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Research on long-term care insurance: status quo and directions for future research

Abstract

We provide a structured literature review of long-term care (LTC) insurance using main path analysis, a mathematical tool to identify the most significant paths in a citation network. We identify three major research areas (financing, demand, and insurability) and systematically evaluate them based on standard frameworks. We further review established and innovative (insurance) solutions for LTC financing. Our results illustrate the immense difficulties of insuring LTC both on the demand side (e.g., low value of consumption while in care, existence of substitutes) and supply side (e.g., lack of predictability and asymmetric information), explaining the marginal contribution of insurance mechanisms to LTC financing. Combined products that bundle the risks, and public–private partnerships that integrate LTC into the pension systems might help to overcome the insurability limitations. In addition, alternative financing methods that go beyond the idea of risk pooling (LTC bond, LTC put option, equity release) could help to improve the sustainability of LTC financing.

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Notes

  1. The statistics on LTC financing in OECD countries presented in Colombo et al. (2011) emphasise the low relevance of insurance. Private insurance as a source of LTC funding ranges from 0% (Netherlands, Czech Republic) to a maximum of 9.8% (Belgium). Social security, which might also use some insurance mechanisms is, however, important in some countries (90% in the Netherlands, 70% in the Czech Republic).

  2. There are a few previous literature surveys on LTC with a slightly narrower focus. The most comprehensive study is by Norton (2000), in which the taxonomy of LTC, supply, demand and demographic trends based on previous theoretical and empirical research are discussed. Cremer et al. (2012) and Klimaviciute and Pestieau (2018) review more recent publications, seeking a sustainable public LTC scheme that combines both market and family solutions. Pestieau and Ponthière (2012) study the vicious circle in the LTC market (i.e., the market for LTCI is thin because people may find most of the current LTCI products too expensive and at the same time, insurance companies provide LTCI with higher prices due to the thinness of its market) from both the demand and supply sides. Brown and Finkelstein (2011) discuss the LTC expenditure, the relevant nature of the private market for LTCI along with Medicaid in the U.S.

  3. We discuss 20 factors whose impact on LTCI demand has been empirically studied. Of these, 12 are clear in their prediction and are in line with observations from other insurance markets. For the other eight factors, however, the literature is either inconclusive or contradictory.

  4. See, e.g., Liu et al. (2016) for an application to data envelopment analysis or Huang et al. (2017) for an application to 3D printing.

  5. Four measuring traversal counts discussed in the literature are Search Path Link Count (SPLC), Search Path Node Pair (SPNP), Node Pair Projection Count (NPPC), and Search Path Count (SPC). Although there are small variations in these indexes, Batagelj (2003) recommends SPC over other traversal counts.

  6. We choose global rather than local search to connect the links in a way that delivers the largest traversal counts (see Liu and Lu (2012) for further explanation of this idea and a discussion of different approaches).

  7. See, e.g., Eling et al. (2014) for an application to microinsurance demand.

  8. Biener and Eling (2012) and Biener et al. (2015) show applications to micro and cyber insurance.

  9. For identifying main research areas in LTC (applying main path analysis and text mining) we use the Web of Science database. However, in the later discussion we also include relevant studies and working papers from other sources.

  10. A complete list of all articles and papers included in the review is available upon request.

  11. For clarity, each node on the graph mentions the family name of the first author along with the first letter of the family name of other authors followed by the year of publication.

  12. There are many ways to categorise LTC financing. Wittenberg et al. (2003) propose five categories: private savings, private insurance, private insurance with public-sector support, public-sector tax-based support, and social insurance. Costa-Font et al. (2015) distinguish ex ante (insurance) from ex post financing (public sector, family). We rely on Chen’s (2001) categorisation because it allows us to make a statement on the empirical relevance using Colombo et al. (2011).

  13. Colombo et al. (2011, p. 79).

  14. Murtaugh et al. (2001).

  15. Murtaugh et al. (2001) and Brown and Warshawsky (2013).

  16. There are some alternative products for traditional LTCI such as short-term care insurance (STCI or convalescent insurance) and critical illness insurance (critical care) which provide partial LTC coverage in terms of period (STCI is generally less than 1-year coverage) or limited risks (critical care covers only specified serious illnesses such as cancer or stroke).

  17. Hsieh et al. (2017).

  18. See Shilling (1991) for the formation, structure and operation of the consortium. In his study, he defines a consortium as a group of proprietary facilities that provides LTC.

  19. Some LTCI demand studies consider actual purchase decisions (e.g., Mellor 2001; Sperber et al. 2017), while others test demand and willingness to pay (e.g., Brau and Bruni 2008). Both approaches have their limitations and are included in our review. While studies with actual purchase decisions might be biased towards risks that are not excluded by insurance companies, studies that test the intention to purchase do not know whether it really reflects real-world behaviour. We also note that a few other demographic aspects were studied in the literature such as race, ethnicity and their effect on the ownership of LTCI (Headen Jr. 1992; Sloan and Norton 1997; Cramer and Jensen 2006; McGarry et al. 2013).

  20. Courbage and Roudaut (2008) illustrate a non-linear bell-shaped effect of income on demand, emphasising that only a fraction of the population is interested in LTCI; poor people cannot afford it while extremely wealthy people can pay the potential costs out-of-pocket.

  21. Only Kumar et al. (1995) report a negative effect of experience on demand for LTCI. They state that most of LTCI policies available at the time of survey did not provide significant home health coverage.

  22. Costa-Font and Rovira-Forns (2008) explain their insignificant results by the fact that a part of the gender effect is captured by the effect of the individual’s own disability risk perceptions.

  23. Our focus in this study is on the demand for LTCI in general. However, articles such as Meier (1999) focus on the postponement of purchasing LTCI by the young generation and its reasons.

  24. Costa-Font and Rovira-Forns (2008) attribute this insignificant effect to the fact that risk-averse people may prefer to protect themselves through other means such as protective savings and self-insurance.

  25. The same is observed for other measures related to home equity, such as having a larger home equity to wealth ratio (see Davidoff 2008).

  26. McCall et al. (1998).

  27. Other types of private insurance such as (the surrender value from) classical life insurance without LTC rider or critical illness insurance (for diseases that might result in LTC) might be interpreted as a third category of substitutes alongside public insurance and the family.

  28. Cramer and Jensen (2006) note that the demand for coverage is price-inelastic; a USD 1,000 decrease in the annual premium would cause an increase of only 0.01 (on a scale of 0 to 1) in the probability of an individual purchasing LTCI. It thus seems that premium subsidies or intense price competition may not stimulate the demand for insurance. Overall, it is not fully understood under which conditions premium subsidies or tax incentives are an effective and efficient tool for promoting LTCI demand.

  29. Kumar et al. (1995) state that as the probability of the LTC need increases, the utility value of purchasing fair (or constant load) insurance to meet that need falls, and self-insurance becomes more attractive. An additional explanation is the age variation in prices.

  30. Wang et al. (2018) also mention that higher demand in younger cohorts in China may be due to the strict one-child policy that was in effect at the time of their birth. They may feel a greater need for LTC coverage than their elders.

  31. Ambiguous results with respect to age are also documented in other insurance markets. These results, however, may reflect the U-shaped relationship as identified in Cohen and Einav (2007) and Halek and Eisenhauer (2001). Similar tests seem warranted in the LTCI market.

  32. While Courbage and Zweifel (2011) theoretically predict that more parental wealth and a higher level of expected inheritance induce intergenerational moral hazard, with the net effect leading to the purchase of less LTCI coverage, Xu and Zweifel (2014) argue that the lack of such predictions in China may be interpreted as the traditional Chinese view of the importance of filial piety, which means children do more to support their ailing parents, no matter how much LTC coverage their parents have.

  33. The insurability model is defined from an insurance carrier point of view and analyses the risks at the aggregate portfolio level (not individually). It maps out the major factors that should be taken into account by practitioners (for product design) or policymakers (for institutional framework) to improve insurability. Although leading to some overlap in the demand factors, we believe that the consideration of both the Outreville (2013) insurance demand framework and the Berliner (1982) insurability criteria provides added value because they highlight the fundamental points from two different perspectives.

  34. An example is that future dementia might be overestimated just by connecting the number of elderly people in the future with today’s dementia occurrences and neglecting changes in dementia occurrences.

  35. Crimmins and Beltrán-Sánchez (2011).

  36. Stallard (2016).

  37. Crimmins and Beltrán-Sánchez (2011).

  38. Brewster and Gutterman (2014).

  39. In a personal conversation, Christian Mumenthaler, CEO of Swiss Re, called LTC ‘science fiction’ insurance, because insurance companies have no idea what LTC will look like in 10 or 20 years. While insurance companies can limit the amount or coverage period, it is unclear whether this amount and time is sufficient.

  40. See Levikson and Mizrahi (1994), Pitacco (1995), and Haberman and Pitacco (1998) for early works.

  41. See Levantesi and Menzietti (2012) and Fong et al. (2015).

  42. Pritchard (2006).

  43. Biessy (2017).

  44. Fuino and Wagner (2018).

  45. The actuarial basis for calculating LTCI premiums is not well developed in many countries (the probability estimates of LTC are more accessible in the U.S. than in European and Asian countries), but is getting better (see, e.g., Fuino and Wagner 2018 for Switzerland). Furthermore, the cost of care that is covered by LTCI is exposed to intertemporal risk. Cutler (1996) argues that inflation affects all members of the insurance pool by gradual increase of the costs of services. Hence, it would be more difficult for an insurance company to pool such an interdependent risk. See Karlsson (2002).

  46. They use data from individuals at risk from Huntington disease (HD) and find that those who carry the HD genetic mutation are up to five times more likely to buy LTC than individuals from the population without that mutation.

  47. 17% of applicants who tested positive changed their LTCI coverage in the year following the test. This rate was 2% for people who tested negative, and 4% for those who did not receive the disclosure of their APOE (Apolipoprotein E). Studies of lapse behaviour in LTCI also discuss dynamic adverse selection, in which the insured may decide to cancel the policy when health improves. See Finkelstein et al. (2005) mentioning this inefficiency in the private LTCI market. Konetzka and Luo (2011) mention factors such as the characteristics of the individuals who cancel their LTC policies. They state that health status plays a small role in the decision to allow a policy to lapse. They also find little evidence of ex post adverse selection based on health status, and conclude that LTCI lapse is usually based on financial problems rather than changes in health risk. Basu (2016) also confirms the low economic conditions of the individuals as the factor affecting the lapse decision, and finds evidence of ex post advantageous selection.

  48. See Pauly (1990), Zweifel and Strüwe (1996), Zweifel (1996) and Zweifel and Strüwe (1998).

  49. See Klimaviciute (2017).

  50. See Hendren (2013) for existence of more private information for those who are rejected by an insurer.

  51. Barr (2010).

  52. Colombo et al. (2011).

  53. Brown and Finkelstein (2007, 2008). Focusing on the demand side, Ameriks et al. (2018) study the under-insurance in late-life risks by designing an idealised insurance product that does not have the defects of the LTCI policies available in the market. They then quantify the demand for their new product based on survey data and compare it with the demand for a normal LTCI product; their model predicts 59% of the sample will want to purchase their policy, while only 22% of the sample already has LTCI. This gap sheds light on unmet demand in the market.

  54. See Finkelstein and McGarry (2006), Webb (2009) and Sloan and Norton (1997).

  55. For example, the development of better data sets is mainly a to-do for practitioners, but if they exist they are an important input for research. At the same time, the development of better models for calculating risk and prices is a primary task for researchers, but if the models exist, they might be well applied in the industry. It is thus difficult, if not impossible, to identify which recommendations specifically address researchers or practitioners.

  56. De la Maisonneuve and Martins (2015) categorise the determinants of LTC expenditure in demographic and non-demographic drivers. In the demographic part, the transformation of the population towards older ages increases the number of old people. However, whether this leads to a higher number of people needing LTC depends on the development of disability rates among older generations (de Meijer et al. 2012). Disability trends seem to vary across countries; while some countries experience a decline in disability prevalence rates in elderly people (e.g., Denmark, Finland, and Italy), others experience stability (such as Australia and Canada) or even growth (Belgium, Japan, and Sweden); see Colombo et al. (2011). The dynamics of disability prevalence rates in different periods and countries is one of the major obstacles for predicting the effect of demography on future LTC expenditure and needs to be better understood. Non-demographic drivers such as the price of LTC (driven for example by productivity and labour costs), increase in demand for formal LTC (decline in availability of informal caregivers) and income effects (rise in real incomes leading to demand for higher quality services) are other factors determining LTC expenditure.

  57. Colombo and Mercier (2012) and Angelis et al. (2017).

  58. See Xu and Zweifel (2014) on the analysis of public expenditure in China, and Courbage and Zweifel (2015) on the effect of means-tested public provision on crowding-out effects of private savings and informal care.

  59. See Pla-Porcel et al. (2016), Ventura-Marco and Vidal-Meliá (2016), Vidal-Meliá et al. (2018) and Pla-Porcel et al. (2017).

  60. See Meier (1996) for the analysis of moving from a private funding system to a social aid regime and from the latter to the compulsory insurance regime, and their effects on LTCI and savings.

  61. Stallard (2016).

  62. Crimmins and Beltrán-Sánchez (2011).

  63. Lakdawalla and Philipson (2002) and Spillman and Lubitz (2000).

  64. See de Meijer et al. (2012) for trends in mild and severe disability; Stearns et al. (2007) and Freedman et al. (2013) on late-life activity limitations.

  65. Bartkowiak (2012) and Blake et al. (2006).

  66. Chen (2016).

  67. Society of Actuaries (2011).

  68. ENEPRI (2012).

  69. According to Argentum (2016), more than 1.2 million employees will be needed by 2025 to provide the required care for the growing ageing population.

  70. While most of these articles are empirical, approximately 106 are purely theoretical.

  71. The indirect effect is captured not only by the citation count but also by traversal weights between the nodes in the citation network.

  72. See Ho et al. (2017).

  73. Local Citation Score (LCS) counts the number of the times the paper is cited within the local network (extracted from HistCite Software). Global Citation Score (GCS) is the number of the times the paper is globally cited (i.e., citation count from all the articles available in Web of Science). We also provide the rank of the paper based on its GCS in our network.

  74. Table 9 also identifies the most important data sets used in the academic literature which are the U.S. National Nursing Home Survey (NNHS) mainly used in the 1990s by five main path studies, the U.S. Health and Retirement Study (HRS) used in six main path studies, and the Survey of Health, Ageing and Retirement in Europe (SHARE) used by three main path studies.

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Appendices

Appendices

Appendix 1: Increasing relevance of long-term care insurance

Figure 4 documents the exponential growth in the academic interest of the topic of LTCI by showing the number of papers (Fig. 4, left) and the number of citations (Fig. 4, right) from Web of Science. We depict the extracted records of total LTCI literature along with the ones related to financing of LTCI for comprehensiveness of our results. The early peaks in the number of citations are 135 citations for the Pauly (1990) article and 67 and 64 citations for Sloan and Norton (1997) and Ikegami (1997). The three papers cited most often are by Van Houtven and Norton (2004), Finkelstein and McGarry (2006) and Ikegami and Campbell (2002) with 192, 187 and 152 citations, respectively.

Fig. 4
figure 4

Number of papers (left) and number of citations (right) in Web of Science

We found 1280 articles based on search criteria of ‘long term care’ and ‘insurance’ from Web of Science and manually filtered out those unrelated to the insurance aspect of LTC. This left us with 591 articles for our citation network. Table 8 illustrates the authors and journals with the most publications in our database of 591 articles.

Table 8 Authors and journals with most publications in the data set

Appendix 2: Main path analysis

Figure 5 illustrates the raw citation network of 591 records exported from Web of Science.Footnote 70 Liu and Lu (2012) identify the following advantages of main path analysis. First, it simplifies the citation network with hundreds of nodes into a smaller number of nodes and links. This gives us a satellite view of the network. Second, it demonstrates the historical evolution of a topic via main contributions in the literature. Third, it shows which papers have attracted the most attention in the historical development of a topic. Although the citation count illustrates the direct effect of the articles on a certain topic, the main path analysis also considers the indirect effects.Footnote 71 Review papers are not included in the path because of the bias they could introduce to the analysis.Footnote 72 Hence, after removing the review papers on the path, we list the top 40 routes based on global main path analysis, as depicted in Fig. 3.

Fig. 5
figure 5

Citation network of the 591 papers

The results show that the most important path is from Brown and Finkelstein (2007) to Finkelstein and McGarry (2006). Their relative Global Citation Scores (GCS) are 85 and 187, Local Citation Scores (LCS) are 64 and 41 and their GCS ranks are 10 and 2.Footnote 73 While there are papers with higher GCS and LCS in our database, the global key-route main path has chosen the above path as the most important in the evolution of knowledge. There are 31 papers on the main path, of which 21 are empirical, five theoretical, and five policy papers.Footnote 74 Table 9 illustrates the main path studies along with a summary of their findings.

Table 9 Main path studies

Appendix 3: Review of future research

In this appendix we present potential future research that we extracted from the articles on the main path (from 2000 onwards) and conclusion part of all the recent publications (106 publications from 2016 to 2018) in our data set. We use this to derive directions for future research in the final section of the paper (Table 10).

Table 10 Potential future directions based on main path and recent studies

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Eling, M., Ghavibazoo, O. Research on long-term care insurance: status quo and directions for future research. Geneva Pap Risk Insur Issues Pract 44, 303–356 (2019). https://doi.org/10.1057/s41288-018-00114-6

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Keywords

  • Long-term care
  • Long-term care insurance
  • Main path analysis
  • Citation network
  • Demand
  • Financing
  • Insurability
  • Literature survey