Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 638

The DOI matches its Crossref record.

Measuring the Cyclicality of Real Wages: How Important is Composition Bias?

Solon, G., Barsky, R., Parker, J. A.·1994·The Quarterly Journal of Economics

Abstract

In the period since the 1960's, as in other periods, aggregate time series on real wages have displayed only modest cyclicality. Macroeconomists therefore have described weak cyclicality of real wages as a salient feature of the business cycle. Contrary to this conventional wisdom, our analysis of longitudinal microdata indicates that real wages have been substantially procyclical since the 1960's. We also find that the substantial procyclicality of men's real wages pertains even to workers that stay with the same employer and that women's real wages are less procyclical than men's. Numerous longitudinal studies besides ours have documented the substantial procyclicality of real wages, but none has adequately explained the discrepancy with the aggregate time series evidence. In accordance with a conjecture by Stockman (I983), we show that the true procyclicality of real wages is obscured in aggregate time series because of a composition bias: the aggregate statistics are constructed in a way that gives more weight to low-skill workers during expansions than during recessions. We conclude that, because real wages actually are much more procyclical than they appear in aggregate statistics, theories designed to explain the supposed weakness of real wage cyclicality may be unnecessary. and theories that predict substantially procyclical real wages become more credible.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2307/2118426

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 638

Metadata

Authors
Solon, G., Barsky, R., & Parker, J. A.
Venue
The Quarterly Journal of Economics
Published
1994-02-01
Volume
109
Issue
1
Pages
1-25
ISSN
  • 0033-5533 (ISSN-L)
  • 1531-4650
OpenAlex
W2890163679
Cited by
638
References
0
FWCI
28.74
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Measuring the Cyclicality of Real Wages: How Important is Composition Bias? https://app.cochat.ai/papers/10.2307/2118426
The paper
Solon, G., Barsky, R., & Parker, J. A. (1994). Measuring the Cyclicality of Real Wages: How Important is Composition Bias? The Quarterly Journal of Economics. https://doi.org/10.2307/2118426

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.