Global Distribution of Poverty

Table of Contents

Global Poverty Data Set

National-level Poverty Data Sets

• Indirect Estimation Techniques
  • Small Area Estimates Poverty & Inequality Data Set
  • Poverty and Food Security Country Case Studies
• Other Indirect Estimation Techniques
  • Combining Surveys with Remote Sensing Data
• Direct Estimation Techniques
  • Unsatisfied Basic Needs Data Set

Combining Surveys with Remote Sensing Data

Researchers at the Pro-Poor Livestock Policy Initiative (PPLPI) combined survey data with satellite data sets to predict poverty. Using this approach, the authors examined the correlations between environmental land-surface conditions and processes (temperature, rainfall, vegetation growth etc.) and poverty (as measured in household survey data). A similar study was undertaken in Nigeria, where in the absence of a reliable Census, researchers combined survey and remote sensing data to estimate poverty for small areas.

Rogers, D., T. Emwanu , and T. Robinson. (2006) “Poverty Mapping in Uganda: An Analysis Using Remotely Sensed and Other Environmental Data”. Pro-Poor Livestock Policy Initiative (PPLPI), Rome. [Document]

Legg, C., P. Kormawa, B. Maziya-Dixon, R. Okechukwu, S. Ofodile and T. Alabi. Report on mapping livelihoods and nutrition in Nigeria using data from the National Rural Livelihoods Survey and the National Food Consumption and Nutrition Survey. International Institute for Tropical Agriculture. [Document]