April 2022 / RETIREMENT INSIGHTS
How Do You Evaluate a Glide Path?
Glide path evaluation is not an easy task.
- In evaluating target date glide paths, T. Rowe Price looks at economic utility—their potential to satisfy investors’ retirement income and wealth preferences.
- A numerical utility score means relatively little to most investors. So our model generates metrics that measure possible retirement outcomes more directly.
- We believe the metrics in our evaluation process make it easier for plan sponsors and investors to assess whether a glide path reflects their own preferences.
Over the course of two decades of research, T. Rowe Price has developed a proprietary framework for glide path design that is centered on a structural model incorporating the inputs, parameters, and mathematical techniques that we believe are necessary to represent accurately the challenges faced by retirement investors.
In a previous T. Rowe Price Insights paper, we highlighted certain aspects of our model to demonstrate how we evaluate the range of possible outcomes associated with a particular glide path.1 As we progress through our Making the Benefit Connection series, this information will be essential to understanding how the presence of defined benefit (DB) plans potentially affect the appropriate level and shape of the glide paths for target date offerings in companion defined contribution (DC) plans.
The primary metric that T. Rowe Price uses to evaluate a glide path design is economic utility, which measures the degree of satisfaction a person experiences from possessing or consuming an economic good. In the case of glide path evaluation, the economic goods in question are income for spending and accumulated wealth. Income and wealth both provide levels of satisfaction that can be measured in terms of investor utility. And, in both cases, there is a governing principle that economic theory typically treats as universal: the law of diminishing marginal utility.
To illustrate this principle, consider a simple example involving a favorite meal. Even though the entire meal is satisfying, the last bite will not be as satisfying as the first bite. While we address this issue mathematically—which provides a rigorous way to combine our utility model’s many features—our approach also fits naturally with the way we prefer to express the problem: How can we potentially make an investor as satisfied as possible given their preferences? As John Dewey, the prominent American philosopher, once said: “A problem well‑put is half‑solved.”2
Utility is based on a set of individualized preferences. However, expressed simply as a number, the concept has relatively little meaning for the typical investor, in our view. To convey why certain glide paths potentially are appropriate for specified preferences, we have compiled a set of complementary metrics to express possible retirement outcomes. Our metrics measure risk and reward in ways that we believe investors actually care about, rather than simply in terms of portfolio return and volatility.
In our view, the metrics generated using our model make it easier for plan sponsors to assess whether, on balance, a particular glide path reflects their preferences. However, we recognize that this might not be obvious on first impression. So, instead of focusing on just one glide path, our model analyzes a range of glide paths that takes into consideration slight adjustments to investor preferences and the potential trade‑offs associated with those choices. We refer to this spectrum of glide paths as a “suitability range.”
To help explain the benefits of utility theory, we first argue for the need for a more capable approach than those typically in use today. We then provide a high‑level explanation of our utility model. Finally, we explain some of the model’s key inputs, including preference values and plan demographics, and discuss our results metrics and our suitability range. We believe this discussion will help lay a solid foundation for understanding the effects of DB plans on companion DC plans.
Two Important Effects
Earned pension benefits often are similar in nature to Social Security benefits. Both payment streams represent deferred labor income that has a measurable present value. An investor receiving defined benefits has a higher guaranteed fixed income than another investor with the same salary and financial capital but no DB plan. Assuming the two individuals are using the same DC glide path, the investor receiving defined benefits, in effect, has a higher overall fixed income allocation.
Other things being equal, this dynamic suggests that to be properly diversified across all their assets, investors receiving defined pension benefits should shift more of their financial capital to equity‑like assets (i.e., they should have higher equity‑like exposures in their DC glide paths) to adjust for the effect of their defined benefits on their overall allocations. We call this the “substitution effect.”
The substitution effect may seem relatively straightforward, but does it actually make sense? Suppose, for example, that two investors have identical salaries, savings rates, employer matching contribution rates, and account balance histories. However, one also receives significant payments from a DB plan.
- Clearly, the individual with the DB plan should be able to expect a more securely funded retirement than the person without a DB plan.
- Greater income security should mean that the DB plan beneficiary has less need for the potential long‑term growth advantages conveyed by higher equity exposure.
- Being risk averse, the DB beneficiary ordinarily could be expected to lower equity exposure rather than raise it.
For our hypothetical defined benefit recipient, the outcome of the utility function is the opposite of the one predicted by the substitution effect—equity exposure in the preferred glide path should be lower rather than higher. We call this offsetting preference the “wealth effect.”
These arguments are cogent because our research confirms that the substitution and wealth effects are both real. Their relative strengths are tied to individual preferences and circumstances that need to be assessed and considered together. To incorporate both effects in a parsimonious glide path design model, we must develop a rich and nuanced approach to glide path evaluation. We intend to explore this concept further in the fourth paper in this series.
Seeking to Maximize Investor Utility
The personality traits that influence economic satisfaction are tied to certain goals and preferences that help define that person. Everyone has a unique blend of these preferences. In our view, the utility function is a rigorous way to describe the interactions of these characteristics and to measure the level of satisfaction a given set of outcomes can provide an individual.
We measure these preferences with explicit parameters. Furthermore, our model ascribes utility to two distinct sources. On the one hand, people enjoy the goods they consume that are paid for out of their retirement savings. Measuring utility as a function of consumption is a common approach. However, we believe that people also derive value from the security, flexibility, and autonomy derived from maintaining or growing their wealth.
Uniquely, our model factors both sources of satisfaction into its utility score. However, reflecting the contravening dynamics of seeking to both maintain and consume wealth, efforts to increase the utility score by improving investors’ outcomes along one of these two dimensions inherently come at the expense of the other. Individual preferences are used to establish a tipping point that seeks to balance the two sources of utility in a unique way for each person or group of people.
Behavioral preferences are just one of three classes of variables simulated in our framework (Figure 1). Capital market assumptions and demographic factors also play key roles.
T. Rowe Price’s Glide Path Designs Are Based on Three Input Types
(Fig. 1) Input classes
T. Rowe Price has built a proprietary cascading model for generating capital market returns based on economic factors and calibrated to certain assumptions. While this is an essential design component, unless a plan sponsor has a significantly different outlook for asset class returns compared with our inputs, different capital market assumptions are relatively less important for differentiating glide paths and their suitability.
Far more influential are demographic characteristics and behaviors. We model investor cash flows including income, savings, Social Security benefits, and behaviorally representative spending patterns. These draw on our capital markets model but also incorporate mortality rates and employer matches. Changes in these flows can meaningfully impact the indicated shape of a glide path.
To tie all of these variables together, we use Monte Carlo simulation to generate thousands of hypothetical scenarios for quantities such as macroeconomic variables, asset class returns, salary trajectories, portfolio balance growth, spending policies in retirement, and sampled preference values. The suggested glide path is the one that provides the highest utility for a population described by its behavioral preferences and demographics under our definition of utility. We then use the hypothetical outcomes produced by the suggested glide path as inputs to the set of metrics we cited at the beginning of this paper and that we will discuss in more detail later.
Our approach allows us to incorporate these three classes of inputs into objective criteria and apply a consistent investment evaluation process across a variety of retirement goals and expectations.
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