This document discusses BRAC Uganda's implementation and use of a poverty scorecard tool. It provides background on poverty levels in Uganda and introduces the poverty scorecard as a way to monitor client poverty status and improve products. It describes how BRAC Uganda developed a poverty scorecard for Uganda using 10 indicators. The document outlines how BRAC Uganda integrated the scorecard into its operations, conducted training, and did quality checks. Key success factors and lessons learned are also presented.

1. Implementation of Poverty
Scorecard
Experience from BRAC Uganda
Abebual Zerihun
April 08, 2010
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2. The Poor and Microfinance
% of people below poverty line
Region Total Who can borrow
from MFI
Uganda (2005) 31 17
Central 16 9
Eastern 36 24
Western 21 11
Northern 61 35
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3. 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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4. 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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5. Poverty likelihood conversion chart
Score range Poverty likelihood for people
with score in range (%)
0-4 95.2
5-9 97.2
10-14 93.6 This client’s
15-19 80.6
household has a
20-24 76.1
score of 21 25-29 71.2 76.1% likelihood of
30-34 62.5 falling below
35-39 47.7 poverty line-$1/day.
40-44 44.0
45-49 38.7
50-54 27.0
55-59 26.2
60-64 13.3
65-69 8.1
70-74 6.5
75-79 2.7
80-84 3.1
85-89 1.3
90-94 0.7
95-100 0.0
Total n = 5,112
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6. Poverty scorecard for Uganda
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7. 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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8. Implementation
• Branch level MF data
entrant
• Periodic analysis of
client poverty status
• Sept 2010
longitudinal survey
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9. Contextualizing Score
Naigaga Annette
Semujju Luiga Sells used clothes and BRAC CHP
Sells cooked food and condiments First Loan: Ush 300,000
First Loan: Ush 300,000 Second Loan: Ush 600,000
Second Loan: Ush 500,000 Poverty Score: 65
Majeri Odu Poverty Score: 57
Handicrafts & Poultry
First Loan: Ush 200,000
Poverty Score: 39
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10. 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 Total monitoring % discrepancy
score score
Wall material 4547 4641 -2.02
Number of children 2855 2713 5.23
Household head’s education 1704 1700 ~0
Over all average (June 09-Oct09) 2.4
Over all average (Nov09-Feb10) ~0
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11. Key success factors
• Senior level management commitment
• Good and reliable system
• Triangulation to control quality
• Piloting
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12. 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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13. Thank You
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