Food Economy in Situations of Chronic Political Instability

Introduction

This paper illustrates how the food economy approach has been used in situations of chronic conflict and political instability (SCCPI). The food economy analytical framework is a useful tool for integrating outcomes arising from different political contexts into a livelihood-based analysis because it is designed to connect changes in the macro-level context to micro-level consequences. Recent experiences have emphasised the need to incorporate an analysis of the wider political, social, and economic context into livelihood assessments, and to increase the synergy and communication between political economists and food security analysts (Jaspars and Shoham, 2002).

In unstable political situations, aid agencies typically need to take decisions quickly – either due to the extreme and changeable nature of events that tends to arise under these circumstances, or because of the limitations placed by a funding agency. At the same time, decisions on the allocation of resources are greatly facilitated by quantified information. Yet the best kind of information does more than cater to decision-makers; it marries their needs with the words, views and realities of poor rural and urban households.

In SCCPI, perhaps more than in other situations, change is an important factor for decision-makers. They take decisions because something has changed, or because they want to make something change. It follows that effective assessment approaches must allow one to analyse and predict the effects of macro-level changes on real people. Such changes are today often propagated by political power plays or changing market conditions, and it is no longer enough to think in terms of handling natural hazards on the one hand and man-made hazards on the other – livelihood analysis must be able to incorporate both.

Food economy analysis, built from field experience over the past 10–20 years, has developed into a model that is designed to link livelihood information to an analysis of the effects of change. The method has been employed in SCCPI (Angola, Sierra Leone, Somalia, Sudan); situations of sub-national conflict (Burundi, the Democratic Republic of Congo (DRC), pastoral Kenya); andsituations in recovery from conflict (The Balkans, Eritrea, Mozambique, Rwanda). While the original food economy work in Ethiopia and southern Sudan aimed to simply get a handle on how people made ends meet, current work in such settings as rural Mozambique and urban Zimbabwe have developed this basic model into more sophisticated analyses of the household effects of macro-political and economic changes.

Conclusions

The following points summarise key arguments made in this paper related to how food economy contributes to assessment principles and outcomes in situations of chronic conflict and political instability.

• Food economy analysis functions around a model that makes it possible to analyse the effects of both natural and man-made hazards on household access to food and income. In SCCPI, this facility is absolutely crucial if the objective is to design strategies for supporting and promoting livelihoods rather than just reducing the immediate effects of a hazard.
• Food economy translates livelihoods analysis into practical, quantified information for decision-makers, with a practical economic geography attached. It is based on foods but tells a rounded story of how poor people cope and how communities are internally differentiated.

Quantification and disaggregation are essential for prioritisation and targeting in SCCPI.

• It is a robust and transparent approach remarkably adaptable to war and peace, town and country– incorporating the essential elements of livelihood analysis in an approach that can be used to rapidly understand livelihood outcomes in complex emergencies.
• Because the framework logically organises and structures different types and levels ofinformation, it provides a powerful impetus for coordinated information gathering and analysis.This same facility helps build consensus around findings and conclusions, leading to faster and more accurate decision-making.

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