IPC Integrated Food Security Phase Classification. Lesson: IPC Classification Procedures Step by Step

Transcription

1 IPC Integrated Food Security Phase Classification Version 2.0 Lesson: IPC Classification Procedures Step by Step Text-only version In partnership with:

2 In this lesson LEARNING OBJECTIVES... 2 WHERE YOU ARE IN THE IPC PACKAGE... 2 INTRODUCTION... 2 CLASSIFICATION PROCESS AND ORGANIZATION... 3 HOW THE 8 STEPS LINK WITH THE ANALYSIS WORKSHEET S SECTIONS... 3 PRE-STEPS... 5 THE 8 STEPS PROCESS... 6 STEP 1 - Define Analysis Area & HH Analysis Groups... 7 STEP 2 - Document Evidence in Repository STEP 3 - Analyse Evidence STEPS 4 and 5 HAGs and Phase Classification Conclusions STEP 6 Impact of Humanitarian Assistance STEP 7 Risk Factors to Monitor STEP 8 Limiting Factors Matrix SUMMARY ANNEX - POTENTIAL INDIRECT EVIDENCE TO SUPPORT IPC ANALYSIS ANNEX: BASELINE SUMMARY FOR TAMBAI DISTRICT REVISED IN OCTOBER

3 LEARNING OBJECTIVES At the end of the lesson, you will be able to: describe the eight-step process needed to classify severity and causes of food insecurity; explain the use of the Analysis Worksheets to complete the eight steps; explain the purpose and relevance of each step; and utilize and complete each step of the IPC Acute Analysis Worksheet. WHERE YOU ARE IN THE IPC PACKAGE Functions Building Technical Consensus Classifying Severity and Causes Communicatin g for Action Quality Assurance Tools TWG MATRIX ANALYTIC FRAMEWORKS REFERENCE TABLES ANALYSIS WORKSHEETS COMMUNICATIO N TEMPLATE SELF ASSESSMENT PEER REVIEW Multi- Understanding Referencing Transparently, Transforming Assuring Procedures for agency stakeholder s to do collaborativ e analysis evidence with an integrated Analytical Framework evidence against international standards methodically and consensually analysing analyses into concise information for action quality evidence INTRODUCTION This lesson outlines the procedures for IPC classification of the severity and causes of food insecurity. It illustrates the process to complete the IPC Acute Analysis Worksheets; in particular, the eight steps to follow to classify both the Current and Projected food security situation. 2

4 Although the focus of the process is on the Worksheet, the analysis is based on interpretation of the Analytical Framework and the Reference Tables, and, therefore, the three need to be looked at together. CLASSIFICATION PROCESS AND ORGANIZATION The process that guides the classification of the Current and Projected Situation Analysis consists of eight steps, listed in the next screen. By following the steps you will complete the four sections of the IPC Acute Analysis Worksheet. The Analysis Worksheet enables the collection, organization, documentation and analysis of evidence in order to classify the severity of acute food insecurity and to identify immediate causes. One Analysis Worksheet should be completed for each area analysed. A single Analysis Worksheet can be used for conducting analysis of the Current and Projected Situations. The number of steps to be completed depends on whether the classification is Area only or HH Groups + Area. The parts of the Analysis Worksheet that don t need to be completed for Area only classification are shaded in a diagonal light grey. THE 8 STEPS PROCESS OVERVIEW Steps to classify severity and causes STEP 1 Define Analysis Area and HH Analysis Groups STEP 2 Document Evidence in Repository STEP 3 Analyse Evidence STEP 4 Household Analysis Groups Conclusions STEP 5 Phase Classification Conclusions STEP 6 Impact of Humanitarian Assistance STEP 7 Risk Factors to Monitor STEP 8 Limiting Factors Matrix HOW THE 8 STEPS LINK WITH THE ANALYSIS WORKSHEET S SECTIONS The flow chart below illustrates the process for completing the Acute Analysis Worksheet: 3

5 the 8 STEPS show how data are processed and follow an analytical order; while the 4 worksheet SECTIONS depict how IPC analysis is organized and follow a reporting order. Section A : Area and Household Analysis Group Definition STEP 1: Define Analysis Area and HH Analysis Groups (if conducting HH Group Analysis) Section C : Causes STEP 8: Limiting Factors Matrix Section D : Evidence Documentation and Analysis STEP 2: Document Evidence in Repository STEP 3: Analyze Evidence for Contributing Factors, Outcomes, Phase Classifications Section B : Phase Classification Conclusion STEP 4: Household Analysis Groups Conclusions (only if conducting HH Group Analysis) STEP 5: Phase Classification Conclusions STEP 6: Impact of Humanitarian Assistance STEP 7: Risk Factors to Monitor Sections A, B and C of the IPC Acute Analysis Worksheets are included in the Communication Template. The Communication Template The IPC presents and describes core aspects of situation analysis using maps, charts, tables and text in a standardized Communication Template which enables communicating for action and decision making. The IPC Communication Template includes four parts: 1. IPC map. 2. A summary of key findings, issues, methods and recommendations for next steps. 3. Population tables. 4. Core sections of the Analysis Worksheet. You can find more detailed information on the communication template in Lesson 9, Communication for Action. 4

6 PRE-STEPS Before starting the 8-step process, your Technical Working Group must decide about the following presteps, whether to conduct: Area only or Household Groups + Area Analysis The decision should be informed by the pros and cons of Area- and Household Group-based classification. As a minimum standard, an IPC classification must be Area-based. Ideally, however, if time, data and capacities exist, TWGs are encouraged to conduct analysis of Household Groups and Areas. For more information on the Pros and Cons of Area and HH Group classification see Lesson 7, Key Parameters for the IPC Classification. Pros and Cons of Are-a and HH Group-based Classification Pros Cons Area only Less complicated Requires less data and analysis Nutrition and mortality data typically provided at areal level only Good for general severity analysis and geographic targeting Provides no details on severity of food insecurity for different HH groups within a given area Provides no information for the strategic design of responses tailored to different HH groups. Area and Details the severity of food insecurity More complicated HH for different HH groups Requires more data, analysis and time Group Important for strategic responses Requires the identification of various HH tailored to the needs of different HH groups groups Difficult to use nutrition and mortality Enables analysts to examine data (i.e. typically provided for the vulnerability of different HH groups. whole population - not by HH group) Current or Projected Situation Analysis Both Current and Projected Situation Analysis can be conducted on the same Analysis Worksheet. The spatial area of the classification can vary widely and is determined by the Technical Working Group depending on the situation and the needs of decision-makers. At the top of the Analysis Worksheet there is space for noting: the name of the area, whether it is Current and/or Projected Analysis and the validity period(s), as well as the date when the analysis was completed: 5

7 Area name Current and/or Projected ACUTE FOOD INSECURITY ANALYSIS WORKSHEET ANALYSIS AREA DATE OF ANALYSIS: VALID FOR: [ ] CURRENT [ ]PROJECTED (which area) (created on) (from when to when) (from when to when) Date of creation Validity period(s) For detailed information on Current and Projected Situation Analysis see Lesson 7 Key Parameters for the IPC Classification. Technical Working Group (TWG) The national IPC TWG is a group of food security analysts from a variety of Stakeholder organizations, including Government, UN agencies, national and international NGOs, academia and technical agencies. The TWG is usually chaired by the government, open to relevant stakeholders and embedded in relevant existing institutions. TWG members are technically proficient in their sector with a good knowledge of food security analysis. For detailed information on TWG see Lesson 3 Building Technical Consensus. THE 8 STEPS PROCESS You will go through the eight STEPS of the process for classifying severity and causes of food insecurity using fictional datasets and a partially-filled worksheet to classify a fictional area called Tambai. Your exploration will include all steps for the Area only classification. You will also complete selected steps for HH and Area- based classification and Projection. For each step you will find the following information:! IMPORTANCE OF THE STEP TOOLS HOW TO brief explanation of its importance; presentation of the related tools; and description of the procedures to carry out the step. The final classification of severity and identification of causes will build upon small exercises following each step s explanation. 6

8 STEP 1 - Define Analysis Area & HH Analysis Groups! IMPORTANCE OF THE STEP In STEP 1 of the classification you will define the area of concern and the household analysis groups within it. STEP 1 is important to: establish basic units of analysis (geographical area to be analysed, HH Analysis Groups and time frame of analysis) and population estimates; and provide background information that improves overall quality of analysis by: o enabling livelihoods-based analysis; o contextualizing Contributing Factors. Example: The importance of background information Several IPC acute analyses have been conducted in South Sudan since the start of the conflict in December The conflict has had wide impact on the food security and nutrition security in the country, so understanding the situation and its impact is very important for proper analysis. For example, malnutrition rates in many areas affected by the conflict in South Sudan are alarmingly high. In several cases, these rates have also been influenced by a breakdown in health services and by disease such as measles outbreaks, rather than by significant gaps in food consumption alone. Understanding the context has been fundamental for proper analysis of nutrition outcomes and for estimating to what extent high malnutrition rates are due to gaps in food consumption rather than to non-food security specific factors. TOOLS The tool for STEP 1 is Section A of the Analysis Worksheet. 7

9 Section A - Area and HH Analysis Group Definitions ACUTE FOOD INSECURITY ANALYSIS WORKSHEET ANALYSIS AREA DATE OF ANALYSIS: VALID FOR: [ ] CURRENT [ ]PROJECTED (which area) (created on) (from when to when) (from when to when) Section A: Area and HH Analysis Group Definitions STEP 1: Area Description, HH Analysis Group Definitions, and Map Brief Area and Livelihood Description Estimated # of People in Area (specify source of pop. data) Current Projected (with assumed in and out migration) Chronic Food insecurity Level for the area (if available) HH Analysis Group (HAG) Definitions Identify groups of relatively homogenous households with regard to their food security situation (consider contributing factors and likely outcomes). These HH Analysis Groups will be analysed independently for their respective Phase Classifications. Map and Seasonal calendar of Analysis Area (insert image of map identifying spatial extent of analysis area and seasonal calendar indicating major seasons and annual events) The number of groups will depend upon analytical needs, data availability and desired level of precision. Label of HAG A B c D (...) Brief Description of each HAG [Specify Source(s): ] # of people in HAG % of pop in HAG HOW TO -- Consider the following activities for STEP1 a) Determine Analysis Area and Time Periods. b) Insert a map of the area and a seasonal calendar. c) Provide a brief description of the area. d) Provide an estimate of resident population in the area. e) Specify level of Chronic Food Insecurity (if available). f) Identify and describe Household Analysis Groups. 8

10 a) DETERMINE ANALYSIS AREA AND TIME PERIOD(S) OF THE ANALYSIS ACUTE FOOD INSECURITY ANALYSIS WORKSHEET ANALYSIS AREA DATE OF ANALYSIS: VALID FOR:[ ] CURRENT [ ]PROJECTED (which area) (created on) (from when to when) (from when to when) Section A: Area and HH Analysis Group Definitions STEP 1: Area Description, HH Analysis Group Definitions, and Map To accomplish STEP 1, you should first determine Analysis Area and Time Period(s). Analysis Area is determined by one or a combination of: spatial extent of a hazard; variation of livelihood patterns and vulnerability; availability of data and information; practicality of doing the multiple analyses; and needs of decision-makers; Time Periods of analysis indicate: date of completion of Current and/or Projected Situation Analysis; validity periods, which however are optional (analysis is a snapshot, but you can specify a time period for which the situation is not expected to change). The population in the area should be as homogenous as possible with regards to likely FS Outcomes and causes. Analysis Area The IPC is adaptable and applicable to any spatial size. It is up to you as IPC analysts to determine the spatial extent of the Analysis Area. A Single Phase Classification will be determined for this area. The determination of the Analysis Area can be informed by, but not limited to, units such as livelihood zones, hazard zones, administrative boundaries, market catchment zones and others. b) INSERT A MAP OF THE AREA AND A SEASONAL CALENDAR After you ve indicated Analysis Area and Time Periods: Insert a map of the analysis area that shows its spatial extent. The map could be a proper GIS map, a picture or even a hand drawn sketch. Insert a seasonal calendar identifying agricultural seasons and any other major events. 9

11 C) PROVIDE A DESCRIPTION OF THE AREA Briefly describe the area based on baseline data (whenever possible) and other background information. You may include socio-economic, livelihood, environmental, and other relevant contextual information to support the analysis. Brief Area and Livelihood Description --- description here -- D) PROVIDE A POPULATION ESTIMATE Provide estimate of resident population in the area for the current and/or projected time periods: typically, this estimate is the total population in the area unless the analysis is for a subgroup (e.g. internally displaced people); specify sources. Estimated # of People in Area (specify source of pop. data) Current Projected (with assumed in and out migration) E) SPECIFY LEVEL OF CHRONIC FOOD INSECURITY If available, specify level of Chronic Food Insecurity, based on analysis using the IPC protocols for chronic analysis. If not available, leave this field blank. Chronic Food insecurity Level for the area (if available) F) IDENTIFY AND DESCRIBE HH ANALYSIS GROUPS If doing Household Group Analysis: Identify and describe Household Analysis Groups (HAGs 1 ). These groups are hypothesized as likely to have different Phase classifications pending evaluation and analysis of the evidence. Label of HAG Brief Description of each HAG [Specify Source(s): ] # of people in HAG % of pop in HAG A 1 HAGs (Household Analysis Groups) relatively homogenous groups of households with regards to their food security situation, including Contributing Factors and likely Outcomes. 10

12 These groups may be defined by any relevant characteristics, such as: exposure to hazard; livelihood; wealth; gender; ethnic affiliation; other factors that make these groups distinct. Specify the estimated number of people in each HAG and their percentage of the total population in the area. The number of HAGs depends on complexity of situation, decisionmaking needs; and data availability. See Annex Baseline Summary for the Tambai District area Example Brief Area Livelihood description based on Baseline Summary for the Tambai District area: Tambai frequently suffers from drought, which occurred on four occasions between 2000 and Other frequent hazards include mine closures and border control, which affect remittances, as well as localized floods, as these tend to result in road closures. Tambai has high levels of chronic food insecurity and even in reference years most households are not able to meet minimal food needs. The Demographic Health Survey (DHS) has reported stunting levels of more than 40% in the district. Access to infrastructure, such as health facilities and schools, is inadequate in the area. Tambai is characterized by subsistence farming, with HHs accessing food primarily through own production; many depend on small fields and wild foods. The main crops found in the area include maize, cassava and beans. Other crops, such as vegetables, are seldom found in small plots. Soils are sandy which do not allow for good harvests. Many raise chickens, but only the wealthier households own substantial amounts of other larger livestock. The area is on the coast with a few fresh water bodies within its limit. Although fishing potential exists, only a few households engage in fishing, usually for subsistence purposes. Those who can afford nets earn some seasonal income from fishing. Access to the area is reasonable, with public transport running twice a day. There are no commercial farms or other sources of employment. Access to schools and health facilities is limited; the poor generally lack access to these services. 11

13 STEP 2 - Document Evidence in Repository! IMPORTANCE OF THE STEP The STEP 2 Evidence Repository is a place to file and keep track of pieces of evidence. The possibility to refer back to this repository is crucial as the IPC process is conducted. Your IPC TWG will likely be reviewing large volumes of data or evidence. The capacity to document that evidence - and to find it again when needed - is essential! STEP 2 is a simple method to ensure your TWG can do this! STEP 2 is important to: organize wide-ranging data from multiple sources for ease of access and reference; and note down sources and dates of evidence to assist in the evaluation of reliability and relevance. TOOLS The tool for STEP 2 is the last sheet of Section D of the Worksheet that serves as the Evidence Repository. The Repository is, quite simply, a place to file and keep track of pieces of IPC evidence. The objective of building an evidence base for IPC Analysis is to document and analyse the necessary amount of evidence in order to: substantiate a Phase classification with at least acceptable confidence; and understand the basic causes. The point is NOT to document all that is known about the area, nor to analyse questions beyond the scope of the IPC. Section D: Evidence Documentation and Analysis STEP 2: EVIDENCE REPOSITORY Document Code To link to template in Step 3. Reference Multiple pieces of evidence in Step 3 can link to a single source. Order is not important. Source Date Raw Evidence When possible, insert raw evidence (e.g. graph, image, table, quote). 12

14 6 HOW TO In order to carry out STEP 2 you should consider the following activities. a) State the source of the evidence. b) Specify dates of the report release and data collection. c) Assign a Documentation Code (DC) to each source. d) Archive raw evidence in any format. a) STATE THE SOURCE OF THE EVIDENCE and b) SPECIFY DATES Note down the source of each piece of evidence: specify the agency or group conducting the studies; and include methodological notes if relevant. Reference Multiple pieces of evidence in Step 3 can link to a single source. Source Date Report the date of the report release and of each of the pieces of evidence gathered C) ASSIGN A DOCUMENTATION CODE TO EACH SOURCE and D) ARCHIVE RAW EVIDENCE IN ANY FORMAT Document Code Reference To link to template in Multiple pieces of evidence in Step 3. Step 3 can link to a single source. Order is not important Source 1 2 Assign each source a unique Documentation Code to link IPC evidence in the Repository to analysis in Step 3. You can add as many documents as necessary. Raw Evidence When possible, insert raw evidence (e.g. graph, image, table, quote). Evidence can be in any format: graphic e.g. tables, graphs, charts; or 13

15 Order of filing does not matter! Each code, i.e. each source, can have multiple pieces of raw evidence (view some examples in the next screen). narrative - e.g. text, conclusions. Codes are used to cross-reference evidence with Analysis Worksheet templates in Step 3 It is not mandatory to add raw evidence, but it should be added as much as possible, as referring to it during the analysis may help, especially when writing evidence statements. Tips and good practice for STEP 2 See Annex Table of Potential Indirect Evidence to Support IPC Analysis for suitable examples of evidence that can be used to support your analysis. A data mapping tool can assist with the collection of evidence. The data mapping tool used in the IPC is the IPC Data Mapping Matrix for Acute Analysis. The matrix is a tool to help analysts to identify the available evidence in the country by the IPC food security Outcomes and Contributing Factors, and to point out possible gaps in available evidence. The matrix lists available evidence on the main IPC direct indicators, as well as indirect indicators (if available), including sources and timing of data collection. It is good practice to fill in the matrix prior to IPC analysis, so that it can guide the exercise and highlight data gaps. IPC National TWGs can request the IPC Data Mapping Matrix Tool by writing to IPC@fao.org A good practice is to complete STEP 2 prior to the analysis workshop; evidence may be added and/or deleted during the analysis workshop. Completing STEP 2 will result in an easy analysis workshop! STEP 3 - Analyse Evidence! IMPORTANCE OF THE STEP In STEP 3, you will write evidence statements for Outcomes and Contributing Factors. You will also indicate a Documentation Code (DC) to link the statement back to the Evidence Repository. Then you 14

16 will assess the statements for reliability by considering the quality of the source and methods used in producing the evidence and the time relevance of the data. STEP 3 is important to: document and organize evidence to facilitate complex analysis and ensure transparency; transform raw evidence (documented in Step 2) into meaningful statements and conclusions which will be used to assign Phase classification; and evaluate reliability of each piece of evidence, ensuring transparency of analysis, allowing for greater quality assurance of the findings. Situation Imagine that you have to analyse and make conclusions about food access in one area... You would need to skim through many reports to look at food price charts, then to flip through other reports to look for income levels and finally to look at other reports to find employment rates. After that, to understand the impact a flood has had, you would need to look through another three to five reports. Wouldn t this be a mess?!! In STEP 3 all evidence is documented so that it can be converged to carry out complex analysis in a transparent way. TOOLS The tool for STEP 3 is the first part of Section D of the Analysis Worksheet. This part of the Worksheet is dedicated to documenting key evidence and conclusions: for Contributing Factors and Outcomes in Current and Projected situations. Besides the Analysis Worksheet, STEP 3 requires support from the Analytical Framework and Reference Tables. These three tools and their procedures are used together to help determine the Phase classification. These three tools organize the food security elements in the same way: 15

17 Contributing Factors: Hazard and Vulnerability, Food Availability, Access, Utilization and Stability. The Contributing Factors, Hazards and Vulnerability need to be analysed together in order to understand causes of food insecurity. It is not possible to look in isolation at either Hazards or Vulnerability. Note that in the Analytical Framework their relationship is implied, and in the Reference Table their interaction is referenced against Phase descriptions. Food Availability, Access, Utilization and Stability are all separated, similarly to how they are presented in the Analytical Framework. Even though they have an impact on each other, it is necessary to analyse each one in isolation to understand what the most important limiting factors are. However, in order not to repeat each reference against Phase description for the four pillars in the Reference Table, one single reference is presented, which is applicable to all of them. More detailed information on the IPC Analytical Framework is available in Lesson 5 and information on IPC Reference Tables can be found in Lesson 6 Outcomes: Food Consumption, Livelihood Change, Nutrition, Mortality. HOW TO For STEP 3 you need to complete the following tasks: a) Write evidence statements for each Contributing Factor and Outcome element and assign Reliability Scores. b) Write conclusion statements for each Contributing Factor and Outcome. c) Determine indicative Phase classification for Outcome elements only. A) WRITE EVIDENCE STATEMENTS FOR EACH CONTRIBUTING FACTOR AND OUTCOME ELEMENT AND ASSIGN RELIABILITY SCORES. In this first task of STEP 3 you simply explain the raw data by carrying out the following activities: 1. Write evidence statements from the raw evidence reported in Step 2 (including direct and indirect evidence). TIP: Bear in mind that the statements are simpler, paraphrased versions of what the evidence notes. 2. Specify the Documentation Code to link to Step 2 Repository. Usually there is more than one piece of evidence for each DC. 16

18 Remember that the order of the DCs does not matter as they are used only as a reference number. 3. Assign Reliability Score to each piece of evidence. Often, evidence from the same Document Code will share the same Reliability Score. However, in some cases, the same DC includes evidence that might be collected with different methods. For example, the FAO/WFP Crop and Food Supply Assessment Mission includes evidence on price markets, observation, interviews, satellite images among others. In this case the pieces of evidence in the same DC might have different Reliability Scores. Outcom e l Food consumption CURRENT HAG A: HAG B: HAG c: HAG D: AReA: Household Dietary Diversity Score (HDDS): Analysis done in Feb/12 shows that ( DC1 R3 Poorest: 3 food groups Poor: 5 food groups Middle: 6 food groups Better off: 8 food groups Document Code Reference Source 1 Multi Agency Emergency Assessm Save the Children and Ministry of Reliability score The major advantage of having inserted the raw data in STEP 2 here is clear: it is easy to find, to analyse and later to refer to for transparency of analysis. Example: evidence statement Raw evidence: HIV/AIDS Central Province: The entire country suffers from lack of available treatment. From the estimated 1.5 million people in rural areas who are HIV positive and in need of treatment, it is estimated that less than 20,000 receive ongoing antiretroviral treatment. This is primarily due to the lack of reasonably close health facilities. The problem is especially pronounced in the more isolated northern and northwestern areas of Central Province where treatment is unavailable. Evidence statement for the Hazard and Vulnerability element: Prevalence of HIV: HIV is increasing, affecting as many as 35% of the adult population in the province. From the 1.5 million people in the country in need of antiretroviral treatment, it is estimated that <20,000 people receive ongoing treatment (DC 6, R 1).? How to assign Reliability Scores 17

19 Reliability Scores indicate the trustworthiness of the evidence. Assigning Reliability Scores requires critical evaluation of the source, method and time relevance of the data. The table below provides a general guide by summarizing the criteria to be used.! If evidence is not considered at least "Somewhat Reliable" it should not be included in IPC analysis. Reliability Rating Criteria The critical distinction is between R1 and R2, because only R2 scores and R1. Somewhat Reasonable but questionable above are used to assign overall source, method or time-relevance Reliable confidence: of data. R2. Reliable Data is from a reliable source, using scientific methods and reflecting current or projected conditions. The number of "Reliable" and "Very Reliable" evidence will be used to assign R3. Very Reliable Source, method and time relevance of data are all the confidence level of analysis. trustworthy. Examples -- Example 1-- Evidence statement The National Remote Sensing Unit has issued a report stating that the rainfall patterns during the main agricultural season for the area were only 20% of the average. Note on methods The National Remote Sensing Unit assessed satellite images for rainfall estimates selected for the area being analysed. The analysis includes both values for the current agricultural season and comparison with 15-year average. Reliability score: R2 (Reliable) Reasoning: From a reliable source, using scientific method, with data reflecting current or projected conditions. Note that the satellite image is measuring rainfall and not vegetation index or food production. This data may seem "very reliable", as satellite imagery is a scientific method. However, as it is based on cold cloud duration which is a proxy indicator for rainfall, it should only be considered as "reliable". 18

20 -- Example 2-- Report: A month-old report from an NGO survey records that the Household Dietary Diversity Score (HDDS) for the district is only four food groups. Note on methods: The survey included 600 HHs in the district allowing 95% confidence of findings with a 2.5% precision rate. The sample followed a Random Population Proportion to Size. Experienced enumerators were trained for five days and received close supervision. Error was calculated to be minimal (less than 0.05 from the mean number of food groups). Reliability score: Very Reliable (R3) These are trustworthy sources. Furthermore, the methodology of the survey is exceptionally rigorous and the time relevance of the survey is very high. --Example 3-- Report: An NGO secretarial staff claims that the fields in the districts are completely dry and harvests are expected to be minimal. Note on methods: A member of the NGO went on holiday in the district that is being analysed two months before the IPC analysis. During his travels by car, he passed a few agricultural fields where he observed the conditions of the crops. Reliability score: Not Reliable. The evidence is collected through random observations rather than with any scientific methods. Moreover, the evidence is not backed up by any data issued by reputable sources (for example, NGOs, UN agencies, government agencies) in terms of written reports, graphs or statements. As a result, the evidence is not reliable and should not be included in IPC analysis --Example 4-- Report: A trip report by an Oxfam programme officer claims the poor do not have as much to eat in the district as they normally do. Note on methods: The NGO staff carried out a two-day field trip to monitor the conditions in the field in the week preceding the IPC analysis. The staff carried out informal discussions with various NGO field staff who work closely with the communities. Reliability score: Somewhat Reliable (R1). The sources, methods or time relevance of data are reasonable, but questionable. In this situation, the time relevance is high thanks to the report being recent. The source is a staff member of reputable NGO, even though the report itself is a trip report and not an official report of the NGO. Also, no proper scientific methods were used for data collection (e.g. questionnaires or discussion guides). Instead, the discussions were informal, though even these can yield 19

21 valuable information. -- Example 5-- Report: A well-respected NGO claims the poor have effectively no more stocks of food and their harvests will be insignificant in the areas of the district where they work. Note on methods: The NGO carried out a formal qualitative study, where community members participated in Focus Group Discussions (total of 18 FGDs with groups of males and females in 9 communities). Key Informant Interviews were also conducted with 12 staff from the Government and community leaders. Communities selected to be visited were sampled to ensure good diversity in terms of viewpoints. Findings were triangulated by field observation. Reliability score: Very Reliable (R3) The source, method and time relevance of data are all reliable. Very rigorous qualitative methods have been used to collect the data, the survey is recent and, therefore, has good time relevance, and the survey was conducted by a reputable agency. B) WRITE CONCLUSION STATEMENTS FOR EACH CONTRIBUTING FACTOR AND OUTCOME In the second task of STEP 3 you will analyse the evidence statements and then formulate brief conclusions for each element. Conclusion Statement for Outcomes should include: direct and indirect evidence related to an element; and any relevant evidence from Contributing Factors.! Conclusions may also consider indicators that are not included in the Reference Table (such as nation/region specific indicators). IN DEPTH: INFERENCE OF OUTCOMES Inference - A conclusion reached on the basis of evidence and reasoning. Logical deduction. There are three main ways to infer Outcomes from indirect evidence: 1. Interpreting Contributing Factors within their context. 2. Calibrating other measures of Outcomes not included in the Reference Tables. 3. Extrapolating evidence over space, time and scale. Interpreting Contributing Factors within their context The first - and very important - way to infer Outcomes is to interpret Contributing Factors within their context. The basis for inference of Outcomes from Contributing Factors comes from an understanding of 20

22 how food security is interlinked by consequential phenomena: the interaction between vulnerability and shocks impacts food security dimensions which in turn ultimately impact Outcomes. The Livelihood-based Analysis explicitly draws together different elements of FS producing estimation of impacts on food consumption and/or livelihood changes. The following simplified example shows that: by understanding, livelihood strategies, hazards and their impact on the four food security dimensions (either at a baseline year or in the studied year). you will be able to estimate (or infer) outcome levels of food consumption, livelihood changes, nutrition and mortality. This is especially useful for projections, where we never have Outcome data and we need to infer what is most likely to happen. CALIBRATING OTHER MEASURES OF OUTCOMES NOT INCLUDED IN THE REFERENCE TABLES The second way to infer Outcomes is to calibrate evidence, which consists of analysing other measures (indicators) of Outcomes that are not included in the Reference Table. For example, evidence can be collected using varying methods found to be locally appropriate. IPC is open and encourages any potential evidence! However, because these locally used indicators are not globally comparable, they need to be approximated to globally comparable evidence that is included in the Reference Table. The IPC Manual V.20 encourages countries to develop thresholds for context-specific indirect evidence. Below is a potential example of calibrating evidence that can be included. Example It is important to understand what the Reference Table means for the element in each Phase: there is always an explanation for the element. For example, food consumption evidence pointing at Phase 4 implies that there is a "Large Food Gap; much below 2,100 kcal per day". This definition, together with the cut-offs of the globally comparable evidence, should support analysts in calibrating their locally specific method for measuring food consumption according to the IPC Reference Table. Mozambique Dietary Adequacy Tool (MDAT) - In this example, the indicator used is the Mozambique Dietary Adequacy Tool (MDAT). This tool involves a 24-hour recall of the frequency of meals (0 to 3) 21

23 consisting of any food items from four basic groups (cereals and tubers; legumes and pulses; vegetables and fruits; meats) that were eaten in the HH. The frequency is multiplied by a weight, which relates to the normal quantity and nutritional value of the foods eaten. A score is then developed and cut offs are set for "Acceptable", "Low Quality" and "Very Low Quality". Following the Phase definitions and element descriptions and merging with similar indicators, (such as the FCS which also has only three categories and is similar in concept), evidence from this tool could be calibrated according to the Reference Table. EXTRAPOLATING EVIDENCE OVER SPACE, SCALE AND TIME The third way to infer Outcomes consists of extrapolating evidence that is presented over different space, scale and time. Essentially, this way of inferring relates to data collected for larger or smaller areas, groups or time periods different from the ones you are analysing. This kind of inference is quite difficult, as you need to understand the differences and similarities between the area or group you are analysing and those ones given in the evidence. You will need to contextualize evidence, make assumptions and interpret what the data means for outcomes. Examples of indirect evidence for different scales, groups and times FCS for a province was "borderline", but analysts are only classifying an area that suffered from crop pests. The HHS for a province was moderate, however analysts are only classifying the poorest HAGs, which were highly affected by the sharp price rises. Wasting levels were taken from a survey carried out before harvest but IPC analysis is being carried out after a successful harvest. You need to think in terms of... How the sub-area/specific group/current situation is different from the one presented? Does evidence from contributing factors show that the studied sub-area/specific group/current situation is likely to be worse or better than one presented? To what extent? C) DETERMINE INDICATIVE PHASE CLASSIFICATION FOR OUTCOME ELEMENTS ONLY The last analytical task of this step is to assign indicative Phase classification for Outcomes only. 22

24 Indicate the likely Phase classification for an element, if interpreted on its own based on the evidence and the conclusion for each Outcome element. The indicative phase must be done based on convergence of evidence, not only on direct evidence. Base your analysis on the indicators and descriptions in the Reference Table. Do so for each Household Analysis Group (HAG) separately. Indicative phases for HAGs are only applicable for Food Consumption and Livelihood Changes, because Nutrition and Mortality are not collected at the HH level. For projections, follow the same procedures, inserting key assumptions and the justification for each of them. What about Contributing Factors? Contributing Factors are not given indicative Phases because they cannot be looked at in isolation. However, their consequential and interlinked impact on food security Outcomes needs to be included as inferences for Outcomes. IN DEPTH: Area Classification The 20 Percent Rule The indicative Phases for Food Consumption and Livelihood Change for the Area classification are based on the 20% rule. An area is going to be classified in a certain Phase when at least 20% of the population in the area are in that Phase or worse. In this case, the classification is based on whether or not at least 20 % of the population is in a particular Phase or worse. There are two ways of estimating if at least 20% of the population is in a certain Phase or worse: analyse the whole population but always look for the worst-off 20% (Non-Stratified Approach); and analyse only the worst-off 20% of the population (Pre-Stratified Approach). 1. Non-Stratified Approach This approach consists of getting evidence for the whole area (e.g. one province) and for each direct or inferred evidence assessing what is the worst-off Phase that affects at least 20% of the population. Then, convergence of evidence should be used to decide what is the worst Phase at least 20% of the population is in. The final population in that Phase may be counted in various ways, including: 23

25 just stating that at least 20% of the population is in that Phase; giving a range based on reliable direct evidence. The important thing is that the group agrees with the approach and that the assumptions are understood and well documented. 2. Pre-Stratified Approach In this approach you will only analyse one group - that which has the worst-off expected food security level - given that this group cumulatively adds up to at least 20% of the population. The defining characteristics of the worst-off 20% can be socio-economic, ethnic, exposure to hazards etc. depending on the factors driving food insecurity in the area. Group Name/Description Relative likely food % of population Cumulative security status Poorest Worst-off 25% 25% Poor 30% 55% Middle 15% 70% Wealthier Better-off 30% 100% Group Name/Description Relative likely food % of population Cumulative security status HHs living in highly flood area Worst-off 15% 15% HHs living in moderate flooded area 10% 25% HHs living in slightly flooded area 50% 75% HHs living in outskirts of flooded area Better-off 25% 100% Once you have identified the correct group to analyse, you will only look at this group. This is very similar to doing a full HH Groups Analysis but here you are only using one HH Analysis Group - the one that cumulatively adds up to at least 20%. HDDS Condition of the worstoff Finding for the Indicative group (25% of pop.) group Area Phase HDDS 3 FGS Phase 4 FCS Condition of the worstoff Finding for the Indicative group (25% of pop.) group Area Phase FCS Poor Phase 4 Livelihood Change Condition of the worstoff Finding for the Indicative group (25% of pop.) group Area Phase You will identify the Phase for that group. Because you have not classified other groups, you can only say that "at least xx% of HHs are in this Phase (or worse)". In this example it is 25%. 24

26 Coping Distress Phase 4 Consider the following raw evidence and the related statement STEP 2 Evidence Repository DC Reference Multiple pieces of evidence in Step 3 can link to a single source Source Date 5 Child 30x30 Survey, done in collaboration between UNICED and the Ministry of Health July 2012 note: Survey conducted just after harvest Raw Evidence When possible, insert raw evidence (e.g. graph, image, table, quote). Table 14: Wasting levels: % of children wasted District % of children 0-59 months wasted (<2sd) % of children 0-59 months severely wasted (<3sd>) Mati Section D: Evidence Documentation and Analysis STEP 3: Key Evidence and Conclusions for Contributing Factors and Outcomes Contributing Factor Elements Nutritional Status CURRENT AREA: Acute Malnutrition: Based on the child survey carried out in July 2012 (after harvest), 13.9% of children were acutely malnourished (<-2 SD) and 5.8% were severely malnourished (<-3 SD) (DC 5, R 1). Narrative conclusion based on the evidence in the example above: Element Conclusion (including inference and controlling for non-food issues): Although nutritional findings would classify the area as Phase 3, the survey was conducted just after the harvest (July 2012). Because food consumption as well as livelihood changes show that the current situation is severe, one would expect high levels of acute malnutrition. Because this is only 1.1% less than what would be required to classify the area as Phase 4 and the situation is expected to have deteriorated after the harvest, the malnutrition rate points to Phase 4. Because of the difference between analysis groups in terms of food consumption and livelihood changes, it is expected that many children living in the poorest and poor households are acutely malnourished. STEPS 4 and 5 HAGs and Phase Classification Conclusions 25

27 ! IMPORTANCE OF THE STEP In STEPS 4 and 5 you will decide on the overall Area Phase classification, and also on Phase classifications for each Household Analysis Group if they have been used in the analysis. In addition justifications are provided for classifications. STEPS 4 and 5 are important to transform diverse and complex evidence into concise and meaningful information (Phases and population estimates) for effective, strategic and timely action. While STEP 4 is only done if analysis also focuses on HH Groups, STEP 5 is done for all analyses as it includes the overall Area classification. TOOLS The tool for STEPS 4 and 5 is Section B of the Analysis Worksheet.! As for STEP 3, the completion of STEPS 4 and 5 require support from the Analytical Framework and Reference Table. Therefore the three tools are used together to assist in the Phase classification. When conducting a HAG analysis, you will use the STEP 4 template as an indicator for each Household Analysis Group (HAG): the estimated phase; the estimated number of people in the HAG; and a summary statement justifying the conclusions. Section B: classification conclusions and Justification STEP 4: HH Analysis Group (HAG) classification conclusions Classify each HH Analysis Gro convergence of evidence (from STEP 3). If a single HH Analysis Group is determined Label of HAG A B c D ( ) Current Situation Phase # of People and % of total pop Summary Justification If a single HAG consists of two or more phases, indicate the percentage share and the population number for each phase as well. Then, you will use the STEP 5 template to indicate the total number of people from STEP 4 who are in the same Phase. You should indicate: the estimated number of people in each Phase overall; 26

28 their percentage of the total population in the given area; and the overall level of confidence for the classification. If you are classifying Area only, you should also indicate the appropriate Phase for the Area and the estimated number of people who are in that Phase or worse. STEP 5: Phase classification conclusions. Combine different HH Analysis Groups with th d f ti t d # f l d % th l ti b f l i Current Situation Phase [Confidence Le el for O erall Anal sis: ] ** Justification Estimated % of (key evidence and rationale of directly pop or total pop measured or inferred outcomes: food range or range HOW TO For STEPS 4 and 5 you need to complete the following tasks: a) Classify each HH Analysis Group (if doing HAG analysis). b) Classify the Area c) Calculate number and percentage of people in each Phase d) Write justification statements. e) Assign Confidence Level for overall analysis A) CLASSIFY EACH HH ANALYSIS GROUP (if doing HAG analysis) Use convergence of evidence to assign the overall Phase classification for each HAG, considering: Outcome Elements Food Consumption HAG A: 1 HAG A: 2 CURRENT HAG A: 3 HAG A: 4 AREA: Food Consumption and Livelihood Change, including evidence from Contributing Factors (HH Group Reference Table); and Livelihood Change HAG A: 2 HAG B: 3 HAG C: 3 HAG D: 4 AREA: 27

29 Nutrition and mortality (Area-based Reference Table). Outcome Elements Nutritional Status Mortality CURRENT AREA: 3 AREA: NA! Final Phase classification is not based on averaging Outcomes STEP 4: HAG Classification Conclusions Current Situation Label of # of Pp and % of total HAG Phase Summary Justification pop. A 2 50,000 (50%) B 3 20,000 (20%) C 3 20,000 (20%) D ( ) 4 10,000 (10%) Insert findings into STEP 4 and population estimates from STEP 1 (HAG definition). If a HAG was broken into two during the analysis here is the place where the analysts would clearly identify that the group needed to be broken, for example into: poorest receiving food aid; and poorest not receiving food aid. Explanation will need to be included in the summary justification.! Final Phase classification of each HAG is inserted in STEP 4 together with information about population that is taken from STEP 1 Background. B) CLASSIFY THE AREA There are two ways to classify an area, depending whether or not HAGs were also classified. When HAGs are also classified For the overall Area classification you will follow the Area Reference Table to look for what is the worst Phase in which at least 20% of the population has been classified. 28

30 Identify populations in each Phase and their % (from STEP 4) Identify the Phase of the worst-off group that crosses the 20% threshold. STEP 4: HAG Classification Conclusions Current Situation Label of # of Pp and % of HAG Phase Summary Justification total pop. A 2 50,000 (50%) B 3 20,000 (20%) C 3 20,000 (20%) D ( ) 4 10,000 (10%) Area classification: 3 Converge HAG findings with area-based indicators of nutrition and mortality to determine overall Phase classification. Outcome Elements Nutritional Status Mortality AREA: 3 AREA: NA CURRENT Example In an overall Area classification (when HAGs are also classified), the most appropriate for the following situation is phase 3. The classification is allocated to the most severe Phase that has at least 20% of the HHs. As neither Phase 4 nor Phase 3 has 20% of the HHs separately, they are summed up and as a result 25% of the total HHs are in Phase 3 or worse. Phase 3 becomes the overall Phase for the area. A 1 2 C 3 D ( ) 4 30,000 40% 26,250 35% 11,250 15% 7,500 10% When HAGs are not classified In this approach, you will give the indicative Phase classification of Outcomes for the whole area and, when converging evidence you need to decide if the worst-off 20% of the population is, indeed, in that Phase. Use convergence of evidence to assign the overall Phase for the area, considering: 29

31 Food Consumption and Livelihood Change, including evidence from Contributing Factors (HH Group Reference Table); TIP Use the 20% rule, since the focus is on the worst-off 20% of households. Nutrition and Mortality (Area Reference Table). C) CALCULATE NUMBER AND PERCENTAGE OF PEOPLE IN EACH PHASE There are two ways to calculate the population, depending on whether or not HAGs are also classified. When also HAGs were classified The calculation of population is very straightforward and consists of: summing the populations of all HAGs that are in the same Phase (from STEP 4); inserting findings in STEP 5 template. When HAGs were not classified The second way of calculating the population in each phase is when no HAGs have been classified. This is a much more difficult task as it will call for reasoning and decisions to be made, such as determining: How population will be calculated The IPC is not prescriptive on how the evidence will be used to come up with population estimates. Rather, the IPC highlights approaches that can be used. How to estimate populations in Area-based analysis The final decision on how to estimate populations in Area-based analysis will depend on: local circumstances; reliability of evidence; and analytical decisions. How to use the 20% rule when estimating an Area Phase When doing an estimation of population in the Area Phase, the approach will depend on how the 20% rule is used: Most common (non-stratified) approach From reliable evidence you try to find a range or an absolute number. That result will depend on the context, reliability, and existence of evidence as well as on analytical decisions. This approach can be combined with focusing the analysis and classification on one group (e.g. the worst off that adds up to at least 20% of HHs the so-called Pre-Stratified approach). With this approach you don t know what is happening to all the other groups, as 30