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Long-term care risk misperceptions

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Abstract

This paper reports survey evidence on long-term care (LTC) risk misperceptions and demand for long-term care insurance (LTCI) in Canada. We study three LTC risks: needing help with at least one activity of daily living, needing access to a nursing home, and living to the age of 85. We contrast subjective (stated) probabilities with objective (predicted) probabilities for these three dimensions, and define misperceptions as the differences between them. We first provide descriptive statistics about objective and subjective probabilities. We then analyse how risk misperceptions correlate with individual characteristics and how they affect intentions and actual purchase of LTCI. We find that although misperceptions significantly affect both intention to buy LTCI and actual purchase of LTCI, they cannot explain the low take-up rate of LTCI in our sample. Correcting simultaneously the perceptions of LTC risks on the three dimensions would increase LTCI take-up by at most one percentage point.

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Notes

  1. LTC is defined as the care for people needing daily living support over a prolonged period of time. Support can be provided with activities of daily living (such as bathing, dressing, eating, getting in and out of bed, toileting and continence) or instrumental activities of daily living (which include preparing meals, cleaning, doing the laundry, taking medication, getting to places beyond walking distance, shopping, managing money affairs, using the telephone, and nowadays, the Internet).

  2. https://longtermcare.acl.gov/costs-how-to-pay/costs-of-care.html.

  3. LTC policies in Québec and Ontario (the other province studied in this paper) are quite similar except that the number of for-profit facilities is much higher in Ontario. See Boyer et al. (2018) for a more detailed discussion of the LTC system in Canada.

  4. These values were obtained from the Canadian Life and Health Insurance Association (CLHIA 2018).

  5. De Donder and Leroux (2013) show that misperception biases regarding the probability of dependency can explain why so few governments have implemented a public LTCI programme.

  6. See Hurd (2009) for a review of studies which elicit subjective risk assessments in surveys.

  7. Similarly, Riddel and Hales (2018) study the link between cancer risk misperceptions and selection in health insurance markets.

  8. One can compare the distribution of respondents’ characteristics before weighting (Supplementary Table 1 in Online Appendix B) and after weighting (Table 1 in section ‘General statistics’). The proportions after weighting correspond to the population distribution (according to the Labor Force Survey of 2014) for the age, gender, province of residence and education level criteria. Comparing both tables, we see that respondents in our sample were slightly older than the population on average (59.86 vs 59.06), slightly less often female (50% vs 50.5%) and that Québec was over-represented (50% of respondents by design, as opposed to a share of 38% in the joint population of Québec and Ontario). The largest differences between sample and population concerned education, with more high-school graduates in the sample than in the population (29.7% vs 26.8%), more college graduates (66.6% vs 58.9%) and thus fewer high-school drop outs (3.7% vs 14.3%). Supplementary Table 2 in Online Appendix B shows that the fraction of respondents who hold LTCI is barely affected by the weighting procedure. Finally, most results reported below are not affected significantly by the weighting of the sample.

  9. Note that in this table the statistics on objective probabilities are computed only on the sample of respondents who answered the corresponding question.

  10. In the rest of the paper, when we test whether distributions are statistically different, we always perform a Kolmogorov–Smirnov test at the 99% confidence interval, but we do not mention it again.

  11. A variance equivalence test cannot reject the assumption that variances are equal.

  12. We thank a referee for this observation.

  13. This is related to the distribution of \(\tilde{p}_{ADL}\)having three modes at 0, 1/2 and 1.

  14. Regressing pADL over \(\tilde{p}_{ADL}\), we find a statistically significant coefficient estimate equal to 0.013 (p-value  =  0.024).

  15. We could not, however, reject the assumption that variances are the same at the 99% confidence level.

  16. Regressing pNH over \(\tilde{p}_{NH}\), we find that the coefficient of the regression line is not significantly different from zero.

  17. The regression coefficient they obtain is equal to 0.091. The difference in significance between our studies may be explained in part by differences in sample sizes, as they have around 5000 observations.

  18. The regression line yields a statistically significant coefficient estimate of 0.1286 (p-value close to 0).

  19. We thank a referee for this suggestion.

  20. We randomised which 5 contracts we presented to each respondent among this set of 3 × 3 potential contracts.

  21. Market premiums were obtained from CAA-Québec, which is the Québec equivalent of the AAA in the United States.

  22. This is not surprising, since Boyer et al. (2017), using the same data set as this paper, show that a majority of respondents are either not aware that LTCI exists, or have never been proposed any contract. By proposing to subjects specific LTCI contracts close to being actuarially fair, we reveal intentions to buy LTCI that are not tapped in the current market.

  23. Introducing separately objective and subjective probabilities in a regression instead of misperceptions together with the objective risk, does not change our results.

  24. For instance, in Québec, the out-of-pocket cost of a public nursing home (CHSLD) is at most CAD 20,000 a year. This amount is conditional on the individual resources and could even be reduced to zero.

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Author information

Authors and Affiliations

  1. HEC Montréal, Montréal, Canada

    Martin Boyer & Pierre-Carl Michaud

  2. ESG UQAM, Montréal, Canada

    Philippe De Donder

  3. Université Laval, Montréal, QC, Canada

    Claude Fluet

  4. Department of Economics, Ecole des Sciences de Gestion (ESG), University of Québec at Montréal (UQAM), Case Postale 8888, Succursale Centre-ville, Montréal, QC, H3C 3P8, Canada

    Marie-Louise Leroux

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  1. Martin Boyer
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  2. Philippe De Donder
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  3. Claude Fluet
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  4. Marie-Louise Leroux
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  5. Pierre-Carl Michaud
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Corresponding author

Correspondence to Marie-Louise Leroux.

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Appendix

Appendix

Table 8 Regression of subjective probabilities on individuals’ characteristics
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Boyer, M., De Donder, P., Fluet, C. et al. Long-term care risk misperceptions. Geneva Pap Risk Insur Issues Pract 44, 183–215 (2019). https://doi.org/10.1057/s41288-018-00116-4

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