Analysis of poverty data by small area estimation

Analysis of poverty data by small area estimation

Detalles Bibliográficos
Otros Autores: Pratesi, Monica, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Chichester, West Sussex, United Kingdom : Wiley 2016.
Colección:Wiley ebooks.
Acceso en línea:Conectar con la versión electrónica
Ver en Universidad de Navarra:https://innopac.unav.es/record=b40609613*spi
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

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