Katica Roy – The Economic Opportunities of Closing the Gender Equity Gap

Connect with Katica:

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Connect with Dwight:

• 
• dwight.brown@salary.com

Connect with David:

• 
• 
• david.turetsky@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• Katica's Blog
• Pipeline Equity
• HR Data Labs Season 6 Episode 11
• HR Data Labs Season 6 Episode 9

• “There’s often this assumption that [the gender pay gap] just impacts women, and that’s actually not true. . . . the American taxpayer actually subsidizes the pay gap.”
• “In the United States, we could add $3.1T to the economy if we closed the gender equity gap—i.e., the pay gap, labor force participation, and occupational segregation.”

Announcer: 0:02

Here's an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what's happening in people analytics today. Give them the technology to connect, hit record for their discussions into a beaker. Mix thoroughly. And voila, you get the HR Data Labs podcast, where we explore the impact of data and analytics to your business. We may get passionate, and even irreverent, that count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here's your host, David Turetsky.

David Turetsky: 0:46
Hello, and welcome to the HR Data Labs podcast. I'm your host, David Turetsky alongside our co-host, Dwight Brown. Hey, Dwight, how are you?

Dwight Brown: 0:53
Hey, David, I'm good. How you doing?

David Turetsky: 0:54
I'm doing great. Like always, we try and find the best guests talk to you about what's going on in the world of HR and business. Today, we have with us a very special guest Katica Roy. Hey, Katica, how are you?

Katica Roy: 1:07
I'm doing well. How are you?

David Turetsky: 1:08
Very good. Katica, what's your role at Pipeline Equity?

Katica Roy: 1:12
I am the founder and CEO.

David Turetsky: 1:14
So tell us a little bit about Pipeline Equity and what you do for them.

Katica Roy: 1:18
So Pipeline is a software platform that increases the financial performance of companies through closing the intersectional gender equity gap. So gender plus race and ethnicity and age. We actually started with research. So we did research across 4000 companies in 29 countries. And what we found was that for every 10% increase in intersectional, gender equity, there's a 1 to 2% increase in revenue. And so what we do is actually enable companies to realize that gain through the decisions that they're already making, the people decision. So internal hiring, pay performance, potential and promotion, we actually ensure that they're equitable before they make those decisions.

David Turetsky: 2:01
It is a phenomenal thing to do as well, not just to close the pay gap. But also I'm sure shareholders are saying yes, 1 to 2%. We love it. And it's a great thing to do anyways. So before we get into our topic, Katica, what's the one fun thing that no one knows about you?

Katica Roy: 2:21
People know so much about me, because so much of my life is public! So people may or may not know this about me, but I grew up in Napa Valley, California, my parents had a winery, which they have subsequently sold. But I, when I was born, they were just starting that winery. So we, all of us, were a part of helping to plant the vines.

Dwight Brown: 2:45
Oh, that's cool.

David Turetsky: 2:46
It's very cool.

Dwight Brown: 2:47
So you know, you know a lot about grapes and the fruits that go into wine?

Katica Roy: 2:51
A little bit. I know a little bit. I don't, I don't have a degree.

Dwight Brown: 2:58
No, you just planted them! You didn't learn about them, right?

Katica Roy: 3:01
I wasn't even allowed to plant them! What my job was was to take the milk cartons and to put that over, like over the vines and they're they're protected as they're growing. That was the job that I was allowed to do.

David Turetsky: 3:16
If you don't mind, I'm gonna take a video outside of my grape vines. I just love a little bit of assistance with making sure they grow appropriately.

Katica Roy: 3:22
I can connect you to some folks in Napa if you'd like.

David Turetsky: 3:27
That'd be great, yeah, that'd be great. They're like, no, we're not going to use wine grapes from Boston. So that's okay.

Dwight Brown: 3:34
You don't hear that very often. Yeah, Boston grapes go into this wine!

David Turetsky: 3:39
Yeah, doesn't sound like a winner to me. So today, we're going to talk about something very special. And Katica gave us a little bit of a preview, we're going to talk about the economics of gender equity. And I'm really excited about this, given the fact that I'm an economist and I love pay. So our first question, Katica, is what does a gender economist actually do? It sounds like a really cool job.

Katica Roy: 4:12
It is a cool job. So there are two things that agenda economist does. One is that they look at the economy through the lens of gender. So for instance, not this last jobs report, the one last Friday, but the one before that, if you look at that jobs report through the lens of gender, one of the things that we know is that there are still 812,000 women missing from the labor force since the beginning of the pandemic. And we have a 4 million worker shortage between the number of folks that are looking for a job and the number of jobs open. So if we brought those women back, we can actually close that gap by just over 20%. So those are the kinds of things that a gender economist looks at. The second thing that agenda economists does, is to look at equity, not as the right thing to do, though it is, but actually as a massive economic opportunity. So for instance, you mentioned pay, one of the things that we know is that we could actually expand the US economy by $512 billion if we close the gender pay gap,

Dwight Brown: 5:17
Wow.

David Turetsky: 5:18
Which is really important these days, because, well, first of all, we have a lot of underemployment in those in those areas, which you're talking about. But also that pay gap needs to get closed today, or as soon as possible. I mean, obviously won't be close today. But as soon as humanly possible.

Katica Roy: 5:35
Well, and we all pay for it. I mean, that's I think the thing that isn't often understood about the gender pay gap. There's often this assumption that it just impacts women. And that's actually not true. What we know is that the American taxpayer actually subsidizes the pay gap. Because when women aren't compensated equitably, they're more likely to rely on social welfare programs. Well, we all pay for that. So it's actually in our interest to close the gender pay gap. In addition to the fact that we could actually close the Social Security savings gap by a third, if we close the gender pay gap. And that's because most women, from an income perspective, actually sit under that Social Security cap.

Dwight Brown: 6:19
Wow.

David Turetsky: 6:20
So they're not paying into the extent at which it would close the gap.

Katica Roy: 6:23
That's right, that it would be more fully funded, or at least close the gap by a third.

Dwight Brown: 6:29
That's interesting, because this is, these are the kinds of statistics we don't hear about often. And so as you're talking about it, it's total news to me, but it makes total sense at the same time.

David Turetsky: 6:42
And so I'll go further on that. So Katica, why aren't we talking about this more? Why isn't it part of the jobs report? And why isn't that part of what we hear about?

Katica Roy: 6:52
That's a great question. What I can tell you is that there are some outlets that do cover it. So and I know because I'm on them. Because they have me on to talk about it. So Yahoo Finance and Bloomberg as well as MSNBC, particularly in particular, Mika Brzezinski's Know Your Value, those are outlets that do talk about it. More broadly, the reason why it isn't talked about as often in mainstream media, is because we aren't lensing the information through intersectional gender equity through gender, specifically through gender and race and ethnicity. Age is also an issue but gender plus race and ethnicity. So for instance, when the jobs report comes out, we'll talk about unemployment, and whether unemployment went up or down. And we'll talk about maybe wages. And maybe we get a little bit one more step down in that data, like what industries gained or lost jobs. But we don't really get into the details behind the report. For instance, the unemployment rate is a rate, it's made up of two numbers, right. And it's either a good thing that unemployment is low, which means that fewer people are looking for work, or it's a bad thing, because the labor force shrank. And when you begin to look at the numbers, through gender, you can actually see the comparison of what does it look like for black women? What does it look like for Latinas? From a jobs job gain perspective, who is actually gaining jobs? So for instance, we know in the July jobs report that men gained jobs at I think it was like seven times the rate of women. Those are things that we should be talking about, it's not good from an economic perspective for any gender to be left out. So I'm not just this is not a right on women thing, right? This is we don't want to leave men out either. We don't want to leave anyone out. But in order for us to do that, we have to take what's often called a gender mainstreaming approach, which is that we're actually looking at that data through the lens of gender. And that then informs policy, right, so whether that's the Fed, or Congress, or the White House, how we're actually looking at our policies and ensuring that those are actually equitable.

David Turetsky: 9:17
But I think it's also the state houses as well, because there's a lot of legislation that happens in the states that should be happening in the states. And I think, you know, if I might double click a little bit on the Rubik's cube that you're talking about, it's not just male, female, it's also people of color, women of color, who gets significantly impacted and did get significantly impacted by the pandemic. And their level of pay is grossly under what what even, you know if I can say that the advantaged group in this case is white women.

Katica Roy: 9:50
Yeah, absolutely.

David Turetsky: 9:51
But so if we did double click a little bit and got to that second lens, or that third lens, then that would actually enable at least the constituents, not just the state houses because they should know this already on, we know who is actually getting disadvantaged, or now who is actually trending higher from a, you know, the jobs report perspective.

Katica Roy: 10:11
Yeah, and specifically talking about women of color. That's why I say gender plus race and ethnicity and age, right? That's that when you actually look at that it's 120 intersections of data,

David Turetsky: 10:21
Oh my gosh, yeah.

Katica Roy: 10:22
when you break that out.

David Turetsky: 10:23
Right. But but people can't even conceptualize that. Because, you know, because all those permutations right, their head would explode!

Katica Roy: 10:30
Yeah. Well, that's why we created Pipeline. That's the beauty of technology and artificial intelligence. The one thing I will tell you is that the other thing that we don't talk about, so we talk about Equal Pay Day, and equal pay day relative to gender plus race and ethnicity, right, we just passed the date for black women's Equal Pay Day, which is the number of days into the next year that black women have to work in order to be paid equitably, to their white male peers. Right? But one of the things that isn't talked about as often, right, so it's, it's almost three, you know, three, it's over three quarters of a year, right? And we haven't even reached Latinas' Equal Pay Day. But one of the things that we know is that you if you look at moms, and specifically breadwinner moms, and in the United States, moms are the breadwinners, in 40% of US households with children under the age of 18. And as a cohort, they have the largest gender pay gap of any women in the workforce, it's 66 cents on the dollar. I did this research. And then the second piece is if you look at that through, if you double click like you were talking about David, to gender plus race and ethnicity, we know that black breadwinner moms have the largest gender pay gap of any women in the workforce, it's 44 cents on the dollar,

David Turetsky: 11:51
Oh my God.

Katica Roy: 11:51
and they support the majority of all black children in the United States and have for over 40 years.

David Turetsky: 11:58
Wow.

Dwight Brown: 11:59
That's horrible.

Katica Roy: 12:00
So you're not just impacting today's labor force, you're actually impacting your future labor force, because the economic standing of parents is the top indicator of the future economic standing of children.

David Turetsky: 12:15
And let's not even talk about where this comes into play when we're talking about affirmative action and how that gives people not a leg up but at least a chance of being able to be successful and have the opportunity to break those cycles. We're not talking about that today. We can't, we only have a half hour to talk about stuff!

Katica Roy: 12:34
There is a negative reaction. I will say the one thing about affirmative action, just we don't have to dive too far. I will tell you two things. One, is there are predictive predicted negative economic impact to to the overturning of affirmative action. That's one. And the second is it was about it wasn't about giving people a leg up what it was about, which I'm not saying, David, that you were saying that, but just for folks listening, it was it was actually about recognizing people's different starting lines. And meeting them where they are. And what the

David Turetsky: 13:05
No, yeah. research shows is that, for instance, if you have a more privileged student with a certain GPA, and a certain SATs score, and you take an underprivileged student, or an underestimate, whatever, you doesn't come from that background, with the same GPA and the same SAT, the economic bump is bigger for that underprivileged student. Absolutely.

Katica Roy: 13:32
And that's what we've just wiped out.

Announcer: 13:35
Like what you hear so far? Make sure you never miss a show by clicking subscribe. This podcast is made possible by Salary.com. Now back to the show.

David Turetsky: 13:46
Let's go back to one of the other things you said before and start talking about the massive economic opportunity. And what do you mean exactly by gender equity is a massive economic opportunity?

Katica Roy: 13:58
Well, in the United States, we could add $3.1 trillion to the US economy if we closed the gender equity gap. So that's not just the pay gap, the 512 billion I talked about as part of that. The other two pieces are labor force participation. Labor force participation is about half that number, a little less than half that number. So one of the things that we know is that women added $2 trillion to the US economy from 1970 to 2016, through their increased labor force participation, but even though they're 58% of college graduates, they're still only 47% of the labor base, right? So increasing their labor force participation is part of that number. And then the last is actually dealing with occupational segregation. So it's actually, which is for those who are like, that sounds like a really deep cut. What does that mean? That basically means that some occupations are female dominated, and some are male dominated. And if we actually made that more equitable, we would get an economic bump from that.

David Turetsky: 15:01
Right. Just to kind of translate. If we talk about women in STEM, which has always been a problem, but now it's at least getting a focus where women are being recognized for their accomplishments in STEM in history. But also the fact that that there is this kind of lack, whether it's lack of leadership, or whether it's and when I say lack of leadership, lack of representation in leadership, not the lack of, of leadership from females in the in STEM, saying that there's lack of appropriate representation of females in the STEM based occupations that then would enable people to say, hey, I'm just like her. Why can't I do that job? Because if you look at an about us page of, you know, some career site, and it's got the Chief Technology Officer is this graying old white guy? Well, that kind of disenfranchises a lot of people who say, well, I wouldn't want to join that organization, because that person doesn't represent me. But now if we start seeing people of color, women, other people who see themselves in there, right, then they actually can say, hey, that person represents me, I can succeed, I could be that person one day, you know, is that is that kind of? Does that resonate? When you're talking about the opportunities?

Katica Roy: 16:17
It does. Yes. And what's interesting is actually, women in STEM has gotten worse, not better over the years. So if you look at it over the last 30 years, it's, it's actually gotten worse. It's not. And so that's an issue and it is a particular issue because during the pandemic, we actually leapt forward five years in terms of digital acceleration, and we can see it, you know, with generative artificial intelligence now, but there's a lot a lot of other technological advancements where if we don't have women with a seat at the table, the real concern is that, right now, we can hardwire our biases into the algorithms, right, or we can hardwire them out. The choice is ours. So that's like, the acute concern right now, is that right? And we've already seen some of that with Chat GPT, and some of the other generative artificial intelligence tools, because it's, without that equity lens, we're just basically spitting out our biases, the other piece

David Turetsky: 17:19
We're gonna re, we're gonna replay our biases is for the next 1000 years!

Katica Roy: 17:23
And we will hardwire them in because what happens with artificial intelligence, is that its intelligent, it is learning whatever you're feeding it. And so it starts to, the more you have that you feed, the stronger it gets,

Dwight Brown: 17:37
The worse it gets.

Katica Roy: 17:38
That's yeah, that's right. That's, it's, that's because every time for instance, chat GPT generate and not to call them out, they're not the big, you know, they're the most commonly, when but when they generate artificial intelligence that basically goes back into the engine, and back into the engine, and back into the engine.

Dwight Brown: 17:56
It's getting recycled over and over and perpetuated more and more.

Katica Roy: 18:00
Yes.

David Turetsky: 18:01
Katica. Think about in another way, it's because we're training it from old thought, right? We don't know how to interact with with the generative AI yet, we don't know how to ask the right questions. We don't know how to feed it the right data until we learn. And then we learn what it's trying to tell us what to do. So in order for us to hopefully get to a better place, we need to tell it, that treating people the same is a rule. It's like, you know, when you when you watch that movie, I think it was the guy and he was there with his dog in Philadelphia, and he was a doctor, and he wanted to try and save the world and he was the last person? Well, you need to have that that same thought process of you need that person to train it to do the right thing. You need to have that programmer to do the right thing and make sure it doesn't happen again, right?

Katica Roy: 18:54
Yeah, yeah, well, you need two pieces. And then I want to talk about the question around the if you can see it, you can be it. But you need two things. One is you need to understand where the biases are in, where they actually exist in the dataset. That's one piece. And then the second is you need to actually ensure that the team that is developing and refining your algorithms is inclusive, so that you have those lived experiences so that they can both understand where the biases would be in that data. But also when they're programming and writing the algorithms, they understand how to code that to ensure that it's inclusive. So it's a both and in terms of ensuring that and that's the real concern with the lack of gender equity, intersectional gender equity, in tech and in artificial intelligence. One of the more acute concerns this year, is that women make up about 26% of the tech workforce, but they've been 65% of the layoffs. So not only is it bad, it's actually a particularly bad this year given the layoffs. The other thing that we find in terms of looking at the about us page and not seeing someone who looks like yourself, is that often we talk about the glass ceiling publicly, as has a woman ever had that role? Right? And one of the things that we find is that the pipeline leaks early and often. So on average, that men are promoted at a rate of 21% greater than women. That's what we've found. And then, David, if you do that double click to gender plus race and ethnicity, we found that on average men are promoted at a rate of 42% greater than black women. So that about us page that inequity starts with that very first promotion, it's starting 20, 25 years earlier, that's really the crux of what we need to solve.

Dwight Brown: 20:59
And and, you know, I think the important point is the stats that you're coming up with. You know, it seems like in recent years, we've we've started to hear more and more about that inequity. But so often, it's not coupled with the numbers. And so, you know, it kind of sits out in the ether. Yeah, where now with what you're doing there, you're actually quantifying this. I mean, I'm flabbergasted as you're, as you're quoting these stats, I'm like, God, I didn't, you know, I knew it was there. I didn't realize it was that big. I didn't realize the impact was that big. So it and I think that'll help to move the ball forward, if we have a chance to move the ball forward.

David Turetsky: 21:41
Well, let's talk about moving the ball forward. What are some things that some companies are doing to actually fix this? You know, what are they doing right? And what are they doing wrong?

Katica Roy: 21:52
96% of CEOs put equity in their top priorities, but only 22% of employees regularly see it shared and measured. So you have this 74 point gap between what CEOs and companies say is important. Essentially their employer branding, and the actual employee experience. And what we have a lot of is awareness. So much so that we're getting some backlash. But you know, but there's a lot of awareness around this issue. Where it falls down is that there's not as much execution. So we've had a fair amount of what what is often been called, like checkbox diversity. So we send everyone through unconscious bias training, that's the most common one, companies spend $8 billion a year on unconscious bias training. And what the data shows is, not only does it not work, but it actually can make it it can actually make equity worse, it can make you less equitable, because it reinforces stereotypes. And so what we really need to do is move to ensuring that our, like the crux of it is, our people decisions, right? So there are five people decisions that companies make, which is internal hiring, mobility, pay, performance, potential and promotion. And there's three that they make across their employees each year, which is performance, potential and pay. So for the average fortune 500 company that has 60,000 employees, that's 180,000 opportunities to move toward equity each and every year. But bigger than that, what's also important is that's how we can actually leap forward to toward equity.

David Turetsky: 23:39
So is there a solution? Or is there something that we can do to get things right, and what have you seen for for organizations that are getting things right?

Katica Roy: 23:47
Well, the companies that are moving toward equity, are ensuring that their people decisions are equitable. And they're actually thinking more broadly about how they ensure equity within their organization through the decisions that they're making about their people and through the benefits that they're offering their people so that they're addressing the total labor force.

David Turetsky: 24:12
One of my colleagues and friendsfrom early in my career started talking about DEI as far as benefits go. And that there are is actually a difference in how people adopt benefits by the group that they're in, and making sure that we're trying to solve problems for all the different groups that we have in our organization by offering the right benefits to them. Do you see a lot of organizations that are changing the way they offer benefits to focus more on the diverse people that they have in their organization?

Katica Roy: 24:40
Well, really, it's more inclusive, I would say, that is rather than offering benefits that we think people want, offering them the ones that they actually want. And there is, I think the beginning of the movement toward that it's, it's my observation is that it's very much at its infancy. For instance, I'll give you an example. There's conversation around flexibility, right? So whether that's flexibility and remote work or whatever, women want flexibility. That's not necessarily true. So that's a hypothesis. It's not actually a research conclusion. So that's one thing. And the second is, I have said this, and I know other women who I know have said this, which is give me opportunity, and I can pay for my flexibility!

David Turetsky: 25:27
Right.

Katica Roy: 25:27
This sort of idea that, I don't need you to help I can manage my life just fine on my own. What I need is opportunity. I think that's when we talk about the things that companies are doing, unconscious bias training being one, but a lot of it is around fixing the solutions that don't work, in addition to the checkbox diversity, our solutions that are focused, focused on fixing the subjects of inequity. So women, women of color, etc. And, and the issue is that the system is not equitable by design. It is inequitable by default. So when we do things like send women to leadership development programs, and teach them how to negotiate, or have them apply for more jobs, we're essentially saying it's a women problem. But they're actually in a system that doesn't value them equitably. We'd be much better from a systems perspective, to actually ensure that the system is equitable by design, rather than trying to fix the subjects of inequity.

David Turetsky: 26:42
And that takes more than just one employer. And that takes the government or takes the culture to overcome those things.

Katica Roy: 26:50
I would say and technology, I would say technology. Culture can be a loaded word to be honest with you like, yeah, so we want to have inclusive cultures. And yet, that has been a you know, like, oh, they're not a culture fit has been used as a way to exclude people in the past. So culture can be like when you have a company that has a name for someone who exhibits their culture. So Google has Googlers, or Charles Schwab has Schwabbies not to call them out, just as examples that folks know, one of the things that we know is that women are held to account to demonstrate those cultural values to get ahead. Men are not. They can, but if they, it doesn't matter if they do in order for them to be promoted. So that's like, that's one example of where like culture, yes, we need to have inclusive cultures, but it can be a bit of a loaded word.

David Turetsky: 27:49
Yeah. That sucks.

Dwight Brown: 27:53
It makes total sense. It's, yeah, I mean, it totally sucks.

David Turetsky: 27:59
Hey, are you listening to this and thinking to yourself, Man, I wish I could talk to David about this? Well, you're in luck. We have a special offer for listeners of the HR Data Labs podcast, a free half hour call with me about any of the topics we cover on the podcast, or whatever is on your mind, go to Salary.com/HRDLconsulting, to schedule your FREE 30 minute call today!

Dwight Brown: 28:25
Would it be would it be safe to say that kind of a starting point for that, because you talked about the systems and I can definitely see that, do we start at the point of awareness? Is that? Is that a starting point to really start to move that ball? Or what would you what would you say is the starting point with that?

Katica Roy: 28:46
With making this system? So so what what we know from behavioral science, and so sort of separate from the equity conversation is just human behavior, right? So if you look at it from a human behavior perspective, what we know is that the system will trump the individual every single time. So if you want to change human behavior, you have to change the system, that humans are in.

David Turetsky: 29:13
Sure.

Katica Roy: 29:14
Right? And that's why a lot of diversity solutions don't work is because they're trying to fix the people, but they're not fixing the system. If we fix the system, then what happens is, like I've managed 1000s of people in my career, right? Obviously very committed to equity, breadwinner mom who fought to be paid equitably twice and won. And yet in those systems, I had to choose to be equitable. We need to change the system where it if it is equitable by design, then I am choosing to be inequitable.

David Turetsky: 29:52
Right.

Katica Roy: 29:52
That's the way that you actually move it forward. And that's what we haven't done. And that's why the solutions haven't been successful.

Dwight Brown: 30:00
Yeah, no. And that makes, that makes total sense. I think what I'm, what I'm wondering about is, how do I know, in the context of changing the system, how do I know where to start with that? And I'm wondering if it's sort of an awareness of what's actually broken in the system.

Katica Roy: 30:21
I think we've done a lot of that though.

Dwight Brown: 30:23
Okay.

Katica Roy: 30:24
To be honest, like, we have done a lot of that, right? So we've seen from the end of 2019, to the end of 2022, there was 174% increase in companies that sign public pledges committed to equity. You know, we've seen a lot of awareness and awareness is great, but awareness is not execution.

Dwight Brown: 30:45
Sure.

Katica Roy: 30:46
Awareness is not changing the system. And so what we need to do, our people decisions, as one example, that's not the only example. Our benefits, as another example, we need to ensure that those are equitable by default, or equitable by design, excuse me, rather than inequitable by default. I'll give you a public policy example. Which is that in the United States, the Equal Pay Act was signed into law by President Kennedy in 1963. But, but still.

Dwight Brown: 31:17
And yet, here we are!

Katica Roy: 31:20
Right! And the reason is that the only way that works, is if I'm experiencing pay inequity, I have to speak up.

David Turetsky: 31:31
Right.

Katica Roy: 31:33
And by definition, if I am experiencing inequity, I am not in a position of power. Because why would I do that to myself. So our system is still inequitable by default. That's the issue, rather than for instance, companies proving that they're paying people equitably, because it's a cost to the American taxpayer, as an example. And that's just one public policy example. But that that's where if we actually want equity, let's fix the system.

Dwight Brown: 32:08
Sure.

David Turetsky: 32:08
But there is some, good news is there is some hope and pay transparency laws are meant to at least give the information and make information more ubiquitous so that people who are making those decisions can at least have a lot more level playing field. And there are no more bargains! I pray there are no more bargains, where people say, well, I don't know, you know, why don't we just pay me what I got paid before, or a little bit more? That just just makes things worse, not better. But now with pay transparency, at least there's that representation of understanding with a candidate as to what it is that pay range might be for that job. I'm not saying it's the solution. I'm saying that from a public policy perspective, Katica, it's actually getting to there. Slowly.

Katica Roy: 32:57
It's a guardrail. It's a step forward.

David Turetsky: 32:59
At least! Yeah, it is. Right.

Katica Roy: 33:00
But it's not... Yeah, I mean look, Colorado, which is where I live, was the first state in the union to have pay transparency. I was one of two expert witnesses for the state legislature and the only economic expert witness. So I am an advocate for pay transparency, but what pay transparency is, like, it's the name we've given it, but we're not really making everyone's pay transparent. We're really making a pay range transparent, right? So it's a step forward. And it provides guardrails, and that's good. There's still more solutions in that. The other thing I would just add is, and I think, you know, pay has gotten a fair amount of airtime because it's a it's a number, and we can quantify it, and we can say it quickly, and people can understand it. One of the things that we know is that pay is the symptom, it's not the disease. So in other words, pay is the quantitative value that we place on our talent, but the actual value comes before that in performance and potential. And so we have to ensure, for instance, that our performance reviews are equitable, that our performance ratings are equitable, that we're actually developing talent equitably for those future roles. So for instance, when you're talking about the CTO, that the pipeline of folks in, who are essentially the bench that we're creating to take on those roles, is equitable. They're getting those highly visible media assignments that would put them in line to actually take those roles.

David Turetsky: 34:37
I would go back a step and say the candidate experience as well, and making sure that one is fair, so that the people who actually come into our organization, or the people who are inviting in to interview, that should be fair as well. And hiring them for the right roles, not just a role, not just a junior role. They should be hired for the role that they're qualified for.

Katica Roy: 34:59
And that's also something that's different between men and women. So we judge men on their future potential, which is a pattern matching, right? Because we, we pattern match them to someone who's currently in that role, and the majority of all leaders in corporations are men. And we judge women based on their past performance. So we're more likely to put men into a future leadership role or, David to your point, actually think of hiring them for a position, than we are to do that with women.

David Turetsky: 35:30
When I was talking about culture, that's what's got to change. That crap. Bullshit, pardon my French, that's got to, it's got to change! We've got to stop being those people!

Katica Roy: 35:40
Which then we have to change the system so that you are choosing!

David Turetsky: 35:44
Exactly.

Katica Roy: 35:44
So let's say we were all interviewing for the same role. Right? Then David, if you, or maybe Dwight and I are interviewing for the same role. And David, you're the hiring manager. That by default, we, Dwight and I, are included in that pool and you would have to choose to exclude me.

David Turetsky: 36:03
Unless the damn AI excludes you first!

Katica Roy: 36:06
This is after like, you know, ensuring, right, yeah. For sure. Yeah. But that's what I mean like

David Turetsky: 36:15
Katica could be in that role, because she's female. And we've seen the pattern shows over the last few years.

Katica Roy: 36:20
And we've seen that! Right? Like Amazon had a hiring app, but that did that, did that very thing.

Dwight Brown: 36:25
Yep. Yeah, exactly.

David Turetsky: 36:26
Well, it goes back to your point before, Katica. It's, we've got to make sure that the algorithms are being programmed by the people who it's supposed to serve, and a representative sample the people, so we can look at it critically and make sure we're doing the damn right thing. Katica, It was a pleasure having you. Thank you so much. You're awesome.

Katica Roy: 36:48
Thank you! Thanks for having me. I enjoyed it.

Dwight Brown: 36:51
It's been great having you on. This is fascinating.

David Turetsky: 36:54
Yeah. We could sit here and listen to your numbers all day, by the way we love that stuff! We're numbers guys

Dwight Brown: 37:00
You can tell we're data geeks, you know?

Katica Roy: 37:03
I mean, that's where I spend most of my, my brain works in numbers, right? That's how I that's how I think right? And I'm a researcher by trade, so I have hypotheses and then I figure out whether or not they're true.

David Turetsky: 37:15
And we love that. Well, thank you so much. And hopefully, we'll get you back on to talk about how much progress we've made in a very short period of time.

Katica Roy: 37:23
That would be great!

David Turetsky: 37:24
Thank you very much. Dwight, thank you.

Dwight Brown: 37:26
Thank you. Appreciate it. This has been fascinating.

David Turetsky: 37:28
And thank you all for listening. Take care and stay safe.

Announcer: 37:32
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