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Social Protection
At the heart of social protection is the idea that the state has a responsibility to ensure a minimum standard of living for all citizens. Actions aimed at meeting this goal cover a wide range of economic and rights-based interventions, including direct support, disability allowances, supplementary feeding, health insurance, agricultural input subsidies, conditional cash transfers (based on school or Mother-Child-Health clinic attendance, for instance), public works support, and so on.
Two significant design challenges face all social protection programs:
- customizing appropriate social protection packages that will address gaps in people's current living standards; and
- identifying beneficiaries who fall below these living standards.
To adequately address these challenges a range of questions need to be answered:
- What are the current actual living standards of people in different parts of the country? How can we compare poverty levels?
- Should one standard be applied? Or should the standard vary by livelihood zone?
- How much of a gap exists between the current standards and the program's goals?
- What kinds of interventions will work best to fill these gaps? a direct transfer? a change in policy? an insurance scheme?
- How can the intervention(s) be delivered in such a way as to shore up local livelihoods, rather than supplanting or disrupting them?
FEG provides a multidisciplinary team of consultants who have provided support in the area of social protection programming, both in using creative applications of HEA to help answer some of the important design questions listed above, and in dealing with the tricky process issues related to multi-donor and government initiatives. Click here to read more about how HEA has been applied in the area of social protection.
In addition to providing important guidance on the program planning side, FEG , through its customized application of HEA analytical tools, can help design livelihoods-based monitoring and evaluation programs aimed at helping judge the effectiveness of an intervention. This can be done in a predictive way, modeling the potential effect on household income and expenditure of different grant or wage levels in order to design the most effective program. It can also be done to evaluate impact once an intervention is underway.
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