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RT Book, Whole SR Electronic DC OPAC T1 Analysis of poverty data by small area estimation / edited by Monica Pratesi A1 Pratesi, Monica YR 2016 FD 2016 SP 1 online resource K1 Poverty -- Statistical methods K1 Poverty -- Econometric models K1 Poverty -- Measurement K1 Income distribution -- Econometric models K1 Pauvret�e -- M�ethodes statistiques K1 Pauvret�e -- Mod�eles �econom�etriques K1 Revenu -- R�epartition -- Mod�eles �econom�etriques K1 BUSINESS & ECONOMICS -- Economics -- Macroeconomics K1 POLITICAL SCIENCE -- Economic Conditions K1 Income distribution -- Econometric models K1 Poverty -- Econometric models K1 Poverty -- Measurement K1 Poverty -- Statistical methods PB Wiley PP Chichester, West Sussex, United Kingdom SN 9781118815007 SN 1118815009 SN 9781118814987 SN 1118814983 SN 9781118814963 SN 1118814967 SN 1118815017 SN 9781118815014 LA English (英語) CL LCC:HC79.P6 CL DC23:339.4/60727 NO Includes bibliographical references and index NO Print version record and CIP data provided by publisher NO Cover -- Title Page -- Copyright -- Contents -- Foreword -- Preface -- Acknowledgements -- About the Editor -- List of Contributors -- Chapter 1 Introduction on Measuring Poverty at Local Level Using Small Area Estimation Methods -- 1.1 Introduction -- 1.2 Target Parameters -- 1.2.1 Definition of the Main Poverty Indicators -- 1.2.2 Direct and Indirect Estimate of Poverty Indicators at Small Area Level -- 1.3 Data-related and Estimation-related Problems for the Estimation of Poverty Indicators NO 1.4 Model-assisted and Model-based Methods Used for the Estimation of Poverty Indicators: a Short Review -- 1.4.1 Model-assisted Methods -- 1.4.2 Model-based Methods -- References -- Part I Definition of Indicators and Data Collection and Integration Methods -- Chapter 2 Regional and Local Poverty Measures -- 2.1 Introduction -- 2.2 Poverty -- Dilemmas of Definition -- 2.3 Appropriate Indicators of Poverty and Social Exclusion at Regional and Local Levels -- 2.3.1 Adaptation to the Regional Level -- 2.4 Multidimensional Measures of Poverty NO 2.4.1 Multidimensional Fuzzy Approach to Poverty Measurement -- 2.4.2 Fuzzy Monetary Depth Indicators -- 2.5 Co-incidence of Risks of Monetary Poverty and Material Deprivation -- 2.6 Comparative Analysis of Poverty in EU Regions in 2010 -- 2.6.1 Data Source -- 2.6.2 Object of Interest -- 2.6.3 Scope and Assumptions of the Empirical Analysis -- 2.6.4 Risk of Monetary Poverty -- 2.6.5 Risk of Material Deprivation -- 2.6.6 Risk of Manifest Poverty -- 2.7 Conclusions -- References -- Chapter 3 Administrative and Survey Data Collection and Integration -- 3.1 Introduction NO 3.2 Methods to Integrate Data from Different Data Sources: Objectives and Main Issues -- 3.2.1 Record Linkage -- 3.2.2 Statistical Matching -- 3.3 Administrative and Survey Data Integration: Some Examples of Application in Well-being and Poverty Studies -- 3.3.1 Data Integration for Measuring Disparities in Economic Well-being at the Macro Level -- 3.3.2 Collection and Integration of Data at the Local Level -- 3.4 Concluding Remarks -- References -- Chapter 4 Small Area Methods and Administrative Data Integration -- 4.1 Introduction -- 4.2 Register-based Small Area Estimation NO 4.2.1 Sampling Error: A Study of Local Area Life Expectancy -- 4.2.2 Measurement Error due to Progressive Administrative Data -- 4.3 Administrative and Survey Data Integration -- 4.3.1 Coverage Error and Finite-population Bias -- 4.3.2 Relevance Error and Benchmarked Synthetic Small Area Estimation -- 4.3.3 Probability Linkage Error -- 4.4 Concluding Remarks -- References -- Part II Impact of Sampling Design, Weighting and Variance Estimation -- Chapter 5 Impact of Sampling Designs in Small Area Estimation with Applications to Poverty Measurement -- 5.1 Introduction NO 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 NO John Wiley and Sons Wiley Online Library: Complete oBooks NO HTTP:URL=https://onlinelibrary.wiley.com/doi/book/10.1002/9781118814963 NO 書誌ID=EB00004465; LK [E Book]https://onlinelibrary.wiley.com/doi/book/10.1002/9781118814963 OL 30