Targeting food aid for vulnerable populations more effectively

The fight against world hunger must recognise the fundamental right of vulnerable people to access sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life (World Food Summit 1996). With more than 22.5 million refugees in the world today (HCR 2016) and uncertainty surrounding the funding of international food aid programmes, food security for vulnerable people has become an increasingly pressing policy issue. When aid is distributed, those households whose food or economic needs and vulnerabilities are the greatest must be prioritised.

Researchers at INRA, working with their colleagues at the American University of Beirut, studied the mechanisms used by United Nations agencies to distribute aid to vulnerable refugee populations. Their aim was to improve targeting processes to identify the most vulnerable households with greater precision and lower costs, thereby limiting leakage (aid to non-vulnerable households) and under-coverage (failure to identify vulnerable households). They used United Nations data on the more than one million Syrian refugees in Lebanon receiving assistance from the World Food Programme (WFP) to develop a model to identify vulnerable households.

The research team found that the simultaneous consideration of individual data and regional and community-based aggregates (access to drinking water and sanitation facilities, access to healthcare, incidence of chronic disease) improves the overall targeting effectiveness of aid programmes for vulnerable populations.

When poverty rather than food insecurity — which is difficult to assess with precision — is used as a indicator, targeting effectiveness is higher. Coverage rates reached 85%, compared to 54% when referencing food insecurity.

The model combining both individual and aggregate data thus provided the best overall performance in terms of targeting effectiveness: more vulnerable households were covered while leakage was reduced. There was a global gain of 9% over existing practices.

The work presents an original method that combines individual and larger-scale community aggregate data to reduce instances of food aid being misallocated and to increase the targeting effectiveness for populations that are food insecure and/or economically vulnerable. The work also has the potential to reduce costs associated with data collection because developing aggregate data indicators is less intensive than collecting data on households. When aid is better targeted it can only be more effective. The model creates opportunities for analysts and policymakers to consider new models for aid distribution, food aid in particular.