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IPC Analysis: Estimating

Population in Crisis August 2010

Kampala

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1. Concepts

• IPC Analysis provides a Situation Analysis

Overall objective is to generate analysis on the situation that is evidence based, linked to international standards and informs appropriate type and level of response to populations in crisis

IPC Analysis is not a method and does not, in itself, offer guidance on how to estimate the number of people in crisis…

whatever method is used to estimate populations, it is necessary to have a consistent and meaningful way to represent those findings

(IPC Technical Manual page 40)

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2. Purpose

• Estimation of the number of people in each IPC Phase (3, 4 and 5) not all people in an area will be affected in the same way provide in-depth analysis and not an overall picture

• Provides a Situation Analysis not Response analysis population estimates for Phases 3, 4 & 5 not “number of people in need”enables maintain the objectivity of the analysis

• Inform decision makers provide information on the depth and severity of the probleminformation for further in-depth analysis of potential response options

There is no set way to do the population estimates and it is necessary for countries to develop their own methods… that

allows you to estimate populations in the same way over time and space... making the estimates in the same way each time… in a

transparent way

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3. Guiding principles

• Objectivity estimated without judgment about possible needs or response options it is a situational analysis and not response analysis

• Estimate in terms of degree or severity of the hazard

• Within a crisis phase, populations are affected differentlynot all people within a crisis phase face same degree of hazardsome people may be in ‘HE’ level while other might be in ‘AFLC’

• Understanding of the differentiation between groups within the phase expert knowledge of population dynamics in the area

• Estimates are based on convergence of evidence not just one evidence

• Population estimates are estimates – not exact figures they provide an indication of the magnitude of the hazard

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Proximate indictors defining the Severity of the Situation

IPC Key Reference Outcomes

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Data

• Organized population data disaggregated to lower unit of analysis

• administrative/livelihood zones develop an analysis framework risk populations e.g. flood prone areas

• Baseline data wealth ranking assets or poverty ranking

• Expert knowledge livelihood and population dynamics objective expert opinion

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• Degree of differentiation within groups in terms of access to income, food and coping are all the households in the poor wealth group – all at the same level? is there wide variation from the better of the poor and the poorest poor?

• Magnitude this is affected by the homogeneity of the households the more homogenous the wealth group the more likely the shock will affect all people

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• Demographics & wealth phase classification is systematic poor are affected first, then middle, then better-off

• exceptions are natural disasters• Chronology

analysis follows previous classification if situations worsens, it is expected population estimated to increase

• Evidence convergence of evidence and not only one evidence continuity and consistency rational

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Sources of population data • 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

Uganda situation

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Scenario 1

Scenarios 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 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

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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

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Scenario 2

Scenario 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 unitMultiply 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

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District 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

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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

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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

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Somalia example

District Livelihood Zone

UNDP 2005

Population

% population

in LZ(establishe

d by FSNAU)

Total LZ population

affected(calculated)

Belet Weyne Agro pastoral

135,580  

56% 75,328

Belet Weyne Hawd Pastoral 22% 30,126

Belet WeyneRiverine

11% 15,063

Belet Weyne S. Inland Pastoral 11% 15,063

District Livelihood Zone

% population breakdown by livelihood zone and wealth group

(FSNAU baseline assessments)

Very poor Poor Middle Better off

Belet Weyne Agro pastoral 0% 35% 55% 10%

Belet Weyne Hawd Pastoral 0% 45% 35% 20%

Belet Weyne Riverine 3% 32% 55% 10%

Belet Weyne S. Inland Pastoral 2.5% 22.5% 45% 30%

Estimating proportions of overall population in given phase:

Total number of people in AFLC in District 1= (D1 * X1 *X2 *X3)

Where:D1 = is the district population (from UNDP)X1 =is the percent of Population in that LZ in that district (established by FSAU)X2 = is the percent of the poor wealth group (or other analytical unit) in that LZ (from baselines)X3 = is the percent of poor wealth group in AFLC in LZ1 (from the analysis & evidence)