The Growth Process of Individual Retirement

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Accounts: Evidence from Administrative Data

Asher Dvir-Djerassi

University of Michigan’s Ford School of Public Policy

January 26, 2026

The fndings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily refect

the views or the ofcial positions of the U.S. Department of the

Treasury or the Internal Revenue Service. All results have been

reviewed to ensure that no confdential information is disclosed.

Abstract

Over the past two decades, Individual Retirement Accounts (IRAs) have grown

faster than any other component of the retirement system—now standing at

over one-third of U.S. retirement assets. Yet the sources of this growth remain difcult to assess with existing data. This paper contributes to flling

this gap by leveraging IRS administrative microdata to decompose IRA asset growth. Using a representative 1% sample of the 2019 U.S. adult resident population and leveraging the direct reporting of IRA fair-market values

to the IRS (Form 5498, Box 5)—a feature not shared by other major asset

classes—together with information on direct contributions and rollovers from

Forms 1099-R and 5498, the paper decomposes IRA asset growth from 2000

to 2019 into three components: direct contributions, rollovers from employersponsored plans, and compounded returns on pre-2000 balances. The results

show that rollovers from employer-sponsored plans—primarily defned contribution (DC) plans such as 401(k)s—account for approximately 50% of asset

growth over the period, compared to 20% from direct contributions and 30%

from pre-2000 legacy balances. These fndings imply that rollover contributions from DC plans, rather than direct contributions, are the core engine of

IRA growth. A central implication of this fnding is that treating DC plans

and IRAs as distinct systems may warrant reconsideration. Leading measures

of household wealth—such as the U.S. Financial Accounts—and federal taxexpenditure estimates—such as those produced by the Ofce of Management

and Budget (OMB) and the Joint Committee on Taxation (JCT)—treat the

DC and IRA systems as separate. By treating these systems as separate, estimates of the combined fscal cost of the retirement system, for instance, are

likely lower than they would be if DC plans and IRAs were treated as a single,

integrated system. In sum, this paper highlights the central role of employerplan rollovers in shaping IRA asset growth and clarifes the close integration of

the DC and IRA systems, with important implications for tax administration.

Introduction

Individual Retirement Accounts (IRAs) are the fastest-growing component of

the U.S. retirement system, now comprising more than one-third of all retirement assets. Despite their growing importance, the underlying drivers of

IRA asset accumulation remain poorly understood. In particular, the relative

roles of direct contributions and rollovers from employer-sponsored retirement

plans—such as 401(k)s and 403(b)s—have not yet been systematically quantifed using administrative data at scale.

This paper addresses two core questions: frst, what share of IRA asset accumulation is attributable to direct contributions versus rollovers from employersponsored plans; and second, how does the relative importance of these sources

vary across the population? By answering these questions using linked administrative data, the analysis also speaks to a broader set of measurement issues,

which have broad implications for tax administration. Specifcally, the results

inform how IRAs and defned contribution (DC) plans are treated in the Financial Accounts of the United States, household wealth measures, and tax

expenditure estimates, where assumptions about the independence of the DC

and IRA systems shape characterizations of household wealth and the fscal

cost of retirement policies.

To answer these questions, the paper leverages a 1% random sample of the

2019 adult U.S. resident population, constructed from full-population IRS administrative data. Using linked records from 1999 to 2019, the analysis reconstructs the evolution of IRA balances over time for this cohort. It decomposes

asset growth into three components: (1) direct contributions, (2) rollovers

from DC and defned beneft (DB) plans, and (3) the compounded value of

initial balances held in 1999. The analysis isolates the long-run impact of each

component by estimating counterfactual scenarios in which contributions or

rollovers are removed.

Assessing the importance of rollovers and direct contributions to IRA accumulation cannot be inferred from aggregate fow volumes alone. The contribution

of rollovers to long-term IRA growth depends not only on the scale of incoming transfers, but also on whether those assets are retained, earn investment

returns, and their pattern of decumulation. If rollovers are disproportionately

concentrated among older individuals, rapidly withdrawn, annuitized, or allocated conservatively, their role in sustained asset accumulation may be greater

than their aggregate volume would suggest. Conversely, if rollover assets are

broadly distributed across the population and exhibit retention and return

profles similar to other IRA balances, their central role in asset growth would

be warranted. Because these behavioral and demographic patterns are not observable from aggregate volume data alone, this paper employs counterfactual

simulations applied to micro-data to quantify the role of rollovers versus direct

contributions to IRA growth.

The results show that approximately 50% of IRA asset growth over the 2000–2019

period is attributable to rollovers, less than 20% to direct contributions, and

slightly more than 30% to legacy balances—i.e., IRA assets held prior to

the study period. This pattern is broadly consistent across economic subgroups. However, among individuals with lower net worth and smaller IRA

balances—likely early-career individuals—direct contributions account for a

relatively larger share of growth. Taken together, these fndings indicate that

the employer-based DC system and the IRA system operate as an integrated

pipeline of asset accumulation, rather than as separate retirement vehicles.

By providing the frst population-wide accounting of the processes underlying

IRA accumulation, this paper ofers new empirical clarity on how IRA assets

grow over time. The results and methods developed here have direct relevance

for revenue forecasting, national accounting, and the estimation of retirementrelated tax expenditures by clarifying the extent to which employer-sponsored

plans and IRAs operate as a single, integrated system. In doing so, the paper

highlights the limits of frameworks that the DC and IRA systems as institutionally or economically independent.

Background & Literature

IRAs were established under the Employee Retirement Income Security Act of

1974 (ERISA) to give workers without access to employer-sponsored pension

plans a means to save for retirement with comparable tax advantages. Since

their creation, IRAs have been repeatedly expanded and modifed through

successive pieces of legislation and administrative rulings, substantially broadening both eligibility and the forms of tax-advantaged saving available through

these accounts.

The Economic Recovery Tax Act of 1981 (ERTA) expanded IRA eligibility to

all workers with earned income, including those already covered by workplace

pensions. The Tax Reform Act of 1986 introduced income-based limits on

the tax deductibility of contributions, leading to the creation of nondeductible

IRAs, in which contributions are made with after-tax dollars but investment

Figure 1: IRA Policy Evolution and Asset Growth, 1974–2022

ERISA

Traditional IRAs

created

IRA eligibility

made

universal

Income

limits

introduced

Roth

IRAs

introduced

Catch-up

contributions

introduced

IRAs

surpass

DC assets

SECURE

RMD from 70.5

to 72

SECURE 2.0

RMD

to 73

1974

1981

1986

1997

2001

2015

2019

2022

$26 Bil.

$141 Bil.

$644 Bil.

$2.5 Tril.

$3.8 Tril.

$10.6 Tril.

$11.8 Tril.

$12.2 Tril.

1978 Revenue Act

Creates

401(k) plans

Notes: Asset values are expressed in constant 2019 U.S. dollars using the CPI-U.

Source: United States Financial Accounts.

gains accumulate outside of the tax system; the reform also introduced partially or fully deductible IRAs based on income. The Taxpayer Relief Act of

1997 introduced Roth IRAs—another type of nondeductible IRA—allowing

tax-free withdrawals in retirement, provided certain conditions are met, and

permitted nonworking spouses to contribute to IRAs within specifed income

thresholds.

In addition to direct contributions, rollover contributions from employer-sponsored

retirement plans became increasingly common over the 1980s and 1990s. Prior

to the early 1980s, DC plans were rare in the United States. This changed

with the emergence of the 401(k), authorized under a 1978 amendment to the

Internal Revenue Code and clarifed by IRS guidance in 1981, which permitted

the widespread use of elective pre-tax salary deferrals from regular wages into

employer-sponsored retirement plans. As access to 401(k) and 403(b) plans

expanded across the workforce, so too did the volume of rollovers from these

plans into IRAs. Rollovers typically occur when individuals change jobs or retire and choose to move their retirement savings out of an employer-sponsored

plan and into an IRA, which ofers greater individual control and investment

fexibility.

Over the period from 1999 to 2020, IRA assets experienced the fastest growth

among all major components of retirement assets. As shown in Figure 2, the

mean annual growth rate of IRA assets was 7.9%, exceeding that of DC assets

(6.3%), annuities (5.4%), and DB entitlements (4.6%). This rapid growth has

transformed the composition of private retirement assets. In 2000, the total

value of IRA assets was well below that of DC assets—such as 401(k) and

Figure 2: Mean annual growth rate of retirement assets, 1999–2020.

Source: Author’s analysis of the Financial Accounts of the United States, Table L.117.

403(b) plans—but IRA balances grew steadily, overtaking DC assets around

2015. Likewise, IRA assets were roughly twice the size of annuity holdings in

2000 and are now nearly four times as large. These shifts are illustrated in

Figure 3, which shows the relative trajectories of the main retirement asset

categories over time. IRAs have thus outpaced the growth of other retirement

assets and have become the largest component of the private retirement system

outside of public DB entitlements.

2.1

IRA Varieties and their Features

There are two primary types of IRAs available to individuals:

1. Traditional IRAs: Anyone with earned income, along with a nonworking spouse under specifc conditions, can contribute to a Traditional IRA.

Whether contributions are tax-deductible depends on the individual’s

income and their participation in a workplace retirement plan. Investment earnings grow tax-deferred, and withdrawals taken after age 59½

are taxed as regular income. Withdrawals must begin no later than April

1 following the year in which the account holder turns 70½.

2. Roth IRAs: These accounts allow for tax-free retirement savings. Contributions are made with after-tax income, meaning they are not deductible, but investment earnings and withdrawals after age 59½ are taxfree if the account has been maintained for at least fve years. Unlike

Traditional IRAs, Roth IRAs do not require minimum withdrawals at

any age.

Figure 3: Retirement Assets (Excluding DB Assets): Levels and Shares Over Time

Source: Author’s analysis of the Financial Accounts of the United States, Table L.117.

Table 1: Number and Share of Households that Own IRAs by Type in 2019

IRA Type

Year Created

Households

Number

Percent

Trad. IRA

1974 (ERISA)

36.1 million

28.1%

SEP IRA

SAR-SEP IRA

SIMPLE IRA

1978 (Revenue Act)

1986 (Tax Reform Act)

1996 (Small Business Job Protection Act)

7.8 million

6.1%

Roth IRA

1997 (Taxpayer Relief Act)

24.9 million

19.4%

Any IRA

–

46.4 million

36.1%

Notes: Households may own more than one type of IRA. SEP IRAs, SAR-SEP IRAs, and SIMPLE IRAs

are employer-sponsored IRAs.

Sources: Holden and Schrass (2019).

There are annual limits on how much individuals can contribute to IRAs, with

a higher limit for those age 50 and older, allowing them to make “catch-up”

contributions. A tax unit’s adjusted gross income may restrict the maximum

contribution amount for Roth IRAs and afect whether Traditional IRA contributions are deductible.

Then there are employer-sponsored IRAs:

1. Simplifed Employee Pension (SEP) Plans: These plans allow employers to make tax-deferred contributions on behalf of their employees.

Self-employed individuals can also use SEP IRAs to save for retirement.

2. Savings Incentive Match Plans for Employees (SIMPLE) IRAs:

These allow both employer contributions and employee salary deferrals

to be made on a tax-deferred basis. Employers are required to either

match employee contributions or make fxed contributions to employee

accounts.

2.2

IRA Decumulation

A substantial empirical literature examines changes in IRA assets through

the lens of withdrawals and decumulation, drawing primarily on high-quality

administrative data from the IRS Statistics of Income (SOI) division and industry sources such as the Employee Beneft Research Institute (EBRI) IRA

Database. This work has made signifcant progress in documenting how tax

rules and institutional design shape the timing and magnitude of IRA withdrawals.

Withdrawal behavior varies sharply across account types and age groups, with

tax rules governing required minimum distributions (RMDs) playing a central role. RMDs impose mandatory withdrawals from tax-deferred retirement accounts beginning at a specifed age, a threshold that has shifted over

time—from 70½ prior to 2020, to 72 under the SECURE Act of 2019, and to

73 following SECURE 2.0. Argento, Bryant, and Sabelhaus (2015) show that

many IRA balances remain untouched until RMDs take efect. Subsequent

work demonstrates that households respond strategically to these requirements, adjusting withdrawal behavior around statutory thresholds (Brown,

Poterba, and Richardson 2017; Mortenson, Schramm, and Whitten 2019).

Lifecycle models developed by Hornef, Maurer, and Mitchell (2023) further illustrate how RMD incentives interact with broader retirement planning, shaping asset drawdowns over the retirement period.

A related strand of the literature focuses on leakage, defned as withdrawals

from retirement accounts prior to retirement age. Leakage typically refects

responses to income shocks or liquidity needs and entails both tax penalties

and the loss of tax-advantaged growth (Argento et al. 2015; Goodman et al.

2021). Munnell and Webb (2015) estimate that roughly 1.5 percent of assets

leak annually from the combined 401(k)/IRA system, implying that aggregate retirement balances are at least 20 percent lower than they would be in

the absence of current withdrawal rules. This work highlights the cumulative

impact of small, repeated withdrawals and underscores the importance of distinguishing between voluntary early withdrawals and mandatory RMD-driven

decumulation (Sabelhaus 2000; Mortenson et al. 2019).

2.3

IRA Accumulation

On the accumulation side, evidence from SOI annual reports and the EBRI

IRA Database consistently shows that direct IRA contributions are relatively

rare. In any given year, only a minority of IRA holders make contributions,

and fewer than half of contributors reach the statutory maximum (Statistics

of Income 2018). Although traditional IRAs hold the majority of aggregate

IRA assets, Roth IRAs receive more frequent new contributions. Contribution

activity is concentrated among younger individuals, whereas older account

holders primarily accumulate IRA balances through rollovers from employersponsored plans (Copeland 2015). Related work highlights how tax incentives,

such as the Saver’s Credit, infuence participation and contribution patterns

Table 2: Aggregate IRA Flows and Assets (2019 Adult Resident Population)

Year

Contributions

Rollovers

Withdrawals

Assets

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

73.2B

67.0B

65.9B

62.8B

61.6B

60.2B

57.2B

51.6B

50.2B

51.4B

49.6B

53.8B

56.1B

54.4B

51.3B

43.5B

39.1B

37.6B

31.8B

509.4B

461.8B

417.7B

378.6B

400.2B

365.4B

328.8B

275.6B

236.5B

231.5B

193.3B

218.7B

240.9B

196.8B

159.1B

143.6B

113.5B

123.6B

112.7B

138.7B

383.1B

352.9B

305.1B

269.7B

263.7B

245.0B

231.1B

213.6B

193.3B

231.9B

140.8B

154.2B

135.1B

113.9B

95.9B

84.5B

73.3B

71.9B

67.4B

70.8B

10.76T

8.88T

9.04T

7.66T

6.99T

6.79T

6.21T

5.19T

4.57T

4.87T

3.84T

3.12T

3.99T

3.44T

2.74T

2.56T

2.11T

2.65T

2.09T

1.89T

Notes: All values are in nominal dollars. B denotes billions and T denotes trillions.

Source: Author’s analysis of IRS administrative data.

across groups (Bryant 2020).

While this literature provides a detailed accounting of contribution behavior,

comparatively less research has examined the mechanisms through which IRA

assets have accumulated over time, particularly in light of the rapid growth of

IRAs relative to employer-sponsored DC plans. Existing empirical work nevertheless suggests that rollovers play a central role in shaping aggregate IRA

balances. As shown in Table 2, rollover fows consistently exceed direct contributions by a wide margin: in 2019, households transferred over $500 billion

from employer-sponsored plans into IRAs through rollovers, compared with

less than $75 billion in direct contributions. Aggregate volumes suggest that

rollovers account for a substantial share of new IRA funding and thus accumulation. This perspective is refected in industry analyses noting that rollovers

from employer-sponsored retirement plans have been a major contributor to

IRA growth (Holden and Schrass 2019).

Further evidence underscores the prominence of rollovers in IRA accumulation. In 2019, 59 percent of households with traditional IRAs had experienced

at least one rollover, and nearly half of newly opened traditional IRAs in 2020

Table 3: Traditional IRA Investors with Rollovers by Investor Age, 2020

Age

18 to 24

25 to 29

30 to 34

35 to 39

40 to 44

45 to 49

50 to 54

55 to 59

60 to 64

65 to 69

70 to 74

All

Aggregate (Millions $)

Share (%)

Median ($)

Mean ($)

901

3,975

9,209

14,568

19,054

25,100

36,032

59,994

109,522

94,043

40,896

413,295

0.2

1.0

2.2

3.5

4.6

6.1

8.7

14.5

26.5

22.8

9.9

100.0

2,090

2,780

4,110

6,220

9,130

13,770

18,240

29,000

58,300

68,130

32,380

11,270

4,250

7,820

16,900

30,480

47,090

66,220

87,710

127,730

189,270

200,720

152,510

87,520

Source: The IRA Investor Database™

Notes: This group consists of traditional IRA investors aged 18 to 74 who had rollovers into their

traditional IRAs in tax year 2020. Components may not add to totals because of rounding.

were initiated solely with rollover funds (Holden and Schrass 2019). By contrast, direct contributions are infrequent: between 2007 and 2019, only about

12 percent of IRA holders made a contribution in the survey year.

Table 3 documents the age distribution of rollover activity. Although rollovers

occur across the age spectrum, they are concentrated among individuals aged

55 to 74, who account for more than 70 percent of aggregate rollover volume.

Mean rollover amounts peak near retirement age, exceeding $200,000 among

those aged 65 to 69. Together, these patterns suggest that most traditional

IRAs originate through discrete rollover events rather than sustained contribution behavior.

2.4

Existing Research Based on Administrative Data

Existing empirical work on IRA accumulation and decumulation is often grounded

in high-quality administrative data produced by the IRS. These data enable

careful measurement of IRA balances, contributions, and withdrawals and are

widely used in both academic research and policy analysis.1

At the same time, the structure of these data shapes the types of questions

that are addressed. The workhorse survey used in much research based on

IRS-based estimates are also used by the Federal Reserve in constructing the Financial

Accounts of the United States, including the estimates of IRA assets reported in Table L.117

(Board of Governors of the Federal Reserve System, 2024).

IRS data is the INSOLE (INdividual and SOLE proprietor), a repeated crosssectional, stratifed random sample of individual income tax returns rather

than a longitudinal panel. Analyses based on these data typically emphasize

aggregates or cross-sectional patterns, rather than longitudinal dynamics, such

as tracking the evolution of IRA balances for the same individuals over time. In

addition, because the data are drawn from tax flers, IRA holdings among nonflers are not directly observed. These features do not detract from the value

of existing work, but they do point to constraints—rather than immutable

limitations—on the extent to which existing data structures can be used to

decompose the growth of IRA assets into its underlying components.

In particular, while aggregate evidence points to an important role for rollovers,

existing research has not directly assessed how rollover fows translate into

sustained asset accumulation at the individual level, or how their contribution

compares with other sources of growth over the life cycle. One reason for

this gap is data limitations. The data used in this study are designed to

complement this literature by enabling longitudinal analysis of IRA balances

and transactions and by incorporating information on non-flers.

Data

This paper estimates the processes through which IRA assets accumulated

for the 2019 U.S. resident population.2 To do so, I construct a retrospective

balanced panel that enables the study of long-term IRA asset accumulation

and the relative role of diferent infow mechanisms. This retrospective panel

follows the 2019 U.S. resident adult population across a twenty-year window,

allowing for detailed accounting of the infows (contributions and rollovers),

outfows (withdrawals), and investment returns that shaped their IRA balances

over time.

By reconstructing the histories of IRA-related fows for this fxed population,

the analysis sheds light on the role of rollovers and direct contributions in

IRA accumulation. Since rollovers often occur as large, one-time events and

contributions tend to be smaller and recurring (when they occur at all), a

Only U.S. residents are included, although individuals who were not residents in 2019

could also have held IRAs. However, this defnition aligns with the population concept

used by the Federal Reserve’s Financial Accounts. While this choice excludes a subset of

individuals, it ensures consistency with national accounts. Moreover, this exclusion would

only afect the analysis if non-resident IRA holders were both systematically diferent from

residents and held sizable IRA assets; neither condition is likely.

dynamic, multi-year perspective is essential for assessing their long-run impact.

The construction of this panel rests on administrative tax data that report

IRA balances and transactions at the individual level. The next subsections

describe the data sources, the method used to defne the 2019 population, the

procedures for linking individuals and accounts over time to form a consistent

and analyzable panel, and the estimation of household wealth used for analysis

of IRA accumulation processes by economic subgroups.

3.1

Determining the 2019 Population

This paper uses a 1% sample of the U.S. adult resident population in 2019.

Constructing such a sample is nontrivial in the IRS data environment. While

creating a 1% sample of tax flers is relatively straightforward, developing a representative 1% sample of the entire U.S. adult resident population—including

both flers and non-flers—requires a more complex approach that triangulates

multiple IRS and administrative data sources.

The target population includes all individuals who were at least 20 years old

in 2019 (or younger if married) and who resided in the United States for the

majority of that year. The core challenge lies in accurately identifying both

residency and survival to 2019, particularly for individuals who did not fle

taxes that year.

The starting point for constructing the population is the Social Security Administration’s Death Master File, which contains year of birth and year of

death information. Individuals are initially selected based on age and recorded

survival to 2019. However, the absence of a recorded year of death is not suffcient to conclude that someone was alive in 2019.

To more rigorously establish evidence of life in that year, individuals are considered present in 2019 if they either have a recorded year of death after 2019

or appear between 2019 and 2023 in at least one qualifying data source. These

include tax returns (fled as primary, secondary, or dependent), any of 45 different informational returns (such as W-2s and 1099s), passport applications,

records of Economic Impact Payments in 2020 or 2021, or information returns

associated with the Afordable Care Act. For individuals aged 85 or older, only

tax returns and ACA forms are considered acceptable evidence of life. This

process involves searching across billions of records to determine presence in

the country during the reference year.

An additional challenge involves identifying marital status and linking spouses.

This is particularly important for constructing household-level variables (e.g.,

household wealth). The standard approach in literature using tax data is to

infer marital status from the fling status reported on tax returns, and, in the

case of joint or separately fled married returns, to use the return to identify the

spouse (e.g., Saez and Zucman 2016). However, this approach is imperfect.

In other cases, spousal identifers are missing from returns fled as married.

There are also instances in which the identifed spouse is deceased, and some

individuals appear married to multiple people or are classifed as both single

and married across diferent returns.

For non-flers, the difculty is greater. In many studies using tax data—including

Chetty et al. (2014)—non-flers are simply assumed to be single. While this

simplifying assumption eases sample construction, it is not appealing.

To overcome these limitations, a sequential algorithm is utilized that draws on

a multiplicity of data sources to identify marital status and establish consistent

spousal linkages. This procedure results in a demographically plausible and

internally consistent marital structure for the 2019 population.

To align more closely with a widely used population concept in existing research, a further adjustment is made following the procedures used by Piketty,

Saez, and Zucman (2018). For the years 1999 to 2019, they construct their

population using the SOI’s INSOLE and supplement it with “synthetic” nonflers to ensure full population coverage—see Saez (2016) for further details.

Importantly, for their synthetic non-flers they exclude those who died during

the reference year. In line with this logic, the sample used for this paper eliminates all non-flers and their spouses (when those spouses are not directly in

the 1% sample) if they died in 2019. Death is determined using multiple indicators: individual and spousal death dates, as well as a separate verifcation

process to confrm survival at least through the frst day of 2020.3

This sample construction procedure identifes 248.1 million individuals as part

of the U.S. adult resident population in 2019. For comparison, the American Community Survey (ACS) yields a slightly smaller population under

This adjustment is not required for analyses focused exclusively on IRAs, but is adopted

for wealth-based comparisons across economic subgroups. Handling within-year deaths

poses a measurement problem for wealth estimates based on capitalized income, since many

income fows are observed only for the portion of the year an individual is alive. Mechanically capitalizing such income can severely understate underlying asset holdings for those

who die within the year. There is no uniquely correct solution to this issue. Excluding

non-flers who die during the reference year prioritizes a consistent population concept and

avoids mechanically distorted wealth estimates driven by the timing of death, refecting a

pragmatic response to a limitation of annual administrative data.

the same age and residency conditions: 246.7 million, or about 0.5% lower

than the IRS-based estimate. The modest discrepancy between the two fgures may refect defnitional diferences between data sources. For instance,

the ACS applies a two-month residence rule, which excludes individuals temporarily abroad—such as those living overseas for ten weeks during the survey year—whereas such individuals may still appear in IRS administrative

records. In general—and particularly for the purposes of this paper—such a

small discrepancy is negligible and does not materially afect the analysis or

its alignment with national population benchmarks.

3.2

Construction of IRA Flows and Balances

A longitudinal panel is constructed that consists of each member of the 2019

population and tracks key features of individual IRA activity over the period

1999–2019. The panel includes four core elements for each individual and year:

(1) rollovers into IRAs, (2) contributions to IRAs, (3) year-end IRA asset

balances, and (4) withdrawals from IRAs. These measures are derived from

IRS information returns—specifcally Forms 5498 and 1099-R—and provide

the basis for evaluating the accumulation of IRA assets over time.

For each individual in the 2019 population, all relevant information returns

fled between tax years 1999 and 2019 are collected. To be included in the

2019 population, individuals must be both living and U.S. residents in 2019.

However, the residency requirement does not apply to earlier years. Nonetheless, individuals who were not US residents for any year from 1999 to 2018,

but residents in 2019, remains relevant for understanding patterns of asset

accumulation.

All information returns used in this analysis are fltered to remove duplicate

forms and originals replaced by amended form. After this data cleaning process, each core element—rollovers, contributions, withdrawals, and year-end

balances—is aggregated at the individual-level.

Rollovers from Employer-Sponsored Plans into IRAs

Rollovers from employer-sponsored retirement plans into IRAs are identifed

using a joint procedure based on Form 1099-R and Form 5498. Form 1099-R

reports distributions from retirement accounts, including employer-sponsored

DC plans and, less commonly, certain DB plans, while Form 5498 reports

rollover contributions received by IRAs (Box 2).

Rollover-eligible distributions from DC and DB plans are identifed on Form

1099-R (Box 1) using the distribution codes G and H reported in Box 7.

The analysis is further restricted to forms for which the IRA/SEP/SIMPLE

checkbox is not marked, thereby excluding distributions originating from IRAs

and limiting attention to rollovers plausibly sourced from employer-sponsored

plans. In parallel, rollover contributions reported on Form 5498 (Box 2) are aggregated to the individual level. These contributions refect amounts received

by IRAs that are designated as rollovers but do not, on their own, identify the

source account.

For each individual and year, employer-plan rollovers into IRAs are defned as

the minimum of the rollover-eligible distribution amount reported on Form 1099R and the rollover contribution amount reported on Form 5498. This minimumbased construction treats the overlap between distributions and contributions

as the portion that can be reliably attributed to completed rollovers from

employer-sponsored plans into IRAs. Any excess on either side—such as distributions not redeposited into IRAs, rollover contributions sourced from IRAs

or other accounts, or timing and reporting discrepancies—is intentionally excluded.

By design, this approach eliminates intra-IRA rollovers and other transactions that cannot be confdently classifed as DC/DB-to-IRA transfers using

information returns alone. Bounding rollover activity by both reported distributions and reported contributions produces a conservative measure that

prioritizes classifcation certainty.

Applying this procedure yields aggregate employer-sponsored plan rollovers

into IRAs of approximately $509 billion in 2019. This total is lower than sum

reported in SOI aggregates ($554 billion) and the sum obtained if exclusively

reliant on Form 1099-R information ($598 billion). The diferences partially

refect the exclusion of transactions that cannot be unambiguously attributed

to employer-to-IRA rollovers.

Lastly, indirect rollovers cannot be cleanly identifed using administrative tax

records alone. Neither Form 1099-R, which reports distributions from retirement accounts, nor Form 5498, which reports rollover contributions to IRAs,

indicates whether a rollover was executed as an indirect transfer, nor do the

forms reliably trace the movement of funds across accounts. Brady and Bass

(2020) develop a substantially more complex methodology that combines tax

return information with account-level fows to approximate indirect rollovers.

Their estimates indicate that such transactions are quantitatively small: in

Table 4: IRA Contribution Limits (1999–2019)

Year

Trad/Roth IRA

Trad/Roth Catch-Up

SIMPLE IRA

SIMPLE Catch-Up

SEP IRA

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

$6,000

$5,500

$5,000

$4,000

$3,000

$2,000

$1,000

$500

N/A

$13,000

$12,500

$12,000

$11,500

$10,500

$10,000

$9,000

$8,000

$7,000

$6,500

$6,000

$3,000

$2,500

$2,000

$1,500

$1,000

$500

N/A

$56,000

$55,000

$54,000

$53,000

$52,000

$51,000

$50,000

$49,000

$46,000

$45,000

$44,000

$42,000

$41,000

$40,000

$35,500

$30,000

Source: IRS Publication 560 and Publication 590-A, various years.

Notes: (1) Catch-up contributions apply to those 50 and older. They do not apply to SEP IRAs but do

apply to SIMPLE IRAs, Traditional IRAs, and Roth IRAs. (2) SEP contributions reported on Form 5498

(box 8) include only employer contributions. Employees cannot contribute directly to a SEP IRA. (3)

SIMPLE IRA contributions reported on Form 5498 (box 9) include both employee salary deferrals and

employer contributions. (4) Traditional & Roth IRA limits are combined: one can contribute a total of

$6,000 (or $7,000 if age 50+), but not $6,000 to each. (5) An individual cannot contribute to both a SEP

IRA and a SIMPLE IRA within the same employer. Multiple employers may allow contributing to both.

tax year 2010, only $0.7 billion in indirect rollovers from DC and DB plans

to IRAs were completed, compared with $267.7 billion in direct rollovers, and

were associated with approximately 55,000 flers. Given their limited aggregate magnitude, greater susceptibility to failed redeposits and classifcation

inconsistencies, and declining prevalence as direct rollovers have become increasingly automated, no separate procedure is implemented to estimate indirect rollovers explicitly—and could not be implemented for non-flers, for

whom validation opportunities are limited.

Direct Contributions to IRAs

Contributions are drawn from Form 5498, which records total contributions

to traditional IRAs (Box 1), Roth IRAs (Box 10), SEP IRAs (Box 8), and

SIMPLE IRAs (Box 9). Contributions are frst aggregated across plan types

within individuals. They are then subjected to year-specifc statutory limits

based on age and plan rules. The contribution caps vary by account type and

change over time, as shown in Table 4. For those aged 50 or older, catch-up

provisions allow for higher limits.

To refect legal contribution caps, each individual’s total contributions are

compared to the allowable limit in a given year, and excess contributions are

capped accordingly. This procedure addresses a small number of observations with implausibly large reported contributions, which are likely driven

by rare instances of reporting error, miscoding, or other forms of data contamination rather than genuine violations of statutory limits. The impact of

this adjustment is limited: applying contribution caps reduces aggregate IRA

contributions by approximately 3 percent.

The allowable limit is defned as:

(

Ctbase + Ctcatchup , if agei,t ≥ 50

Capi,t =

Ctbase ,

otherwise

where Ctbase and Ctcatchup are the base and catch-up limits for year t. These

limits vary by plan type and are applied accordingly. An additional rule ensures that SIMPLE IRAs and SEP IRAs are not double-counted when plan

participation overlaps.

Fair Market Value of IRA Assets

End-of-year market values of IRA accounts are reported on Form 5498 (Box

5). These values refect the total IRA holdings of each individual, including

assets accumulated through contributions, rollovers, and investment returns.

Reported balances represent the market values of IRA investments as of December 31 for each tax year.

However, several caveats apply to the interpretation of these reported values.

In rare instances, particularly in the case of decedents, the market values reported in Box 5 of Form 5498 may refect the value of the account on the date

of death rather than at year-end. Additionally, custodians may occasionally

report a missing or zero market value when they are unable to assign a market value to the assets in the account. This typically arises when IRAs hold

non-publicly traded or illiquid assets—such as closely held business interests,

real estate, or limited partnerships—for which no readily ascertainable market price exists. While these cases are uncommon, they introduce potential

measurement challenges in assessing IRA balances in a small subset of the pop18

ulation; this issue is also addressed in greater detail below in the discussion of

implicitly missing market values.

Non-Rollover IRA Withdrawals

Withdrawals are also captured using Form 1099-R, but under a separate classifcation from rollovers. To isolate true non-rollover distributions taken by the

account holder, records are restricted to those in which the IRA/SEP/SIMPLE

checkbox is marked—indicating that the distribution originated from an IRA—and

where Box 7 does not include codes associated with rollovers, corrections, or

exceptional cases. Specifcally, the analysis excludes forms with Box 7 distribution codes 4, 5, 6, C, G, H, N, and R, which refect death distributions,

prohibited transactions, rollovers, recharacterizations, and return of contributions, among others. Withdrawals are then aggregated at the individual-byyear level.

3.3 Construction of Economic Subgroups

To support additional analyses, individuals in the 2019 population are classifed according to the value of their IRA holdings and their total household

net worth under the equal split assumption. These classifcations are created

by sorting individuals based on the reported value of IRA accounts and the

estimated value of total assets minus liabilities, respectively. Each classifcation is fxed relative to the 2019 population and remains unchanged over the

retrospective panel. From this, estimates are produced by group in order to

determine whether IRA growth processes are uniform across the population.

The categorization of individuals by IRA holdings is based on observed account

balances. Using these values, individuals are grouped based on ordered levels

of account size. The classifcation of individuals based on household wealth is

derived from an estimation framework that builds upon that used by Saez and

Zucman (2020), but which introduces a set of methodological modifcations.

The scope of asset and liability components is broadened to include both

funded and unfunded DB pension entitlements, which is consistent with the

wealth concept used in the Financial Accounts. Additionally, IRA assets are

directly assigned from Form 5498 rather than estimated via capitalization of

Form 1040 IRA distributions and imputation.

Unlike prior eforts to estimate household net worth, which rely solely on

Table 5: Information Returns and Fields Used to Produce Non-Filer Estimates

simulated or imputed data for non-flers (e.g., Saez and Zucman 2016, 2020;

Smith, Zidar, and Zwick 2023), this approach uses information returns to

estimate non-flers’ income fows as they would appear on a Form 1040 had

they fled. These income fows are then used to estimate non-flers’ net assets

using the same method applied to flers. A detailed account of the felds and

forms used in this reconstruction is provided in Table 5.

To validate the non-fler estimation procedure, single tax flers in 2019 are

processed twice: once using full Form 1040 data and once using only the

information returns that would be available if they had not fled; the resulting

aggregates are then compared to assess accuracy. As shown in Table 6, the

information return approach reproduces the vast majority of reported income

with a high degree of accuracy. Wages—the largest component—are captured

almost exactly (99.9% match). Aggregate totals for income relevant to asset

and liability assignment difer by just 3.4%. These results suggest that the nonfler estimation procedure reliably approximates key fnancial characteristics,

lending confdence to its application across the broader population.

To estimate net worth for all individuals, the framework begins by adopting

a comprehensive accounting of asset and liability categories as used in the

Financial Accounts of the United States Table B.101.h—excluding consumer

durables, as is standard in comparable approaches—such that aggregate net

asset levels produced through this procedure mechanically match those reported by the Federal Reserve.4

See Board of Governors of the Federal Reserve System, Financial Accounts of the United

States, Table B.101.h: “Balance Sheet of Households and Nonproft Organizations.” Documentation available at https://www.federalreserve.gov/apps/FOF/Guide/B101h.pdf.

Table 6: Validation of Information Return Based Estimates Using Single Filers

F1040 Measure /

Information Return Measure

F1040 Measure

(Billions USD)

Information Return Measure

(Billions USD)

Wages

Taxable Retirement Distributions

Capital Gains

Profts from Pass-Throughs

Income from Sole-Proprietorships

Rental Payments on Tenant Occupied Real Estate

Qualifed Dividends

Interest Income

Property Taxes on Owner-Occupied Real Estate

Income for Fixed-Income Assets held in Mutual Funds

Mortgage Payments on Tenant Occupied Real Estate

100%

129%

121%

93%

95%

104%

100%

81%

n/a

88%

94%

$2,215

$348

$152

$114

$101

$88

$62

$54

$30

$23

$12

$2,212

$271

$126

$123

$107

$85

$62

$66

n/a

$26

$13

Income Relevant to Asset and Liability Assignment

104%

$3,199

$3,089

F1040 Total Income net of OASDI Income and UC

99%

$2,907

$2,939

Income Variable

Source: Author’s analysis of IRS administrative data.

Notes: (1) Estimates use 2019 data; all dollar fgures are in nominal 2019 dollars. (2) Mortgage payments

on tenant-occupied real estate cannot be directly observed and are estimated using fler-level average ratios

of rental income from tenant-occupied properties to associated mortgage payments. (3) Interest income

combines taxable and tax-exempt interest; for non-fler estimation, these components are disaggregated.

(4) Pass-through profts combine S-corporation and partnership income; because passive and non-passive

profts cannot be directly observed in information returns, fler-level averages are used to disaggregate

these fows. (5) Property taxes paid are observed only for itemizers and are therefore unavailable in the

information-return-based approach.

Administrative records are then used to approximate individual asset or liability levels. As stated, IRA balances are directly assigned using information from

Form 5498. Where feasible, income fows are linked to underlying assets and

fows are capitalized to recover underlying stocks. In cases where this link is

noisy or unavailable—such as for many liabilities or non–income-generating assets—imputation is performed using the Survey of Consumer Finances (SCF),

following the approach in Saez and Zucman (2020). Specifcally, individuals are sorted into cells defned by age, marital status, and income grouping.

Within each cell, the probability of owning a given asset or liability and the

average holding size are drawn from the Survey of Consumer Finances. Each

individual is probabilistically assigned asset or liability ownership based on

these probabilities. If ownership is assigned, the mean value for the cell is

applied.

Methods

This section outlines the method used for systematically quantifying how account balances evolve over time based on observable fows—contributions,

rollovers, and withdrawals. At its core, the method relies on a transition

framework in which the IRA balance for individual i at the end of year t,

denoted IRAi,t , is modeled as the result of four components: the prior year’s

ending balance IRAi,t−1 , infows (contributions and rollovers), outfows (withdrawals), and investment returns. While balances and fows are directly observed in administrative data, individual-year-specifc returns are not. The

unknown rate of return, ri,t , is treated as a residual term that captures the

change in balances not explained by observed fows.

To operationalize this framework, consider the following general formulation:

IRAi,t = f (IRAi,t−1 , Ci,t , Rolli,t , Wi,t , ri,t )

where Ci,t denotes contributions, Rolli,t rollovers into IRAs, and Wi,t withdrawals. The function f (·) summarizes how investment returns apply to both

prior balances and net fows over the year and may depend on assumptions

about the timing of fows (e.g., mid-year versus end-of-year).

4.1

Estimating Year-on-Year Rates of Return

Within this framework, year-on-year rates of return, ri,t , are estimated for

each individual from 2000 to 2019. The preferred return specifcation follows

what is referred to here as the mid-year weighted assumption. This approach

builds on standard methods for estimating returns with annual data (Fagereng

et al. 2020), which assume that contributions and withdrawals occur evenly

throughout the year, but adapts them to the specifc timing patterns of IRA

fows. Rather than assuming that all net contributions are evenly distributed,

the specifcation treats half of direct IRA contributions as occurring at yearend and the other half as spread evenly throughout the year. This adjustment

refects administrative data from the Investment Company Institute showing

that 40–50% of direct IRA contributions are made between January and April

of the following calendar year—after the tax year to which they apply—and

thus do not earn returns in the year they are attributed to.5

This mid-year weighted specifcation—the preferred specifcation used throughout this paper—treats rollovers and withdrawals as occurring evenly throughout the year, while direct contributions are treated diferently: half of direct

contributions are assumed to arrive at the end of the year and earn no return,

while the remaining half are assumed to be distributed evenly throughout the

year. As a result, only half of contributions receive return exposure, and those

exposed contributions earn only half of the annual return, yielding an efective

return weight of 0.25.

IRA contributions made before tax day in April can still be designated for the prior tax

year.

Table 7: Rate of Return Variants and Formulas

RoR Variant

Adjustment and Formula

Mid-Year Weighted

Assumes rollovers and withdrawals are spread evenly throughout the year. Half of direct

contributions (Ci,t ) are treated as arriving at year-end and therefore do not earn returns;

the other half are assumed to earn returns for half the year.

IRAi,t − IRAi,t−1 − Ci,t − Rolli,t + Wi,t

IRAi,t−1 + 0.25 · Ci,t + 0.5 · Rolli,t − 0.5 · Wi,t

mid-wgt

ri,t

Beginning-of-Year

Assumes all fows occur at the start of the year.

beg

ri,t

Mid-Year

IRAi,t − IRAi,t−1 − Ci,t − Rolli,t + Wi,t

IRAi,t−1 + Ci,t + Rolli,t − Wi,t

Assumes fows are evenly distributed throughout the year.

IRAi,t − IRAi,t−1 − Ci,t − Rolli,t + Wi,t

IRAi,t−1 + 0.5 · (Ci,t + Rolli,t − Wi,t )

mid

ri,t

End-of-Year

Assumes no intra-year fows.

end

ri,t

IRAi,t − IRAi,t−1 − Ci,t − Rolli,t + Wi,t

IRAi,t−1

Under these assumptions, the transition equation takes the following form:

IRAi,t = IRAi,t−1 +Ci,t +Rolli,t −Wi,t +ri,t ·(IRAi,t−1 + 0.25 · Ci,t + 0.5 · (Rolli,t − Wi,t )) .

Solving for ri,t gives the mid-year weighted rate of return formula:

mid-wgt

ri,t

IRAi,t − IRAi,t−1 − Ci,t − Rolli,t + Wi,t

IRAi,t−1 + 0.25 · Ci,t + 0.5 · (Rolli,t − Wi,t )

In addition to this preferred specifcation, three alternative return variants

are estimated based on diferent assumptions about the timing of fows (see

Table 7). Each variant is implemented using the same administrative data on

IRA balances, contributions, rollovers, and withdrawals. While these return

formulations difer in how they treat the timing of fows, the resulting estimates

are nearly identical in practice. Figure 4 shows the average annual return on

IRAs assets by year and return formula specifcation. Variation across the

specifcations is minimal. For consistency, reasons of parsimony, and the justifcations provided above, the mid-year weighted return formulation is solely

used throughout the subsequent analysis.

Capping Extreme Returns

To limit the infuence of extreme outliers, annual rates of return are capped

at ±1000%, with estimates exceeding this threshold in absolute value set to

Figure 4: Average Individual IRA Return by Year and Return Formula

Source: Author’s analysis IRS administrative data.

Note: ±1000% cap has been imposed on all returns. The fgure shows the mean individual-level rate of

return, calculated as the average of each individual’s annual return.

the cap. In addition, an alternative set of return estimates is generated in

which returns are capped at ±100%. These alternative estimates, reported in

the appendix, serve as a robustness check and are nearly identical to those

obtained using the preferred and less restrictive cap.

Extreme estimates are exceedingly rare. When they do occur, they typically

refect data limitations or mechanical artifacts—often arising from indirect

rollovers not observed in the data or from missing IRA values for which market

valuation is difcult—rather than genuinely extraordinary investment performance. Figure 5 shows the frequency of these extreme estimates by year and

return specifcation. Under the preferred mid-year weighted specifcation, only

about 0.2% of observations in any year have returns greater than +1000% or

less than –1000%.

Validating Estimates Against an External Benchmark

Figure 6 compares the estimated annual mean returns on IRAs for the 2019

population with the aggregate annual mean return on DC accounts as reported

in Form 5500 data from 2000 to 2019. Form 5500 is submitted annually by

ERISA covered employer-sponsored retirement plans to the U.S. Department

of Labor to comply with ERISA reporting requirements. The dataset includes

information from all DC plans sponsored by private employers with 100 or

Figure 5: Percent of RoR Estimates Subject to the ±1000% Cap

Source: Author’s analysis of IRS administrative data.

more participants, making it a high-quality external benchmark that captures

the vast majority of assets held in the DC system.

As shown in Figure 6, the estimated IRA return series closely tracks the Form

5500-based DC return benchmark throughout the entire period. The alignment

is strong in all years and becomes nearly identical in the years closest to 2019.

While some divergence is expected—since the Form 5500 returns refect the full

U.S. DC participant population, whereas the IRA estimates pertain to a subset

of individuals alive and residing in the U.S. in 2019—the close correspondence

provides additional confdence in the reliability of the estimated mean returns,

the underlying data, and the method.

Addressing Implicitly Missing Prior-Year Balances

In a small number of cases (0.72% in 2019), individuals exhibit positive IRA

balances in year t, but no recorded IRA assets in year t − 1 and neither direct

contributions or rollovers in year t. If used directly in the return formulae,

this combination would yield undefned values due to division by zero in the

denominator or implausible estimates when only withdrawals are present.

These cases imply either an unobserved rollover and/or contribution in year

(t), or a missing IRA balance in year (t − 1). In most instances, they likely

refect unobserved indirect rollovers, as evidenced by the declining incidence

Figure 6: Annual Mean IRA Returns for the 2019 Cohort Compared to Aggregate DC Plan Returns (Form 5500), 2000–2019

Source: Author’s analysis of IRS administrative data and U.S. Department of Labor’s analysis of Form

5500 information in annual Private Pension Plan Bulletin.

of such observations over time—consistent with the declining frequency of

indirect rollovers—or, alternatively, the absence of reliable market valuation

for IRA balances (e.g., when accounts contain hard-to-value assets).

While this represents a relatively small share of the total sample—typically

fewer than 1% of cases—it still warrants a principled correction. To address

these cases, a retrospective imputation procedure is adopted. The idea is to

estimate a plausible prior-year IRA balance using the inverse of the annual IRA

transition equation, under the assumption that the individual experienced the

average mid-year weighted rate of return in year t. The imputation formula is:

mid-wgt

IRAi,t−1 =

· IRAi,t − Ci,t − Rolli,t + Wi,t − r̄t

· (0.25Ci,t + 0.5(Rolli,t − Wi,t ))

1 + r̄tmid-wgt

This procedure for recovering missing IRA asset information is applied frst to

2019 and then proceeds iteratively backward to recover information for earlier

years, through 1999. This sequencing is necessary because the current-year

information is used to estimate the prior-year balance. Beginning with the

most recent year ensures that the imputation process maximizes the recovery

of earlier-year values by drawing on the fullest available data.

The aggregate magnitude of this correction is modest (0.43% of observed IRA

assets in 2019) but important for internal consistency. From 1999 to 2019, the

share of observations requiring imputation increases as one moves backward

in time, with the highest concentration in the early 2000s. This pattern refects both the greater prevalence of indirect rollovers in earlier years and the

fact that imputation is more feasible when information from a future year is

available. Additional details are provided in the appendix.

4.2

Baseline Asset Accumulation Micro-Simulation

The evolution of IRA balances over time is simulated by applying a transition

equation that maps asset values from one period to the next. This transition

rule implies a forward accumulation process that expresses IRA balances in

any future year as a function of the initial balance, net fows, and investment

returns.

Recall the period-to-period transition equation under the mid-year weighted

return assumption, defned for individual, i, between years t − 1 and t:

IRAi,t = IRAi,t−1 +Ci,t +Rolli,t −Wi,t +ri,t ·(IRAi,t−1 + 0.25 · Ci,t + 0.5 · (Rolli,t − Wi,t )) .

Iterating this transition equation yields an accumulation formula for IRA assets of each individual, i. Starting from the base-year balance in 1999, the

implied IRA balance in 2019 can be written as:

IRAi,2019 = IRAi,1999

2019

(1 + ri,t ) +

t=2000

2019

X h

t=2000

(0.25 · Ci,t + 0.5 · (Rolli,t − Wi,t ))

2019

(1 + ri,s )

s=t

+ (0.75 · Ci,t + 0.5 · (Rolli,t − Wi,t ))

2019

(1 + ri,s )

s=t+1

This expression represents the 2019 IRA balance of each individual, i, as the

sum of three components: (1) the compounded growth of the initial 1999 balance, (2) the return-weighted accumulation of within-year infows and outfows

according to their assumed timing, and (3) the non-compounding portion of

net fows.

In implementation, the simulation applies additional observational anchoring

and boundedness constraints in rare edge cases to preserve numerical stability, including a zero lower bound on simulated balances and an upper bound

in extreme divergence cases. These adjustments introduce mild nonlinearities but do not alter the structure of the underlying accumulation equation

characterized above. These further adjustments are discussed below in detail.

Table 8: Simulated Aggregate IRA Assets for the 2019 Population, 2000–2019

Year

(A)

Observed

IRA (T)

(B)

Simulated

IRA (T)

(C)

Dif. (T)

Col(B) – Col(A)

(D)

% Dif.

Col(C)/Col(A)

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

10.81

8.94

9.14

7.79

7.11

6.94

6.36

5.34

4.71

5.04

3.99

3.25

4.17

3.67

2.99

2.73

2.32

2.77

2.25

2.05

10.38

8.55

8.72

7.44

6.79

6.60

6.06

5.09

4.49

4.80

3.82

3.08

3.91

3.45

2.88

2.63

2.27

1.85

1.98

2.04

-0.43

-0.39

-0.42

-0.35

-0.32

-0.34

-0.30

-0.25

-0.22

-0.23

-0.17

-0.16

-0.25

-0.22

-0.11

-0.09

-0.06

-0.92

-0.27

-0.01

-4.01%

-4.36%

-4.63%

-4.47%

-4.50%

-4.83%

-4.71%

-4.62%

-4.67%

-4.62%

-4.24%

-5.02%

-6.02%

-5.92%

-3.52%

-3.34%

-2.38%

-33.19%

-12.07%

-0.71%

Source: Author’s analysis of IRS administrative data.

Notes: All monetary values are expressed in trillions (T) of nominal USD. Simulated

values in Column (B) are generated using the iterative method outlined in this section.

Column (C) reports the diference between simulated and observed IRA assets. Column

(D) reports the percent diference relative to observed assets.

By parameterizing this accumulation equation with observed rollovers (Rolli,t ),

contributions (Ci,t ), withdrawals (Wi,t ), estimated annual rates of return on

IRA assets (ri,t ), and the base-year account value (IRAi,1999 ), the model can

be used to assess how closely the accumulation process approximates observed

IRA balances in each year. Table 8 compares the simulated aggregate market

value of IRA assets for each year from 2000 to 2019—constructed using the

iterative summation formula for the analytic sample—with the corresponding

observed aggregates derived from administrative records. The simulated values

closely track the observed totals, with diferences generally on the order of 4%.6

Larger discrepancies appear in 2001 and 2002. These diferences refect irregularities

in the underlying administrative data in the early years of IRA reporting, including incomplete identifcation of duplicate or amended records in 2000–2002. Importantly, the

micro-simulation incorporates both observational anchoring and explicit boundedness constraints, ensuring that such early-period measurement error does not propagate mechanically

through the accumulation process. As an additional safeguard, the annual aggregate percent

diference between simulated and observed IRA assets is used to rescale counterfactual simulated values in subsequent analyses, preventing baseline structural error in the accumulation

model from infuencing estimated counterfactual results.

4.3

IRA Growth: Rollovers vs. Direct Contributions

To evaluate the contribution of specifc account fows to the growth of IRA

assets, the simulation model allows for counterfactual manipulation of key parameters: rollovers (Rolli,t ) and contributions (Ci,t ). The analysis focuses on

two central counterfactuals—see the appendix for supplemental simulations.

The primary objective is to estimate upper-bound efects of rollovers and contributions on long-term IRA asset accumulation.

The two core simulations proceed as follows:

• In the No Rollovers simulation, all rollovers between 2000 and 2019 are

set to zero, while all other components remain unchanged. This isolates

the cumulative impact of rollover fows on asset accumulation and, when

compared to observed 2019 balances, allows for an evaluation of their

long-run efect.

• In the No Contributions simulation, all contributions over the same period are set to zero, while other components are unchanged. This isolates

the role of direct contributions in shaping account balances and enables

assessment of their long-run impact relative to observed outcomes.

To ensure internal consistency and to prevent mechanically implausible outcomes, several limited adjustments are applied during the iterative simulation

procedure.

First, simulated IRA balances are constrained to be non-negative. In rare

cases—most commonly under counterfactual scenarios in which rollovers and/or

contributions are removed while withdrawals remain—simulated values can become negative as a mechanical consequence of applying observed outfows in

the absence of prior infows. In these cases, balances are reset to zero.

Second, the simulation handles a small set of observations that are inconsistent with the transition equation given the observed data. Specifcally, when

an individual has no observed prior-year balance (IRAi,t−1 = 0), no observed

fows in year t (Ci,t = 0, Rolli,t = 0, Wi,t = 0), but a positive observed balance

in year t (IRAi,t > 0), the simulated value is reset to the observed current-year

balance. These cases are consistent with unobserved indirect rollovers and/or

missing market valuation of IRA balances that were not handled through the

imputation procedure. Replacing the simulated value with the observed balance prevents artifcial attrition of IRA assets arising from missing data in the

transition equation, rather than from genuine economic processes. Without

this correction, mechanically zeroing such accounts would cause them to drop

out of subsequent periods and artifcially lowering aggregate IRA assets.

Third, simulated values are monitored for divergence from observed balances.

Extreme divergence cases are constrained: when simulated assets exceed observed balances by a factor of 100 or more (conditional on a positive observed

balance), the simulated value is capped at the observed level. This thresholdbased correction is rare—afecting roughly 0.01% of observations—and curbs

implausible compounding driven by edge cases (e.g., small initial balances

combined with timing assumptions). The cap is applied independently each

year to avoid compounding distortions.

Together, these adjustments refect a conservative modeling strategy: corrections are introduced only to prevent mechanically unstable or empirically

implausible outcomes, while preserving the underlying accumulation dynamics

implied by observed fows and the return specifcation.

Results

Two simulations are used to assess the sources of IRA asset growth between

2000 and 2019, each introduces counterfactual changes to isolate the contribution of specifc infow mechanisms. In the frst counterfactual, all direct

IRA contributions are set to zero, while rollovers, withdrawals, and returns

remain unchanged. In the second, all rollovers from employer-sponsored retirement plans—predominantly defned contribution plans, and in rare cases

DB plans—are set to zero, with all other components held fxed. Together,

these counterfactuals allow the overall growth of IRA assets over the 2000–2019

period to be decomposed into three components: (i) direct contributions, (ii)

rollover contributions from employer plans, and (iii) legacy balances held prior

to 2000, which themselves refect earlier contributions or rollovers. The results

from these simulations are then examined both in the aggregate and across

economic subgroups.

5.1

The Cumulative Role of Rollovers and Contributions

The aggregate consequences of removing rollovers difer substantially from

those of removing direct contributions. As shown in Table 9, when all rollovers

from employer-sponsored retirement plans into IRAs are removed, simulated

aggregate IRA assets in 2019 fall to $5.55 trillion—roughly half of the observed

$10.81 trillion. This divergence emerges early in the simulation period and

Figure 7: Decomposition of IRA Asset Growth by Source

Source: Author’s analysis of IRS administrative data.

Notes: The fgure decomposes aggregate IRA asset growth into direct contributions, rollovers from

employer-sponsored retirement plans, and the compounded value of pre-existing balances. The

decomposition is based on linked IRS administrative records and follows a 1% representative sample of the

U.S. adult resident population observed in 2019 backward over the 2000–2019 period.

widens steadily over time. By the mid-2000s, simulated balances are already

more than 25 percent below observed levels, and by the late 2010s the shortfall

approaches 50 percent.

In contrast, removing direct IRA contributions produces a considerably smaller

reduction in aggregate balances. Table 10 shows that under the no-contributions

counterfactual, simulated IRA assets in 2019 decline to $9.07 trillion, corresponding to a 16 percent reduction relative to the observed total. The

contribution-driven gap is negligible in the early 2000s and grows gradually

over time, refecting the incremental nature of annual contributions and their

more limited capacity to generate large balances. While contributions clearly

matter for IRA accumulation, their aggregate impact is modest relative to that

of rollovers.

Taken together, the two counterfactuals account for roughly two-thirds of observed IRA assets in 2019, with the remaining one-third attributable to balances already held prior to 2000. Figure 7 summarizes this decomposition

visually.

Legacy balances cannot be separately decomposed into contributions and rollovers

given data limitations. If pre-2000 balances were assigned using the same

Table 9: Counterfactual IRA Assets Without Rollovers

Year

(A)

Observed IRA

Assets (T)

(B)

No Rollovers IRA

Assets (T)

(C)

Diference (T)

Col(B) – Col(A)

(D)

% Diference

Col(C)/Col(A)

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

10.81

8.94

9.14

7.79

7.11

6.94

6.36

5.34

4.71

5.04

3.99

3.25

4.17

3.67

2.99

2.73

2.32

2.77

2.25

2.05

5.55

4.72

4.97

4.35

4.08

4.10

3.87

3.35

3.05

3.31

2.75

2.30

3.06

2.80

2.36

2.23

1.96

2.42

2.05

1.95

-5.26

-4.22

-4.18

-3.44

-3.04

-2.84

-2.49

-1.99

-1.66

-1.73

-1.24

-0.95

-1.10

-0.87

-0.63

-0.50

-0.36

-0.35

-0.19

-0.10

-48.7%

-47.2%

-45.7%

-44.2%

-42.7%

-40.9%

-39.1%

-37.3%

-35.2%

-34.3%

-31.2%

-29.3%

-26.5%

-23.7%

-20.9%

-18.2%

-15.4%

-12.7%

-8.6%

-4.9%

Source: Author’s analysis of IRS administrative data.

Notes: All monetary values are expressed in trillions (T) of nominal USD. Column (A) reports

observed IRA asset totals for the 2019 population. Column (B) reports counterfactual totals under

the removal of all DC/DB-to-IRA rollovers, scaled to maintain aggregate consistency. Column

reports the percentage diference relative to observed assets.

rollover-to-contribution mix observed over the 2000–2019 period, rollovers

would account for approximately three-quarters of legacy assets. Under this

assumption, rollovers would explain roughly 76 percent of total IRA assets in

2019, with the remaining 24 percent attributable to direct contributions.

5.2

Results by Economic Subgroups

Table 11 reports the role of rollovers and direct contributions in shaping IRA

accumulation over the 2000–2019 period across economic subgroups, with outcomes measured as fnal (2019) balances. Results are shown for the full population (Panel A), IRA asset quantiles (Panel B), and net worth quantiles

(Panel C). Across nearly all groups, rollovers account for a substantially larger

share of accumulated IRA assets, while the contribution channel plays a more

limited role, especially as IRA assets and net worth increase.

Panel B shows that the removal of rollovers substantially reduces IRA balances throughout the IRA asset distribution. For individuals in the bottom

80 percent of IRA balances, eliminating rollovers reduces simulated balances

Table 10: Counterfactual IRA Assets Without Direct Contributions

Year

(A)

Observed IRA

Assets (T)

(B)

No Contributions IRA

Assets (T)

(C)

Diference (T)

Col(B) – Col(A)

(D)

% Diference

Col(C)/Col(A)

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

10.81

8.94

9.14

7.79

7.11

6.94

6.36

5.34

4.71

5.04

3.99

3.25

4.17

3.67

2.99

2.73

2.32

2.77

2.25

2.05

9.07

7.54

7.73

6.62

6.06

5.93

5.46

4.62

4.10

4.42

3.52

2.90

3.75

3.34

2.75

2.54

2.19

2.66

2.19

2.03

-1.74

-1.40

-1.42

-1.18

-1.05

-1.00

-0.90

-0.72

-0.61

-0.62

-0.47

-0.35

-0.42

-0.33

-0.24

-0.18

-0.13

-0.11

-0.06

-0.02

-16.1%

-15.7%

-15.5%

-15.1%

-14.7%

-14.5%

-14.1%

-13.5%

-13.0%

-12.3%

-11.7%

-10.6%

-10.0%

-9.1%

-8.0%

-6.6%

-5.6%

-4.0%

-2.5%

-1.2%

Source: Author’s analysis of IRS administrative data.

Notes: All monetary values are expressed in trillions (T) of nominal USD. Column (A) reports

observed IRA asset totals for the 2019 population. Column (B) reports counterfactual totals under

the removal of all direct IRA contributions, scaled to maintain aggregate consistency. Column

reports the percentage diference relative to observed assets.

by roughly 28 percent relative to observed values. The magnitude of the efect

increases sharply moving up the distribution: balances fall by 46 percent in the

80–90th percentile, nearly 58 percent in the 90–99th percentile, and more than

60 percent among the top 0.1 percent. These patterns indicate that rollovers

are especially central to the accumulation of large IRA balances, but remain

quantitatively important even for the broad majority of IRA holders.

Panel C shows a closely related pattern when groups are defned by net

worth rather than IRA holdings. Removing rollovers reduces IRA balances

by roughly 37 percent for households in the bottom 80 percent of the wealth

distribution, and by more than 50 percent for households in the top decile.

In contrast, the efects of removing direct contributions are smaller and vary

more across groups. As shown in Panels B and C, eliminating contributions

reduces IRA balances most sharply for lower- and middle-ranked groups, with

declines of roughly 20–30 percent among the bottom 80 percent of the IRA and

wealth distributions. The efect attenuates steadily moving up the distribution,

falling below 15 percent for the top IRA and wealth groups and below 5 percent

for the very top.

Table 11: Counterfactual 2019 IRA Balances by Simulation Type & Group

Panel A: Full Population Total

Simulation Type

Group

No Rollovers

No Contributions

All

Observed

IRA (T)

Simulated

IRA (T)

Dif. (T)

% Dif.

10.81

5.55

9.07

-5.26

-1.74

-48.7%

-16.1%

Panel B: By IRA Asset Quantile Bins

Observed

IRA (T)

Simulated

IRA (T)

Dif. (T)

% Dif.

No Rollovers

0–80%

80–90%

90–99%

99–99.9%

99.9+%

2.487

1.986

4.501

1.387

0.404

1.786

1.081

1.893

0.526

0.163

-0.701

-0.905

-2.607

-0.861

-0.241

-28.2%

-45.6%

-57.9%

-62.1%

-59.6%

No Contributions

0–80%

80–90%

90–99%

99–99.9%

99.9+%

2.487

1.986

4.501

1.387

0.404

1.714

1.626

4.003

1.318

0.390

-0.773

-0.360

-0.498

-0.069

-0.014

-31.1%

-18.1%

-11.1%

-5.0%

-3.4%

Panel C: By Net Worth Quantile Bins

Observed

IRA (T)

Simulated

IRA (T)

Dif. (T)

% Dif.

No Rollovers

0–80%

80–90%

90–99%

99–99.9%

99.9+%

1.689

2.224

5.516

1.204

0.177

1.070

1.168

2.615

0.583

0.099

-0.619

-1.057

-2.902

-0.622

-0.078

-36.6%

-47.5%

-52.6%

-51.6%

-44.1%

No Contributions

0–80%

80–90%

90–99%

99–99.9%

99.9+%

1.689

2.224

5.516

1.204

0.177

1.326

1.832

4.693

1.067

0.159

-0.363

-0.392

-0.823

-0.137

-0.018

-21.5%

-17.6%

-14.9%

-11.4%

-10.4%

Source: Author’s analysis of IRS administrative data.

Notes: All dollar values are expressed in trillions (T) of nominal USD. Groups are defned

using 2019 values. IRA asset quantiles are defned among individuals with positive IRA

balances; wealth quantiles are defned over the full adult population. The 0–80 percent

group aggregates the bottom four quintiles.

Taken together, the subgroup results reinforce the aggregate fndings in Panel A:

both rollovers and contributions matter for IRA accumulation, but rollovers

largely dominate across the distribution. Their infuence is large, persistent,

and broadly shared, while the contribution channel plays a secondary and more

heterogeneous role.

Conclusion

This paper provides the frst population-level accounting, using administrative microdata, of how IRA assets accumulated between 2000 and 2019. By

decomposing IRA balances into direct contributions, rollovers from employersponsored plans, and the compounded value of pre-2000 balances, the analysis

clarifes the mechanisms underlying the rapid growth of IRAs over the past

two decades. The results show that rollovers—primarily from DC plans—are

the dominant driver of IRA asset growth, accounting for roughly half of accumulated balances, compared to a substantially smaller role for direct contributions.

6.1

DC plans and IRAs are an integrated system

A central implication of these fndings is that DC plans and IRAs should not

be understood as standalone or parallel savings vehicles. In practice, they

operate as tightly linked stages of a single accumulation process, with assets

routinely fowing from DC plans into IRAs over the life course. IRA balances

therefore refect not only saving behavior within the IRA system, but also the

cumulative outcomes of participation in employer-sponsored retirement plans.

Treating DC plans and IRAs as separate systems obscures their close integration and can lead to misleading inferences about both household wealth

dynamics and the fscal footprint of retirement saving. Increases in IRA assets should therefore not be interpreted mechanically as evidence of expanded

IRA contributions or greater reliance on IRAs as a primary savings vehicle.

The results instead indicate that a substantial share of observed IRA asset

growth refects the reclassifcation of assets initially accumulated in DC plans

and later transferred through rollovers.

Recognizing the integrated nature of the DC–IRA system has important consequences for how retirement wealth is measured, how tax expenditures are

evaluated, and how retirement policy is assessed over time.

6.2

Implications for Household Wealth Measurement

Leading measures of household wealth—including the Financial Accounts of

the United States—do not treat IRAs symmetrically with other forms of retirement wealth. In the main household balance sheet (Table B101.h), DB entitlements and DC assets are reported explicitly as retirement wealth, whereas

IRA assets are not. Instead, the underlying assets held in IRAs are allocated across standard asset categories such as corporate equities, mutual fund

shares, and bonds. This convention carries over the Distributional Financial

Accounts (DFA), which inherits the same accounting structure as the Financial

Accounts.7

As a result, DC plans and IRAs—despite forming a continuous accumulation

pipeline in practice—are treated as analytically distinct in the core infrastructure used to measure household wealth. The fndings in this paper highlight

the tension between this accounting convention and the underlying economic

reality. This separation can lead to misleading interpretations of household

wealth dynamics. Given this accounting structure in the Financial Accounts,

growth in IRA balances may be read as a shift toward non-retirement saving,

even though the assets remain fully embedded within the retirement system.

Taken together, these results suggest that DC plans and IRAs should be conceptualized as components of a unifed retirement wealth pipeline rather than

as separate asset classes. Incorporating this perspective into household wealth

frameworks would improve consistency across accounts, clarify the sources of

observed asset growth, and provide a more accurate picture of how retirement

wealth accumulates over the life course.

6.3

Implications for Tax Expenditure Accounting

Conceptualizing DC plans and IRAs as an integrated accumulation pipeline

has important implications for how retirement-related tax expenditures are

measured. In both systems, tax expenditures operate through two core mechanisms: the deductibility of contributions (for non-Roth accounts) and the

exclusion of investment returns from current taxation. Contributions reduce

taxable income at the time they are made, while returns on accumulated as7

See Federal Reserve Board, Distributional Financial Accounts, and Batty et al. (2019).

sets—interest, dividends, and realized capital gains—accrue on a tax-free basis

rather than being taxed annually, as they would be in a taxable brokerage account.

Current tax-expenditure estimates produced by the Ofce of Management and

Budget (OMB) and the Joint Committee on Taxation (JCT) evaluate the

revenue efects of DC plans and IRAs separately (U.S. Department of the

Treasury 2025; Joint Committee on Taxation 2024). This practice follows

statutory distinctions across account types. It does not, however, refect how

tax-deferred assets are accumulated and insulated from taxation across the

retirement system as a whole, particularly when assets move between DC

plans and IRAs through rollovers.

Evaluating DC and IRA tax expenditures in isolation can understate their

combined fscal cost, especially under a progressive income tax schedule. Contribution limits apply separately to DC plans and IRAs, allowing tax units

to deduct income across both systems. When contributions are made to both

accounts in the same year, the combined deductions can shift a larger share of

income out of higher marginal tax brackets than would be implied by either

contribution alone. As the OMB itself notes, when tax provisions interact,

the total revenue efect of the system can difer from the sum of its individual

components.

In addition to contribution deductions, the insulation of investment returns

also operates jointly across account types. Consider a tax unit whose retirement assets are split between DC plans and IRAs. Returns that would

otherwise generate taxable interest, dividends, or realized capital gains instead accrue on a tax-free basis across both accounts. Under a progressive tax

schedule, the combined tax beneft associated with insulating returns across

both systems is larger than what would be obtained by estimating DC and

IRA return exclusions separately and adding them together. Put diferently,

the efective tax subsidy associated with jointly holding assets in DC plans and

IRAs exceeds the subsidy that would be inferred by evaluating each system

independently.

Taken together, these dynamics imply that the combined tax advantage of DC

plans and IRAs exceeds the sum of their separately estimated tax expenditures

whenever a tax unit contributes to, or holds assets in, both systems. Given,

as this paper has shown, that most IRA assets originate in DC plans through

rollovers, and that DC plans and IRAs operate in practice as a connected accumulation system, treating them jointly in tax-expenditure accounting would

therefore better refect observed patterns of asset accumulation, and would

imply a larger total fscal cost than estimates that evaluate the two systems

separately.

As IRAs continue to grow as a share of household wealth, incorporating

accumulation-based and system-level perspectives into tax-expenditure analysis will become increasingly important. The framework developed here provides a foundation for such analyses and can inform future work on retirement

saving, tax policy, and the governance of long-term wealth accumulation in

the United States.

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This is a copy of a public record, reproduced as it was published. It is not legal advice, and it may not be the version a court would rely on. Check the official source before you cite it.