The Washington Post recently reported that participants in the Federal Long Term Care Insurance Program now face a premium increase of 5% to 25% (Joe Davidson, “Buyers of Long-Term Care Insurance Riled by Premium Increase,” 8/13/09). According to the U.S. Office of Personnel Management, the increase reflects revised pricing assumptions, including investment returns and lapse rates.
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What guidance is available to financial advisors whose clients ask about long term care insurance (LTCI)?
One thing that I’ve learned from articles about the financial merits of LTCI is that you have to pay attention to what is being studied. Articles that seem to be addressing the same question may actually be dealing with different questions, so it is no surprise that they reach different conclusions.
Here’s a list of the important contingencies that are relevant to LTCI:
• You can have a claim, or not.
• You can die or survive.
• You can keep or drop the policy.
• Unless the premium is guaranteed, it can stay the same, increase or decrease.
• The insurer can pay the claim, or not, depending on whether (1) it agrees that the claim is covered by the contract; (2) it treats policyholders with covered claims fairly; and (3) it is still solvent.
Each subset of these five dimensions of uncertainty potentially leads to a different study of whether LTCI is a good deal. There are 32 possible combinations of five objects taken zero, one, two, three, four and five at a time, so there are many studies for researchers to do.
Also, there are several possible metrics — such as wealth, expected utility or probability of ruin — to use for each combination of contingencies. And for each combination of contingencies and each metric, there are many other modeling decisions to make, such as mathematical techniques, data sources, investment returns, income tax treatment of premiums, and Medicaid and estate planning.
Let’s take a quick tour of a small part of the literature with all of this in mind.
“LTCi can provide benefits for pennies on the dollar. A 65-year-old could pay $2,000 per year for a $3,000 monthly benefit. One year of care (12 months x $3,000/month) would be worth 18 years of premiums (18 years x $2,000/year).”
Sales pitches like this involve no contingencies; there is no uncertainty about morbidity, mortality, lapsation, price or claims payment. Of course, the goal of these pitches is to induce people to buy LTCI, so they assume that there is a large claim in relation to the premiums.
2. Actuarial pricing
When actuaries determine the price of LTCI, they take account of morbidity, mortality and lapsation. They ignore price uncertainty (which they control) and claims payment risk. The actuaries’ profit tests provide the expected loss ratio; that is, the expected present value of claims divided by the expected present value of premiums. The actual loss ratio can be higher or lower than the expected loss ratio, depending on the actual results for all of the pricing factors.
A June 2008 report
by the U.S. Government Accountability Office summarized the use of loss ratios in state regulation of LTCI. Prior to 2000, the model regulation of the National Association of Insurance Commissioners (NAIC) required an expected loss ratio of at least 60%. In other words, for every dollar of premium that you paid, you could expect to get back at least 60 cents in benefits; the rest was for the insurer’s expenses and profit. In 2000, the NAIC refined the model regulation to discourage price increases. Under the revised regulation, there is no minimum required loss ratio at the time of sale. Instead, actuaries must certify that the initial pricing is adequate under “moderately adverse conditions,” and any future price increase must have an expected loss ratio of at least 85% (combined with 58% for the initial premium). This is just a model regulation; actual regulations vary by state.
According to Conning’s
midyear 2009 report on LTCI, the loss ratio has been above 100% since 2006, although this measure is based on statutory accounting data (which is cross-sectional) rather than on profit tests (which are longitudinal). You could say that this actual experience means that LTCI has been a good deal; however, it would be prudent to assume that insurers will adjust their prices in the future to make a profit.
3. Stephen C. Goss, “Who Should Buy Long-Term Care Insurance? What Type of Policy Makes Sense?”, Contingencies
, July/August 1990
Goss, who is now the chief actuary of the Social Security Administration, modeled the effect of various LTCI policy designs on the probability of asset depletion. He considered morbidity and mortality; he ignored lapsation, price and claims payment. He found that LTCI policies with a loss ratio of about 70% could reduce the risk of asset depletion for a broad segment of the population by up to 12%. A long waiting period (one to two years), lifetime benefits and inflation protection would be the best design for many people.
4. William A. Dreher, “Long-Term Care Insurance Coverage: Educating Clients and Evaluating Its Merits as an Investment (An Actuary’s Solution to the Consumer’s Dilemma),” Long-Term Care News
, December 2004
Dreher, a consulting actuary, constructed a cash-flow model to examine the value of LTCI — measured by net present value and internal rate of return on premiums — for selected scenarios with a specified policy design, lifespan, timing and duration of claims, after-tax discount rate, income tax treatment of premiums, and assumed probability of occurrence. He ignored lapsation, price and claims payment.
The selected scenarios ranged from no claim to a large claim and showed the potential risk of self-insurance and the potential reward from buying LTCI.
5. Michael D. Everett, Murray S. Anthony and Gary Burkette, “Long-Term Care Insurance: Benefits, Costs, and Computer Models,” Journal of Financial Planning
, February 2005
Everett, Anthony and Burkette, professors of economics and accounting, constructed a retirement cash-flow model to show the adequacy of savings to fund long term care expenses in selected scenarios, defined by financial assets, rate of return, retirement income and household expenses. They briefly discussed lapsation, price and claims payment risk.
Using estimated morbidity and mortality for the selected scenarios, they concluded that the risk of exhausting one’s assets as a result of long term care costs was greater than other risks, such as fire, for which people routinely bought insurance, and that people should seriously consider buying LTCI.
6. Joel I. Gold, David VanderLinden and John S. Herald, “The Financial Desirability of Long-Term Care Insurance Versus Self-Insurance,” Journal of Financial Planning
, November 2006
Gold, VanderLinden and Herald, two finance professors and a graduate student, estimated the expected net present value of benefits minus outlays for a LTCI policy providing nursing home care. They considered morbidity; it is unclear if they considered mortality (it may be implicit in their data sources). They briefly mentioned price risk. They ignored lapsation and claims payment.
Using 2006 LTCI prices, they found that LTCI is a better deal for women than for men, because of unisex pricing. For men, the expected NPV was generally negative except at young issue ages and low discount rates. For women, the expected NPV was positive at all issue ages and discount rates below 9.4%.
7. Jeffrey R. Brown and Amy Finkelstein, “Why is the market for long-term care insurance so small?”, Journal of Public Economics, November 2007
Brown and Finkelstein, economists, sought to explain why private LTCI covers less than 10% of total LTC expenditures. They concluded that part (but not all) of the explanation is the high loading on LTCI policies. Using 2002 prices and a variety of data sources, they found that the estimated load — that is, the percentage of premiums not paid in benefits, or the complement of the loss ratio — depends on issue age (the load rises with issue age), gender (the load is lower for women than men) and lapsation (the load is higher if you take the possibility of lapse into account). On a unisex basis and for base-case assumptions, the load was 18% with no lapses (44% for men; -4% for women) and 51% with expected lapses (65% for men; 39% for women).
They also briefly discussed price and claims payment risk.
8. Aparna Gupta and Lepeng Li, “Integrating long-term care insurance purchase decisions with saving and investment for retirement,” Insurance: Mathematics and Economics, November 2007
Gupta and Li, a professor and graduate student in operations research and decision analysis, looked at the optimal time to buy LTCI with the goal of maximizing the consumer’s expected utility. They modeled the evolution of the consumer’s wealth and health (morbidity and mortality) and used nonlinear and dynamic programming to solve the optimization problem. LTCI premiums were based on a regression analysis of actual prices in the marketplace (which have since increased). They ignored lapsation (although their model addressed affordability, which is one cause of lapsation), price and claims payment.
They modeled a 10-year decision period, starting at age 55. They found that most utility-maximizing people would choose to buy LTCI, but the timing of the purchase is affected by policy design and the assumed utility function.
A final note:
What I take away from my journey into this area of human knowledge is that (1) you shouldn’t expect to make money from LTCI (unless you’re selling it); (2) LTCI is a better deal for women than for men; and (3) there are worse things to spend your money on.
Last year I bought a LTCI policy with a $200 daily benefit, a 90-day waiting period, a six-year benefit period and a guaranteed purchase option for a 5% compounded annual benefit increase. I paid extra to get an insurance company with a well-deserved reputation for fair treatment of policyholders. I expect that this policy will be just one piece of my total long term care funding solution, and that this piece will have higher loading than the rest of my evolving solution.