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Aon Retirement and Investment Blog

Why Retirement Models are Perfect, While People are Not

Aon Hewitt’s 2015 study The Real Deal projects the average person needs about 11 times[1] their final salary in future retirement assets at age 65 to adequately meet their retirement income needs, in addition to Social Security. Only one-in-five are on track to meet or exceed their needs in retirement at age 65. An additional 20% may be close to having reasonably adequate savings with some lifestyle adjustments. This gap leaves 60% of workers unable to afford to retire at age 65. Aon Hewitt projects that U.S. workers will be able to retire with sound financial security at a median age of 68, while 16% of workers are not expected to have enough to retire even by age 75.
 
Model vs. Reality
Can defined contribution (DC) plans really help people secure the assets needed by age 65 to meet their goals?  If you ask a model, the answer is YES. Then why does reality often not live up to our models?
 
Based on participant trend data, simplistic models often assume a person in a perfect, frictionless bubble with persistent savings for 40 working years, at an average 7.3%[2] contribution rate. With an uncertain investment return, we assume future returns match the 8%[3] median DC return over the 10-year period, acknowledging this is a generous assumption for forward-looking returns. Exhibit 1 projects the “perfect” person[4] who contributed $346,000 while earning $1,259,000 in compounded investment returns by age 65 (the equivalent to 7 times final salary of $230,000). Participants need to save early and stay invested throughout their careers because of the power of compound returns on early dollars, but even then, this participant is projected to fall short of 11 times final pay at retirement.


 
In reality, that perfect bubble pops by everyday frictions, such as low employer defaulted savings rates (3% vs. 7.3%), disruptions in persistent savings, loans/withdrawals, employer changes, and overwhelming investment menus leading to indecision, and possibly below average investment returns.
 
The “Typical” Participant is a Fantasy
Shown through a model participant, trend data can be misleading, especially when looking at independent data for “typical” (median or average) participants. Data can also be misleading shown simply (Exhibit 2) by average vs. median balances. Both intend to reference the middle balance, but the measurement produces completely different results. Today, quarterly data reports such as Exhibit 2 are primarily in one dimension.
 
The commonly quoted average balance attempts to tell a sponsor where the plan’s middle dollar is located. Mostly likely, participants with high balances skew the average balance in their favor, whereas, a median balance tells sponsors how their middle person is tracking for retirement adequacy. Sponsors offer DC plans to help people, not to track the mythical average dollar. 
 
Sponsors accessing deeper cuts of their data (e.g., quartile or decile) will better understand which participant cohorts need help and to what extent. Sponsors can make today’s data more meaningful by pairing it with other variables such as age, wage, or tenure (Exhibits 3-5[5]). Most record keepers are capable of providing this, but often need to be asked. 
 
Exhibit 3, as expected, trends higher at older ages due to more time to save.
 

Exhibit 4 is also directionally intuitive.


Exhibit 5 appears obvious, but in fact the differences are somewhat dramatic. Tenure could be indifferent to wage and age for the job-hoppers changing employers every 5.5[6]  years. The first time average and median balances reach amounts closer to the simple model is when we look at cohorts with over 20 years of service. Long tenure exemplifies the benefit of a persistent savings environment attempting to realize the model’s perfect bubble.

Consolidate Asset Allocations
As shown through Exhibit 5, plan data samples individuals in isolation by employer. Therefore, participants switching jobs every 5 years could have as many as 8 different employer plans by age 65. Multiple plans could be producing below median investment returns due to outdated asset allocations in forgotten employer plans. Consolidating former employer plans into their current plan could ensure that a participant’s asset allocation matches their current objectives.
 
DC Evolution
Insightful data management will drive the next evolution in DC plan design. Plan-specific data will help sponsors improve adequacy for the “typical” participant by helping those who need it the most. Exhibit 6 shows an illustrative example of the evolution of data tools to analyze participant information. Shown are distributions of expected participant outcomes—we see how many participants are far above and below the median. The “typical” participant is a fantasy, as each person is unique, and data helps sponsors see how each unique participant is on (or off) track for adequate retirement savings.  


Action Items
Use Plan Data
  • Pairing data by multiple variables like age, tenure, and pay unveils what cohorts need a sponsor’s time and help.
  • Discover who needs help by looking beyond the “typical” participants, through distributions of outcomes.
Encourage Persistent Savings
  • Auto-enroll at a default rate of at least 6%.
  • Stretch savings through 1-2% annual auto-escalations.
Reduce Leakage
  • Allow consolidation of former employer plans into the plan.
  • Limit plan loan features by considering waiting periods, adding pop-up messaging, or even required counseling.
Improve Asset Allocations
  • Re-enroll into the Qualified Default Investment Alternative (QDIA), helping maintain an age appropriate asset allocation.
  • Maintain a small core fund menu, possibly 4 – 6 objective-based funds that are functional and not redundant.
 
William Ryan is an Associate Partner and Head of Aon Hewitt’s target date fund research team, based in Chicago. 


[1] The Real Deal: 2015 Retirement Income Adequacy at Large Companies
[2] 2015 Universe Benchmarks: Measuring Employee Savings and Investing Behavior in Defined Contribution Plans
[3] 2015 Universe Benchmarks: Measuring Employee Savings and Investing Behavior in Defined Contribution Plans
[4] Assumptions: 7.3% contribution, $50,000 starting wage at age 25, 4% salary increase, 8% investor return, retirement age is 65
[5] 2015 Universe Benchmarks: Measuring Employee Savings and Investing Behavior in Defined Contribution Plans
[6] Bureau of Labor Statistics U.S. Department of Labor: Employee Tenure in 2014

 
Content prepared for US subscribers, but available to interested subscribers of other regions.

The information contained above should be regarded as general information only. That is, your personal objectives, needs or financial situation were not taken into account when preparing this information. Accordingly, you should consider the appropriateness of acting on this information, particularly in the context of your own objectives, financial situation and needs.Nothing in this document should be treated as an authoritative statement of the law on any particular issue or specific case, nor should it be treated as investment advice. Use of, or reliance upon any information in this post is at your sole discretion. It should not be construed as legal or investment advice. Please consult with your independent professional for any such advice. The blog content is intended for professional investors only.


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