Beyond the 4% Rule: The Life-Expectancy Problem in Dynamic Withdrawal Strategies

The 4% Rule Is Finally Getting a Rethink

The 4% rule has anchored retirement income planning for thirty years. Bill Bengen's 1994 research showed that a retiree with a balanced portfolio could withdraw 4% in year one, adjust for inflation thereafter, and not run out of money across any rolling 30-year window since 1926. It is simple, teachable, and durable. It is also, by now, widely understood to be too rigid for real clients.

A recent Wall Street Journal piece (June 2026) laid out the mainstream case for moving past it, and the framework it described is one most thoughtful advisors have already been drifting toward. The criticisms are familiar: the rule assumes a fixed 30-year horizon that not everyone will hit, it bakes in a century of unusually strong returns, and it is unresponsive in both directions. A portfolio can double in a bull market and the rule still holds spending flat. A portfolio can fall by a third and the rule still tells the client to withdraw the same inflation-adjusted dollar amount, accelerating the path to ruin.

The estimates themselves keep moving, too. Morningstar has revised its "safe" starting figure between roughly 3.3% and 3.9% in recent years; Bengen himself has suggested the number could now be as high as 4.7%. When a single rule produces a range that wide, it is a sign the rule was never the point. The horizon was.

This article is about the fix the industry is converging on, why that fix is genuinely better, and the one input it quietly depends on that almost no one is getting right.

The Fix Everyone Is Converging On

The replacement for the 4% rule is not a different fixed percentage. It is a dynamic approach that adjusts spending to two things the static rule ignores: how the portfolio is actually performing, and how long the client is actually expected to live. It usually arrives in three layers.

Layer 1: The actuarial method. Instead of locking in a dollar amount, you divide the current portfolio balance by the client's remaining life expectancy and spend roughly that much this year. Next year, you recompute with the new balance and the new remaining life expectancy. A 70-year-old with about 16 years of remaining life expectancy spends roughly one-sixteenth of the portfolio; a year later, one-fifteenth, and so on. Spending rises when the portfolio is up and falls when it is down, which is exactly the responsiveness the 4% rule lacks. (Practitioners sometimes call this the "RMD method," because it mirrors the way required minimum distributions divide a balance by an IRS life-expectancy factor.)

Layer 2: Guardrails. Pure actuarial spending is too jumpy. So you band it. Set a floor and a ceiling around the familiar 4% anchor, say 3% on the low end and 6% on the high end. Run the actuarial calculation each year; if the implied withdrawal lands inside the band, take it. If it would exceed the ceiling, cap it. If it would fall below the floor, spend the floor. The 4% rule survives, demoted from "the plan" to "the center of the band."

Layer 3: A shock absorber. Even guardrails can demand a brutal one-year cut after a market drop. So you cap the year-over-year change in spending, often at plus or minus 5%, smoothing the ride at the cost of a bit more drawdown risk in a deep, sustained downturn. Pair this with the classic essential-versus-discretionary split, covering non-negotiable expenses with guaranteed income (Social Security, an annuity) and running the dynamic strategy only on the discretionary remainder, and you have a genuinely more honest plan than a flat 4%.

This is good. It is responsive, it keeps a familiar reference point, and it can be run on a spreadsheet. But look closely at Layer 1, because every layer above it inherits whatever Layer 1 gets wrong.

The Number the Whole Method Divides By

The static 4% rule had one virtue that the dynamic method gives up: it did not require you to know how long the client would live. The horizon was a fixed 30-year assumption, wrong for almost everyone, but at least it was wrong in a known, bounded way.

The dynamic method removes that crutch. The moment you divide the portfolio by "remaining life expectancy," that number stops being a background assumption and becomes the single most load-bearing input in the plan. Get it wrong and you do not just misjudge the horizon, you misprice every annual withdrawal and you miscalibrate the guardrails that are supposed to protect the client.

So where does that number come from? In practice, advisors reach for a population table. The Journal points readers to the Social Security Administration's period life tables. Many advisors instead use the IRS Uniform Lifetime Table, the same factors behind RMDs. They are different tables, but for this purpose they share the same fatal property: they describe a population, not your client.

Why the Life-Expectancy Table Is Wrong, in a Predictable Direction

A population life table is built from one year of nationwide death data and answers a deliberately narrow question: if current mortality rates held fixed at every age, how long would an average person of this age and sex live? That construction introduces three errors, and for the typical advisory client they all push the same way.

It is a period table, not a cohort table

Period tables are a snapshot. They assume mortality rates never improve. In reality, mortality at most ages has been declining for decades thanks to better cardiovascular care, improved cancer survival, and falling smoking rates. The Society of Actuaries publishes mortality-improvement scales (currently MP-2021) precisely to project that forward. A model that incorporates improvement will project a longer life expectancy for most healthy people than a static period table, often by 2 to 4 years for a healthy 65-year-old. The actuarial method, fed a period table, systematically understates how long a healthy client will live.

It is a population average, and your clients are not average earners

This is the error almost no one accounts for. Life expectancy is steeply correlated with income and wealth. In the landmark Chetty et al. analysis (JAMA, 2016), the wealthiest 1% of American men lived to roughly 87 while the poorest 1% lived to roughly 73, a gap of nearly 15 years; for women the gap was about 10 years. Population tables blend all of those people together.

But an advisor's book is not a random sample of the country. Clients with investable portfolios are, almost by definition, drawn from the upper end of that income-and-wealth distribution, the end that outlives the population average. Using a population table for an affluent 65-year-old is like pricing a preferred-risk life with a standard mortality assumption. It is the wrong reference class.

It ignores health entirely

A population table has exactly two inputs: age and sex. It cannot see whether your client has well-controlled hypertension or untreated heart failure, a family history of longevity to 95 or of cancer at 60, an active lifestyle or limited mobility. The Journal piece concedes this directly, noting the tables "don't factor in health issues or family medical history" and suggesting readers simply guess high or low. Guessing is not a method. Two 65-year-olds with identical table values can have a real-world life-expectancy spread of 15 years or more.

Look at the spread above. Four people the table treats as identical, with genuine life expectancies ranging from the early 70s to the low 90s. Now ask what happens when you divide a portfolio by the wrong one of those numbers, every single year.

Which Way the Error Runs, and Why It Matters

Here is the part that should worry anyone adopting the actuarial method. The three errors above are not random noise that washes out. For the typical healthy, affluent advisory client they compound in the same direction: the population table understates true life expectancy.

A too-short life expectancy in the denominator means a too-large withdrawal. Divide a $1.5M portfolio by a table life expectancy of 16 years and you spend about $94K. Divide it by a health-and-wealth-adjusted 22 years and you spend about $68K. The actuarial method, fed a population table, will tell your healthiest, longest-living clients to spend roughly 30% more than their actual horizon supports, every year, while reporting that it has "responsibly" right-sized their spending. That is precisely the outliving-your-money risk the dynamic method was supposed to solve, reintroduced through the back door.

The error runs the other way for impaired clients. For someone with serious health conditions, the population table overstates life expectancy, the actuarial method underfunds current spending, and the client spends their healthiest remaining years more frugally than they ever needed to.

Neither failure is exotic. They are the two most common client types in a real practice: the healthy high-earner and the client whose health has quietly deteriorated. The method works beautifully on the mechanics and fails on the input.

What to Use Instead: A Health-Adjusted Denominator

The fix is not to abandon the dynamic method. The mechanics are sound. The fix is to feed it a life expectancy that actually describes the person, not the population.

A health-adjusted estimate starts from actuarial-grade mortality tables (such as the SOA 2015 VBT, built on insured lives and far more granular than population tables), layers in mortality improvement, and then adjusts for the individual: their specific conditions, the severity of each, the way comorbidities interact, family history, and lifestyle. Instead of two inputs, it uses dozens. And rather than returning a single point estimate, a Monte Carlo simulation returns a full distribution, so you can plan around a median and a 90th-percentile longevity rather than a single number that is wrong by construction.

That distribution is what the dynamic method actually needs. The denominator each year should reflect the client in the chair, and the guardrails should be set against their real tail risk, not a generic one.

How It Plugs Into the Dynamic Method

You do not rebuild the strategy. You replace one input and recalibrate one set of bands.

1. Swap the denominator. Each year, divide the portfolio by the client's health-adjusted remaining life expectancy instead of the table value. Everything downstream, the actuarial spend, the guardrail check, the shock absorber, runs exactly as before.
2. Set guardrails against the real tail. A client whose 90th-percentile longevity is 100 needs a different floor than one whose 90th percentile is 85. Use the distribution to choose bands that match the client's actual downside, not a one-size 3-6%.
3. Right-size the guaranteed-income floor. The essential-versus-discretionary split depends on how many years of essentials you are guaranteeing. Health-adjusted longevity tells you whether to floor 20 years or 30.
4. Revisit on health changes, not just market moves. The static table only updates with age. A health-adjusted denominator updates when the client's health does, which is often the more consequential event for the plan.

The result is the responsiveness of the dynamic method plus an input that is calibrated to the individual. The strategy stops being precise about the wrong number.

Frequently Asked Questions

Is the 4% rule outdated?

The 4% rule is not wrong so much as rigid. It assumes a fixed 30-year horizon and does not respond to markets or to how long a specific client is likely to live. Most advisors now use it as a reference point inside a dynamic strategy rather than as the whole plan. The bigger issue is that its modern replacement, the actuarial withdrawal method, depends heavily on an accurate life-expectancy input, which population tables do not provide for individual clients.

What life expectancy should I use for the actuarial (RMD) withdrawal method?

Not a population table on its own. SSA period tables and the IRS Uniform Lifetime Table both describe a national average and ignore the client's health, family history, and the fact that affluent clients outlive the average. For an individualized plan, use a health-adjusted life expectancy with a confidence range, ideally one that incorporates mortality improvement and the client's specific conditions.

What is the difference between the SSA and IRS life-expectancy tables?

The SSA period life tables estimate average remaining years by age and sex from nationwide mortality data. The IRS Uniform Lifetime Table provides the divisors used to calculate required minimum distributions. They produce different numbers, but for retirement-spending purposes they share the same limitation: both are population averages with no adjustment for individual health, family history, or income.

Does the dynamic withdrawal method work for healthy clients?

The mechanics work for everyone, but the standard population-table input works against healthy clients. Because healthy, affluent clients outlive the average, a population table understates their life expectancy, which causes the actuarial method to recommend spending more than their true horizon supports, reintroducing the very longevity risk the method was meant to reduce.

How do guardrails and a shock absorber fit with health-adjusted life expectancy?

Guardrails band the actuarial result; the shock absorber caps year-over-year spending swings. Both work better when the underlying life-expectancy distribution is individualized, because you can set the floor and ceiling against the client's actual tail risk rather than a generic assumption. Health-adjusted modeling improves the input; the guardrails and shock absorber still do their job on top of it.

The shift from population tables to health-adjusted modeling is not about adopting new software for its own sake. It is about making the most important number in the plan describe the person it is supposed to be planning for.

Related reading: Why SSA Life Tables Fail Your Clients, How Longevity Risk Affects Retirement Plans, and Social Security Claiming and Life Expectancy.

Try the free longevity calculator to see how a health-adjusted life expectancy compares to the population table you would otherwise divide by. For a full health-adjusted assessment with confidence intervals and planning horizons, get a longevity report. Advisors can bring health-adjusted longevity into the practice and use it as the denominator in every dynamic withdrawal plan.