Big data for poverty measurement: insights from a scoping review

This research presents a scoping review of 53 systematically selected studies that employ big data to measure and monitor poverty concepts. The primary aim of the review is to explore if and how big data can be used as a replacement or complementary to national and international statistics to identify, measure, and monitor poverty, economic development and inequality on a macro level. The analysis reveals that (1) the relevance of the field so far is driven by data availability, (2) researchers from different fields are involved as data types and analytics employed stem from various research domains, however, researchers from the global south are underrepresented, (3) the main data types used are Call Detail Records (CDR) and satellite image data while night-light is frequently associated with economic development, (4) the choice for certain data types is based on the hypothesis that the manifestations of poverty and development leave traces that are captured by big data sources, (5) big data techniques are so far mainly applied for feature extraction while classical statistical techniques are preferred for analysis. With this in mind, the review highlights challenges and opportunities of using big data for development statistics and briefly discusses the implications for monitoring and evaluation showing that it is highly unlikely that big data statistics will replace traditionally generated development data any time soon. Many barriers need to be overcome, including some technical challenges, stability and sustainability issues as well as institutional and legal aspects. In the meantime, big data offers undoubtfully a major opportunity to play a role to improve accuracy, timeliness and relevance of socio-economic indicators especially where no data is available, or where quality is highly disputable.