Analysis of Poverty Data by Small Area EstimationMonica Pratesi John Wiley & Sons, 23 feb 2016 - 480 pagine A comprehensive guide to implementing SAE methods for poverty studies and poverty mapping There is an increasingly urgent demand for poverty and living conditions data, in relation to local areas and/or subpopulations. Policy makers and stakeholders need indicators and maps of poverty and living conditions in order to formulate and implement policies, (re)distribute resources, and measure the effect of local policy actions. Small Area Estimation (SAE) plays a crucial role in producing statistically sound estimates for poverty mapping. This book offers a comprehensive source of information regarding the use of SAE methods adapted to these distinctive features of poverty data derived from surveys and administrative archives. The book covers the definition of poverty indicators, data collection and integration methods, the impact of sampling design, weighting and variance estimation, the issue of SAE modelling and robustness, the spatio-temporal modelling of poverty, and the SAE of the distribution function of income and inequalities. Examples of data analyses and applications are provided, and the book is supported by a website describing scripts written in SAS or R software, which accompany the majority of the presented methods. Key features:
Analysis of Poverty Data by Small Area Estimation offers an introduction to advanced techniques from both a practical and a methodological perspective, and will prove an invaluable resource for researchers actively engaged in organizing, managing and conducting studies on poverty. |
Sommario
IMPACT OF SAMPLING DESIGN WEIGHTING AND VARIANCE | 1 |
3 | 20 |
5 | 34 |
3 | 41 |
3 | 48 |
4 | 56 |
1 | 61 |
2 | 62 |
SPATIOTEMPORAL MODELING OF POVERTY | 207 |
Modelbased Methods | 227 |
Spatial Information and Geoadditive Small Area Models | 245 |
Modelbased Direct Estimation of a Small Area Distribution Function | 263 |
Small Area Estimation for Lognormal Data | 279 |
2 | 287 |
5 | 291 |
1 | 295 |
2 | 87 |
4 | 96 |
5 | 102 |
Modelassisted Methods | 109 |
4 | 115 |
7 | 129 |
5 | 130 |
9 | 146 |
4 | 162 |
5 | 168 |
6 | 178 |
Nonparametric Regression Methods for Small Area Estimation | 187 |
16 | 299 |
4 | 305 |
2 | 306 |
4 | 318 |
173 | 327 |
An Overview of the U S Census Bureaus Small Area Income and Poverty | 349 |
Poverty Mapping for the Chilean Comunas | 379 |
Poverty Dilemmas of Definition | 405 |
4 | 408 |
the Estimation of Poverty and Inequality Parameters in Small Areas 21 4 13 A Quick Guide to Chapter 17 Empirical Bayes and Hierarchical | 425 |
431 | |