1 / 12

Model based estimation of indicators of poverty and social exclusion

Thomas Glaser Statistics Austria Directorate Social Statistics European Conference on Quality in official Statistics 5 th June 2014. Model based estimation of indicators of poverty and social exclusion. Overview. Europe 2020 indicators: Break in time series

malise
Download Presentation

Model based estimation of indicators of poverty and social exclusion

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Thomas Glaser Statistics Austria Directorate Social Statistics European Conference on Quality in official Statistics 5th June 2014 Model based estimation of indicators of poverty and social exclusion

  2. Overview • Europe 2020 indicators: Break in time series • Data basis for modelling: EU-SILC 2011 • Different modelling variants • Application of modelled register data effect toEU-SILC 2008-2010 • Conclusions and outlook

  3. Register basedhouseholdincome • Usageofregisterfrom EU-SILC 2012 onwards • Results in a break in time seriesforincomebased Europe 2020 indicators • At-risk-of-poverty: AROP(REG) • At-risk-of-povertyorsocialexclusion: AROPE(REG) • Data fromincomeregisters also availableforEU-SILC 2011 • Revision of time series 2008-2012 desired • Noincomeregisterdatafor 2008-2010 availableyet • Model basedregisterhouseholdincomeassolution

  4. Choice ofmodels: Variant 1 • Directestimationofindicators • Likelihoodof AROP(REG)and AROPE(REG)estimatedbylogisticregressions • Estimateofindicators: meanvalueofestimatedprobabilities on personal level • Advantage: • Directestimationofindicators • Disadvantages: • Possibilityofinconsistentestimatesofindicators • Only moderate model fit • Underestimationof AROP(REG)and AROPE(REG)

  5. Choice ofmodels: Variant 2 • Estimationofregisterbasedhouseholdincome HINC(REG) • Linear regression: Natural log ofHINC(REG)asdependent variable • Estimationofindicators • Equivalisedincomefromwith interview baseddata • Calculationof AROP(REG)and AROPE(REG) • Advantages: • Consistentindicators • Estimatesofhouseholdincome on sample level • Verygoodmodel fit (R2=89%) • Disadvantages: • Loss ofvariance due toregression • Underestimationof AROP(REG)and AROPE(REG)

  6. Choice ofmodels: Variant 2a • Addition ofiid N(0,σ2) stochasticerrortermstoestimatesresultingfrom linear regression in variant 2 • Advantages: • Compensationoftendencytowardsthemean • EstimatedHINC(REG)distributionsimilartoHINC(REG) • EstimatesclosetoactualAROP(REG)and AROPE(REG)(EU-SILC 2011) • Disadvantage: • Additional variancecontainsno additional information on structureofhouseholdincome

  7. Choice ofmodels: Variant 3 • Estimationofdifference HINC - HINC(REG) • Twostepmodelling • 1) Classificationof relevant difference (discriminantanalysis) • 2) Linear regressionsimilarto variant 2 fordifference • Estimateddifferenceisaddedto HINC • Advantages: • Explicit estimationofregisterdataeffect • Disadvantages: • OverestimationofAROP(REG)and AROPE(REG) • Low model fit • Errors oftwomodellingsteps

  8. Weighting • Calibrationincorporatesregisterincomedata • Additional modellingstepforestimatedweights in every variant wouldbenecessary • All modelsfittedwithoutweights • Characteristics relevant forweightingare also predictors in themodels • Marginal differencesweighted – unweighted • OLS mostefficientfor linear regression

  9. Chosen model • Variant 2a: EstimationofregisterbasedhouseholdincomeincludingiidN(0,σ2) stochasticerrorterms • More advantagesthandisadvantages • Easy application • Coefficientsfromregressionandstochasticerrortermswereappliedto interview baseddataof EU-SILC 2008-2010 • Socio-economicstructurereflected in predictor variables foreachyearcanbe incorporated in estimation

  10. Register and interview based time series

  11. Conclusions and outlook • Unbroken time seriesofEurope 2020 indicatorsfor EU-SILC 2008-2012 achieved • Europe 2020 targetscanbemeasuredfrom 2008 onwardswithregisterbasedindicators • Next task: recalculationofregisterbasedhouseholdincomefor EU-SILC 2008-2010 • Revision of EU-SILC micro-data 2008-2011 until 09/2014 • Publication of revised time-series 2008-2013 in autumn/winter 2014

  12. Please address queries to: Thomas Glaser thomas.glaser@statistik.gv.at Contact information: Guglgasse 13, 1110 Vienna Thank you for your attention!

More Related