Food, Agriculture and Natural Resources Policy Analysis Network (FANRPAN) Food, Agriculture and Natural Resources Policy Analysis Network (FANRPAN)
 


Understanding HIV/AIDS and livelihoods: The contribution of longitudinal data and cluster analysis
Please visit www.livelihoods.org/static/fsamuels_NN359.html?em=0107 for the full briefing paper
August 2006
Fiona Samuels, Michael Drinkwater, Margaret McEwan

Acknowledgements: FANRPAN acknowledges Livelihoods Connect as the source of this briefing paper summary.


Summary

How do people use their social networks to resist the negative livelihoods effects of HIV/AIDS? What can analysing ‘clusters’ tell us about livelihood changes that analysing households can’t?

This ODI briefing paper draws on longitudinal research carried out in Zambia in 1993 and 2005 to suggest that the ‘cluster’ is a better unit for capturing the complexity and fluidity of people’s livelihoods than the household. A ‘cluster’ is a group of people between which there are multiple resource exchanges, usually based on kinship, labour and food exchange, and/or common access to draught power. These studies used this approach to investigate the impact of AIDS on livelihoods in two rural areas of Zambia: one remote, and one close to the copperbelt towns. The briefing highlights that the approach:

  • allows the most important relationships between individuals of different generations and gender, marital and kinship statuses to be identified and understood.
  • by understanding vulnerability in the context of multiple resource flows and relationships, it reveals the resilience provided by social networks. Nevertheless, even better-off farmers have been gravely affected by the combined effects of the AIDS epidemic, livestock disease and changes in the economic and policy environment. The briefing recommends that:

    • Diversification has been an important nutritional strategy and requires a policy emphasis on local knowledge.
    • Programmes working with ‘the poor’ should be directed at resource-poor clusters, rather than poor households. Exploring how clusters can be used in future research should be a priority.

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