Journal of Financial Planning: December 2017
Jason K. Branning, CFP®, specializes in retirement planning at Branning Wealth Management, LLC (a DBA of Asset Dedication LLC). He is co-founder of Modern Retirement Theory, LLC (modernretirementtheory.com), a retirement education company.
M. Ray Grubbs, Ph.D., is a professor of management at Millsaps College where his primary teaching areas include strategic management, leadership, and entrepreneurship. He is co-founder of Modern Retirement Theory, LLC.
Financial planners are driven by the passionate desire to serve clients with evidence-based convictions. Yet, planners often seem locked in a battle with each other over the superior method to deliver retirement income to clients. Often, this leads to staking out a position that one method is universally superior to others. In theory and in practice there are multiple paths to forming a retirement plan for clients.
An individual client’s retirement income plan that attempts to account for unbounded conditions cannot be solved by math; a more robust framework is needed. This different logical framework must ask, “What is the question under the question of the best solution for retirement income?” We believe that deeper question is, “What kind of income is preferred and desired during retirement?”
In retirement planning, planners should consider a logical, definitional-based framework as convictional where empirical, evidence-based information is not available or is unclear. Our view is that the only clear answer to this deeper question for retirement income is found in what we call the 3-S model framework: it must be simultaneously stable, secure, and sustainable. Because retirement income is the first and most important issue in retirement planning, we must get this answer right. As an individual’s longevity is unknowable, income planning should align with the 3-S model income framework. If 3-S model income is the answer to the deepest question of retirement income, then the next question is, “What options can reasonably be expected to deliver 3-S model income in retirement?”
Unknown Versus Unknowable
Planning against the unknowable offers a different starting point than simply planning for unknowns. We can know that a client will ultimately die, but it is not just unknown as to when, but individually unknowable. The distinction between unknown conditions and unknowable conditions is an important one for financial planners and clients.
Unknown implies that there is something that can be done to provide knowledge on which actions can be taken. Unknowable implies that there is nothing that can be done to add knowledge to a particular client situation. The unknowable aptly describes individual client issues such as longevity and conditions within longevity. Conducting empirical tests requires assumptions or constraints to get a meaningful mathematical solution. Math is useful if conditions can be controlled and limited, as math measures quantity.
Upon retirement, wages typically cease, therefore, wages must be replaced by new sources of income. To maximize the use of a client’s assets, retirement income should provide for base or mandatory expenses (the ongoing expenses of life, such as food, clothing, housing, taxes, health care, insurance, transportation, etc.). A minimum coverage ratio of 1:1 base expense covered by a base income source is required. It is important to distinguish expenses as mandatory versus discretionary to offer the potential for better use of remaining assets and for greater tax efficiency of income. Liability matching is not just about generating retirement income, it is about generating net cash flow. According to various studies, retirees tend to actually spend less over the retirement years (an inflation-adjusted decline of about 1 percent per year, according to David Blanchett’s May 2014 Journal paper, “Exploring the Retirement Consumption Puzzle”), so dramatically overfunding base expenses will lead to paying more than necessary in taxes over the retirement horizon. This also leads to additional stress on other goals if base expenses are overfunded.
Strategy Approaches for Covering the Base
We propose that there are three broad approaches for satisfying base income for retirement planning. These approaches are through agreement-based arrangements or contracts, mortality-based tools, and historically based investing. Each has unique advantages, disadvantages, and trade-offs. Each approach individually or in combination must meet the 3-S model. Ideally, using multiple approaches will serve to mitigate the risks of adopting a solo strategy.
As one makes choices among the three different approaches of managing base income, the assignment of risk to the individual must be considered. These risk assessments are important distinctions to make so clients can make more informed choices. Each strategy has nuances.
Agreement-based. Agreement-based income arrangements offer retirement income from sources that commit to pay for a specific time, and therefore offer 3-S model income that is time-bound. Examples of agreement-based income are wages, whole life insurance withdrawals, and rents from houses, land, or commercial property, as well as royalties from intellectual property.
For example, human capital can serve a valuable role in retirement success and sustainability. Wages will mitigate the effect of retirement expenses on retirement assets. The trade-off in the human capital equation is that while working longer benefits a client’s retirement sustainability, if the client dies early, they would have exchanged their time for excess financial resources. Human capital is classified as time-bound 3-S model income.
Another example of agreement-based, time-bound 3-S income is a withdrawal or series of withdrawals from a whole life insurance contract. Whole life policies can be structured to pay a period certain, monthly, or annual income during retirement.
Mortality-based. With mortality-based approaches of providing base income, the risk of failure is most often spread over numerous other individuals, as with a lifetime annuity. Risk is assumed by an insurance company or other entity with a perpetual life and access to wider and deeper capital markets.
An individual can never know with certainty how long they will live. To offset longevity risk, transferring a part of a retiree’s wealth to a large pool of individuals can help mitigate the negative financial effects of a long life. Mortality-based retirement income solutions, tools, or strategies offer a guarantee or third-party backstop through risk pooling.
Many of these solutions are tax advantaged. Where possible, using mortality-based solutions at age 70 or older will offer higher sustainability through greater payouts (also known as mortality credits). Social Security is the ultimate pension benefit, as it offers inflation-adjusting, lifetime income.
Historically based portfolios. The primary goal of the historically based asset allocation portfolio is to examine how different pressures (capital assumptions, market cycles, depression, recession, interest rate cycles, war, unemployment, etc.) affect different portfolio allocations. Studying the effects of historical data on portfolio assets offers guidance on how those allocations may potentially perform in future cycles. The various methods to back-test portfolios include Monte Carlo analysis and historical audit. Probabilistic, historically based strategies are conditionally 3-S as they would have historically guaranteed stable, secure, sustainable portfolio income.
Allocations of total return portfolios are generally determined through a risk tolerance profile and subsequent categorizations of conservative, moderate, and aggressive. Using investments in generating retirement income has been an accepted method for decades, but starting with Bengen’s 1994 development of the safe withdrawal rate (SWR), acceptance has grown in SWR for total return portfolios.
Withdrawal strategies using a total return approach for retirement income have seen improvements through the work of Jonathan Guyton’s decision rules, David Blanchett’s sustainable withdrawal rates, and Michael Kitces’ ratcheting 4 percent rule. Additionally, if a planner thinks past performance in the U.S. was exceptional, Wade Pfau created an international safe withdrawal rate that includes historical data from 20 developed countries.
These sustainable withdrawal rate strategies provide sharpening in retirement income delivery from total return portfolios. All widely known SWR strategies offer conditional 3-S model income, because they deliver retirement income informed through extensive historical analysis. Although the future is always uncertain, clients are being offered a prudent approach for retirement income.
Finally, within a historically based portfolio approach stands the dedicated portfolio. This strategy originated in the 1930’s with “cash matching” portfolios (immunization). In the 2004 book Asset Dedication: How to Grow Wealthy with the Next Generation of Asset Allocation, co-author Stephen Huxley argues that a dedicated portfolio, “reduces sequence risk because it insulates the growth portion of the portfolio from withdrawals.” Time segmentation highlights an asset’s historical best, which is then applied to the client horizon and plan, resulting in a dedicated portfolio. Dedicating a portfolio through the time segmentation strategy offers the maximum customized allocation for an individual client. If historically based, probabilistic approaches to base income fail to provide necessary returns, or if these returns are not sequenced favorably, the risk of failure falls on the individual.
Conclusion
The 3-S model answers the deepest question about retirement income: what kind of income is preferred and desired during retirement? Any 3-S approach (agreement-based, mortality-based, or historically based) can be reasonable and prudent to cover base expenses with a minimum coverage ratio of 1:1. Planners should use their knowledge, judgement, and discretion about their client to offer solutions or the best combination of solutions to craft retirement income in an unknowable context.