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Global poverty estimation using private and public sector big data sources.

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This study integrates satellite, Facebook, and OpenStreetMap data to estimate wealth levels and changes across 59 countries. This novel approach offers a cost-effective alternative to traditional household surveys for poverty assessment.

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Area of Science:

  • Geospatial analysis
  • Development economics
  • Data science

Background:

  • Household surveys provide accurate poverty data but are expensive and infrequent.
  • Estimating wealth and its changes globally requires innovative, scalable methods.

Purpose of the Study:

  • To develop and validate a model using diverse data sources for estimating wealth levels and changes.
  • To assess the model's generalizability across a wide range of countries.

Main Methods:

  • Trained machine learning models on 63,854 survey cluster locations in 59 countries.
  • Integrated data from satellites, Facebook Marketing, and OpenStreetMap.
  • Evaluated model performance in explaining wealth variation at cluster and district levels.

Main Results:

  • The model explained 55% of wealth level variation at the cluster level and 59% at the district level on average.
  • Variation in wealth changes explained was 4% at the cluster level and 6% at the district level.
  • Nighttime lights, OpenStreetMap, and land cover data were key predictors for wealth levels.

Conclusions:

  • Combining multiple public and private data sources effectively estimates wealth levels and changes.
  • The model shows strong performance, particularly in lower-income countries and those with high wealth variance.
  • This approach offers a scalable and cost-efficient complement to traditional poverty measurement surveys.