dc.contributor.author | Walter, Paul | de |
dc.contributor.author | Groß, Marcus | de |
dc.contributor.author | Schmid, Timo | de |
dc.contributor.author | Weimer, Katja | de |
dc.date.accessioned | 2024-02-06T12:43:33Z | |
dc.date.available | 2024-02-06T12:43:33Z | |
dc.date.issued | 2022 | de |
dc.identifier.issn | 2001-7367 | de |
dc.identifier.uri | https://www.ssoar.info/ssoar/handle/document/91944 | |
dc.description.abstract | The estimation of poverty and inequality indicators based on survey data is trivial as long as the variable of interest (e.g., income or consumption) is measured on a metric scale. However, estimation is not directly possible, using standard formulas, when the income variable is grouped due to confidentiality constraints or in order to decrease item nonresponse. We propose an iterative kernel density algorithm that generates metric pseudo samples from the grouped variable for the estimation of indicators. The corresponding standard errors are estimated by a non-parametric bootstrap that accounts for the additional uncertainty due to the grouping. The algorithm enables the use of survey weights and household equivalence scales. The proposed method is applied to the German Microcensus for estimating the regional distribution of poverty and inequality in Germany. | de |
dc.language | en | de |
dc.subject.ddc | Sozialwissenschaften, Soziologie | de |
dc.subject.ddc | Social sciences, sociology, anthropology | en |
dc.subject.other | direct estimation; interval-censored data; non-parametric estimation; OECD scale; prediction; Mikrozensus 2012 | de |
dc.title | Iterative Kernel Density Estimation Applied to Grouped Data: Estimating Poverty and Inequality Indicators from the German Microcensus | de |
dc.description.review | begutachtet (peer reviewed) | de |
dc.description.review | peer reviewed | en |
dc.source.journal | Journal of Official Statistics | |
dc.source.volume | 38 | de |
dc.publisher.country | DEU | de |
dc.source.issue | 2 | de |
dc.subject.classoz | Erhebungstechniken und Analysetechniken der Sozialwissenschaften | de |
dc.subject.classoz | Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods | en |
dc.subject.thesoz | Mikrozensus | de |
dc.subject.thesoz | microcensus | en |
dc.subject.thesoz | Bundesrepublik Deutschland | de |
dc.subject.thesoz | Federal Republic of Germany | en |
dc.subject.thesoz | Armut | de |
dc.subject.thesoz | poverty | en |
dc.subject.thesoz | Ungleichheit | de |
dc.subject.thesoz | inequality | en |
dc.subject.thesoz | Schätzung | de |
dc.subject.thesoz | estimation | en |
dc.subject.thesoz | Daten | de |
dc.subject.thesoz | data | en |
dc.subject.thesoz | Indikator | de |
dc.subject.thesoz | indicator | en |
dc.identifier.urn | urn:nbn:de:0168-ssoar-91944-5 | |
dc.rights.licence | Creative Commons - Namensnennung, Nicht kommerz., Keine Bearbeitung 4.0 | de |
dc.rights.licence | Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0 | en |
ssoar.contributor.institution | FDB | de |
internal.status | formal und inhaltlich fertig erschlossen | de |
internal.identifier.thesoz | 10052284 | |
internal.identifier.thesoz | 10037571 | |
internal.identifier.thesoz | 10036765 | |
internal.identifier.thesoz | 10041153 | |
internal.identifier.thesoz | 10057146 | |
internal.identifier.thesoz | 10034708 | |
internal.identifier.thesoz | 10047129 | |
dc.type.stock | article | de |
dc.type.document | Zeitschriftenartikel | de |
dc.type.document | journal article | en |
dc.source.pageinfo | 599-635 | de |
internal.identifier.classoz | 10105 | |
internal.identifier.journal | 201 | |
internal.identifier.document | 32 | |
internal.identifier.ddc | 300 | |
dc.identifier.doi | https://doi.org/10.2478/jos-2022-0027 | de |
dc.description.pubstatus | Veröffentlichungsversion | de |
dc.description.pubstatus | Published Version | en |
internal.identifier.licence | 20 | |
internal.identifier.pubstatus | 1 | |
internal.identifier.review | 1 | |
internal.pdf.valid | false | |
internal.pdf.wellformed | true | |
internal.pdf.encrypted | false | |