By Monica Pratesi
A entire consultant to imposing SAE equipment for poverty reports and poverty mapping
There is an more and more pressing call for for poverty and residing stipulations information, relating to neighborhood parts and/or subpopulations. coverage makers and stakeholders want signs and maps of poverty and residing stipulations with a view to formulate and enforce rules, (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 bargains a finished resource of knowledge in regards to the use of SAE equipment tailored to those specific positive factors of poverty facts derived from surveys and administrative information. The publication covers the definition of poverty signs, facts assortment and integration equipment, 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 information analyses and functions are supplied, and the booklet is supported via an internet site describing scripts written in SAS or R software program, which accompany nearly all of the provided methods.
- Presents a entire evaluate of SAE tools for poverty mapping
- Demonstrates the functions 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 info through Small region Estimation bargains an creation to complex ideas from either a pragmatic and a methodological viewpoint, and should turn out a useful source for researchers actively engaged in organizing, dealing with and carrying out reports on poverty.
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Extra resources for Analysis of Poverty Data by Small Area Estimation
Also to protect against possible model failures, benchmarking procedures make the total of small area estimates match a design consistent estimate for a larger area. With respect to benchmarking, all the families of methods offer a solution. There are two kinds of benchmarked estimators: estimators that are internally benchmarked (or self-benchmarked) and those that are externally benchmarked. Self-benchmarked predictors are the GREG estimator and the pseudo-EBLUP introduced by You and Rao (2002).
Statistical Science 28(1), 40–68. Prasad NGN and Rao JNK 1990 The estimation of the mean squared error of small-area estimators. Journal of the American Statistical Association 85, 163–171. Prasad NGN and Rao JNK 1999 On robust small area estimation using a simple random effects model. Survey Methodology 25, 67–72. Introduction on Measuring Poverty at Local Level Using Small Area Estimation Methods 17 Pratesi M and Petrucci A 2014 Methodological and operational solutions to gaps and issues on methods for producing agricultural and rural statistics at small domains level and on methods for aggregation, disaggregation and integration of different kinds of geo-referenced data for increasing the efficiency of agricultural and rural statistics.
Papers in Regional Science 93, 685–701. Fabrizi E, Salvati N, Pratesi M, and Tzavidis N 2014b Outlier robust model-assisted small area estimation. Biometrical Journal 56, 157–175. Foster J, Greer J, and Thorbecke E 1984 A class of decomposable poverty measures. Econometrica 52, 761–766. Frey J and Cressie N 2003 Some results on constrained Bayes estimators. Statistics and Probability Letters 65, 389–399. Gosh M and Meeden G 1997 Bayesian Methods for Finite Population Sampling. London: Chapman & Hall.
Analysis of Poverty Data by Small Area Estimation by Monica Pratesi