Finding out which people in any given community live below the poverty line is actually quite hard. Why do it? To target services like microfinance (let’s not get into the targetting v universal provision argument here); comparing poverty rates in different regions and countries, and tracking changes over time.
But both income and consumption poverty are hard to assess directly – poor people tend not to have wage slips or supermarket receipts handy to give to people with clipboards. In Viet Nam, Oxfam researchers tried a different approach, working with communities to identify different levels of poverty – who owned land, a buffalo, a tin roof, a motor bike etc, producing a real and nuanced picture of what poverty means in those villages. But that can be an expensive business – labour intensive household surveys, difficult problems gathering and interpreting reliable data etc etc.
But researchers at the microfinance.com website have come up with a much cheaper version, ‘poverty scorecards’ based on the World Bank’s regular and highly detailed ‘Living Standards Measurement Study’ (LSMS) surveys. The scorecards boil down a vast amount of data from the LSMS to identify easily detectable signs of poverty – the tin roof or buffalo test for each country.
Here’s an example from Bangladesh. Fill it in, and if a person gets 24 points, there’s an 80% chance probability that they are living on less than $1 a day. The website already crunches the numbers for a number of countries, in alphabetical order: Bangladesh, Bolivia, Ecuador, Ethopia, Haiti, India, Indonesia, Kenya, Malawi, Mali, Mexico, Morocco, Nepal, Nigeria, Pakistan, Palestine, Peru, Philippines, South Africa, Vietnam, Yemen. They plan to add more countries to the list.
One complaint, though – the actual scorecards are buried in the appendices for each paper, after you’ve waded through a bunch of maths. Any chance of putting them up separately, guys?
In any given place, such a scorecard could be improved by adapting it to local circumstances (rural poverty is different from urban, nomadic pastoralists from settled small farmers etc), but it looks like an excellent initial short cut – our monitoring and evaluation people are certainly intrigued. Has anyone other than microfinance people used them? Got any comments, eg about whether individual country examples work for your countries?