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Analysis of Poverty Data by Small Area Estimation

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A comprehensive guide to implementing SAE methods for poverty studies and poverty mapping There is an increasingly urgent demand f... Weiterlesen
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Beschreibung

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.



Monica Pratesi, Department of Economics and Management, University of Pisa, Italy.
Monica's research field includes small area estimation, inference in elusive populations, nonresponse, design effect in fitting statistical models. Monica is currently involved as researcher and reference person of the DEM-UNIPI in the project EFRAME(European FRAmework for MEasuring progress) funded under the 7th FP (eframeproject.eu/).



Autorentext

Monica Pratesi, Department of Economics and Management, University of Pisa, Italy.
Monica's research field includes small area estimation, inference in elusive populations, nonresponse, design effect in fitting statistical models. Monica is currently involved as researcher and reference person of the DEM-UNIPI in the project EFRAME(European FRAmework for MEasuring progress) funded under the 7th FP (eframeproject.eu/).



Inhalt
Foreword xv

Preface xvii

Acknowledgements xxiii

About the Editor xxv

List of Contributors xxvii

1 Introduction on Measuring Poverty at Local Level Using Small Area Estimation Methods 1
Monica Pratesi and Nicola Salvati

1.1 Introduction 1

1.2 Target Parameters 2

1.2.1 Definition of the Main Poverty Indicators 2

1.2.2 Direct and Indirect Estimate of Poverty Indicators at Small Area Level 3

1.3 Data-related and Estimation-related Problems for the Estimation of Poverty Indicators 5

1.4 Model-assisted and Model-based Methods Used for the Estimation of Poverty Indicators: a Short Review 7

1.4.1 Model-assisted Methods 7

1.4.2 Model-based Methods 12

References 15

Part I DEFINITION OF INDICATORS AND DATA COLLECTION AND INTEGRATION METHODS

2 Regional and Local Poverty Measures 21
Achille Lemmi and Tomasz Panek

2.1 Introduction 21

2.2 Poverty Dilemmas of Definition 22

2.3 Appropriate Indicators of Poverty and Social Exclusion at Regional and Local Levels 23

2.3.1 Adaptation to the Regional Level 23

2.4 Multidimensional Measures of Poverty 25

2.4.1 Multidimensional Fuzzy Approach to Poverty Measurement 25

2.4.2 Fuzzy Monetary Depth Indicators 26

2.5 Co-incidence of Risks of Monetary Poverty and Material Deprivation 30

2.6 Comparative Analysis of Poverty in EU Regions in 2010 31

2.6.1 Data Source 31

2.6.2 Object of Interest 31

2.6.3 Scope and Assumptions of the Empirical Analysis 32

2.6.4 Risk of Monetary Poverty 32

2.6.5 Risk of Material Deprivation 33

2.6.6 Risk of Manifest Poverty 37

2.7 Conclusions 38

References 39

3 Administrative and Survey Data Collection and Integration 41
Alessandra Coli, Paolo Consolini and Marcello D'Orazio

3.1 Introduction 41

3.2 Methods to Integrate Data from Different Data Sources: Objectives and Main Issues 43

3.2.1 Record Linkage 43

3.2.2 Statistical Matching 46

3.3 Administrative and Survey Data Integration: Some Examples of Application in Well-being and Poverty Studies 50

3.3.1 Data Integration for Measuring Disparities in Economic Well-being at the Macro Level 51

3.3.2 Collection and Integration of Data at the Local Level 53

3.4 Concluding Remarks 56

References 57

4 Small Area Methods and Administrative Data Integration 61
Li-Chun Zhang and Caterina Giusti

4.1 Introduction 61

4.2 Register-based Small Area Estimation 63

4.2.1 Sampling Error: A Study of Local Area Life Expectancy 63

4.2.2 Measurement Error due to Progressive Administrative Data 65

4.3 Administrative and Survey Data Integration 68

4.3.1 Coverage Error and Finite-population Bias 68

4.3.2 Relevance Error and Benchmarked Synthetic Small Area Estimation 70

4.3.3 Probability Linkage Error 75

4.4 Concluding Remarks 80

References 81

Part II IMPACT OF SAMPLING DESIGN, WEIGHTING AND VARIANCE ESTIMATION

5 Impact of Sampling Designs in Small Area Estimation with Applications to Poverty Measurement 85
Jan Pablo Burgard, Ralf Münnich and Thomas Zimmermann

5.1 Introduction 85

5.2 Sampling Designs in our Study 87

5.3 Estimation of Poverty Indicators 90

5.3.1 Design-based Approaches 90

5.3.2 Model-based Estimators 92

5.4 Monte Carlo Comparison of Estimation Methods and Designs 96

5.5 Summary and Outlook 105

Acknowledgements 106

References 106

6 Model-assisted Methods for Small Area Estimation of Poverty Indicators 109
Risto Lehtonen and Ari Veijanen

6.1 Introduction 109

6.1.1 General 109

6.1.2 Concepts and Notation 110

&...

Produktinformationen

Titel: Analysis of Poverty Data by Small Area Estimation
Editor:
EAN: 9781118814987
ISBN: 978-1-118-81498-7
Digitaler Kopierschutz: Adobe-DRM
Format: E-Book (pdf)
Herausgeber: Wiley
Genre: Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
Anzahl Seiten: 472
Veröffentlichung: 29.12.2015
Jahr: 2015
Untertitel: Englisch
Dateigrösse: 9.4 MB