February 2022 / DEFINED CONTRIBUTION
Beyond Averages: A More Robust Approach to Glide-Path Design
Key Inputs Should Reflect Diversity of DC Plan Populations.
- T. Rowe Price believes that failing to account for the heterogeneity of defined contribution participant populations in glide‑path design may lead to poor retirement outcomes.
- Many target date providers use simple averages to represent key participant characteristics and behavioral preferences in their glide‑path models.
- By using distributions of values within plan populations instead of averages, T. Rowe Price seeks to produce more robust target date offerings for all participants.
Many target date providers use averages as the primary population inputs when designing glide paths for defined contribution (DC) plans. We believe this method is not effective given that target date strategies are designed to be used by broad, diverse populations. These populations are not homogeneous—just the opposite, in fact. Plan participants may exhibit a variety of demographic characteristics and differing investment preferences.
We believe that not accounting for the heterogeneous nature of DC participants when designing and implementing target date glide paths ultimately may lead to retirement outcomes that fall wide of the mark for many plan participants.
Our research and insights into DC populations and participant behaviors suggest that glide‑path models that use distributions of the key participant characteristics as design inputs instead of simple averages potentially can do a better job of capturing the diversity of participant characteristics within DC plan populations.
In solutions customized at the participant level, such as managed accounts, the problem of heterogeneity is avoided entirely, as glide paths can be designed based on each participant’s specific circumstances, objectives, and preferences. However, use of managed accounts as the default option is neither typical nor necessarily desirable among DC plans due to a number of potential downsides, including potentially higher costs and the need for participants to engage and provide personalized inputs in order for the benefits to be achieved.
At T. Rowe Price, our glide‑path work draws on our recordkeeping database of over 2 million DC plan participants. Based on aggregate, depersonalized data from this source, we have developed inputs that reflect the key demographic attributes and behavioral preferences of a real‑world universe of actual plan participants. In our view, this approach represents a more practical, lower‑cost alternative to individually customized solutions while avoiding the inherent limitations of a design methodology based on simple averages.
The role of asset accumulation within DC plans as a critical lever in retirement planning emphasizes how important it is for plan sponsors and their investment advisors to understand the implications of different target date design approaches. In our view, plan sponsors would be well served by selecting target date strategies with glide paths that are based on realistic assumptions about participant demographics and preferences and, thus, seek to improve retirement outcomes for the entire plan population.
Designing Glide Paths for Diverse Populations
Target date strategies provide DC plan participants with appropriately diversified portfolios designed to pursue long‑term retirement investment objectives. By moving along preset glide paths, target date asset allocations can reflect the evolving needs and risk tolerances of participants as they pass through the accumulation phase of their investment life cycle and into retirement.
However, DC plan populations inevitably include a range of individuals with differing demographic characteristics—such as age, current and expected earnings, and savings behavior—and risk preferences. Yet, many investment providers seek to design their glide paths based on inputs that reflect a single “average” participant.
With this as the backdrop, consider the mathematical definition of “average”—the result obtained by adding multiple quantities together and then dividing the total by the number of quantities. While simple averages are easy to understand and likely adequate for many uses, we believe using them as inputs in the glide‑path design is a vast oversimplification.
Given the impact that glide‑path design can have on long‑term retirement outcomes, we believe the use of oversimplifying assumptions can create significant downside risks for plan sponsors and participants.
We can illustrate these risks by taking a closer look at two key participant demographic inputs that are integral to glide‑path modeling:
- expected earnings levels
- savings behavior
Each of these inputs has substantial influence in glide‑path design—in particular, in setting the appropriate allocation between equity and fixed income assets at any given point on the path, both before and after retirement.
However, the ranges of salaries and contribution rates within a DC plan population may be quite wide. A design methodology based on simple averages may result in suboptimal asset allocations and retirement outcomes for a significant number of participants.
Take, for example, the hypothetical income groups in Figure 1, which displays both their plan participation rates and their projected income replacement from Social Security. The lower‑income cohorts (such as the participants in the second quintile) have less discretionary income and, thus, may not be able to save as large a share of their earnings as the middle‑ and higher‑income cohorts. The lower‑income groups are likely to need more growth from their DC plan accounts to mitigate their relatively low saving rates and close the gap between their expected Social Security benefits and their consumption needs in retirement.
Our model for glide‑path design has evolved to emphasize the role of nondiscretionary consumption in the spending model in a systematic manner. Recognizing that the share of total expenses dedicated to discretionary spending tends to be lower for lower‑earning participants, we tie this observation to a preference for avoiding balance depletion that is a feature of our behavioral spending model.1 Namely, lower income reflects a lower share of discretionary spending and, thus, a lower preference for avoiding depleting assets. Likewise, higher income reflects a larger share of income devoted to discretionary spending, greater ability to save, and a reduced focus on immediate consumption—resulting in a higher preference for avoiding the depletion of assets.
Next, consider a participant who falls somewhere in the middle of the earnings distribution (in the fourth quintile in Figure 1, for example) but also has a relatively low savings rate. This individual is likely to experience an even larger shortfall in his or her ability to cover consumption needs in retirement because Social Security benefits will close less of the gap. This example illustrates the potential pitfalls in relying on simple averages when seeking to design optimal glide paths, which may miss the mark in terms of serving the entire plan population well.
Lower and Higher Income Groups Are Both Likely to Need More Portfolio Growth
(Fig. 1) Social Security Replacement1 and Retirement Plan Access by Income2
Longevity Risk Is an Increasingly Critical Design Factor
Savers face many risks throughout the course of financially preparing for retirement. These may include market risks (such as price volatility and the erosion of real portfolio values by inflation) as well as behavioral risks (such as insufficient savings or failed attempts to time the market). We believe that longevity risk—a shortfall of funds during retirement—has become one of the most important risks that must be addressed in retirement planning.
Americans now are living longer, which means a career’s worth of savings might need to fund a retirement lasting many decades. For example, recent actuarial estimates indicated that for a hypothetical male‑female couple, both 65 years old, at least one member had an 80% probability of living to age 85, or another two decades, a 60% probability of living to age 90, or two and a half decades, and a nearly 33% chance of living to 95—three full decades of retirement (Figure 2).
Today’s DC Plan Participants Can Expect Lengthy Retirements
(Fig. 2) Actuarial Estimates of Expected Survival Rates at Age 651
Our modeling approach reflects these considerations by thinking of glide‑path optimization as a planning problem. Namely, we allow each individual to have a specific retirement planning horizon, which is tied to a timing preference in our behavioral spending model. The mortality discount that we apply to each time period scenario in our analysis is conditional on an individual living until their specific plan horizon, rather than simply living until retirement age. In practice, we calibrate these planning horizons to the preferences of plan sponsors, allowing us to incorporate sponsors’ views into our glide‑path design in a systematic manner.
In our view, relying on a default asset allocation for the “average” person ignores the full participant population in favor of building a glide path designed to serve a mathematical value, not real people.
Our modeling work suggests that the use of inputs based on distributions within actual participant populations can have a significant impact both on glide‑path design and real‑world retirement outcomes. Figure 3, for example, shows the “optimal” glide path suggested by our methodology using simple average values for participant savings, earnings, and preferences, and, alternatively, distributions of those same values across a hypothetical plan population.2
Glide Paths Based on Distributions May Feature Higher Equity Exposure
(Fig. 3) Hypothetical Glide Paths Based on Average Earnings and Preferences and on Distributions of Those Values Within a Plan Population1
As can be seen, the “distributions” and “averages” glide paths run in parallel in the initial years of the accumulation cycle but begin to diverge about 30 years before retirement. The distributions‑based glide path then begins to allocate a higher weight to equities relative to the averages‑based path, a positive differential that climbs to almost 14 percentage points by the 10th year before retirement. After that point, the two glide paths begin to converge, although the distributions‑based glide path continues to maintain a higher equity weight through the 30th year of retirement.
The higher equity weights suggested by glide paths designed using value distributions rather than simple averages have obvious implications for portfolio performance and retirement outcomes. Historically, the compounding of the equity risk premium—essentially, the additional return on stocks relative to bonds—has led to meaningful differences in outcomes for investors. Our research suggests that the potential benefits of capturing this equity premium outweigh the potential risks, such as the possibility of a large market decline at or near retirement, for investors with a longer‑term focus on longevity.
Changes in participant behavior also have strengthened the case for higher long‑term equity exposure in our view. Until fairly recently, many plan sponsors assumed that participants would exit their plans at or soon after retirement, rolling their balances over into individual retirement accounts. This presumption may have led some sponsors to have concerns about adopting higher‑equity glide paths, out of concern that a major market decline might force some exiting participants to “lock in” their losses upon retirement.
While we believe plan sponsors should make target date and glide‑path decisions based on their own beliefs and intended objectives, we encourage them to consider that participants today are expected to live longer in retirement and are likely to depend more heavily on their DC plans for income during retirement. Data also show that participants now are more likely to keep assets in their DC plans after retirement while plan sponsors have grown more interested in seeing them stay there:
- T. Rowe Price’s recordkeeping data show that in 2018 over 61% of DC participant balances at the time of retirement were still invested in plan accounts one year after retirement, up from 55.4% in 2017 and only 48.7% in 2016.3
- A 2018 T. Rowe Price survey of 289 DC plan sponsors found that almost 70% believed retention of participant assets was preferable to retirees transitioning their account balances out of the plan. Almost 30% said that keeping retired participants in the plan had become more of a priority for them recently. Only 15% preferred that their participants roll their balances out of the plan at retirement.4
We believe this behavioral evolution has significantly strengthened the need for careful evaluation of the desired level of potential growth exposure leading up to and into retirement. However, we also recognize that for some plans there may be relatively unique characteristics or preferences that could justify lowering equity exposure in the glide path near the age of retirement.
Measuring the Impact of Distributions‑Based Inputs
We believe using real distributions of participant variables, such as those based on our own DC plan database, to inform glide‑path design guards against one key pitfall of relying on simple averages: the tendency for such a methodology to recommend glide‑path allocations with relatively low potential growth trajectories. Use of distributions as design inputs typically results in glide paths with a higher growth potential due to their higher equity allocations, with a corresponding improvement in their potential to mitigate income shortfalls in retirement.
As part of their design research, T. Rowe Price analysts compared the hypothetical performances of two glide paths, one based on simple averages and one based on distributions within an assumed plan population.5
- The exercise was based on T. Rowe Price’s glide‑path design model, which incorporates a variety of demographic and behavioral characteristics and preferences.
- One version of the glide path was designed using simple mathematical averages for the key inputs, while the other was based on distributions of those values across the hypothetical participant population.
- Scenario analysis was used to estimate the likelihood that the distributions‑based glide path could outperform the averages‑based one, given the design model’s economic and capital market assumptions, such as expected economic growth, inflation, and asset returns (expressed as probability distributions).6
Figure 4 highlights the results of this analysis across two critical outcome metrics: consumption replacement during retirement and portfolio values at retirement. We defined consumption as income minus savings. The consumption replacement rate was the percentage of preretirement income, net of savings, that could be sustainably withdrawn from the portfolio over a defined period after retirement—in this case, 30 years.
Hypothetical Distributions-Based Glide Paths Outperformed Averages-Based Glide Paths in a Majority of the Scenarios We Tested
(Fig. 4) Results of 10,000 Scenarios1
Relative to the glide path based on average values, the glide path that was designed using participant distributions resulted in an improved consumption replacement in almost 76% of the 10,000 hypothetical scenarios generated. It also produced larger portfolio values at retirement in almost 76% of those hypothetical scenarios. Put differently, in more than three out of four hypothetical scenarios, portfolio values at retirement were higher for the glide paths designed using distributions‑based inputs.
Figure 5 shows the hypothetical gains in consumption replacement made possible in the analysis described above. The 10,000 scenario results were ranked according to the relative performance of the distributions‑based glide path versus the averages‑based glide path. The resulting hypothetical improvements in consumption replacement are shown for each year of retirement, out to 30 years.
Distribution-Based Glide Parths May Lead to Better Retirement Outcomes
(Fig. 5) Improvements in Post-retirement Consumption Replacement by Scenario Quantile
Distributions-Based Glide Path Versus Averages-Based Glide Path
Results of 10,000 Scenarios1
Using the 50th percentile (the median scenario) in each year as the benchmark, the distributions‑based glide path produced hypothetical improvements in real (after inflation) consumption across the first 30 years of retirement that ranged from almost 2.4% to more than 5.4% per year relative to the averages‑based glide path. Those gains increased most rapidly in the fifth through the 20th year of retirement, before tapering off slightly near the end of our 30‑year post‑retirement horizon. For many retirees, these may be the key spending and consumption years, depending on health care‑related costs, and, thus, the retirement segment where higher portfolio balances may be most desirable to guard against unforeseen spikes in health care or other expenses.
In percentage terms, the improvements in consumption replacement demonstrated in our analysis might appear relatively modest. However, for individuals who will need to support themselves in what could be a lengthy retirement, the benefits in dollar terms could be quite meaningful.
For a hypothetical plan participant who retired at age 65 with a USD 100,000 final salary and an average annual post‑retirement consumption of USD 80,000 after inflation, a 4% increase in salary replacement made possible by following the distributions‑based glide path instead of the averages‑based glide path in our model would add USD 3,200 in additional average annual after‑inflation resources.
Even larger hypothetical gains in post‑retirement consumption replacement were indicated in the top two scenario groups in our analysis (i.e., the 75th and 90th percentile results). The probabilities associated with those hypothetical outcomes were correspondingly smaller, however.
There are many levers that impact retirement outcomes, and we believe plan sponsors need to carefully consider the many trade‑offs involved in the design of their plan’s target date strategies and the impact those choices may have on retirement outcomes.
In this paper, we have sought to illustrate the importance of glide‑path modeling, with an emphasis on the key inputs that drive those models—the risk tolerances, investment goals, and other individual preferences of the participants they are intended to serve.
T. Rowe Price’s approach to glide‑path design is guided by the fact that the characteristics and preferences of plan participants are heterogeneous. A glide path based on a profile of the “average” participant is unlikely to be highly desirable for at least some participants.
Our glide‑path designs are based on distributions, not averages, derived from our database of over 2 million DC plan participants. We believe this methodology produces more robust offerings in that it seeks to minimize the degree to which potential outcomes for one group of participants are sacrificed in favor of any other group.
One effect of our methodology is to increase the recommended exposure to equities and other growth‑oriented assets across both the pre‑ and post‑retirement portions of a given glide path. This tendency largely accounts for the hypothetical performance of the distributions‑based glide paths in our modeling work and reinforces our belief that longevity risk—the possibility that retirees might outlive their resources—has increasingly become one of the most important risks DC plan participants face and should be a key factor to consider in building portfolios for retirement.
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