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Employee Pay Equity & Wage Gap Perception Survey

Measures how fairly employees believe compensation is distributed across gender, race, tenure, and role, and pinpoints which pay decisions or comparisons shape that perception. Built for HR and People Analytics teams auditing pay equity, with an AI follow-up that reconstructs the specific story behind a respondent's lowest fairness rating instead of just the number.

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Sample questions

A preview of what’s in the template. Every question is editable before you launch.

16 questions · ~8 min
Q01
Message

Thanks for taking a few minutes to share how you see pay fairness at our organization. Your answers are anonymous and feed directly into our pay equity review — this takes about 5-6 minutes.

Q02
Multiple ChoiceRequired

Are you aware of this organization's approach to ensuring equal pay for equal work (e.g., published pay bands, regular pay equity audits)?

  • Yes, I've seen specific information (bands, audit results, etc.)
  • I've heard it's a priority but haven't seen details
  • No, I'm not aware of any approach
  • Not sure
Q03
Opinion ScaleRequired

Overall, how fairly do you believe your pay compares to others doing similar work at this organization?

Scale: 1 – 7
Min:Very unfairlyMax:Very fairly
Q04
MatrixRequired

Based on what you've observed or heard, how does each of the following tend to affect pay outcomes at this organization?

6 rows × 5 columns
  • Gender
  • Race or ethnicity
  • Age
  • Tenure at the company
  • Educational background
  • +1 more
Columns: Much lower pay · Somewhat lower pay · No noticeable difference · Somewhat higher pay · Much higher pay
Q05
Multiple Choice

In the last 12 months, have you compared your pay to publicly available salary data or to coworkers' pay?

  • Yes, using public salary data (e.g., a salary benchmarking site)
  • Yes, by talking with coworkers
  • Yes, both
  • No, I haven't compared
Q06
Best–Worst Trade-off (MaxDiff)Required

Which of the following do you think has the biggest influence on pay differences between employees here?

  • Job performance
  • Years of experience
  • Negotiation skill
  • Manager relationships
  • Job level or title
  • Location or remote-work status
  • Demographic characteristics (e.g., gender, race)
Pick best & worst per setBest:Most influences pay differences hereWorst:Least influences pay differences here
Q07
Opinion ScaleRequired

How confident are you that pay decisions here — raises, promotions, starting offers — are made without bias toward any group?

Scale: 1 – 5
Min:Not at all confidentMax:Completely confident
Q08
AI InterviewRequired

Reconstruct the specific experience behind this respondent's fairness rating and their confidence-in-bias-free-decisions rating. If either score was low or mid-range, ask for a concrete example — a raise, promotion, or comparison with a colleague — that shaped that view, and which demographic factor (if any) they suspect was involved. If both scores were high, ask what evidence or communication gave them that confidence, so we can identify what's working.

Q09
Multiple Choice

Has your manager or HR ever proactively explained how your pay was determined?

  • Yes, in detail
  • Yes, briefly
  • No, never
  • Don't recall
Q10
Rating Scale

How satisfied are you with how clearly this organization communicates about pay decisions and pay equity efforts?

Range: 1 – 5
Min:Very dissatisfiedMax:Very satisfied
Q11
Long Text

What one change would most improve your confidence that pay is determined fairly here? (Optional)

Q12
Message

Just a few optional background questions to help us spot patterns across groups — feel free to skip any of these.

Q13
Multiple Choice

Which gender do you identify with?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q14
Multiple Choice

Which best describes your race or ethnicity?

  • American Indian or Alaska Native
  • Asian
  • Black or African American
  • Hispanic or Latino
  • Native Hawaiian or Pacific Islander
  • White
  • Two or more races
  • Prefer to self-describe
  • Prefer not to say
Q15
Multiple Choice

How long have you been with this organization?

  • Less than 1 year
  • 1-3 years
  • 4-7 years
  • 8+ years
  • Prefer not to say
Q16
Message

Thank you for your honesty. Your responses will be combined anonymously with others and reviewed by HR leadership as part of our pay equity assessment — no individual answers are shared with managers.

What’s included

  • AI follow-ups

    Adaptive probes on open-ended answers that pull out detail a static form would miss.

  • Attention checks

    Built-in safeguards against rushed answers and low-quality respondents.

  • AI-drafted copy

    Wording, ordering, and branching written by the AI — tuned to your research goal.

  • Auto report

    Themes, quotes, and a plain-English summary write themselves once responses come in.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

  • Goes beyond a single fairness rating with an AI follow-up interview that reconstructs the specific pay decision or comparison behind a respondent's lowest score
  • Includes a max-diff exercise to rank which factors (gender, race, tenure, role) employees believe most drive pay gaps, not just whether one exists
  • Pairs quantitative measures (opinion scales, matrix, rating) with an open-ended question and optional demographic breakdowns for gender, race, and tenure to segment results
  • Publishes the AI's questions and follow-up logic transparently and auto-generates a report, with a free tier and $50/mo Business plan (no academic tier)

SurveyMonkey

Wage Gap Evaluation Template & Questions

This is a fielding-ready static template focused specifically on wage gap perception, making it a direct topical match. It relies on fixed question sets (likely scales and multiple choice) rather than adaptive probing, so any 'why' behind a low fairness score has to be inferred rather than reconstructed. It's a solid, established option for teams that just need standard wage-gap metrics without deeper narrative context.

What it does well

  • Purpose-built specifically for wage gap perception rather than generic employee evaluation
  • Backed by SurveyMonkey's large template library and familiar survey-building tools
  • Likely quick to deploy given its templated, ready-to-use format

Where it falls short

  • No adaptive AI follow-up to dig into the specific story behind a low fairness rating — respondents just leave a static score
  • No voice AI interview option or guided screen-share tasks for richer qualitative context
  • No published methodology or prompt-level transparency for how any follow-up questions (if present) are generated

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

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