Review of urban food security targeting methodology and emergency triggers
Summary of report1
People queuing at an Oxfam feeding programme in Mbare, Harare
A recent report compiled by Oxfam GB, Concern Worldwide and Action Contre la Faim (ACF) aims to assist in capacity building and guidance during emergency responses in urban areas, focusing on food security, livelihoods, and nutrition. It covers emergency triggers and targeting emergency responses in urban areas.
Findings on triggers in urban areas
Triggers are events or indicators that precipitate the beginning or end of an emergency response. Existing frameworks provide a foundation for assessment and for gathering information in various sectors, but there are no urban-specific indicator cut-offs to trigger emergency responses in urban areas. There are a wide range of tools and frameworks used by non-governmental organisations (NGOs) and international organisations, but the most promising analysis framework for urban areas is the Integrated Food Security Phase Classification (IPC), incorporating elements of the HEA and ‘Indicator Development for Surveillance of Urban Emergencies’ (IDSUE), an attempt by Concern Worldwide to develop urban-specific indicators in Nairobi. In addition, Oxfam has been piloting a combined Household Economy Approach (HEA)/Participatory Capacity and Vulnerability Analysis (PCVA) approach which aims to combine risk mapping and identification of opportunities to strengthen protect and restore livelihoods.
The most common short-coming with these approaches is that they have not been piloted in or adapted to urban contexts. This lack of adaptation of tools in combination with the absence of baseline data (which has been disaggregated by informal settlements) means that it is currently very difficult to establish, with consensus, that an urban area has moved from a chronic to an acute crisis. This slows and confuses responses and blurs the distinction between emergency relief and development programming.
Recommendations on triggers
Key recommendations made in the report include:
- Use existing coordination mechanisms such as the Food Security Cluster urban working group2, and work with the WFP and FAO (who are tasked with taking forward the food security element of the Inter-Agency Standing Committee (IASC) urban strategy), to pilot and adapt the IPC tool for use in urban contexts.
- Identify and agree with the Food Security Cluster urban working group the top five cities at risk of urban emergencies. Develop urban working groups in these cities, i.e. Port au Prince, Kathmandu, Manila, Dhaka, Nairobi, Harare, and Gaza.
- Within ‘at risk’ cities, identify ‘high risk’ urban areas where emergencies are likely to occur (i.e. those vulnerable to natural disasters or price spikes), and develop geographical vulnerability mapping that supports contingency planning.
- Through the Food Security Cluster urban working group, agree on an assessment approach and baseline mapping indicators, which can disaggregate different urban areas within one city, to ensure there is political consensus amongst key stakeholders and donors prior to an emergency.
- Once there is consensus on IPC urban indicators, there will need to be a greater focus on urban data collection to feed into urban situation analysis. Although IPC indicators will need to be universally applied, there may be some locally specific adaptations for data collection. As an IPC chronic tool is being developed3, this is also a good opportunity to ensure that it represents urban contexts:
- Some additional locally specific indicators may be required, and both quantitative and qualitative indicators are likely to be important.
- The indicators used need to clearly identify when the acute phase is over.
- The data analysis process and regular re-analysis must be very responsive to change given the pace of change in urban areas.
- The system must be sensitive enough to identify emergency situations in small areas of the city.
- Table 1 presents a suggested trigger indicator framework that can be used as a basis for this system.
- Establish a clear baseline format for these areas prior to an emergency.
- Baselines (which can be based on markets assessments such as EMMAs4) should include calculations of the cost of living including food, travel, fuel, rent, sanitation access, water purchase, education, health and market functionality so that the gap between ‘normal times’ and the shock can be quickly calculated.
- Use this information to construct a baseline for the vulnerability, risk, coping situation and market access / availability, based on the system in 5 above.
- Use this information to plan geographic and household targeting (see 7 below).
- Utilise all primary and secondary data available, being aware that many other organisations are likely to have information available.
- Explore the possibility of using technology to develop the information basis, using digital data gathering, and using smartphones, digital platforms and GPS to improve cost efficiency over the long-term. In areas prone to natural disasters (e.g. earthquakes), a low tech alternative should also be prepared.
- Ensure that contingency planning incorporates building capacity in areas such as cash transfer logistics and finance, to ensure standard operating procedures on cash transfers are available to be applied during an emergency.
- Ensure that emergency responses form part of an integrated ‘One Programme Approach’ linking humanitarian and development responses.
|Table 1: Suggested trigger indicator framework|
|Indicator area||Specific indicator||Threshold||Measurement||Challenges||Comments|
|Food security and socioeconomic status||Household Hunger Score (HHS)||Severe (4-6)||Household hunger scale||
Need to define the geographical area narrowly to focus on slums (and poorest areas within them if possible).
May need to focus on particular population groups.
High frequency reporting may be a challenge and need to consider frequency of surveys.
|HHS shorter than Household Food Insecurity Access Scale (HFIAS) but seems to vary more. HHS is median of HHS of all households in sample.|
|Household Dietary Diversity Score (HDDS)||>4 out of 12 food groups||Household Dietary Diversity Scale||HDDS gives average of score of all households. May need to look at individuals as households usually contain one member who eats out, skewing the data.|
|A local indicator of food insecurity, such as consumption of street food or food availability||Accelerated depletion/ erosion strategies and assets leading to high food consumption gaps||HEA, EMMA||HEA should reveal survival deficit > 20%. EMMA will identify market opportunities.|
|Prevalence of negative coping strategies||Greater than usual, increasing crisis and distress strategies||HEA, surveys, key informants, focus group discussions (FGD), Coping Strategy Index (CSI)||Negative coping strategies are defined locally (e.g. reducing food consumption quantity or quality, prostitution, crime, dumpsite scavenging, selling productive assets, unseasonal migration|
|Greater than usual, increasing To be determined locally, > 20% reduction in access to informal credit mechanisms||HEA, surveys, key informants||Indicator specific to local areas (sometimes implies resilience, sometimes emergency). Changes in remittances, savings, loans, credit, rent arrears and debt should be captured.||Particularly important to understand the local context; for instance in Gaza, debt may indicate likely loss of social network, and therefore a critical situation.|
|Displacement||Movement forced by disaster or destitution||Concentrated, increasing||Surveys, key informants, slum analysis, camp registrars, UNHCR data||Qualitative indicator meant to capture populations forced to move; threshold is where they are appearing in large numbers and changing the health and protection characteristics of the destination or forced displacement (e.g. earthquake, or slow onset droughts that lead to displacement).||Includes newly displaced or long term refugees or internally displaced population (IDP’s)|
|Hazards & vulnerability||Increasing incidence disease outbreaks||Greater than usual, increasing|
|Availability of assistance||Functioning of regular social protection systems||Poorly functioning, low coverage||Key informants, Government statistics||Qualitative indicator intended to capture changes in government provision for vulnerability||This can be a very important indicator where there are no other sources of assistance (as in Gaza, for example).|
|Functioning of informal sharing mechanisms||Strained to nonfunctional||HEA, surveys, key informants||Reference to a baseline figure.|
|Essential goods availability and prices||Price of main staple food||>20% seasonal reference, increasing||Consumer Price Index (CPI) from local statistics office, local price monitoring, EMMA, HEA||Need to account for wage inflation, subject to rapid change.||Also useful to assess drivers of prices such as agricultural production, exchange rate, import markets|
|Price of fuel||>20% seasonal reference, increasing||CPI from local statistics office, local price monitoring, EMMA, HEA||Need to account for wage inflation, subject to rapid change.||Also useful to assess drivers of prices such as agricultural production, exchange rate, import markets|
|Rent cost or loss/ change of tenure||>20% seasonal reference, increasing, or forced eviction||CPI from local statistics office, local price monitoring, EMMA||Need to account for wage inflation, subject to rapid change - difficult to define standard unit, depends on size of house, number of rooms, neighbourhood, building materials, etc. Loss of housing should indicate if it is owner occupied, tenant owned, or if the tenant is squatting, living in makeshift housing or protection related issues.||Also useful to assess drivers of prices such as legislative changes, regularisation|
|Access to water (litres per person per day (pppd))||4-7.5 litres pppd, or decreasing against a baseline||HEA, focus groups, surveys||SPHERE specifies <15 litres pppd and this may be an appropriate cut-off in urban areas where more water is needed for personal hygiene.|
|Price of water / quality of water||>20% seasonal reference, increasing||CPI, local price monitoring, EMMA||Need to account for wage inflation, subject to rapid change.|
|Health||Prevalence of illness in last two weeks||Greater than usual for season, increasing||DHS, surveillance systems such as NUHDSS in Nairobi, clinic reporting||Needs to be specific to different diseases to reflect public health risks.||WHO also use case fatality rates (of 1%). Can also have different thresholds for cases/week of specified diseases.|
|Security||Conflict||Widespread, high intensity||Key informants||Highly changeable.||Meant to cover violence such as postelection violence in Nairobi.|
|Prevalence of insecurity (mugging, stabbing, rape, robbery)||Greater than usual, increasing||Surveys, key informants, crime records|
|Area outcome: Nutrition||Global acute malnutrition||Greater than usual, increasing, exceeds the seasonal norm||Anthropometric measurements from household surveys such as DHS or MICS, clinic measurements, admissions, anthropometric surveys||Late indicator of crisis. Frequency of reporting is a challenge, and need to focus on specific area and groups.||IPC includes also >15% GAM but this is very difficult to measure accurately in urban areas because it requires high levels of data disaggregation e.g. by slums|
|Capacity of nutrition clinics||Unable to cope with demand/ sharp increase in admissions||Clinic reporting||Does spare capacity indicate poor outreach or healthy population? Need to verify whether increases in demand are due to emergency or more health seeking behaviour.||The most vulnerable households do not always utilise clinics which they may associate with stigma or because of the transaction costs associated with choosing between attending clinic versus income generation.|
Area outcome: Mortality
|Crude mortality rate (deaths/10,000 people/day)||1-2, increasing, >2x reference rate||DHS, surveillance systems such as NUHDSS in Nairobi, local surveys||In many countries, these rates can be above 2 in 'normal' situations. Very difficult to measure frequently in an emergency.||May need to use the ‘increasing’ threshold.|
|Under five mortality rate (deaths/10,000 U5s/day)||2-4, increasing||DHS, surveillance systems such as NUHDSS in Nairobi||In many countries, these rates can be above 2 in 'normal' situations. Very difficult to measure frequently in an emergency.||May need to use the ‘increasing’ threshold.|
EMMA: Emergency Market Mapping and Analysis; HEA: Household Economy Analysis; DHS: Demographic Health Survey; NUHDSS: Nairobi Urban Health and Demographic Surveillance System; HHS: Household Hunger Score; HDDS: Household Dietary Diversity Sore; HFIAS: Household Food Insecurity Access Scale; CSI: Coping Strategy Index; CPI: Consumer Price Index
Findings on targeting in urban areas
Good targeting in urban areas takes time, resources and good preparedness and contingency planning. This includes the development of risk and power analysis so that stakeholders, including the government, can identify their capacity to respond and identify where and how many people might be affected by various scenarios, as well as putting in place agreements and modalities for cash transfer mechanisms. NGOs have commonly applied community-based targeting (CBT) in urban areas, but this is very challenging in large cities as urban communities are hard to define. Furthermore, communities and leaders typically lack the coherence, power, confidence and knowledge of their neighbours to do this, given the densely populated and fluid nature of many urban areas.
A number of NGOs have experimented more recently with combinations of scorecards and community key informants instead of CBT. These can often be effective, but need careful tailoring to a specific context. For instance, programme evaluations in Port-au-Prince suggest that given the scale of disaster, blanket targeting, or targeting using an indicator that included isolation (e.g. geographic distance from markets) or displacement (e.g. whether the household has been forced to move by disaster), might have used resources more effectively.
Governments often prefer categorical targeting (e.g. ‘orphans’ or ‘older persons) because this is simpler to explain and justify to their constituencies, and graduation is simpler (i.e. through no longer being a child, or through death of the older person). However, these categories do not always overlap well with poverty or vulnerability, or crisis affectedness, so this approach will not always prioritise the most vulnerable in emergencies. .
Advantages and disadvantages of different targeting methods are summarised in Table 2. Most methods will use variations of the following indicators:
- Food security. Household hunger score and dietary diversity are comparatively easy and fast to measure, though it can be hard to get reliable information.
- Demographic indicators. Often (but not always) relevant and quite easy to collect.
- Livelihoods and income. Income is critical in urban areas but hard to measure directly, hence the use of proxies. Questions on type of employment are more likely to succeed and are often useful. Questions on debt are important but can be unreliable and sometimes ambiguous.
- Expenditure. Highly relevant but hard and time-consuming to collect. Proxies are better.
- Assets and housing. Easy and reliable because can be verified by visiting targeting teams, but not always well correlated to poverty following an emergency (therefore weakening the usefulness of proxy means tests).
- Nutritional status. Reliable and highly relevant but can be expensive to collect.
- Health status. Relevant but not always reliable.
- Receipt of assistance from formal or informal sources. Usually highly relevant but can be difficult to interpret in contexts where informal sharing is very common.
|Table 2: Summary of targeting methods|
|Administrative targeting||Beneficiaries are selected from a population list; the criteria used for selection differ by programme. CBT is a type of administrative targeting, in which the list of population members is based on community leaders’ knowledge of their fellow villagers. This often uses categorical approaches to targeting.||• Simple to use when lists are available • Community engagement (if CBT is used)||• Risk of exclusion if lists are incomplete or out of date • Prone to exclusion if community leaders favour one group|
|Communitybased targeting (CBT)||Community leaders and members identify beneficiary households based on vulnerability criteria identified in FGD and is then triangulated and verified by the implementing agency.||• Community engagement • Not limited to small number of proxy criteria||• Risk of exclusion of marginal social or political groups or new arrivals|
|Geographic targeting||Beneficiaries are selected on the basis of their geographic location (e.g, selecting the poorest and most food-insecure districts, and providing assistance to all households in district).||• Easy and quick||• Low targeting accuracy if vulnerable households are widely dispersed|
|Institutional targeting||Beneficiaries are selected based on affiliation with a selected institution (e.g. enrolled at a selected school, lives in selected orphanage, or receives antenatal services at a selected clinic).||• Relatively easy – only institutions are selected and beneficiaries are those that attend the institution||• Excludes people that would be eligible but who do who are not registered to receive services at targeted institutions e.g. IDPs|
Beneficiaries are selected on the basis of their income, expenditures, wealth or assets.
|• High potential targeting accuracy||• Time/resource intensive; requires census of all potential beneficiaries|
|Proxy targeting||Beneficiaries are selected on the basis of an observable characteristic or set of characteristics. Examples of single-proxy categorical targeting include: targeting by anthropometric status, by age and by physiological status (e.g. pregnancy/ lactation).||• Easy to use if selection traits are obvious • Multi-proxy targeting increases targeting accuracy but may be costlier than single proxy||• Risk of exclusion and inclusion error with single proxy targeting • Proxies may be difficult to observe directly and objectively|
|Self-targeting||Beneficiaries ‘self-select’ by deciding to participate. Incentives to participate, e.g. cash for work pay is set at a level just below or equal to daily labour rates, which acts as a self-selection mechanism. Aspects of programme design encourage the intended target group to participate and others not to participate.||• Avoids time and resource expenses of other targeting approaches||• Risk of significant leakage unless programme is designed to maximise targeting accuracy|
Many grandmothers have been left to care for young children due to massive economic migration and the HIV and Aids pandemic
Recommendations on targeting
Targeting should be approached as follows:
- Use urban coordination mechanisms to identify vulnerable geographic areas within cities and establish population numbers, key stake-holders’ capacity to respond and the gap between them. City-wide vulnerability mapping can reflect population numbers and concentration, livelihood and industrial activity zoning, service provision (both government and commercial), and infrastructure access (e.g. transport, communications, housing etc).
- Adapt integrated baseline HEX/PCVA assessments and analysis including power analysis to provide data on vulnerable groups and risks, as well as highlighting risky geographical zones. In the future, this may include markets assessment methodologies based on EMMA, as there are discussions underway about combining HEA and EMMA approaches.
- In high risk areas, baseline data can provide clear targeting indicators in advance of the emergency. These can be verified once the emergency has hit to ensure that they reflect all of the affected population groups. Joint baseline data collection and contingency planning can help to build consensus prior to the disaster on who is vulnerable and where, and what the community and states capacity to respond and recover is. Targeting in urban contexts needs to take particular care to ensure that vulnerable groups are not overlooked. These include slum dwellers, refugees, IDPs, and socially marginalised groups. The most effective way of tackling this is by breaking the city into grids or predefined areas, and then delineating these areas into sub-units, such as neighbourhoods or street groups to better facilitate analysis. Care must be taken because not all slums and informal settlements are marked on official city maps.
- Apply an adapted IPC framework to urban contexts to enable stakeholders to reach consensus on the level of emergency, and use the response analysis framework to decide on the type of response required and the subsequent targeting.
- For many emergencies, starting with blanket provision is likely to be appropriate, but targeting will subsequently be required.
- Base the decision on a calculation of the scale of need and the resources that are currently available or that will be available in the future.
- Try to ensure that local government officials are involved in the decision from the outset, and utilise government mechanisms where possible. For example, use existing social protection programmes that can be scaled up in emergencies to deliver cash transfer programmes. Following this decision, begin planning for targeting immediately.
- Decide what geographical areas, vulnerable groups, households or individuals to target.
- Most targeting criteria will specify both areas and types of households.
- Understand and take account of local political issues to identify targeting criteria that make sense in the local politics.
- Work closely with government representatives to ensure all targeting processes are integrated into government programmes.
- The choice of targeting criteria will need to take into account the feasibility of identifying these areas and individuals.
- The feasibility of targeting mechanism and indicators will to some extent depend on the information available.
Specify a targeting methodology, including indicators to identify areas or households.
- Existing targeting methods should be used or adapted where possible, and targeting must be time- and place-specific.
- Urban targeting indicators need to be more responsive to change than rural indicators because the pace of change in urban areas is very high.
- Agree where possible on targeting methodology in advance.
Each targeting method has limitations, outlined above. Targeting design and implementation will have significant impacts on the political credibility of the programme, which is vital in volatile urban areas. There is no perfect methodology that can be recommended in every case. In general, census-based scorecards are likely to be most effective if time and money permit, and if not, carefully implemented community based targeting (CBT) systems will be best (see Box 1).
Finally, when implementing any targeting approach:
- At least 10% of selected households should be visited for verification. If 30% of visited households do not meet the criteria, selection should be re-run.
- A computerised data entry and management system should be designed in advance to track, monitor and provide accountability around targeting.
- Local organisations will need to be involved in implementation, but the name of an international organisation can sometimes help with credibility.
Box 1: Targeting design options
Oxfam provided fertiliser and seeds as well as promotion of community nutrition gardens
Census-based score cards
Census approaches using targeting scorecards or proxy means tests are usually the most effective methods in urban areas for identifying the poorest most fairly, and also generate a longer list of households for future scaling up of responses, but:
- Organisations may lack funding or time to develop proxy means tests, particularly in rapid onset emergencies. However, scorecards are more straightforward than proxy means tests and templates are available and can be adapted.
- Care needs to be taken adapting scorecards or tests using knowledge of the local context and time to verify indicators.
- They must be implemented with the consent and participation of community members, but not with their full control.
- Surveyors should not be able to take final targeting decisions in households as this can undermine their credibility and cause resentment. Ideally, NGO staff should visit households directly to improve credibility.
- Decisions should be made at head office or with an algorithm in the field.
- Results should have some possibility of ‘human over-ride’ to correct obvious exclusions generated by the tests.
- Digital data gathering can improve the speed and reliability of the process.
Community based targeting (CBT) systems
CBT can identify the poorest households in urban areas and is comparatively fast and cheap to design and implement. If resources are limited, this may be the best option, However:
- Urban populations often do not know each other well and communities are hard to define, which usually results in greater reliance on community ‘leaders’, who do not always have the knowledge or incentives to target fairly.
- Targeting through community leaders can generate significant resentment, particularly in already fragmented or tense urban areas.
- Strong facilitation and great care are therefore required to ensure that community members and leaders have the knowledge and incentives to participate fairly, and to avoid putting too much pressure on community leaders. This can increase the cost of targeting.
1Review of urban food security targeting methodology and emergency triggers. Final Report. Ian MacAuslan and Maham Farhat. July 2013. See summary this issue of Field Exchange.
4Emergency Market Mapping and Analysis. http://emmatoolkit.org/
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Reference this page
Review of urban food security targeting methodology and emergency triggers. Field Exchange 46: Special focus on urban food security & nutrition, September 2013. p30. www.ennonline.net/fex/46/urbanfood