Retirement Readiness Surges with Focus Shift to Actual Data

  “Sure, it will probably be more work, and generate fewer clicks,” commented one industry source, “but it’s the right thing to do.”

Yes, after years of relying on uninformed “guesses” from individuals ignorant of their financial needs and situation, the retirement industry, major media outlets, and a large number of academics have made a commitment to focus on actual data, rather than hypothetical extrapolations from incomplete datasets.

Another explained, “we always thought that exaggerating the depth of the retirement crisis would encourage people to save more — but that turns out not to be the case.” Those projections affixed labels like “magic” to those extrapolated numbers based on surveys of uninformed workers, which not only ignored real differences in incomes, location and age, but were typically also averaged to further obscure accurate results. Likely fueled by previous reports of needed retirement savings, surveys of individuals routinely exaggerated the real needs of retirement finances — fueling future projections as well. 

“While self-assessment can be a critical foundation for retirement needs planning, we are committed to sharing real-world perspectives on actual retirement needs,” noted one industry expert. We’re ready to call “bs” on inflated, uneducated and unrealistic “estimates.”     

Part and parcel of this previous approach — and reinforcing its messages — were academic studies that mixed results of those who participated in a workplace retirement plan with those who never had, and individuals within five years of retirement with those who had just started working. All breathless reported by a media then-clamoring for click-bait ready headlines.     

An academic noted that, “I’m not sure what people expected since we routinely built our projections on the projections of others, who were — as it turned out — based on survey data from — well, questionable sources. No wonder we kept coming up with the same results.”

Indeed, new research, published by the Oxford Newfound Institute of Nihilism (ONION), finds that workers — no longer persuaded by “retirement crisis” headlines that there wasn’t any point in trying — now are taking proactive steps to understand their situation, often with the help of trained advisors. Previous research had shown that fewer than half of workers had made even a single attempt to assess their retirement needs, and many of those had simply … guessed.

Ironically, despite this newfound and dramatic increase in confidence, the new retirement savings goals were not only more likely to produce a successful outcome, they were generally higher than the goals previously set by workers who had gone through the process.

In fact, some of the most dramatic impacts were recorded by participants in plans where employers had not only provided for automatic enrollment immediately upon hire, but who applied automatic enrollment retroactively to existing hires as well. “All these years, I just assumed my employer thought it was too late for me to start saving,” said one long-time worker who had just been automatically enrolled under such a program. 

A separate, plan sponsor-focused report found that the renewed focus and confidence translated into tangible workforce management benefits as well. “We found that a growing number of older workers were simply hanging on to their old jobs, afraid to retire because they had no idea how much they would need to have in retirement,” observed one. “Now, for the first time in a long time, we’re seeing workers actively plan for their retirement date with confidence. We should have done this years ago!”

No foolin’.

• Nevin E. Adams, JD

Note: Sure, it's April Fool’s, but while the post above has a certain tongue-in-cheek character, the implications are not as fictional as you might think. In fact, they are well within the realm of a very potential reality for millions more — with a little help from plan advisors, their plan sponsor clients and the cooperation of plan participants. Not holding my breath on the shift in focus by the media, academia, or — sadly — even the retirement industry itself.

‘Scare’ Tactics

 Could someone please explain to me why the retirement industry keeps publishing ridiculous, uninformed and often ludicrous notions of retirement income needs?

Honestly, I have no earthly idea what value any rational thinking person would attach to the guesses that an uninformed public makes about retirement income needs. But then why any credible source would take those guesses and then AVERAGE them (cause you know how much more accurate an average is[i]) for publication is — well, it’s the kind of thing that makes my head hurt (particularly after repeated banging of my head on a table after reading another).

The latest I stumbled across came from BlackRock, which — based on a survey of “1,000 national registered voters in the United States” — declared that $2.191 million is the “average expected amount of savings needed for retirement.”

Seriously?

No wonder among that same group just 22% were deemed to be “extremely or very confident they will have enough money to live on throughout their retirement years.” I’m surprised it was that high.

Speaking of high, take just a second and apply the 4% drawdown “rule” to that wild-eyed estimate, and you’d find that produces $87,640 in annual income — on top of Social Security! Think people could muddle by on THAT?

Sadly, the sponsors of this survey have the ability to shrug, and say “well, that’s what people think.” But shouldn’t knowledgeable people in this industry have a responsibility to call “BS” on that kind of crazy assumption? 

Unfortunately, there’s not even a footnote here to suggest anything other than the perceived need is real — juxtaposed, I should add by the numbers this same (likely equally misinformed) group puts forth as the amount of savings they have.    

Look, BlackRock is not the only — and probably not the last — to put forth this kind of nonsense. Northwestern Mutual did so last April — even having the temerity to label it a “magic” number (though they “only” said it was $1.46 million). This being an annual “event” of theirs, I’m sure an “update” is forthcoming. More’s the pity. 

One assumes that the purveyors of these data points see it as a “wake up” call to folks, a motivation. But I think this “scare tactic” — there’s really no other word for it — is more likely just another sign to regular folks that they’ll NEVER manage to reach it — and surely some, perhaps most, just give up, or don’t even try in the first place. Not to mention the encouragement it doubtless provides to those who want to proclaim the system is “broken.”

What people think they’ll need is one thing — but I would argue that we have a responsibility to help them understand what they really need. 

And it’s not exaggerated, uninformed “scare tactic” guesses.

• Nevin E. Adams, JD

 

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[i] Averages are easy math — but misleading. In this case that average tells us nothing about the relative breakdown on age brackets, incomes, where they live, their health, etc.  What someone needs (or thinks they need) living in New York City is (or should be) considerably different from the projections of someone living in Dubuque, Iowa.   

Baby 'Steps'

I recently ran across a survey that claimed 7 in 10 DC plan sponsors were “taking steps” to solve the retirement income challenge… but that looks to have been “aspirational.”

While the survey’s[i] intro cautioned that there was more to be done, that struck me as a remarkably high (and reassuring) finding, though it didn’t mesh with my sense of the world at present. Sure enough, turns out, there is apparently a retirement income “journey”—one that apparently has several stages—all of which were (apparently) classified as “steps.” Those included:

• 34% – INITIAL (my emphasis, their wording) stages of learning about retirement income approaches
• 14% – in the process of better understanding participants’ retirement income needs
• 8% – in the process of evaluating specific retirement income solutions/products
• 7% – implementing/implemented a retirement income solution/product

Indeed, the survey goes on to comment that another 8% have evaluated these type solutions, and decided not to pursue them. And more than a quarter (27%) say retirement income is “not currently a topic of interest or need.”  

So, not to put too fine a point on it, but that looks to me like (only) 15% are taking actual steps to solve the problem, though I suppose there’s something to be said that nearly half are at least keeping an open mind on the subject.    

And even those who are considering a solution seem a bit confused. Consider that when it comes to products and solutions designed to support retirement income, plan sponsors cited stable value funds (70%). The income fund in a target-date series was a distant second (46%), though products most likely to be considered for future inclusion in 401(k) plans include annuities, long-duration fixed income funds, and managed accounts that support decumulation.

Don’t get me wrong—despite some significant legislative enhancements in SECURE 1.0 (and some modest encouragements in SECURE 2.0)—there remain plenty of legitimate, rational reasons why a plan fiduciary might rationally defer or delay action here. While today there are (more) solutions available, and more regulatory/legislative clarity—the traditional concerns (still) loom large in rationalizing inertia.  Mostly I think it still boils down to a question as to whether it’s the employer’s responsibility to provide these options (and to take on additional fiduciary exposure), particularly if nobody is asking for it.

More’s the pity, because in my experience if you want employees to feel comfortable about retiring, they need to know how much income they will have to live on. For most, that’s going to be a function of their Social Security benefit (diminished by their Medicare premiums), and what kind of income stream their retirement savings can produce. The latter isn’t hard math, but it’s more complicated than most participants will want to pursue (particularly with their financial future at stake). They may not be lining up at HR’s door demanding these solutions, but the physical—and fiscal—reality is that they need them. 

It's been said that “a journey of a thousand miles begins with a single step.”

The sooner, the better. 

- Nevin E. Adams, JD

 

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[i] The research was conducted by Coalition Greenwich from May 23 to Aug. 26, 2022, using an online, quantitative approach with 155 DC plan sponsors who have at least one 401(k) plan and at least $100 million in 401(k) assets. Plan breakdown by AUM: 36 plans with $100-$249M AUM; 37 plans with $250-$499M AUM; 31 plans with $500-$999M AUM; 32 plans with $1-$4.9B AUM; 19 plans with over $5B AUM.

Survey Says—Or Does It?

When you see a headline that confirms your sense of the world, you’re naturally predisposed to embrace, remember (and these days “share”) it as a validation of what you already perceive reality to be.

Indeed, as human beings, we’re drawn to perspectives, surveys, and studies that validate our sense of the world. This “confirmation bias,” as it’s called, is the tendency to search for, interpret, favor, and recall information in a way that confirms our preexisting beliefs or hypotheses. It also tends to make us discount or dismiss findings that run afoul of our existing beliefs—even if the grounds supporting that premise are shaky, sketchy, or (shudder) downright scurrilous.

Here are some things to look for—likely in the fine print or footnotes—as you evaluate those findings.

There can be a difference between what people say they will (or might) do and what they actually will.

No matter how well targeted they are, surveys (and studies that incorporate the outcome of surveys) must rely on what individuals tell us they will do in specific circumstances, particularly in circumstances where the decision is hypothetical. When you’re dealing with something that hasn’t actually occurred, or doesn’t actually exist, there’s not much help for that, but there’s plenty of evidence to suggest that, once given an opportunity to act on the actual choice(s), people do, in fact, act differently than their response to a survey might suggest.

Let’s face it, people tend to be less prone to action in reality than they indicate they will be—inertia being one of the most powerful forces in human nature. Also, sometimes survey respondents indicate a preference for what they think is the “right” answer, or what they think the individual conducting the survey expects, rather than what they might actually think (particularly if it’s something they haven’t previously thought about). That, of course, is why the positioning and framing of the question can be so important (as a side note, whenever possible, it helps to see the actual questions asked, and the responses available).

Now, survey takers will inevitably champion the higher accuracy rate of in-person surveys (or at least phone calls) versus online surveys, though the latter are ever more common (and less expensive to conduct).  

The bottom line is that when what people tell you they will do, and if you later find that they don’t—just remember that there may be more “powerful” forces at work.

There can be a difference between what people think they have, what they say they have, and reality.

Since, particularly with retirement plans, there are so few good sources of data at the participant level, much of what gets picked up in academic research is based on information that is “self-reported,” which is to say, it’s what people tell the people taking the survey. The most prevalent is, perhaps, the Survey of Consumer Finance (SCF), conducted by the Federal Reserve every three years.

The source is certainly credible, but it’s based on phone interviews with individuals about a variety of aspects of their financial status, including a few questions on their retirement savings, expectations about pensions, etc. In that sense, it tells you what the individuals surveyed have (or perhaps wish they had), but not necessarily what they actually have.

Perhaps more significantly, the SCF surveys different people every three years, so it pays to be wary of trendlines that are drawn from its findings—such as increases or decreases in retirement savings. Those who do are comparing apples and oranges—more precisely the savings of one group of individuals to a completely different group of people… three years later.

The survey sample size and composition matter.

Especially when people position their findings as representative of a particular group, you want to make sure that that group is, in fact, adequately represented. Perhaps needless to say, the smaller the sampling size—or the larger the statistical error—the less reliable the results.

Case in point: Several months ago, I stumbled across a survey that purported to capture a big shift in advisors’ response to the Labor Department’s fiduciary regulation. Except that between the two points in time when they assessed the shift in sentiment, they wound up talking to two completely different types of advisors. So, while the surveying firm—and the instrument—were ostensibly the same, the conclusions drawn as a shift in sentiment could have been nothing more than a difference in perspective between two completely different groups of people—at two completely different points in time.

When you ask may matter as much as what is asked.

Objective surveys can be complicated instruments to create, and identifying and garnering responses from the “right” audiences can be an even more challenging undertaking. That said, people’s perspectives on certain issues are often influenced by events around them—and a question asked in January can generate an entirely different response even a month later, much less a year after the fact.

For example, a 2020 survey of plan sponsor sentiment on a topic like ESG litigation is unlikely to produce identical results to one conducted in the past 30 days, any more than an advisor survey about the potential impact of the fiduciary regulation prior to its publication would likely match that of advisors dealing with those realities six months after publication. Down in those footnotes about sample size/composition, you’ll likely find an indication as to when the survey was conducted. There’s nothing wrong with recycling survey results, properly disclosed. But things do change, and you need to be careful about any conclusions drawn from old data.

Consider the source(s).

Human beings have certain biases—and so do the organizations that conduct and pay to conduct surveys and studies conducted. And sometimes the organizations paid to conduct such surveys are aware of those biases, and—consciously or unconsciously—that filters in to the way questions are posed, or in the way results are evaluated.

Not that sponsored research can’t provide valuable insights. But approach with caution the conclusions drawn by those who tell you that everybody wants to buy the type of product(s) offered by the firm(s) that have underwritten the survey.   

Be wary of sentiment ‘aggregation.’

It’s rare that the authors of a particular survey don’t have a preferred/expected outcome in mind—but legitimate surveys, objectively worded, sometimes receive a more tepid response than those authors might prefer. Typical are those that claim a “majority” are in favor of a certain outcome—a majority that requires combining what is generally a small minority who are strongly in favor with a (much?) larger number who are (only) somewhat in favor (for example, 16% strongly in favor, 35% somewhat favor turns into “A Majority Favor…”). 

It’s not exactly exaggerating to say that the combined result is at least somewhat supportive—but it can produce a result that is positioned far more enthusiastically in favor of a particular outcome than a discerning look at actual adoption/take-up later reveals.

Compound ‘Interests’

One of the more obvious ways to get people’s attention is to publish a survey/study that purports to find a dramatic impact of some kind. Basically, the authors will state an assortment of assumptions (and they’ll make no bones about THAT), and then take those assumptions, multiply them and…voila a gigantic impact that warrants attention (or at least clicks, likes and shares). 

The math checks out, so next thing you know it’s a headline where, as Mark Twain once noted, a “lie” travels around the world while the truth is still getting its boots on. It does so by being picked up, uncritically, by news media outlets which (apparently) draw comfort from the academic credentials of the authors—and their ability to lay the veracity of the claims at THEIR feet. 

When, in fact, all they’re doing is compounding the problem(s).     

- Nevin E. Adams, JD

5 Ways Industry Surveys Can Be Misleading

As human beings, we’re drawn to perspectives, including surveys and studies that validate our sense of the world. This “confirmation bias,” as it’s called, is the tendency to search for, interpret, favor, and recall information in a way that confirms our preexisting beliefs or hypotheses. It also tends to make us discount findings that run afoul of our existing beliefs.

In its simplest terms then, when you see a headline that confirms your sense of the world, you’ll be naturally inclined to embrace and remember it as a validation of what you already perceive reality to be. Even if the grounds supporting that premise are shaky, sketchy, or (shudder) downright scurrilous.

Here are some things to look for – likely in the fine print – as you evaluate those findings.

There can be a difference between what people say they will (or might) do and what they actually will.

No matter how well targeted they are, surveys (and studies that incorporate the outcome of surveys) must rely on what individuals tell us they will do in specific circumstances, particularly in circumstances where the decision is hypothetical. When you’re dealing with something that hasn’t actually occurred, there’s not much help for that, but there’s plenty of evidence to suggest that, once given an opportunity to act on the actual choice(s), people act differently than their response to a survey might suggest.

For example, people tend to be less prone to action in reality than they indicate they will be – inertia being one of the most powerful forces in human nature, apparently. Also, sometimes survey respondents indicate a preference for what they think is the “right” answer, or what they think the individual conducting the survey expects, rather than what they actually think. That, of course, is why the positioning and framing of the question can be so important (as a side note, whenever possible, it helps to see the actual questions asked, and the responses available).

The bottom line is that when what people tell you they will do, or even what kind of product they would like to buy, if you later find that they don’t, just remember that there may be more powerful forces at work.

There can be a difference between what people think they have and reality.

Since, particularly with retirement plans, there are so few good sources of data at the participant level, much of what gets picked up in academic research is based on information that is “self-reported,” which is to say, it’s what people tell the people taking the survey. The most prevalent is, perhaps, the Survey of Consumer Finance (SCF), conducted by the Federal Reserve every three years.

The source is certainly credible, but the basis is phone interviews with individuals about a variety of aspects of their financial status, including a few questions on their retirement savings, expectations about pensions, etc. In that sense, it tells you what the individuals surveyed have (or perhaps wish they had), but not necessarily what they actually have.

Perhaps more significantly, the SCF surveys different people every three years, so be wary of the trendlines that are drawn from its findings – such as increases or decreases in retirement savings. Those who do are comparing apples and oranges – more precisely the savings of one group of individuals to a completely different group of people… three years later.

The survey sample size and composition matter.

Especially when people position their findings as representative of a particular group, you want to make sure that that group is, in fact, adequately represented. Perhaps needless to say, the smaller the sampling size – or the larger the statistical error – the less reliable the results.

Case in point: Several months ago, I stumbled across a survey that purported to capture a big shift in advisors’ response to the Labor Department’s fiduciary regulation. Except that between the two points in time when they assessed the shift in sentiment, they wound up talking to two completely different types of advisors. So, while the surveying firm – and the instrument – were ostensibly the same, the conclusions drawn as a shift in sentiment could have been nothing more than a difference in perspective between two completely different groups of people.

Consider the source.

Human beings have certain biases – and so do the organizations that conduct and pay to surveys and studies conducted. Not that sponsored research can’t provide valuable insights. But approach with caution the conclusions drawn by those that tell you that everybody wants to buy the type of product offered by the firm(s) that have underwritten the survey.

When you ask may matter as much as what you ask.

Objective surveys can be complicated instruments to create, and identifying and garnering responses from the “right” audiences can be an even more challenging undertaking. That said, people’s perspectives on certain issues are often influenced by events around them – and a question asked in January can generate an entirely different response even a month later, much less a year after the fact.

For example, a 2015 survey of plan sponsor sentiment on a topic like 401(k) fee litigation is unlikely to produce identical results to one conducted in the past 30 days, nor would an advisor survey about the fiduciary regulation prior to the publication of the final rule as to its impact. Down in those footnotes about sample size/composition, you’ll likely find an indication as to when the survey was conducted. There’s nothing wrong with recycling survey results, properly disclosed. But things do change, and you need to be careful about any conclusions drawn from old data.

Not to mention the conclusions you might be otherwise inclined to draw from conclusions about old data.

- Nevin E. Adams, JD

See also:

• EBRI Report Calls Out Pew, CRR on Retirement Conclusions
• 4 Things That Make Me Go ‘Huh?’
• Could Fees Cost Millennials $590,000 in Retirement Savings?
• A Preference on ‘Preferences’?
• 3 (More) Pervasive Retirement Myths — an Academic Perspective

Lessons Learned

I joined EBRI with a passion for the insights that quality research can provide, and a modest concern about the dangers that inaccurate, sloppy, and/or poorly constructed methodologies and the flawed conclusions and recommendations they support can wreak on policy decisions.  While my tenure here has only served to increase my passion for the former, on (too) many occasions I have been struck not only by the breadth of assumptions made in employee benefit research, but just how difficult – though not impossible – it is for a non-researcher to discern those particulars.

We have over the past couple of years devoted some of this space to highlighting some of the most egregious instances, but as I close this chapter of my professional career, I wanted to share with you a “top 10” list of things I have learned in my search to find reliable, objective, actionable research:

1. There are always assumptions in research; find out what they are. Garbage in, garbage out, after all (the harder you have to look, the more suspicious you should be).
2. Just because research validates your sense of reality doesn’t make it “right.” But just because it invalidates your sense of reality doesn’t necessarily make it right, either.
3. Take the conclusions of sponsored research with a grain of salt.
4. Self-reported data can tell you what the individual thinks they have, but not necessarily what they actually have.
5. Sample size matters. A lot.
6. “Averages” (e.g., balances/income/savings) don’t generally tell you much.
7. There’s a certain irony that those who propose massive changes in plan design, policy, or tax treatment, frequently assume no behavioral changes in response.
8. When it comes to research findings, “directionally accurate” is an oxymoron.
9. In assessing conclusions or recommendations, it’s important to know the difference between partisan, bipartisan and nonpartisan.
10. In an employment-based benefits system, the ability to accurately gauge employee response to benefits change is dependent on the reaction of the employers who provide access to those benefits.

One of the things that I’ve always loved about the field of employee benefits was that there was always something new to learn, and with each position along the way I have gained a new and fresh perspective.  I’ll always treasure my time here as a member of the EBRI team, the opportunity I’ve had to contribute to this body of work – and I’m looking forward to continuing to draw on EBRI research for insights and analysis in my new position, as I have for most of my professional career.

That said, I’ll close by commending to your attention one of my favorite “lessons” – a quote attributed to Mark Twain, and one worth keeping in mind along with the 10 “lessons” above:

“It’s easier to fool people than to convince them they have been fooled.” 

Here’s to not being fooled.

- Nevin E. Adams, JD