AdvancesinBusinessStatistics,Methods andDataCollection
Editedby
GerSnijkers StatisticsNetherlands
MojcaBavdaž UniversityofLjubljana
StefanBender
DeutscheBundesbankandUniversityofMannheim
JacquiJones AustralianBureauofStatistics
SteveMacFeely WorldHealthOrganizationandUniversityCollegeCork
JosephW.Sakshaug InstituteforEmploymentResearchandLudwigMaximilianUniversityofMunich
KatherineJ.Thompson U.S.CensusBureau
ArnoutvanDelden
StatisticsNetherlands
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Contents
ListofContributors xxix
Section1IntroductiontoNewMeasures/IndicatorsfortheEconomy 1
1AdvancesinBusinessStatistics,MethodsandDataCollection: Introduction 3
GerSnijkers,MojcaBavdaž,StefanBender,JacquiJones,SteveMacFeely, JosephW.Sakshaug,KatherineJ.Thompson,andArnoutvanDelden
1.1TheICES-VIEditedVolume:ANewBookonEstablishmentStatistics Methodology 3
1.2TheImportanceofEstablishmentStatistics 5
1.3ICESTrends 8
1.4OrganizationofThisBook 11
1.4.1Section1:IntroductiontoNewMeasures/IndicatorsfortheEconomy 12
1.4.2Section2:TopicsintheProductionofOfficialEstablishmentStatisticsand OrganizationalFrameworks 12
1.4.3Section3:TopicsintheUseofAdministrativeData 13
1.4.4Section4:TopicsinBusinessSurveyDataCollection 14
1.4.5Section5:TopicsintheUseofNewDataSourcesandNewTechnologies 15
1.4.6Section6:TopicsinSamplingandEstimation 16
1.4.7Section7:TopicsinDataIntegration,LinkingandMatching 16
1.5ToConclude 17 Disclaimer 17 References 18 Appendix:AvailableICES-VIIntroductoryOverviewLecture(IOL)Videos 21
2GDPandtheSNA:PastandPresent 23
SteveMacFeelyandPetervandeVen
2.1Introduction 23
2.2TheOriginsofNationalIncomeStatistics–ABriefHistory 23
2.2.1EarlyDevelopments 23
2.2.2InventionofGrossNationalProduct(GNP) 24
2.2.3TheDebateonIncludingGovernment 26
2.2.4TowardaSystemofNationalAccounts 27
2.2.5GlobalProliferationofGDP 27
2.3SNAandGDPToday 28
2.3.1TheSystematLarge 28
2.3.2SupplyandUseTables 28
2.3.3InstitutionalSectorAccounts 31
2.3.4TheLinkBetweenSupplyandUseTablesandtheInstitutionalSectorAccounts 33
2.3.5ConsistencyandCoherence 33
2.3.6TheRelationshipBetweenNationalAccountsandBusinessStatistics 34
2.3.6.1DefinitionalAdjustments 35
2.3.6.2AdjustmentsforExhaustiveness 35
2.3.6.3AdjustmentsforTimeConsistency 35
2.3.6.4BalancingAdjustments 35
2.4MostRecentandImportantRevisionstoSNA(Implicationsfor BusinessStatistics) 36
2.4.1InternationalStandardsNotSetinStone 36
2.4.2FromSNA1968toSNA1993 37
2.4.3FromSNA1993toSNA2008 38
2.4.4TheSNAandSourceStatisticsforEnterprises 39
2.5ConclusionsandImplicationsforBusinessStatistics 40 References 41
3GDPandtheSNA:FutureChallenges 43 SteveMacFeelyandPetervandeVen
3.1Introduction 43
3.2AnAgendafortheFuture 44
3.3TheTangledWebofGlobalization 44
3.4TheDigitalRevolution 47
3.5MovingBeyondGDP:GDPImpeached 48
3.6IncludingaMeasureofWell-being 50
3.7PuttingaValueontheEnvironment 52
3.8ChallengesReplacingGDP 53
3.9ConclusionsandImplicationsforBusinessStatistics 54 References 56
4BridgingtheGapBetweenBusinessandMacroeconomicStatistics: MethodologicalConsiderationsandPracticalSolutions 63 TimoKoskimäkiandKristianTaskinen
4.1Introduction 63
4.2GlobalProductionandStatistics 63
4.2.1ConceptsofNationalityandEconomicOwnership 64
4.2.2CaseFinland:GlobalProductioninEconomicStatistics 65
4.2.2.1IdentificationofEnterprisesInvolvedinGlobalProduction 65
4.2.2.2CaseonAutomotiveIndustry 66
4.2.2.3ForeignTradeofGoodsBasedonEconomicOwnership 67
4.2.2.4ChallengesRelatedtoGlobalProductionRecordings 68
4.3Co-operationBetweenNationalStatisticalOfficesandNationalCentralBank StatisticsFunctionsTacklingGlobalizationProblems 69
4.3.1ForeignDirectInvestmentNetworkasanExampleofCo-operation 69
4.3.2Early-WarningSystem(EWS) 70
4.3.3ARoadmapforSolvingtheGlobalization-RelatedIssuesinMonetary,Financial, andBalanceofPayments–Statistics 71
4.4BridgingtheGapBetweenBusinessandEconomicStatisticsThroughGlobalData Sharing 72
4.4.1ProductInnovation–One-OfforRegularDataSharingforBetterQuality 72
4.4.2ServiceInnovation–ImprovingRespondentServiceforMNEs 73
4.4.3ProcessInnovationtoStatisticalProductionbyDataSharing 73
4.4.4InnovatingUserExperience–BetterRelevanceandConsistencyforUsers 74
4.4.5OrganizationalInnovation–ChangingtheBusinessModelofOfficialStatistics 74
4.4.6CulturalInnovation–KeytoMakingitHappen 75
4.4.7InnovationinOtherIndustriestoLearnFrom 75 References 76
5MeasuringInvestmentinIntangibleAssets 79 MojcaBavdaž,AhmedBounfour,JoshMartin,AlbertoNonnis,GiulioPerani, andTjašaRedek
5.1Introduction 79
5.2DataSourcesonIntangibles 80
5.2.1PastSurveysonIntangibles 81
5.2.2ComparisonofPastSurveysonIntangibles 85
5.3MeasurementChallengesinSurveys 86
5.3.1IntangiblesAreIntangibleandMobile 86
5.3.2Own-AccountInvestmentPrevails 87
5.3.3PricingofIntangiblesIsDifficult 87
5.3.4InSearchoftheMostSuitableRespondentforIntangibles 88
5.3.5InvestmentsinIntangiblesTakeTime 88
5.3.6DataExistenceQuestioned 89
5.3.7EvidenceofInconsistentRespondentBehavior 89
5.3.8SummarizingtheChallengesinIntangibleSurveys:The4“F”Words 90
5.4IntangiblesandtheProductivityPuzzle 91
5.4.1AnalyticalConsiderations 91
5.4.2RoleofGlobalValueChains 91
5.5CollectingDataonIntangibles:TheWayAhead 93
5.5.1MethodologicalImprovements 93
5.5.2DataNeedsTodayandTomorrow 94
5.5.2.1CurrentandPotentialUsers 94
5.5.2.2A(Single)IASurveyoraBundleofDataSources? 95
5.5.2.3AParallelDevelopmentPath:AssessingIntangibleAssetStocks 95
5.6Conclusion 96 Acknowledgment 98 References 98
6MeasuringtheUSDigitalEconomy 105
JessicaR.Nicholson,ThomasF.HowellsIII,andDavidB.Wasshausen
6.1Introduction 105
6.2ExperimentalDigitalEconomyMeasures 105
6.2.1Methodology 106
6.2.1.1DefiningtheDigitalEconomy 107
6.2.1.2CalculatingResults 110
6.2.2DomesticTrends 110
6.2.2.1ValueAdded 110
6.2.2.2GrossOutput 112
6.2.2.3Prices 112
6.2.3InternationalCollaborationandAlignment 112
6.2.3.1TheOrganizationforEconomicCo-operationandDevelopmentWorkingPartyon NationalAccounts 113
6.2.3.2InternationalComparisons 114
6.2.4OtherAreasofResearch 115
6.2.4.1“Free”DigitalMedia 115
6.2.4.2MeasurementandTreatmentofData 116
6.2.4.3Prices 117
6.3MeasuringDigitalServicesTrade 118
6.3.1DefiningDigitalServicesforInternationalTrade 119
6.3.2TrendsinICTandICT-enabledServices 119
6.3.3AreasofResearch 121
6.4ConclusionandWayForward 122 References 123
7EstablishmentBasedInformalSectorStatistics:AnEndeavorof MeasurementfromEconomicCensus2018ofNepal 125 MaheshC.Pradhan
7.1Introduction 125
7.2IssuesofInformalSectorinLegislationandPoliciesinNepal 125
7.2.1Constitution2015 126
7.2.2LaborAct2017 126
7.2.3ContributionBasedSocialSecurityAct2017 126
7.2.4FifteenthPeriodicPlan(2019/20–2023/24) 126
7.2.5NationalEmploymentPolicy2014 126
7.3ConceptandDefinitionofInformalSector 127
7.3.1DefinitionofInformalSectorfromStatisticalPerspective 127
7.4EndeavorsofMeasuringInformalEconomicActivitiesinNepal 128
7.4.1NepalLaborForceSurvey 128
7.4.2NepalLivingStandardSurveys(NLSS) 129
7.4.3PopulationCensuses 129
7.5EconomicCensus2018 130
7.5.1ContentsofEconomicCensus2018 130
7.6StatusoftheInformalSectorStatistics 131
7.6.1InformalSectorStatisticsfromNepalLaborForceSurvey1998and2008 131
7.6.2InformalSectorStatisticsfromNepalLaborForceSurvey2017/18 131
7.6.3InformalSectorStatisticsfromNationalPopulationCensus2011 131
7.6.4InformalSectorStatisticsfromNationalEconomicCensus2018 133
7.6.5StatusofKeepingAccountingRecord 133
7.6.6InformalityinMicroSmallandMediumEstablishments(MSME) 133
7.6.7StreetBusinessSituation 135
7.7AnnualRevenues/Sales,OperatingExpensesinNot-RegisteredEstablishments 137
7.8NeedofRegularMeasurementInformalSector 140
7.9Conclusion 141 References 142
Section2TopicsintheProductionofOfficialEstablishmentStatistics andOrganizationalFrameworks 145
8StatisticalProducersChallengesandHelp 147
JacquiJonesandHollyO’Byrne
8.1Introduction 147
8.2ABriefOverviewoftheEvolutionofEconomicStatistics,andtheEstablishmentof NationalStatisticalInstitutes 147
8.3OurStatisticalEcosystem 150
8.4HelpAvailabletoUs 152
8.4.1InternationalGovernance 152
8.4.2StatisticalPrinciplestoProduceandDisseminateOfficialStatistics 154
8.4.3StatisticalProductionModelsandFrameworks 155
8.4.3.1QualityAssuranceFrameworks 156
8.4.4StatisticalManualsandHandbooks 156
8.4.5Classifications 156
8.4.5.1ClassifyingBusinesses 157
8.4.5.2ClassifyingEmploymentandWorkers 157
8.4.5.3ClassificationsOverview 158
8.4.6StatisticalTools 158
8.4.7InternationalCollaborationandSupport 158
8.5SummaryBeforetheCaseStudy 159
8.6StandardizationLeadstoEfficiency:Canada’sIntegratedBusinessStatistics Program 159
8.7IBSPObjectives 160
8.8CornerstonesofanIntegratedInfrastructureSystem 160
8.9Metadata-DrivenModel 161
8.10IntegratedInfrastructure 161
8.11InformationManagement 162
8.12StandardizationandCooperationWithinIBSP 162
8.13TheBusinessRegister 163
8.13.1TheBRastheCommonFrame 163
8.13.2AllocationFactorsontheBR 163
8.13.3CommoditiesandActivitiesontheBR 164
8.13.4RobustMethodologiesandGeneralizedSystems 164
8.14StandardToolsforDevelopingEQ 164
x Contents
8.15DevelopingaHarmonizedContentModel 165
8.16TheIBSPDataMartandAnalyticalTools 165
8.17ManagingResponseBurden 166
8.18ElectronicQuestionnaires 166
8.19LargeandComplexEnterprises 166
8.19.1EPM/LAOSPrograms 167
8.19.2CustomizedCollection 167
8.20TaxReplacementStrategy 167
8.21ActiveCollectionManagement 168
8.22RollingEstimateModel 168
8.23IBSPGrowthandAdaptation 169
8.24EfficienciesGainedandLearned 170
8.25Conclusion 170 References 171
9TheDevelopmentandMaintenanceofStatisticalBusinessRegistersas StatisticalInfrastructureinStatisticsIndonesiaandtheAustralianBureauof Statistics 175 ImamMachdi,RatihPutriPertiwi,Rr.Nefriana,andWillemErasmus
9.1Introduction 175
9.2TheIndonesianandAustralianContext 175
9.3TheDefinitionofaStatisticalBusinessRegister 176
9.4TheEvolutionofSBRsinStatisticsIndonesiaandtheAustralianBureauof Statistics 176
9.4.1DevelopmentoftheStatisticalBusinessRegisterinStatisticsIndonesia 177
9.4.1.1Phase1(1970–2012):BusinessDirectory 177
9.4.1.2Phase2(2013–2015):IntegratedBusinessRegister 177
9.4.1.3Phase3(2015–2021):TheStatisticalBusinessRegister 178
9.4.2DevelopmentoftheStatisticalBusinessRegisterintheAustraliaBureauof Statistics 178
9.5StatisticalBusinessRegisterDesigns 180
9.5.1TheDesignoftheBPSSBR 180
9.5.1.1StatisticalUnitModel 180
9.5.1.2UnitCoverage 180
9.5.1.3DataSources 181
9.5.1.4MainProcesses 181
9.5.1.5SBRIntegration 183
9.5.2TheDesignoftheABSStatisticalBusinessRegister 183
9.5.2.1CentralizedMaintenance 184
9.5.2.2Dissemination 184
9.5.2.3Uses 185
9.6StatisticalBusinessRegisterBenefits 185
9.7StatisticalBusinessRegisterChallenges 186
9.7.1SBRGovernanceandPolicy 186
9.7.2BusinessProcessIntegration 187
9.7.3SystemDevelopment 187
9.8OpportunitiesinSBRImplementation 188
9.8.1TransformationProgram 188
9.8.2NationalPolicyandInitiative 189
9.9TheFutureSpineConcept 190
9.10Conclusion 191 Acknowledgment 192 References 192
10ManagingResponseBurdenforOfficialStatisticsBusiness Surveys–ExperiencesandRecentDevelopmentsatStatisticsNetherlands, StatisticsPortugal,andStatisticsSweden 193 JohanErikson,DeirdreGiesen,LeanneHouben,andPauloSaraiva
10.1Introduction 193
10.2UnderstandingandMeasuringResponseBurden 194
10.2.1TheConceptofResponseBurden 194
10.2.2MeasuringandMonitoringResponseBurden 194
10.3OrganizationofResponseBurdenManagement 197
10.3.1LegalContextandCooperationwithOtherGovernmentBodies 197
10.3.2OrganizationofBurdenManagementWithintheNSI 198
10.4BurdenReductionMeasures 198
10.4.1UsingAlternativeSources 199
10.4.2ImprovingPrimaryDataCollection 199
10.4.2.1RedesigningContenttoFitDataProvisionCapacities 200
10.4.2.2SampleCoordination 200
10.4.2.3BusinessSurveyCommunication 201
10.4.2.4Feedback 202
10.4.2.5FileTransferandOtherTechniques(HybridDataCollection) 203
10.4.3Survey-assistedModelingwithMixedSources 204
10.4.4ReducingBurdenThroughCooperation 204
10.4.4.1CoordinationofMetadata 205
10.4.4.2TechnicalCooperationandStandards 206
10.4.4.3StandardBusinessReporting 206
10.5Discussion 208
DisclaimerandAcknowledgments 209 References 210
Appendix10.A:BurdenMeasurementQuestions 214
Appendix10.B:ExampleofStatisticsNetherlands’CommunicationAboutMandatory Reporting 218
Appendix10.C:ExampleofAdaptationCommunicationtoDataProvidersduetoCOVID-19 Crises–WebsiteStatisticsNetherlands2021 219
Appendix10.D:PersonalizedFeedbacktoBusinessDataProvider–MonthlyReport 220
Appendix10.E:PersonalizedFeedbacktoBusinessDataProvider–YearlyReport 221 Appendix10.F:ExampleofRCSFIImportinStatisticsNetherlandsStructuralBusiness StatisticsQuestionnaire 222
11ProducingOfficialStatisticsDuringtheCOVID-19Pandemic 225 JacquiJones,LuisaRyan,A.J.Lanyon,MarieApostolou,TanyaPrice,CorinnaKönig, MariekeVolkert,JosephW.Sakshaug,DaneMead,HelenBaird,DuncanElliott, andCraigH.McLaren
11.1Introduction 225
11.2ManagingtheAustralianStatisticalBusinessRegisterDuringCOVID-19 226
11.2.1ABSBusinessRegisterandCOVID-19 227
11.2.2ChangestoBusinessReporting 228
11.2.3PotentialImpactstotheABSBR 229
11.2.4IncreasingNumberofEmployers 229
11.2.5IndustryRecoding 232
11.2.6BusinessCancellations 232
11.2.7TheNewNormal? 233
11.3MitigatingCOVID-19ResponseRateRisksintheCollectionoftheABSProducer andInternationalTradePriceIndexes 234
11.3.1OverviewofABSProducerandInternationalTradePriceIndexes 234
11.3.2ABSProducerandInternationalTradePricesDataCollection 235
11.3.3DevelopingtheCOVID-19Response 235
11.3.4TheOptimizingResponseStrategy 236
11.3.5PrefieldPreparation 236
11.3.6FieldDevelopmentandOperations 236
11.3.7OutcomesoftheOptimizingResponseStrategy 239
11.3.8LessonsLearned 240
11.4TheImpactofChangingDataCollectionModesintheIABEstablishmentPanelin ResponsetotheCOVID-19Pandemic 241
11.5ClassificationandStatisticalImplementationofAustralianCOVID-19Government Policies 247
11.5.1BuildingaNewWorkProgram 247
11.5.2ChangestoBusinessSurveys 248
11.5.3PolicyCaseStudy–JobKeeper 248
11.5.3.1ClassificationofthePolicy 249
11.5.3.2ImplementationofPolicyClassification 249
11.5.4LessonsfortheOrganization 250
11.6SeasonalAdjustmentandTrendDuringandPost–COVID-19 251
11.6.1Pre-COVIDPublicationandPresentationofData 252
11.6.2SeasonalAdjustmentandTrendEstimationinPractice 252
11.6.3PublicationandPresentationDuringCOVID-19 253
11.6.4OptionsforTimeSeriesPublicationsDuringCOVID-19 254
11.6.5ModelingOutliers 254
11.6.6ForwardFactors 256
11.6.7OptiontoSuspendSeries 257
11.6.8UseofHigh-FrequencyEstimates 259
11.6.9OtherTimeSeriesChallenges 259
11.6.10Conclusion 259
Appendix 260 References 261
Section3TopicsintheUseofAdministrativeData 265
12MethodologyfortheUseofAdministrativeDatainBusinessStatistics 267 ArnoutvanDeldenandDanniLewis
12.1Introduction 267
12.2ReceivetheData 269
12.3InspecttheData 270
12.4LinktoPopulationFrame 270
12.4.1BasicLinkageMethods 271
12.4.2LinkageofDataSetsinthePresenceofDifferentUnitTypes 271
12.5FromActualtoTargetPopulation 272
12.5.1EstimationMethodstoAdjustforUndercoverage 273
12.5.2TemporaryCoverageIssues 274
12.6FromObservedtoTargetedVariables 274
12.6.1HarmonizationMethods 274
12.6.2EditingMethodsforMeasurementErrors 276
12.6.3CorrectingforBiasDuetoDecentralizedandAutonomousOrganizations 277
12.7FromObservedtoTargetedPeriods 278
12.7.1EstimationMethodsWhenDataAreNotAvailableonTime 278
12.7.1.1Benchmarking 278
12.7.1.2ForecastingfromPreviousCompleteData 279
12.7.1.3EstimationTechniques 279
12.7.2EstimationMethodstoAdjustforPeriodicity 280
12.8AssessDataQuality 280
12.8.1ThroughputQuality 280
12.8.2OutputQuality 281
12.8.3AnalysisofDifferencesBetweenSurveyandAdministrativeDataEstimates 282
12.9UnsolvedIssues 283
12.10Conclusion 285 References 285
13DevelopingStatisticalFrameworksforAdministrativeDataandIntegratingIt intoBusinessStatistics.ExperiencesfromtheUKandNewZealand 291 NicholasCox,CraigH.McLaren,ClaireShenton,TomTarling,andEllaW.Davies
13.1Introduction 291
13.1.1Background 291
13.1.2AdministrativeDataMethodsResearchProgram 292
13.2QualityFrameworksforAdministrativeData 293
13.2.1StatisticsNetherlandsandUNECEFramework 295
13.2.2StatsNewZealandandZhangFramework 295
13.2.3TheESSnetKomusoProject 296
13.2.4TheSouthamptonUniversityErrorProject 296
13.3CaseStudyOne–TheUseofValueAddedTaxDataintheUnitedKingdom 297
13.3.1OrganizationalContextWithintheOfficeforNationalStatistics 297
13.3.1.1AdministrativeDatainPractice 298
13.3.1.2DealingwiththeData 298
13.3.1.3ComplexUnitsinUKTaxData 299
13.3.2DevelopingStatisticalPipelinesforProcessing 300
13.3.3TheUseofAdministrativeDatainUKMonthlyShort-termIndicators 301
13.3.4TheUseofAdministrativeDataforRegionalEstimation 301
13.3.5Example:ComparisonofVATDatatoSurveyData 303
13.4CaseStudy2–AGreaterUseofAdministrativeDatainNewZealand’sLaborMarket Statistics 305
13.4.1OrganizationalContext 305
13.4.2StatsNZ’sNewMonthlyEmploymentIndicator 307
13.4.3RedesigningtheQuarterlyEmploymentSurvey 308
13.4.3.1PhaseOne 309
13.4.3.2PhaseTwo 310
13.4.4IntroducingStatsNZ’sNewQuarterlyBusinessEmploymentData 310
13.5ConcludingRemarks 311 References 311
14TheEvolutionofIntegratingAdministrativeDatainBusinessStatisticsin Ireland 315 ColinHanleyandSorchaO’Callaghan
14.1Introduction 315
14.2AdministrativeData 316
14.2.1Benefits 316
14.2.1.1Resources 317
14.2.1.2Coverage 317
14.2.1.3Timeliness 317
14.2.1.4ResponseBurden 317
14.2.2Challenges 318
14.2.2.1Access 318
14.2.2.2Quality 318
14.2.2.3StatisticalUnits 318
14.3AdministrativeDatainCSOBusinessStatistics 319
14.3.1LegalMandate 319
14.3.2BusinessRegister 319
14.3.3StructuralBusinessStatistics(SBS) 321
14.4DataLinkageUsingAdministrativeData 322
14.4.1ExportingEnterprisesinIreland 322
14.4.1.1TradeDataSources 322
14.4.1.2Linking 323
14.4.1.3LinkingTradetoBusinessRegister 323
14.4.1.4Results 324
14.4.2BusinessSignsofLife 325
14.4.2.1Linking 326
14.4.2.2Results 327
14.4.3LessonsLearned 329
14.4.3.1Quality 329
14.4.3.2Profiling 329
14.4.3.3Coverage 329
14.4.3.4Transparency 329
14.5TheUseofVATDatainBusinessStatistics 330
14.5.1TheCurrentSituation 330
14.5.2VATDataAvailableinIreland 330
14.5.3VATDataforShort-TermBusinessStatistics 330
14.5.4VATDataasaTimelyIndicatorofBusinessSignsofLife 331
14.5.5BusinessSignsofLifeSeries 331
14.6Summary 331 References 332
Section4TopicsinBusinessSurveyDataCollection 335
15WhatComputerizedBusinessQuestionnairesandQuestionnaire ManagementToolsCanOffer 337
GustavHaraldsen
15.1Introduction 337
15.2BusinessSurveyChallenges 338
15.2.1Concepts 338
15.2.2Units 339
15.2.3TimeReferences 340
15.3ThePathtoCompetentandMotivatedRespondents 341
15.4WhatComputerizationCanOffer 343
15.4.1Source-OrientedInstruments 343
15.4.2CombinedCommunicationMeans 345
15.4.3DesignedDialogues 348
15.5WhatWeKnowThatWeDon’tKnow 351 Acknowledgments 352 References 352
16TailoringtheDesignofaNewCombinedBusinessSurvey:Process,Methods, andLessonsLearned 357
GerSnijkers,LeanneHouben,andFredDemollin
16.1Introduction 357
16.2TowardtheNew“CBS-DNBFinancesofEnterprisesandBalanceofPayments” Survey 359
16.3AchievingCoherentStatistics 359
16.4QuestionnaireCommunication:TailoringtheDesign 361
16.4.1StepsintheQuestionnaireDevelopmentProcess 362
16.4.2TheFeasibilityStudy 363
16.4.3WhatDataandWhereAretheData? 365
16.4.4TheBusinessResponseProcess 366
16.4.5How:QuestionnaireDesignRequirements 367
16.4.6QuestionnaireDevelopment 368
16.5IntroducingtheSurveyintheField:TheSurveyCommunicationStrategy 370
16.5.1TheThreePhasesintheSurveyCommunicationStrategy 370
16.5.1.1Pre-fieldPhase 370
16.5.1.2FieldPhaseoftheFirstWaveoftheSurvey 372
16.5.1.3Post-fieldPhaseoftheFirstWaveoftheSurvey 373
16.5.2EvaluationoftheCommunicationStrategy:WastheStrategyEffective? 374
16.5.2.1EffectivenessofthePre-fieldStrategy 374
16.5.2.2TheResponseRateDevelopmentintheNextQuartersof2019 378
16.6LessonsLearned 379
Acknowledgment 381
Disclaimer 381
References 382
17AdvancesinQuestion(naire)Development,Pretesting,andEvaluation 387
DianeK.Willimack,HeatherRidolfo,AmyAndersonRiemer,MelissaCidade,andKathyOtt
17.1Introduction 387
17.2AdaptationandInnovationinPretestingMethods 388
17.2.1CaseStudy#1:EmergingTopicofRobotics– ittakesavillage389
17.2.1.1Background 389
17.2.1.2PretestingMethodology 389
17.2.1.3Summary 391
17.2.2CaseStudy#2:MultipleModesandMethods– differentstrokesfordifferentfolks391
17.2.2.1Background 391
17.2.2.2PretestingMethodology 392
17.2.2.3Summary 393
17.2.3CaseStudy#3:Record-KeepingStudy–“Idon’tkeepmyrecordsthatway”394
17.2.3.1Background 394
17.2.3.2PretestingMethodologyforPhaseI 394
17.2.3.3PretestingMethodologyforPhaseII 395
17.2.4CaseStudy#4:UsabilityTesting– whenhumanmeetscomputer397
17.2.4.1Background 397
17.2.4.2PretestingMethodology 398
17.2.4.3Summary 401
17.2.5CaseStudy#5:RemoteTesting,Logistics,andCOVID– realityisvirtual401
17.2.5.1Background 401
17.2.5.2RemoteTestingatNASS 401
17.2.5.3RemoteTestingattheCensusBureau 401
17.2.5.4Summary 403
17.2.6CaseStudy#6:PretestingPlusParadata– alookunderneaththehood403
17.2.6.1Background 403
17.2.6.2PretestingMethodology 403
17.3PretestingMethodologies:CurrentFeaturesandFutureNeeds 405
17.3.1CurrentFeaturesandConsequences 405
17.3.1.1Finding“theMissingLink”:CollaborativePartnerships 405
17.3.1.2TheOdyssey:ExploratoryMethods 405
17.3.1.3LeaveNoStoneUnturned:MultipleMethods 405
17.3.1.4Methuselah:TheManyRolesofTechnology 406
17.3.1.5TrusttheProcess:TheResponseProcess 406
17.3.2FutureNeedsandImplications 406 Acknowledgments 408
References 408
18UsingParadatainElectronicBusinessSurveyQuestionnaires 413 GerSnijkers,SusanDemedash,andJessicaAndrews
18.1Introduction 413
18.2Paradata 414
18.3QuestionnaireCompletionParadata 417
18.4LookingInsidetheQuestionnaireCompletionProcess 419
18.4.1CompletingtheCBS-DNBQuarterlySurveyonFinancesofEnterprisesandBalance ofPayments 421
18.4.1.1QuestionnaireCompletionProfiles 421
18.4.1.2UsageofDownloadandImportFunctions 423
18.4.1.3UsageofDutchandEnglishVersionsoftheQuestionnaire 424
18.4.1.4TimeNeededtoCompletetheQuestionnaire 424
18.4.1.5EffectoftheCommunicationStrategy 426
18.4.2BusinessSurveyUseofParadataatStatisticsCanada 427
18.5Conclusions 431
Acknowledgment 432
Disclaimer 433 References 433
19RecentFindingsfromExperimentsinEstablishmentSurveys 437 JoshLangeland,HeatherRidolfo,JakiMcCarthy,KathyOtt,DougKilburg,KarenCyBulski, MelissaKrakowiecki,LarryVittoriano,MattPotts,BenjaminKüfner,JosephW.Sakshaug, andStefanZins
19.1Introduction 437
19.2ExperimentswithMailedSurveyPacketstoImproveRecruitmentStrategiesina NationalEstablishmentSurvey(BLS) 438
19.2.1Motivation 438
19.2.2AdvanceLetterStudy 439
19.2.2.1ExperimentDesign 439
19.2.2.2Results 440
19.2.3FolderDesignStudy 441
19.2.3.1ExperimentDesign 441
19.2.3.2Results 441
19.2.4Discussion 443
19.3ExperimentsTestingChangestoDataCollectionTimingandContentofContactsinthe USCensusofAgriculture(NASS) 443
19.3.1Motivation 443
19.4ComparingFedExtoTraditionalPostageinaSurveyofSubstanceAbuseandMental HealthFacilities(byMathematicaforSAMHSA) 451
19.4.1Motivation 451
19.4.2ExperimentDesign 452
19.4.3Results 454
19.4.4Discussion 458
19.5AddressingItemNonresponsewithClarifyingInformation–EvidencefromtheIAB JobVacancySurvey(IAB) 458
19.5.1Motivation 458
19.5.2ExperimentDesign 459
19.5.3Results 461
19.5.3.1ItemDuration 461
19.5.3.2ItemNonresponse 461
19.5.3.3SpilloverEffects 462
19.5.4Discussion 463
19.6Summary 464 Acknowledgment 465 References 465
20WebPortalsforBusinessDataCollection 469 BenteHoleandLeanneHouben
20.1Introduction 469
20.2TheNSIWeb-PortalStudy 470
20.2.1MoreAbouttheSurvey 470
20.2.2SurveyResultsandOtherFindings 471
20.2.2.1TypeandSizeofPortals 472
20.2.2.2CommonFeaturesandStatus 474
20.2.2.3Registration,Authentication,andAuthorization 476
20.2.2.4DataImportandTransfer 481
20.2.2.5IdentifyingtheRightBusinessUnit 483
20.2.2.6ReturningDatatotheRespondents 484
20.2.2.7ContactOptionsandCommunication 485
20.2.2.8StrengthsandWeaknesses 487
20.3InvestigatingHowtoBuildaCustomizedPortalatStatisticsNetherlands 488
20.4RecommendationsandFutureDevelopments 489
20.4.1Recommendations 489
20.4.2FutureWebPortalDevelopments 491 DisclaimerandAcknowledgements 493
Appendix20.AListofNSIstheQuestionnairewasSentto 493 Appendix20.BWordCopyofQuestionnaire 494 References 498
21ACreativeApproachtoPromotingSurveyResponse 501 CharlesF.Brady,Jr.andKariL.Klinedinst
21.1Introduction 501
21.2Background 502
21.3ApproachandMethods 503
21.3.1PublicSector–PrivateSectorPartnership 503
21.3.2StrategicObjectives 504
21.3.3TargetSegmentationandFocusGroups 505
21.3.4CommunicationsPlan 506
21.4ResultsfromFocusGroups 507
21.4.1FocusGroups–RoundOne 507
21.4.2FocusGroups–RoundTwo 508
21.4.3IntermediaryMeetingatCensus 508
21.4.4FocusGroupsRoundThree 509
21.4.5AdditionalMeetings 509
21.5DevelopmentofCampaignMaterials 510
21.5.1Brochures 511
21.5.2Videos 512
21.5.3CampaignWebsite 512
21.5.4PartnerBriefingPresentationContent 513
21.5.5IslandAreas 513
21.6CampaignImplementation 513
21.6.1“9-8-7”Campaign 513
21.6.2Webinar 514
21.6.3EmailAwarenessCampaign 514
21.6.4InternalCommunications 514
21.6.5EconomicCensusDayforCensusBureauStaff 514
21.6.6MediaRelations 514
21.6.7CongressionalandIntergovernmentalAffairs 514
21.6.8SocialMedia 515
21.6.9MeetingsandEvents 515
21.7MovingForward 515
21.7.1RespondentPortalChanges 515
21.7.2AdaptationforCurrentSurveys 516
21.8Conclusion 516 References 517
Section5TopicsintheUseofNewDataSourcesandNew Technologies 519
22StatisticalDataProductioninaDigitizedAge:TheNeedtoEstablish SuccessfulWorkflowsforMicroDataAccess 521 StefanBender,JannickBlaschke,andChristianHirsch
22.1Introduction 521
22.2BuildingBlocksforSuccessfulWorkflowsEnablingAccesstoMicroData 522
22.2.1BuildingBlock1:LayingtheTechnicalandProceduralFoundations 523
22.2.2BuildingBlock2:GeneratingSafeResults 525
22.2.3BuildingBlock3:GeneratingValueforAllStakeholders 526
22.3AnAlternativeApproachtoMeasuringValue:FAIRData 527
22.4ApplyingtheBUBMICModeltoResearchDataCenters 528
22.4.1BuildingBlock1:LayingtheTechnicalandProceduralFoundations 529
22.4.2BuildingBlock2:GeneratingSafeResults 530
22.4.3BuildingBlock3:GeneratingValueforAllStakeholders 531
22.4.4ExamplesofGeneratingValueforAllStakeholders 531
22.4.4.1RulesforVisitingResearchersattheRDSC 531
22.4.4.2SDCPackagesinStataandR 532
22.4.4.3Dobby,theBBk-RDSC’sHigh-PerformanceandStreamlinedDataProduction Pipeline 532
22.4.4.4RDSCContractGenerator 532
22.4.4.5AnnodataSchema 532
22.5Conclusion 534 Acknowledgments 534 Disclaimer 534 References 534
23MachineLearninginGermanOfficialStatistics 537 FlorianDumpert
23.1Introduction 537
23.2TerminologyandaShortIntroductiontoMachineLearning 537
23.3MachineLearninginOfficialStatistics–InternationalOverview 541
23.4HistoryandCurrentStatusofMachineLearninginGermanOfficialStatistics 543
23.4.1FederalStatisticalOfficeofGermany 543
23.4.2HistoryandCurrentStatusintheGermanOfficialStatisticsNetwork 544
23.5SomeCurrentProjectsattheFederalStatisticalOffice 545
23.5.1Overview 545
23.5.2MachineLearningtoIncreaseAnalysisCapabilitiesintheAreaofMinimumWage UsingOfficialStatistics 545
23.5.3MachineLearningforEditingandImputation 550
23.5.3.1Relevance 550
23.5.3.2EditingandImputationintheNewDigitalEarningsSurvey 550
23.5.3.3StudiesonthePreservationoftheDistributionunderImputation 552
23.6SummaryandOutlook 555 References 557
24SixYearsofMachineLearningintheBureauofLaborStatistics 561 AlexanderMeasure
24.1Introduction 561
24.2WhyOfficialStatistics? 561
24.3HowShouldWeDoIt? 562
24.4IsItGoodEnough? 564
24.5HowShouldWeUseIt? 565
24.6HowDoWeIntegrateIt? 567
24.7HowDoWeMaintainIt? 568
24.8Conclusion 570 References 570
Appendix:F1-scoreandMacroF1-score 572
25UsingMachineLearningtoClassifyProductsfortheCommodityFlow Survey 573 ChristianMoscardiandBenjaminSchultz
25.1Background 573
25.1.1CommodityFlowSurvey(CFS)Background 573
25.1.2CFSDataCollectionChallenges 573
25.1.2.1Nonrespose 573
25.1.2.2DataQuality 574
25.1.2.3RespondentBurden 574
25.1.2.4RelatedWork 575
25.2Data 575
25.3Methods 577
25.3.1FilteringandTextCleansing 577
25.3.1.1FilteringCFSResponseData 577
25.3.1.2TextPreprocessing 578
25.3.1.3De-duplicationandDisambiguation 578
25.3.2DerivingVariables(Features)fromTextData 580
25.3.3OtherFeaturesIncorporatedintoModel–NAICSCode 580
25.3.4ResolvingPreviouslyUnseenVariablesduringPrediction 581
25.3.5Model 581
25.3.6TrainingandEvaluation 582
25.3.7ImputingandCorrectingData–EditRuleAgreement 583
25.4Results 584
25.4.1ModelResults 584
25.4.2Applicationsto2017CFSandImpactofML 586
25.4.2.1Applications 586
25.4.2.2ImpactofML 587
25.5ConclusionandFutureWork 588 Disclaimer 590 References 590
26AlternativeDataSourcesintheCensusBureau’sMonthlyStateRetailSales DataProduct 593
RebeccaHutchinson,ScottScheleur,andDeannaWeidenhamer
26.1Introduction/Overview 593
26.2HistoryofState-LevelRetailSalesatCensus 594
26.3OverviewoftheMSRS 595
26.4Methodology 597
26.4.1DirectlyCollectedDataInputs 597
26.4.2FrameCreation 598
26.4.3EstimationandImputation 599
26.4.3.1CompositeEstimator 599
26.4.3.2SyntheticEstimator 600
26.4.3.3HybridEstimator 601
26.4.4QualityMetrics 602
26.5UseofAlternativeDataSourcesinMSRS 602
26.5.1InputtoMSRSModel 602
26.5.2Validation 605
26.6Conclusion 609 Disclaimer 610 References 610
Section6TopicsinSamplingandEstimation 613
27IntroductiontoSamplingandEstimationforBusinessSurveys 615
PaulA.SmithandWesleyYung
27.1Introduction 615
27.2StatisticalBusinessRegisters 615
27.3Sampling 619
27.3.1StratifiedSampling 619
27.3.2Cut-offSampling 622
27.3.3ProbabilityProportionaltoSizeSampling 622
27.3.4IndirectSampling 623
27.3.5BalancedSampling 624
27.4Estimation 624
27.4.1Model-assistedandCalibrationEstimation 624
27.4.2Outliers 628
27.5Model-basedEstimation 629
27.5.1SmallAreaEstimation 629
27.5.2Nowcasting 630
27.5.3Model-basedEstimators 631
27.6Conclusion 631 References 632
28SampleCoordinationMethodsandSystemsforEstablishmentSurveys 637
AlinaMateiandPaulA.Smith
28.1Introduction 637
28.2SampleCoordination 638
28.2.1NotationandDefinitions 638
28.2.2MethodsforSampleCoordination 638
28.2.3MethodsBasedonPRNs 639
28.2.4Non-PRNMethods 642
28.3ComparingSampleCoordinationMethods 644
28.3.1MeasuresUsedinSampleCoordination 644
28.3.2CriteriaforSampleCoordination 645
28.4SampleCoordinationSystems 647
28.4.1OptimizationMeasuresinSampleCoordinationSystems 648
28.5OverviewofSampleCoordinationSystems 648
28.5.1CoordinatedPoissonSampling/ConditionalSelection 648
28.5.2SAMU 650
28.5.3SynchronizedSampling 650
28.5.4Burden-BasedCoordination 651
28.5.5CoordinationFunctions 651
28.6Discussion 652
28.6.1DistinguishingSampleCoordinationMethodsandSampleCoordinationSystems 652
28.6.2FurtherChallenges 653
28.7Conclusion 653
Acknowledgments 654 References 654
29VarianceEstimationforProbabilityandNonprobabilityEstablishment Surveys:AnOverview 659
JillA.DeverandDanLiao
29.1EstimationforProbabilityBusinessSurveyData 660
29.1.1ProbabilitySamplinginPractice 660
29.1.2TheoriesofPopulationInference 660
29.1.3BasicWeightingSteps 662
29.1.4VarianceEstimationforProbabilitySurveys 662
29.1.4.1ExactFormulas 663
29.1.4.2LinearizationMethods 664
29.1.4.3ReplicationMethods 665
29.1.5VarianceEstimationwithImputedValues 671
29.1.6VarianceEstimationApplicationsAmongProbabilityEstablishmentSurveys 671
29.2EstimationwithNonprobabilityEstablishmentSurveyData 673
29.2.1NonprobabilitySamplinginPractice 673
29.2.2AnalyticObjectives 673
29.2.2.1MethodsforNonprobabilityEstimation 675
29.2.2.2MethodsforHybridEstimation 676
29.2.3VarianceEstimators 677
29.2.3.1Quasi-randomizationMethods 677
29.2.3.2SuperpopulationandModel-basedMethods 678
29.3ConcludingRemarks 678 References 679
30BayesianMethodsAppliedtoSmallAreaEstimationforEstablishment Statistics 685
PaulA.Parker,RyanJanicki,andScottH.Holan
30.1Introduction 685
30.2BayesianHierarchicalModelingforDependentData 688
30.3Area-LevelModels 690
30.4Unit-LevelModels 692
30.4.1BasicUnit-LevelModel 692
30.4.2AccountingforSurveyDesign 693
30.4.3ModelsforNon-GaussianData 694
30.5EmpiricalSimulationStudy 695
30.6DataAnalysis 698
30.7Discussion 701 Acknowledgments 702 References 702
31VarianceEstimationUnderNearestNeighborRatioHotDeckImputationfor MultinomialData:TwoApproachesAppliedtotheServiceAnnualSurvey (SAS) 705
RebeccaAndridge,JaeKwangKim,andKatherineJ.Thompson
31.1Introduction 705
31.2BasicSetup 709
31.3SingleImputationVarianceEstimation 710
31.4MultipleImputationVarianceEstimation 711
31.5SimulationStudy 712
31.5.1DataGeneration 712
31.5.2ImputationMethodsImplemented 714
31.5.3EvaluationofPerformance 715
31.5.4Results 716
31.6EmpiricalApplication 718
31.6.1Background 718
31.6.2Results 720
31.7GeneralConclusion 722 Acknowledgments 724
Disclaimer 724 References 724
32MinimizingRevisionsforaMonthlyEconomicIndicator 727
NicoleCzaplicki,StephenKaputa,andLauraBechtel
32.1Introduction 727
32.2MARTSandMRTSBackgroundandMotivation 729
32.2.1SampleDesign 729
32.2.2UnitDefinitions 730
32.2.3ResponseRates 730
32.2.4EstimationMethodology 730
32.2.5ImputationMethodologyandProcedures 732
32.3EstimationEvaluation 733
32.3.1EstimationMethodsConsidered 733
32.3.1.1LinkRelativeEstimation 733
32.3.1.2ModifiedLinkRelativeEstimator 734
32.3.1.3WeightingClassEstimator 734
32.3.1.4RatioEstimator 735
32.3.2EstimationEvaluationCriteria 735
32.3.3EstimationEmpiricalResults 736
32.3.4EstimationDiscussion 737
32.4AutomatingtheDetectionofHigh-PriorityUnitsforImputation 738
32.4.1MethodsforIdentifyingUnitsforAnalystImputation 738
32.4.1.1InfluenceMeasureMethod(Month-to-MonthChange) 738
32.4.1.2SizeIdentificationMethod 739
32.4.1.3Prioritization 741
32.4.2IdentifyingHigh-PriorityUnitsforAnalystImputationEvaluation 742
32.4.3IdentifyingHigh-PriorityUnitsforAnalystImputationDiscussion 742
32.5AutomatingImputationProcedures 743
32.5.1ImputationModel 743
32.5.1.1RegARIMATimeSeriesModel 744
32.5.1.2HierarchicalBayesianRegression(HBR)Model 745
32.5.2SimulationStudy 745
32.5.2.1SimulationStudyDesign 746
32.5.2.2EvaluationStatistics 746
32.5.2.3SimulationResults 747
32.5.3ImputationDiscussion 750
32.6Conclusion 751
Acknowledgments 752
Disclaimer 752 References 752
Section7TopicsinDataIntegration,LinkingandMatching 755
33RecordLinkageforEstablishments:Background,Challenges,andan Example 757
MichaelD.LarsenandAlanHerning
33.1Introduction 757
33.2VariablesforLinkingRecords 758
33.3Exact,Deterministic,andProbabilisticMatching 759
33.3.1ExactMatchingandDeterministicMatchingwithMultiplePasses 759
33.3.2ProbabilisticMatching 761
33.3.3CombiningDeterministicandProbabilisticLinkage 763
33.4AdditionalConsiderationsinRecordLinkage 763
33.4.1StructuralConsiderationsinRecordLinkage 763
33.4.2One-to-OneMatching 764
33.4.3UsingLinkedFilesinAnalysis 765
33.4.4ConfidentialityandComputing 766
33.5APracticalExampleofBusinessDataLinking:TheBusinessLongitudinalAnalysis DataEnvironment 768
33.5.1Overview 768
33.6BLADELinkingMethodology 768
33.6.1BLADEIntegratingFrame 768
33.6.2LinkingDatatoBLADE 770
33.6.2.1ABSSurveyData 770
33.6.2.2BLADECore 770
33.6.2.3LinkageofOtherAdministrativeData 771
33.6.3MaintainingBLADE 772
33.6.3.1StaticandDynamicBLADE 773
33.7BLADEAccessModel 773
33.7.1AccessingBLADE 774
33.7.2CustomizedBLADEProducts 775
33.7.3HowBLADEIsBeingUsed 775
33.8Conclusion 775 References 776
34MethodsforEstimatingtheQualityofMultisourceStatistics 781 ArnoutvanDelden,SanderScholtus,TondeWaal,andIreneCsorba
34.1Introduction 781
34.2RepresentationError 783
34.2.1EstimatingandCorrectingSelectivity 783
34.2.1.1EstimatingOutputAccuracywithRespecttoRepresentationErrors 783
34.2.1.2CorrectingforRepresentationErrors 785
34.2.2CaseStudyonEstimatingBiasDuetoSelectivity 785
34.2.3CaseStudyonCorrectingOutputforSelectivity 787
34.3LinkageError 788
34.3.1EstimatingtheEffectofLinkageErrorsonOutputs 788
34.3.2AdjustingOutputstoCorrectforLinkageErrors 790
34.3.3SimulationStudyonCorrectingContingencyTablesforLinkageErrors 792
34.4MeasurementError 793
34.4.1QuantifyingMeasurementError 794
34.4.2EstimatingtheEffectofMeasurementErroronOutputs 795
34.4.3CaseStudyonTurnoverGrowthRatesbyEconomicActivity 796
34.4.4CaseStudyonInternetPurchases 797
34.4.5ObtainingaBias-correctedEstimatorforMeasurementErrors 798
34.4.6SimulationStudyComparingtheBias-correctedEstimators 799
34.5Conclusion 800
References 801
35AdoptingPreviouslyReportedDataintothe2022CensusofAgriculture: LessonsLearnedfromthe2020SeptemberAgriculturalSurvey 805
LindaJ.Young,JosephB.Rodhouse,ZacharyTerner,andGavinCorral
35.1Introduction 805
35.2AgriculturalSurveyandPreviouslyReportedData(PRD) 806
35.3StudyDesign 807
35.3.1SurveyQuestionsandPRDIncludedintheStudy 807
35.3.2DesignoftheStudy 808
35.3.3DataCollection 810
35.3.4AnalysisoftheStudy 810
35.4StudyResults 810
35.4.1ComparisonofResponseRates 811
35.4.2ComparisonofCompletionTimes 812
35.4.3AnalysisofUpdateRates 814
35.4.4AnalysisofEditRates 816
35.5Discussion 816
Acknowledgments 818
References 819
36IntegratingAlternativeandAdministrativeDataintotheMonthlyBusiness Statistics:SomeApplicationsfromStatisticsCanada 821 Marie-ClaudeDuval,RichardLaroche,andSébastienLandry
36.1ContextforIntegratingAlternativeandAdministrativeData 821
36.2ReplacementofSurveyDatabyTaxDataintheMonthlySurveyofFoodServicesand DrinkingPlaces 822
36.2.1PreviousMethodologyUnderaSurveyDesign 822
36.2.2TheGSTAdministrativeFile 823
36.2.3TheNewMethodologyProposedtoReplaceSurveyDatawithAdministrative Data 824
36.2.4AssessmentandRequirementsBeforetheImplementation 825
