The availability of poverty data has increased over the last 20 years but large gaps remain
About half the countries we studied in our recent paper, Data Deprivation, Another Deprivation to End are deprived of adequate data on poverty. This is a huge problem because the poor, who often lack political representation and agency, will remain invisible unless objective and properly sampled surveys reveal where they are, and how they’re faring. The lack of data on human and social development should be seen as a form of deprivation, and along with poverty, data deprivation should be eradicated.
As many middle-income countries are moving towards embracing cash transfers with or without co-responsibilities attached (and the recent hype of handing cash directly to the poor), there is an important wave of programs that provide “cash plus” intervention.
Location: Sarfuddinpur, Bihar
In June this year, I was in Sarfuddinpur, a village in Muzaffarpur district in north-central Bihar. This was my tenth round of qualitative data collection in this village and I wanted to document the stories of a few Self-Help Group, or SHG, leaders; Shakuntala Devi was one of them. I first observed her presiding over an SHG meeting under the village peepal tree in July 2013. She was expertly facilitating a discussion with other SHG members around loans, but also around child health issues and the challenges faced by women in the marketplace. She disciplined free riders and rewarded contributors with a respected leader’s ease. Since then, I have seen her conduct many other meetings.
The limited availability of data on poverty and inequality poses major challenges to the monitoring of the World Bank Group’s twin goals – ending extreme poverty and boosting shared prosperity. According to a recently completed study, for nearly one hundred countries at most two poverty estimates are available over the past decade.Worse still, for around half of them there was either one or no poverty estimate available.* Increasing the frequency of data on poverty is critical to effectively monitoring the Bank’s twin goals.
Against this background, the science of “Big Data” is often looked to as providing a potential solution. A famous example of this science is “Google Flu Trends (GFT)”, which uses search outcomes of Google to predict flu outbreaks. This technology has proven extremely quick to produce predictions and is also very cost-effective. The rapidly increasing volumes of raw data and the accompanying improvement of computer science have enabled us to fill other kinds of data gaps in ways that we could not even have dreamt of in the past.