Trends in Earnings Volatility Using Linked Administrative and Survey Data

James Ziliak, Charles Hokayem and Christopher Bollinger

Journal of Business & Economic Statistics, 2022, vol. 41, issue 1, 12-19

Abstract: We document trends in earnings volatility separately by gender using unique linked survey data from the CPS ASEC and Social Security earnings records for the tax years spanning 1995–2015. The exact data link permits us to focus on differences in measured volatility from earnings nonresponse, survey attrition, and measurement between survey and administrative earnings data reports, while holding constant the sampling frame. Our results for both men and women suggest that the level and trend in volatility is similar in the survey and administrative data, showing substantial business-cycle sensitivity among men but no overall trend among continuous workers, while women demonstrate no change in earnings volatility over the business cycle but a declining trend. A substantive difference emerges with the inclusion of imputed earnings among survey nonrespondents, suggesting that users of the ASEC drop earnings nonrespondents.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2022.2102023 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Trends in Earnings Volatility using Linked Administrative and Survey Data (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:41:y:2022:i:1:p:12-19

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2022.2102023

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().