Nominal Wage Adjustments and the Composition of Pay: New Evidence from Payroll Data
Daniel Schaefer () and
Carl Singleton ()
Additional contact information
Daniel Schaefer: Institut für Volkswirtschaftslehre, Johannes-Kepler-Universität Linz
No em-dp2020-01, Economics & Management Discussion Papers from Henley Business School, Reading University
We use representative employer payroll data from Great Britain and the period 2006-2018 to document novel facts about nominal wage adjustments, focusing on workers who stay in the same firm and job from one year to the next. The richness of these data allows us to analyse separately basic pay and the other components of earnings, such as overtime and incentive pay, while controlling for hours worked. Weekly and hourly basic pay show signs of downward nominal rigidity, but non-basic pay components adjust more commonly. Unusually, these payroll-based data also report the pay rates of hourly-paid employees. A quarter of these workers, who stay in the same job between years, typically see no change in their rate of pay, and very few experience wage cuts. Finally, we exploit the employer-employee link in our data and find some evidence that wage setting is state-dependent rather than time-dependent.
Keywords: downward nominal wage rigidity; payroll records; components of pay; hourly pay (search for similar items in EconPapers)
JEL-codes: E24 J31 J33 (search for similar items in EconPapers)
Pages: 48 pages
New Economics Papers: this item is included in nep-eur, nep-hrm, nep-lma and nep-mac
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rdg:emxxdp:em-dp2020-01
Access Statistics for this paper
More papers in Economics & Management Discussion Papers from Henley Business School, Reading University Contact information at EDIRC.
Bibliographic data for series maintained by Marie Pearson ().