The Library
PERFORMANCE REVIEW · Performance Management

Self-ratings and bias in performance reviews - Harvard Kennedy School

Publisher not yet verifiedOctober 8, 2025 3 min read
performance reviews bias gender gap racial equity self-ratings anchoring manager bias natural experiment financial services demographic disparities

Editorial summary. This is our text summary of an article published by gnews-performance-review. Charts, figures, and the author’s full voice are at the original — read it there .

Editorial verdict

Credible field research. The natural experiment design is methodologically sound and the findings on anchoring effects are compelling — but the race gap persistence in manager ratings is the critical finding organisations cannot afford to overlook.

Executive summary

This article summarises a peer-reviewed field study published in the Journal of Economic Behavior & Organization (2025), co-authored by Iris Bohnet, Oliver P. Hauser, and Ariella S. Kristal of Harvard Kennedy School. The study investigates whether gender and race disparities in performance appraisals stem from employees' self-ratings, managers' evaluations, or the interaction between the two. Leveraging a natural experiment at a multinational financial services firm — a 2016 software glitch that prevented managers from viewing self-evaluations — the researchers analysed four review cycles (2015–2018) to isolate the causal effect of self-rating visibility on final performance scores. Key findings indicate that women, particularly women of colour, consistently assigned themselves lower self-ratings; managers rated people of colour lower regardless of self-rating visibility; and concealing self-ratings reduced anchoring effects but did not eliminate demographic disparities because managers substituted historical ratings as an alternative anchor. Notably, newcomers with no rating history who also had hidden self-ratings showed reduced gaps for women of colour. The article concludes that addressing performance rating bias requires both process-level interventions (withholding self-ratings) and manager-level interventions (structured criteria, bias interrupters, and demographic auditing).

researchRelevance: 9/10Multi-Region

Key insights

  • 1Women, and especially women of colour, consistently self-rate lower than men — a pattern attributed to social norms and anticipated backlash against self-promotion rather than actual performance differences.
  • 2Race-based penalties in manager ratings persisted even when self-evaluations were hidden, indicating that managerial bias operates independently of self-rating anchoring and is not resolved by process design changes alone.
  • 3When self-ratings were hidden and no historical rating data existed (newcomers), women of colour achieved parity with white women and men — suggesting that anchoring on past scores is a secondary mechanism that perpetuates inequity.

Practical takeaways

  • Withholding self-evaluations from managers until after initial ratings are submitted can reduce anchoring effects, with the strongest benefit observed for employees without prior rating histories.
  • Because race gaps in manager ratings persist independently of self-rating visibility, demographic outcome auditing and structured calibration processes represent a distinct and necessary layer of intervention beyond process sequencing changes.

References

  1. Journal of Economic Behavior & Organization (2025).Can gender and race dynamics in performance appraisals be disrupted? The case of social influence.

Source & Provenance

Verified
Publisher / Source

gnews-performance-review

Author

Not specified

Publication Date

October 8, 2025

Article Type

Research Study

Geography

Multi-Region

Content Type
Unknown Source Type
Original Source

Original source metadata is preserved. AI analysis is generated separately.

Like this? Get the Monday Decision Brief — free, every week.

No spam, unsubscribe anytime.

Rate this article

Want the full article? Read it at the original source — free, no paywall.

Read original article
All content belongs to original publishers. AI analysis is for research purposes only. View original source

Where Peoplense used this source

This source is cited in the Decision Brief below — our own answer to the question it bears on, with the evidence weighed and a verdict.