Table Of Content(cid:2)
Analysis of Poverty Data by
Small Area Estimation
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WILEYSERIESINSURVEYMETHODOLOGY
EstablishedinpartbyWalterA.ShewhartandSamuelS.Wilks
Editors:MickP.Couper,GrahamKalton,LarsLyberg,J.N.K.Rao,NorbertSchwarz,
ChristopherSkinner
EditorEmeritus:RobertM.Groves
Acompletelistofthetitlesinthisseriesappearsattheendofthisvolume.
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Analysis of Poverty Data by
Small Area Estimation
Editedby
MonicaPratesi
UniversityofPisa,Italy
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Thiseditionfirstpublished2016.
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ISBN:9781118815014
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1 2016
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Contents
Foreword xv
Preface xvii
Acknowledgements xxiii
AbouttheEditor xxv
ListofContributors xxvii
1 IntroductiononMeasuringPovertyatLocalLevelUsingSmallArea
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EstimationMethods 1
MonicaPratesiandNicolaSalvati
1.1 Introduction 1
1.2 TargetParameters 2
1.2.1 Definitio oftheMainPovertyIndicators 2
1.2.2 DirectandIndirectEstimateofPovertyIndicatorsatSmallAreaLevel 3
1.3 Data-relatedandEstimation-relatedProblemsfortheEstimationofPoverty
Indicators 5
1.4 Model-assistedandModel-basedMethodsUsedfortheEstimationofPoverty
Indicators:aShortReview 7
1.4.1 Model-assistedMethods 7
1.4.2 Model-basedMethods 12
References 15
PartI DEFINITIONOFINDICATORSANDDATACOLLECTIONAND
INTEGRATIONMETHODS
2 RegionalandLocalPovertyMeasures 21
AchilleLemmiandTomaszPanek
2.1 Introduction 21
2.2 Poverty – DilemmasofDefinition 22
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vi Contents
2.3 AppropriateIndicatorsofPovertyandSocialExclusionatRegionaland
LocalLevels 23
2.3.1 AdaptationtotheRegionalLevel 23
2.4 MultidimensionalMeasuresofPoverty 25
2.4.1 MultidimensionalFuzzyApproachtoPovertyMeasurement 25
2.4.2 FuzzyMonetaryDepthIndicators 26
2.5 Co-incidenceofRisksofMonetaryPovertyandMaterialDeprivation 30
2.6 ComparativeAnalysisofPovertyinEURegionsin2010 31
2.6.1 DataSource 31
2.6.2 ObjectofInterest 31
2.6.3 ScopeandAssumptionsoftheEmpiricalAnalysis 32
2.6.4 RiskofMonetaryPoverty 32
2.6.5 RiskofMaterialDeprivation 33
2.6.6 RiskofManifestPoverty 37
2.7 Conclusions 38
References 39
3 AdministrativeandSurveyDataCollectionandIntegration 41
AlessandraColi,PaoloConsoliniandMarcelloD’Orazio
3.1 Introduction 41
3.2 MethodstoIntegrateDatafromDifferentDataSources:Objectivesand
MainIssues 43
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3.2.1 RecordLinkage 43
3.2.2 StatisticalMatching 46
3.3 AdministrativeandSurveyDataIntegration:SomeExamplesofApplicationin
Well-beingandPovertyStudies 50
3.3.1 DataIntegrationforMeasuringDisparitiesinEconomicWell-being
attheMacroLevel 51
3.3.2 CollectionandIntegrationofDataattheLocalLevel 53
3.4 ConcludingRemarks 56
References 57
4 SmallAreaMethodsandAdministrativeDataIntegration 61
Li-ChunZhangandCaterinaGiusti
4.1 Introduction 61
4.2 Register-basedSmallAreaEstimation 63
4.2.1 SamplingError:AStudyofLocalAreaLifeExpectancy 63
4.2.2 MeasurementErrorduetoProgressiveAdministrativeData 65
4.3 AdministrativeandSurveyDataIntegration 68
4.3.1 CoverageErrorandFinite-populationBias 68
4.3.2 RelevanceErrorandBenchmarkedSyntheticSmallAreaEstimation 70
4.3.3 ProbabilityLinkageError 75
4.4 ConcludingRemarks 80
References 81
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Contents vii
PartII IMPACTOFSAMPLINGDESIGN,WEIGHTINGANDVARIANCE
ESTIMATION
5 ImpactofSamplingDesignsinSmallAreaEstimationwithApplications
toPovertyMeasurement 85
JanPabloBurgard,RalfMünnichandThomasZimmermann
5.1 Introduction 85
5.2 SamplingDesignsinourStudy 87
5.3 EstimationofPovertyIndicators 90
5.3.1 Design-basedApproaches 90
5.3.2 Model-basedEstimators 92
5.4 MonteCarloComparisonofEstimationMethodsandDesigns 96
5.5 SummaryandOutlook 105
Acknowledgements 106
References 106
6 Model-assistedMethodsforSmallAreaEstimationofPovertyIndicators 109
RistoLehtonenandAriVeijanen
6.1 Introduction 109
6.1.1 General 109
6.1.2 ConceptsandNotation 110
6.2 Design-basedEstimationofGiniIndexforDomains 111
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6.2.2 SimulationExperiments 114
6.2.3 EmpiricalApplication 116
6.3 Model-assistedEstimationofAt-risk-ofPovertyRate 117
6.3.1 AssistingModelsinGREGandModelCalibration 117
6.3.2 GeneralizedRegressionEstimation 119
6.3.3 ModelCalibrationEstimation 120
6.3.4 SimulationExperiments 122
6.3.5 EmpiricalExample 123
6.4 Discussion 124
6.4.1 EmpiricalResults 124
6.4.2 InferentialFramework 125
6.4.3 DataInfrastructure 125
References 126
7 VarianceEstimationforCumulativeandLongitudinalPovertyIndicators
fromPanelDataatRegionalLevel 129
GianniBetti,FrancescaGagliardiandVijayVerma
7.1 Introduction 129
7.2 Cumulativevs.LongitudinalMeasuresofPoverty 130
7.2.1 CumulativeMeasures 130
7.2.2 LongitudinalMeasures 131
7.3 PrincipleMethodsforCross-sectionalVarianceEstimation 131
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viii Contents
7.4 ExtensiontoCumulationandLongitudinalMeasures 133
7.5 PracticalAspects:SpecificationofSampleStructureVariables 134
7.6 PracticalAspects:DesignEffectsandCorrelation 136
7.6.1 ComponentsoftheDesignEffect 136
7.6.2 EstimatingtheComponentsofDesignEffect 138
7.6.3 EstimatingotherComponentsusingRandomGroupingofElements 139
7.7 CumulativeMeasuresandMeasuresofNetChange 140
7.7.1 EstimationoftheMeasures 140
7.7.2 VarianceEstimation 141
7.8 AnApplicationtoThreeYears’Averages 141
7.8.1 ComputationGivenLimitedInformationonSampleStructurein
EU-SILC 141
7.8.2 DirectComputation 144
7.8.3 EmpiricalResults 145
7.9 ConcludingRemarks 146
References 147
PartIII SMALLAREAESTIMATIONMODELINGANDROBUSTNESS
8 ModelsinSmallAreaEstimationwhenCovariatesareMeasured
withError 151
SerenaArima,GauriS.DattaandBruneroLiseo
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8.1 Introduction 151
8.2 FunctionalMeasurementErrorApproachforArea-levelModels 153
8.2.1 FrequentistMethodforFunctionalMeasurementErrorModels 154
8.2.2 BayesianMethodforFunctionalMeasurementErrorModels 156
8.3 SmallAreaPredictionwithaUnit-levelModelwhenanAuxiliaryVariableis
MeasuredwithError 156
8.3.1 FunctionalMeasurementErrorApproachforUnit-levelModels 157
8.3.2 StructuralMeasurementErrorApproachforUnit-levelModels 160
8.4 DataAnalysis 162
8.4.1 Example1:MedianIncomeData 162
8.4.2 Example2:SAIPEData 165
8.5 DiscussionandPossibleExtensions 169
Acknowledgements 169
Disclaimer 170
References 170
9 RobustDomainEstimationofIncome-basedInequalityIndicators 171
NikosTzavidisandStefanoMarchetti
9.1 Introduction 171
9.2 DefinitionofIncome-basedInequalityMeasures 172
9.3 RobustSmallAreaEstimationofInequalityMeasureswithM-quantile
Regression 173
9.4 MeanSquaredErrorEstimation 176
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Description: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 con
