Analysis of Poverty Data by Small Area Estimation

A comprehensive guide to implementing SAE methods for povertystudies and poverty mapping

There is an increasingly urgent demand for poverty and livingconditions data, in relation to local areas and/or subpopulations.Policy makers and stakeholders need indicators and maps of povertyand living conditions in order to formulate and implement policies,(re)distribute resources, and measure the effect of local policyactions.

Small Area Estimation (SAE) plays a crucial role in producingstatistically sound estimates for poverty mapping. This book offersa comprehensive source of information regarding the use of SAEmethods adapted to these distinctive features of poverty dataderived from surveys and administrative archives. The book coversthe definition of poverty indicators, data collection andintegration methods, the impact of sampling design, weighting andvariance estimation, the issue of SAE modelling and robustness, thespatio–temporal modelling of poverty, and the SAE of thedistribution function of income and inequalities.  Examples ofdata analyses and applications are provided, and the book issupported by a website describing scripts written in SAS or Rsoftware, which accompany the majority of the presentedmethods.

Key features:

Analysis of Poverty Data by Small Area Estimation offersan introduction to advanced techniques from both a practical and amethodological perspective, and will prove an invaluable resourcefor researchers actively engaged in organizing, managing andconducting studies on poverty.