Employer-to-Employer Transitions and Time Aggregation Bias

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Employer-to-Employer Transitions and Time Aggregation Bias. / Bertheau, Antoine; Vejlin, Rune Majlund.

I: Labour Economics, Bind 75, 102130, 2022.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

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

APA

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

Vancouver

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

Author

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

Bibtex

@article{51ed93887c644206890109dbb3e35b90,
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 = "Faculty of Social Sciences, Labour market flows, Employer-to-employer flows, Measurement problems, Time aggregation bias",
author = "Antoine Bertheau and Vejlin, {Rune Majlund}",
year = "2022",
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 Majlund

PY - 2022

Y1 - 2022

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 - Faculty of Social Sciences

KW - Labour market flows

KW - Employer-to-employer flows

KW - Measurement problems

KW - Time aggregation bias

U2 - 10.1016/j.labeco.2022.102130

DO - 10.1016/j.labeco.2022.102130

M3 - Journal article

VL - 75

JO - Labour Economics

JF - Labour Economics

SN - 0927-5371

M1 - 102130

ER -

ID: 290056198