Webinar - How to Prepare for a Pay Equity Analysis Series Ep 2 Diagnose

PayScale, Inc.
PayScale, Inc.Director of Demand Generation at PayScale, Inc.
How to Prepare for a Pay Equity
Analysis, Ep. 2: Diagnose
Today's Presenters
Becky Wood
Director, Insights & Analytics
Ruth Thomas
Chief Product Evangelist
Chris Iversen
Principal Pay Equity Consultant
3
Pay equity analysis series
What you will learn:
• Key considerations before starting your
journey
• How to prepare for pay equity analysis
• How to plan for remediation and take
action
• How to work towards sustainable fair pay
• How technology can support you
Payscale’s pay equity approach
Commit Diagnose Take action Sustain
Today’s agenda
• How to diagnose your pay gaps
• What's next in this series
• Q&A
Poll 1: Has your organization
conducted pay equity analysis?
❑ No, never
❑Yes, but many years ago
❑Yes, every few years
❑Yes, annually
❑Yes, continuously
❑Unsure
Diagnose stage
Define scope and methodology
Group employees
Determine your factors
Run analysis
Define scope and methodology
Do you have the budget
for pay remediation?
Which populations and
locations are in scope?
What frequency do you
plan to conduct analyses?
How will this relate to your
compensation cycle?
Who will
participate?
What timeframe is
achievable?
What are your key
milestones?
How long will it take ?
Group employees
How do you determine hierarchy?
Can you use an existing job
classification framework or
benchmarking levels?
Job groupings should be legally
defensible How can you group
employees doing like work or
work that is substantially
similar?
Consider group size – impact on
analysis
What are the main issues in
grouping employees?
Determine your factors
What protected characteristics data do
you have/want to collect?
How consistent and complete
is your data?
What factors do you think should
drive pay variance, and which
shouldn’t?
Are your factors defensible?
Does your pay vary by location
or employee segment?
Do you have good data for each
of the factors?
Poll 2: What compensable
factors do you analyze for pay
equity?
❑ Experience
❑ Location
❑ Performance
❑ Skills
❑ Certifications
❑ Hierarchy
❑ Other
Some explanatory variables
are biased, even though they
seem objective.
“At Payscale, we believe a holistic pay equity
audit provides a complete understanding of
factors that drive wage inequality.”
16
Run analysis: how do you measure pay equity?
Controlled Pay Gap
(also known as adjusted or equal pay gap)
Measures equal pay for equal work - legislation
has broadened this definition to equal pay for
comparable, substantially similar and work of equal
value.
It is perfectly reasonable to pay people in the same
job differently if this is due to justifiable factors.
These are typically called compensable factors
and should be legally justifiable and ideally aligned
to your compensation philosophy.
Uncontrolled Pay Gap
(also known as unadjusted, raw or opportunity pay
gap)
The average earnings of one group of employees
compared to another.
Gives an overall position on equity.
Often isn’t directly related to pay but to systemic
issues such as cultural bias, societal assumptions
and a lack of progress in workplace design.
Recognizing the root causes of pay gaps allows
employers to address these systemic issues rather
than just the symptoms evidenced by pay gaps.
Poll 3: Which pay gaps to
you currently measure?
❑ The controlled gap (employees with the
same job characteristics)
❑The uncontrolled gap (overall regardless
of job)
❑Yes, for both the controlled and
uncontrolled gap
❑No
❑Unsure
Run analysis – uncontrolled pay gap
(Average Male Pay) – (Average Female Pay)
Average Male Pay
Median and Mean both have value
Valuable for smaller groups as cohort analysis
Important to use with regression at the PAG level - it adds
another lens, and you can still have pay equity issues without
statistical significance
Run analysis – regression analysis
• Regression analysis tests whether a pay gap reduces after key compensable
factors that are legally defensible and align to your compensation philosophy
are considered.
• The analysis statistically investigates the factors that determine pay. The effects
are measured simultaneously, i.e., the model attempts to estimate the contribution of
each variable in the presence of the others.
• Statistical modelling can generate predictions of what an employee would be paid,
according to the model, based on their individual values on permitted factors.
• Regression analysis modelling provides clues and directions for where to go next,
what to analyze further.
Run analysis - key considerations
• What does the R2 model value indicate? How important is it?
• Always test the validity of models - may need to re-run with different
variables or clean the data. And always compare against your goals and
comp philosophies.
• Importance of outliers, regression, and other mathematical tools, and
taking them all into account. Don’t just rely on one analytical result.
• You can still have pay equity problems without statistical significance!
• The model is only a model, it is not real life! It is important to
contextualize.
Key takeaways
Be intentional in defining the scope and planned outcomes for your pay
equity work.
Data, data, data!
Grouping employees is probably the most important step and often requires
pre-work, allow time for this.
Careful consideration of compensable factors is required, you may not have
all you need to begin your first analysis, but that’s OK.
It is not always about the numbers and there is more than one way of doing
this!
This is a technical but is a capability we should all be learning.
What’s next?
Watch for episode 3 in the
How to Prepare for Pay
Equity Analysis Series:
Take Action
Dig into more pay equity
resources from Payscale
Q&A
Feel free to ask any questions in the chat!
1 de 23

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Webinar - How to Prepare for a Pay Equity Analysis Series Ep 2 Diagnose

  • 1. How to Prepare for a Pay Equity Analysis, Ep. 2: Diagnose
  • 2. Today's Presenters Becky Wood Director, Insights & Analytics Ruth Thomas Chief Product Evangelist Chris Iversen Principal Pay Equity Consultant
  • 3. 3 Pay equity analysis series What you will learn: • Key considerations before starting your journey • How to prepare for pay equity analysis • How to plan for remediation and take action • How to work towards sustainable fair pay • How technology can support you
  • 4. Payscale’s pay equity approach Commit Diagnose Take action Sustain
  • 5. Today’s agenda • How to diagnose your pay gaps • What's next in this series • Q&A
  • 6. Poll 1: Has your organization conducted pay equity analysis? ❑ No, never ❑Yes, but many years ago ❑Yes, every few years ❑Yes, annually ❑Yes, continuously ❑Unsure
  • 7. Diagnose stage Define scope and methodology Group employees Determine your factors Run analysis
  • 8. Define scope and methodology Do you have the budget for pay remediation? Which populations and locations are in scope? What frequency do you plan to conduct analyses? How will this relate to your compensation cycle? Who will participate? What timeframe is achievable? What are your key milestones?
  • 9. How long will it take ?
  • 10. Group employees How do you determine hierarchy? Can you use an existing job classification framework or benchmarking levels? Job groupings should be legally defensible How can you group employees doing like work or work that is substantially similar? Consider group size – impact on analysis
  • 11. What are the main issues in grouping employees?
  • 12. Determine your factors What protected characteristics data do you have/want to collect? How consistent and complete is your data? What factors do you think should drive pay variance, and which shouldn’t? Are your factors defensible? Does your pay vary by location or employee segment? Do you have good data for each of the factors?
  • 13. Poll 2: What compensable factors do you analyze for pay equity? ❑ Experience ❑ Location ❑ Performance ❑ Skills ❑ Certifications ❑ Hierarchy ❑ Other
  • 14. Some explanatory variables are biased, even though they seem objective.
  • 15. “At Payscale, we believe a holistic pay equity audit provides a complete understanding of factors that drive wage inequality.”
  • 16. 16 Run analysis: how do you measure pay equity? Controlled Pay Gap (also known as adjusted or equal pay gap) Measures equal pay for equal work - legislation has broadened this definition to equal pay for comparable, substantially similar and work of equal value. It is perfectly reasonable to pay people in the same job differently if this is due to justifiable factors. These are typically called compensable factors and should be legally justifiable and ideally aligned to your compensation philosophy. Uncontrolled Pay Gap (also known as unadjusted, raw or opportunity pay gap) The average earnings of one group of employees compared to another. Gives an overall position on equity. Often isn’t directly related to pay but to systemic issues such as cultural bias, societal assumptions and a lack of progress in workplace design. Recognizing the root causes of pay gaps allows employers to address these systemic issues rather than just the symptoms evidenced by pay gaps.
  • 17. Poll 3: Which pay gaps to you currently measure? ❑ The controlled gap (employees with the same job characteristics) ❑The uncontrolled gap (overall regardless of job) ❑Yes, for both the controlled and uncontrolled gap ❑No ❑Unsure
  • 18. Run analysis – uncontrolled pay gap (Average Male Pay) – (Average Female Pay) Average Male Pay Median and Mean both have value Valuable for smaller groups as cohort analysis Important to use with regression at the PAG level - it adds another lens, and you can still have pay equity issues without statistical significance
  • 19. Run analysis – regression analysis • Regression analysis tests whether a pay gap reduces after key compensable factors that are legally defensible and align to your compensation philosophy are considered. • The analysis statistically investigates the factors that determine pay. The effects are measured simultaneously, i.e., the model attempts to estimate the contribution of each variable in the presence of the others. • Statistical modelling can generate predictions of what an employee would be paid, according to the model, based on their individual values on permitted factors. • Regression analysis modelling provides clues and directions for where to go next, what to analyze further.
  • 20. Run analysis - key considerations • What does the R2 model value indicate? How important is it? • Always test the validity of models - may need to re-run with different variables or clean the data. And always compare against your goals and comp philosophies. • Importance of outliers, regression, and other mathematical tools, and taking them all into account. Don’t just rely on one analytical result. • You can still have pay equity problems without statistical significance! • The model is only a model, it is not real life! It is important to contextualize.
  • 21. Key takeaways Be intentional in defining the scope and planned outcomes for your pay equity work. Data, data, data! Grouping employees is probably the most important step and often requires pre-work, allow time for this. Careful consideration of compensable factors is required, you may not have all you need to begin your first analysis, but that’s OK. It is not always about the numbers and there is more than one way of doing this! This is a technical but is a capability we should all be learning.
  • 22. What’s next? Watch for episode 3 in the How to Prepare for Pay Equity Analysis Series: Take Action Dig into more pay equity resources from Payscale
  • 23. Q&A Feel free to ask any questions in the chat!