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Health and Lapse of Long-Term Care Insurance

Health and Lapse of Long-Term Care Insurance. R. Tamara Konetzka, PhD University of Chicago Ye Luo, PhD University of North Florida June 2008 Funding: NORC and University of Chicago Center on Aging. Background.

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Health and Lapse of Long-Term Care Insurance

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  1. Health and Lapse of Long-Term Care Insurance R. Tamara Konetzka, PhD University of Chicago Ye Luo, PhD University of North Florida June 2008 Funding: NORC and University of Chicago Center on Aging

  2. Background • Despite high cost of LTC and substantial probability of need, LTC insurance market remains small. • Most LTC financing proposals include expansion of LTC insurance. • Policies becoming more standardized and awareness increasing. • Issues of affordability, adverse selection, and moral hazard remain. • Lapse rates are high, especially soon after purchase.

  3. Why study lapse? • Consumers who lapse lose the investment they made in insurance premiums paid. • Thus lapse = renewed risk of the financial burden of LTC, but with less income. • We know very little about the attributes of people who lapse.

  4. Research Questions • What is the probability of lapsing a LTC insurance policy, controlling for competing risks? • What are the health characteristics of people who are likely to let a LTC insurance policy lapse? • Can income or health shocks explain lapse?

  5. Prior Research • Vast majority of studies were cross-sectional, focus on prevalence. • Important factors associated with having LTC insurance: middle income, middle assets, good health, higher education. • Purchase and retention of LTCI is an inherently dynamic process. • Only one rigorous study of policy lapse.

  6. Prior studies using longitudinal data • Cramer and Jenson 2006 • Study of purchase using HRS • Predictors of purchase similar to prevalence studies • Finkelstein, McGarry, and Sufi 2005 • Study of lapse using HRS • “dynamic inefficiency” in LTC markets • Lapsers are ex-post less likely to use a nursing home, though they are poorer and less educated

  7. Conceptual Framework • Purchasers assess value of LTC insurance policy in terms of cost, probability of needing LTC, and payout if LTC needs arise. • Must weigh cost of LTCI against other uses • Revisit decision over time as health and financial shocks are introduced

  8. Data • Health and Retirement Study, 5 waves 1996 – 2004. • Respondents born between 1924 and 1947 (age 49-72 in 1996). • 55,663 two-wave intervals, pooled. • Examined smaller subgroups for lapse analysis (up to 5,067)

  9. Determining Insurance Status • Used the question each wave: “Do you have any LTC insurance which specifically covers nursing home care for a year or more or any part of personal or medical care in your home? • Lapse = “yes” at time 1 and “no” at time 2. • “Have you ever cancelled/lapsed” question is very inconsistent over time; also unable to identify timing of lapse; also subject to recall bias.

  10. Potential Data Issues • Measurement error • Do people really know if they have LTCI? • Some inconsistent patterns over time • Solution: Group data by likely extent of measurement error and run sensitivity analyses • Lapse rates are sensitive to these groupings • However the predictors of lapse appear to be robust to this measurement error

  11. Transition Framework Status at time 1 = one of 2 discrete states: • In community without LTC insurance • In community with LTC insurance Outcome at time 2 = one of 4 discrete states: • In community without LTC insurance • In community with LTC insurance • Institutionalization • Death

  12. Methods Multinomial logit estimation of: • Transition probabilities from time 1 to time 2 • Effects of demographics, health, and financial characteristics at time 1 on lapse in time 2 Change Model-- Logit estimation of: • Effects of changes in health status between time 1 and time 2 on lapse in time 2.

  13. age gender race self-rated health education marital status income assets has kid has daughter number of kids number of nearby kids whether kids own home expect to leave a bequest Potential Predictors

  14. 2-Year Lapse Rates

  15. More likely to lapse: Poor self-rated health status Black Hispanic Lower income Lower assets Less educated More children Less likely to lapse: Very good health Female Retired & not working at all Kids work full-time Kids own homes Expect to leave a bequest Results: Predictors of Lapse

  16. Preliminary Conclusions/Implications • Sicker individuals less likely to buy LTCI but also more likely to lapse if they do buy it. • Unlikely that reassessment of risk can explain most lapse – more likely that affordability is the issue. • Expansions of the LTC insurance market to those who can marginally afford it may be inefficient and leave frail elderly worse off.

  17. Caveats/Limitations • Little information on attributes of policies, most importantly prices • Remaining measurement error • Still need to analyze 3-period transitions to assess the effects of changes in health and income over time

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