Household Portfolios and Retirement Saving: Parker, Schoar, Cole & Simester (2025)

What this is. The paper’s core results, the identification design exploiting the Pension Protection Act of 2006 as a natural experiment, and the estimating equations: enough to know what it found and how, without reading all 49 pages. To replicate or extend the analysis, read the full source at the original.

TL;DR

Using account-level data from a large U.S. financial institution on millions of middle-class investors (2006 to 2018), the paper documents three facts. First, middle-class investors hold roughly 71% of their investable wealth in equities, about 10 percentage points more than in the 1990s. Second, the life-cycle profile of equity shares is now hump-shaped: investors increase their stock allocation from age 25 to around 50 and then reduce it as they approach retirement, a pattern absent before 2000. Third, retirement contribution rates have increased steadily with age by about 4% to 5% over working lives and are stable across cohorts. The PPA of 2006, which permitted target date funds (TDFs) as qualified default investment alternatives, is shown to have caused the portfolio changes, particularly for younger and lower-income workers, while having little persistent effect on contribution rates. The savings behavior documented here is broadly consistent with prescriptive life-cycle portfolio models, unlike the 1990s patterns.

Core results

Magnitudes and significance are as reported; locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Average equity share of investable wealth is 71%, 10pp above the 1990s, and inversely related to age in the cross-section	Table III Panel A, p. 2753; Figure 5, p. 2757	Mean equity share 71.0% (median 77.3%) in 2016; cross-sectional age gradient from ~74% at ages 25-27 to ~55% at ages 64-65; SCF shows only 54.5% (underreporting argued)
R2	Within-person, equity shares follow a hump-shaped life-cycle: up ~7% from age 25 to 50, down ~7% from 50 to 65; in the 1990s the profile was flat or upward-sloping	Figure 6, p. 2759; Figure 9 bottom panel, p. 2763	Person fixed effects regression shows monotone rise then fall; 1990s comparison (Ameriks and Zeldes (2004)) shows opposite pattern; McKenzie (2006) double-differencing confirms the hump
R3	Younger cohorts hold higher equity shares at every overlapping age; each cohort born later has 15-20pp more in equities by 2018 than those born around 1945	Figure 7, p. 2760; Figure 8, p. 2761	Monotone cohort shift in equity shares for births before age 40; cohorts with high initial TDF share show steeper hump-shaped profiles
R4	TDF adoption as QDIA (PPA 2006) raises equity share of young new enrollees by ~5.5pp (age 25-35) and ~6pp for low-income workers	Table IV col. (1) and (3), pp. 2766-2767	Treated coefficient 0.0552 (SE 0.0907) for full sample; 0.0599 bottom income tercile; <2% for top income tercile; firm fixed effects DiD, two-year window
R5	Same TDF default reduces equity share of older new enrollees by ~13pp (age 55-65), consistent with TDF glidepath moving near-retirees out of stocks	Table IV col. (1), pp. 2766-2767	Interaction Age 55 to 65 x Treatment: -0.1325 (SE 0.0012); effect is robust to controlling for income and restricting to those with no prior retirement assets
R6	Medium-run (5-year) effect: treated young workers’ 5-year coefficient 0.0134 (~1.3pp above control); treated older workers’ 5-year coefficient -0.0564 (~5.6pp below control), declining from year-1 peak/trough as control group converges voluntarily	Table V col. (4) and (7), pp. 2770-2771	Year-0 through Year-5 treatment interaction coefficients trace declining but persistent gap; paper text approximates these as “~3%” (young) and “~2%” (old), likely based on Figure 10 predicted-equity-share comparisons
R7	Retirement contribution rates rise ~4-5pp over working lives and are stable across cohorts; PPA had a transitory negative effect of -0.4% to -1.2% on contribution rates, fading to near zero after five years	Figure 11, pp. 2774-2775; Table VI col. (1), p. 2778; Table VII col. (1), p. 2780	Realized contribution rate increases from ~4.6% at age 25 to ~8.5% at age 65 (cross-section); within-person increase ~5%; PPA DiD coefficient -0.0043 at year of treatment, converges to ~0 by year 5

Overall (paper’s conclusion). The rise of TDFs following the PPA of 2006 caused middle-class American investors to hold more equity earlier in their careers and to progressively de-risk as they approach retirement, bringing the life-cycle portfolio profile closer to prescriptive life-cycle models. This rebalancing occurred largely via portfolio composition changes, not changes in saving rates. The PPA provisions intended to raise contribution rates had little lasting effect, leaving open whether regulatory design of retirement plans is an effective tool for increasing saving quantities.

Theory / model

The paper has no formal utility or equilibrium model; it instead tests hypotheses derived from the life-cycle portfolio-choice literature against administrative panel data. The paper’s identification logic and the hypotheses it tests are as follows.

Life-cycle portfolio hypotheses. The paper is motivated by Campbell (2016), who surveys how financial product design and regulation can improve household financial well-being. Classical models (Merton (1969), Samuelson (1969)) imply constant portfolio allocations in scale-invariant settings. Richer models with non-tradable human capital that declines with age (Viceira (2001), Heaton and Lucas (2000), Campbell and Viceira (2002), Gomes, Michaelides, and Zhang (2020)) recommend reducing risky asset holdings over the working life. TDFs embed exactly this prescription: a glidepath that holds ~90% in equity roughly 20 years before retirement and reduces to 40-50% at target retirement date. The paper’s first hypothesis is that current life-cycle profiles of equity shares are broadly consistent with these prescriptions, in contrast to the 1990s patterns documented by Ameriks and Zeldes (2004).

PPA identification assumption. The Pension Protection Act of 2006 permitted TDFs as Qualified Default Investment Alternatives (QDIAs) in employer-sponsored defined-contribution plans. The key assumption is that employees’ employment decisions were not affected by whether their employer adopted a TDF as QDIA: workers were typically unaware of plan feature changes at the time of hiring, chose jobs on many other dimensions, and employer selection of a TDF default was driven by liability concerns (the safe harbor provision), not worker characteristics (pp. 2764-2765). Workers at the same employer enrolling in the two years before the switch serve as the control group; those enrolling in the two years after serve as the treated group. The design follows the tradition of Madrian and Shea (2001), who showed that default investment options have large effects on 401(k) choices. Mitchell and Utkus (2022) use Vanguard data to document similar TDF effects; this paper extends that work using a different large institution and focusing on life-cycle patterns and causal PPA effects.

Contribution rate hypotheses. Optimal saving models (Gomes et al. (2018), Poterba (2014)) predict significant shortfalls in U.S. retirement saving. The paper tests whether the PPA’s provisions intended to raise saving (autoenrollment, autoescalation) had measurable effects on the realized rate at which employees saved. Broader aggregate implications of TDF growth are analyzed in Parker, Schoar and Sun (2023).

Method

The main empirical strategy is a difference-in-differences (DiD) design exploiting the Pension Protection Act of 2006. The two estimating equations are given in Sections III.A and III.B of the paper.

Short-run DiD (equation 2, p. 2765). For workers starting a new job between 2005 and 2008 and observed for the first two years, the specification is:

$$y_{ift} = \beta_1 \times D_{\text{treated}} + \beta_2 \times D_{\text{treated}} \times \text{AgeEnrolled}_i + \beta_3 \times \text{AgeEnrolled}_i + \lambda_f + \epsilon_{ift}, \tag{2}$$

where $y_{ift}$ is the portfolio equity share (or reported contribution rate) of individual $i$ starting at firm $f$ in year $t = 0$; $D_{\text{treated}}$ equals one if the worker enrolled after the firm adopted a TDF as its default (in 2007 or 2008); $\text{AgeEnrolled}_i$ is a vector of 10-year age-group indicators at enrollment, included both alone and interacted with the treatment to capture the age-heterogeneous TDF glidepath effect; and $\lambda_f$ is a firm fixed effect so the comparison is within-employer across the two cohorts. Standard errors are clustered at the household level.

Medium-run DiD (equation 3, p. 2769). To track dynamics over five years after enrollment:

$$y_{ift} = \beta_1 \times D_{\text{treated}} + \beta_2 \times D_{\text{treated}} \times \text{AgeEnrolled}_i + \beta_3 \times \text{AgeEnrolled}_i + \beta_4 \times D_{\text{treated}} \times \lambda_t + \beta_5 \times \lambda_t + \lambda_f + \epsilon_{ift}, \tag{3}$$

where $\lambda_t$ are year fixed effects and their interaction with treatment captures how the treatment gap evolves year by year. Separate regressions by age group replace the interacted $\text{AgeEnrolled}$ terms.

Life-cycle descriptive regressions (equation 1, p. 2756). Cross-sectional and within-person regressions of equity share on age-group indicators document the cross-sectional age profile and the within-cohort life-cycle hump:

$$y_{it} = \boldsymbol{\beta}_1' \mathbf{Age}_{it} + \beta_2 \, Inc_{it} + \epsilon_{it}, \tag{1}$$

where $\mathbf{Age}_{it}$ is a vector of three-year age-group indicators and $Inc_{it}$ is the log deviation of the individual’s income from the sample mean income. Person fixed effects are added for the within-person version (Figure 6, Figure 11 Panel B). The McKenzie (2006) double-differencing method is also applied as a robustness check to recover pure age effects free of cohort and time effects (Internet Appendix Section II).

Empirical specifications

Descriptive life-cycle profiles (R1, R2, R3). The full RI sample of investors aged 25-65 observed annually from 2006 to 2018 (millions of individual-year observations). The cross- sectional regression (eq. 1) uses age-group dummies with and without a log-income control; the within-person version adds person fixed effects. Cohort analysis splits by 10-year birth cohorts (Figure 7). For R2, the McKenzie (2006) approach differences within-cohort over adjacent ages, then differences again over time, to eliminate cohort and time effects and identify the second partial derivative of the age profile (Internet Appendix II). Standard errors are clustered at the individual level (and at the employer level in robustness checks, Internet Appendix Table IA.XI).

Short-run PPA DiD (R4, R5). Sample restricted to workers who started a new job 2005-2008, observed for two years. Treatment defined as enrollment in 2007 or 2008 at an employer that switched its default to a TDF. Control: enrollment in 2005 or 2006 at the same employer. Firm fixed effects absorb all employer-level differences. The key identifying variation is the timing of the plan-level switch relative to enrollment date. Columns (3)-(4) of Table IV repeat the analysis by income tercile; columns (5)-(6) restrict to those with no prior retirement assets at the institution. The portfolio equity share is the dependent variable for R4 and R5; the reported contribution rate for R7.

Medium-run PPA DiD (R6, R7). Same base sample, extended to track individuals for five years after enrollment. Year-of-treatment and year-after interactions (eq. 3) trace convergence between treated and control. Columns (4) and (7) of Table V split by age at enrollment (25-34 vs. 55-65); columns (4) and (7) of Table VII do the same for contribution rates. Standard errors are clustered at the household level throughout.

Robustness checks. (i) Restricting the contribution-rate PPA sample to 2007 enrollees only (Table IA.XXIV) to reduce financial-crisis confounds. (ii) Using price-constant equity shares that ignore passive price appreciation (Internet Appendix Table IA.IX). (iii) Repeating DiD with standard errors clustered at the employer (Table IA.XVII). (iv) The ex-ante designated equity share as an alternative equity-share measure (Table IA.X).

Datasets used

Dataset	Role in paper	Wiki page
Proprietary account-level administrative data from a large U.S. financial services company	Primary data: individual portfolios, contribution rates, demographics, employer identifier, 2006-2018	No page yet
Survey of Consumer Finances (SCF), 2016 wave	Comparison sample of U.S. retirement investors (RIs) for calibration and validation of representativeness	No page yet
Administrative data from Ameriks and Zeldes (2004)	1990s comparison for life-cycle equity share patterns	No page yet

The paper’s headline results are derived entirely from proprietary account-level data provided by a single unnamed financial services firm. Reproducing the results requires access to that confidential dataset, which is not publicly available.

Sample scope: investors aged 25-65 with retirement wealth in the middle 80% of the age-adjusted distribution (retirement investors, RIs), observed annually from December 2006 to December 2018. Sample covers millions of individuals and trillions of dollars in investable wealth (Table II, p. 2750).

When to read the full paper

Read the original if you are: studying the design of 401(k) plans and the life-cycle portfolio implications of TDF defaults; calibrating or testing life-cycle portfolio-choice or saving models against administrative microdata; investigating the effects of the Pension Protection Act of 2006 on investor behavior; or interested in the representativeness of SCF survey data relative to administrative data on portfolio equity shares. The Internet Appendix (available on the Journal of Finance website) contains 25+ robustness tables, estimation moments, and the McKenzie (2006) double-differencing reconstruction.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(5). This distillation was extracted by an LLM on 2026-06-05 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Parker, Jonathan A., Antoinette Schoar, Allison Cole, and Duncan Simester. “Household Portfolios and Retirement Saving over the Life Cycle.” The Journal of Finance 80, no. 5 (October 2025): 2739-2787. DOI: 10.1111/jofi.13473. (C) 2025 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.