Analysis of Poverty Data by Small Area Estimation by Monica Pratesi

By Monica Pratesi

A complete consultant to imposing SAE tools for poverty stories and poverty mapping

There is an more and more pressing call for for poverty and dwelling stipulations info, in terms of neighborhood parts and/or subpopulations. coverage makers and stakeholders desire signs and maps of poverty and residing stipulations that allows you to formulate and enforce regulations, (re)distribute assets, and degree the impression of neighborhood coverage actions.

Small sector Estimation (SAE) performs a very important position in generating statistically sound estimates for poverty mapping. This ebook deals a accomplished resource of data concerning the use of SAE equipment tailored to those particular positive factors of poverty facts derived from surveys and administrative records. The e-book covers the definition of poverty symptoms, facts assortment and integration equipment, the effect of sampling layout, weighting and variance estimation, the difficulty 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 purposes are supplied, and the e-book is supported via an internet site describing scripts written in SAS or R software program, which accompany nearly all of the provided methods.

Key features:

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

Analysis of Poverty info by means of Small sector Estimation deals an advent to complex suggestions from either a realistic and a methodological viewpoint, and may turn out a useful source for researchers actively engaged in organizing, coping with and carrying out stories on poverty.

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Additional resources for Analysis of Poverty Data by Small Area Estimation

Sample text

It can be illustrated and represented by building poverty maps. Poverty maps can be constructed using censuses, surveys, administrative data and other data. Here we refer to poverty mapping to visualize the spatial distribution of poverty indicators. This is particularly useful, as it is shown in Chapter 2, to monitor the localization of poverty and the individuation of the most vulnerable areas. 2 Direct and Indirect Estimate of Poverty Indicators at Small Area Level The estimates of the different poverty indicators at area level can be done under the design-based (Hansen et al.

Published 2016 by John Wiley & Sons, Ltd. com/go/pratesi/poverty 2 Analysis of Poverty Data by Small Area Estimation However the main sources of statistical data on monetary and non-monetary poverty are from sample surveys on income and living conditions. These rarely give credible estimates at sub-regional and local level. From this comes the importance of the Small Area Estimation (SAE) methods for measuring poverty at local level. This is confirmed also by the large amount of literature on these local estimates resulting from many projects, conferences and books in the last decade.

A high frequency of zeros can occur also when the study variable is a characteristic of the households, such as presence of households not able to keep their home adequately warm or with arrears on utility bills in a local area where living conditions are acceptable. In this case the problem is different and should be treated under the umbrella of SAE for a rare population. (iv) The outlier. Outlier detection in the study variable have always been an interesting challenge when examining data to prepare the estimation of small area target parameters.

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