food and the house, 22% more likely to report higher savings, 17% more likely to report more customers, 17% more likely to reportable to pay suppliers, and 8% more likely to report having more employees.

The complete regressions can be found in the appendices.

The regressions also show positive correlations with net income and savings as would be expected. All else held constant, respondents who have larger current incomes are more likely to report improvements in the number of customers they have, their ability to pay suppliers and their number of employees. Similarly, respondents who save more are also more likely to report higher sales income, higher net income, and higher savings compared to a year ago. This agrees with common sense – those who make more are able to save more, for example – but by including these variables in the regression, they are controlled for when looking at the explanatory variables, especially the client variable. This means that even when it would be expected for a respondent who has a higher income to also be more likely to report improvement in income, the fact that the client variable is still significant even when including income in the regression signals that the clients on average do better than the comparison group even at the same income levels. In sum, the differences between clients and the comparison group in the

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key change variables in the table above hold even while controlling for factors like how much a respondent currently makes.

Other factors seen in these regressions are that males were 9% less likely to report improvement in being able to pay their suppliers while those who own their own house were 7% more likely to report higher net income and family spending. Business age was also negatively correlated with reporting higher sales and net income as well as with number of customers and ability to pay suppliers; every additional year a business had been in operation resulted in 1% reduction in the likelihood of reporting higher sales income, net incomes and number of customers, and 3% less for reporting more ability to pay suppliers. While perhaps a bit surprising at first, the effects are not particularly strong. Either way, a plausible and likely explanation is that, just like businesses in the developed world, older microbusinesses mature and as they do, growth rates slow, causing those who have been in business longer to report less improvement over the years.

The next table summarizes the results of the regressions of variables related to personal life aspects in survey question 2. Perceptions of change (the dependent variables) were regressed against the same independent variables that were used for the business activities Logit regressions. A plus signifies that the independent and the dependent variables are positively correlated; a negative sign signifies a negative correlation. Any independent variable was considered significant at the 90% confidence level.

Dependent Variables

Δ Overall Quality of Life

+

Δ Comfort of Your Home

+

Δ Assets

+

Δ Children's Education

+

Δ Your Health

‐

‐ +

+

‐ ‐

+ +

+ + +

‐

‐

+

Δ Family's Health Δ Amount and Nutrition of Meals

Client

Limbe

Years of Education

Business Age

Agriculture

Retail

Service

Manufacturing

Own House

Male

Age

Savings

Direction of 90% Significant Personal Impact Variables

Net Income

Independent Variables

+ +

Δ Availability of Electricity Δ Availability of Clean Water to House Δ Availability of Medical Assistance Δ Strength of Religious Belief

‐

13

+

+

+

+

‐

+

+

+

+

+

+

+

+

The results show that the client variable is significant and positively correlated for eight of the 11 dependent variables, often at well above the 90% confidence level (>99.4% for overall quality of life, home comfort, assets, family health and children’s education). Compared with 12 months ago, holding all other variables constant, clients were 15% more likely to report higher overall quality of life, 17% more likely to report higher comfort of their home, 18% more likely to report more assets, 17% more likely to report better education for their children, 7% more likely to report better personal health, 9% more likely to report better family health, 8% more likely to report higher frequency and nutrition of meals, and 7% more likely to report stronger religious belief.

In contrast, the client variable was not found to be significant for the three variables related to the availability of electricity, clean water, and medical assistance, corresponding to the very small difference between the client and comparison groups mentioned in the basic analysis section for these variables.

Interestingly, there is, however, a strong correlation between those three availability variables and the number of years of formal education a respondent has had. For each additional year of education, a respondent was 4% more likely to report better access to electricity, 3% more likely to report better access to clean water to their house, and 1% more likely to report better access to medical assistance. Since most of these services are provided by the government, it would appear that differences between the two groups would have to correspond to a small number of respondents who perhaps moved to an area with better services or were successful at lobbying for more services. This data suggests that it tends to be the better educated who actually see improvements.

Education was also positively correlated to reporting higher assets, frequency and nutrition of meals and stronger religious belief, but only had a small effect. Respondents in the Limbe region were also 11% more likely to report better access to electricity and 8% more likely to report higher access to medical assistance, most likely due to government expansion of services, as well as 9% more likely to report higher frequency and nutrition in meals and 8% more likely to report stronger religious belief.

Similar to the analysis of business activities, there are positive correlations between a number of the dependent variables and income, as would be expected. All other variables held constant, respondents with higher current net incomes were more likely to report higher overall quality of life, home comfort, assets, children’s education as well as frequency and nutrition of meals. The fact that income is not correlated to availability of electricity, clean water or medical assistance confirms that it is a more complicated process than simply being able to afford these services and also coincides with the explanation that there will not be a large difference in improvement between the client and comparison groups for the same reasons.

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Finally, age was only significant in the personal health regression; every 10 years corresponded to being 4% less likely to report better health (as would be expected). Males were also 5% less likely to report better quality of life, 7% less likely to report better health, and 7% less likely to report stronger religious belief. Those who own their house were 7% more likely to report higher comfort of their home.

Business age was significant in six of the 11 variables, but had a only weak effect; each additional year of operation corresponded to no more than a 1% difference.

Linear Analysis of Income The third and final analysis performed was a pair of linear regressions attempting to predict the current absolute sales income and net incomes for respondents. The same independent variables from the Logit analysis were used. The only difference between the two analyses is that while the Logit tried to explain variables that described change over the last year, these regressions try only to explain the current values of business activity. The following table summarizes the results in semi-log format.12 Full regression data can be found in the appendices.

Male

Own House

Industry

Business Age

Years of Education

Limbe

Client

Independent Variables

Age

Dependent Variables

LN Average Weekly Sales Income

+

‐

+

+

+

LN Average Weekly Net Income

+

+

‐

+

+

+

Direction of 90% Significant Business Activity Variables

Again, the client binary variable is significant across both regressions, at a very high confidence level (>99%). All other variables held constant, clients reported earning 41% more sales income than the comparison group and 32% more net income. Education is also an important indicator of high performance business activities. Everything else held constant, each additional year of formal education predicts a 6% increase in both sales and net incomes. Business age in this regression also has positive effect; all else equal, each additional year that a business is in operation predicts a 3% increase for sales and net incomes. Males on average earn 34% more than females in sales income and 22% more in net

12

Regressions with the normal dependent values failed Breusch-Pagan heteroskedasticity tests (a test of the null hypothesis that the residuals vary by the same amount for all predicted values of the dependent variable) and so dependent variables were transformed using natural logarithms so that regressions satisfied both the Breusch-Pagan and scatter plot residuals vs. predicted value linearity tests.

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income. For every 10 additional years of age, someone tends to make 8% more net income (and 7% more sales income though the confidence level is at 89.6% so was not included in the table).

confidence level

Age

Male

Own

Manufacturing

Service

Retail

Agriculture

Business Age (Months)

Education (Years)

Limbe

Client

LN Sales Income coefficient

Constant

Dependent Variables

Independent Variables

9.094

0.007

0.342

‐0.012

‐0.737

‐0.484

‐0.413

‐0.475

0.003

0.064

0.027

0.413

100.00% 89.67% 100.0%

11.42%

87.78%

69.64%

62.60%

68.30% 99.98% 100.00% 24.23% 100.00%

‐0.013

‐0.887

‐0.632

‐0.679

‐0.745

13.56%

95.64%

85.47%

88.64%

91.12% 99.98% 100.00% 41.55%

LN Net Income coefficient

8.364

confidence level

0.008

0.223

100.00% 95.37% 99.64%

0.003

0.065

0.044

It should also be noted that in the analysis that the four industries were condensed into one category. For sales income, none of the four were significant individually, but after testing for multicolinearity (which measures to what extent different variables move in the same direction), it was found that all four were jointly significant (meaning that at least one of the four is significant). Each industry was measured against a “none of the above” base value so the fact that they are all negative does not yield much information, nor is it matter for concern. In the net income regression, both manufacturing and agriculture were significant at the 90% confidence level though, as with sales income, were also multicolinear with retail and service. Again, all four were correlated in the negative direction and so for simplicity were collapsed into just one variable. It should be noted that these linear regressions do not prove causality, but only serve to show that the differences seen in the basic analysis between the business metrics in the basic analysis section are statistically significant.

Case Studies of Spillover Effect In addition to the effects on individual borrowers themselves, there was a desire to see if microfinance loans might also cause “spillover” effects in which non-clients, through contact with an Opportunity client, might benefit from their client neighbors. In the most basic sense, it bears remembering from the analysis above that on average, each client borrower employs (with financial compensation) slightly more (.2) people than comparison group business owners, reported 19% higher educational improvement for their children, and reported 9% higher spending on family expenditures.

Beyond this impact, however, it was desired to investigate further to see whether first, any cases of other

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0.316 99.99%

spillover effects could be found and second, to discover some possible forms these effects would take.

This case study section is purely anecdotal and no claims can be made to which extent that spillover similar to the following examples occurs.

The three case studies presented here each show a different way of how spillover might take place. A table summarizing the impact is given here with the full stories found in the appendices.

Client Name

Type of Effect

Business

Susan Kapasule

Cluster Leader

Cross-border trade

Jisenga PTB Group

Orphan Care

Traditional food and drinks

Yamakani Phiri

Employer

Bakery

Impact Recruited 130 new clients who have built 76 houses, bought 84 refrigerators and own 3 cars Group of 8 women care for and send 16 extra children to school in addition to their own; 7 from poorer relatives, 9 orphans Employs 7 people and supplies over 60 other small business owners

Conclusions and Limitations In general, the differences seen between client and comparison responses in perception of change questions compared to 12 months ago are striking. Not only do clients report better results on all 15 indicators where there is reasonable expectation that microfinance could make a difference, but the magnitude of the impact itself is surprising. For example, in many cases, the norm for the comparison group is a roughly 50/50 chance that a respondent reports improvement, as can be seen in the basic analysis for the variables of improvement in sales income, net income, number of customers, amount of assets, and children’s education. The analysis shows that the norm for clients is improvement roughly three times out of four. In two other variables – improvement in the amount saved and the ability to pay suppliers – the comparison group reported improvement roughly one times in three while clients reported improvement over 50% of the time. In addition, the very high degree of confidence that the client variable plays a significant role in these regressions also is worth repeating. For regressions of sales income, net income, number of customers, ability to pay suppliers, and number of employees, for overall quality of life, home comfort, assets, family health and children’s education – ten out of the 15 regressions where the client variable would be expected to be significant – the client variable was significant at a confidence level of greater than 99.4%.

Considering these results, one might argue that client respondents were biased towards reporting more positive changes than the comparison group. It is worth noting again, however, that while there was significant, positive impact in 15 of the 18 improvement variables for the client group, there were three variables where there was no statistically significant difference between in the clients and comparison – in

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the change in availability of electrical supplies, clean water to one’s house, and medical assistance. As discussed earlier, the provision of these services will almost always depend on government programs largely outside of a respondent’s control. Consequently, the fact that there is no significant difference in improvement in these three variables suggests that a client bias might be minimal since if this were the case, one would expect to see roughly constant levels of reported improvement across all variables including these three.

Significant differences between clients and the comparison group in reported absolute values of sales income, net income, family expenditures and savings are also surprising. These figures suggest that clients make more, spend more on their families, and yet also save more than their peers who have never had a loan from a formal financial institution. The fact that clients currently make more money than the comparison group does not imply that this is a result of Opportunity Malawi services. Indeed, it is very plausible to conclude that once one has money is it easier to make more money. A critic would then question whether Opportunity Malawi simply loans to clients who are wealthier than average. However, this line of thinking merely leads back to the Logit analysis which controls for this very possibility. By including net income as one of the explanatory variables in the Logit regressions, the impact of the binary client variable can be observed independent of income. In fact, the model predicts that holding all of the included variables constant, there is still a significant impact in moving from the comparison to the client group. For example, for all client and comparison group 40 year old females with nine years of education who make $35 of net income a week‌, etc., it would still be expected that 20% more of the clients would report higher net incomes. So, while the fact that clients on average make more than the comparison group does not imply that this is a result of Opportunity Malawi loans, the Logit analysis shows that clients are more likely to show improvement in net income over the last year. While unobservable characteristics might also play a role, these two pieces of data taken together suggest that clients might have higher incomes not because they always had higher incomes, but rather due in large part because their lives are improving as a result of borrowing from Opportunity Malawi.

It also bears repeating that the comparison group appears to be a comparable population to the clients. The two groups are virtually identical when it comes to the number of years of formal education, days per week they can afford to eat meat, distribution by business industry, household size, ratio of children in school, number of employees, ownership of some form of transportation, as well as the type of house they live in (indoor toilet, separate sleeping room, security from theft, brick construction, metal roof and a concrete floor). Meanwhile, many observable differences between the two groups (sales and net income, spending, savings, electricity, television, refrigerators, and percentage that own their house)

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are all in areas where it is plausible that the differences are due to improvement derived from having access to credit, given the improvement seen in the Logit analysis.

With regards to limitations of the study, several should be mentioned. First, the study is completely dependent on the accurate reporting of both clients and the comparison group alike. Most of the key survey questions do not involve “hard” numbers measured over the course of a year, but rather the respondents’ perception of how much each of these metrics had changed. Perceptions are susceptible to factors other than the key quality being asked about. Business performance in the more recent past, for example, might unduly influence a respondent’s perception, biasing the answer. Clients especially might feel the need to overstate their performance or to please the interviewer. Second, as mentioned in the Methodology section, there is a potential omitted variable bias in the client sample in which an unobservable characteristic such as ambition that is correlated with being a client plays some role in the outcome. Third, due to the database setup, multiple entries on the industry type could not be entered when they appeared, affecting 14% of the surveys, and meaning that only the first industry listed was recorded. This could potentially have added some “noise” to the industry type data. Finally, with regards to the case study section, while these stories prove that spillover can and is in fact happening, there are obvious limitations in the usefulness of anecdotal evidence, though this could be an interesting area for future study and development in the microfinance industry.

Acknowledgements This study would have been possible without both the assistance and dedicated support of many individuals. First, thanks go to Paul Christensen, who teaches the Microfinance class at Kellogg, for agreeing to be a part of this project from the very beginning, giving input, suggestions and encouragement along the way as well as reading drafts from both phases of this study; this study would never have gotten off the ground otherwise. Thanks to Professor Wioletta Dziuda, who on multiple occasions gave much more time than she needed to in helping to think through the statistics behind the analysis; this was much beyond the call of duty of DECS 434. Thanks as well to Professor Leemore Dafny for letting us crash her Advanced Statistics class and even go to office hours. Thanks to my fellow Kellogg compatriots Jimena Garcia, Josh Engel and Patrick Rios, who were willing to bite off more than they had to in agreeing to this project.

There are almost more people to thank at Opportunity Malawi than can be counted. Thank you to Francis Pelekamoyo, Aleksandr Alain Kalanda, Alice Abillu for your warm welcome and for setting this project up for success. Thank you to Luckwell Ng’ambi for coordinating every aspect of the project on the ground in Malawi and for being so responsive to outlandish requests as well as last minute changes; it

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is truly appreciated! Thanks to John Fromm for letting us camp in his office and to the managers that graciously hosted us at each location and released personnel to accompany us while in Malawi: Boyce Njunga in Kasungu and Alefa Mtunga and Maureen Nalikungwi in Limbe. Thank you to the staff who planned out daily routes, hired cars, and generally made things happen: Owen Kabaghe, Kamuzu Nyangulu, and Ernest Chikacha. Thanks to the Opportunity Malawi main office staff that were so accommodating and helpful, especially to Gift Livata, Arthur Nkosi, Grace Mpinganjira, Daud Sulemani, Lumbani Manda, and to Temwa Kanchewa for doing whatever was needed to negotiate good deals with shady minibus owners. Thanks to George Phuza for your friendship and conversation – I thoroughly enjoyed our microfinance debates while watching World Cup games! Of course, thanks to all the dedicated loan officers for your hard work and commitment to serve the poor; we wouldn’t have found a single client without you, especially Lucius Kasakala, Paulina Mzumara, Faliot Amanzi, Brighton Mwamadi, Lazarous Mbemba, Wezzie Mwale, Paul Mazingaliwa, Tiwonge Gondwe, Cromwell Ngwalo, Shupikayi Walani, Robert Mayawo, Fredrick Chikwekwe, Kingsley Namizinga, Esmie Pagona, and Mtendere Mchakama.

Thanks to our partner at CDM, Bright Sibale, for your knowledge and expertise as well as to the team of hardworking enumerators who tramped all over Malawi tracking down clients and comparison group: Abel, Ellen, Mastano, Gladys, Felix, Anganile, Faith, Cosmas, Brenda and Mayeso. Thanks to Van who faithfully and safely transported us around the country; sorry we never found the traditional dancers.

Finally, thanks to Dianna Andrade, whose efforts were instrumental in executing the research on the ground; I am amazed at your ability to attract an entire village of children with only a camera. Thanks to Mary Pennington for your initial guidance. Thanks to Beth Houle for believing in this project and giving it wings. Thanks to my parents for supporting me all along the somewhat circuitous path I’ve chosen as well as to the Big Man who gives minds with which to think, hearts with which to love, and who sets before us life to the full.

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Author Biography Ryan Mohling: Ryan is a graduate student pursuing an MBA from the Kellogg School of Management at Northwestern University and a Master of Public Administration at the John F. Kennedy School of Government at Harvard University where he is a Presidential Public Service Fellow. Before graduate school, Ryan worked in Costa Rica for five years where he was the Latin America Regional Coordinator of International Schools for Young Life, a nonprofit youth outreach and mentoring organization. A native of Boulder, Colorado, he holds a Bachelor of Science degree cum laude in mechanical engineering from Duke University and is a travel enthusiast, having visited 45 countries on six continents.

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Appendix 1: Client/Comparison Survey

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Appendix 2: Data Cleaning Log Question Change Net Income Change Savings Change # Customers Change # Suppliers Change # Employees Change Quality of Life Change Comfort of Home Change # Assets Change Your Health Change Family Health Change Nutrition Change Electricity Change Med Assistance Savings Savings Eat Meat Age Age Rent/Own Business Age Business Age Employees Years of Education Years of Education Years of Education Years of Education Years of Education Number of Loans (Client) Indoor Toilet Separate Sleeping Security Brick House Concrete Floor TV Fridge Cell Transport School aged Loans Change Children’s Education

# Removed 1 3 3 8 1 1 1 1 5 2 4 1 1 1 2 4 11 1 12 3 1 1 6 1 1 1 1 1 3 2 5 1 4 6 3 1 1 8 1 6

Reason Bad Value Valid Responses No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 Bad Value 1,325,000 0 to 1,000,000, ‐99 No response ‐99 No response ‐99 No response ‐99 Bad Value 5 16 to 85, ‐99 No response ‐99 No response ‐99 Bad Value 980 0 to 480, ‐99 No response ‐99 No response ‐99 Bad Value 40 0 to 20, ‐99 Bad Value 28 0 to 20, ‐99 Bad Value 24 0 to 20, ‐99 Bad Value 24 0 to 20, ‐99 No response ‐99 ‐99 No response No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 No response ‐99 School age children > 0 but answered N/A on # that attend school (not 0) Bad Value 65 0 to 50, ‐88, ‐99 No response ‐99

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Appendix 3: Survey Control Sheets

25

26

Appendix 4: Logit Regressions of Perception of Change Variables Change in Sales Income

27

Change in Net Income

28

Change in Family Expenditures

29

Change in Savings

30

Change in Number of Customers

31

Change in Ability to Pay Suppliers

32

Change in Number of Employees

33

Change in Overall Quality of Life

34

Change in Comfort of Your Home

35

Change in Amount of Assets

36

Change in Education of Your Children

37

Change in Your Health

38

Change in Health of Your Family

39

Change in Amount and Nutrition of Your Meals

40

Change in Availability of Reliable Electrical Supplies

41

Change in Availability of Clean Water to Your House

42

Change in Availability of Medical Assistance

43

Change in Strength of Your Religious Belief

44

Change in Strength of Your Religious Belief (2)

*Two regressions are included for the strength of religious belief variable. In the original regression, the male, education, limbe, and client variables were significant but in the calculation of marginal effects analysis, variable p-values were significantly different. Therefore a second regression was performed without the industry variables in which the same 4 variables were significant and the p-values correlated to the initial regression. Marginal effects from the second regression are discussed in the analysis.

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Appendix 5: Linear Analysis of Income Variables LN Sales Income

46

LN Net Income

47

Appendix 6: Summary of Findings on Gender Opportunity Malawi serves a much higher percentage of women (75% of total clients) compared to the 41% of women respondents found in the comparison group. The results of the study are particularly interesting then, since it was found that clients on average make more money than the comparison group, while in general, being male is correlated with higher incomes as seen in the linear analysis. This suggests that the effects of receiving microfinance loans actually outweighs the disadvantage women have in income disparity. (Indeed, the client coefficient is larger than the male coefficient in the linear analysis). It should also be noted that in general women (both clients and nonclients) are more apt to report improvement over the past year across the perception of change variables.

Four of the 18 regressions showed that women were improving more than men at a 90% confidence level (ability to pay suppliers, overall quality of life, your health, and strength of religious belief) and while not significant at 90%, another 12 regressions also showed the same. In fact, there were only two out of 18 dependent variables that showed that men might be more likely than women to report improvement: number of employees and availability to medical assistance (though again, at less than a 90% confidence level).

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Appendix 7: Spill Over Case Studies Susan Kapasule – Opportunity Malawi Cluster Leader Mrs. Kapasule, as she is known, is the community matriarch. In light of her beginnings, however, it is clear she was not always destined for this fate.

Before she became a client with Opportunity International’s Bank of Malawi, she lived in the most humble of circumstances. On the outskirts of Lilongwe, the capital city of Malawi, her livelihood was selling tea on the corner of the street that she lived on – a business so small that even her loan officer, Kingsley Namizinga, explained that it was almost not a business at all. This mother of seven children described life as being “tough” before she started receiving loans from Opportunity Malawi. She was using profits to pay for the school fees for her children, but there was never enough. At one point, short of cash, she was forced to withdraw her son from private school (which costs about $15 per trimester) and enroll him in the government schools that average 100 children per classroom.

Then, about 8 years ago, Mrs. Kapasule heard about some neighbors who had obtained small loans from an NGO that would become Opportunity Malawi. She applied for and received a loan and from there she started to see changes in her life. With the working capital from the loan, she was able to grow her business; she started baking selling donuts, scones and cassava and began travelling to across the border to Tanzania to buy goods for resale in Malawi. She now is able to rent a store to sell her wares and has also built a house along with a second one that she rents out.

Mrs. Kapasule’s success didn’t go unnoticed in her community and the positive impact in her life soon began to spread to those around her. “People started to notice how my life was changing”, she explains. While other banks might treat their clients harshly, she says that “here at Opportunity Malawi, I have been empowered.” Mrs. Kapasule began introducing friends and neighbors to Opportunity International. She estimates that over the years, she has personally recruited over 170 new clients.

Currently, there are 14 active Opportunity Malawi trust groups that trace their roots to Mrs. Kapasule.

Clients in those groups have also experienced success in their businesses; since receiving the first loan, the 130 clients in the 14 groups have built 76 houses, bought 84 refrigerators, and now own three cars.

Mrs. Kapasule is now like a den mother with the women in the trust groups she oversees. She dispenses business advice to anyone who needs it, teaches Opportunity Malawi training sessions on business skills, budgeting and saving, and on multiple occasions has taken others with her to Tanzania to train them in cross-border trade. Women in her groups unanimously agreed that the loans and subsequent business success have brought them newfound respect in the community. Before the loans, most didn’t have any income of their own which they blamed for creating inequality in their marriages; they

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explained that husbands saw them as useless and mistreated them. But they claim that this has all changed since the loans and their financial success. They say they are respected by men in their communities and, more importantly, by their husbands as well. They are unanimous that mistreatment has stopped and explain that husbands won’t divorce them now because they have the ability to stand on their own. Multiple women describe how there is peace in their homes now; couples work together to agree on a family budget now where before the woman had no say.

The group is unanimous in declaring Mrs. Kapasule as their role model and inspiration. When asked whether they could be the “next Mrs. Kapasule” they are adamant that they are already on their way; indeed, the nine group members had already recruited 33 new clients.

Jisenga Premium Trust Bank Group – Orphan Care The eight women in the Jisenga Premium Trust Group live in a rural village of mud huts with thatched roofs near the town of Mponela, Malawi. They own and run a variety of small businesses, selling traditional food and drinks to neighbors and travelers on their way to the city.

Life in the village was and still is hard. But before being able to grow their businesses through loans from Opportunity International, there was little hope. One woman, Nelia, described being so poor that she couldn’t even afford the salt she would need to cook meals for her family. She was forced to beg from neighbors to have even this small basic necessity. Nelia described the hopelessness of not being able to afford to pay the very minimal school fees for her kids (around $1 per term). Without any other options, she had to send one of her children away to live with relatives who would be able to provide for her.

Yet now with businesses growing, things have changed for the better. Nelia was able to bring her daughter back from the relatives and instead is now the one caring for two nephews. The amazing part is that Nelia’s actions are the norm for the Jisenga group. Between the eight members, the group cares for 16 extra children in addition to their own. Seven of those are from poor relatives and the other nine are orphans. When asked why relatives send children to them, they explained that “everyone knows that they have resources” now. The women describe it as merely their duty. Whatever the motivation, the fact is that 16 children are being provided education who otherwise wouldn’t be receiving it.

Yamakani Phiri Yamakani is a young man who has overcome much. One of eight children, he was sent away to live with an uncle at the age of nine when his parents began having difficulty providing for the family.

His uncle had enough money to care for him and was able to pay the school fees for him to attend school.

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But when Yamakani was in 6th grade, his uncle died, and with him, Yamakani’s means of going to school.

Yamakani dropped out and in 1998, he started working for another uncle in a bakery. With a month’s salary, Yamakani bought a 50 kg bag of flour and was able to start baking bread on his own using his uncle’s equipment. In 2003, Yamakani bought a building that would serve as both a bakery and his house and built a brick oven in the back. At the time, he had one employee.

A few years later, Opportunity International began operating a mobile banking van in his village.

Yamakani opened a savings account with Opportunity Malawi but heard that people were also able to get small loans. Because he owned his bakery which could serve as collateral, Yamakani was able to take out an individual loan of about $1,000 US dollars. When he paid that back, he took out a second loan of about $2,000. With the loans he built two more ovens and bought a minibus to ferry bags of flour from the city. The impact on his business has been impressive: before the loans he was using about eight 50 kg bags of flour per week which translated into $25 of profit every day. Currently, he goes through 70 bags of flour per week and records $110 of profit per day.

Yamakani’s success has spilled over to others as well. He now employs 7 other people, providing stable jobs in an economy with few employment options. Five employees live in the apartments Yamakani has built behind the bakery free of charge. The bakery sells bread and scones itself but also sells wholesale, supplying more than 60 other micro-entrepreneurs that transport the goods for resale in the surrounding villages.

Now 29 years old and with a family, Yamakani is a respected businessman in the community. He would probably tell you what it feels like, but there’s bread to bake.

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