Why Do Some Retirement Benchmarks Only Count 401(k) and IRA Balances?
A chart in a retirement article claims the average person your age has saved a certain amount, and the fine print says it only counts what sits in a 401(k) or IRA. Meanwhile your own numbers include a pension estimate, a taxable brokerage account, and equity in a paid-down home, none of which show up in that figure. It’s worth understanding why the benchmark drew its line where it did before deciding what the number means for you.
In a nutshell
Many widely cited retirement savings benchmarks pull their data from payroll-linked or account-provider records, such as workplace plan administrators or IRA custodians, because that data is large-scale, standardized, and relatively easy to aggregate. The narrow scope is a practical research choice, not a judgment that other assets don’t count toward retirement. Pensions, home equity, taxable brokerage accounts, and cash savings are often real and relevant, but harder to measure consistently across a large population, so they get left out of the count rather than declared unimportant.
Where the data actually comes from
Studies built on 401(k) and IRA balances typically draw from a handful of large plan providers or custodians who can hand over anonymized account data covering millions of participants. That’s a genuine strength: the sample size is large and the numbers are precise, because they come straight from account records rather than self-reported survey answers. The tradeoff is scope. A provider only sees the accounts it administers, so anyone whose retirement savings live somewhere else, or who saves through a different structure entirely, is invisible to that dataset by design.
What tends to get left out
- Employer pensions. A traditional pension promises future income rather than showing up as an account balance today, so it’s difficult to fold into a simple net-worth-style number.
- Taxable brokerage accounts. Money invested outside a retirement-specific account is still retirement savings for many people, but it isn’t tagged that way in provider data.
- Home equity. A paid-down mortgage represents real wealth, though it isn’t liquid in the same way an account balance is, and researchers often treat it separately.
- Cash and other savings. Emergency funds, high-yield savings accounts, and other cash reserves rarely appear in retirement-specific benchmarks even though they cushion a retirement transition.
Why researchers narrow the scope on purpose
Combining every asset type into one comprehensive retirement wealth figure would require survey data, since no single institution holds records of a household’s pension value, home equity, and outside investments all at once. Survey-based studies exist and often produce different, sometimes lower, numbers because they rely on people accurately recalling and reporting their own finances. Account-based studies avoid that recall problem but only ever describe the slice of wealth sitting in the accounts they can see. Neither approach is more “correct” than the other; they’re answering slightly different questions, which is part of why survey-based retirement data draws skepticism from people who don’t see their own situation reflected in it.
What this means when comparing yourself to a chart
Because the underlying data source shapes what gets counted, the same person could look either behind or ahead of a benchmark depending on which study produced it. Someone with modest 401(k) savings but substantial home equity might read as behind by one measure and comfortably positioned by another. Before reacting to where you land against an average, it’s worth checking what the study actually measured, since a headline number rarely spells out its own boundaries. The gap between median and average figures adds another layer worth understanding for the same reason.
What to weigh
A benchmark that counts only 401(k) and IRA balances isn’t hiding anything or getting the math wrong; it’s describing a specific, measurable slice of retirement wealth because that’s the slice available in the data. Reading past the headline number to see what was actually counted turns a potentially alarming comparison into a more useful, and more accurate, one.