amerms workshop 13: cutting edge in spm (ppt by abebual demilew)
DESCRIPTION
FULL TITLE: What is the Cutting Edge in Managing and Measuring Social Performance? ROOM: Tsavo A Translated session: English & French PANEL: Chair: Mr. Christian Loupeda, Director Imp-Act Consortium, Freedom from Hunger (FFH), USA Panelist: Mr. Abebual Zerihun Demilew, Research Manager, BRAC, Uganda Panelist: Ms. Refilwe Mokoena, Research & Development Manager, Small Enterprise Foundation (SEF), South Africa Panelist: Ms. Ging Ledesma, Manager of Social Performance, Oikocredit, The NetherlandsTRANSCRIPT
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Implementation of Poverty Scorecard
Experience from BRAC UgandaAbebual Zerihun
April 08, 2010
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The Poor and Microfinance
% of people below poverty line
Region Total Who can borrow from MFI
Uganda (2005) 31 17Central 16 9Eastern 36 24Western 21 11Northern 61 35
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Poverty Scorecard
• Reliable and practical monitoring and targeting tool
• Can monitor movements in and out of a poverty line
• Can be used to improve products and services
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Construction of PSC for Uganda
• Developed by BRAC Africa research unit
• 10 simple multidimensional indicators [demographic, asset, and housing] screened from UNHS-2005
• Sum of scores to each indicator yields the poverty score for the household
• Provides poverty likelihoods in different score range
• Public knowledge goods
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Poverty likelihood conversion chartScore range Poverty likelihood for people
with score in range (%)
0-4 95.25-9 97.2
10-14 93.615-19 80.620-24 76.125-29 71.230-34 62.535-39 47.740-44 44.045-49 38.750-54 27.055-59 26.260-64 13.365-69 8.170-74 6.575-79 2.780-84 3.185-89 1.390-94 0.7
95-100 0.0Total n = 5,112
score of 21
This client’s household has a 76.1% likelihood of falling below poverty line-$1/day.
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Poverty scorecard for Uganda
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Implementation• Integrated with MIS and loan appraisal system
• Full coverage across all the 89 branches including all the borrowers, regardless of their loan cycle
• Orientation during monthly managers meetings
• For new staff, training mainstreamed through the microfinance management trainings.
• Credit officers collect data for poverty scorecards
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Implementation
• Branch level MF data entrant
• Periodic analysis of client poverty status
• Sept 2010 longitudinal survey
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Contextualizing Score
Majeri OduHandicrafts & PoultryFirst Loan: Ush 200,000Poverty Score: 39
Semujju LuigaSells cooked food and condimentsFirst Loan: Ush 300,000Second Loan: Ush 500,000Poverty Score: 57
Naigaga AnnetteSells used clothes and BRAC CHPFirst Loan: Ush 300,000Second Loan: Ush 600,000Poverty Score: 65
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Quality Control
• Periodic checks by monitoring unit• Data capture for 20 sample branches
– Credit Officers tend to ‘overscore’, i.e. identify households to be more better of than they actually are.
Sample Question Total reported score
Total monitoring score
% discrepancy
Wall material 4547 4641 -2.02Number of children 2855 2713 5.23Household head’s education 1704 1700 ~0Over all average (June 09-Oct09) 2.4
Over all average (Nov09-Feb10) ~0
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Key success factors
• Senior level management commitment
• Good and reliable system
• Triangulation to control quality
• Piloting
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Lessons
• Monitor or target, but not both
• Quality, quality and quality
• Get management buy-in
• Keep it real. Understand limitations. Manage expectations
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Thank You