Analysis of poverty data by small area estimation / edited by Monica Pratesi.

Format:

Book

Contributor:

Pratesi, Monica, editor.

Series:

Wiley series in survey methodology.

THEi Wiley ebooks.

Wiley Series in Survey Methodology

THEi Wiley ebooks

Language:

English

Subjects (All):

Poverty--Statistical methods.

Poverty.

Poverty--Econometric models.

Poverty--Measurement.

Income distribution--Econometric models.

Income distribution.

Physical Description:

1 online resource (471 p.)

Edition:

1st ed.

Place of Publication:

West Sussex, England : Wiley, 2016.

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

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.

Contents:

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

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

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

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

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

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

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

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

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

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(θ)

11.A.2 Calculation of g2(θ)

11.A.3 Calculation of g3(θ)

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

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

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

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

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.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781118815007

1118815009

9781118814987

1118814983

OCLC:

935255288