Another Way to Calculate How Much Clients Can Spend in Retirement

In addition to discussing this problem with Morningstar, Tharp has also written in depth on the topic on Kitces.com. Ultimately, he says, the key point is that outcomes, not probabilities, are what matter to clients, and any way of communicating Monte Carlo results should be clear about what those results mean in terms of real spending to the client over time.

In some cases, it may even make sense to avoid framing Monte Carlo results in terms of probabilities entirely, and to instead communicate results in terms of the actual dollar spending adjustments that would be triggered in specific scenarios.

The Guardrails Approach

Tharp says he likes to explain this approach using “guardrails” terminology, as that seems to resonate with clients.

He encourages advisors to utilize ongoing Monte Carlo simulations as a means of tracking the client’s probability of success as an ongoing issue, and to put pre-defined guardrails in place that will trigger specific spending changes as the probability of success rises and falls over time.

“Advisors can use withdrawal-rate guardrails, which are guidelines to increase or decrease spending when portfolio withdrawal rates reach certain levels,” he says. For example, if an initial 4% withdrawal rate calls for $5,000 in monthly spending, the spending amount could be adjusted higher if it reaches 2% of the portfolio value or lower if it hits 6%.

Of course, even withdrawal-rate guardrails can be flawed, Tharp warns, because the relatively steady withdrawal rate patterns that are often assumed in the underlying Monte Carlo simulations do not necessarily align with how retirees actually pull distributions from a portfolio in retirement.

In reality, Tharp says, what is more commonly seen is a “retirement distribution hatchet” in which the initial retirement distribution rates from a portfolio are highest early in retirement, and then they significantly decline when deferred Social Security is claimed as late as age 70.

Spending tends to fall even further later in life, Tharp says, as older retirees tend to spend less on discretionary items like travel. Another factor to consider is that there are often other sources of income in retirement, such as pensions or rental income, that are not directly factored into the Monte Carlo simulations.

To compensate for these issues, Tharp says, advisors should consider using holistic risk-based guardrails, which reflect current longevity expectations, expected future cash flows, expected future (real) income changes and other factors.

With this approach, probability of success via traditional Monte Carlo analysis can serve as the risk metric to guide the implementation of risk-based guardrails. According to Tharp, there is still a possibility of causing anxiety for clients if the risk is presented in terms of the success or failure of their plan as a whole, but advisors can instead use the language of “income risk,” which may be less stress-inducing.

Ultimately, Tharp says, the key point is that a risk-based guardrails model can provide clients with a more accurate picture of how much they can sustainably spend than can models based on static withdrawal rates or withdrawal-rate guardrails. While Tharp says risk-based guardrails can be less efficient to calculate manually than withdrawal-rate guardrails because of the many factors considered in the risk-based model, when properly assisted by technology, risk-based guardrails can be implemented and maintained as efficiently as withdrawal-rate guardrails.

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