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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...

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Detalles Bibliográficos
Autores principales: Borowczyk-Martins, Daniel, Lalé, Etienne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
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
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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.
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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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