Previously in our Benefit Connection series, we explored how the additional wealth provided by a defined benefit (DB) plan can impact the target date glide path in an accompanying defined contribution (DC) plan, particularly if the substitution effect isn’t considered.1 We showed how this additional wealth generally pushes down the equity allocation across most ages. However, we didn’t consider the impact that the presence of a DB plan might have on when participants choose to retire. We do so here.
Data from U.S. corporate pension plan regulatory filings suggest that participants who have DB plan benefits often retire earlier than the Social Security full retirement age, which is transitioning from age 66 to age 67 (Figure 1). This is particularly true when plans offer early retirement subsidies, which creates a retirement benefit that is more valuable than the actuarially reduced benefit.
While aggregated U.S. public plan data are harder to come by, the typical design of many local government, firefighter, and law enforcement DB plans leads us to expect that the average retirement age for these participants is lower than the Social Security full retirement age, just as we see in the U.S. corporate sector. If a DB plan encourages employees to retire earlier than they otherwise would, the glide path for a companion DC plan’s target date offering should reflect this earlier transition from accumulation to decumulation.
“An earlier retirement date will impact postretirement wealth and spending in several ways for participants who have both DB and DC benefits.”
Many DC plan glide paths, including the ones offered by T. Rowe Price in our flagship commingled vehicles, are built on the assumption that participants will retire at a specific age, typically 65. An earlier retirement date will impact postretirement wealth and spending in several ways for participants who have both DB and DC benefits.
(Fig. 1) Weighted average retirement ages for DB plans with 10+ participants
Source: U.S. Employee Benefits Security Administration, 2022 Form 5500 dataset (n = 17,526). Data analysis by T. Rowe Price.
To assess how much glide paths should change to reflect lower retirement ages, we modeled four hypothetical scenarios, all of which are based on the baseline safe harbor DC plan that we have used throughout the Benefit Connection series:
Not surprisingly, and consistent with the findings in the other papers in the Benefit Connection series, the addition of the FAP plan brought the hypothetical optimal glide path equity allocation down significantly throughout both the accumulation and decumulation phases (Figure 2). The largest disparity occurred in the peak earning ages for someone retiring at age 65. However, when we took the same DB plan and allowed a participant to retire at age 61 with an actuarially equivalent benefit, the impact on the hypothetical glide path equity allocation was much more muted (Figure 3).
The biggest difference in equity allocations between the FAP plan with early retirement (scenario 3) and the baseline scenario (i.e., a DC plan without a companion DB plan) occurred well into retirement and was only about eight percentage points in magnitude. The longer retirement period required significant DC plan portfolio growth throughout the accumulation phase in order to be sustainable.
By its very nature, the early retirement subsidy provided in scenario 4 increased retirement wealth, so we saw a two- to four-percentage-point reduction in the hypothetical optimal equity allocation throughout the glide path in comparison with the unsubsidized early retirement glide path in scenario 3. The impact of the subsidy was largest in the years right around retirement, since those were the years when the additional wealth from the subsidy would have been realized.
(Fig. 2) Hypothetical optimal glide path equity allocations
Source: T. Rowe Price.
For illustrative purposes only. Not representative of an actual investment. This analysis contains information derived from a Monte Carlo simulation. This is not intended to be investment advice or a recommendation to take any particular investment action. See Appendix for more information.
(Fig. 3) An unsubsidized FAP plan reduced equity by less than eight percentage points vs. the baseline scenario
Source: T. Rowe Price.
For illustrative purposes only. Not representative of an actual investment. This analysis contains information derived from a Monte Carlo simulation. This is not intended to be investment advice or a recommendation to take any particular investment action. See Appendix for more information.
While the addition of a DB plan to an existing DC plan can improve participants’ overall retirement wealth, if the existence of the DB plan encourages employees to retire early, there could be several offsetting factors that affect glide path design.
If their early-retirement DB benefits are unsubsidized, participants still will need significant equity exposure in their DC plans to sustain their longer retirement periods. In this case, the DB benefit would likely be lower due to both a shorter career service multiplier and a reduction to reflect the actuarial impact of mortality and the time value of money.
“Even with a subsidy, the full wealth effect of having a DB plan is not realized for early retirees when compared with those who retire at a later retirement age.”
Even with a subsidy, the full wealth effect of having a DB plan is not realized for early retirees when compared with those who retire at a later retirement age. Higher equity allocations and investment returns would still be needed to support a longer decumulation horizon.
Hypothetical DC plans: Our baseline assumption was a safe harbor plan design with the employer matching up to 100% of the first three percentage points of salary deferrals and 50% of the next two percentage points.
Hypothetical DB plan: A final average pay plan that pays a single life annuity with the following benefit formula: normal retirement benefit at normal retirement date = 1% x the average of the final five years of pay x years of service. For the final average pay plan with an early-retirement subsidy, we applied a 3% reduction to the normal retirement benefit per year of early retirement. This subsidy compares favorably with the roughly 6.8% annual reduction in benefits that we estimate is approximately actuarially equivalent based on the RP‑2014 healthy annuitant mortality table with MP‑2021 mortality improvement scale published by the Society of Actuaries and the January 2024 minimum present value segment rates published by the IRS.
Demographic analysis: We assumed that participant incomes grew in line with a proprietary salary growth model calibrated on T. Rowe Price’s recordkeeping platform. Participants were assumed to begin taking Social Security benefits at age 65 and to begin withdrawing income from their DC plans to support a steady, inflation‑adjusted level of spending over the full retirement period, including early retirement where applicable.
Projections or other information generated regarding the likelihood of certain outcomes are not guarantees of future results. This analysis is based on assumptions, and there can be no assurance that the projected results will be achieved or sustained. Actual results will vary, and such results may be better or worse than the assumed scenarios.
1Justin Harvey, Adam Langer, Aaron Stonacek, and James Tzitzouris, Understanding the Substitution Effect (2024).
2We show results for age 61 retirement because it is the youngest age given as the average retirement age by a critical mass (at least 2%) of U.S. corporate DB plans in their 2022 Form 5500 filings.
3For the subsidized plan, we applied a 3% reduction to the normal retirement benefit per year of early retirement. This compared favorably with the roughly 6.8% annual reduction in benefits that we estimate is actuarially equivalent. See the Appendix for further details on the modeling methodology.
Additional Disclosure
Monte Carlo simulations model future uncertainty. In contrast to tools generating average outcomes, Monte Carlo analyses produce outcome ranges based on probability—thus incorporating future uncertainty.
Material Assumptions include:
Modeling Assumptions:
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