The Hire thru Retire Podcast
How Employers Can Help Improve Financial Outcomes for a Diverse Workforce with Voya's Tom Armstrong & Carole Mendoza
In this episode, we’re focused on the discussion of helping to close benefits and savings gaps to improve financial outcomes for a diverse workforce. Sharing some pioneering research conducted at Voya, with the support of our clients, to get some valuable insights and opportunities for employers to consider in their own practice. To engage in the discussion, Bill is joined by Tom Armstrong, VP, customer insight and analytics and head of the Behavioral Finance Institute – and Carole Mendoza, VP of benefits at Voya. Tom and Carole share more about the research which takes a deep dive on understanding of how employees from different ethnic and racial groups, think, feel, and act when it comes to saving for retirement.
Bill Harmon is a registered representative of Voya Financial Partners, LLC (member SIPC).
CN2791846_0325
In this episode, we’re focused on the discussion of helping to close benefits and savings gaps to improve financial outcomes for a diverse workforce. Sharing some pioneering research conducted at Voya, with the support of our clients, to get some valuable insights and opportunities for employers to consider in their own practice. To engage in the discussion, Bill is joined by Tom Armstrong, VP, customer insight and analytics and head of the Behavioral Finance Institute – and Carole Mendoza, VP of benefits at Voya. Tom and Carole share more about the research which takes a deep dive on understanding of how employees from different ethnic and racial groups, think, feel, and act when it comes to saving for retirement.
Bill Harmon is a registered representative of Voya Financial Partners, LLC (member SIPC).
CN2791846_0325
You are listening to The Hire Through Retire podcast with Voya's Bill Harmon tackling all things from 401ks to HSAs and everything in between. We are talking to the best and brightest in the industry to bring you the latest in benefits, savings, and investment trends in the workplace. Come along with us on our journey to help all Americans become well-planned, well-invested, and well protected.
Speaker 2:Hello everyone and welcome back to the Hire Through Retire podcast. I'm your host, bill Harmon, and thank you so much for joining me here today. You know, it is spring and final. Whatever part of the country you're in, I'm sure you are ready to help really be two feet into spring and, you know, spring's, uh, a season where everything's fresh, maybe fresh ideas. And, you know, that really lines up with this topic that we're gonna talk about today cuz it's one that's gained a lot of interest and attraction from our own clients. So we thought there'd be no better, uh, way to take this opportunity to share with our, uh, podcast audience as well. The topic that we're gonna discuss is closing the benefits and savings gaps to improve financial outcomes for a diverse workforce. And we've done some pioneering research here at Voya with the support of our clients to get some really valuable insights and opportunities that we'd like to share with you today. So joining me for the discussion are Tom Armstrong, a familiar voice. If you're a fan of our podcast, Tom leads customer insight and analytics at Voya, and also heads up our Behavioral finance Institute. We also have Carol Mendoza and Carol's the VP of benefits here at Voya. Tom and Carol are gonna share more about this research, which really took, uh, look at some of our largest plan sponsors that we support to get a better understanding about how employees from different ethnic and racial groups think, feel, and act when it comes to saving for retirement. So Tom Carol, welcome. I'm really excited to have you here today to talk about this great topic.
Speaker 3:Thank you for having me.
Speaker 4:Thanks for welcoming back to the podcast, bill, and I'm actually really excited to be here with, uh, our friend Carol too.
Speaker 3:Thank you. Likewise.
Speaker 2:So let's start with you, Tom. I know data and research, I mean, I think you have tattoos saying data and research as like many people do, but I know it's near and dear to your heart, but, but why don't you share with us some background about this particular research effort, what the goals were and how it came to be to gaining some insights from our own clients.
Speaker 4:Sure, thanks Bill. And you made me laugh. I'm not sure if I have data tattoos, but that might be
Speaker 2:Something. Oh, oh, you don't, okay. I shouldn't presume.<laugh>
Speaker 4:<laugh>, uh, no, the, the impetus for the research actually is, is one that came out of Voya's purpose statement, which is together we fight for everyone's opportunity for a better financial future. And, you know, I think for me, one of the operative words of our, our purpose statement is everyone, uh, we're not just fighting for a few, we're not just fighting for some, we're really looking to help all of the employees, uh, that we serve with their unique backgrounds and needs and goals. And, you know, ultimately the reason why we did this research is we wanted to provide our employers, um, starting with Voya and, and Carol here, uh, as the Voya representative, right, with a new lens to help all of our employers see their people fully so that we can design more prescriptive solutions and, and really help all the, all the, the folks that we're here to serve. So I think that was the mission. You know, we did start out by partnering with Carol to get our own data on our own plan and then use some samples of that data to go talk to employers that we could provide them with this new lens. If we could get, get data from them, uh, that would help us, uh, give them that more clear picture. So that's really how it started and, and we're excited to be on this journey together. We're certainly not done.
Speaker 2:That's great. And so let's turn it to you, Carol. So Tom mentioned that Voya's own plan was a key part of the analysis and data and really was kind of at the very beginning to see if we could then take that to our customers. So can you tell us a little bit more about what you were hoping to get out of this analysis when Tom approached
Speaker 3:You? As Tom said, it's very focused on Voya's purpose. Um, historically we've looked at things like, um, wealth equity across genders and compensation levels. I think some employers who were interested in equity have gotten that data in the past, but this was a new way of looking at our data, uh, pulling in d potential disparities with racial and ethnic groups in wealth. So we can't help to address those disparities if we don't know what they are. So this was a terrific opportunity to understand those, those differences.
Speaker 2:So let's dig into it. So to Tom, you mentioned, uh, some of the specific measures you were looking at within the analysis specific to health plan, health, uh, engagement, financial wellness. When it comes to plan health specifically, what are some of the things that you learned when considering participation, um, and savings rates within the plans?
Speaker 4:Yeah, so first we started with, um, studying, uh, a really wide cross section of data. We had over 163,000 employees that we analyzed their data for and a and across a number of different employers that, that did provide us with that ethnicity data. And, um, sadly what we found was that we did see lower participation as well as lower savings rates within some of the communities that we serve as specifically the black African American, Hispanic and Latino populations. We saw lower participation and, and balances and savings rates even when we controlled for salary. I think there was a sense that, well, you know, maybe there's a fun, this is a function of salary and we're not paying people enough. But even when we looked at, uh, within the, the different salary cohorts, uh, we still saw, saw these gaps, we also saw lower match utilization and higher incidents of hardships and loans within those groups. So we certainly have some work to do, but again, now that we have the data, we and the employers that we're supporting and working with really are, are digging in and we're trying to take action. And I will say we found some bright spots and solutions to closing for some of those gaps that, that I can just discuss, uh, in a, in a moment.
Speaker 2:Well, that'd be great. I'm sure the audience really wanna know now that you've found the gaps, well what can we do about it? And did it work? Turning over to you, Carol. You know, one thing we found from this research is, is that as employers focus on recruitment and retention, it's a very competitive labor market. The d e I programs can help increase diverse hiring. Is there anything that you've learned from what you've found, uh, what was found here in this analysis that you would recommend to employers to help consider helping with their specific d e i recruiting efforts?
Speaker 3:Yeah, I think it's really important to be honest about gaps in wealth equity and how we're addressing those gaps is really compelling to the market. It also drove us to enhance benefits that are attractive to the market, like adding student loan reimbursement to help those who have crippling student loan debt address that so that they can then contribute more to their retirement accounts. But I think the, the most important thing is that addressing wealth equity is the right thing to do and it's an important message to all of our candidates, uh, especially diverse candidates, but to all candidates.
Speaker 2:So Tom, you started off, I wanna talk to both Tom, you and Carol about let, let's kind of think about what we learned from it. You've mentioned that you found the data, there are some alarming data in there, but there were some things that, you know, that we could do to address it and address it pretty quickly. So one in particular, let's talk about auto features specifically for a minute. We've all seen research suggesting that auto-enrollment and then auto-escalation are powerful tools to help not only get participants into a plan and saving, but also to make sure that they're kind of increasing that engagement, increasing their savings rate. So Tom, why don't we start with you and if you could share a little bit about what you found here, uh, within the analysis, and then Carol, if you could share your perspective after that about how employers might consider this for their own practice.
Speaker 4:Sure. Yeah. So to start out auto features, um, and we saw this, you know, before we pulled all of the data together that they really work at a macro level in terms of driving plan health. But the, the really interesting thing, and for me really cool thing was that we saw auto features really help to close gaps between ethnicities here as well. So in addition to improving plant health more broadly, auto features actually we believe are, are somewhat of a secret sauce in terms of making sure that we can start to close some of the, the gaps that we did observe in participation and, and other key metrics. So for example, for plans that didn't utilize automatic enrollment within our dataset, only about 31% of the black African American employees were actually participating in those plans. And that compares to 49% of their white peers, which ultimately represents an 18 percentage point variance, right? The difference between 49% for whites and 31% for black African Americans. I think the good news though is when we look at automatic enrollment plans, not only do we see higher rates, but we see that gap closed significantly. So with automatic enrollment plans, we saw 87% of black African American employees participating in the plan versus 90%, uh, for their white peers, which is only a three percentage point variance. So still something we want to close for, right? We're not gonna give up and say 3% is great, but certainly a, a much smaller variance than than where we see in those, uh, non-automatic enrollment plans. We, so we not only see, I'll say three times greater participation rate for automatic enrollment plans for black African American employees as an example, but then we see very little gaps, uh, versus their peers when we institute these auto features. So they're really, really powerful when we think about, um, just putting in some foundational plan design changes that can really help our employees.
Speaker 2:And Carol, maybe what did you, uh, find in any, anything you could help the audience with?
Speaker 3:Sure. A a couple of things that we have in place today at Voya, um, we do auto enrollment in the four oh[inaudible] plan at 3% after 60 days. We're fortunate that many of our employees already elect up to 6% where they get the full match before we get to auto enrollment. But this auto enrollment ensures that inertia doesn't prevent employees from getting the most they can out of the retirement plan. We also have auto-escalation in place, so a 1% increase in the savings rate on the anniversary of planned participation. And then something I know that the city of Milwaukee has done that we're also considering is re-enrollment, auto re-enrollment, so that those who didn't participate in the past might get a nudge to participate in the future.
Speaker 2:That is great. And, and boy, those numbers are just, um, so impressive, Tom to go ahead and narrow the gap, but even to take it from a 39% participation rate up to 87, phenomenal, just to get that going. And then now we can even address just that three percentage point difference, but we took it down from 18% of a, a gap in addition to the groups that, um, you mentioned already today. We also know that women and the special needs community also need greater financial care and attention as these communities continue to face challenges such as income gaps, career interruptions, due to family caregiving responsibilities, added health considerations, name a few these items, they're, they're what impacts the ability to build wealth and achieve financial wellness. So do you have any insight that you found within these groups, Tom?
Speaker 4:So we did study, um, some gender differences and, um, within the dataset we did see that women are generally, uh, have even lower participation rates versus their male, uh, counterparts across the ethnicities with both lower participation, lower savings rates, and just more so I guess within those underserved communities. So, you know, I think the good news there though, again, is that with auto features we tended to close gaps the gender gap as well. And so I think we did see some of those differences, but again, uh, it, it struck us that, um, auto features are also closing for across the gender gaps, not just ethnicity, unfortunately on the special needs committee. We just don't have a lot of data bill. And, and this is something that I would encourage our entire audience listening, let's continue to work together in the financial services industry as an employer to get more data because I think we would love to get data from our employers, uh, that we support and quite frankly, more broadly for who might be caring for, um, someone with a special need and, and someone in that special needs community so that we can help to include that in the analysis in the future. Um, so I, I guess it's my friendly plea for more data along with the tattoos that I might be getting, uh, in
Speaker 2:The near future.<laugh>, Carol, do you have anything you wanted to add?
Speaker 3:Yeah, I, I agree. Um, data, data, data, both on the, the health side and the wealth side, the, the more that you can, um, look at and scrub your data so that you understand where there are gaps, that the better opportunity you have to address potential issues.
Speaker 2:And it goes back to one of the first things you said, Tom, everyone. And so if there are different communities that have different challenges, boy, the what can we do collectively to address those? Any of those hindrances challenges, you know, whatever it might be in plan design like we've just talked about is a great one. Tom Carroll, I, I want to thank you again as this really is pioneering research that many employers and advisors can consider in discussions to help these diverse groups of individuals. And with that, maybe we can leave with one final thought for our listeners. If we were to summarize the research with a few proven action steps that plan sponsors can take to help narrow the retirement savings gaps experienced by diverse employee populations, what would they be? Tom,
Speaker 4:It's gonna be hard for me to summarize in one, but, uh, maybe it maybe humor me with, uh, two or three thoughts. So first, foundationally, as we discussed auto features, re-enrollment, auto-escalation, they really work, um, not just to broadly improve, you know, employee retirement outcomes for, for the employer-sponsored retirement plans, but also to help close gaps between ethnicity and gender. Where automatic enrollment isn't always possible, whether it's because of, um, you know, state legislation or expense or whatever it might be. I would say then make sure you're working with these communities, whether it's an E R G or a council, there's an opportunity to engage your employees or all of the employees that we support in a dialogue about how to overcome these challenges and working to really help together to close these gaps that we're seeing across the community. And then, then the final thing I would just say is, is a tip to consider language and messaging really matters. In fact, we did some behavioral finance research last year with Steve Shu at Cornell University and a couple other folks, um, on our bfi uh, team in BFI Institute here at Voya that showed that we could close gaps in savings rates by just reframing the way we ask someone to enroll as pennies per dollar instead of percent, for example. And so I think auto features are certainly key building blocks, but then we want to talk to the audience, understand their needs, understand where they are, and then speak to them in terms that really relate to them. And if we can do that with some creative language even, or creative reframing of what are more traditional industry jargon, we may actually have a chance at really helping to, um, serve all of those, those employees, uh, and meet them where they are.
Speaker 2:That's great. The pennies per dollars. Really fascinating research on how people react to that cause that's just some more, so much more real. Carol, do you have any thoughts to add?
Speaker 3:Sure. Um, just to piggyback on what Tom was saying about talking in terms that employees understand, I do think it's important to analyze the data but also understand what's driving discrepancies. So, you know, maybe a decade or two ago we would talk to employees about cutting their Starbucks happen. If you don't get your Starbucks every day, then you could save more money. Well, I think that could be a little insulting to people who are concerned about student loan debt or putting food on the table. So if we understand what's causing gaps in savings rates as an example, we can better address those gaps. And it, the example I had with our different savings rate, we determined that we, we should help with student loans because that was a, a real concern. So finding not only the gaps, but what's driving those gaps can help us to address them.
Speaker 2:That's fantastic. And, and thank you so much Tom, Carol, great insights today. Really appreciate you being here. Thank
Speaker 4:You. Thanks for having us, bill. Yeah, it was great.
Speaker 2:I also wanna thank our listeners. This is certainly a topic that we are planning to revisit and hope to do more research, um, as you heard Tom say. So I certainly encourage you to come back here for some further insight and tips, uh, to take back to your own practices. Thank you so much for joining us today. Stay well.
Speaker 1:This information is provided by Voya for your education only. Neither Voya North Representatives offer tax or legal advice. Any opinions expressed within, do not necessarily reflect those of the Voya family of companies or its representatives and are not intended to provide specific advice or recommendations for any individual. Please consult your tax or legal advisor before making a tax related investment or insurance decision.