The Relative Wealth Index predicts the relative standard of living within countries using de-identified connectivity data, satellite imagery and other nontraditional data sources.It has been built by researchers at the University of Carlifornia - Berkeley and Facebook. The estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Facebook. They train and calibrate the estimates using nationally-representative household survey 20 data from 56 LMICs, then validate their accuracy using four independent sources of household survey data from 18 countries. They also provide confidence intervals for each micro-estimate to facilitate responsible downstream use.The data is provided for 93 low and middle-income countries at 2.4km resolution. It covers the time between April 01, 2021 and December 22, 2023. An interactive map of the Relative Wealth Index is available here: http://beta.povertymaps.net/Please cite / attribute any use of this dataset using the following:Microestimates of wealth for all low- and middle-income countries Guanghua Chi, Han Fang, Sourav Chatterjee, Joshua E. Blumenstock Proceedings of the National Academy of Sciences Jan 2022, 119 (3) e2113658119; DOI: 10.1073/pnas.2113658119
Publications Citing This Dataset:
Smythe, I. S., & Blumenstock, J. E. (2022). Geographic microtargeting of Social Assistance with high-resolution poverty maps. Proceedings of the National Academy of Sciences, 119(32). https://doi.org/10.1073/pnas.2120025119
Variables:
Name
Description
quadkey
ID of place
latitude
Latitude of region
longitude
Longitude of region
rwi
Relative Wealth Index score in that region
error
Confidence bounds arround the RWI score
Details:
Publisher:
University of California, Berkeley; Data for Good at Meta (previously Facebook)