Meet Manju Devi* – a young woman from rural Bhagalpur who came to a World Health Partners (WHP) SkyHealth center seeking help. She lives in a home without electricity, though, ironically, she owns an electric fan. Her family does not own a phone or TV, nor do they own a bicycle or a cooking stove. They do not have a set cement floor, a source of water, or a toilet. They do, however, own a cow and a few chickens.
Why is this household information important? Though income can be used to define poverty (internationally, having less than $1.25 per head per day in purchasing power is considered extreme poverty), in countries where either income and expenditure information is not easily available, or in areas where overall wealth is defined more in terms of goods and possessions than monetary income, the alternative is to measure wealth by collecting equity data as a proxy. Equity data typically includes household ownership of select assets, including items such as a television, bicycle, furniture, mobile phone, and bank account. Data is also collected on quality of living standards, such as housing structure, access to water/electricity, sanitation and space available.
Recently, as a member of the Social Franchise Metrics Working Group (SFMWG), WHP participated in the selection of the most relevant and feasible metric to measure equity – the proportion of clients receiving franchised services who are within the lowest two national wealth quintiles. These wealth indices, adapted from the Demographic and Health Survey (DHS), measure equity in terms of asset ownership and household characteristics. Using DHS as the source for questions allows for rigorous analysis by sub-populations, useful comparisons within a country context and comparisons across countries. As one of the pilot sites, WHP collected equity data of clients for over a year, and this data was then weighted using Demographic Health Survey 2005-2006 data, considered nationally representative.
How does this relate to our very own Manju Devi? Based on the equity data we collected on her family and the subsequent analysis/weighting, we were able to conclude that Manju (and her family) fall into the poorest 20% of people nationally (quintile 1), something we may not have been able to determine using monetary income as our only measure.
One of the biggest doubts about the franchising approach to delivering social services (known as “social franchising”) is whether or not the mechanism allows those most in need to access services. Franchising thrives on mobilizing the private sector through existing entrepreneurs, in this case, rural providers. However, because the private sector is relatively unregulated (particularly financially), there is concern that without regular monitoring, the risk of exploitation increases. If a private “fee-for-service” model is used, how will the poorest of the poor be able to utilize services? It is a valid concern, and by understanding the economic status of clients we can attempt to understand the effect of wealth on health outcomes as well as measure the extent to which our health care services (those of a fee-for-service model) are reaching low-income families. The data WHP has been collecting is showing positive signs: overall, 51% of WHP clients fall into the lowest 2 quintiles, meaning that more than half of clients served by WHP fall into the poorest 40% of the national population.