Read e-book online Analysis of Poverty Data by Small Area Estimation PDF

By Monica Pratesi

ISBN-10: 1118815017

ISBN-13: 9781118815014

A accomplished advisor 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, with regards to neighborhood components and/or subpopulations. coverage makers and stakeholders desire symptoms and maps of poverty and residing stipulations for you to formulate and enforce rules, (re)distribute assets, and degree the impact of neighborhood coverage actions.

Small sector Estimation (SAE) performs a very important position in generating statistically sound estimates for poverty mapping. This e-book bargains a accomplished resource of knowledge concerning the use of SAE equipment tailored to those particular good points of poverty info derived from surveys and administrative documents. The ebook covers the definition of poverty symptoms, facts assortment and integration equipment, the influence 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 information analyses and functions are supplied, and the ebook is supported by means of an internet site describing scripts written in SAS or R software program, which accompany nearly all of the offered methods.

Key features:

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

Analysis of Poverty information via Small sector Estimation bargains an advent to complex recommendations from either a pragmatic and a methodological standpoint, and may end up a useful source for researchers actively engaged in organizing, handling and undertaking stories on poverty.

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Extra resources for Analysis of Poverty Data by Small Area Estimation

Sample text

Also in the SAE context the model-assisted approach has become popular and in this section we briefly review the most common estimators under this approach. 8 Analysis of Poverty Data by Small Area Estimation Among design-based methods assisted by the specification of a model for the study variable there are three families of methods that have been recently applied in poverty mapping: Generalized Regression (GREG) estimators; pseudo-EBLUP estimators; and M-quantile weighted estimators. The GREG approach can be used to estimate several poverty indicators.

Pfeffermann D and Barnard CH 1991 Some new estimators for small-area means with application to the assessment of farmland values. Journal of Business & Economics Statistics 9, 73–84. Pfeffermann D, Terryn B, and Moura FAS 2008 Small area estimation under a two-part random effects model with application to estimation of literacy in developing countries. Survey Methodology 34, 235–249. Pfeffermann D 2013 New important developments in small area estimation. Statistical Science 28(1), 40–68. Prasad NGN and Rao JNK 1990 The estimation of the mean squared error of small-area estimators.

It can be applied to obtain benchmarking MQ small area estimates. Introduction on Measuring Poverty at Local Level Using Small Area Estimation Methods 11 The treatment of the outliers is not the focus of the estimators of GREG type nor of those under the pseudo-EBLUP approach, while the weighted M-quantile approach this issue. There are studies under the AMELI (2008) project that illustrate the behavior of the GREG-like estimators in the presence of different models of outlier-contamination of the observed data.

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Analysis of Poverty Data by Small Area Estimation by Monica Pratesi

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