Causal misperceptions of the part-time pay gap

B-Tier
Journal: Labour Economics
Year: 2023
Volume: 83
Issue: C

Authors (3)

Backhaus, Teresa (Rheinische Friedrich-Wilhelms-...) Schäper, Clara (not in RePEc) Schrenker, Annekatrin (not in RePEc)

Score contribution per author:

0.670 = (α=2.01 / 3 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

This paper studies if workers infer from correlation about causal effects in the context of the part-time wage penalty. Differences in hourly pay between full-time and part-time workers are strongly driven by worker selection and systematic sorting. Ignoring these selection effects can lead to biased expectations about the consequences of working part-time on wages (‘selection neglect bias’). Based on representative survey data from Germany, we document substantial misperceptions of the part-time wage gap. Workers strongly overestimate how much part-time workers in their occupation earn per hour, whereas they are approximately informed of mean full-time wage rates. Consistent with selection neglect, those who perceive large hourly pay differences between full-time and part-time workers also predict large changes in hourly wages when a given worker switches between full-time and part-time employment. Causal analyses using a survey experiment reveal that providing information about the raw part-time pay gap increases expectations about the full-time wage premium by factor 1.7, suggesting that individuals draw causal conclusions from observed correlations. De-biasing respondents by informing them about the influence of worker characteristics on observed pay gaps mitigates selection neglect. Subjective beliefs about the part-time/full-time wage gap are predictive of planned and actual transitions between full-time and part-time employment, necessitating the prevention of causal misperceptions.

Technical Details

RePEc Handle
repec:eee:labeco:v:83:y:2023:i:c:s0927537123000714
Journal Field
Labor
Author Count
3
Added to Database
2026-01-24