What about the ethnicity pay gap? Analysing beyond gender
A lot has been written about the gender pay gap, but a lot less is being said about analysing pay gaps linked to other D&I factors, such as age, disability and ethnicity.
Some recent statistics from a joint study of pay equity practices conducted by World at Work and Korn Ferry caught my attention:
· 46% of organisations consider both gender and ethnicity in pay equity analyses.
· 36% of organisations take additional demographics (e.g., age) into account.
No doubt like many of you, I’m curious about quality analysis of pay gap indicators beyond gender. Being able to report on ethnicity is closest to my heart as I’m doubly impacted by these biases in that I’m Indian and I’m female. A quick Google search doesn’t show me how this is (possibly) being achieved in NZ organisations. Is reporting on ethnicity still sitting in the ‘too hard basket’?
I asked our RemNet membership using a Speedback Survey with the following response from 14 companies:
· Only 36% analyse pay gaps by factors other than gender
· Surprisingly, only one organisation analyses ethnicity; and
· Perhaps more alarmingly, only 21% of member organisations indicated that they’ll look at analysis by ethnicity in the future.
We know there’s an ethnicity pay gap in NZ. A 2018 white paper from Statistics NZ: Statistical Analysis of Ethnic Wage Gaps in NZ, using data from the 2016 and 2017 Household Labour Force Surveys found significant ethnicity pay gaps. Statistics NZ estimate the average hourly wage earned by Māori employees as 82% of the average hourly Pākehā wage, and the average wage earned by Pacific employees at only 77% of the Pākehā average.
We know Boards across NZ have set targets for achieving a gender balance and more diverse representation in terms of ethnicity in their leadership teams. It makes sense that pay parity across gender and ethnicity would also be expected in working towards these goals.
So what’s holding us back from analysing ethnicity pay gaps within our organisations?
The thought starters below are intended to start the ethnicity pay gap conversation within our network. My hope is that collectively we’re able to identify solutions to the challenges we’re all facing.
1. Robust data is the biggest barrier to ethnicity pay gap reporting. This challenge has existed on a national level since well before we started thinking about pay gaps – even with something as important as our national census.
NZ doesn’t have strict reporting guidelines like the UK or USA so our employees can choose not to report their ethnicity. Do we wait till NZ follows suit and implements stricter reporting rules? Or, can we work with our employees to raise awareness and help them understand why we need their data and what exactly it will be used for.
Fonterra started this journey last year by conducting a ‘diversity census’ for our NZ-based employees, encouraging them to update all their personal details on our HR system. We then celebrated our diversity, communicating the results in an infographic.
2. How do we treat employees with mixed ethnicity? Do we only report on the most dominant ethnicity or on all ethnicities an employee identifies with?
Statistics NZ introduced ‘Kiwi’ in our national census. ‘Kiwi Asian’ and ‘Kiwi Indian’ have become increasingly popular. Are we confusing nationality with ethnicity?
Are we at risk of giving more ‘weighting’ to one ethnicity over another in order to make our stats shine? For example, if an organisation has programs for Maori/Pasifika employees might they only include an employee’s Maori/Pasifika background and disregard any other ethnicity they have recorded?
Is it appropriate to ask employees to ‘rank’ all the ethnicities they identify with?
3. Are we trying to be too specific? Do we need to analyse pay gaps by all unique ethnicities, or should we group by continent?
Perhaps we could turn to Statistics NZ for some best practice guidelines to kickstart our ethnicity pay gap analysis. Their “Ethnicity NZ Standard Classification 2005” is an attempt at standardising ethnicity classifications.
4. How do we manage gaps in our data where no ethnicity has been specified? Do we report on ‘other’, or are we excluding this part of our employee population and pretending we have a full data set?
5. Should we analyse ethnicity on its own as we’ve been doing for gender? Will analysing the interaction of multiple D&I factors give us a more accurate picture or complicate our story?
Research shows the impacts of both gender and diversity are more profound than gender alone.
We’d love to hear from you. Who’s already begun to tackle these challenges? Where to from here?