Fighting Poverty with Big Data: A Conversation with Joshua Blumenstock
This post, written by Rachel Pizatella-Haswell, UC Berkeley Goldman School of Public Policy MPP ‘18, was originally published on The Blum Center’s Blog.
Joshua Blumenstock is an Assistant Professor at the UC Berkeley School of Information, where he directs the Data-Intensive Development Lab, and a member of the Blum Center’s Development Engineering faculty. His research lies at the intersection of machine learning and development economics, and focuses on using novel data and methods to better understand the causes and consequences of global poverty. Blumenstock has a Ph.D. in Information Science and a M.A. in Economics from U.C. Berkeley, and Bachelor’s degrees in Computer Science and Physics from Wesleyan University.
What can remote sensing and geographic information system data and cell phone data tell us about a person living in poverty?
Blumenstock: We have partial answers to that question. The work that’s been done indicates we can estimate very basic things: population density, household average wealth, basic indices of relative socio-economic status. Of course, there are lots of different ways to measure poverty and inequality and welfare. People working in developing countries tend to like consumption because it seems to be most closely correlate to how someone is actually doing. There has been some work looking at whether you can estimate consumption and expenditures from remote data sources, and initial results are promising here too. Aside from measuring basic welfare, all sorts of work is being done to use these data to learn about migration, social network structure and the spread of disease, to give a few examples.
What can data tell us about poverty indicators such as the incidence or depth of poverty?
Blumenstock: What these models actually spit out are sub-regional estimates of welfare. We can define welfare however we want. In general, as long as you can measure it in the traditional way, you can use these non-traditional data and models to try to estimate it. However, depending on what you want to measure, and what data source you’re using — such as phone data or satellite data — your estimates may be more or less accurate. But once you have your estimate of the distribution of wealth, you can do all of the things you could do with traditional data. You can back-out the poverty incidence, Gini curves and other constructs you derive from the poverty distribution.
To read the complete blog post, see here.
