How to measure microfinance impact on poverty alleviation

How to measure microfinance impact on poverty alleviation: what does available evidence tell us? Some Lessons Emerging from Buusaa Gonofaa’s System of Tracking Improvements in Clients’ Livelihood By Teshome Dayesso, General Manager bgmfi@ethionet. et contact@e-mfp. eu www. e-mfp. eu

Buusaa Gonofaa MFI - Introduction n Started by a local NGO, HUNDEE, later transformed into NBFI in 1999, regulated by the central bank of Ethiopia n Provides micro credit and savings services through joint liability groups of 15 -20 members; n Short term general-purpose loans (8 – 12 months) for income generating activities, microenterprises, farm inputs, productive assets (ox, cow), housing improvements, consumption smoothening n Community managed saving and credit facilities in hard-toreach remote rural areas n Current outreach >50, 000 active clients; total assets of >US $6 million; outstanding portfolio of US$5 million contact@e-mfp. eu www. e-mfp. eu

Buusaa Gonofaa MFI: Our Intentions n Mission: - To provide flexible & efficient microfinance service on a sustainable basis to improve the livelihood of the resource poor, particularly women, landless youth & smallholder farmers Flexible & efficient Sustainable/profitable Financial performance Outreach to target group – the poor Client profile, poverty level Improved livelihood contact@e-mfp. eu Client satisfaction Progress overtime www. e-mfp. eu

Why BG Wanted Client Assessment Scorecard (or ‘Social Ledger’) n Double-bottom line: standardized measurement of financial sustainability; but tracking social goals relied on simple success stories, and intuitive decision making; but this was not enough and we wanted data ‘evidence’ to describe it. n With financial maturity, we wanted a more systematic way to understanding what is happening out there, ¡ Whom do we reach? How poor are they? ¡ Is there a change (+ve, -ve) in our clients’ livelihood? ¡ Where do we succeed in changing client’s livelihood? Where do we fail? Why? ¡ Who benefits from BG most? Does our loan assist either survivalists or entrepreneurial poor? Or both? contact@e-mfp. eu www. e-mfp. eu

Poverty as ‘lack’ and how the poor measure their progress (or improvements) in overcoming it What the poor ‘lack’ in comparison to the not-so-poor n Food security n Clothing n Shelter n Income n Education n Health n Housing ownership n Access to electricity, water n Land n Capital/savings contact@e-mfp. eu n n n What the poor consider as progress or improvement 25% - Expanding the business (ox/cow fattening, more inputs for farming) 16% - Improvements to the house or house construction 14% - Buying ox, cow, sheep 13% Acquire basic household furniture, utensils 13% Buying “luxuries” (radiocassette, TV, jewellery, etc) www. e-mfp. eu

BG’s Poverty Indicators & Score Weight Measurable Indicators Date of scoring as Month/Year: Weight Year/Round of Scoring m 1/yr m 2/yr m 3/yr m 4/yr m 5/yr o Roofing material: - T=thatch; I=iron; P=plastic, O=other O Number of rooms/huts o Housing/improvement o # Oxen o # Cows o # Sheep/goats (shoats) o # Bed type – Metal o # Bed type - Wood/Mosvold o # Tape recorder o # TV Total Score of HH Assets: % Change in Asset score contact@e-mfp. eu 18 16 2 2 4 2 24 T T T I I 4 0 0 1 0 2 2 5 1 1 1 2 3 10 3 1 4 2 3 4 3 3 1 2 3 9 2 2 1 0 1 100 1 127 21% 2 1 1 183 44% 2 1 1 213 14% 2 1 1 188 -12% 6 www. e-mfp. eu

Poverty category & cut-off points Score range Approximate Income range Very poor 0 – 16 ≤ $1/day Poor Not so poor 17 – 60 $1 – $2/day >60 ≥ $2/day Poverty category n A person with total score of 5 is poorer than a person with score of 15, and vice versa n Collection of data (scoring) from clients by LO as part of routine loan application on every cycle – baseline at Intake, wealth Scoring on each loan cycle 7 contact@e-mfp. eu www. e-mfp. eu

Improvements in the poverty status of clients between the 1 st and 3 rd Scoring (N=2, 113) 55% 50% 38% 26% 20% 13% Very Poor 1 st Scoring contact@e-mfp. eu Poor Not so Poor 3 rd Scoring www. e-mfp. eu

Mean Score Growth by Components Asset score-1 st scoring Asset score-3 rd scoring Biz score-1 st scoring Biz score-3 rd scoring Total wealth-1 st scoring Total wealth-3 rd scoring Asset score growth (%) Biz score growth (%) Wealth score growth (%) Very Poor 2 27 33 41 37 64 Poor 35 38 46 53 78 88 Not so poor 83 72 36 49 120 125 1, 005% 24% 71% 8% 15% 12% -14% 35% 4% 9 contact@e-mfp. eu www. e-mfp. eu

What contributed towards assets score growth? Ox score growth Cow score growth Shoat score growth Bed score growth Tape score growth TV score growth Asset Score Growth Pearson Correlation Sig. (2 -tailed). 683 (**). 000. 638 (**). 000. 153 (**). 001. 107 (**). 000. 093 (**). 003. 439 (**). 000 1 ** Correlation is significant at the 0. 01 level (2 -tailed) contact@e-mfp. eu www. e-mfp. eu

n n Some Emerging Lessons and conclusion It is very important to know what matters most for our target groups – identification of indicators from the bottom up, thus relevant ‘evidence’ The system provided us with good insight about what is happening ‘out there’ – more informed decision to better serve the very poor and poor; ‘evidence’ to refine our services and refocus on improvement areas that matter most for the target group It would not tell us what loan size or mix of financial services are most likely to keep the poor healthy or enable them send their children to school; but it may help us in understanding how the poor might progress overtime towards those ideals of dignified living. Our intention is to improve livelihoods; the target groups have diverse priorities and hence there is no single goal that can be measured with a single indicators. But the system complements our judgments and decisions and helps us to be mindful of our promises. contact@e-mfp. eu www. e-mfp. eu