The Mistake Financial Planners Make with Life Expectancy

(You may download a copy of the Life Expectancy Table)

Planners use the wrong numbers for life expectancy and it means that much of the planning that’s been done needs to be revised. Planners typically consult life expectancy tables published by the IRS and most are well aware that these are the average life expectancies. A person reaching age 70 will live 17 years longer on average, with half dying before age 87 and half living longer. A planner adds a few more years for a margin of error (let’s plan to age 94 Mrs. Smith) and completes their plan. And that’s where most planners stop.

Stopping at this point presents a double jeopardy. Financial instruments may not perform for a sufficiently long period and the client may live too long. We could burn the client from both ends. The first danger we have already encountered with life insurance policies whose premiums did not vanish and variable investments (mutual funds, variable life, annuities, IRAs) whose balances collapsed insuring they would not last a lifetime.

We already make the second mistake with Social Security and in a minute, I will show you how to avoid this same error with your clients. Here’s a snippet from the Social Security web site to illustrate the problem:

If we look at life expectancy statistics from the 1930s we might come to the conclusion that the Social Security program was designed in such a way that people would work for many years paying in taxes, but would not live long enough to collect benefits. Life expectancy at birth in 1930 was indeed only 58 for men and 62 for women, and the retirement age was 65. But life expectancy at birth in the early decades of the 20th century was low due mainly to high infant mortality, and someone who died as a child would never have worked and paid into Social Security. A more appropriate measure is probably life expectancy after attainment of adulthood.
….the majority of Americans who made it to adulthood could expect to live to 65, and those who did live to 65 could look forward to collecting benefits for many years into the future. So we can observe that for men, for example, almost 54% of the them could expect to live to age 65 if they survived to age 21, and men who attained age 65 could expect to collect Social Security benefits for almost 13 years (and the numbers are even higher for women).

In other words, Social Security’s fundamental problem is based on life expectancy estimates that are too short and we as planners have made the same error. We have consulted data that often leads us to plan for too few years of retirement.

It’s always bothered me that the life expectancy tables and the averages tell me little. What I want to know is the probability of my client living to age 94 once the client has already reached age 70 (a table that shows life expectancy at birth is of no value). Your client wants the same information but probably doesn’t know the proper question to ask.

Why do we go to such lengths selecting our asset classes and using the standard deviation from the appropriate time frame for our Monte Carlo simulations and then just “wing it” with life expectancy? Why isn’t life expectancy one more factor that we input to our simulations with an average and standard deviation? After all, life expectancy, a natural phenomenon, adheres better to a normal distribution and the underlying statistics than do investment returns.

There are some crude attempts to provide this information to investors and advisors. On Fidelity’s website, buried in their pages on annuities, is a probability-based calculator (http://personal.fidelity.com/products/annuities/income/income_intro.shtml). However, it shows only the 25% and 50% chance of living to selected ages. It will not answer the client’s question “How can I be 90% sure that my money will outlive me?” So I created the table to help you answer that question.

I took the data from the Center for Disease Control Vital Statistics report, February 2004 and constructed a probability table just to see how surprised I might be. Using the table, I can quickly see that my client, age 70 has a 13% chance of living to age 94. At least now (before the programmers add this to their simulation software as I recommend), I can ask Mrs. Smith, “With high comfort, your portfolio can generate $xx per year until age 94 but there’s a 13% chance you’ll live longer. Can you do with less so that we can further decrease the probability of outliving your money?”

The client may then ask, “If I want to reduce the risk of outliving my money to 10%, how much do I need to reduce spending?” First, I can look at my life expectancy table and see that someone age 70 has a 10% chance of living to age 95. I can then use this forecasted age in my planning software or calculator to determine how much Mrs. Smith can spend.

Yes people are living longer. And that makes us responsible for informing clients of more accurate probabilities of outliving their money.