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Income Source Confusion Using the SILC

We use a unique panel of household survey data—the Austrian version of the European Union Statistics on Income and Living Conditions (SILC) for 2008–2011—which have been linked to individual administrative records on both state unemployment benefits and earnings. We assess the extent and structure o...

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Autores principales: Bollinger, Christopher Robert, Tasseva, Iva Valentinova
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496567/
https://www.ncbi.nlm.nih.gov/pubmed/37705921
http://dx.doi.org/10.1093/poq/nfad025
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author Bollinger, Christopher Robert
Tasseva, Iva Valentinova
author_facet Bollinger, Christopher Robert
Tasseva, Iva Valentinova
author_sort Bollinger, Christopher Robert
collection PubMed
description We use a unique panel of household survey data—the Austrian version of the European Union Statistics on Income and Living Conditions (SILC) for 2008–2011—which have been linked to individual administrative records on both state unemployment benefits and earnings. We assess the extent and structure of misreporting across similar benefits and between benefits and earnings. We document that many respondents fail to report participation in one or more of the unemployment programs. Moreover, they inflate earnings for periods when they are unemployed but receiving unemployment compensation. To demonstrate the impact of income source confusion on estimators, we estimate standard Mincer wage equations. Since unemployment is associated with lower education, the reports of unemployment benefits as earnings bias downward the returns to education. Failure to report unemployment benefits also leads to substantial sample bias when selecting on these benefits, as one might in estimating the returns to job training.
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spelling pubmed-104965672023-09-13 Income Source Confusion Using the SILC Bollinger, Christopher Robert Tasseva, Iva Valentinova Public Opin Q Article We use a unique panel of household survey data—the Austrian version of the European Union Statistics on Income and Living Conditions (SILC) for 2008–2011—which have been linked to individual administrative records on both state unemployment benefits and earnings. We assess the extent and structure of misreporting across similar benefits and between benefits and earnings. We document that many respondents fail to report participation in one or more of the unemployment programs. Moreover, they inflate earnings for periods when they are unemployed but receiving unemployment compensation. To demonstrate the impact of income source confusion on estimators, we estimate standard Mincer wage equations. Since unemployment is associated with lower education, the reports of unemployment benefits as earnings bias downward the returns to education. Failure to report unemployment benefits also leads to substantial sample bias when selecting on these benefits, as one might in estimating the returns to job training. Oxford University Press 2023-08-09 /pmc/articles/PMC10496567/ /pubmed/37705921 http://dx.doi.org/10.1093/poq/nfad025 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of American Association for Public Opinion Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Bollinger, Christopher Robert
Tasseva, Iva Valentinova
Income Source Confusion Using the SILC
title Income Source Confusion Using the SILC
title_full Income Source Confusion Using the SILC
title_fullStr Income Source Confusion Using the SILC
title_full_unstemmed Income Source Confusion Using the SILC
title_short Income Source Confusion Using the SILC
title_sort income source confusion using the silc
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496567/
https://www.ncbi.nlm.nih.gov/pubmed/37705921
http://dx.doi.org/10.1093/poq/nfad025
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