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Data for the ins and outs of involuntary part-time employment
Data are US monthly time series of involuntary part-time employment stocks and flows from 1976 until 2019 (covering five economic downturns), derived from the US Current Population Survey (CPS). Stocks and flows are cleared from discrepancies over time caused by the 1994 redesign of the CPS, and the...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776922/ https://www.ncbi.nlm.nih.gov/pubmed/33426243 http://dx.doi.org/10.1016/j.dib.2020.106686 |
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author | Borowczyk-Martins, Daniel Lalé, Etienne |
author_facet | Borowczyk-Martins, Daniel Lalé, Etienne |
author_sort | Borowczyk-Martins, Daniel |
collection | PubMed |
description | Data are US monthly time series of involuntary part-time employment stocks and flows from 1976 until 2019 (covering five economic downturns), derived from the US Current Population Survey (CPS). Stocks and flows are cleared from discrepancies over time caused by the 1994 redesign of the CPS, and they are adjusted to control for margin error problems and time aggregation biases. Data are available in two different formats: unadjusted and adjusted for misclassification errors – another important sources of biases in worker flows data. The time series obtained through these adjustments allow for a comprehensive account of the cyclical dynamics of involuntary part-time employment. |
format | Online Article Text |
id | pubmed-7776922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-77769222021-01-07 Data for the ins and outs of involuntary part-time employment Borowczyk-Martins, Daniel Lalé, Etienne Data Brief Data Article Data are US monthly time series of involuntary part-time employment stocks and flows from 1976 until 2019 (covering five economic downturns), derived from the US Current Population Survey (CPS). Stocks and flows are cleared from discrepancies over time caused by the 1994 redesign of the CPS, and they are adjusted to control for margin error problems and time aggregation biases. Data are available in two different formats: unadjusted and adjusted for misclassification errors – another important sources of biases in worker flows data. The time series obtained through these adjustments allow for a comprehensive account of the cyclical dynamics of involuntary part-time employment. Elsevier 2020-12-26 /pmc/articles/PMC7776922/ /pubmed/33426243 http://dx.doi.org/10.1016/j.dib.2020.106686 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Borowczyk-Martins, Daniel Lalé, Etienne Data for the ins and outs of involuntary part-time employment |
title | Data for the ins and outs of involuntary part-time employment |
title_full | Data for the ins and outs of involuntary part-time employment |
title_fullStr | Data for the ins and outs of involuntary part-time employment |
title_full_unstemmed | Data for the ins and outs of involuntary part-time employment |
title_short | Data for the ins and outs of involuntary part-time employment |
title_sort | data for the ins and outs of involuntary part-time employment |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776922/ https://www.ncbi.nlm.nih.gov/pubmed/33426243 http://dx.doi.org/10.1016/j.dib.2020.106686 |
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