Calculation of population numbers

Uganda

Sources

Wealth quartiles (UDHS 2006) Census population projections done by the bureau of

statistics Region District Sub counties

Seasonal Assessment figures derived for percentage of populations that are affected by recent hazard say drought/ dry spell % of the population expecting harvest of <50% of

their normal harvest/ previous season harvest Reports of most affected sub-counties

Scenario 1Scenarios Process Assumptions

1. If 1 overall phase classification is has been assigned for a population

Apply the wealth rankings to the rural population or area classifiedCheck with popn estimation in previous analysisCheck with popn estimation in previous analysisCheck with popn estimation in previous analysis

Even if an area is classified in one phase there are parts of the population that belong to different phasesThe lowest quartile (poorest) are the most affected by food insecurity therefore belong to the worst phasemiddle quartiles (to the middle phases)Upper quartile usually in the upper phases e.g 1/2Classification done for rural popn bse urban popns are likely to skew classification- able to purchase &use a variety of food sources

Population and wealth rankings

Selected population indicators by district

District % Urban % RuralTotal Pop

(1,000) Total Population Urban Pop Rural Pop Lowest second middle fourth

Kampala 100.0 0.0 1480.2 1,480,200.00 1,480,200.00 -

Wealth quartiles

Kalangala 8.5 91.5 50.8 50,800.00 4,318.00 46,482.00 0.06 0.10 0.19 0.28 Masaka 10.6 89.4 816.2 816,200.00 86,517.20 729,682.80 0.06 0.10 0.19 0.28 Mpigi 2.5 97.5 441.9 441,900.00 11,047.50 430,852.50 0.06 0.10 0.19 0.28 Rakai 4.5 95.5 231.5 231,500.00 10,417.50 221,082.50 0.06 0.10 0.19 0.28 Sembabule 2.2 97.8 202.3 202,300.00 4,450.60 197,849.40 0.06 0.10 0.19 0.28 Wakiso 7.7 92.3 1158.2 1,158,200.00 89,181.40 1,069,018.60 0.06 0.10 0.19 0.28

2,694,967.80 0.06 0.10 0.19 0.28 Kayunga 6.7 93.3 330.8 330,800.00 22,163.60 308,636.40 0.05 0.15 0.20 0.30 Kiboga 5.2 94.8 293.3 293,300.00 15,251.60 278,048.40 0.05 0.15 0.20 0.30 Luwero 12.2 87.8 396.5 396,500.00 48,373.00 348,127.00 0.05 0.15 0.20 0.30 Mubende 7.3 92.7 525.3 525,300.00 38,346.90 486,953.10 0.05 0.15 0.20 0.30 Mukono 17.2 82.8 929.2 929,200.00 159,822.40 769,377.60 0.05 0.15 0.20 0.30 Nakasongola 5.1 94.9 143.6 143,600.00 7,323.60 136,276.40 0.05 0.15 0.20 0.30

2,327,418.90 0.05 0.15 0.20 0.30 Bugiri 4.1 95.9 543.9 543,900.00 22,299.90 521,600.10 0.11 0.19 0.21 0.29 Busia 16.3 83.7 265.4 265,400.00 43,260.20 222,139.80 0.11 0.19 0.21 0.29 Iganga 5.6 94.4 661.4 661,400.00 37,038.40 624,361.60 0.11 0.19 0.21 0.29 Jinja 22.1 77.9 451.0 451,000.00 99,671.00 351,329.00 0.11 0.19 0.21 0.29 Kamuli 1.6 98.4 670.0 670,000.00 10,720.00 659,280.00 0.11 0.19 0.21 0.29 Mayuge 2.7 97.3 399.4 399,400.00 10,783.80 388,616.20 0.11 0.19 0.21 0.29

2,767,326.70 0.11 0.19 0.21 0.29 Kaberemaido 1.8 98.2 168.1 168,100.00 3,025.80 165,074.20 0.29 0.28 0.21 0.15 Katakwi 2.0 98.0 150.3 150,300.00 3,006.00 147,294.00 0.29 0.28 0.21 0.15 Kumi 2.3 97.7 345.5 345,500.00 7,946.50 337,553.50 0.29 0.28 0.21 0.15 Pallisa 4.5 95.5 471.7 471,700.00 21,226.50 450,473.50 0.29 0.28 0.21 0.15 Soroti 11.3 88.7 499.8 499,800.00 56,477.40 443,322.60 0.29 0.28 0.21 0.15

1,635,400.00 1,543,717.80 0.29 0.28 0.21 0.15 Kapchorwa 4.6 95.4 182.3 182,300.00 8,385.80 173,914.20 0.29 0.28 0.21 0.15 Mbale 9.9 90.1 392.9 392,900.00 38,897.10 354,002.90 0.29 0.28 0.21 0.15 Sironko 4.0 96.0 328.8 328,800.00 13,152.00 315,648.00 0.29 0.28 0.21 0.15 Tororo 6.5 93.5 440.0 440,000.00 28,600.00 411,400.00 0.29 0.28 0.21 0.15

1,254,965.10 0.29 0.28 0.21 0.15 Apac 1.5 98.5 507.2 507,200.00 7,608.00 499,592.00 0.582 0.246 0.069 0.058 Lira 10.9 89.1 626.5 626,500.00 68,288.50 558,211.50 0.582 0.246 0.069 0.058

1,057,803.50 0.582 0.246 0.069 0.058 Adjuman 9.8 90.2 292.1 292,100.00 28,625.80 263,474.20 0.23 0.40 0.14 0.12 Arua 8.8 91.2 491.5 491,500.00 43,252.00 448,248.00 0.23 0.40 0.14 0.12 Moyo 6.2 93.8 303.8 303,800.00 18,835.60 284,964.40 0.23 0.40 0.14 0.12 Nebbi 14.4 85.6 509.2 509,200.00 73,324.80 435,875.20 0.23 0.40 0.14 0.12 Yumbe 6.1 93.9 398.1 398,100.00 24,284.10 373,815.90 0.23 0.40 0.14 0.12

1,806,377.70 0.23 0.40 0.14 0.12 Gulu 25.1 74.9 353.5 353,500.00 88,728.50 264,771.50 0.69 0.22 0.05 0.02 Kitgum 14.8 85.2 357.0 357,000.00 52,836.00 304,164.00 0.69 0.22 0.05 0.02 Pader 2.7 97.3 436.0 436,000.00 11,772.00 424,228.00 0.69 0.22 0.05 0.02 Amuru 25.1 74.9 208.3 208,300.00 52,283.30 156,016.70

1,149,180.20 0.69 0.22 0.05 0.02 Kotido 6.9 93.1 179.3 179,300.00 12,371.70 166,928.30 0.760 0.073 0.034 0.088 Moroto 3.9 96.1 265.3 265,300.00 10,346.70 254,953.30 0.760 0.073 0.034 0.088 Nakapiripiti 1.1 98.9 217.5 217,500.00 2,392.50 215,107.50 0.760 0.073 0.034 0.088 Abim 6.9 93.1 54.1 54,100.00 3,732.90 50,367.10 0.760 0.073 0.034 0.088 Kaabong 6.9 93.1 301.2 301,200.00 20,782.80 280,417.20 0.760 0.073 0.034 0.088

1,017,400.00 967,773.40 0.760 0.073 0.034 0.088 Bundibugyo 6.6 93.4 282.1 282,100.00 18,618.60 263,481.40 0.12 0.21 0.30 0.27

Scenario 2Scenario Process Assumptions2.Affected areas/ sub-counties could be identified through assessments

Usually assessment are done by administrative zonesAffected sub-counties are isolated through assessmentsEstablish numbers of households affected by drought/dry spell/ hazard in that administrative zone% of affected households of total hhd in admin unit multiply by total no.of households in admin unit= affected householdsMultiply by the average household size to get affected population per sub-county affectedTotal affected popn =to sum of all affected popn for all affected sub-countiesCheck with popn estimation in previous analysis

We set some categories:<50% of normal harvest- worst hit/most affected 50-75% of a normal harvest- fair to Normal harvest>75% of a normal harvest- good harvest

For most areas that are reliant on crop production and income derived from crop sales and casual labour opportunities

Worst hit sub countiesDistrict Sub-county

Kaabong

Kalapata

Loyoro

Kaabong

Sidok

Katile

Kapedo

Kotido

Panyangara

Kotido T/C

Nakapelimolu

Rengen

NakapiriritLorengedwat

Lolachat

Moroto

Rupa

Nadunget

Katikekile, Lopeei

Abim Nyakwaye

Rupa

Lokopo

Loyoro

Karenga

Iriiri

Alerek

Kacheri

Namalu

Kathile

Karita

Moruita

Kotido

Lolachat

Sidok

Kapedo

Katikekile

Matany

Panyangara

Lopei

Loroo

Ngoleriet

Nadunget

Abim

Lotome

Nabilatuk

Rengen

Kalapata

Lolelia

Nyakwae

Nakapelimoru

Morulem

Kakomongole

Lorengedwat

Amudat

Kaabong

Moroto

Kaabong

Kotido

Nakapiripirit

AbimRupa

Lokopo

Loyoro

Karenga

Iriiri

Alerek

Kacheri

Namalu

Kathile

Karita

Moruita

Kotido

Lolachat

Sidok

Kapedo

Katikekile

Matany

Panyangara

Lopei

Loroo

Ngoleriet

Nadunget

Abim

Lotome

Nabilatuk

Rengen

Kalapata

Lolelia

Nyakwae

Nakapelimoru

Morulem

Kakomongole

Lorengedwat

Amudat

Kaabong

KARAMOJA PRODUCTION ZONES

Agro-PastoralPastoral

AgricultureSubcounty boundaryDistrict boundary

scenario 3

Scenario Process Assumptions

3. We want to include the Livelihood aspect but lack livelihood information butAEZ information is availableAssessment information shows that one livelihood group is more affected than others in a particular sub county

Get AEZ information or mapOverlay Affected sub-counties maps over the AEZPopulation estimations are made based on which sub-counties are covered by a particular AEZ/LZ group that is affectedSummation of populations in most affected LZ/ sub-counties gives the affected populationCheck with popn estimation in previous analysis

AEZ usually concede with live hoods

Challenges Can’t use livelihoods because of lack of livelihood based data and

profiling

Wealth quartiles are outdated and lump together populations that now have different characteristics for instance for the north IDPs and non IDP areas; this has since changed in the last 2 years because of resettlement

The whole of northern Uganda (Acholi and Lango) is lumped together yet regions like lango faced less displacement and earlier resettlement than the rest; production resumed much earlier and it is evident that population wealth indicators are now different between the 2 regions.

Karamoja has its wealth quartiles but if applied for each district doesn’t make much sense as you will always have the same number of or proportions in the worst phases

Opportunities

Wealth quartiles from the CFSVA