{"product_id":"analysis-of-poverty-data-by-small-area-estimation-isbn-9781118815014","title":"Analysis of Poverty Data by Small Area Estimation","description":"\u003cp\u003e\u003cb\u003eA comprehensive guide to implementing SAE methods for poverty studies and poverty mapping\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThere 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.\u003c\/p\u003e \u003cp\u003eSmall 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eKey features\u003c\/i\u003e:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents a comprehensive review of SAE methods for poverty mapping\u003c\/li\u003e \u003cli\u003eDemonstrates the applications of SAE methods using real-life case studies\u003c\/li\u003e \u003cli\u003eOffers guidance on the use of routines and choice of websites from which to download them\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAnalysis of Poverty Data by Small Area Estimation\u003c\/i\u003e 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.\u003c\/p\u003e Foreword xv \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcknowledgements xxiii\u003c\/p\u003e \u003cp\u003eAbout the Editor xxv\u003c\/p\u003e \u003cp\u003eList of Contributors xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction on Measuring Poverty at Local Level Using Small Area Estimation Methods 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMonica Pratesi and Nicola Salvati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Target Parameters 2\u003c\/p\u003e \u003cp\u003e1.2.1 Definition of the Main Poverty Indicators 2\u003c\/p\u003e \u003cp\u003e1.2.2 Direct and Indirect Estimate of Poverty Indicators at Small Area Level 3\u003c\/p\u003e \u003cp\u003e1.3 Data-related and Estimation-related Problems for the Estimation of Poverty Indicators 5\u003c\/p\u003e \u003cp\u003e1.4 Model-assisted and Model-based Methods Used for the Estimation of Poverty Indicators: a Short Review 7\u003c\/p\u003e \u003cp\u003e1.4.1 Model-assisted Methods 7\u003c\/p\u003e \u003cp\u003e1.4.2 Model-based Methods 12\u003c\/p\u003e \u003cp\u003eReferences 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I DEFINITION OF INDICATORS AND DATA COLLECTION AND INTEGRATION METHODS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Regional and Local Poverty Measures 21\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAchille Lemmi and Tomasz Panek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.2 Poverty – Dilemmas of Definition 22\u003c\/p\u003e \u003cp\u003e2.3 Appropriate Indicators of Poverty and Social Exclusion at Regional and Local Levels 23\u003c\/p\u003e \u003cp\u003e2.3.1 Adaptation to the Regional Level 23\u003c\/p\u003e \u003cp\u003e2.4 Multidimensional Measures of Poverty 25\u003c\/p\u003e \u003cp\u003e2.4.1 Multidimensional Fuzzy Approach to Poverty Measurement 25\u003c\/p\u003e \u003cp\u003e2.4.2 Fuzzy Monetary Depth Indicators 26\u003c\/p\u003e \u003cp\u003e2.5 Co-incidence of Risks of Monetary Poverty and Material Deprivation 30\u003c\/p\u003e \u003cp\u003e2.6 Comparative Analysis of Poverty in EU Regions in 2010 31\u003c\/p\u003e \u003cp\u003e2.6.1 Data Source 31\u003c\/p\u003e \u003cp\u003e2.6.2 Object of Interest 31\u003c\/p\u003e \u003cp\u003e2.6.3 Scope and Assumptions of the Empirical Analysis 32\u003c\/p\u003e \u003cp\u003e2.6.4 Risk of Monetary Poverty 32\u003c\/p\u003e \u003cp\u003e2.6.5 Risk of Material Deprivation 33\u003c\/p\u003e \u003cp\u003e2.6.6 Risk of Manifest Poverty 37\u003c\/p\u003e \u003cp\u003e2.7 Conclusions 38\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Administrative and Survey Data Collection and Integration 41\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAlessandra Coli, Paolo Consolini and Marcello D’Orazio\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 41\u003c\/p\u003e \u003cp\u003e3.2 Methods to Integrate Data from Different Data Sources: Objectives and Main Issues 43\u003c\/p\u003e \u003cp\u003e3.2.1 Record Linkage 43\u003c\/p\u003e \u003cp\u003e3.2.2 Statistical Matching 46\u003c\/p\u003e \u003cp\u003e3.3 Administrative and Survey Data Integration: Some Examples of Application in Well-being and Poverty Studies 50\u003c\/p\u003e \u003cp\u003e3.3.1 Data Integration for Measuring Disparities in Economic Well-being at the Macro Level 51\u003c\/p\u003e \u003cp\u003e3.3.2 Collection and Integration of Data at the Local Level 53\u003c\/p\u003e \u003cp\u003e3.4 Concluding Remarks 56\u003c\/p\u003e \u003cp\u003eReferences 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Small Area Methods and Administrative Data Integration 61\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLi-Chun Zhang and Caterina Giusti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 61\u003c\/p\u003e \u003cp\u003e4.2 Register-based Small Area Estimation 63\u003c\/p\u003e \u003cp\u003e4.2.1 Sampling Error: A Study of Local Area Life Expectancy 63\u003c\/p\u003e \u003cp\u003e4.2.2 Measurement Error due to Progressive Administrative Data 65\u003c\/p\u003e \u003cp\u003e4.3 Administrative and Survey Data Integration 68\u003c\/p\u003e \u003cp\u003e4.3.1 Coverage Error and Finite-population Bias 68\u003c\/p\u003e \u003cp\u003e4.3.2 Relevance Error and Benchmarked Synthetic Small Area Estimation 70\u003c\/p\u003e \u003cp\u003e4.3.3 Probability Linkage Error 75\u003c\/p\u003e \u003cp\u003e4.4 Concluding Remarks 80\u003c\/p\u003e \u003cp\u003eReferences 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II IMPACT OF SAMPLING DESIGN, WEIGHTING AND VARIANCE ESTIMATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Impact of Sampling Designs in Small Area Estimation with Applications to Poverty Measurement 85\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJan Pablo Burgard, Ralf Münnich and Thomas Zimmermann\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 85\u003c\/p\u003e \u003cp\u003e5.2 Sampling Designs in our Study 87\u003c\/p\u003e \u003cp\u003e5.3 Estimation of Poverty Indicators 90\u003c\/p\u003e \u003cp\u003e5.3.1 Design-based Approaches 90\u003c\/p\u003e \u003cp\u003e5.3.2 Model-based Estimators 92\u003c\/p\u003e \u003cp\u003e5.4 Monte Carlo Comparison of Estimation Methods and Designs 96\u003c\/p\u003e \u003cp\u003e5.5 Summary and Outlook 105\u003c\/p\u003e \u003cp\u003eAcknowledgements 106\u003c\/p\u003e \u003cp\u003eReferences 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Model-assisted Methods for Small Area Estimation of Poverty Indicators 109\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRisto Lehtonen and Ari Veijanen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 109\u003c\/p\u003e \u003cp\u003e6.1.1 General 109\u003c\/p\u003e \u003cp\u003e6.1.2 Concepts and Notation 110\u003c\/p\u003e \u003cp\u003e6.2 Design-based Estimation of Gini Index for Domains 111\u003c\/p\u003e \u003cp\u003e6.2.1 Estimators 111\u003c\/p\u003e \u003cp\u003e6.2.2 Simulation Experiments 114\u003c\/p\u003e \u003cp\u003e6.2.3 Empirical Application 116\u003c\/p\u003e \u003cp\u003e6.3 Model-assisted Estimation of At-risk-of Poverty Rate 117\u003c\/p\u003e \u003cp\u003e6.3.1 Assisting Models in GREG and Model Calibration 117\u003c\/p\u003e \u003cp\u003e6.3.2 Generalized Regression Estimation 119\u003c\/p\u003e \u003cp\u003e6.3.3 Model Calibration Estimation 120\u003c\/p\u003e \u003cp\u003e6.3.4 Simulation Experiments 122\u003c\/p\u003e \u003cp\u003e6.3.5 Empirical Example 123\u003c\/p\u003e \u003cp\u003e6.4 Discussion 124\u003c\/p\u003e \u003cp\u003e6.4.1 Empirical Results 124\u003c\/p\u003e \u003cp\u003e6.4.2 Inferential Framework 125\u003c\/p\u003e \u003cp\u003e6.4.3 Data Infrastructure 125\u003c\/p\u003e \u003cp\u003eReferences 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Variance Estimation for Cumulative and Longitudinal Poverty Indicators from Panel Data at Regional Level 129\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGianni Betti, Francesca Gagliardi and Vijay Verma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 129\u003c\/p\u003e \u003cp\u003e7.2 Cumulative vs. Longitudinal Measures of Poverty 130\u003c\/p\u003e \u003cp\u003e7.2.1 Cumulative Measures 130\u003c\/p\u003e \u003cp\u003e7.2.2 Longitudinal Measures 131\u003c\/p\u003e \u003cp\u003e7.3 Principle Methods for Cross-sectional Variance Estimation 131\u003c\/p\u003e \u003cp\u003e7.4 Extension to Cumulation and Longitudinal Measures 133\u003c\/p\u003e \u003cp\u003e7.5 Practical Aspects: Specification of Sample Structure Variables 134\u003c\/p\u003e \u003cp\u003e7.6 Practical Aspects: Design Effects and Correlation 136\u003c\/p\u003e \u003cp\u003e7.6.1 Components of the Design Effect 136\u003c\/p\u003e \u003cp\u003e7.6.2 Estimating the Components of Design Effect 138\u003c\/p\u003e \u003cp\u003e7.6.3 Estimating other Components using Random Grouping of Elements 139\u003c\/p\u003e \u003cp\u003e7.7 Cumulative Measures and Measures of Net Change 140\u003c\/p\u003e \u003cp\u003e7.7.1 Estimation of the Measures 140\u003c\/p\u003e \u003cp\u003e7.7.2 Variance Estimation 141\u003c\/p\u003e \u003cp\u003e7.8 An Application to Three Years’ Averages 141\u003c\/p\u003e \u003cp\u003e7.8.1 Computation Given Limited Information on Sample Structure in EU-SILC 141\u003c\/p\u003e \u003cp\u003e7.8.2 Direct Computation 144\u003c\/p\u003e \u003cp\u003e7.8.3 Empirical Results 145\u003c\/p\u003e \u003cp\u003e7.9 Concluding Remarks 146\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III SMALL AREA ESTIMATION MODELING AND ROBUSTNESS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Models in Small Area Estimation when Covariates are Measured with Error 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSerena Arima, Gauri S. Datta and Brunero Liseo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 151\u003c\/p\u003e \u003cp\u003e8.2 Functional Measurement Error Approach for Area-level Models 153\u003c\/p\u003e \u003cp\u003e8.2.1 Frequentist Method for Functional Measurement Error Models 154\u003c\/p\u003e \u003cp\u003e8.2.2 Bayesian Method for Functional Measurement Error Models 156\u003c\/p\u003e \u003cp\u003e8.3 Small Area Prediction with a Unit-level Model when an Auxiliary Variable is Measured with Error 156\u003c\/p\u003e \u003cp\u003e8.3.1 Functional Measurement Error Approach for Unit-level Models 157\u003c\/p\u003e \u003cp\u003e8.3.2 Structural Measurement Error Approach for Unit-level Models 160\u003c\/p\u003e \u003cp\u003e8.4 Data Analysis 162\u003c\/p\u003e \u003cp\u003e8.4.1 Example 1: Median Income Data 162\u003c\/p\u003e \u003cp\u003e8.4.2 Example 2: SAIPE Data 165\u003c\/p\u003e \u003cp\u003e8.5 Discussion and Possible Extensions 169\u003c\/p\u003e \u003cp\u003eAcknowledgements 169\u003c\/p\u003e \u003cp\u003eDisclaimer 170\u003c\/p\u003e \u003cp\u003eReferences 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Robust Domain Estimation of Income-based Inequality Indicators 171\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNikos Tzavidis and Stefano Marchetti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 171\u003c\/p\u003e \u003cp\u003e9.2 Definition of Income-based Inequality Measures 172\u003c\/p\u003e \u003cp\u003e9.3 Robust Small Area Estimation of Inequality Measures with M-quantile Regression 173\u003c\/p\u003e \u003cp\u003e9.4 Mean Squared Error Estimation 176\u003c\/p\u003e \u003cp\u003e9.5 Empirical Evaluations 176\u003c\/p\u003e \u003cp\u003e9.6 Estimating the Gini Coefficient and the Quintile Share Ratio for Unplanned Domains in Tuscany 180\u003c\/p\u003e \u003cp\u003e9.7 Conclusions 183\u003c\/p\u003e \u003cp\u003eReferences 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Nonparametric Regression Methods for Small Area Estimation 187\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eM. Giovanna Ranalli, F. Jay Breidt and Jean D. Opsomer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 187\u003c\/p\u003e \u003cp\u003e10.2 Nonparametric Methods in Small Area Estimation 188\u003c\/p\u003e \u003cp\u003e10.2.1 Nested Error Nonparametric Unit Level Model Using Penalized Splines 189\u003c\/p\u003e \u003cp\u003e10.2.2 Nested Error Nonparametric Unit Level Model Using Kernel Methods 191\u003c\/p\u003e \u003cp\u003e10.2.3 Generalized Responses 192\u003c\/p\u003e \u003cp\u003e10.2.4 Robust Approaches 192\u003c\/p\u003e \u003cp\u003e10.3 A Comparison for the Estimation of the Household Per-capita Consumption Expenditure in Albania 195\u003c\/p\u003e \u003cp\u003e10.4 Concluding Remarks 202\u003c\/p\u003e \u003cp\u003eReferences 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV SPATIO-TEMPORAL MODELING OF POVERTY\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Area-level Spatio-temporal Small Area Estimation Models 207\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMaría Dolores Esteban, Domingo Morales and Agustín Pérez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 207\u003c\/p\u003e \u003cp\u003e11.2 Extensions of the Fay–Herriot Model 209\u003c\/p\u003e \u003cp\u003e11.3 An Area-level Model with MA(1) Time Correlation 212\u003c\/p\u003e \u003cp\u003e11.4 EBLUP and MSE 214\u003c\/p\u003e \u003cp\u003e11.5 EBLUP of Poverty Proportions 215\u003c\/p\u003e \u003cp\u003e11.6 Simulations 216\u003c\/p\u003e \u003cp\u003e11.6.1 Simulation 1 216\u003c\/p\u003e \u003cp\u003e11.6.2 Simulation 2 217\u003c\/p\u003e \u003cp\u003e11.7 R Codes 220\u003c\/p\u003e \u003cp\u003e11.8 Concluding Remarks 220\u003c\/p\u003e \u003cp\u003eAppendix 11.A: MSE Components 221\u003c\/p\u003e \u003cp\u003e11.A.1 Calculation of g1(𝜽) 221\u003c\/p\u003e \u003cp\u003e11.A.2 Calculation of g2(𝜽) 221\u003c\/p\u003e \u003cp\u003e11.A.3 Calculation of g3(𝜽) 222\u003c\/p\u003e \u003cp\u003eAcknowledgements 224\u003c\/p\u003e \u003cp\u003eReferences 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Unit Level Spatio-temporal Models 227\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMaria Chiara Pagliarella and Renato Salvatore\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Unit Level Models 230\u003c\/p\u003e \u003cp\u003e12.2 Spatio-temporal Time-varying Effects Models 232\u003c\/p\u003e \u003cp\u003e12.3 State Space Models with Spatial Structure 234\u003c\/p\u003e \u003cp\u003e12.4 The Italian EU-SILC Data: an Application with the Spatio-temporal Unit Level Models 236\u003c\/p\u003e \u003cp\u003e12.5 Concluding Remarks 239\u003c\/p\u003e \u003cp\u003eAppendix 12.A: Restricted Maximum Likelihood Estimation 240\u003c\/p\u003e \u003cp\u003eAppendix 12.B: Mean Squared Error Estimation of the Unit Level State Space Model 241\u003c\/p\u003e \u003cp\u003eReferences 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Spatial Information and Geoadditive Small Area Models 245\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChiara Bocci and Alessandra Petrucci\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 245\u003c\/p\u003e \u003cp\u003e13.2 Geoadditive Models 247\u003c\/p\u003e \u003cp\u003e13.3 Geoadditive Small Area Model for Skewed Data 248\u003c\/p\u003e \u003cp\u003e13.4 Small Area Mean Estimators 250\u003c\/p\u003e \u003cp\u003e13.5 Estimation of the Household Per-capita Consumption Expenditure in Albania 251\u003c\/p\u003e \u003cp\u003e13.5.1 Data 251\u003c\/p\u003e \u003cp\u003e13.5.2 Results 253\u003c\/p\u003e \u003cp\u003e13.6 Concluding Remarks and Open Questions 258\u003c\/p\u003e \u003cp\u003eAcknowledgement 259\u003c\/p\u003e \u003cp\u003eReferences 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V SMALL AREA ESTIMATION OF THE DISTRIBUTION FUNCTION OF INCOME AND INEQUALITIES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Model-based Direct Estimation of a Small Area Distribution Function 263\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHukum Chandra, Nicola Salvati and Ray Chambers\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 263\u003c\/p\u003e \u003cp\u003e14.2 Estimation of the Small Area Distribution Function 264\u003c\/p\u003e \u003cp\u003e14.3 Model-based Direct Estimator for the Estimation of the Distribution Function of Equivalized Income in the Toscana, Lombardia and Campania Provinces of Italy 268\u003c\/p\u003e \u003cp\u003e14.4 Final Remarks 275\u003c\/p\u003e \u003cp\u003eReferences 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Small Area Estimation for Lognormal Data 279\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eEmily Berg, Hukum Chandra and Ray Chambers\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 279\u003c\/p\u003e \u003cp\u003e15.2 Literature on Small Area Estimation for Skewed Data 280\u003c\/p\u003e \u003cp\u003e15.3 Small Area Predictors for a Unit-Level Lognormal Model 282\u003c\/p\u003e \u003cp\u003e15.3.1 The Linear Unit-Level Mixed Model 282\u003c\/p\u003e \u003cp\u003e15.3.2 A Synthetic Estimator 283\u003c\/p\u003e \u003cp\u003e15.3.3 A Model-Based Direct Estimator 285\u003c\/p\u003e \u003cp\u003e15.3.4 An Empirical Bayes Predictor 286\u003c\/p\u003e \u003cp\u003e15.4 Simulations 287\u003c\/p\u003e \u003cp\u003e15.4.1 Comparison of Synthetic, TrMBDE, and EB Predictors 287\u003c\/p\u003e \u003cp\u003e15.4.2 Bias and Robustness of the EB Predictor 291\u003c\/p\u003e \u003cp\u003e15.4.3 Comparison of Lognormal and Gamma Distributions 291\u003c\/p\u003e \u003cp\u003e15.5 Concluding Remarks 294\u003c\/p\u003e \u003cp\u003eAppendix 15.A: Mean Squared Error Estimation for the Empirical Best Predictor 295\u003c\/p\u003e \u003cp\u003eReferences 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Bayesian Beta Regression Models for the Estimation of Poverty and Inequality Parameters in Small Areas 299\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eEnrico Fabrizi, Maria Rosaria Ferrante and Carlo Trivisano\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 299\u003c\/p\u003e \u003cp\u003e16.2 Direct Estimation 300\u003c\/p\u003e \u003cp\u003e16.3 Small Area Estimation of the At-risk-of-poverty Rate 302\u003c\/p\u003e \u003cp\u003e16.3.1 The Model 302\u003c\/p\u003e \u003cp\u003e16.3.2 Data Analysis 304\u003c\/p\u003e \u003cp\u003e16.4 Small Area Estimation of the Material Deprivation Rates 305\u003c\/p\u003e \u003cp\u003e16.4.1 The Model 305\u003c\/p\u003e \u003cp\u003e16.4.2 Data Analysis 306\u003c\/p\u003e \u003cp\u003e16.5 Joint Estimation of the At-risk-of-poverty Rate and the Gini Coefficient 308\u003c\/p\u003e \u003cp\u003e16.5.1 The Models 308\u003c\/p\u003e \u003cp\u003e16.5.2 Data Analysis 310\u003c\/p\u003e \u003cp\u003e16.6 A Short Description of Markov Chain Monte Carlo Algorithms and R Software Codes 312\u003c\/p\u003e \u003cp\u003e16.7 Concluding Remarks 312\u003c\/p\u003e \u003cp\u003eReferences 313\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Empirical Bayes and Hierarchical Bayes Estimation of Poverty Measures for Small Areas 315\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJohn N. K. Rao and Isabel Molina\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 315\u003c\/p\u003e \u003cp\u003e17.2 Poverty Measures 316\u003c\/p\u003e \u003cp\u003e17.3 Fay–Herriot Area Level Model 317\u003c\/p\u003e \u003cp\u003e17.4 Unit Level Nested Error Linear Regression Model 319\u003c\/p\u003e \u003cp\u003e17.4.1 ELL\/World Bank Method 319\u003c\/p\u003e \u003cp\u003e17.4.2 Empirical Bayes Method 321\u003c\/p\u003e \u003cp\u003e17.4.3 Hierarchical Bayes Method 322\u003c\/p\u003e \u003cp\u003e17.5 Application 323\u003c\/p\u003e \u003cp\u003e17.6 Concluding Remarks 324\u003c\/p\u003e \u003cp\u003eReferences 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI DATA ANALYSIS AND APPLICATIONS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Small Area Estimation Using Both Survey and Census Unit Record Data 327\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eStephen J. Haslett\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 327\u003c\/p\u003e \u003cp\u003e18.2 The ELL Implementation Process and Methodology 329\u003c\/p\u003e \u003cp\u003e18.2.1 ELL: Implementation Process 329\u003c\/p\u003e \u003cp\u003e18.2.2 ELL Methodology: Survey Regression, Contextual Effects, Clustering, and the Bootstrap 331\u003c\/p\u003e \u003cp\u003e18.2.3 Fitting Survey-based Models 334\u003c\/p\u003e \u003cp\u003e18.2.4 Residuals and the Bootstrap 335\u003c\/p\u003e \u003cp\u003e18.2.5 ELL: Linkages to Other Related Methods 338\u003c\/p\u003e \u003cp\u003e18.3 ELL – Advantages, Criticisms and Disadvantages 339\u003c\/p\u003e \u003cp\u003e18.4 Conclusions 344\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 An Overview of the U.S. Census Bureau’s Small Area Income and Poverty Estimates Program 349\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eWilliam R. Bell, Wesley W. Basel and Jerry J. Maples\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 349\u003c\/p\u003e \u003cp\u003e19.2 U.S. Poverty Measure and Poverty Data Sources 351\u003c\/p\u003e \u003cp\u003e19.2.1 Poverty Measure and Survey Data Sources 351\u003c\/p\u003e \u003cp\u003e19.2.2 Administrative Data Sources Used for Covariate Information 354\u003c\/p\u003e \u003cp\u003e19.3 SAIPE Poverty Models and Estimation Procedures 356\u003c\/p\u003e \u003cp\u003e19.3.1 State Poverty Models 357\u003c\/p\u003e \u003cp\u003e19.3.2 County Poverty Models 363\u003c\/p\u003e \u003cp\u003e19.3.3 School District Poverty Estimation 368\u003c\/p\u003e \u003cp\u003e19.3.4 Major Changes Made in SAIPE Models and Estimation Procedures 372\u003c\/p\u003e \u003cp\u003e19.4 Current Challenges and Recent SAIPE Research 374\u003c\/p\u003e \u003cp\u003e19.5 Conclusions 375\u003c\/p\u003e \u003cp\u003eReferences 376\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Poverty Mapping for the Chilean Comunas 379\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eCarolina Casas-Cordero Valencia, Jenny Encina and Partha Lahiri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 379\u003c\/p\u003e \u003cp\u003e20.2 Chilean Poverty Measures and Casen 381\u003c\/p\u003e \u003cp\u003e20.2.1 The Poverty Measure Used in Chile 381\u003c\/p\u003e \u003cp\u003e20.2.2 The Casen Survey 382\u003c\/p\u003e \u003cp\u003e20.3 Data Preparation 383\u003c\/p\u003e \u003cp\u003e20.3.1 Comuna Level Data Derived from Casen 2009 383\u003c\/p\u003e \u003cp\u003e20.3.2 Comuna Level Administrative Data 387\u003c\/p\u003e \u003cp\u003e20.4 Description of the Small Area Estimation Method Implemented in Chile 391\u003c\/p\u003e \u003cp\u003e20.4.1 Modeling 394\u003c\/p\u003e \u003cp\u003e20.4.2 Estimation of A and 𝛽 395\u003c\/p\u003e \u003cp\u003e20.4.3 Empirical Bayes Estimator of 𝜃i 395\u003c\/p\u003e \u003cp\u003e20.4.4 Limited Translation Empirical Bayes Estimator of 𝜃i 395\u003c\/p\u003e \u003cp\u003e20.4.5 Back-transformation and raking 396\u003c\/p\u003e \u003cp\u003e20.4.6 Confidence intervals for the poverty rates 396\u003c\/p\u003e \u003cp\u003e20.5 Data Analysis 397\u003c\/p\u003e \u003cp\u003e20.6 Discussion 399\u003c\/p\u003e \u003cp\u003eAcknowledgements 401\u003c\/p\u003e \u003cp\u003eReferences 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Appendix on Software and Codes Used in the Book 405\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAntonella D’Agostino, Francesca Gagliardi and Laura Neri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 405\u003c\/p\u003e \u003cp\u003e21.2 R and SAS Software: a Brief Note 406\u003c\/p\u003e \u003cp\u003e21.3 Getting Started: EU-SILC Data 409\u003c\/p\u003e \u003cp\u003e21.4 A Quick Guide to the Scripts 410\u003c\/p\u003e \u003cp\u003e21.4.1 Basics of the Scripts 410\u003c\/p\u003e \u003cp\u003e21.4.2 A Quick guide to Chapter 5 (Impact of Sampling Designs in Small Area Estimation with Applications to Poverty Measurement) 412\u003c\/p\u003e \u003cp\u003e21.4.3 A Quick guide to Chapter 6 (Model-assisted Methods for Small Area Estimation of Poverty Indicators) 412\u003c\/p\u003e \u003cp\u003e21.4.4 A Quick Guide to Chapter 7 (Variance Estimation for Cumulative and Longitudinal Poverty Indicators from Panel Data at Regional Level) 414\u003c\/p\u003e \u003cp\u003e21.4.5 A Quick Guide to Chapter 8 (Models in Small Area Estimation when Covariates are Measured with Error) 415\u003c\/p\u003e \u003cp\u003e21.4.6 A Quick Guide to Chapter 9 (Robust Domain Estimation of Income-based Inequality Indicators) 416\u003c\/p\u003e \u003cp\u003e21.4.7 A Quick Guide to Chapter 10 (Nonparametric Regression Methods for Small Area Estimation) 417\u003c\/p\u003e \u003cp\u003e21.4.8 A Quick Guide to Chapter 11 (Area-level Spatio-temporal Small Area Estimation Models) 418\u003c\/p\u003e \u003cp\u003e21.4.9 A Quick Guide to Chapter 12 (Unit Level Spatio-temporal Models) 419\u003c\/p\u003e \u003cp\u003e21.4.10 A Quick Guide to Chapter 13 (Spatial Information and Geoadditive Small Area Models) 420\u003c\/p\u003e \u003cp\u003e21.4.11 A Quick guide to Chapter 14 (Model-based Direct Estimation of a Small Area Distribution Function) 422\u003c\/p\u003e \u003cp\u003e21.4.12 A Quick Guide to Chapter 16 (Bayesian Beta Regression Models for the Estimation of Poverty and Inequality Parameters in Small Areas) 423\u003c\/p\u003e \u003cp\u003e21.4.13 A Quick Guide to Chapter 17 (Empirical Bayes and Hierarchical Bayes Estimation of Poverty Measures for Small Areas) 424\u003c\/p\u003e \u003cp\u003e21.4.14 A Quick Guide to Chapter 18 - (Small Area Estimation Using Both Survey and Census Unit Record Data: Links, Alternatives, and the\u003c\/p\u003e \u003cp\u003eCentral Roles of Regression and Contextual Variables) 425\u003c\/p\u003e \u003cp\u003eReferences 426\u003c\/p\u003e \u003cp\u003eAuthor Index 427\u003c\/p\u003e \u003cp\u003eSubject Index 431\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMonica Pratesi, Department of Economics and Management, University of Pisa, Italy\u003c\/b\u003e.\u003cbr\u003eMonica'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\/).\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eA comprehensive guide to implementing SAE methods for poverty studies and poverty mapping\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThere 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.\u003c\/p\u003e \u003cp\u003eSmall 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eKey features\u003c\/i\u003e:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents a comprehensive review of SAE methods for poverty mapping\u003c\/li\u003e \u003cli\u003eDemonstrates the applications of SAE methods using real-life case studies\u003c\/li\u003e \u003cli\u003eOffers guidance on the use of routines and choice of websites from which to download them\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAnalysis of Poverty Data by Small Area Estimation\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988734492901,"sku":"NP9781118815014","price":117.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118815014.jpg?v=1761781380","url":"https:\/\/k12savings.com\/es\/products\/analysis-of-poverty-data-by-small-area-estimation-isbn-9781118815014","provider":"K12savings","version":"1.0","type":"link"}