Analysis of Poverty Data by Small Area Estimation by Monica Pratesi

By Monica Pratesi

A entire advisor to enforcing SAE equipment for poverty stories and poverty mapping

There is an more and more pressing call for for poverty and dwelling stipulations info, when it comes to neighborhood components and/or subpopulations. coverage makers and stakeholders desire signs and maps of poverty and residing stipulations as a way to formulate and enforce regulations, (re)distribute assets, and degree the impression of neighborhood coverage actions.

Small zone Estimation (SAE) performs a very important function in generating statistically sound estimates for poverty mapping. This ebook bargains a accomplished resource of data concerning the use of SAE equipment tailored to those specific gains of poverty information derived from surveys and administrative records. The booklet covers the definition of poverty signs, info assortment and integration tools, the influence of sampling layout, weighting and variance estimation, the problem of SAE modelling and robustness, the spatio-temporal modelling of poverty, and the SAE of the distribution functionality of source of revenue and inequalities. Examples of knowledge analyses and functions are supplied, and the e-book is supported by way of an internet site describing scripts written in SAS or R software program, which accompany the vast majority of the awarded methods.

Key features:

  • Presents a finished overview of SAE equipment for poverty mapping
  • Demonstrates the purposes of SAE tools utilizing real-life case studies
  • Offers tips at the use of exercises and selection of sites from which to obtain them

Analysis of Poverty information by way of Small sector Estimation bargains an creation to complicated concepts from either a realistic and a methodological viewpoint, and should end up a useful source for researchers actively engaged in organizing, handling and carrying out stories on poverty.

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Extra info for Analysis of Poverty Data by Small Area Estimation

Example text

Among poverty indicators, the so-called Laeken indicators are very often used to target poverty and inequalities. They are a core set of statistical indicators on poverty and social exclusion agreed by the European Council in December 2001, in the Brussels suburb of Laeken, Belgium. Referring to the monetary poverty and starting from the Income distribution the most frequently used indicators are the average mean of the equalized income, the Head Count Ratio (HCR) and the Poverty Gap (PG). The HCR measures the incidence of poverty and it is the percentage of individuals of households under a poverty line, that can be defined at national or regional level.

Models presented in Parts IV and V of this book rely on and often enlarge the assumptions of this popular approach: Chapter 8 introduces the issue of measurement error in the covariates; Chapter 10 extends it to a non-parametric regression environment; and Chapters 11, 12 and 13 extend it to take into account spatial and temporal correlations and the characteristics of geographical patterns. 17) on the logarithmically transformed values of income yjd , the most widely used method for small area poverty mapping is the so-called World Bank (WB) or Elbers, Lanjouw and Lanjouw (ELL) method (Elbers et al.

Suppose that a sample s is drawn according to some, possibly complex, sampling design such that the inclusion probability of unit j within area d is given by ????jd , and that area-specific samples sd ⊂ Ud of size nd ≥ 0 are available for each area. Note that non-sample areas have nd = 0, in which case sd is the empty set. The set rd ⊆ Ud contains the Nd − nd indices of the non-sampled units in small area d. Values of yjd are known only for sampled values while for the p-vector of auxiliary variables ̄ d or individual values xjd are accurately known it is assumed that area level totals Xd or means X from external sources.

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