Podcast-Intro: Welcome to
Testing, Testing 123, a podcast

brought to you by TestGenius.

Jenny Arnez: everyone.

My name is Jenny Arnez and you
are watching the Testing, Testing,

1-2-3 podcast by TestGenius.

I'm your host today.

I'm joined with Mike Callen.

He's my co host and
President of TestGenius.

Also back with us today is Dr.

Brian Marentette.

Brian is the Director of People
Insights at Berkshire Associates.

And we are starting our part two of our
conversation with Brian about pay equity.

Guys, do you mind if I just go
ahead and just jump right in?

Brian Marentette, PhD: Let's do it

Jenny Arnez: So Brian, We talked
a lot about pay equity analysis

last time, some basic principles.

And as I thought about this,
I have a couple questions.

If there seems to be perhaps two different
ways that, that one might conduct a pay

equity analysis in their organization.

They could either do it software driven.

Right, or they could hire
a third party consultant.

Can you talk a little bit about that?

Brian Marentette, PhD: Sure, so I am on
the consultant side of things, so I will

try not to be too biased in my discussion
of this, but we too use software.

We all have to use software
at the end of the day.

And ultimately those pieces of
software are really just tools

that allow us to run the analysis.

Now, within the last several years
several companies have made the software

more user friendly, more available
to, HR analysts and compensation

analysts to be able to do some of
these pay equity types of analyses.

by themselves, which is great.

The more that you can introduce this
concept to the world and have more

of it going on, the better, right?

We're all looking to address pay equity
and make it more equitable and fair.

Now there's obviously some caveats there.

What, what comes with, Software
typically is limited support, right?

You do off offer customer
service and different technical

support resources and things.

But the level of support that,
we provide in on the consulting

side is pretty extensive.

We have gone through years of schooling
and years of research and experience

on how to do these analyses and how
to overcome obstacles and challenges

and resolve data issues that, if
you're just using software and have

not been trained in this space are
going to be brick walls for you.

It's going to be extremely difficult.

So to an experienced analyst that
has been doing pay equity work that.

Maybe understands the fundamental
steps that are involved and has

the ability to work with the data.

Certainly, they could be using
software and doing it effectively.

And that's great to somebody
that is new to the space has not

been trained in statistics or,
understands how to set up statistical

models for analyzing compensation.

Generally, that's where,
consulting and the service side

is going to be more beneficial.

There's a lot of complexity and
nuance in some of the data that we

use and how we set up the analysis
that again, can be confusing.

Multiple routes that you can take
and how do you decide which way

to set this up and how to run it?

And then most importantly on the backend.

Once you run everything,
you get your results.

What does it all mean?

How do you interpret these findings?

What is it really telling you?

Cause it's easy to just set something up
with a dataset, hit run, get your results,

and then start putting money towards
potential problem areas that you've got.

And in our experience, we've seen a
few clients that have gone from using

software internally to then coming to us.

When they've got a claim or they're being
audited by the department of labor Saying

we're being told we have pay equity
problems and we've been doing it for

years and we don't see anything in our
results Okay, let us give it a shot and

we run it and then we see a completely
different story than what they've told

us so yeah to the experienced user and
analyst of course using software would

be a viable route for everybody else They
might find more benefit in a Consultant

relationships that guides them through it.

Jenny Arnez: The personal example
that immediately popped into my head

was trying to do my income taxes.

Do I use this software?

And I go, Oh, I have no idea
what that question means.

Or do I go to an accountant
who understands the law?

Brian Marentette, PhD: Yeah, exactly.

Yeah.

And I, for years, I was able
to do my own taxes because like

back in grad school, I was broke.

I didn't make any money.

I didn't have anything to really do.

So yeah, like a smaller employer.

And we had this example earlier,
which was, if you have 10 employees,

can you really be doing pay equity?

Sure.

And guess what?

You don't need software.

You won't even open a software program.

You might look at Excel once to see what
everybody's making, but in that situation,

you're gonna be doing what's called like
a cohort analysis, which is literally

looking at each person and saying.

What are they making?

Okay.

How does that job compare
to this other person?

Because all 10 of your employees
are probably doing different things.

Do we value that work
that much more than this?

And does that person have that
much more experience that we should

be paying them that much higher?

And it's really like a
pretty manual review.

That's considered a pay equity analysis.

Now it's on a very small sample
or small head count, but yeah.

When you get to be a much more complicated
organization, I can promise you, Companies

like Google or Microsoft, they are not
doing their pay equity analysis through

an HR analyst, internally on software,
things get much more complicated.

I'm

Jenny Arnez: sure.

I would think too, that having a
third party consultant involved means

a less of a likely that you're going
to be biased in your own findings.

Brian Marentette, PhD: Potentially.

Yeah.

And I think, yeah, there's a
potential there for somebody

internally to maybe steer away, even

unintentionally steering away from
setting an analysis up that might produce,

disparity or a red flag in their findings.

But I think the, Probably one
of the larger benefits of having

it done externally is the ties
into the transparency piece.

Hey, we're having our books
audited by an external firm.

They're coming in, they're running
the analysis, they're telling us

what the problems are, they're
telling us how much we need to

adjust salaries to resolve them.

It's not done internally.

This is all done externally and they
have no vested interest in how we do it.

We operate.

So

Mike Callen: is it potentially a
legal strategy to have a third party

handle this issue like they might
handle any other potentially legally

legal minefield issue to keep things
at arm's length and have some sort

of attorney client privilege or some
sort of similar type of protection.

Okay.

Brian Marentette, PhD: Yeah, absolutely.

Great question there on the point
with legal counsel and privilege

because a lot of what, pay equity
analysis, even if you're not doing

it for legal purposes, even if you're
on more of a proactive side of the

house, you're going to be surfacing.

What we refer to as statistically
significant differences.

When we do these pay equity analyses,
we're looking for differences that

are large enough to be meaningful,
not just due to random chance.

So of course there's going to be
differences along, gender or race

lines and things, but are they big
enough to really be meaningful?

Do we know that they're not just
due to random occurrence and.

Through that exercise, you are
producing results that could be used

against you in the court of law.

If you're not doing the work under
attorney client privilege could be sued.

Any employer could be sued by an
individual employee or a class

action, a group of employees and
whatever analyses you've done

internally, if they are not protected.

They can say, Oh, let's
bring those in as evidence.

You found disparities.

You didn't address all of them.

And you let that work, go on you're in
a bad position there as an employer.

Mike Callen: Do you get
hired by attorneys then?

How does that relationship get
triggered such that some information

is attorney client privileged or
can in house counsel hire you?

And now all of a sudden, that,
that sort of veil is in place.

I just don't, I don't
know anything about that.

Brian Marentette, PhD:
I'm not an attorney.

I don't pretend to be one on TV either.

And or podcasts or anything.

Yeah.

But it, there's some gray area there.

I'd say the safest route is using external

legal counsel who coordinates through
internal counsel, external engages us

to do the analysis, advise external
counsel so that they can advise the

company on interesting legal system.

That's the safest route.

Mike Callen: I do remember being in
a conversation with you and I don't

know what it was a year ago, maybe.

And so this is going back to the soft
software versus the consultant driven.

And I hope I.

I characterize this question correctly.

And if not, if I don't, hopefully
you understand what I'm referring to.

But I remember that you were suggesting
that there can oftentimes be a

tendency within The results that are
given by the software program to have

this big series of overcorrections
to the left or, up and down within

classifications or positions that
actually end up causing more problems.

And that's another reason why you
might want to have a consultant engage

and look at the results, whether
they're software results from you.

Your program or software
results from some platform.

Did I say that anywhere near correctly?

Brian Marentette, PhD: Yeah, I think so.

Yeah.

Yeah.

Yeah.

No, you're on track.

And it goes back to
setting up your analysis.

Of course, if you analyze everything by
job title if you're looking at individual

jobs, see a gap and it's controlling for
relevant factors, you can feel pretty

confident that you need to address that.

Now, not every job title has
enough people to be able to

analyze them as a standalone group.

You need lots of head counts
really to run these like 30

employees in a particular grouping.

So if you don't hit that that
group doesn't get analyzed.

Now to address that people doing this on
their own might start to aggregate groups.

Of jobs together.

They might even run it company wide and
maybe try to control for things like the

department that somebody is in or, the job
group that they're in or your job level.

And so you get these big, we
call them like big models, right?

There are hundreds, maybe thousands
of people in them and you run it and

you find, gosh, we have a gap of 1.

2 million.

Females are impacted by 1.

2 million.

Now.

They didn't set that up correctly.

And so they take 1.

2 million to close that gap and
they distribute it to these females.

And then they run it
again and it looks good.

But what they didn't realize is that
for all these jobs that are within

that, everybody's pretty comparable.

But they didn't really control
for the job that somebody's doing.

And so they give all this money to
people in all these jobs that are female.

And then now when you
look at it by job title.

Females are, you get that
huge overcorrection, right?

So in all these jobs, now females are
making more than their male counterparts.

And you've basically created a
pain, another problem, inequity.

So yeah, you went from probably
having not very much of an issue to

now you actually have a major issue
that's swung the other direction.

And so that's where the complexity,
can lead people down the wrong path

without them really even realizing it.

You can run all these analyses
in a software program and it'll

tell you how much money you need.

Get that bad result to go away,
but that bad result is due

to a faulty analysis, really.

And again, that's in our
experience when we get called in.

It's because that exact
thing has happened.

Mike Callen: Interesting.

Thank you.

Jenny Arnez: So let's talk about
best practices for a minute.

Can you share with us what, what should
an employer pay attention to when having

a pay equity analysis done for them by
an outside consultant or internally?

Brian Marentette, PhD: Yeah.

Yeah.

Yeah.

Yeah.

So there's, three primary factors
that they should be looking at.

One is going to be the statistical
model that, that they're

using, that they're running.

Even if it's a consultant coming
in The goal of the pay equity

analysis is to really reflect
how they make pay decisions.

So ensuring that your statistical
model reflects the factors

that you use to set pay.

So tenure, maybe the time of the job,
maybe performance, or if you have prior

experience all those things that you
use internally as a comp team to set pay

need to be factored into your analysis.

If you leave some of those out you're
really, you've got an incomplete picture.

And that can also lead to that problem of,
kind of like, flagging false positives.

Really of, red hotspots in your results.

And so making sure that you are, you
understand your pay decisions, how

do you set pay and then making sure
you've got data that reflects that.

And if you don't have data in your HRS,
that ties to some of those things like

prior experience be ready to go, pull your
sleeves up when you find problem areas.

And you think it might be
due to prior experience.

You got to go look and maybe pull
resumes potentially and see if prior

experience plays a factor there.

So that's one piece is making sure you
understand your compensation practice

so that the analysis that's intended
to account for that can really do that.

The other thing is knowing
your pay variables.

Like what do you want to analyze?

Those different types of pay,
your base salary of courses is

probably the place to start.

That's the most important
really it's hardest to change.

It's the the largest sum of money,
usually that's the place to start.

But then looking at other forms
of pay and do you know what

influences those elements of pay.

So if you want to look at like overtime
do you really want to look at overtime?

Is there any opportunity for
discretion or bias to enter the

equation of who's earning overtime?

Government agencies might look at
it, but from a, more of a pay equity

standpoint, maybe over time is not
as much of a concern potentially.

Bonuses, if you have bonuses that
are tied to a formulaic kind of

bonus plan, okay it's going to be
the result of the formula that you've

instituted versus discretionary bonuses.

Maybe we want to look really more at just
those end of year discretionary lump sums.

So that's the other thing is understanding
your pay and which forms of pay you

want to analyze and how should they be
analyzed what factors influence them.

And the last piece is the grouping of
how do you want to group your employees?

Like I mentioned with job
title, that's the most

straightforward, it's the easiest.

To look at everybody within the same job
should be making roughly the same amount.

But then are there other what we would
refer to as pay analysis groups that

you could look at, and then how do
you interpret those outcomes as well?

So maybe you want to analyze all employees
within a single pay grade, right?

You have grades one through 12.

Maybe we use pay grade
as our means of grouping.

That.

legal concerns.

It's outside of the Title VII Civil
Rights Act framework, but it will tell

you another look at whether you might
have differences that, again, are

falling on race or gender lines and
that'll include larger groups of people.

Almost all of your pay grades are
going to have at least 30 people

that you can use to run the analysis.

So that's one set.

I'd say that's the starting
place for a pay equity analysis.

Those are things you need to pay attention
to of, how do you set up your model?

What pay are you going to look at?

How are you going to group your employees?

Now within each of those,
there's other more nuanced.

Discussion points and things that
should be worked out with you and the

consultant or within the software.

But those are the three big
categories to pay attention to.

Jenny Arnez: So on that third question,
how do you want to group your employees?

How do you even decide that?

Brian Marentette, PhD:
Oh, good question there.

Questions that are being asked,
what's the, what are you seeking

out of this pay equity analysis?

Is it strictly a legal review?

Okay, we can limit it to job title.

No need to look any further.

That's where really the
legal standard would stop.

Typically more of a DNI lens.

Okay.

Maybe we go and run it by departments
and see that I talked about the

pay gap, let's look at it without
controlling for levels, not factoring

in what job level somebody's at.

Just look at it by department,
what departments are showing.

the largest spread there, the largest
gap after we factor in performance

and some of these other variables.

And so it's really incumbent upon
the client or the end user of

the pay equity analysis to define
what do we want out of this?

If it's really just making
sure at a, job title.

We have equal pay for equal work.

That's great.

If you want to know some of those
other broader questions do we

have talent acquisition issues?

Do we have an uneven distribution of
men and women throughout our hierarchy

or our pay pay grades, then we can
look at broader lenses and that will

certainly open up that can for you.

Mike Callen: I have a question

if

it's okay.

In the first episode, a little bit of
an echo in the first episode, there was

a scenario that you described, Brian,
where there was a business that had an

underground parking and a daycare center.

There were valets and
there were daycare workers.

And you brought up a really interesting
scenario where the valet Parkers were

getting maybe $20 an hour and the
daycare workers, or sorry, 25 and

the daycare workers were getting 20.

The valets were mostly men.

The daycare workers were mostly
women and you were contending that,

the, maybe the more difficult job
was the daycare center operator.

And so they were clearly
not similar job titles.

Yet they.

When you compare them and maybe you
could make a case for one of them being,

a more difficult, more challenging job.

And the women who predominantly
worked there were getting paid

less versus the other one that was
on the other end of the spectrum.

That's clearly something that
human has to be able to sort out.

How do you even start diving
into something like that?

Because that seems it seems like
finding pay equity issues within job

titles is going to be relatively easy.

Identifying these is going to be much more
difficult and probably the bigger problem.

Brian Marentette, PhD: Yeah.

Yeah.

So a lot of it from our perspective,
like we can assist clients with

that if they have what we refer
to as a job architecture in place.

Job architecture, meaning, they've
got more than just job titles and

like divisions, or departments,
they might have job families.

They might have job levels like
individual contributor one, two, three.

Supervisor one, two, three manager,
one, two, three, et cetera.

And when you have that, now you
can start to look at Hey, we know

this job family, IT and finance are
our two highest paid job family.

They have the largest market
demand, highest salaries.

We can maybe we can analyze them
together and look for our our P1s, our

supervisor ones compared to each other.

And we just put them all in
together and we analyze it.

Just by job level with this grouping.

And then you can start to see jobs
that, again, if they're at the same

level and they're in like similar
complexity job families that's where

you can start to highlight some of that.

And again, from our seat as a consultant,
we can look at the job architecture and

say this is how you potentially could
start combining things and let's run it.

We'll see.

How do things look if we see wildly
disparate results, then it's okay,

let's step that back and maybe
break this out a little bit more.

But it's an iterative process and
working with the client on, okay,

using the data you've got, how
could we potentially run this?

And let's give it a shot.

Or you can leave it incumbent
upon the organization.

If they don't have that documented
architecture in place, it's incumbent

on them to say okay, we know these
jobs are all within like an entry level

contributor role.

We could probably start looking
at them together over here.

Yeah.

That sort of thing.

It's a lot more work.

Mike Callen: It occurs to me that with
the, everybody's got a human capital

management system now that's in place.

Most every organization who's got
any sizable number of employees.

And it's, it occurs to me that potentially
those could be used to add certain

amounts of metadata to the structure so
that you did, now you would group entry

level service, entry level professional,
and you could add areas of expertise

or degrees of expertise or degrees of
education or degrees of experience,

and you could start looking at.

All sorts of job titles along
those sort of lines, rather than,

some of the more traditional job,
family, job title kinds of aspects.

Is that anything, has anybody ever tried
to really slice and dice HR differently,

because everything's all database now?

Or would that be helpful?

Brian Marentette, PhD: Yeah, absolutely.

I think some providers are doing
that within the HRIS, they have some

of that architecture built into it
so that as you're structuring, as

you're integrating into that database
you have the ability to map jobs to

certain levels and that sort of thing.

And we're always advocates of
data and management of data.

Yeah.

Organization database,

the better and easier it will be for us
as consultants or as your analysts to

yeah, be able to parse out and explain
things that otherwise require the human

to go in and qualitatively review.

Mike Callen: That was a
bit of a rabbit trail.

I'm sorry about that, but
it just, it did seem to

so maybe make sense and it could
be something that could, maybe

potentially solve some analysis
issues or make some analyses

easier over time and in your space.

We're all here because of the
computer and because of the database,

all being ever present in HR.

If that hadn't happened, back in the
early nineties, it wouldn't have created

all of these aspects of this cottage
industry that have come about your work,

even your service work, our software
work, it's all there because of that.

So it's a fun, exciting territory.

Brian Marentette, PhD: Yeah.

Yeah.

I don't know how the world
existed without computers,

Mike Callen: reams of paper.

Brian Marentette, PhD: Yah

Jenny Arnez: Yes.

I know that we're winding down our
time and I know that you've got

somewhere to go with your children
and certainly want to support that.

If I, or an HR professional wanted
to learn more about pay equity

just to be aware of what laws to
pay attention to, just being able

to recognize a fair pay structure.

What resources would you
suggest that they go to?

Brian Marentette, PhD: Gosh there's a
lot on the EEOC's website I don't have it

handy to share to you, but there's really,
gosh, at a federal level, it's going to

be your civil rights act, 1964, Title VII.

There's also the Equal Pay Act.

You have a state level, a number
of different states particularly

California and Illinois currently.

That require you as an employer to
submit employee level compensation data

to the States for regulatory purposes.

The best place to go is going to
be our website, frankly, and I

will follow up with you to give
you the pertinent information.

There's just a lot of information out
there and, there's some associations,

obviously things like SHRM and World
at Work, which is a compensation

specific organization but really
there's again, probably too much

information out there when it comes to
pay equity in that it's it's not like

a relatively new field by any means.

We've been looking at
compensation for decades.

But in, in terms of how people go about
doing pay equity analyses and, how

they're approaching it, even companies
that are based in Europe come over to

the U S and offer pay equity services.

It's fundamentally different than
how we might operate as more of

a U S centric, EEOC compliance.

So I'd say, first of all, maybe consider
contacting legal counsel to ask them

what legal requirements they have
given the state that they're in or how

large their organization is, whether
or not they have a contract with the

federal government to do business.

If they are, under the Department
of Labor and the Office of Federal

Contract Compliance Programs, the
OFCCP there's a number of factors

that might influence your decision
of how you might go about doing a pay

equity analysis given who you are.

And of course, a conversation
with me, I can work through that

with you if you shoot me an email.

Mike Callen: Does your education,
training, BCGi, do they handle?

Topic of, oh, okay.

Okay.

So there's another
resource available as well.

Brian Marentette, PhD: Absolutely.

We do free webinars almost
a couple of times a month.

You can usually count on maybe once
once a month or every other month,

there'll be a topic related to
pay equity or compensation either

hosted by myself or one of our other
team members, we have a number of

articles, white papers, blog posts.

Like I said, all on our website.

Jenny Arnez: Okay we'll include
in our show notes link to BCGI.

Sounds like to the blog as well, right?

For those articles and, um, I guess if
they want to reach, I don't want to put

your email address on the show notes.

Otherwise you'll be, you'll get lots
of spam bots reaching out to you,

but we'll make sure that we link

to the website so that they can
get in touch with you there.

Yeah.

Brian, this has been a pleasure.

It's been really fun to have
you on here and to give us

some basic information about
what pay equity looks like

and did a few deep dives.

And so Mike, any other final
thoughts you'd like to share?

Mike Callen: No, I just, I
really appreciate it as well.

Sometimes when we have these podcasts,
they're squarely in our space and this

is really, squarely outside of our space
within the same silo of HR, but it's

really interesting to, to learn about
this and, we've had the, great pleasure

of knowing you for decades and working
with you for a long amount of time.

We just don't have an opportunity
to really get together and sit down.

And plumb the depths of your knowledge.

And so it's been a really great
time and look forward to, doing

it again sometime down the road.

So thank you very much for
being here with us very much.

Appreciate it.

Brian Marentette, PhD: Thank you.

I appreciate the kind words and it's been
a pleasure speaking with both of you.

I'm happy to come back anytime I was
going to say, check me out on LinkedIn.

Jenny, you are reading my mind.

Jenny Arnez: Yeah.

I noticed that you're
pretty active on there.

And so that's a great place
to get in touch with Brian.

And.

Again, thank you so much.

We hope today has been of value to you,
our listeners, and our, to our viewers.

Again, reach out if
you have any questions.

We're here to help you.

Thanks again.

Mike Callen: Thanks very much.

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