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Employer-to-Employer Transitions and Time Aggregation Bias

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Employer-to-Employer Transitions and Time Aggregation Bias. / Bertheau, Antoine; Vejlin, Rune.
In: Labour Economics, Vol. 75, 102130, 04.2022.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Harvard

Bertheau, A & Vejlin, R 2022, 'Employer-to-Employer Transitions and Time Aggregation Bias', Labour Economics, vol. 75, 102130. https://doi.org/10.1016/j.labeco.2022.102130

APA

Bertheau, A., & Vejlin, R. (2022). Employer-to-Employer Transitions and Time Aggregation Bias. Labour Economics, 75, Article 102130. https://doi.org/10.1016/j.labeco.2022.102130

CBE

Bertheau A, Vejlin R. 2022. Employer-to-Employer Transitions and Time Aggregation Bias. Labour Economics. 75:Article 102130. https://doi.org/10.1016/j.labeco.2022.102130

MLA

Bertheau, Antoine and Rune Vejlin. "Employer-to-Employer Transitions and Time Aggregation Bias". Labour Economics. 2022. 75. https://doi.org/10.1016/j.labeco.2022.102130

Vancouver

Bertheau A, Vejlin R. Employer-to-Employer Transitions and Time Aggregation Bias. Labour Economics. 2022 Apr;75:102130. doi: 10.1016/j.labeco.2022.102130

Author

Bertheau, Antoine ; Vejlin, Rune. / Employer-to-Employer Transitions and Time Aggregation Bias. In: Labour Economics. 2022 ; Vol. 75.

Bibtex

@article{6c0df00f2c12436c9e8d60e1054f7478,
title = "Employer-to-Employer Transitions and Time Aggregation Bias",
abstract = "The rate at which workers switch employers without experiencing a spell of unemployment is one of the most important labor market indicators. However, Employer-to-Employer (EE) transitions are hard to measure in widely used matched employer-employee datasets such as those available in the US. We investigate how the lack of the exact start and end dates for job spells affect the level and cyclicality of EE transitions using Danish data containing daily information on employment relationships. Defining EE transitions based on quarterly data overestimates the EE transition rate by approximately 30% compared to daily data. The bias is procyclical and is reduced by more than 10% in recessions. We propose an algorithm that uses earnings and not just start and end dates of jobs to redefine EE transitions. Our definition performs better than definitions used in the literature.",
keywords = "Employer-to-employer transitions, Labor market flows, Measurement problems, Time aggregation bias",
author = "Antoine Bertheau and Rune Vejlin",
note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2022",
month = apr,
doi = "10.1016/j.labeco.2022.102130",
language = "English",
volume = "75",
journal = "Labour Economics",
issn = "0927-5371",
publisher = "Elsevier BV * North-Holland",

}

RIS

TY - JOUR

T1 - Employer-to-Employer Transitions and Time Aggregation Bias

AU - Bertheau, Antoine

AU - Vejlin, Rune

N1 - Publisher Copyright: © 2022 The Author(s)

PY - 2022/4

Y1 - 2022/4

N2 - The rate at which workers switch employers without experiencing a spell of unemployment is one of the most important labor market indicators. However, Employer-to-Employer (EE) transitions are hard to measure in widely used matched employer-employee datasets such as those available in the US. We investigate how the lack of the exact start and end dates for job spells affect the level and cyclicality of EE transitions using Danish data containing daily information on employment relationships. Defining EE transitions based on quarterly data overestimates the EE transition rate by approximately 30% compared to daily data. The bias is procyclical and is reduced by more than 10% in recessions. We propose an algorithm that uses earnings and not just start and end dates of jobs to redefine EE transitions. Our definition performs better than definitions used in the literature.

AB - The rate at which workers switch employers without experiencing a spell of unemployment is one of the most important labor market indicators. However, Employer-to-Employer (EE) transitions are hard to measure in widely used matched employer-employee datasets such as those available in the US. We investigate how the lack of the exact start and end dates for job spells affect the level and cyclicality of EE transitions using Danish data containing daily information on employment relationships. Defining EE transitions based on quarterly data overestimates the EE transition rate by approximately 30% compared to daily data. The bias is procyclical and is reduced by more than 10% in recessions. We propose an algorithm that uses earnings and not just start and end dates of jobs to redefine EE transitions. Our definition performs better than definitions used in the literature.

KW - Employer-to-employer transitions

KW - Labor market flows

KW - Measurement problems

KW - Time aggregation bias

UR - http://www.scopus.com/inward/record.url?scp=85124245783&partnerID=8YFLogxK

U2 - 10.1016/j.labeco.2022.102130

DO - 10.1016/j.labeco.2022.102130

M3 - Journal article

AN - SCOPUS:85124245783

VL - 75

JO - Labour Economics

JF - Labour Economics

SN - 0927-5371

M1 - 102130

ER -