ArticlePDF Available

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



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.
Geneva Pap Risk Insur Issues Pract
Research onlong‑term care insurance: status quo
anddirections forfuture research
MartinEling1· OmidGhavibazoo1
Received: 4 June 2018 / Accepted: 8 October 2018
© The Geneva Association 2018
Abstract We provide a structured literature review of long-term care (LTC)
insurance using main path analysis, a mathematical tool to identify the most sig-
nificant paths in a citation network. We identify three major research areas (financ-
ing, 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 sub-
stitutes) 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 inte-
grate LTC into the pension systems might help to overcome the insurability limi-
tations. 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 sus-
tainability of LTC financing.
Keywords Long-term care· Long-term care insurance· Main path analysis·
Citation network· Demand· Financing· Insurability· Literature survey
* Martin Eling
Omid Ghavibazoo
1 Institute ofInsurance Economics, University ofSt. Gallen, Girtannerstrasse 6, 9010St.Gallen,
M.Eling, O.Ghavibazoo
Motivation andaim ofthepaper
The organisation and financing of LTC is arguably one of the most important soci-
etal tasks of the twenty-first century. Although significant uncertainties in terms
of potential need, intensity and duration of LTC provide a powerful rationale for
sharing this risk across individuals (Colombo etal. 2011), the market for long-term
care insurance (LTCI) is very limited.1 From the academic side, the low demand
for LTCI remained largely unexplored until the late 1990s. However, the academic
literature on LTC and insurance is now growing exponentially with more than 1200
articles in Web of Science alone (see Appendix 1). While a considerable amount of
research on LTC has been published in medical or gerontological journals, a grow-
ing number of studies are documented in the fields of business and economics, call-
ing for a structured and timely review of this emerging academic field.2
We provide a structured review of LTC in the context of insurance (see Fig.1)
using an identification technique—main path analysis—that to our knowledge has
not yet been used in the insurance field. Based on the review results, we evaluate
the literature using three established frameworks (Chen 2001; Outreville 2013; Ber-
liner 1982) and compare our results with the findings from other insurance markets.
Our review includes a summary of alternative insurance and financing models that
we use to identify innovative insurance and financing solutions. Finally, we offer an
overview of potential research topics from the perspectives of academics and practi-
tioners to encourage future work on this important topic. The focus of the analysis is
on the business and economics literature in the risk and insurance field.
To summarise our main results, there is a combination of ex ante (insurance)
and ex post (public sector, family) funding mechanisms with some variations in
all advanced economies. When it comes to private insurance, high premium load-
ings and limited coverage are needed on the supply side because of uncertainty with
respect to probability, intensity and duration of LTC, and pronounced information
asymmetries. On the demand side, only well-educated middle-income people with
LTC experience and a pessimistic outlook tend to be interested in buying LTCI,
which is a rather small group. Limited knowledge, low value of consumption while
in care, and the availability of public and private substitutes all reduce the demand
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 insur-
ance mechanisms is, however, important in some countries (90% in the Netherlands, 70% in the Czech
2 There are a few previous literature surveys on LTC with a slightly narrower focus. The most compre-
hensive 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 Kli-
maviciute 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 prod-
ucts 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.
Research onlong‑term care insurance: status quo anddirections…
for LTCI.3 Combined products that bundle 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 improve the sustain-
ability of LTC financing.
The remainder of this paper is structured as follows. We begin with a descrip-
tion of our methodology and then present our data and identification results. Subse-
quently, we cluster the literature according to the three research topics. Finally, we
discuss areas of future work from practitioners’ and researchers’ perspectives.
We use main path analysis and text mining to identify the main research topics in
the field of LTCI. Main path analysis is a methodological improvement on classi-
cal citation network analysis. Citation network analysis, introduced by Garfield etal.
(1964), is a non-weighted directional graph representing the idea that knowledge
flows from older to newer articles. Main path analysis, first suggested by Hummon
and Dereian (1989), simplifies the citation network using predefined weighting
indexes and has become state of the art for literature screening in a variety of fields.4
A citation network consists of nodes (articles) and links (arrows) between the
nodes representing the citations between the articles directed towards the cited arti-
cle. We produce our citation network and main path as follows. First, the data is
exported from Web of Science to the HistCite (Garfield 2009)software to produce
a citation network. Then, the resulting file is imported to the Pajek software intro-
duced by Batagelj and Mrvar (1998) for path analysis. Figure5 illustrates our cita-
tion network extracted from Web of Science. To extract the main path from such
a citation network, a weighting index is needed for the links between the nodes.
Hummon and Dereian (1989) and Batagelj (2003) present various weighting indexes
along with a discussion of their pros and cons.5 We follow Batagelj (2003) and use
Search Path Count (SPC) as a weighting index of the links. This index counts the
number of times a link is traversed through certain starting nodes (source nodes) to
end nodes (sinking nodes).
Figure2 illustrates a simple example of citation network based on the SPC index.
Here we follow Ma and Liu (2016), Liu etal. (2016) and Lu and Liu (2016) and
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.
M.Eling, O.Ghavibazoo
choose the global6 key-route main path analysis as our methodology for identify-
ing the most important publications in the citation network. Global key-route main
path finds the most significant link globally and searches forwards and backwards
(until meeting a sinking or source node) to build the path. In the following example,
nodes A and B are source nodes and G, H, I and J are sinking nodes. The global key-
route main paths (the path with the highest traversal count globally) are identified
as A-C-E-G, A-C-E-H, A-C-E-I, B-C-E-G, B-C-E-H and B-C-E-I. The thickness of
the arrow is an indicator of the importance of the research indicating the traversal
For more details on main path analysis, we refer to De Nooy etal. (2011). Build-
ing upon the identified networks, we analyse the underlying research topics with text
Summary of existing knowledge on LTC and insurance
Financing solutions for long-term care
Demand for long-term care insurance
Insurability of long-term care
Derivation of potential future work
(practical perspective)
Derivation of potential future research
(academic perspective)
Data and identification results
List of 591 articles from Web of Science
Three research clusters based on main path analysis and text mining
Identification methods: main path analysis, text mining
Evaluation methods: Chen’s (2001) financing framework; Outreville’s (2013)
insurance demand framework; Berliner’s (1982) insurability framework
Fig. 1 Conceptual framework and research approach
6 We choose global rather than local search to connect the links in a way that delivers the largest tra-
versal counts (see Liu and Lu (2012) for further explanation of this idea and a discussion of different
Fig. 2 A simple citation network with SPC index
Research onlong‑term care insurance: status quo anddirections…
mining. We use the R package ‘tm’ (Feinerer and Hornik 2012)to identify the most
frequently used words in titles, keywords and abstracts.
We then rely on three widely used frameworks to systematically evaluate the
main results in the identified research fields (see Table1). We begin by using Chen’s
(2001) financing framework to categorise standard LTC financing approaches and
to identify innovative financing solutions that do not fit into the standard categories.
Then, we follow Outreville (2013) and categorise insurance demand in four catego-
ries.7 Finally, we address the insurability of LTC risk based on the Berliner (1982)
insurability framework.8
Data andidentification results
To ensure scientific quality, we limit our review to articles included in the Thomson
Reuters Web of Science.9 The Web of Science covers 18,000 journals in all academic
disciplines. We conducted our search on March 8, 2018 using ‘long term care’ AND
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.
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.
Table 1 Evaluation frameworks
a Outreville (2013) and Berliner (1982) do not differentiate between subsidised price at point of utilisation
or tax price, although they should. We refer to studies on the effects of changes in price on the demand
for LTCI and we do not further differentiate between different price concepts
Chen (2001) financing framework Outreville (2013) demand frame-
Berliner (1982) insurability
Public sector: general governmen-
tal (excl. social security)
Economic factors:
Bequest motives
Actuarial criteria:
Randomness of loss occurrence
Maximum possible loss
Average loss per event
Loss exposure
Information asymmetry
Public sector: social security
Intergenerational model
Intra-generational model
Social and cultural factors:
Education and knowledge
Preferences, experience and
Market criteria:
Insurance premium
Cover limits
Private sector: private LTC
Stand-alone LTC policy
Combined policy
Structural factors:
Substitute for insurance
Tax incentives
Social criteria:
Public policy
Legal restrictions
Private sector: out-of-pocket Personal and demographic fac-
Age and gender
Marital status and number of
M.Eling, O.Ghavibazoo
‘insurance’ as keywords; publications from 1984 to February 2018 were retrieved
by the search engine for further analysis, leading to 1280 articles with ‘long term
care’ and ‘insurance’ in their title, abstract or keywords. Due to overlap with medi-
cal, gerontology and other journals irrelevant to our topic, we did not exclude these
journals ex ante, because some articles relevant to economics are included in those
journals (e.g., Cramer and Jensen 2006). Hence, to be more comprehensive in our
selection, we examined each paper individually and included all studies on LTC and
LTCI from a business, finance and economics point of view, resulting in 591 articles
used for further analyses.10
The main path based on the 591 articles is shown in Fig.3 and can be separated
into three categories: insurability, demand and country-specific studies.11 Early stud-
ies, beginning with Wiener etal. (1987), focus on restrictions, conditions and insur-
ability of LTC. The group of studies beginning with Pauly (1990) deal with demand
and market inefficiencies for LTCI, with a peak of research activity after 2000,
especially with the contributions from Finkelstein and McGarry (2006) and Brown
and Finkelstein (2007, 2008). Among the most recent works on the main path are
country-specific studies and regional comparisons of LTC mechanisms (e.g., Doty
etal. 2015; Nadash and Cuellar 2017). Text mining within the 591 articles (Table2)
also lets us identify five clusters: ‘demand’, ‘financing’, ‘country-specific studies’,
‘demography’ and ‘insurability’. In the section ‘Evaluation of results’ we focus on
demand and insurability, included in both the main path and text mining. As our
overarching framework we take financing as the third research focus; demographic
questions are not the focus of our research. Appendix 2 contains a review of coun-
try-specific studies and complete documentation of the results.
Evaluation ofresults
Financing oflong‑term care
Table3 illustrates sources of financing LTC with their pros and cons as discussed
in academic papers. We also present the relative importance of the different sources
in terms of funding, emphasising the marginal role of insurance today. Our model
of classifying LTC financing schemes follows Chen (2001)12 who proposed the
three-legged funding model for the U.S., consisting of social insurance, private
insurance and personal savings. Mandatory social insurance with basic coverage
serves as a safety net. Other voluntary coverages can supplement the base coverage
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).
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.
Research onlong‑term care insurance: status quo anddirections…
with funding from private sources. Chen (2001) suggests using trade-off principles
to compensate for the inefficiencies in both private and public LTC funding. To
improve the attractiveness of the private LTC products, he proposes combined (or
hybrid) policies. Some of them are addressed comprehensively in the literature, such
as life insurance or annuities with LTC riders (e.g., Kyle 2013). Combined products
attempt to increase the use of LTCI, especially by addressing adverse selection. In
Fig. 3 Main path based on 591
M.Eling, O.Ghavibazoo
the public sector, Chen (2001) suggests collecting funds by employment-based con-
tributions so that the majority of the population contributes to the financing and thus
ensures the sustainability of the safety net.
Both ex ante and ex post funding mechanisms, with some variations, exist in all
advanced economies. A system that relies exclusively on private insurance is not
likely to work, because fluctuating income and unemployment will endanger its
financial sustainability (Rothgang and Engelke 2009). However, by comparing com-
prehensive LTC financing schemes of social insurance such as those in Japan or Ger-
many, Campbell etal. (2010) argue that reining in moral hazard effects (as done by
private insurers) is also necessary for sustainability. There have also been diverging
trends such as an increase in public coverage of LTC expenditure in France, Japan,
Spain and Korea, but a decrease in relative terms in Germany, Sweden and the Neth-
erlands.13 Overall, it is empirically unclear what the optimal mix of financing should
look like. It is also likely that there is not one optimal model; the best model might
depend on economic, social, cultural and demographic factors. One simple example
is that a pay-as-you-go financing scheme will work well in countries with growing
populations and economies but not so well in countries whose economies are stag-
nating or even declining.
The private insurance market is small but growing. The number of insured in the
U.S. rose from 1.7 million in 1992 to 7.2 million in 2014 (NAIC and The Center for
Insurance Policy Research 2016, p. 9). In Germany, the number of private insurance
Table 2 Text mining results Research area/cluster Keywords (Frequency)
1. Demand Market (214)
Demand (159)
Purchase (94)
2. Financing Financing (194)
Income (153)
Financial (125)
3. Country-specific studies Japan (171)
Germany (112)
Korea (70)
China (59)
United States (41)
4. Demography Population (212)
Age (202)
Children (56)
Demographic (53)
5. Insurability Benefits (167)
Coverage (164)
Premium (68)
Moral hazard (35)
Underwriting (35)
Adverse selection (34)
13 Colombo etal. (2011, p. 79).
Research onlong‑term care insurance: status quo anddirections…
Table 3 Financing LTC expenditure
a Average percentage of funding based on Colombo etal. (2011) comparing LTC financing in 23 countries.
The remaining 1.3% to complete 100% are other sources mentioned in Colombo etal. (2011). The funding
from private insurance ranges from 0% (e.g., the Netherlands) to 9.8% (Belgium) with an average of 0.9%
b Feder etal. (2000)
c Ikegami and Campbell (2002)
d Glendinning etal. (2004)
e Kitao (2015)
f Yang etal. (2016). It can be considered as both an advantage and disadvantage because a regular politi-
cal discussion is necessary
g Barr (2010). While democratic legitimacy of restrictions on coverage is mentioned as an advantage of social
insurance, Rothgang (2010) mentions lack of adjustment (or late adjustment) of LTCI benefits as a weakness
which may result in increasing copayments and encouraging delegitimisation of the insurance system
h Rothgang (2010)
i Yang etal. (2016)
j Frank etal. (2013)
k Comas-Herrera etal. (2012)
l Brown and Finkelstein (2007)
m Rothgang and Engelke (2009) and Konetzka and Luo (2011)
n Murtaugh etal. (2001)
o Weston (2012)
Source of funding Pros (+) and cons (−) discussed in the literature Empirical
(e.g., U.S.
+: Provides a safety net for those who are in needb. Income and wealth
are taken into account (solidarity between rich and poor)c. Inclusion
of entire population.d Immediate benefits possible (from day one)
−: Labour income taxes may discourage work and decrease
savings and consumption (negative purchasing power effects).e
Vulnerable to government budget constraints;f reduced incen-
tive to take care of the costs
tional model
(pay as you
+: Mandatory enrolment avoids adverse selection problems. Pro-
vides coverage for risks that are uninsurable in private insurance.
Can adjust to changing circumstances. Democratic legitimacy of
restrictions on coverage, e.g., extending or tightening the eligibil-
ity based on medical advancementsg
−: Intergenerational models ensure solidarity between genera-
tions but depend on demographics.h If limited to employees,
non-employed have no coverage.i For intra-generational models
immediate benefits are not possible, but models do not depend
on demographics and diversification of risk
tional model
LTC policy
+: Covers individuals not eligible for public LTC; offers coverage
expansion on top of public system; heritage protection; insurers
have an incentive to manage costs
−: Information asymmetry.j Costly medical tests and exclusion of
high risks (e.g., older or chronically ill people).k Not affordable
for many people because of high premium loadings.l Lapse risk
when income/liquidity variesm
+: Combining LTC with annuities might mitigate adverse selec-
tion and lower administrative costs.n Annuities mitigate the risk
of illiquidity during disability.o Use of available equity (e.g.,
reverse mortgage). Expanding coverage to population which is
excluded from stand-alone LTCI
−: Relatively complex products
+: Prevents moral hazard and increases individual responsibility 13.5%
M.Eling, O.Ghavibazoo
policies for LTC increased by 65% to 3.5 million from 2012 to 2017 (Brüss 2018;
Nadash and Cuellar 2017). The low demand for stand-alone LTC policies has also
motivated insurance companies to design combined products that bundle LTC with
other risks. Getzen (1988) is one of the early researchers discussing long-life insur-
ance to combine annuities, health insurance and LTC. Bundling different risks not
only has the advantage of cost-effectiveness compared to separate purchases14 but
also addresses the insurability problems caused by asymmetric information. For
example, by purchasing combined LTCI products that cover both the LTC risks and
longevity risk, the problem of adverse selection can be mediated, and strict medical
exclusions might be reduced.15
Panel A of Table4 illustrates alternative LTCI policies. The products can be dis-
tinguished by (1) purchase time (at younger ages, during retirement, or at the time
of LTC need), (2) timing of premium payment (at younger ages, during retirement,
at the time of LTC need, or after death using whole life insurance or other assets
such as real estate, collateral) and (3) coverage (complete or only part, in cash or
in kind).16 One of the latest innovations discussed in the literature is the variable
life care annuity with guaranteed lifetime withdrawal benefits,17 providing protec-
tion against shortfalls of income through guaranteed income streams and including
an LTC rider.
Panel B of Table4 depicts some alternative financing methods that go beyond
the idea of risk pooling provided by insurance. The equity release uses the value of
a house to finance LTC expenditure, but no risk pooling is done. The LTC bond is a
pure savings product that is inheritable in case of non-use for LTC services, and the
consortium18 issues the right to future LTC services in exchange for payments today.
Demand forlong‑term care insurance
Table 5 categorises the research findings on LTCI demand using the Outreville
(2013) demand framework in four major groups—economic, social and cultural,
structural and demographic factors—for a total of 20 factors.19 Twelve of the 20
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 demo-
graphic 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).
Research onlong‑term care insurance: status quo anddirections…
Table 4 Alternative insurance and financing models
Category Definition
Panel A: Insurance models
Annuity Life care annuity Combination of a deferreda or immediateb annuity with LTC disability coverage at retirement; pays retirement
income along with increased benefit; the amount of annuity may be set according to the level of frailty.c The
annuity can be paid when the insured is in need of LTC
Enhanced annuity Targets people who are entering or are already in a nursing home. Based on higher mortality assumptions and pay-
ment of single premium, the coverage offers an enhancement in the amount of annuityd
Enhanced pension Specific model of life care annuity offered at the time of retirement. It provides an increased annuity payment if
LTC is needed; the premiums are collected by reduced annuity payments while the retired person is still healthye
Variable life care annuity with guaranteed
lifetime withdrawal benefits (LCA-
Combination of LTCI and a variable annuity with guaranteed lifetime withdrawal benefits. The guaranteed income
component protects the insured against downside risk, and the LTC component protects against LTC expensesf
Life insurance Accelerated life insurancegWhole life insurance with monthly benefits paid to cover LTC expenses up to deathh
Life insurance with LTC rider Pays the actual cost of LTC services up to a limit or a fixed indemnity charge. It is offered to people near their
retirement age. It can be structured either as a reduced benefit amount from the cash value of the life insurance
part of the policy or as an additional benefit requiring a single premium. Universal life policies may be a good
option for this benefiti
Lifestage LTC product Term life insurance up to specific retirement age and change of cover to LTCI afterwards with the same premium
and coverage amountj
Disability insurance Combined disability coverage A disability insurance policy that protects the insured against the shortfall in income caused by disability. The
coverage may give the option to convert the disability insurance policy to LTC policy after retirement without the
need for medical underwritingk
Combination of annuities
and health insurance
Long life insurance Covers costs of chronic illness (i.e., any cost related to nursing care in any facility or any medically necessary home
health care) during retirement based on specific daily benefits for each of the coverages. In addition, it provides
monthly payments after the age of 76, regardless of the health status of the insured. The premium is collected
based on an initial lump sum paid at the beginning of retirement along with monthly premiums up to the end of
long life contractl
Health insurance Whole life health insurancemCombination of permanent health insurance with LTC coverage, covering ‘own or similar occupation’ disability
before retirement and LTC annuity paid based on frailty
Panel B: Financing models
LTC bonds Personal care savings bondsnRegular bond which is liquidated in the event of LTC or death, but a small proportion of its value will be used for
funding periodic cash prizes
M.Eling, O.Ghavibazoo
Table 4 (continued)
Category Definition
LTC put option Consortium Consortium of LTC providers issues securities to the public. They are put options, and their proceeds are to be used
for financing LTC services
Equity release Reverse mortgageoHomeowner receives LTC coverage by taking out a loan (a lump sum or annuity payment) and has lifelong access
to their home; the payments for the loan will be collected after the death of the insured or after selling the equityp
Home reversion planqSelling (partly or in total) the equity in exchange for a lump sum based on private arrangements. The homeowner
has the right to stay in the property as long as they live or until they move out
a Webb (2009)
b Murtaugh etal. (2001) and Spillman etal. (2003)
c Pitacco (1999)
d Ibid
e Ibid
f Hsieh etal. (2017)
g Freiman (2007), Mayhew etal. (2010) and Pitacco (1999): LTC cover as a rider benefit; that is the same concept as equity release (with life insurance as underlying
h Pitacco (1999) and Spillman etal. (2003)
i Weston (2012)
j Own Your Future Minnesota (2015)
k Freiman (2007)
l Getzen (1988)
m Pitacco (1999) and Mayhew etal. (2010)
n Mayhew and Smith (2014) argue that the cash prizes offered by PCSBs are similar to lottery tickets which are more attractive to low-income people
o Ahlstrom etal. (2004)
p The idea is mentioned in earlier studies by Sawyer (1996) and Rasmussen etal. (1997). Mayhew etal. (2017) introduce similar products for the U.K. market called
‘equity-for-insurance’ and ‘equity bank’ products. The former product collects insurance premiums in exchange for the whole or a percentage of the equity value after the
death of the homeowner. The latter provides the homeowner or their dependents with income or annuity during their remaining lifetime, and the debt will be paid after the
death of the homeowner
q See Alai etal. (2014) and Hanewald etal. (2016)
Research onlong‑term care insurance: status quo anddirections…
Table 5 Main factors affecting LTCI demand
Category Subcategory Results with respect to LTCI demand
Economic Factors Price −: Cohen etal. (1992), Cramer and Jensen (2006), Schaber and Stum (2007)a, Brown etal. (2012), and Wang etal. (2018)
Income +: Kumar etal. (1995), McCall etal. (1998), Mellor (2001), Cramer and Jensen (2006), Schaber and Stum (2007), Brau and Bruni (2008), Wang
etal. (2018), and Courbage and Roudaut (2008): non-linear bell-shaped
Wealthb+: McCall etal. (1998), Mellor (2000, 2001), and Chatterjee and Fan (2017)
0: Kumar etal. (1995)
−: Costa-Font and Rovira-Forns (2008): owning house or flat; Davidoff (2008, 2010): home equity.
Bequest motives +: Pauly (1990)c, Cramer and Jensen (2006), Courbage and Roudaut (2008, 2011), Brown etal. (2012), Chatterjee and Fan (2017)
0: Sloan and Norton (1997), Lin and Prince (2016), and Sperber etal. (2017)
−: Zweifel and Strüwe (1996, 1998), Eisen and Sloan (1996), Courbage and Zweifel (2011), Zweifel and Courbage (2016), and Lockwood (2018)
Social and Cultural
Education +: Kumar etal. (1995), Sloan and Norton (1997), McCall etal. (1998), Mellor (2000, 2001), Cramer and Jensen (2006), Chatterjee and Fan
(2017), and Wang etal. (2018)
0: Costa-Font and Rovira-Forns (2008)
Knowledge +: Cohen etal. (1992): lack of infor mation; Schaber and Stum (2007), and Zhou-Richter etal. (2010): adult children awareness; Gottlieb and
Mitchell (2015)d: use of many information sources; Lin and Prince (2016): awareness of Partnership programme and financial literacy;
McGarry etal. (2016): numerical literacy
Experience +: McCall etal. (1998), Coe etal. (2015): having family member or friend in need of LTC; Courbage and Roudaut (2008, 2011): experience of
disability; Finkelstein etal. (2012), Tennyson and Yang (2014): previous caregiving experience
0: Cramer and Jensen (2006): experience of individual’s own parents
−: Kumar etal. (1995): previous caregiving experience
Beliefs +:Kumar etal. (1995): increased expectation of LTC cost; Sloan and Norton (1997)e: probability of being in a nursing home; McCall etal. (1998):
belief of having no caregiver and independence from government in LTC expenditure; Brown etal. (
2012) and Chatterjee and Fan (2017): need
for care in future; Sperber etal. (2017): belief of autonomy; Finkelstein and McGarry (2006): belief about the likelihood of entering a nursing
home; Pincus etal. (2017): promoting emotional frame; Schaber and Stum (2007): belief of higher risk
State-dependent utility −: Brown etal. (2012) and Brown and Finkelstein (2009)
Intra-family moral
0: Mellor (2001)
−: Pauly (1990), Zweifel and Strüwe (1996, 1998), Zweifel (1996), and Sloan and Norton (1997)
Current health status +: McCall etal. (1998), Cramer and Jensen (2006), and Courbage and Roudaut (2008, 2011): having high risk factors of dependencyf
0: Mellor (2001)
−: Schaber and Stum (2007) and Costa-Font and Rovira-Forns (2008)
Risk aversion 0: Finkelstein and McGarry (2006) and Costa-Font and Rovira-Forns (2008)
Trust in insurers +: Finkelstein and McGarry (2006): strong taste for insurance; Brown etal. (2012)g: trust in insurers; Chatterjee and Fan (2017): preference for
risk management through other types of insurance
M.Eling, O.Ghavibazoo
Table 5 (continued)
Category Subcategory Results with respect to LTCI demand
Structural Factors Substitutes for LTCI:
family resources
+: Courbage and Roudaut (2008)
0: Mellor (2001)
−: Brown etal. (2012) and Costa-Font and Courbage (2015): financial support by family.
Substitutes for LTCI:
public insurance
0: Brown etal. (2012); Costa-Font and Courbage (2015): expectation of public payment for insurance
−: Sloan and Norton (1997)h, Brown etal. (2007), Brown and Finkelstein (2008), and Zweifel and Courbage (2016)
Tax Incentives +: Cramer and Jensen (2006)
0: Courtemanche and He (2009), Goda (2011), Nixon (2014), and Lin and Prince (2013): Partnership in Medicaid
Age +: Schaber and Stum (2007) and Costa-Font and Rovira-Forns (2008): higher for middle age
0: Chatterjee and Fan (2017)
−: Kumar etal. (1995) and Wang etal. (2018)
Gender (women) +: Kumar etal. (1995) and Chatterjee and Fan (2017)
0: Costa-Font and Rovira-Forns (2008)
Marital status (mar-
+: Kumar etal. (1995), Courbage and Roudaut (2008, 2011), and Brown and Finkelstein (2009)
0: Sloan and Norton (1997) and Cramer and Jensen (2006): results vary depending on different range of assetsi
Children +: Courbage and Roudaut (2008, 2011)
0: Sloan and Norton (1997) and Costa-Font and Rovira-Forns (2008): household size
−: Cramer and Jensen (2006) and Schaber and Stum (2007): family size
+ (−;0) denotes a positive (negative, insignificant) relationship between the change in a factor and LTCI demand
a They study the effects of group LTCI enrolment decision (and affordability of LTCI instead of price)
b Various categories of wealth are considered here such as net worth, asset, and net asset
c Pauly (1990) argues that individuals protect the bequest by buying insurance, such as LTCI and life insurance, and he believes the demand for LTCI would be higher
among those who purchase (term) life insurance. However, Meier (1998) does not find any systematic correlation between demand for life insurance and the demand for
d Gottlieb and Mitchell (2015) define ‘narrow framing’ as people’s tendency to make decisions in isolation
e They find self-evaluated probability of being in a nursing home in 5years is significant. However, for a time frame of 10–15 years it is not
f These factors are defined as smoking, drinking alcohol and critical level of body mass index
g They find that lack of trust in insurers negatively affects the demand for LTCI
h For people over the age of 70 the correlation is negative, but for the cohort aged 51-64 it is not
i Married people with assets in the middle range have a positive correlation, otherwise negative
Research onlong‑term care insurance: status quo anddirections…
factors are clear in their prediction and consistent with observations from other
insurance markets (see Outreville 2013; Eling etal. 2014). More income,20 educa-
tion, knowledge, experience,21 beliefs about worse future conditions, whether eco-
nomical or social, trust in insurance and providers and being a woman22 all increase
demand for LTCI.23 An increase in price, substitutes for LTC (via public insurance),
intra-family moral hazard and state-dependent utility negatively impact the demand
for LTCI. Finally, risk aversion has an insignificant effect on demand.24
The other eight factors are inconclusive and sometimes even contradictory in
their predictions. While higher wealth generally leads to more demand for LTCI,
some studies report that home equity reduces the demand for LTCI.25 This result is
also linked to the inconclusive results for bequest motives. Lockwood (2018) argues
that inconsistencies in retirees’ saving and insurance choices match with models in
which bequests are considered luxury goods compared to standard life-cycle mod-
els where people care only about their own consumption. He argues that bequest
motives, by reducing the opportunity cost of precautionary efforts, decrease the
value of insurance and might therefore explain a lower demand for late-life risks.
Moreover, financial family support has a negative effect on the demand for LTCI.
Unobserved bad health status leading to more demand for LTCI is one of the fun-
damental information asymmetries in this market. A positive effect of health status
documented in some studies might be due to potential empirical bias that LTCI poli-
cyholders are already filtered by exclusions from the insurers’ underwriting process.26
Courbage and Roudaut (2008) have addressed substitutes such as informal care from
family. In their study of the French population, they cite altruism as a reason for the
positive effect of informal care and the demand for LTCI. However, Mellor (2001)
shows that caregiver availability does not discourage parents from buying LTCI.27
Within the category of structural factors, it is also not clear whether tax incentives
have a positive or insignificant impact on LTCI demand.28
20 Courbage and Roudaut (2008) illustrate a non-linear bell-shaped effect of income on demand, empha-
sising 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 peo-
ple 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
M.Eling, O.Ghavibazoo
Costa-Font and Rovira-Forns (2008) report that middle-aged people have more
demand for LTCI than other age cohorts. Chatterjee and Fan (2017) find an insig-
nificant effect of age on LTCI demand. They argue that while many studies find
demand increases with age, the insignificant effect may be due to the offsetting
effect of health and perceived risk along with increase in premiums based on age.
Moreover, Kumar etal. (1995) and Wang etal. (2018) find that the demand for LTCI
decreases with age, perhaps because of the reduced expected value of coverage.29 A
more detailed study of potential non-linear and interaction effects might be needed
to better explain the ambiguous results of age on LTCI demand.30,31
The effects of marital status and number of children are also mixed. While Cour-
bage and Roudaut (2008, 2011) explain the positive relationship between being
married and having children and LTCI demand by altruistic behaviour, Cramer
and Jensen (2006) argue the opposite: that the negative effect of having children
on LTCI demand may be attributable to expectations on the part of parents for their
children to provide informal care as a substitute for formal care. Courbage and
Zweifel (2011) theoretically prove the existence of a two-sided (bilateral) intergen-
erational moral hazard in which purchase of LTCI by parents decreases the child’s
incentive to provide informal care, and in the meantime, parents purchase less LTC
coverage, expecting their child’s efforts to keep them out of a nursing home. In a
survey conducted in China, Xu and Zweifel (2014) find a similar two-sided intergen-
erational moral hazard on respondents’ statements. However, they do not confirm
the evidence of the effects of exogenous changes, such as more parental wealth and
higher expected inheritance for the child on intergenerational moral hazard.32 Over-
all, it seems that many of the inconclusive and contradictory results are explained by
interactions among economic, social and demographic factors. More research might
thus be needed to untangle those interaction effects in order to derive cleaner predic-
tions for those factors.
29 Kumar et al. (1995) state that as the probability of the LTC need increases, the utility value of pur-
chasing fair (or constant load) insurance to meet that need falls, and self-insurance becomes more attrac-
tive. 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.
Footnote 28 (continued)
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.
Research onlong‑term care insurance: status quo anddirections…
Insurability oflong‑term care
Applying Berliner’s (1982) insurability criteria to LTC risk, we now systematically
analyse the problematic features of LTC risk and identify the major impediments to
the development of the LTCI market (see Table6).33 We identify and discuss four
problem areas.
Randomness ofloss occurrence
There are two criteria for analysing randomness: independence and predictability.
The need for formal LTC depends on many factors such as lifespan (with and with-
out disability) and presence of family support (Kessler 2008), which do not seem
critical to independence. Barr (2010) argues that LTC probability may not be inde-
pendent, but rather interdependent. Because of technological progress and increas-
ing life expectancy, there might be an upward trend in LTC probability due to
multi-morbidity and dementia in later life. For instance, medical advancements have
improved addressing cardiovascular disease, which will increase the life expectancy
of elders who are now more prone to dementia. However, this effect is not trivial,
because connecting future life expectancy and historical morbidity data neglects the
finding that the morbidity of the future population might also be different.34 While
there are difficulties in describing morbidity and variations in the population mor-
bidity,35 some empirical studies conclude that there has been morbidity compression
in recent years,36 while others do not.37 Moreover, conditions such as random fluc-
tuations, lack of complete knowledge of relevant probabilities, longtime dimensions
and the dynamic nature of the environment affecting LTCI make LTC predictions
difficult.38 Not only are multi-state models more difficult to estimate and calibrate
than single-state models (both conceptually and empirically), but there is a limited
understanding of how the future of LTC might look.39
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.
M.Eling, O.Ghavibazoo
Table 6 Assessment of insurability for LTC risk
a Kyle (2013)
b Barr (2010)
c Comas-Herrera etal. (2012)
d Payne etal. (2007) study the impact of ageing on health care expenditure and find that the duration and severity of morbidity increase with age. However, Schut and van
den Berg (2010) address the healthy ageing process that leads to longevity gains for the population. He states that the occurrence of this process is mixed. In addition, Stal-
lard (2016) discusses comprehensively the compression of morbidity and mortality period based on empirical data
e Cutler (1996) illustrates this issue by describing indemnity payments that are fixed in amount (daily benefit) and time, comparing them to the real expenses of LTC ser-
vices, which may exceed the daily benefits
f Ibid. The risks associated with long-term insurance policies arising from decreasing accuracy of predictions
Insurability criteria Main findings Assessment
Actuarial (1) Randomness of loss occurrence Losses are independent (but may be interdependent)
Long time dimension of the risk along with the dynamic nature of the environment affecting LTCI makes predictability difficult
Actuarial basis for calculating LTCI premiums is not yet well developed in many countries
Intertemporal risks such as inflation affecting cost of care cannot be diversified between age cohorts
(2) Maximum possible loss Possible portfolio loss may increase because of demographic factors and medical enhancementsa
Insurance companies protect themselves by limits on either payoutb or duration of coveragec
Not problematic
(3) Average loss per event The average loss for LTC may increase due to long-term nature of the risk and increase in medical costs, or decrease due to morbidity
Insurance companies prefer providing indemnity-based benefit rather than service benefits to better calculate the average lossese
Not problematic
(4) Loss exposure Loss exposure may increase through ageing baby boomers
It depends on the structure of population age cohorts
Not problematic
(5) Information asymmetry Existence of both adverse selection and moral hazard
Possible existence of intra-family moral hazard
Insurers exclude the higher risk individuals by using medical scanning at the inception of cover.
Market (6) Insurance premium High loading needed because of uncer tainty with respect to future losses and information asymmetry
Low demand (only educated middle-income class people with experience and a pessimistic outlook are interested)
Demand is rather inelastic with respect to price, indicating that subsidies may not be very effective
(7) Cover limits Intertemporal riskf encourages insurers to put limitation on their coverage
Exclusion of people with pre-existing health conditions
Society (8) Public policy The coverage is consistent with societal values. Not problematic
(9) Legal restrictions Coverage is allowed in all jurisdictions Not problematic
Research onlong‑term care insurance: status quo anddirections…
While many studies use Markov models for pricing LTCI,40 the research has
focused on the future evolution of mortality and disability transition intensities.41
The complex effects of the processes of ageing42 (i.e., disability and cognitive
impairment), death probabilities for both autonomous and disabled people, patho-
logical information and longevity gains in the late twentieth century43 and time
spent in dependency44 illustrate the need for comprehensive models for pricing LTC
risks. More research is thus needed to improve the modelling and empirical basis for
LTCI as well as the drivers of LTC probability, duration and intensity.45
Information asymmetry
Early studies by Cutler (1996) or Chen (2001) cite both adverse selection and moral
hazard as the main insurability limitations. In the meantime, there is clear empirical
evidence for adverse selection. For example, Oster etal. (2010) find strong evidence
of adverse selection in LTCI analysing individuals’ genetic information.46 Zick etal.
(2005) report that adverse selection exists for people who were diagnosed with
Alzheimer’s disease.47 When it comes to moral hazard, Li and Jensen (2011) and
Konetzka etal. (2014) discuss the effect of having LTCI on the likelihood of using
nursing home care. The moral hazard issue in LTC can also occur as an intra-family
moral hazard in which changes in caregivers’ behaviours may affect LTCI demand
and losses.48 Various kinds of LTCI benefits, such as fixed versus proportional, may
affect the kind and the intensity of intra-family moral hazard.49 Insurance companies
43 Biessy (2017).
44 Fuino and Wagner (2018).
45 The actuarial basis for calculating LTCI premiums is not well developed in many countries (the prob-
ability 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 popula-
tion 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 indi-
viduals 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).
41 See Levantesi and Menzietti (2012) and Fong etal. (2015).
42 Pritchard (2006).
40 See Levikson and Mizrahi (1994), Pitacco (1995), and Haberman and Pitacco (1998) for early works.
M.Eling, O.Ghavibazoo
deal with adverse selection by medical screening and exclusions, especially for those
with pre-existing health conditions.50 Combined policies might help to resolve some
of the information problems.
Insurance premium
Kessler (2008) mentions three problems that insurers face in pricing LTCI. The first
is the substantial uncertainty in LTC cost projections, resulting in substantial uncer-
tainty on future loss payments. The second risk is adverse selection, which requires
insurers to set high loadings on the premiums. The third risk is moral hazard. Obvi-
ously, the insurability problems here are the natural consequence of the latter two
aspects discussed above. Brown and Finkelstein (2007) consider the high premium
loadings to be a signal of supply-side market failure. As discussed above (Cramer
and Jensen 2006), subsidies or tax incentives reducing the effective price of LTCI
are very likely not a general solution to the problem.
Cover limit
Insurance companies need limits on daily benefits and coverage period to protect
against uncertainties51 and inefficiencies such as adverse selection and moral haz-
ard.52 These cover limits also interact with factors such as public LTC. In addition,
the tendency to provide an indemnity benefit rather than a service benefit may dis-
courage individuals from purchasing private LTCI, because they are unsure whether
coverage is sufficient.
To conclude, both supply-side and demand-side factors are used to explain the
small market for private LTCI and thus reduced insurability. Among the supply-
side explanations are high loadings on the actuarial fair price and rationed coverage
(Brown and Finkelstein 2007) that result from significant actuarial uncertainties and
asymmetric information. It seems, however, that supply-side constraints alone are
not significant enough to be blamed for the small market size.53 Limited consumer
knowledge or rationality, state-dependent utility (i.e., a low value of consumption
while in care), and the existence of potential substitutes for formal insurance are
important demand-side explanations that reduce the insurability. Potential substi-
tutes can be any informal financial or in-kind insurance provided by families, illiquid
housing equity that may be liquidated to pay for care, or public insurance provided,
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.
50 See Hendren (2013) for existence of more private information for those who are rejected by an insurer.
Research onlong‑term care insurance: status quo anddirections…
for instance through Medicaid in the U.S. Information asymmetries have also been
the centre of attention in many studies.54
Summary anddirections forfuture research
Table7 summarises our results and proposes avenues for further research. The list
of research directions presented in Table7 is based on a structured review of recent
papers and the papers on the main path which is provided in Table10 in Appen-
dix 3. As one might expect for an applied research topic such as LTCI, the recom-
mendations are intended for both academic researchers and practitioners. While the
majority of research topics are addressed to researchers who must produce concep-
tual work and conduct empirical tests, most of the outcomes of such studies have
implications for a practitioner audience.55
New methods and products are needed for financing the rise of LTC expendi-
ture.56 Some studies propose combined policies and other innovative ideas to com-
pensate for inefficiencies such as adverse selection in the private LTCI market.
Recent examples are reverse mortgages, variable life care annuities with guaranteed
lifetime withdrawal benefits, and personal care saving bonds. While social insurance
and tax-based financing may provide universal coverage to all of the population,
in the long run the significant increases in public expenditure might call into ques-
tion the sustainability of these types of funding.57 Moreover, other effects of public
financing of LTCI, such as crowding-out effects on private savings and informal care
should be treated cautiously.58 Recent studies have taken both information asym-
metry and sustainability problems into account, and propose to embed LTCI costs in
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 calculat-
ing 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 demo-
graphic 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 num-
ber 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 experi-
ence 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 pro-
ductivity and labour costs), increase in demand for formal LTC (decline in availability of informal car-
egivers) 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.
M.Eling, O.Ghavibazoo
Table 7 Summary of results and potential future research
a More income, education, knowledge, experience, future beliefs related to future worse condition, trust in insurance and providers and being female increase the demand
for LTCI. An increase in price, substitutes for LTC, intra-family moral hazard and state-dependent utility reduce the demand for LTCI. Risk aversion has an insignificant
effect on demand
b Wealth, bequest motives, health status, substitutes such as informal care from family, tax incentives, age, marital status and number of children are inconclusive or ambig-
Financing Demand Insurability
Panel A: Summary of results
Mix of ex ante (insurance) and ex post (public sector,
family) used in all advanced economies
Contribution of private LTCI is marginal
Combined insurance policies and alternative financ-
ing models might help to increase demand and
improve sustainability
20 factors have been studied empirically with respect
to their impact on LTCI demand
12 factors are relatively clear in their prediction and in
general in line with observations from other insur-
ance marketsa
The other eight factors are inconclusive or
High premium loadings and rationed quantities needed
because of uncertainty with respect to probability,
intensity and duration of LTC and information asym-
Reduced interest also on the demand side (low value of
consumption while in care, existence of public and
private substitutes)
Panel B: Potential future research
Optimal mix of public and private funding Impact of culture on formal and informal care Morbidity data (more data with better quality, more
Interaction with other parts of welfare (e.g. pension
Non-linear and interaction effects, e.g., with respect to
income or age
Better models for calculating risks and prices
Wealth trajectories in retirement/life-cycle models Role of taxes/premium subsidies Alternative Risk Transfers (ART) to cover LTC risk
Non-linear LTCI policies Two-sided altruism Lapse of LTCI
Swap with cash flows depending on the survival of
disabled people
Emotional framing versus rational framing Risk preferences in the context of asymmetric informa-
Research onlong‑term care insurance: status quo anddirections…
the Notional Defined Contribution (NDC) pension schemes as the optimal method
of financing of LTC expenditure.59 Implementation of such proposals may need fur-
ther study on requirements and major obstacles to the transition from other methods
of financing to new ones.60 Blundell etal. (2016) compare retirement wealth trajec-
tories in the U.S. and England. Their study addresses the importance of construct-
ing a life-cycle model that explains different asset trajectories in various countries
during retirement. While they mention the necessary ingredients of a model such as
bequest motives, risk of health expenses and consideration of housing as a distinct
asset, the literature on this field is still immature. Further research is needed, for
example, to describe the determinants of housing wealth release during retirement.
Four categories of factors (economic, social and cultural, structural, demo-
graphic) influence the demand for private LTCI; social and cultural factors are the
most frequently mentioned in the literature. The recent study by Gentili etal. (2017)
gives new insights into cultural differences and their role in the demand for private
LTCI along with public expenditure. They study the cultural differences in different
regions of Switzerland and their effect on optimal coverage and public plans pro-
vided by the government. This study might be extended to other countries to exam-
ine the role of culture on demand for formal and informal care, as well as its effect
on the optimal method of financing LTCI.
Some studies mention non-linear effects of the demand side. Using data from
France, Courbage and Roudaut (2008) find a bell-shaped relationship between
income and the demand for private LTC; in other words, demand is low for low- and
high-income individuals but higher for middle-income individuals. This non-linear
pattern could be studied in a cross-country setup. Next to country effects, interaction
effects (e.g., with respect to age) might be considered in such a setup, for example to
analyse a potential U-shape between age and LTCI demand (Cohen and Einav 2007;
Halek and Eisenhauer 2001).
Brown and Finkelstein (2008) explore the interaction of public and private insur-
ance in the U.S. They estimate the implicit tax for a median-wealth man to be 60%
and for a median-wealth woman to be 75%. Implicit tax is the amount of premium
that should be paid to private LTCI for similar coverage that would have been cov-
ered by Medicaid (meaning that the private policy replaces 60% of benefits other-
wise covered by Medicaid; so the implicit tax is 60%). It would be worth using this
criterion to compare the different tax-based systems of funding. This will improve
our understanding of the relation between implicit tax and the demand for private
LTCI, and the interaction between public and private activities in general. Moreo-
ver, the dynamics of changes in implicit tax due to recent reforms have the potential
for future research. Given that it is in general not clear whether tax incentives have
a positive or insignificant impact on LTCI demand and given that a lot of money
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.
59 See Pla-Porcel et al. (2016), Ventura-Marco and Vidal-Meliá (2016), Vidal-Meliá etal. (2018) and
Pla-Porcel etal. (2017).
M.Eling, O.Ghavibazoo
might be used to finance tax incentives, more empirical studies should be done to
analyse the real demand effect of tax incentives and their efficiency.
Altruistic behaviour between parents and children also deserves further attention.
Cremer etal. (2016b) propose social LTCI utilising subsidies, payroll, and inherit-
ance taxes. Their model might be extended to cases where parents leave no bequest
or bequeath only when in good health. They also propose to study non-linear LTCI
policies where LTC benefits are linked to the level of informal care provided by
children. Cremer etal. (2017) study the role of private and public insurance in an
uncertain family assistance setting. They analyse optimal conditions and properties
of public LTCI in this environment. Their model can be extended in a way that adds
heterogeneity by considering individuals with different wealth and probability of
The traditional models for framing the LTCI need to be improved. Pincus etal.
(2017) show that emotional narrative frameworks can improve willingness to pay
for LTCI compared to statistical evidence (i.e., level of probability). Further experi-
mental research is needed to illustrate the cognitive and emotional drivers underly-
ing the emotional frameworks and the influence of various emotions generated by
communications. Also, the recent discussion on narrow framing and LTCI demand
(Gottlieb and Mitchell 2015) might provide an alternative avenue to understand the
decision-making process of individuals when buying LTCI. Experimental research
might also provide clean tests in a controlled environment, for example, to more
accurately analyse the effects of adverse selection and moral hazard, both of which
are relevant for LTCI, but cannot be separated in most empirical research.
Longevity and morbidity risks have significant effects on LTC expenditure and
insurability. However, the results of previous studies concerning these risks are
inconclusive. As discussed above, some studies conclude that there has been morbid-
ity compression in recent years61 while others do not.62 Other researchers focus on
longevity63 and disability64 trends and their effects on LTC expenditure. These stud-
ies show that longevity, morbidity and disability change not only based on gender and
age differences but also among age cohorts. It would be interesting to derive precise
results on the effect of morbidity trends on LTC expenditure in countries other than
the U.S. Moreover, further studies should be done on the dynamics of mortality and
morbidity changes and the future predictions based on population growth.
Li etal. (2017) propose a new multiple state functional disability model that uses
systematic trend and uncertainty of mortality and disability for transition rates. Their
new model is suitable for pricing LTCI products based on uncertainties of transition
rates. More studies are needed on the comprehensiveness of their model in mor-
bidity compressions or expansions and its effect on LTCI pricing and risk manage-
ment. Pauses or slowdowns in morbidity improvements and their effects should be
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.
Research onlong‑term care insurance: status quo anddirections…
emphasised. There is also a lack of alternative risk transfers (ART) in the market for
LTCI. While there are longevity securities, such as longevity bonds65 and longevity
swaps66 for hedging the longevity exposure in annuity products, the financial market
suffers from ART mechanisms for risks exposed to LTC.
The literature lacks comprehensive studies on possible ex post adverse or advan-
tageous selection in the lapse of LTCI. Friedberg etal. (2017) mention unintended
lapse as the major source of dynamic advantageous selection. Bauer etal. (2017)
find the advantageous selection in both the decision to purchase and the decision to
extend complementary LTCI coverage in Germany. Finkelstein etal. (2005) claim
that the reason for immediate lapse could be a wrong purchase. Other studies have
found a U-shaped lapse rate curve based on the duration of policy purchase.67 More
studies of lapses, the lapse rate curve, income shocks on lapse decision and the ex
post effects of the lapse on LTCI portfolios are needed for better understanding,
optimal design and improved risk management of LTCI policies. Also, the general
information asymmetries inherent in the LTCI market need attention. Finkelstein
and McGarry (2006) argue that heterogeneity of private information exists not only
in risk types but also in risk preferences. They encourage further study of the effects
of heterogeneity in risk preferences on the costs of asymmetric information about
risk types.
The literature on LTCI thus still lacks empirical and theoretical research on
numerous emerging areas. While more than a decade has passed since the introduc-
tion of combined LTCI products, more empirical research is needed to analyse their
(in)efficiencies in terms of demand and supply. This could not only help practition-
ers to improve current LTCI products but also provide better insights for modelling
the new ones. Moreover, there are a few theoretical observations that might need
better empirical verification. Among them are crowding-out effects of various public
provisions (as addressed e.g. by Courbage and Zweifel 2015), non-linear LTCI poli-
cies and separation of LTCI expenditure by quantity and inflation.
This paper focuses on alternative financing and risk management models, includ-
ing insurance. Such a study should, however, not overlook other means of improv-
ing the sustainability of the LTC system, which might come from areas outside of
financing. When it comes to the organisation of LTC, the introduction of new tech-
nologies to LTC can affect the cost development in this sector. Information, com-
munication and technology can enhance communication among the actors such as
citizens, informal and professional caregivers who provide LTC services.68 The U.S.
Center for Technology and Aging (2009) mentions seven focal areas of technology
to help older adults remain independent and to make LTC more cost-efficient: (1)
medication optimisation, (2) remote patient monitoring, (3) assistive technologies,
(4) remote training and supervision, (5) disease management, (6) cognitive fitness
and assessment technologies, and (7) social networking. New technologies, such as
65 Bartkowiak (2012) and Blake etal. (2006).
66 Chen (2016).
67 Society of Actuaries (2011).
68 ENEPRI (2012).
M.Eling, O.Ghavibazoo
remote monitoring and virtual care, might significantly lower LTC expenditure, for
example, by reducing the amount of labour needed in care provider facilities.69 This
may contribute to a lowering of the premiums for existing products as well as facili-
tating the design of new ones.
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
Fig. 4 Number of papers (left) and number of citations (right) in Web of Science
Table 8 Authors and journals with most publications in the data set
No. Author Records Journal Records
1 Costa-Font, J. 20 Gerontologist 51
2 Courbage, C. 13 Health Affairs 21
3 Pestieau, P. 13 Health Policy 21
4 Finkelstein, A. 8 The Geneva Papers on Risk and
Insurance—Issues and Practice
5 Ikegami, N. 8 Journal of Risk and Insurance 15
6 Nadash, P. 8 Health Economics 14
7 Wiener, J.M. 7 Inquiry: The Journal of Health
Care Organization Provision and
8 Brown, J.R. 7 Journal of Health Economics 12
9 Cohen, M.A. 7 Social Policy Administration 11
10 Cremer, H. 7 Gesundheitswesen 9
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.
Research onlong‑term care insurance: status quo anddirections…
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.
We found 1280 articles based on search criteria of ‘long term care’ and ‘insur-
ance’ from Web of Science and manually filtered out those unrelated to the insur-
ance 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
Appendix2: Main path analysis
Figure5 illustrates the raw citation network of 591 records exported from Web of
Science.70 Liu and Lu (2012) identify the following advantages of main path analy-
sis. 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 litera-
ture. Third, it shows which papers have attracted the most attention in the histori-
cal 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.71 Review papers are not included in the path because of the bias they could
70 While most of these articles are empirical, approximately 106 are purely theoretical.
Fig. 5 Citation network of the 591 papers
71 The indirect effect is captured not only by the citation count but also by traversal weights between the
nodes in the citation network.
M.Eling, O.Ghavibazoo
Table 9 Main path studies
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Insurability and policy conditions
Wiener etal. (1987) Empirical Evaluate affordability,
coverage amounts and
restrictions of available
Find that LTCI policies
are expensive and lim-
ited based on eligibility
Recommend inflation
adjustment of benefits
for improvement of
U.S. 31 private insurance poli-
cies -1986
Price, eligibility to pur-
chase, coverage provi-
sions, payment levels
2 6 164
Wilson and Weissert
Empirical Focus on combinations of
policy restrictions and
Estimate likelihood of
policy compliance
Find two restrictions, prior
hospitalisation and prior
skilled care clauses as
the major limitations of
U.S. National Nursing Home
Survey (NNHS)-1985
Prior hospitalisation,
mental illness exclu-
sions, skilled or prior
skilled-care require-
ments, deductible wait-
ing periods, length of
nursing home stay
6 9 128
Research onlong‑term care insurance: status quo anddirections…
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Liu etal. (1990) Empirical Compare insurability of
LTCI with health insur-
Study the important fac-
tors needed for design-
ing and pricing private
Find that population in
need of LTC is very
dynamic with higher
levels of disability
Implications for financing
U.S. Supplement on Aging
(SOA) of National
Health Interview Survey
(NHIS)-1984, National
Long Term Care Survey
Age, gender, ADLa diffi-
culty by specific chronic
condition, income, num-
ber of ADL, 56 health
and functional variables,
associated measures of
acute and LTC service
use both formal and
informal, nursing home
length of stay
3 18 69
Rice etal. (1991) Empirical Focus on current LTCI
contracts in U.S.
Examine their effects on
out-of-pocket costs
Confirm the restrictions
mentioned in Wilson
and Weissert (1989)
and suggest two other
restrictions, policy
maximums and lack of
inflation adjustment as
major blocks
U.S. NNHS-1985, LTC
policies from 11 leading
companies in 1988
Age, gender, marital sta-
tus, nursing home length
of stay, out-of-pocket
costs paid by policies,
policy restrictions
2 4 209
M.Eling, O.Ghavibazoo
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Norton and Newhouse
Policy paper Analyse U.S. LTC system
Discuss four issues related
to public LTC financing
which are eligibility,
benefits, financing, and
Suggest public LTC rather
than private one
U.S. 12 25 46
Murtaugh etal. (1995) Empirical Focus on underwriting
criteria of LTCI
Discuss medical under-
writing necessary to
purchase LTCI
Discuss efficiency of
underwriting limitations
on identifying high-cost
U.S. National Mortality Fol-
lowback Survey-1986,
interview and NNHS-
Death certificate of
residents aged 25 or
older who died in
1986, nursing home
use information, age,
ADL limitation, major
illnesses, lifestyle
17 22 54
Pauly (1990) Theoretical Discusses rationality of
limited tendency to buy
private LTCI
Argues existence of intra-
family moral hazard
Suggests intervention of
government for LTCI
market failure
94 135 5
Research onlong‑term care insurance: status quo anddirections…
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Crown etal. (1992) Empirical Study the affordability
and potential market of
private LTCI for various
age cohorts
Provide new estimates
on significant potential
market in the group of
individuals aged 65–69
Discuss the role of public
policy on the issue
U.S. Survey of Income and
Program participation,
(SIPP)-1984, Consumer
Expenditure Survey
Age, asset, income, mari-
tal status, household
expenditure data
6 12 94
Cohen etal. (1992) Empirical Study the differences
between purchasers and
non-purchasers of LTCI
Mention various factors
affecting the demand for
private LTCI
U.S. LifePlans Inc., Survey
of purchasers and non-
purchasers; U.S. Bureau
of the Census, 1990
Age, gender, marital sta-
tus, household income,
liquid income, liquid
asset, education, opinion
about LTC
5 11 105
Zweifel and Strüwe
Theoretical Study the existence of
intra-family moral
Discuss the role of bequest
in affecting intergenera-
tional relationships
19 22 55
M.Eling, O.Ghavibazoo
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Sloan and Norton (1997) Empirical Discuss the role of adverse
selection, bequests
and crowding-out on
demand for private LTCI
in U.S.
Find no evidence of
bequest motives
U.S. Asset and Health Dynam-
ics of the Oldest Old
(AHEAD)-1993, Health
and Retirement Study
Variables related to
expectations, adverse
selection, bequest
motive, Medicaid
crowding-out, degree of
risk aversion, income
and wealth, expenditure
risk, family structure,
other demographic
48 67 3
Zweifel and Strüwe
Theoretical Discuss the pros and cons
of compulsory LTCI
Argue that although by
compulsory LTCI the
adverse selection may
be enhanced, the moral
hazard between parents
and children may
24 27 37
McCall etal. (1998) Empirical Study the factors affecting
the purchase of Partner-
ship programme
Find out the determinants
of purchasing Partner-
ship LTCI
U.S. Telephone survey of
Partnership purchasers
and random sample of
population in each Part-
nership state-1995
Health status, opinions
about LTC and LTCI,
financial planning
activities, demographic
and social characteris-
tics, income and assets
20 23 50
Research onlong‑term care insurance: status quo anddirections…
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Mellor (2001) Empirical Studies on substitutability
of children as formal
care for LTCI
Does not confirm the
existence of intra-family
moral hazard
Finds level of income,
assets, and education as
major factors affecting
the demand for LTCI
U.S. AHEAD-1994, Panel
Study of Income
Dynamics (PSID)-1994
Relationship of caregiv-
ers, age, education,
income, net worth,
health status, gender,
marital status, colour,
perspective towards
receiving help, family
size, having a daughter
32 38 27
Cohen (2003) Empirical Summarises the current
knowledge available for
private LTCI
Discusses growing market
for private LTCI and its
effect on public expendi-
ture, policyholders, their
families and providers
U.S. Health Insurance Asso-
ciation of America
Robert Wood Johnson
Foundation (RWJ) and
Department of Health
and Human services
Annual sale, policy char-
acteristics, age, gender,
marital status, benefit
package, wealth profile,
education and aware-
ness, daily cost of care,
length of time receiving
service, health status,
availability of family
7 17 75
Finkelstein etal. (2005) Empirical Discuss the dynamic inef-
ficiencies of the market
for LTCI by addressing
the lapse patterns on
Suggest the immediate
lapses may be due to
wrong purchase
U.S. HRS-1995–2000,
administrative data from
a specific insurance
Age, gender, number of
limitations to IADLsb,
number of limitations to
ADLs, and the presence
of cognitive impairment
11 22 56
M.Eling, O.Ghavibazoo
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Finkelstein and McGarry
Empirical Study various dimensions
of existence of private
information market for
Emphasise two types of
people with private
information that
purchase private LTCI:
high-risk individuals
and people with strong
taste for insurance
U.S. AHEAD cohort of HRS-
Age, gender, marital
status, age of spouse,
over 35 health indica-
tors, limitations in ADL,
limitations in IADL,
cognitive impairment,
average length of benefit
period, wealth status,
preventive activity, rat-
ing category, deductible,
daily benefit, benefit
period, escalation of
41 187 2
Brown and Finkelstein
Empirical Discuss the market fail-
ures for private LTCI
Present the supply-side
market failures of pri-
vate LTCI
Find out the supply-side
factors are not the sole
reason for LTCI market
U.S. Weiss Rating Inc., 132
known insurance
companies-2002, HRS-
2000, MetLife Market
Survey-2002, Society of
Actuaries (SOA)-2002,
Market-wide premium
and benefits, gender,
marital status, age,
termination probabilities
64 85 10
Brown and Finkelstein
Empirical Analyse the effect of
Medicaid on demand
for LTCI
Suggest reforms to reduce
the implicit tax imposed
by Medicaid to stimulate
private LTCI
U.S. MetLife Market Sur-
Average national daily
care cost of nursing
home and assisted living
facilities, hourly costs
of both skilled and
unskilled home health
care, age, wealth
51 88 9
Research onlong‑term care insurance: status quo anddirections…
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Courbage and Roudaut
Empirical Study the LTCI purchase
in France and the factors
affecting the demand for
The findings are altruistic
behaviour, risk behav-
iours and experience of
disability as reason for
low demand for private
France Survey of Health, Age-
ing and Retirement in
Europe (SHARE)-2007
LTCI ownership status,
age, marital status, edu-
cation, income, inherit-
ance, health status, life
insurance ownership,
recent hospitalisation,
risk factors, having
chronic conditions, hav-
ing symptoms, mobility,
ADL, IADL, family
members, receipt or pro-
viding help to relatives,
physical activity
26 29 34
Courtemanche and He
Empirical Study the effect of tax
incentives on the pur-
chase of private LTCI
Conclude even offer-
ing above-the-line
tax deductions may
have minor effects on
expansion of market for
private LTCI
Estimate the price elastic-
ity of demand for LTCI
and discuss the effects of
the taxes on government
U.S. HRS-1996, 1998, 2000,
2002, 2004
LTCI ownership status,
household income
and wealth, mortgage
payment, property tax,
out-of-pocket medical
expenses, demograph-
ics, and a detailed set
of variables on health
12 15 82
M.Eling, O.Ghavibazoo
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Courbage and Zweifel
Theoretical Discuss the two-sided
intergenerational moral
hazard in the purchase
of LTCI, the one in
which purchase of LTCI
by parents protects the
bequests from cost of
nursing home care and
the other one in which
parents purchase less
LTC coverage knowing
that their children will
keep them out of a nurs-
ing home
10 11 109
Goda (2011) Empirical Studies the effects of tax
subsidy programmes on
the number of people
who purchase LTCI
U.S. HRS-2006 (including
AHEAD sample, War
Baby (WB) sample and
Children of the Depres-
sion Age (CODA)
Demographics, health
status, family structure,
housing, employment,
disability, retirement
plans, net worth,
income, and insurance
10 12 101
Research onlong‑term care insurance: status quo anddirections…
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Brown etal. (2012) Empirical Survey to understand the
effect of preferences and
beliefs, likelihood of
becoming disabled, for-
mal care substitutes and
features of the private
market on demand for
private LTCI.
Explain the findings
related to low demand
for LTCI
U.S. Survey field in RAND
American Life Panel-
LTCI ownership, age,
gender, education, eth-
nicity, preferences and
beliefs, state-dependent
utility, bequest motives,
substitutes for insur-
ance, substitutes for
formal care; price and
affordability, counter-
party risk, trust in
15 22 58
Courbage and Eeckhoudt
Theoretical Study the optimal level
of LTCI insurance and
informal care and rela-
tion of these in the case
where the child is also
decision maker for the
purchase of LTCI and
informal care provider
for their parents
3 5 202
M.Eling, O.Ghavibazoo
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Country-specific studies
Siciliani (2014) Policy paper Discusses the factors
affecting expenditure
growth of LTCI and
relative projection in
OECD countries
Addresses the role of
regulations imposed by
government on quality
of care the individuals
Discusses public and
private insurance in dif-
ferent countries
OECD 2 4 237
Doty etal. (2015) Policy paper Study the long-term
services and supports
in France and compare
them with available
policy in U.S.
Provide solutions to
American private
LTCI based on French
France 0 5 208
Research onlong‑term care insurance: status quo anddirections…
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Costa-Font etal. (2016) Empirical Focus on the LTC market
changes during and
after great recession in
European countries
Discuss the determinants
of changing receipt of
informal care during
European Countries SHARE, 2013 Receiving informal care,
number of limitations
to ADL, net worth,
housing wealth, having
one or more children
live with the respond-
ent, having one or more
children live within
1km of the respond-
ent, cohabitation with a
partner, unemployment
change, education,
demographics, born in
country of interview
0 2 311
Gentili etal. (2017) Empirical Study the cultural differ-
ences and its effect on
the market for LTC and
other arrangements for
the elderly in Switzer-
Switzerland Statistics on socio-medical
institutions (SOMED)-
2007–2013, Home
care survey (HCS)-
2007–2013, Voting data
from national refer-
enda 2013, Public use
sample (PUS) of Swiss
census, SHARE-Wave 4,
Release 5, Swiss Statis-
tical Office (SFO)
Dependency level, age
at entry, gender, place
of residence, nurs-
ing home, share of
people voting yes to
amendment of Swiss
Constitution, household
characteristics, control
variables at municipal
and hospital level
0 0 574
M.Eling, O.Ghavibazoo
Table 9 (continued)
Article Data Citation Scores
Author Type Main findings Country Source and year Main Variables LCS GCS Rank
Nadash and Cuellar
Policy paper Study the private LTCI in
Apply the example from
German private LTCI
to U.S. and France for
better performance
Germany 1 1 383
Busse etal. (2017) Policy paper Focus on the evolution
of statutory health
insurance and LTC in
Discuss the challenges
exposed to the German
Germany 0 8 147
a Activities of daily living (ADLs) are the activities most people perform in daily life without assistance. The basic six ADLs are bathing, eating, toileting, dressing, trans-
ferring and continence
b Instrumental activities of daily living (IADLs) are more complicated tasks that are needed for living independently. They include doing laundry, cooking, shopping, using
the telephone, cleaning, accessing transportation, taking medicines, and managing personal finances
Research onlong‑term care insurance: status quo anddirections…
introduce to the analysis.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.
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 2.73 While there are papers with higher GCS and LCS in our data-
base, the global key-route main path has chosen the above path as the most impor-
tant in the evolution of knowledge. There are 31 papers on the main path, of which
21 are empirical, five theoretical, and five policy papers.74 Table9 illustrates the
main path studies along with a summary of their findings.
Appendix3: Review offuture research
In this appendix we present potential future research that we extracted from the arti-
cles on the main path (from 2000 onwards) and conclusion part of all the recent pub-
lications (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).
72 See Ho etal. (2017).
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.
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.
M.Eling, O.Ghavibazoo
Table 10 Potential future directions based on main path and recent studies
Authors (year) Title Potential future research
Main path articles beyond 2000
Finkelstein etal. (2005) Dynamic inefficiencies in insurance markets: Evidence from
long-term care insurance
The empirical relevance of effects of uninsured negative wealth or
income shocks on lapsation
Further study for finding the reasons behind immediate lapse after
purchase of policy
Finkelstein and McGarry (2006) Multiple dimensions of private information: Evidence from the
long-term care insurance market
The necessary conditions related to effect of heterogeneity in risk
preferences on the efficiency cost of asymmetric information
about risk type
Brown and Finkelstein (2008) The interaction of public and private insurance: Medicaid and the
long-term care insurance market
The way private market will be affected by new Medicaid reform
given the existence of market failures such as asymmetric
information, incomplete commitment in contracting and non-
diversifiable risks
Courbage and Roudaut (2008) Empirical evidence on long-term care insurance purchase in
Improve the results of the study by combining market data, such as
characteristics of the products offered, and individual data, such
as risk aversion behaviours
Courtemanche and He (2009) Tax incentives and the decision to purchase long-term care insur-
Estimate elasticity of demand for LTCI using more refined measure
of individuals’ federal marginal income tax rates
Study the efficiency of state-level incentives in promoting private
LTCI market
Siciliani (2014) The economics of long-term care Study the relation between formal home care and institutional care
Analyse the existence of Baumol’s disease in LTC
The role of technology on LTC costs (cost-saving or cost-augmenting)
Study the effect of competition on quality
Review systematically the availability, ownership structure and
funding arrangements in various countries
Empirically determine important factors explaining observed
public–private mix
Theoretical determination of optimal public–private mix
Study the solutions for moral hazard problem in the design of
future LTCI policies
Research onlong‑term care insurance: status quo anddirections…
Table 10 (continued)
Authors (year) Title Potential future research
Gentili etal. (2017) The role of culture in long-term care arrangement decisions Study the determinants of informal caregiving more comprehen-
Busse etal. (2017) Statutory health insurance in Germany: A health system shaped
by 135years of solidarity, self-governance, and competition
Further study of the problems related to discontinuous care and
oversupply in Germany’s health system
Analyse the long-term challenges raised by population ageing,
growth in chronic disease, multimorbidity, migration, digitaliza-
tion and existing urban–rural discrepancies
Recent articles 2016–2018
Stallard (2016) Compression of morbidity and mortality: New perspectives Treat the recent pauses in morbidity improvements (as a new phe-
nomenon disconnected from past trends or as a correction that
restores long-term trend)
Cremer etal. (2016b) Social long-term care insurance with two-sided altruism Extend the context of two-sided altruism to the case where parents
do not leave any bequest or they bequeath only when in good
Introduce private insurance with loading costs and possibility of
non-linear LTCI polices
Blundell etal. (2016) Comparing retirement wealth trajectories on both sides of the
Explore bequest motive differences in cross-country setting
Estimate late-life health expense risk in England
Study the factors that affect households not drawing on their hous-
ing wealth (such as consumption value of housing, the financial
and emotional transaction costs of releasing housing wealth, the
risk–return mix provided by housing)
Constructing life-cycle model that could explain the asset trajecto-
ries in the U.S. and England
Brown etal. (2016) Heterogeneity in state-dependent utility: Evidence from strategic
Further research on whether state dependence in utility is multi-
Assess the reliability of the measures obtained in this study by
asking the same sample similar questions could help determine
whether the responses reflect true measures of state dependence
M.Eling, O.Ghavibazoo
Table 10 (continued)
Authors (year) Title Potential future research
Chatterjee (2016) Reverse mortgage participation in the United States: Evidence
from a national study
Improve the data set of those who have reverse mortgage
Further study on awareness and demand for reverse mortgage
products with better data set including a large number of reverse
mortgage participants
Pla-Porcel etal. (2016) Life care annuities (LCA) embedded in a notional defined contri-
bution (NDC) framework
Adapt the designed actuarial balance sheet for NDC systems to
include the new model with LTC and evaluate the impacts of
the introduction of a minimum pension on the system’s financial
The transition rules of moving from old financing system to the
integrated NDC framework, the importance of including a mini-
mum pension, permanent disability relationship with LTC, the
necessary conditions for updating the annuity divisors and the
required statistical data for computation of real dependency cost
The design of yearly account statement including individual pen-
sion information about retirement and LTC rights, introducing
automatic balance mechanism based on an actuarial balance
sheet in order to take into account in the system the changing
Kreft and Doblhammer (2016)Expansion or compression of long-term care in Germany between
2001 and 2009? A small-area decomposition study based on
administrative health data
Investigate the future trends in the new care level 0 and the diver-
sity causes in the mortality and morbidity effects
Living conditions in countries and their overall change related to
the trends in care need and mortality
Further study to find the underlying mechanisms of health ageing
for better understanding and dealing with the problems of an
increasingly heterogeneous ageing society
Cremer etal. (2016a) The design of long term care insurance contracts Extend their model by also including welfare of caregivers in the
design of social insurance
Research onlong‑term care insurance: status quo anddirections…
Table 10 (continued)
Authors (year) Title Potential future research
Fong (2017) Old-age frailty patterns and implications for long-term care
Study the reversibility of human ageing in ageing models
Use richer data sets on health conditions and mortality rates to
explore the possibility of recovery from frailty and the impact of
population health trends on frailty
Identify seasonality patterns by constructing longer time-series
Pla-Porcel etal. (2017) Converting retirement benefit into a life care annuity with graded
Further research on the effect of ruling out the recovery assump-
tions in their proposed model in the specific case of an annuitant
not being healthy in the initial state
Separate the redistribution of resources when the unisex actuarial
factor is included in computation of the initial benefit
Pincus etal. (2017) Framing the decision to buy long-term care insurance: Losses
and gains in the context of statistical and narrative evidence
Expand future research by focusing more on the type and strength
of emotions generated by communications rather than rational
uncertainty frameworks
Sperber etal. (2017) How can adult children influence parents’ long-term care insur-
ance purchase decisions?
Study on defining specific communication channels and interven-
tion activities
Biessy (2017) Continuous-time semi-Markov inference of biometric laws asso-
ciated with a long-term care insurance portfolio
Extend the one level LTC model proposed in the paper to consider
several levels of LTC
Cremer and Roeder (2017) Long term care policy with lazy rotten kids Study the effect of crowding-out and altruism on a more disaggre-
gate level and country-specific way
Cremer etal. (2017) Uncertain altruism and the provision of long term care Extend the model by adding heterogeneity in such a way that indi-
viduals may have different wealth and probability of altruism
Levantesi and Menzietti (2018) Natural hedging in long-term care insurance Study on developing non-parametric models in a multiple state
framework to examine the effectiveness of natural hedging and
also utilise different data set
Introduce alternative risk management tools rather than natural
Study the possibility of constructing a swap written on the survival
of disabled people and evaluating their hedge effectiveness
M.Eling, O.Ghavibazoo
Table 10 (continued)
Authors (year) Title Potential future research
De la Peña etal. (2018)Long term care pension benefits coverage via conversion factor
based on different mortality rates: More money as age goes on
Extend the data on the different degrees or levels of dependence to
obtain the transition probabilities from one stage to another
Quantify the effect of this factor not only in the Spanish social
security system but also in other countries such as France, Italy
or Germany
Research onlong‑term care insurance: status quo anddirections…
Ahlstrom, A., A. Tumlinson, and J. Lambrew. 2004. Linking reverse mortgages and long-term care
insurance. The Brookings institution report. https ://www.brook nt/uploa
ds/2016/06/20040 317.pdf. Accessed 3 Mar 2018.
Alai, D.H., H. Chen, D. Cho, K. Hanewald, and M. Sherris. 2014. Developing equity release markets:
Risk analysis for reverse mortgages and home reversions. North American Actuarial Journal 18
(1): 217–241.
Ameriks, J., J. Briggs, A. Caplin, M.D. Shapiro, and C. Tonetti. 2018. The long-term-care insurance puz-
zle: Modeling and measurement. National Bureau of Economic Research. Working Paper. http://
ebp-proje cts.isr.umich .edu/VRI/paper s/VRI-LTC-I.pdf. Accessed 12 July 2018.
Angelis, A., D. Tordrup, and P. Kanavos. 2017. Is the funding of public national health systems sustain-
able over the long term? Evidence from eight OECD countries. Global Policy 8 (52): 7–22.
Argentum. 2016. Getting to 2025: A senior living roadmap. Argentum Expanding Senior Living. https ://
www.argen s/Argen tum20 25.pdf. Accessed 1 May 2018.
Barr, N. 2010. Long-term care: A suitable case for social insurance. Social Policy & Administration 44
(4): 359–374.
Bartkowiak, M. 2012. On transferring longevity risk. Zeszyty Naukowe/Uniwersytet Ekonomiczny w Poz-
naniu 222: 14–21.
Basu, R. 2016. Lapse of long-term care insurance coverage in the US. Baylor Scott & White Health,
Texas A&M Health Science Center. https ://pdfs.seman ticsc holar .org/2a18/96800 41f81 99437 3faa3
6e970 962aa 47223 e.pdf. Accessed 15 May 2018.
Batagelj, V. 2003. Efficient algorithms for citation network analysis. https ://arxiv .org/pdf/cs/03090 23.
Accessed 16 Jan 2018.
Batagelj, V., and A. Mrvar. 1998. Pajek-program for large network analysis. Connections 21 (2): 47–57.
Bauer, J.M., J. Schiller, C. Schreckenberger, and M. Trautinger. 2017. Selection behavior in the mar-
ket for private complementary long-term care insurance in Germany. Working Paper. https ://paper s.cfm?abstr act_id=29954 24. Accessed 3 Mar 2018.
Berliner, B. 1982. Limits of insurability of risks. Englewood Cliffs: Prentice Hall.
Biener, C., and M. Eling. 2012. Insurability in microinsurance markets: An analysis of problems and
potential solutions. The Geneva Papers on Risk and Insurance—Issues and Practice 37 (1):
Biener, C., M. Eling, and J.H. Wirfs. 2015. Insurability of cyber risk: An empirical analysis. The Geneva
Papers on Risk and Insurance—Issues and Practice 40 (1): 131–158.
Biessy, G. 2017. Continuous-time semi-Markov inference of biometric laws associated with a long-term
care insurance portfolio. Astin Bulletin 47 (2): 527–561.
Blake, D., A. Cairns, K. Dowd, and R. MacMinn. 2006. Longevity bonds: Financial engineering, valua-
tion, and hedging. Journal of Risk and Insurance 73 (4): 647–672.
Blundell, R., R. Crawford, E. French, and G. Tetlow. 2016. Comparing retirement wealth trajectories on
both sides of the pond. Fiscal Studies 37 (1): 105–130.
Brau, R., and M.L. Bruni. 2008. Eliciting the demand for long-term care coverage: A discrete choice
modelling analysis. Health Economics 17 (3): 411–433.
Brewster, R., and S. Gutterman. 2014. The volatility in long-term care insurance. Society of Actuaries.
Brown, J.R., N.B. Coe, and A. Finkelstein. 2007. Medicaid crowd-out of private long-term care insurance
demand: Evidence from the health and retirement survey. In NBER/Tax Policy and the Economy,
vol. 21, ed. J.M. Poterba. Cambridge: MIT Press.
Brown, J.R., and A. Finkelstein. 2007. Why is the market for long-term care insurance so small? Journal
of Public Economics 91 (10): 1967–1991.
Brown, J.R., and A. Finkelstein. 2008. The interaction of public and private insurance: Medicaid and the
long-term care insurance market. American Economic Review 98 (3): 1083–1102.
Brown, J.R., and A. Finkelstein. 2009. The private market for long-term care insurance in the United
States: A review of the evidence. Journal of Risk and Insurance 76 (1): 5–29.
Brown, J.R., and A. Finkelstein. 2011. Insuring long-term care in the United States. Journal of Economic
Perspectives 25 (4): 119–142.
Brown, J.R., G.S. Goda, and K. McGarry. 2012. Long-term care insurance demand limited by beliefs
about needs, concerns about insurers, and care available from family. Health Affairs 31 (6):
M.Eling, O.Ghavibazoo
Brown, J.R., G.S. Goda, and K. McGarry. 2016. Heterogeneity in state-dependent utility: Evidence from
strategic surveys. Economic Inquiry 54 (2): 847–861.
Brown, J., and M. Warshawsky. 2013. The life care annuity: A new empirical examination of an insurance
innovation that addresses problems in the markets for life annuities and long-term care insurance.
Journal of Risk and Insurance 80 (3): 677–704.
Brüss, M. 2018. Angst vor dem Pflegefall treibt Zusatzversicherungen. https
://www.versi cheru ngsjo urnal .de/markt -und-polit ik/angst -vor-dem-pfleg efall -treib t-zusat zvers icher
ungen -13227 0.php.
Busse, R., M. Blümel, F. Knieps, and T. Bärnighausen. 2017. Statutory health insurance in Germany:
A health system shaped by 135years of solidarity, self-governance, and competition. Lancet 390
(10097): 882–897.
Campbell, J.C., N. Ikegami, and M.J. Gibson. 2010. Lessons from public long-term care insurance in
Germany and Japan. Health Affairs 29 (1): 87–95.
Center for Technology and Aging. 2009. Technologies to help older adults maintain independence:
Advancing Technology Adoption. Briefing Paper, Public Health Institute. http://www.techa ndagi ngpa per.pdf. Accessed 12 May 2018.
Chatterjee, S. 2016. Reverse mortgage participation in the United States: Evidence from a national study.
International Journal of Financial Studies 4 (1): 1.
Chatterjee, S., and L. Fan. 2017. Household demand for private long term care insurance: An exploratory
note. Economics Bulletin 37 (3): 1975–1981.
Chen, C.C. 2016. Hedging the aging society: Challenges to the insurance market and law in Singapore.
Singapore Management University School of Law Research Paper No. 15/2016. https ://
abstr act=28262 31. Accessed 17 April 2018.
Chen, Y.P. 2001. Funding long-term care in the United States: The role of private insurance. The Geneva
Papers on Risk and Insurance—Issues and Practice 26 (4): 656–666.
Coe, N.B., M.M. Skira, and C.H. Van Houtven. 2015. Long-term care insurance: Does experience mat-
ter? Journal of Health Economics 40: 122–131.
Cohen, M.A. 2003. Private long-term care insurance: A look ahead. Journal of Aging and Health 15 (1):
Cohen, A., and L. Einav. 2007. Estimating risk preferences from deductible choice. American Economic
Review 97 (3): 745–788.
Cohen, M.A., N. Kumar, and S.S. Wallack. 1992. Who buys long-term care insurance. Health Affairs 11
(1): 208–223.
Colombo F., A. Llena-Nozal, J. Mercier, and F. Tjadens. 2011. Help wanted? Providing and paying for
long-term care. OECD Health Policy Studies, OECD Publishing. https ://www.oecd-ilibr
conte nt/publi catio n/97892 64097 759-en. Accessed 15 Jan 2018.
Colombo, F., and J. Mercier. 2012. Help wanted? Fair and sustainable financing of long-term care ser-
vices. Applied Economic Perspectives and Policy 34 (2): 316–332.
Comas-Herrera, A., R. Butterfield, J.-L. Fernández, R. Wittenberg, and J.M. Wiener. 2012. Barriers and
opportunities for private long-term care insurance in England: What can we learn from other coun-
tries? In The LSE companion to health policy, eds. A. McGuire and J. Costa-Font, 258–280.
Costa-Font, J., and C. Courbage. 2015. Crowding out of long-term care insurance: Evidence from Euro-
pean expectations data. Health Economics 24: 74–88.
Costa-Font, J., C. Courbage, and K. Swartz. 2015. Financing long-term care: Ex ante, ex post or both?
Health Economics 24 (S1): 45–57.
Costa-Font, J., M. Karlsson, and H. Øien. 2016. Careful in the crisis? Determinants of older people’s
informal care receipt in crisis-struck European countries. Health Economics 25 (S2): 25–42.
Costa-Font, J., and J. Rovira-Forns. 2008. Who is willing to pay for long-term care insurance in Catalo-
nia? Health Policy 86 (1): 72–84.
Courbage, C., and L. Eeckhoudt. 2012. On insuring and caring for parents’ long-term care needs. Journal
of Health Economics 31 (6): 842–850.
Courbage, C., and N. Roudaut. 2008. Empirical evidence on long-term care insurance purchase in France.
The Geneva Papers on Risk and Insurance—Issues and Practice 33 (4): 645–658.
Courbage, C., and N. Roudaut. 2011. Long-term care insurance: The French example. European Geriat-
ric Medicine 2 (1): 22–25.
Courbage, C., and P. Zweifel. 2011. Two-sided intergenerational moral hazard, long-term care insurance,
and nursing home use. Journal of Risk and Uncertainty 43 (1): 65–80.
Research onlong‑term care insurance: status quo anddirections…
Courbage, C., and P. Zweifel. 2015. Double crowding-out effects of means-tested public provision for
long-term care. Risks 3 (1): 61–76.
Courtemanche, C., and D. He. 2009. Tax incentives and the decision to purchase long-term care insur-
ance. Journal of Public Economics 93 (1–2): 296–310.
Cramer, A.T., and G.A. Jensen. 2006. Why don’t people buy long-term-care insurance? Journals of Ger-
ontology Series B-Psychological Sciences and Social Sciences 61 (4): 185–S193.
Cremer, H., F. Gahvari, and P. Pestieau. 2017. Uncertain altruism and the provision of long term care.
Journal of Public Economics 151: 12–24.
Cremer, H., J.M. Lozachmeur, and P. Pestieau. 2016a. The design of long term care insurance contracts.
Journal of Health Economics 50: 330–339.
Cremer, H., P. Pestieau, and G. Ponthière. 2012. The economics of long-term care: A survey. Nordic eco-
nomic policy review 2: 107–148.
Cremer, H., P. Pestieau, and K. Roeder. 2016b. Social long-term care insurance with two-sided altruism.
Research in Economics 70 (1): 101–109.
Cremer, H., and K. Roeder. 2017. Long-term care policy with lazy rotten kids. Journal of Public Eco-
nomic Theory 19 (3): 583–602.
Crimmins, E.M., and H. Beltrán-Sánchez. 2011. Mortality and morbidity trends: Is there compression of
morbidity? The Journals of Gerontology: Series B 66 (1): 75–86.
Crown, W.H., J. Capitman, and W.N. Leutz. 1992. Economic rationality, the affordability of private long-
term care insurance, and the role for public policy. The Gerontologist 32 (4): 478–485.
Cutler, D.M. 1996. Why don’t markets insure long-term risk? Harvard University and National Bureau
of Economic Research. Working Paper. http://schol ar.harva /cutle r/files /ltc_rev.
pdf?m=13600 40872 . Accessed 24 Feb 2018.
Davidoff, T. 2008. Illiquid housing as self-insurance: The case of long term care. UC Berkeley Working
Paper. https ://escho larsh 278. Accessed 12 Feb 2018.
Davidoff, T. 2010. Home equity commitment and long-term care insurance demand. Journal of Public
Economics 94 (1–2): 44–49.
De la Maisonneuve, C., and J.O. Martins. 2015. The future of health and long-term care spending. OECD
Journal: Economic Studies 2014 (1): 61–96.
De la Peña, J.I., M.C. Fernández-Ramos, and N. Peña-Miguel. 2018. Long term care pension benefits
coverage via conversion factor based on different mortality rates: More money as age goes on.
Interciencia 43 (1): 9–16.
De Meijer, C.A., I.M. Majer, M.A. Koopmanschap, and P.H. van Baal. 2012. Forecasting lifetime and
aggregate long-term care spending: Accounting for changing disability patterns. Medical Care 50
(8): 722–729.
De Nooy, W., A. Mrvar, and V. Batagelj. 2011. Exploratory social network analysis with Pajek, vol. 27.
Cambridge: Cambridge University Press.
Doty, P., P. Nadash, and N. Racco. 2015. Long-term care financing: Lessons from France. The Milbank
Quarterly 93 (2): 359–391.
Eisen, R., and F.A. Sloan. 1996. Long-term care: Economic issues and policy solutions, vol. 5. New
York: Springer.
Eling, M., S. Pradhan, and J.T. Schmit. 2014. The determinants of microinsurance demand. The Geneva
Papers on Risk and Insurance—Issues and Practice 39 (2): 224–263.
ENEPRI. 2012. Technological solutions potentially influencing the future of long-term care. European
Network of Economic Policy Research Institutes, ENEPRI RESEARCH REPORT NO. 114. https
:// oad/pdf/10590 334.pdf. Accessed 10 May 2018.
Feder, J., H.L. Komisar, and M. Niefeld. 2000. Long-term care in the United States: An overview. Health
Affairs 19 (3): 40–56.
Feinerer, I., and K. Hornik. 2012. tm: Text mining package. https ://cran.r-proje ges/tm/
index .html. Accessed 17 Jan 2018.
Finkelstein, A., and K. McGarry. 2006. Multiple dimensions of private information: Evidence from the
long-term care insurance market. American Economic Review 96 (4): 938–958.
Finkelstein, A., K. McGarry, and A. Sufi. 2005. Dynamic inefficiencies in insurance markets: Evidence
from long-term care insurance. American Economic Review 95 (2): 224–228.
Finkelstein, E.S., M.C. Reid, A. Kleppinger, K. Pillemer, and J. Robison. 2012. Are baby boomers who
care for their older parents planning for their own future long-term care needs? Journal of Aging &
Social Policy 24 (1): 29–45.
M.Eling, O.Ghavibazoo
Fong, J.H. 2017. Old-age frailty patterns and implications for long-term care programmes. The Geneva
Papers on Risk and Insurance—Issues and Practice 42 (1): 114–128.
Fong, J.H., A.W. Shao, and M. Sherris. 2015. Multistate actuarial models of functional disability. North
American Actuarial Journal 19 (1): 41–59.
Frank, R.G., M. Cohen, and N. Mahoney. 2013. Making progress: Expanding risk protection for long-
term services and supports through private long-term care insurance. In Shaping affordable path-
ways for aging with dignity. Long Beach, CA: The SCAN Foundation.
Freedman, V.A., B.C. Spillman, P.M. Andreski, J.C. Cornman, E.M. Crimmins, E. Kramarow, J. Lubitz,
L.G. Martin, S.S. Merkin, R.F. Schoeni, T.E. Seeman, and T.A. Waidmann. 2013. Trends in late-
life activity limitations in the United States: An update from five national surveys. Demography 50
(2): 661–671.
Freiman, M.P. 2007. Can 1 + 1 = 3? A look at hybrid insurance products with long-term care insurance.
AARP Public Policy Institute. https ://asset ter/il/2007_11_hybri d.pdf. Accessed 8
Mar 2018.
Friedberg, L., W. Hou, W. Sun, and A. Webb. 2017. Lapses in long-term care insurance. Schwartz Center
for Economic Policy Analysis (SCEPA), The New School. https ://www.netsp s/uploa ds/
E2018 1801P aper_webb.pdf. Accessed 12 Feb 2018.
Fuino, M., and J. Wagner. 2018. Long-term care models and dependence probability tables by acuity
level: New empirical evidence from Switzerland. Insurance: Mathematics and Economics 81:
Garfield, E. 2009. From the science of science to Scientometrics visualizing the history of science with
HistCite software. Journal of Informetrics 3 (3): 173–179.
Garfield, E., I.H. Sher, and R.J. Torpie. 1964. The use of citation data in writing the history of science.
Philadelphia: Institute For Scientific Information. http://scima it/docs/Garfi eld19 64use
.pdf. Accessed 16 Jan 2018.
Gentili, E., G. Masiero, and F. Mazzonna. 2017. The role of culture in long-term care arrangement deci-
sions. Journal of Economic Behavior & Organization 143: 186–200.
Getzen, T.E. 1988. Longlife insurance: A prototype for funding long-term care. Health Care Financing
Review 10 (2): 47–56.
Glendinning, C., B. Davies, L. Pickard, and A. Comas-Herrera. 2004. Funding long-term care for older
people: lessons from other countries. Joseph Rowntree Foundation.
Goda, G.S. 2011. The impact of state tax subsidies for private long-term care insurance on coverage and
Medicaid expenditures. Journal of Public Economics 95 (7–8): 744–757.
Gottlieb, D., and O.S. Mitchell. 2015. Narrow framing and long-term care insurance. Michigan
Retirement Research Center. Working Paper. https ://deepb lue.lib.umich .edu/bitst ream/handl
e/2027.42/11309 6/wp321 .pdf?seque nce=1&isAll owed=y. Accessed 22 Mar 2018.
Haberman, S., and E. Pitacco. 1998. Actuarial models for disability insurance. Boca Raton: CRC Press.
Halek, M., and J.G. Eisenhauer. 2001. Demography of risk aversion. Journal of Risk and Insurance 68
(1): 1–24.
Hanewald, K., T. Post, and M. Sherris. 2016. Portfolio choice in retirement-what is the optimal home
equity release product? Journal of Risk and Insurance 83 (2): 421–446.
Headen Jr., A.E. 1992. Time costs and informal social support as determinants of differences between
black and white families in the provision of long-term care. Inquiry 29 (4): 440–450.
Hendren, N. 2013. Private information and insurance rejections. Econometrica 81 (5): 1713–1762.
Ho, M.H.-C., J.S. Liu, and K.C.-T. Chang. 2017. To include or not: The role of review papers in citation-
based analysis. Scientometrics 110 (1): 65–76.
Hsieh, M.-H., J.L. Wang, Y.-F. Chiu, and Y.-C. Chen. 2017. Valuation of variable long-term care annui-
ties with guaranteed lifetime withdrawal benefits: A variance reduction approach. Insurance: Math-
ematics and Economics 78: 246–254.
Huang, Y., D. Zhu, Y. Qian, Y. Zhang, A.L. Porter, Y. Liu, and Y. Guo. 2017. A hybrid method to trace
technology evolution pathways: A case study of 3D printing. Scientometrics 111 (1): 185–204.
Hummon, N.P., and P. Dereian. 1989. Connectivity in a citation network: The development of DNA the-
ory. Social networks 11 (1): 39–63.
Ikegami, N. 1997. Public long-term care insurance in Japan. Jama-Journal of the American Medical
Association 278 (16): 1310–1314.
Ikegami, N., and J.C. Campbell. 2002. Choices, policy logics and problems in the design of long-term
care systems. Social Policy & Administration 36 (7): 719–734.
Research onlong‑term care insurance: status quo anddirections…
Karlsson, M. 2002. Comparative analysis of long-term care systems in four countries. International Insti-
tute for Applied Systems Analysis. http://pure.iiasa Accessed 20 Feb 2018.
Kessler, D. 2008. The long-term care insurance market. The Geneva Papers on Risk and Insurance—
Issues and Practice 33 (1): 33–40.
Kitao, S. 2015. Fiscal cost of demographic transition in Japan. Journal of Economic Dynamics & Control
54: 37–58.
Klimaviciute, J. 2017. Long-term care insurance and intra-family moral hazard: Fixed vs proportional
insurance benefits. The Geneva Risk and Insurance Review 42 (2): 87–116.
Klimaviciute, J., and P. Pestieau. 2018. The public economics of long-term care. A survey of recent con-
tributions. Annals of Public and Cooperative Economics 89 (1): 49–63.
Konetzka, R.T., D. He, J. Guo, and J.A. Nyman. 2014. Moral hazard and long-term care insurance.
Working Paper. https ://busin ess.illin er/mhec/Konet zka.pdf. Accessed 3 May 2018.
Konetzka, R.T., and Y. Luo. 2011. Explaining lapse in long-term care insurance markets. Health Eco-
nomics 20 (10): 1169–1183.
Kreft, D., and G. Doblhammer. 2016. Expansion or compression of long-term care in Germany between
2001 and 2009? A small-area decomposition study based on administrative health data. Population
Health Metrics 14 (1): 24.
Kumar, N., M.A. Cohen, C.E. Bishop, and S.S. Wallack. 1995. Understanding the factors behind the deci-
sion to purchase varying coverage amounts of long-term care insurance. Health Services Research
29 (6): 653–678.
Kyle, P. 2013. Confronting the elder care crisis: The private long-term care insurance market and the util-
ity of hybrid products. Marq. Elder’s Advisor 15: 101.
Lakdawalla, D., and T. Philipson. 2002. The rise in old-age longevity and the market for long-term care.
American Economic Review 92 (1): 295–306.
Levantesi, S., and M. Menzietti. 2012. Managing longevity and disability risks in life annuities with long
term care. Insurance: Mathematics and Economics 50 (3): 391–401.
Levantesi, S., and M. Menzietti. 2018. Natural hedging in long-term care insurance. ASTIN Bulletin 48
(1): 233–274.
Levikson, B., and G. Mizrahi. 1994. Pricing long term care insurance contracts. Insurance: Mathematics
and Economics 14 (1): 1–18.
Li, Y., and G.A. Jensen. 2011. The impact of private long-term care insurance on the use of long-term
care. Inquiry: The Journal of Health Care Organization. Provision and Financing 48 (1): 34–50.
Li, Z., A.W. Shao, and M. Sherris. 2017. The impact of systematic trend and uncertainty on mortality and
disability in a multistate latent factor model for transition rates. North American Actuarial Journal
21 (4): 594–610.
Lin, H., and J. Prince. 2013. The impact of the partnership long-term care insurance program on private
coverage. Journal of Health Economics 32 (6): 1205–1213.
Lin, H., and J.T. Prince. 2016. Determinants of private long-term care insurance purchase in response to
the partnership program. Health Services Research 51 (2): 687–703.
Liu, J.S., and L.Y.Y. Lu. 2012. An integrated approach for main path analysis: Development of the Hirsch
index as an example. Journal of the American Society for Information Science and Technology 63
(3): 528–542.
Liu, J.S., L.Y.Y. Lu, and W.M. Lu. 2016. Research fronts and prevailing applications in data envelopment
analysis. In Data envelopment analysis. International series in operations research & management
science, ed. J. Zhu, vol. 238. Boston, MA: Springer.
Liu, K., K.G. Manton, and B.M. Liu. 1990. Morbidity, disability, and long-term care of the elderly—
Implications for insurance financing. The Milbank Quarterly 68 (3): 445–492.
Lockwood, L.M. 2018. Incidental bequests and the choice to self-insure late-life risks. American Eco-
nomic Review 108 (9): 2513–2550.
Lu, L.Y.Y., and J.S. Liu. 2016. A novel approach to identify the major research themes and develop-
ment trajectory: The case of patenting research. Technological Forecasting and Social Change 103:
Ma, V.C., and J.S. Liu. 2016. Exploring the research fronts and main paths of literature: A case study of
shareholder activism research. Scientometrics 109 (1): 33–52.
Mayhew, L., M. Karlsson, and B.D. Rickayzen. 2010. The role of private finance in paying for long term
care. The Economic Journal 120 (548): F478.
Mayhew, L., and D. Smith. 2014. Personal care savings bonds: A new way of saving towards social care
in later life. The Geneva Papers on Risk and Insurance—Issues and Practice 39 (4): 668–692.
M.Eling, O.Ghavibazoo
Mayhew, L., D. Smith, and D. O’Leary. 2017. Paying for care costs in later life using the value in peo-
ple’s homes. The Geneva Papers on Risk and Insurance—Issues and Practice 42 (1): 129–151.
McCall, N., S. Mangle, E. Bauer, and J. Knickman. 1998. Factors important in the purchase of partner-
ship long-term care insurance. Health Services Research 33 (2): 187–203.
McGarry, B.E., H. Temkin-Greener, B.P. Chapman, D.C. Grabowski, and Y. Li. 2016. The impact of
consumer numeracy on the purchase of long-term care insurance. Health Services Research 51 (4):
McGarry, B.E., H. Temkin-Greener, and Y. Li. 2013. Role of race and ethnicity in private long-term care
insurance ownership. The Gerontologist 54 (6): 1001–1012.
Meier, V. 1996. Long-term care insurance and savings. FinanzArchiv/Public Finance Analysis 53 (3–4):
Meier, V. 1998. Long-term care insurance and life insurance demand. The Geneva Papers on Risk and
Insurance Theory 23 (1): 49–61.
Meier, V. 1999. Why the young do not buy long-term care insurance. Journal of Risk and Uncertainty 18
(1): 83–98.
Mellor, J.M. 2000. Private long-term care insurance and the asset protection motive. Gerontologist 40
(5): 596–604.
Mellor, J.M. 2001. Long-term care and nursing home coverage: Are adult children substitutes for insur-
ance policies? Journal of Health Economics 20 (4): 527–547.
Murtaugh, C.M., P. Kemper, and B.C. Spillman. 1995. Risky business: Long-term care insurance under-
writing. Inquiry: A Journal of Health Care Organization Provision and Financing 32 (3): 271–284.
Murtaugh, C.M., B.C. Spillman, and M.J. Warshawsky. 2001. In sickness and in health: An annuity
approach to financing long-term care and retirement income. Journal of Risk and Insurance 68 (2):
Nadash, P., and A.E. Cuellar. 2017. The emerging market for supplemental long term care insurance in
Germany in the context of the 2013 Pflege-Bahr Reform. Health Policy 121 (6): 588–593.
NAIC & The Center for Insurance Policy Research. 2016. The state of long-term care insurance: The
market, challenges and future innovations. Washington, DC. ents/cipr_
curre nt_study _16051 9_ltc_insur ance.pdf. Accessed 29 May 2018.
Nixon, D.C. 2014. State tax subsidies to bolster the long-term care insurance market. Journal of Public
Policy 34 (3): 415–436.
Norton, E.C. 2000. Long-term care. In Handbook of health economics, vol. 1, Part B, 955–994.
Norton, E.C., and J.P. Newhouse. 1994. Policy options for public long-term care insurance. Jama-Journal
of the American Medical Association 271 (19): 1520–1524.
Oster, E., I. Shoulson, K. Quaid, and E.R. Dorsey. 2010. Genetic adverse selection: Evidence from long-
term care insurance and Huntington disease. Journal of Public Economics 94 (11–12): 1041–1050.
Outreville, J.F. 2013. The relationship between insurance and economic development: 85 empirical
papers for a review of the literature. Risk Management and Insurance Review 16 (1): 71–122.
Own Your Future Minnesota. 2015. Merge term life with long-term care insurance for life stage protec-
tion report. https :// s/OYF-Life-Stage -Produ ct_tcm10 53-26317 3.pdf. Accessed 24
April 2018.
Pauly, M.V. 1990. The rational nonpurchase of long-term-care insurance. Journal of Political Economy
98 (1): 153–168.
Payne, G., A. Laporte, R. Deber, and P.C. Coyte. 2007. Counting backward to health care’s future: Using
time-to-death modeling to identify changes in end-of-life morbidity and the impact of aging on
health care expenditures. The Milbank Quarterly 85 (2): 213–257.
Pestieau, P., and G. Ponthière. 2012. Long-term care insurance puzzle. In Financing long-term care in
Europe, ed. J. Costa-Font and C. Courbage, 41–52. London: Palgrave Macmillan.
Pincus, J., K. Hopewood, and R. Mills. 2017. Framing the decision to buy long-term care insurance:
Losses and gains in the context of statistical and narrative evidence. Journal of Financial Services
Marketing 22 (1): 33–40.
Pitacco, E. 1995. Actuarial models for pricing disability benefits: Towards a unifying approach. Insur-
ance Mathematics and Economics 16 (1): 39–62.
Pitacco, E. 1999. Multistate models for long-term care insurance and related indexing problems. Applied
Stochastic Models in Business and Industry 15 (4): 429–441.
Pla-Porcel, J., M. Ventura-Marco, and C. Vidal-Meliá. 2016. Life care annuities (LCA) embedded in a
notional defined contribution (NDC) framework. Astin Bulletin 46 (2): 331–363.
Research onlong‑term care insurance: status quo anddirections…
Pla-Porcel, J., M. Ventura-Marco, and C. Vidal-Meliá. 2017. Converting retirement benefit into a life care
annuity with graded benefits. Scandinavian Actuarial Journal 10: 829–853.
Pritchard, D.J. 2006. Modeling disability in long-term care insurance. North American Actuarial Journal
10 (4): 48–75.
Rasmussen, D.W., I.F. Megbolugbe, and B.A. Morgan. 1997. The reverse mortgage as an asset manage-
ment tool. Housing Policy Debate 8 (1): 173–194.
Rice, T., K. Thomas, and W. Weissert. 1991. The effect of owning private long-term care insurance poli-
cies on out-of-pocket costs. Health Services Research 25 (6): 907–933.
Rothgang, H. 2010. Social insurance for long-term care: An evaluation of the German model. Social
Policy & Administration 44 (4): 436–460.
Rothgang, H., and K. Engelke. 2009. Long-term care: How to organise affordable, sustainable long-term
care given the constraints of collective versus individual arrangements and responsibilities. Discus-
sion Paper. Peer Review, The Netherlands.
Sawyer, C.H. 1996. Reverse mortgages: An innovative tool for elder law attorneys. Stetson Law Review
26: 617.
Schaber, P.L., and M.S. Stum. 2007. Factors impacting group long-term care insurance enrollment deci-
sions. Journal of Family and Economic Issues 28 (2): 189–205.
Schut, F.T., and B. van den Berg. 2010. Sustainability of comprehensive universal long-term care insur-
ance in the Netherlands. Social Policy & Administration 44 (4): 411–435.
Shilling, D. 1991. Securities funding of long-term care: A step toward a private sector solution. Fordham
Urb. LJ 19: 1.
Siciliani, L. 2014. The economics of long-term care. B.E. Journal of Economic Analysis & Policy 14 (2):
Sloan, F.A., and E.C. Norton. 1997. Adverse selection, bequests, crowding out, and private demand for
insurance: Evidence from the long-term care insurance market. Journal of Risk and Uncertainty 15
(3): 201–219.
Society of Acturaries. 2011. Long term care intercompany experience studypolicy terminations aggre-
gate databases 2000-2011 report. https :// /Resea rch/Exp-Study /2015-ltc-exp-
study -termi natio ns.pdf. Accessed 3 Mar 2018.
Sperber, N.R., C.I. Voils, N.B. Coe, R.T. Konetzka, J. Boles, and C.H. Van Houtven. 2017. How can
adult children influence parents’ long-term care insurance purchase decisions? The Gerontologist
57 (2): 292–299.
Spillman, B.C., and J. Lubitz. 2000. The effect of longevity on spending for acute and long-term care.
The New England Journal of Medicine 342 (19): 1409–1415.
Spillman, B.C., C.M. Murtaugh, and M.J. Warshawsky. 2003. Policy implications of an annuity approach
to integrating long-term care financing and retirement income. Journal of Aging and Health 15 (1):
Stallard, E. 2016. Compression of morbidity and mortality: New perspectives. North American Actuarial
Journal 20 (4): 341–354.
Stearns, S.C., E.C. Norton, and Z. Yang. 2007. How age and disability affect long-term care expenditures
in the United States. Social Policy and Society 6 (3): 367–378.
Tennyson, S., and H.K. Yang. 2014. The role of life experience in long-term care insurance decisions.
Journal of Economic Psychology 42: 175–188.
Van Houtven, C.H., and E.C. Norton. 2004. Informal care and health care use of older adults. Journal of
Health Economics 23 (6): 1159–1180.
Ventura-Marco, M., and C. Vidal-Meliá. 2016. Integrating retirement and permanent disability in NDC
pension schemes. Applied Economics 48 (12): 1081–1102.
Vidal-Meliá, C., M. Ventura-Marco, and J. Pla-Porcel. 2018. An NDC approach to helping pensioners
cope with the cost of long-term care. Journal of Pension Economics & Finance.
Wang, Q., Y. Zhou, X. Ding, and X.H. Ying. 2018. Demand for long-term care insurance in China. Inter-
national Journal of Environmental Research and Public Health 15 (6): 1–15.
Webb, D.C. 2009. Asymmetric information, long-term care insurance, and annuities: The case for bun-
dled contracts. Journal of Risk and Insurance 76 (1): 53–85.
Weston, H. 2012. The imperfect match between long-term care risk and long-term care insurance. Jour-
nal of Financial Service Professionals 66 (4): 37–45.
Wiener, J.M., D.A. Ehrenworth, and D.A. Spence. 1987. Private long-term care insurance: Cost, cover-
age, and restrictions. The Gerontologist 27 (4): 487–493.
M.Eling, O.Ghavibazoo
Wilson, C.E., and W.G. Weissert. 1989. Private long-term care insurance: After coverage restrictions is
there anything left? Inquiry: A Journal of Health Care Organization Provision and Financing 26
(4): 493–507.
Wittenberg, R., B. Sandhu, and M. Knapp. 2003. Funding long-term care: The public and private options.
In Funding health care: Options for Europe, ed. E. Mossialos, A. Dixon, J. Figueras, and J. Kutzin,
226–249. Maidenhead: Open University Press.
Xu, X., and P. Zweifel. 2014. Bilateral intergenerational moral hazard: Empirical evidence from China.
The Geneva Papers on Risk and Insurance—Issues and Practice 39 (4): 651–667.
Yang, W., A. Jingwei He, L. Fang, and E. Mossialos. 2016. Financing institutional long-term care for
the elderly in China: A policy evaluation of new models. Health Policy and Planning 31 (10):
Zhou-Richter, T., M.J. Browne, and H. Gründl. 2010. Don’t they care? Or, are they just unaware? Risk
perception and the demand for long-term care insurance. Journal of Risk and Insurance 77 (4):
Zick, C.D., C.J. Mathews, J.S. Roberts, R. Cook-Deegan, R.J. Pokorski, and R.C. Green. 2005. Genetic
testing for Alzheimer’s disease and its impact on insurance purchasing behavior. Health Affairs 24
(2): 483–490.
Zweifel, P. 1996. Providing for long-term care: Insurance vs. trust saving. The Geneva Papers on Risk and
Insurance—Issues and Practice 21 (79): 284–292.
Zweifel, P., and C. Courbage. 2016. Long-term care: Is there crowding out of informal care, private insur-
ance as well as saving? Asia-Pacific Journal of Risk and Insurance 10 (1): 107–132.
Zweifel, P., and W. Strüwe. 1996. Long-term care insurance and bequests as instruments for shaping
intergenerational relationships. The Journal of Risk and Uncertainty 12 (1): 65–76.
Zweifel, P., and W. Strüwe. 1998. Long-term care insurance in a two-generation model. Journal of Risk
and Insurance 65 (1): 13–32.
About the authors
Martin Eling is Professor of Insurance Management and Director of the Institute of Insurance Econom-
ics at the University St. Gallen, Switzerland. He received his doctorate from the University of Münster,
Germany and his habilitation from the University of St. Gallen. In 2008 he was Visiting Professor at the
University of Wisconsin-Madison. From 2009 to 2011, he was Professor for Insurance at the University
of Ulm, Germany.
Omid Ghavibazoo received an MSc in Actuarial Science from the Allameh Tabataba’i University, Iran.
Since 2017, he has been a doctoral student at the Institute of Insurance Economics, University of St.
... Private insurance can handle risk where the distribution of the adverse event, such as mortality rates by age and gender, is known. It cannot handle uncertainty where the distribution of the adverse event is unknown (Eling & Ghavibazoo, 2019). ...
... It also redistributes resources across the life cycle, since LTC for older people is concentrated in the last years of life but is funded by social insurance contributions or taxes paid during all or much of people's working lives. It could also be regarded as offering support where the family is not able, or no longer able, to provide the support which the person with care needs requires (Barr, 2010;Eling & Ghavibazoo, 2019). ...
... While this might suggest social insurance, it is important to note that it is not only social insurance schemes that pool risks: tax-based schemes also pool risks across the population. The difference between the systems does not lie in whether or not risks are pooled but in the way in which revenues are raised and allocated to fund LTC, as discussed above (Comas et al., 2010;Eling & Ghavibazoo, 2019;Roland et al., 2021;Wittenberg et al., 2002). ...
Full-text available
The issue of how best to finance long-term care (LTC) is the subject of recent reforms, forthcoming reforms or continuing debate in various countries and remains as relevant and challenging as ever. LTC services are crucial to the wellbeing of large numbers of older adults who need help with everyday tasks. Demand for LTC for older adults is projected to rise across developed and developing countries as the number of older adults rises. Supply of care services is likely to remain constrained due to shortages of long-term care workforce and financial constraints in many countries, and the financial risks associated with LTC remain. Financing of LTC is a complicated issue which raises considerations of economic efficiency and incentives, equity including intergenerational equity, the balance of risk between public and private funding, and sustainability of public expenditures. The aim of this paper is to discuss analytically the case for social insurance as an equitable and efficient way to finance LTC. The paper considers social insurance systems, especially in Germany and Japan, in comparison with safety net tax funded systems such as in England and the USA and more generous tax funded systems such as in Sweden and Denmark. Social insurance has advantages and disadvantages compared with these other systems. It tends to be associated with greater clarity and acceptability since it involves collection of revenues ear marked for LTC and, at least in principle, a link between contributions and benefits on the basis of clear eligibility criteria.
... For example, Houde and Gautam [20] reviewed the LTCI program in Japan and the present payment system of LTC services in the USA. Eling and Ghavibazoo [21] carried out a research review on three major research areas of LTCI: financing, demand, and insurability. Chen et al. [22] presented a review of the development of the public LTCI system in four respects, comprising a comparison of public LTCI systems in different countries, the influence and the challenge of public LTCI, and the relationship between public and private LTCI. ...
Full-text available
With the aging population increasing dramatically and the high cost of long-term care (LTC), long-term care insurance (LTCI) has expanded rapidly across the world. This review aims to summarize the status quo, evolution trends, and new frontiers of global LTCI research between 1984 and 2021 through a comprehensive retrospective analysis. A total of 1568 articles retrieved from the Web of Science Core Collection database were systematically analyzed using CiteSpace visualization software (CiteSpace 5.8. R2, developed by Dr. Chaomei Chen at Drexel University (Philadelphia, PA, USA)). The overall characteristics analysis showed that LTCI is an emerging research field in a rapid development stage—nearly 50% of articles were published in the past five years. The most productive LTCI research institutions and authors are located primarily in Japan and the USA. A rigorous analysis based on a dual perspective of references and keywords was applied to reveal that common LTCI hot topics include disability in the elderly, LTC financing, demand for and supply of LTCI, and LTCI systems. In addition, LTCI research trends have shifted from the supply side to the demand side, and from basic studies to practical applications. The new research frontiers are frailty in the elderly, demand for LTCI, and LTCI systems. These findings can provide help and reference for public health practitioners and researchers, as well as help with the sustainable development of LTCI research.
... Therefore, in the context of the advent of an ageing society and an active response to population ageing becoming a matter of national strategy, we have taken a comprehensive approach to assess the overall situation. On the basis of summarizing the pilot experience of the three northeastern provinces and fully absorbing the lessons from the reforms of typical developed countries [13,36,37], we have formulated a unified top-level plan for the nation that is conducive to promoting the rational establishment and steady progress of the LTCI system. ...
Full-text available
China’s population is ageing rapidly and the increase in life expectancy is accompanied by a loss of capability with advancing age, especially in the Northeast. This study adopts qualitative research methods to analyze the overall status and problems of China’s LTCI policy pilots. Taking four LTCI pilot cities in three northeastern provinces as samples, we used purposive sampling to recruit 10 beneficiaries and providers of LTCI in nursing homes of different kinds, as well as 2 operators (Medical Insurance Bureau staff) for semi-structured in-depth interviews. We developed a social welfare policy analysis framework based on Gilbert’s framework, designed interview outlines and conducted a thematic analysis of the interview data along five dimensions: allocation base, type of provision, delivery strategy, finance mode, and external environment. The results of the research indicate that the coverage of the system is narrow and that disability assessment criteria are fragmented; that the substance of service provision is lacking, both in terms of precision and dynamic adjustment mechanisms; that socialized care synergy cannot be achieved, informal care lacking policy support; that there is an over-reliance on medical insurance funds and that unfair financing standards are applied; and that economic and social development is insufficient to cope with ageing needs and uncertain risks. Accordingly, this research proposes several optimization options to promote the full establishment of LTCI.
... In contrast to the classic approach, the one presented above takes into account the mixed, public-private nature of financing, it emphasises the importance of prevention and preventive health care in the creation of long-term care costs, which, according to Eling and Ghavibazoo [25], makes ex ante and ex post mechanisms common in analyses of the long-term care market in many developed economies. ...
Full-text available
Background The concept of care for people in a critical or even terminal health condition, who are in the last stage of their life, has become the mission of palliative care facilities. Therefore, the life of a sick patient poses a number of challenges for health care services to make sure that medical services are tailored to the trajectory of the disease, as well as the various needs, preferences and resources of patients and their families. Methods Health systems financed from public funds need to adopt new methods of management to meet the high and arising demand for a long-term care. There are several ways of assessing the demand for long-term care services. The method recommended by the author and presented in more detail in this paper is the one relying on grey systems, which enables the estimation of forecasting models and, finally, actual forecasts of the number of potential future patients. Results GST can be used to make predictions about the future behaviour of the system, which is why this article aims to present the possibility of using the first-order grey model GM (1,1) in predicting the number of patients of palliative care facilities in Poland. The analysis covers the data from 2014 to 2019, whereas the prediction of the number of patients has been additionally formulated for 2020. Conclusions Health systems, particularly publicly funded ones, are characterised by a certain kind of incompleteness and uncertainty of data on the structure and behaviour of its individual components (e.g. potential patients or payers). The present study aims to prove how simple and effective grey systems models are in the decision-making process.
... Zietz (2003) provides a comprehensive review of theoretical and empirical studies addressing the relationship between risk aversion and life insurance demand and reports mixed results (i.e., negative, positive, and insignificant associations). For long-term care insurance, Eling and Ghavibazoo (2019) document the most important determinants of demand for long-term care insurance. They also report mixed results for the association between risk aversion and long-term care insurance demand. ...
We investigate the relationship between self-reported willingness to take financial risks and ownership of life insurance and long-term care insurance. For a representative sample of individuals aged 50+ from 14 countries and controlling for demographic and socioeconomic determinants of insurance demand, we find a positive link between willingness to take financial risks and ownership of both long-term care insurance and life insurance. The link is stronger for whole life insurance compared to term life insurance and long-term care insurance. Two robustness tests that (i) use risky asset ownership instead of willingness to take financial risks and (ii) focus on specific demographic and socioeconomic groups confirm the results for life insurance, while the results for long-term care insurance are less clear. Our empirical results cannot be explained by the classical expected utility framework and thus support recent research indicating that alternative models (e.g., higher-order risk attitudes or prospect theory) are needed to explain insurance demand.
Full-text available
Este trabajo explora la posibilidad de incluir una prestación adicional (en dinero) en las rentas vitalicias de jubilación (LCA) para ayudar a las parejas jubiladas a hacer frente al coste de la dependencia (LTC). El objetivo principal es evaluar cuánto costaría agregar un flujo adicional de prestaciones a las rentas vitalicias de jubilación para las parejas si uno o ambos miembros caen en el estado de dependencia La valoración de la renta vitalicia para parejas (LACc) se realiza mediante un modelo multiestado de Markov (illness-death multistate model) El impacto de la introducción de la contingencia de dependencia en la renta vitalicia para parejas se evalúa comparando las prestaciones iniciales en ambos casos (LCAc1 y JAc1): Joint life care annuity with a last survivor payout rule (LCAc1) (incluye dependencia; renta vitalicia de jubilación con ayuda a la dependencia para parejas que se extingue al segundo fallecimiento) Joint life annuity with a last survivor payout rule (JAc1) (no incluye dependencia; renta vitalicia de jubilación para parejas que se extingue al segundo fallecimiento) La diferencia en la prestación inicial es consecuencia de los factores actuariales que valoran el coste de la cobertura para el asegurador/promotor en ambos tipos de rentas. En el trabajo también se analiza la disposición de las parejas a elegir este tipo de LCA. Basado en datos de Australia e independencia de los riesgos involucrados, se ilustra numéricamente de manera bastante realista el modelo y algunas de las implicaciones teóricas. El trabajo destaca la importancia de informar sobre: los años esperados de vida de ambos cónyuges (esperanza de vida conjunta)… y los años esperados de viudez del cónyuge sobreviviente (esperanza de vida del sobreviviente) desglosados por estado de salud, dado que: esta información dota de transparencia al cálculo de los factores actuariales … y proporciona información muy útil para ayudar a la pareja a comprender la necesidad de protegerse contra el incremento de coste asociado a los distintos estados de dependencia.
Full-text available
This paper examines the possibility of including cash-for-care benefits in life care annuities (LCAs) to help retired couples cope with the cost of long-term care (LTC). It aims to assess how much it would cost to add an extra stream of payments to annuities for couples should either or both require LTC. We present an actuarial method based on array calculus to value this type of LCA. The impact of introducing the LTC contingency on the annuity is assessed by comparing the initial benefits in both cases. The difference in the initial benefit arises due to the annuity factors used to compute the benefits. We also analyse how willing couples would be to choose this type of LCA. Using Australian LTC transition probability data for a realistic calibration and assuming independence of the risks involved, we numerically illustrate the model and the theoretical findings implied. The paper highlights the importance of reporting the expected years both spouses will be alive (joint life expectancy) and the expected years the surviving spouse will be a widow(er) (survivor life expectancy) broken down by health state, given that this information makes the computation of the actuarial factors transparent and provides highly useful information to help the couple understand the need to be protected against the cost of LTC services. JEL: G22, H55, I13, J14, J26
Aging societies need efficient and flexible systems to finance care for the frail elderly. We study pre‐retirees' demand for flexible insurance that can finance informal long‐term care by paying income in poor health states instead of reimbursing formal care costs. We collect and analyze stated preferences for this long‐term care income product, and preferences for informal care. When asked to allocate wealth to a life annuity, a liquid investment and flexible long‐term care insurance, around 75% of our sample of 1008 pre‐retirees choose some long‐term care cover. Study participants treat long‐term care income insurance as a complement to informal care from their families. Females who expect to rely exclusively on extensive care from family members prefer more cover than similar males. We also find that if long‐term care income insurance were available, some healthier seniors would purchase additional longevity insurance, using liquid funds otherwise set aside to self‐insure long‐term care risk.
Retirement incomes and aged care have both received considerable policy attention in Australia over the last three decades, but as in other countries, the two financing systems have remained largely separate in policy and practice. The problem is located at the intersection of wider policy debates about shifts from public to private welfare and concerns over intergenerational equity. This paper aims to integrate these related debates by incorporating aged care into the “pillars” framework used to understand retirement incomes. We propose an Aged Care Superannuation Levy applied to superannuation fund earnings, winding back the large and growing public tax concessions that support this pillar of private defined contribution pensions in Australia’s retirement income system. This revenue would create a social insurance pillar for aged care to stand alongside the four other pillars of retirement incomes that interact variously with aged care financing. Interest in a social insurance pillar of aged care financing is renewed in the light of the Royal Commission into quality and safety of aged care that reported in February 2021. As the Royal Commission recommendations on financing were divided and not taken up in government policy, the way remains opens for further consideration of the alternative approach proposed here.
Full-text available
This paper explores a notional defined contribution (NDC) approach aimed at helping pensioners to cope with the cost of long-term care (LTC). It develops the necessary technicalities to fully integrate an LTC benefit, graded according to the annuitant's degree of disability, into a generic NDC retirement framework with a minimum pension benefit for both contingencies. It also discusses the policy implications of various issues that should be taken into account before any decision is made to put the model into practice. Finally, to enable policymakers to better understand how the proposal would function, the paper includes a realistic numerical example.
Long-term care insurance (LTCI) has low acceptance by consumers due to cost, failure by consumers to face this risk, and deficiencies in the product. Part of the problem is that insureds want coverage against their long-term care expenses, but must bear the risks of predicting the number of years of long-term care coverage needs and future costs of long-term care itself, as well as rising premiums to keep the policy in force at the coverage level sought. This means a good share of the future long-term care risk is still borne by the insured, even after buying LTCI. Unlike property and liability, disability, and life insurance, where some portion of the pool incurs a loss in the current year, LTCI creates a pool where the losses shift to, and increase in, future years. This presents an imperfect match between risk shifting and risk financing. Hybrid products that combine long-term care benefits with annuities or universal life insurance in a well-integrated format may be more attractive combinations than stand-alone LTCI to meet the multiple finance and estate needs, income constraints, and risk allocations of many consumers.
This study uses the most recent wave of the Health and Retirement Study (HRS) to examine participation of aging households in the long-term care insurance market. Results suggest that households who perceived a need to move to a nursing home within the next five years and house holds with higher preference for risk management through insurance were more likely to have long-term care coverage. Interestingly, the households with higher levels of risk tolerance were less likely to have long-term care c overage. Conversely, households with a bequest motive were more likely to have long-term care coverage. Additionally, the empirical results of this study indicate that the probability of having long-term care coverage was higher for women. Households with greater educational attainment and greater net worth were also more likely to have long-term care coverage.
Despite facing significant uncertainty about their lifespans and health care costs, most retirees do not buy annuities or long-term care insurance. In this paper, I find that retirees’ saving and insurance choices are highly inconsistent with standard life-cycle models in which people care only about their own consumption but match well models in which bequests are luxury goods. Bequest motives tend to reduce the value of insurance by reducing the opportunity cost of precautionary saving. The results suggest that bequest motives significantly increase saving and significantly decrease purchases of long-term care insurance and annuities.
Due to the demographic changes and population aging occurring in many countries, the financing of long-term care (LTC) poses a systemic threat. The scarcity of knowledge about the probability of an elderly person needing help with activities of daily living has hindered the development of insurance solutions that complement existing social systems. In this paper, we consider two models: a frailty level model that studies the evolution of a dependent person through mild, moderate and severe dependency states to death and a type of care model that distinguishes between care received at home and care received in an institution. We develop and interpret the expressions for the state- and time-dependent transition probabilities in a semi-Markov framework. Then, we empirically assess these probabilities using a novel longitudinal dataset covering all LTC needs in Switzerland over a 20-year period. As a key result, we are the first to derive dependence probability tables by acuity level, gender and age for the Swiss population. We find that the transition probabilities differ significantly by gender, age and time spent in the frailty level and type of care states.
Long-term care is a sector of the healthcare industry that is growing in importance with the aging of populations around the world. The risk of needing long-term care is, in theory, an appropriate risk to be insured against, but the private long-term care insurance market has remained small. In a growing body of work, economists have identified and focused on features of the market that distinguish it from other types of insurance. But despite the importance for public policy of economic theory and empirical evidence on long-term care insurance, significant gaps remain in the understanding of this market.