Ubudehe Categorization SEPTEMBER, 20TH 2015

AFTER APPEAL (Data Integration) Progress BEFORE APPEAL AFTER APPEAL (Data Integration) Activity Status Data Collection Completed Data Entry Data Processing Data Cleaning Data Analysis Data Preliminary Analysis In Progress Data Publication Categorization Reporting

UBUDEHE CATEGORIZATION KEY DATA BY PROVINCE Community Based Classification by Province Population Mean HHs   CAT 1 CAT 2 CAT 3 CAT 4 UN CAT Total HHs (Ubudehe) Size Kigali City 26,136 98,864 92,938 7,101 75 225,114 916,549 4.1 Southern 136,991 323,410 137,324 2,004 57 599,786 2,614,156 4.4 Northern 48,033 224,762 132,303 1,614 62 406,774 1,751,987 4.3 Western 92,980 298,802 147,566 2,571 460 542,379 2,476,327 4.6 Eastern 60,431 257,246 252,404 3,189 632 573,902 2,550,422 Rwanda 311,272 977,922 548,619 13,895 675 2,347,955 10,309,441 5.6 % 13% 42% 23% 1% 0% 100%

HOUSEHOLDS SHARE BY CATEGORY (CB)

KEY FINDINGS: SIGNIFICANT GAPS BETWEEN CAT 1 and CAT 2 The difference between CAT 1 and CAT 2 remains significant before and after appeals. These 2 categories cumulate the Population below Poverty Line The difference between CAT 2 and CAT 3 tends to increase after appeals, suggesting that Households in CAT 3 may have been moved in CAT 2.

KEY FINDINGS – DISTRICT LEVEL INFORMATION ON APPEALS PROCESS

RB vs CB CATEGORIZATION (AA)

RB vs CB CATEGORIZATION (BA)

CONSISTENCY WITH EICV4 CAT1 Extrem Pov EICV 4 Diff. Ext pov   CAT1 Extrem Pov EICV 4 Diff. Ext pov CAT1 & CAT2 Pov line EICV 4 No District Cat 1 (CB) 1 Bugesera RB 9.7% 13.4% 57.15% 34.30% 16 Nyamagabe 30.7% 13% 74.52% 41.50% CB 11.6% 1.8% 62.89% 26.9% -13.91% 79.06% 2 Gakenke 12.3% 16.2% 59.23% 42.00% 17 Gisagara 28.7% 20.60% 75.94% 53.30% 10.6% 5.6% 63.70% 23.2% -2.64% 75.49% 3 Karongi 31.2% 21.3% 76.19% 45.30% 18 Kayonza 10.0% 9.50% 42.70% 26.40% 29.8% -8.5% 81.34% 7.9% 1.60% 45.18% 4 Rutsiro 14.1% 23.6% 49.27% 51.40% 19 Huye 20.8% 5.70% 76.00% 32.50% 16.3% 7.3% 52.41% 22.1% -16.38% 77.28% 5 Rulindo 9.5% 20.2% 69.23% 48.10% 20 Gicumbi 8.8% 24.70% 53.01% 55.30% 22.7% -2.5% 74.11% 6.4% 18.31% 53.39% 6 Nyagatare 16.4% 19.5% 63.56% 44.10% 21 Kamonyi 25.7% 6.00% 61.37% 25.90% 19.3% 0.2% 64.54% 18.5% -12.53% 66.19% 7 Rubavu 18.0% 14.2% 71.63% 35.50% 22 Nyarugenge 6.2% 8.40% 42.52% 19.90% 17.3% -3.1% 70.69% 10.8% -2.41% 47.55% 8 Gatsibo 15.1% 61.20% 43.80% 23 Nyamasheke 21.0% 39.20% 83.84% 62.00% 8.6% 9.9% 61.94% 14.4% 24.78% 84.56% 9 Burera 20.4% 23.0% 70.86% 50.40% 24 Rusizi 12.0% 15.80% 66.18% 35.10% 12.2% 74.26% 7.24% 68.36% 10 Ngororero 34.9% 23.5% 82.89% 49.60% 25 Rwamagana 8.00% 54.76% 25.40% 24.6% -1.1% 84.40% 11.2% -3.16% 60.01% 11 Nyaruguru 22.8% 20.0% 65.00% 47.90% 26 Ngoma 4.7% 19.50% 47.67% 46.80% 20.5% -0.5% 73.56% 9.1% 10.39% 50.07% 12 Ruhango 16.9% 12.8% 83.50% 37.80% 27 Nyanza 34.0% 17.60% 80.43% 38.00% 27.5% -14.7% 87.61% 23.9% -6.30% 81.14% 13 Muhanga 23.4% 7.8% 68.81% 30.50% 28 Musanze 16.0% 16.80% 71.92% 34.90% 19.6% -11.8% 73.44% 10.9% 5.85% 73.39% 14 Kirehe 8.0% 17.8% 39.08% 41.80% 29 Gasabo 21.4% 11.30% 64.58% 23.40% 4.8% 13.0% 39.40% 15.4% -4.13% 66.71% 15 Kicukiro 4.2% 6.5% 39.50% 16.30% 30 Nyabihu 8.7% 12.60% 53.14% 39.60% 4.1% 2.4% 39.43% 3.83% 59.55%

CONSISTENCY WITH EICV 4 (Cont.)   Ubudehe CAT1 EICV4 RB CB (Below Extrm Pov Line) Total HHs 18.18% 15.55% 16.30%

CONSISTENCY WITH EICV 4 (Cont.)   Ubudehe CAT 1 & 2 ICV4 RB CB (Below Pov Line) Total HHs 64.45% 66.39% 39.10%

Challenge 1 - Check differences (red) between RB & CB in 11 Districts   Difference RB&CB CAT1 Difference RB&CB CAT2 Difference RB&CB CAT3 Difference RB&CB CAT4 No District 1 Rulindo 13.20% -8.32% -2.98% -0.10% 2 Gatsibo -6.47% 7.21% -2.36% -0.33% 3 Burera -9.60% 12.99% -1.24% -0.08% 4 Ngororero -10.25% 11.76% -0.18% 5 Ruhango 10.63% -6.53% -2.68% -0.13% 6 Muhanga -3.76% 8.39% -1.68% -0.14% 7 Nyamagabe -3.81% 8.35% -1.49% 8 Kamonyi -7.13% 11.95% -2.64% -0.34% 9 Nyamasheke -6.58% 7.30% -0.71% -0.01% 10 Nyanza -10.11% 10.82% -0.61% -0.09% 11 Gasabo -5.96% 8.10% -1.61% -0.52%

Challenge 2 - Address the issue of duplicated data Same IDs and Location with different HH Code Diff. Location/Name with same NID Total HHs Duplicated Cases 52447 20220 72667 % of Total Ubudehe Categorization Households 2.2% 0.8% 3%

Recommendations and Suggested Way Forward Proposed actions Remarks Keep Category 1 and Category 4 classification as they appear in preliminary analysis - Preliminary data analysis confirms coherence between Category 1 criteria’s and extreme poverty situation. Resolve Issues of Category 2 and Category 3 The high number of households in Category 2 suggests either some inconsistencies in the Algorithm or incorrect information's provided by households. Two options are considered to clear this issues: Adjustments should be made in order to upgrade to Category 3 all eligible Households, while not affecting households already classified in Category 3. Physical verification of a sample of 150 HHs from Category 2. Correct NID duplicates Meetings at village level are suggested to verify and check individual cases Identify reasons of the differences between RB & CB in 11 Districts Significant differences are detected between Response Based and Community Based classification in 11 Districts that need to be addressed