1. Integrated Food Security Phase Classification IPC Analysis: Estimating Population in Crisis August 2010 Kampala

2. Integrated Food Security Phase Classification 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)

3. Integrated Food Security Phase Classification 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 problem information 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

4. Integrated Food Security Phase Classification 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 differently not all people within a crisis phase face same degree of hazard some 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

5. Integrated Food Security Phase Classification Proximate indictors defining the Severity of the Situation IPC Key Reference Outcomes

6. Integrated Food Security Phase Classification 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

7. Integrated Food Security Phase Classification Homogeneity and Magnitude 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

8. Integrated Food Security Phase Classification Rules of logic and Evidence 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

9. Integrated Food Security Phase Classification 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

10. Integrated Food Security Phase Classification Scenario 1 ScenariosProcessAssumptions 1. If 1 overall phase classification is has been assigned for a population Apply the wealth rankings to the rural population or area classified Check 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 phases The lowest quartile (poorest) are the most affected by food insecurity therefore belong to the worst phase middle quartiles (to the middle phases) Upper quartile usually in the upper phases e.g 1/2 Classification done for rural popn bse urban popns are likely to skew classification- able to purchase &use a variety of food sources

11. Integrated Food Security Phase Classification Population and wealth rankings

12. Integrated Food Security Phase Classification Scenario 2 ScenarioProcessAssumptions 2.Affected areas/ sub- counties could be identified through assessments Usually assessment are done by administrative zones Affected sub-counties are isolated through assessments Establish numbers of households affected by drought/dry spell/ hazard in that administrative zone % of affected households of total hhd in admin unit Multiply by the average household size to get affected population per sub-county affected Total affected popn =to sum of all affected popn for all affected sub- counties Check 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

13. Integrated Food Security Phase Classification Worst hit sub counties DistrictSub-county Kaabong Kalapata Loyoro Kaabong Sidok Katile Kapedo Kotido Panyangara Kotido T/C Nakapelimolu Rengen Nakapiririt Lorengedwat Lolachat Moroto Rupa Nadunget Katikekile, Lopeei Abim Nyakwaye

14. Integrated Food Security Phase Classification

15. Integrated Food Security Phase Classification scenario 3 ScenarioProcessAssumptio ns 3. We want to include the Livelihood aspect but lack livelihood information but AEZ information is available Assessment information shows that one livelihood group is more affected than others in a particular sub county Get AEZ information or map Overlay Affected sub-counties maps over the AEZ Population estimations are made based on which sub- counties are covered by a particular AEZ/LZ group that is affected Summation of populations in most affected LZ/ sub-counties gives the affected population Check with popn estimation in previous analysis AEZ usually concede with live hoods

16. Integrated Food Security Phase Classification Somalia example DistrictLivelihood Zone UNDP 2005 Populatio n % population in LZ (establishe d by FSNAU) Total LZ population affected (calculated) Belet WeyneAgro pastoral 135,580 56%75,328 Belet WeyneHawd Pastoral22%30,126 Belet Weyne Riverine 11%15,063 Belet WeyneS. Inland Pastoral11%15,063 DistrictLivelihood Zone % population breakdown by livelihood zone and wealth group (FSNAU baseline assessments) Very poorPoorMiddleBetter 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)