Retirement Researcher Advocates Actuarial Concepts for Adjusting Spending in Retirement

Dr.
David Blanchett, Head of Retirement Research at PGIM DC Solutions, has
been recently active on LinkedIn, advocating his 2022 paper “Redefining the Optimal Retirement Income Strategy.”
In this paper, Dr. Blanchett suggests several changes to traditional
Monte Carlo models currently used by many financial advisors. These
changes include:Inclusion of a dynamic process, similar
conceptually to the Actuarial Approach advocated in this website, to
determine how retiree spending should be adjusted from year to year, andDevelopment of a better metric for evaluating scenario results than the traditional “probability of success” metric.Instead
of developing the probability of success for a household with a
spending goal of $X per year for a fixed period of years, Dr.
Blanchett’s model develops a “Goal Completion Score” for a household
with a spending goal of $X per year (with Y% considered “Needs” and
(100%-Y%) considered “Wants”) for a fixed period of years, but with such
household agreeing to increase or decrease future spending in a future
year “t” based on their Funded Status at time t (the present value of
their assets at time t divided by the present value of their spending
liabilities at time t) and other adjustment rules (algorithms). It is
anticipated that the spending adjustments “baked into” Dr. Blanchett’s
Monte Carlo model would affect household “wants” spending first.Dr.
Blanchett’s dynamic algorithm for changing spending in retirement from
year to year is similar to the approach anticipated by the Actuarial
Approach outlined in this website in that both approaches anticipate:an
annual comparison of the present value of household assets and
household spending liabilities to determine the household’s Funded
Status (which Dr. Blanchett calls the Funded Ratio),establishment
of separate funding buckets and asset/liability comparisons for
essential and discretionary spending (which Dr. Blanchett calls needs
and wants), andmapping of assets/investments to the two buckets using Liability Driven Investing (LDI) theories.Dr.
Blanchett’s dynamic spending approach is simpler (for calculation
simplicity purposes) than the Actuarial Approach (Actuarial Financial
Planner model and annual valuation process) for a number of reasons,
includingIt doesn’t anticipate non-recurring expenses (e.g., long-term care, new car purchases, family assistance, etc.)Needs and wants are assumed to be constant percentages of total spending each yearIt
uses a fixed period of years for the household lifetime with no
adjustments upon the first anticipated death within the coupleIt doesn’t anticipate different rates of future increases for different types of assets or expense liabilitiesOur quick assessment of Dr. Blanchett’s proposed changesReaders
of our blog know that we aren’t big fans of Monte Carlo modeling when
it comes to ongoing financial planning during retirement. Generally,
these models are more consistent with one-and-done (static) types of
analyses and can be quite useful for facilitating certain decisions,
such as developing or changing an investment strategy. Having said that,
we believe the changes proposed by Dr. Blanchett are a step in the
right direction. We might even go so far as to agree that his approach
could provide value at the onset of a household’s retirement by
quantifying the range and likelihood of future spending adjustments or
other risks (and would be even better if it used our calculated Funded
Status measure). However, since his approach has spending adjustments
based on future calculations of household Funded Status baked in, we
wonder how useful his approach would be for years subsequent to the
initial application when the household is expected to simply apply a
spending adjustment algorithm (like our Funding Status guardrails) to
determine spending for that future year.ConclusionWe
believe that the process used to adjust spending from year to year is
more important than the model used to calculate a Household’s Funded
Status or project other results. While our Actuarial Financial Planner
(AFP) may not be the most sophisticated model around, it is more robust
in determining Funded Status than the approach anticipated by Dr.
Blanchett. In addition, we believe periodically stress-testing the
assumptions used in the AFP can provide users as good, if not better,
risk information as may be obtained with Monte Carlo modeling (either
traditional or Dr. Blanchett’s improved version). And, for fans of
Occam’s Razor out there, the Actuarial Approach provides a much simpler
and easier to understand solution.