Analysis of Poverty Data by Small Area Estimation

 
 
John Wiley & Sons Inc (Verlag)
  • erschienen am 29. Dezember 2015
  • |
  • 472 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-118-81498-7 (ISBN)
 
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:
* Presents a comprehensive review of SAE methods for poverty mapping
* Demonstrates the applications of SAE methods using real-life case studies
* Offers guidance on the use of routines and choice of websites from which to download them
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.
weitere Ausgaben werden ermittelt
  • 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
  • 5.2 Sampling Designs in our Study
  • 5.3 Estimation of Poverty Indicators
  • 5.3.1 Design-based Approaches
  • 5.3.2 Model-based Estimators
  • 5.4 Monte Carlo Comparison of Estimation Methods and Designs
  • 5.5 Summary and Outlook
  • Acknowledgements
  • References
  • Chapter 6 Model-assisted Methods for Small Area Estimation of Poverty Indicators
  • 6.1 Introduction
  • 6.1.1 General
  • 6.1.2 Concepts and Notation
  • 6.2 Design-based Estimation of Gini Index for Domains
  • 6.2.1 Estimators
  • 6.2.2 Simulation Experiments
  • 6.2.3 Empirical Application
  • 6.3 Model-assisted Estimation of At-risk-of Poverty Rate
  • 6.3.1 Assisting Models in GREG and Model Calibration
  • 6.3.2 Generalized Regression Estimation
  • 6.3.3 Model Calibration Estimation
  • 6.3.4 Simulation Experiments
  • 6.3.5 Empirical Example
  • 6.4 Discussion
  • 6.4.1 Empirical Results
  • 6.4.2 Inferential Framework
  • 6.4.3 Data Infrastructure
  • References
  • Chapter 7 Variance Estimation for Cumulative and Longitudinal Poverty Indicators from Panel Data at Regional Level
  • 7.1 Introduction
  • 7.2 Cumulative vs. Longitudinal Measures of Poverty
  • 7.2.1 Cumulative Measures
  • 7.2.2 Longitudinal Measures
  • 7.3 Principle Methods for Cross-sectional Variance Estimation
  • 7.4 Extension to Cumulation and Longitudinal Measures
  • 7.5 Practical Aspects: Specification of Sample Structure Variables
  • 7.6 Practical Aspects: Design Effects and Correlation
  • 7.6.1 Components of the Design Effect
  • 7.6.2 Estimating the Components of Design Effect
  • 7.6.3 Estimating other Components using Random Grouping of Elements
  • 7.7 Cumulative Measures and Measures of Net Change
  • 7.7.1 Estimation of the Measures
  • 7.7.2 Variance Estimation
  • 7.8 An Application to Three Years' Averages
  • 7.8.1 Computation Given Limited Information on Sample Structure in EU-SILC
  • 7.8.2 Direct Computation
  • 7.8.3 Empirical Results
  • 7.9 Concluding Remarks
  • References
  • Part III Small Area Estimation Modeling and Robustness
  • Chapter 8 Models in Small Area Estimation when Covariates are Measured with Error
  • 8.1 Introduction
  • 8.2 Functional Measurement Error Approach for Area-level Models
  • 8.2.1 Frequentist Method for Functional Measurement Error Models
  • 8.2.2 Bayesian Method for Functional Measurement Error Models
  • 8.3 Small Area Prediction with a Unit-level Model when an Auxiliary Variable is Measured with Error
  • 8.3.1 Functional Measurement Error Approach for Unit-level Models
  • 8.3.2 Structural Measurement Error Approach for Unit-level Models
  • 8.4 Data Analysis
  • 8.4.1 Example 1: Median Income Data
  • 8.4.2 Example 2: SAIPE Data
  • 8.5 Discussion and Possible Extensions
  • Acknowledgements
  • Disclaimer
  • References
  • Chapter 9 Robust Domain Estimation of Income-based Inequality Indicators
  • 9.1 Introduction
  • 9.2 Definition of Income-based Inequality Measures
  • 9.3 Robust Small Area Estimation of Inequality Measures with M-quantile Regression
  • 9.4 Mean Squared Error Estimation
  • 9.5 Empirical Evaluations
  • 9.6 Estimating the Gini Coefficient and the Quintile Share Ratio for Unplanned Domains in Tuscany
  • 9.7 Conclusions
  • References
  • Chapter 10 Nonparametric Regression Methods for Small Area Estimation
  • 10.1 Introduction
  • 10.2 Nonparametric Methods in Small Area Estimation
  • 10.2.1 Nested Error Nonparametric Unit Level Model Using Penalized Splines
  • 10.2.2 Nested Error Nonparametric Unit Level Model Using Kernel Methods
  • 10.2.3 Generalized Responses
  • 10.2.4 Robust Approaches
  • 10.3 A Comparison for the Estimation of the Household Per-capita Consumption Expenditure in Albania
  • 10.4 Concluding Remarks
  • References
  • Part IV Spatio-Temporal Modeling of Poverty
  • Chapter 11 Area-level Spatio-temporal Small Area Estimation Models
  • 11.1 Introduction
  • 11.2 Extensions of the Fay-Herriot Model
  • 11.3 An Area-level Model with MA(1) Time Correlation
  • 11.4 EBLUP and MSE
  • 11.5 EBLUP of Poverty Proportions
  • 11.6 Simulations
  • 11.6.1 Simulation 1
  • 11.6.2 Simulation 2
  • 11.7 R Codes
  • 11.8 Concluding Remarks
  • Appendix 11.A: MSE Components
  • 11.A.1 Calculation of g1(?)
  • 11.A.2 Calculation of g2(?)
  • 11.A.3 Calculation of g3(?)
  • Acknowledgements
  • References
  • Chapter 12 Unit Level Spatio-temporal Models
  • 12.1 Unit Level Models
  • 12.2 Spatio-temporal Time-varying Effects Models
  • 12.3 State Space Models with Spatial Structure
  • 12.4 The Italian EU-SILC Data: an Application with the Spatio-temporal Unit Level Models
  • 12.5 Concluding Remarks
  • Appendix 12.A: Restricted Maximum Likelihood Estimation
  • Appendix 12.B: Mean Squared Error Estimation of the Unit Level State Space Model
  • References
  • Chapter 13 Spatial Information and Geoadditive Small Area Models
  • 13.1 Introduction
  • 13.2 Geoadditive Models
  • 13.3 Geoadditive Small Area Model for Skewed Data
  • 13.4 Small Area Mean Estimators
  • 13.5 Estimation of the Household Per-capita Consumption Expenditure in Albania
  • 13.5.1 Data
  • 13.5.2 Results
  • 13.6 Concluding Remarks and Open Questions
  • Acknowledgement
  • References
  • Part V Small Area Estimation of the Distribution Function of Income and Inequalities
  • Chapter 14 Model-based Direct Estimation of a Small Area Distribution Function
  • 14.1 Introduction
  • 14.2 Estimation of the Small Area Distribution Function
  • 14.3 Model-based Direct Estimator for the Estimation of the Distribution Function of Equivalized Income in the Toscana, Lombardia and Campania Provinces of Italy
  • 14.4 Final Remarks
  • References
  • Chapter 15 Small Area Estimation for Lognormal Data
  • 15.1 Introduction
  • 15.2 Literature on Small Area Estimation for Skewed Data
  • 15.3 Small Area Predictors for a Unit-Level Lognormal Model
  • 15.3.1 The Linear Unit-Level Mixed Model
  • 15.3.2 A Synthetic Estimator
  • 15.3.3 A Model-Based Direct Estimator
  • 15.3.4 An Empirical Bayes Predictor
  • 15.4 Simulations
  • 15.4.1 Comparison of Synthetic, TrMBDE, and EB Predictors
  • 15.4.2 Bias and Robustness of the EB Predictor
  • 15.4.3 Comparison of Lognormal and Gamma Distributions
  • 15.5 Concluding Remarks
  • Appendix 15.A: Mean Squared Error Estimation for the Empirical Best Predictor
  • References
  • Chapter 16 Bayesian Beta Regression Models for the Estimation of Poverty and Inequality Parameters in Small Areas
  • 16.1 Introduction
  • 16.2 Direct Estimation
  • 16.3 Small Area Estimation of the At-risk-of-poverty Rate
  • 16.3.1 The Model
  • 16.3.2 Data Analysis
  • 16.4 Small Area Estimation of the Material Deprivation Rates
  • 16.4.1 The Model
  • 16.4.2 Data Analysis
  • 16.5 Joint Estimation of the At-risk-of-poverty Rate and the Gini Coefficient
  • 16.5.1 The Models
  • 16.5.2 Data Analysis
  • 16.6 A Short Description of Markov Chain Monte Carlo Algorithms and R Software Codes
  • 16.7 Concluding Remarks
  • References
  • Chapter 17 Empirical Bayes and Hierarchical Bayes Estimation of Poverty Measures for Small Areas
  • 17.1 Introduction
  • 17.2 Poverty Measures
  • 17.3 Fay-Herriot Area Level Model
  • 17.4 Unit Level Nested Error Linear Regression Model
  • 17.4.1 ELL/World Bank Method
  • 17.4.2 Empirical Bayes Method
  • 17.4.3 Hierarchical Bayes Method
  • 17.5 Application
  • 17.6 Concluding Remarks
  • References
  • Part VI Data Analysis and Applications
  • Chapter 18 Small Area Estimation Using Both Survey and Census Unit Record Data
  • 18.1 Introduction
  • 18.2 The ELL Implementation Process and Methodology
  • 18.2.1 ELL: Implementation Process
  • 18.2.2 ELL Methodology: Survey Regression, Contextual Effects, Clustering, and the Bootstrap
  • 18.2.3 Fitting Survey-based Models
  • 18.2.4 Residuals and the Bootstrap
  • 18.2.5 ELL: Linkages to Other Related Methods
  • 18.3 ELL - Advantages, Criticisms and Disadvantages
  • 18.4 Conclusions
  • References
  • Chapter 19 An Overview of the U.S. Census Bureau's Small Area Income and Poverty Estimates Program
  • 19.1 Introduction
  • 19.2 U.S. Poverty Measure and Poverty Data Sources
  • 19.2.1 Poverty Measure and Survey Data Sources
  • 19.2.2 Administrative Data Sources Used for Covariate Information
  • 19.3 SAIPE Poverty Models and Estimation Procedures
  • 19.3.1 State Poverty Models
  • 19.3.2 County Poverty Models
  • 19.3.3 School District Poverty Estimation
  • 19.3.4 Major Changes Made in SAIPE Models and Estimation Procedures
  • 19.4 Current Challenges and Recent SAIPE Research
  • 19.5 Conclusions
  • References
  • Chapter 20 Poverty Mapping for the Chilean Comunas
  • 20.1 Introduction
  • 20.2 Chilean Poverty Measures and Casen
  • 20.2.1 The Poverty Measure Used in Chile
  • 20.2.2 The Casen Survey
  • 20.3 Data Preparation
  • 20.3.1 Comuna Level Data Derived from Casen 2009
  • 20.3.2 Comuna Level Administrative Data
  • 20.4 Description of the Small Area Estimation Method Implemented in Chile
  • 20.4.1 Modeling
  • 20.4.2 Estimation of A and ß
  • 20.4.3 Empirical Bayes Estimator of ?i
  • 20.4.4 Limited Translation Empirical Bayes Estimator of ?i
  • 20.4.5 Back-transformation and raking
  • 20.4.6 Confidence intervals for the poverty rates
  • 20.5 Data Analysis
  • 20.6 Discussion
  • Acknowledgements
  • References
  • Chapter 21 Appendix on Software and Codes Used in the Book
  • 21.1 Introduction
  • 21.2 R and SAS Software: a Brief Note
  • 21.3 Getting Started: EU-SILC Data
  • 21.4 A Quick Guide to the Scripts
  • 21.4.1 Basics of the Scripts
  • 21.4.2 A Quick guide to Chapter 5 (Impact of Sampling Designs in Small Area Estimation with Applications to Poverty Measurement)
  • 21.4.3 A Quick guide to Chapter 6 (Model-assisted Methods for Small Area Estimation of Poverty Indicators)
  • 21.4.4 A Quick Guide to Chapter 7 (Variance Estimation for Cumulative and Longitudinal Poverty Indicators from Panel Data at Regional Level)
  • 21.4.5 A Quick Guide to Chapter 8 (Models in Small Area Estimation when Covariates are Measured with Error)
  • 21.4.6 A Quick Guide to Chapter 9 (Robust Domain Estimation of Income-based Inequality Indicators)
  • 21.4.7 A Quick Guide to Chapter 10 (Nonparametric Regression Methods for Small Area Estimation)
  • 21.4.8 A Quick Guide to Chapter 11 (Area-level Spatio-temporal Small Area Estimation Models)
  • 21.4.9 A Quick Guide to Chapter 12 (Unit Level Spatio-temporal Models)
  • 21.4.10 A Quick Guide to Chapter 13 (Spatial Information and Geoadditive Small Area Models)
  • 21.4.11 A Quick guide to Chapter 14 (Model-based Direct Estimation of a Small Area Distribution Function)
  • 21.4.12 A Quick Guide to Chapter 16 (Bayesian Beta Regression Models for the Estimation of Poverty and Inequality Parameters in Small Areas)
  • 21.4.13 A Quick Guide to Chapter 17 (Empirical Bayes and Hierarchical Bayes Estimation of Poverty Measures for Small Areas)
  • 21.4.14 A Quick Guide to Chapter 18 - (Small Area Estimation Using Both Survey and Census Unit Record Data: Links, Alternatives, and the Central Roles of Regression and Contextual Variables)
  • References
  • Author Index
  • Subject Index
  • Series Page
  • EULA

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