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date: 19 October 2019

The Economics of Long-Term Care

Summary and Keywords

Long-term care (LTC) is arguably the sector of the economy that is most sensitive to population aging: its recipients are typically older than 80 years whereas most care providers are of working age. Thus, a number of ongoing societal trends interact in the determination of market outcomes in the LTC sector: trends in longevity and healthy life expectancy interact with changing family structures and norms in shaping the need for services. The supply side is additionally affected by changes in employment patterns, in particular regarding the transition into retirement, as well as by cross-regional imbalances in demographic and economic conditions. The economic literature on long-term care considers many of these issues, aims at understanding this steadily growing sector, and at guiding policy. Key economic studies on long-term care address determinants of the demand for long-term care, like disability and socio-economic status; the two most important providers: informal family caregivers and nursing homes; and the financing and funding of LTC.

Population Aging Puts Economics of Long-Term Care on the Agenda

In the decades to come, long-term care for older people (LTC) is expected to be one of the most important and challenging policy issues for developed societies worldwide. LTC has aroused interest of researchers in different fields such as medicine and nursing, sociology, political science, healthcare management, as well as economics. Although it is receiving increasing attention by the research community, the role of long-term care in health economic research is still rather small compared to its role in society and political debate.

The increasing importance of the economics of LTC is apparent already in the development of LTC expenditure. Figure 1 compares the proportion of total LTC expenditure GDP over time for selected countries: Austria, France, Germany, Italy, the Netherlands, and the United States. For all countries except the United States, a steady increase is observed. This trend is expected to continue. In the EU, the share of public LTC expenditures as percentage of GDP amounted to about 1.5 % and is expected to almost double until 2060 (OECD/European Union, 2016). A similar story applies to other developed countries.

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Figure 1. Long-Term Care Expenditures (as % of GDP).

Source: OECD.

The topic of long-term care is closely related to the phenomenon of demographic change which is characterized by an increase in life expectancy and a reduction in fertility. It is mainly population aging that is responsible for the increase in LTC expenditures:

• Higher life expectancy causes an increase in the number of older people. This probably increases the demand for long-term care; in particular as life expectancy increases faster than healthy life expectancy.

• Low birth rates lead to an increase in the old-age dependency ratio (the number of older people per working age individual) inducing a higher financial burden of health and LTC costs per capita.

• A reduced number of working-age individuals leads to another challenge: Most LTC is provided informally by family members, particularly women. These women will be needed on the labor market in the decades to come, setting the supply side of LTC under pressure.

Life expectancy has been rising significantly throughout the world. Figure 2a shows trends in life expectancy at birth between 1980 and 2015 for selected countries. All countries experienced a clear positive trend in life expectancy. There has been a long debate in the health academic literature of whether these trends coincide with a compression of morbidity (Fries, 1980) or an extension of morbidity (Gruenberg, 1977). A parallel literature in health economics studies whether health expenditures increase with an individual’s age or whether it is only proximity to death that matters (Karlsson, Mayhew, & Rickayzen, 2008). While no clear consensus has emerged on this issue, it is fair to say that age does affect LTC expenditures (see “The Demand for Long-Term Care” and Karlsson, Iversen, & Øien, 2018 for more on this topic).

The challenge of increased longevity is reinforced by another phenomenon, reduction in birth rates. The natural consequence of increasing life expectancy and low birth rates is a rising old-age dependency ratio, i.e., the number of individuals aged 65 and above divided by the working-age (15–64) population (Figure 2b). The old-age dependency ratio can be interpreted as an indicator of the burden on the working-age population due to retired and care-dependent individuals.

These trends imply that not only more and more people potentially demand LTC but also that fewer people are available for provision and financing, leading to a massive burden. Further, adverse effects on health and labor market outcomes of informal caregivers such as spouses and children are possible. Not only informal care obligations but also increased labor demand in the formal LTC sector may indirectly affect other sectors of the economy.

Most economic research on LTC for older people falls within three broad areas. The first deals with social and economic determinants of demand for LTC services. This is instrumental for projections of future costs, but also for considerations of equity in national LTC funding schemes—and for policy questions related to the optimal allocation between prevention and care services. Second, the supply of different types of care services, and their effectiveness and quality in covering needs in the population, has been an important object of study. The different financing and funding regimes for LTC and their implications for economic efficiency and equity is a third important research area.

In this article, we summarize economic research in the field of LTC. We narrowly define LTC as care for older people. Hence, we do not cover issues of care for younger people with

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Figure 2. Demographic Trends.

Source: OECD.

disabilities. We address all three main research areas introduced above and point out gaps in the research. The structure of this chapter is as follows: We provide literature reviews on the demand (see “The Demand for Long-Term Care”) and supply of LTC services (see “The Supply of Long-Term Care”) as well as on financing aspects (see “Long-Term Care Financing and Funding Regimes”). Finally, we conclude with a brief summary and try to identify gaps that require further research (see “Summary and Outlook”).

It is not the aim of this article to provide an exhaustive review of all strands of the literature within the three domains. There are quite a number of excellent surveys that fulfill this goal and we mention some of them in the section “Further Reading.” Instead, we focus on selected aspects only, and aim to put most weight on the—in our opinion—most credible studies in the field. These studies are discussed and evaluated in great depth in order to point out what makes them particularly relevant. This approach comes at the expense of making some striking omissions, such as a detailed discussion of studies in industrial organization or entire subfields of the literature such as the one on formal home care. This is not to be understood as a judgment on the quality of this research; it is merely a necessary action in order to keep the review focused. Most, though not all, of the literature discussed is empirical work using micro data.

The Demand for Long-Term Care

Introduction

Modeling LTC Demand

In order to structure the following exposition, we present a theoretical model of LTC demand within a family decision-making context. Our model is a slightly adapted version of the model used by Stabile, Laporte, and Coyte (2006).

Thus, consider a household with utility defined by the function

$Display mathematics$
(1)

where X represents market goods and services, L leisure, and A the ability of care recipients to perform activities of daily living. The variable A is in turn a function of the care recipient’s disability status and of the utilization of care:

$Display mathematics$
(2)

where F represents formal care and C denotes informal care provided by family members. D is the current disability status of the care recipient. The household faces the resource constraint

$Display mathematics$
(3)

where PX is the unit cost of X , PF the cost of formal care; V represents non-wage income and the wage rate is W. T is the total time available for leisure, care-giving, and labor market work.

The household’s decision problem thus involves choosing (F, C, X, L) to maximize household utility, given the constraints. This leads to the Lagrangian

$Display mathematics$
(4)

Under the additional assumption that the function U (X, L, A (F, C | D)) is concave with respect to (X, L, F, C), an interior solution will be given by the first-order conditions

$Display mathematics$
(5)

$Display mathematics$
(6)

$Display mathematics$
(7)

$Display mathematics$
(8)

$Display mathematics$
(9)

These FOCs provide some useful information on the determinants of LTC utilization. For example, the relationship between formal and informal care will be governed by the relationship $ACAF=WPF$ so that a higher opportunity cost of the caregiver, W, a lower price of formal care services, PF, and greater productivity in formal care, AF will lead to less utilization of informal care. Moreover, the model can be used to evaluate the effects of various changes in the exogenous parameters. Such an exercise would determine the response in demand for informal (C* = C (H, W, V, PF, PX)) and formal (F* = F (H, W, V, PF, PX)) care. The exact shape of these demand functions will depend on the substitutability between formal and informal care in the production of performance ability, A, and on the marginal rate of substitution between different arguments of the utility function.

Evidently, the disability status of the care recipient is a key determinant of the demand for LTC services. Starting from a position where the household’s marginal valuation of consumption and leisure is independent of the functional ability of the care recipient (UAX = 0 and UAL = 0 respectively), a deterioration in physical health will result in increased demand for formal and informal care. This is evident already from the first-order conditions, since UA will take on a larger value when the care recipient is in poorer health. If a deterioration in disability is associated with a change in the marginal utility of consumption, the relationship between health and demand for LTC will either be weaker or stronger than in the baseline case.

This simple model captures some of the most salient aspects of the problem facing households with care needs, and it is helpful for the interpretation of empirical work, which often does not make a clear distinction between demand and utilization. That said, it does have some clear limitations, since, for example, dynamic issues or the family bargaining problem are left aside.

Determinants of LTC Need

Given that the disability status of the care recipient appears to be a key determinant of demand for LTC services, it deserves some further consideration. Economic research has made rapid progress in this area in recent years, by bringing in perspectives from biology. This biological literature in turn draws on reliability theory from engineering. A seminal contribution to this biological literature was made by Gavrilov and Gavrilova (1991), who model the human body as a fixed number of individual parts, which do not age. In this model, the aging process is a progressive reduction in redundancy, which ends with the failure of the organism, i.e., death. This modeling approach is able to capture a number of characteristics of the aging process; most importantly, that it is progressive, as the failure of a component forces other blocks to work harder, which in turn increases the probability of their failure.

Following these insights from reliability theory, empirical work has studied the pace at which human beings accumulate the deficits that eventually lead to physical limitations and death. Mitnitski Mogilner, MacKnight, and Rockwood (2002) show that the following equation captures an individual’s accumulation of deficits very well:

$Display mathematics$
(10)

where D (t) is the number of deficits at age t, µ is a physiological parameter (“the force of aging”) and E represents the impact of nonbiological factors on the aging process. Both parameters have been estimated with great precision in a number of different populations and the functional form appears to be the same everywhere.

Dalgaard and Strulik (2014) introduce this perspective on frailty in economics. In their version of the model, individuals may slow down the accumulation of deficits by undertaking health investments. Hence, Dalgaard and Strulik (2014) model deficit accumulation as

$Display mathematics$
(11)

where a is an autonomous parameter, reflecting influences of the environment which are beyond the individual’s control, h (t) represents health investments at time t, A represents the productivity of these investments, and γ‎ determines the economies of scale.

Dalgaard and Strulik (2014) incorporate this law of motion into an economic model, where individuals maximize their lifetime utility by means of decisions regarding consumption and health investments. They calibrate their model to the U.S. economy in 2000 and find a remarkably good fit on some variables. In counterfactual experiments, they vary a number of exogenous variables and study their impact on the accumulation of health deficit. Accordingly, variation in incomes and in the productivity of health investments has large effects on health deficit accumulation. Subsequent papers have developed the Dalgaard-Strulik model to take some further aspects into account. Dalgaard and Strulik (2017) incorporate a retirement decision and study the effects of education on aging. Education is found to have a large effect on health deficit accumulation and longevity. A paper by Schünemann, Strulik, and Trimborn (2017) studies the gender gap in mortality and finds that gender-specific preferences appear to be responsible for a large part of the gap.

Concluding Remarks

The above exposition has shown that the key determinants of demand for LTC services may be summarized in a relatively parsimonious model. Accordingly, the key variables appear to be the individual’s needs, the household’s wealth and income, potential informal caregivers’ opportunity costs and the prices of formal care services. In addition, the degree of substitutability between formal and informal care, and the impact on frailty on the utility function, are important aspects of the problem as well. An empirical analysis of demand for LTC needs to take into account that most of these determinants are endogenous. This is arguably not necessarily the case for prices and wages, but needs, income and wealth, and the availability of informal caregivers will typically correlate with preferences and other unobserved personal characteristics in a way that may bias estimates of their effects.

In addition, as our treatment of the Dalgaard-Strulik model makes clear, the determinants of LTC needs exhibit a great degree of overlap with the determinants of demand: income, wealth, education, and labor market attachment are variables that likely have an impact on both variables. This adds an additional identification challenge: it is difficult, in some cases impossible, to separate direct effects on demand from those that are mediated through LTC needs. For example, to the extent that education is an important determinant of both outcomes, exogenous changes in education can only identify the combined direct and indirect effects. On the other hand, for variables that vary later in life, such as labor market earnings, it might be possible to identify two sources of exogenous variation, which in turn would allow a separation of direct and indirect effects.

Empirical Studies of Demand for LTC Services

Income as a Determinant of Care Demand

As mentioned above, income is a potential determinant of care needs and of demand for care, and it is likely to be an endogenous variable in both dimensions. Two American studies, Goda, Golberstein, and Grabowski (2011) and Tsai (2015), deal with this endogeneity issue by considering random and permanent shocks to income at older ages, given by the U.S. Social Security Benefits “notch.” This “notch” was introduced due to changes in indexation of social security benefits in the 1970s and generates an arbitrary, and strong, hump in postretirement income affecting individuals born around 1915.

Both studies base their analysis on a regression equation of the type

$Display mathematics$
(12)

where Ui represents utilization of some type of service in for individual i, SSi represents social security income, and Xi a set of exogenous controls. These controls include a number of exogenous characteristics of the individual such as race, education, and age, but exclude variables believed to be endogenous to social security income. To the extent that the income variable SSi can be seen as exogenous, β‎ thus represents the total effect of income on utilization, including all behavioral responses to a change in incomes.

There are a number of reasons to expect ε‎i to be correlated with SSi. For example, health and preferences for independent living are components of ε‎i which are likely to be correlated with income. For this reason, an instrumental variable approach is used in order to address the endogeneity problem. The first stage is specified as

$Display mathematics$
(13)

where Ni is a dummy variable taking on the value 1 if the household head belongs to the pivotal birth cohorts.

Long-Term Care Financing and Funding Regimes

The final part of this article deals with economic research in the field of LTC financing and funding regimes. Two aspects have received great attention by the research community, namely (1) the small size of the market for LTC insurance and (2) fiscal implications of LTC funding regimes. We provide the most important insights from these research areas.

As current projections indicate a continuous increase in the expenditure on LTC in the decades to come and private expenditure accounts for a significant share of total expenditure, it appears puzzling that the LTC insurance market still is relatively small (Brown & Finkelstein, 2011). Several studies were able to identify possible explanations for this phenomenon.

On the demand side, three different factors were found to lead to low take-up: low preferences for existing insurance products, informal care availability and family relationships, and bequest motives. Ameriks, Briggs, Caplin, Shapiro, and Tonetti (2018) develop a structural life-cycle model to evaluate whether low long-term care insurance purchases are caused by a crowding-out due to public care provision, unmet demand and poor-quality insurance products, or simply a lack of desire to insure against the risk. The authors show that a relatively large proportion of the population generally demands LTC insurance but existing products do not fit the demanded quality. Further, heterogeneity in individual preferences explains a substantial part of the LTC insurance puzzle. The dynamic model by Mommaerts (2015) confirms both the importance of individual preferences and a lack of high-quality insurance products as informal care availability appears to reduce LTC insurance purchases especially for wealthy people, and insurance take-up might be increased if insurance products covered informal care.

Not only in theoretical but also in empirical studies, the family is considered as an important determinant of LTC insurance demand. Costa-Font (2010) studies the effect of family ties on LTC insurance take-up. Two different measures for family ties are considered; the first is simply the distance to the child that lives nearest, and the second is a composite index consisting of the distance to the closest child, the respondent’s opinion regarding caregiving duties, and familistic values. The probit model results indicate a negative association between family ties and long-term care insurance demand in Europe. Further, Costa-Font and Courbage (2015) study the effect of expecting family funding for LTC on the expectations of private insurance funding. They take the potential endogeneity of family funding into account by instrumenting it with residential distance to family members and self-reported family ties. The results indicate that family support crowds-out individual incentives to seek LTC insurance. A special role is accounted to the relationship between parents and adult children. However, the distance to the nearest child might be an invalid instrument for informal care provision and thus the interpretation of the results as causal effects becomes questionable.

But the family might not only affect an individual’s LTC insurance purchase via informal care supply. According to a study by Zhou-Richter, Browne, and Gründl (2010), children’s awareness of financial risks due to LTC needs can positively affect their parents’ demand for insurance. Further, experience with LTC histories of parents is suggested to influence insurance take-up as well. Coe, Skira, and Van Houtven (2015) estimate the probability of having LTC insurance as a function of parents’ and parents in-laws’ care receipt, finding that experiencing the previous generation’s nursing home use significantly increases the probability of a LTC insurance purchase. Providing care to an in-law is shown to significantly reduce the LTC insurance purchase probability among wealthier individuals. This is interpreted as a sign of strong family responsibility norms causing individuals to self-insure against LTC in order to leave a large bequest to relatives. Theoretical evidence for the importance of bequest motives is provided by Lockwood (2018) who develops a life-cycle model that suggests bequest motives to increase savings and decrease LTC insurance purchases.

Brown and Finkelstein (2007) show for the United States that there are gender differences in LTC insurance pricing but not in coverage. However, the small market size might also be due to supply-side market failures, as there is evidence of prices being higher than actuarially fair, and of quantity rationing. Braun, Kopecky, and Koreshkova (2018) and Hendren (2013) construct theoretical models that explain insurance rejections as consequences of private information. Hendren (2013) argues that individuals who are not eligible for LTC insurance purchase—such as those who had a stroke—may not only be aware of their own health characteristics that are observable by the insurer but also about unobservable factors and preferences which are direct consequences of their health conditions. Thus, this group would be regarded as so adversely selected that offering insurance contracts to them could not result in positive profits for the insurer, which explains the rejections. Braun et al. (2018) consider administrative costs as important supply-side factors additional to private information.

This article concludes with an overview of studies analyzing fiscal implications of LTC funding regimes. Karlsson, Mayhew, and Rickayzen (2007) as well as Wouterse and Smid (2017) apply scenario analyses to determine the fiscal effects of introducing new LTC funding schemes in the United Kingdom and the Netherlands, respectively. The paper by Karlsson et al. (2007) evaluates hypothetical adoptions of the Swedish, German, and Japanese LTC regimes in the United Kingdom to find that each system is associated with an increase in taxes. Different population groups in terms of gender and age would benefit differently from the alternative systems and the results are highly sensitive to the measure of benefits. Wouterse and Smid (2017) compare alternative policies to finance the growth in LTC utilization. Their results indicate that adverse economic affects via lower labor supply caused by higher premium rates are unavoidable. However, there are differences in the timing of the effects. Direct substantial increases in the premium rates lead to large short-term effects but smaller effects in later years, whereas gradual increases have small short-run and large long-term effects.

Several papers evaluate more modest changes in the national LTC funding schemes. Bergquist, Costa-i-Font, and Swartz (2015) estimate the effect of the LTC partnership program—an initiative to encourage individuals to purchase private LTC insurance in the United States—on Medicaid long-term care expenditures. Their OLS and GLS fixed effects results do not show significant effects on expenditures, which is possibly due to a problematic design in the program and a target group limited to middle-class individuals. Kopecky and Koreshkova (2014) build a life-cycle model calibrated to the United States to determine the welfare effects of extending nursing home cost coverage by social insurance programs. The authors find individuals to benefit from social insurance extensions financed via increases in income taxes. In a study on Germany, Geyer, Haan, and Korfhage (2017) look at the fiscal effects of informal care applying a structural model. According to their results, an increase in formal care financed by a reduction of informal care subsidies leads to positive effects on the government budget.

Summary and Outlook

This article has provided a broad overview of economic research in the field of long-term care. Starting with a brief introduction explaining the relationship between demographic change and long-term care, we identified the increase in life expectancy, the decrease in birth rates, and the increase in healthy life expectancy below the increase of total life expectancy as the main factors making long-term care one of the most important topics in developed countries in the 21st century. A detailed literature review summarized economic research on the three main areas of LTC demand, LTC supply, and LTC financing and funding. Further, as economic research on LTC is rapidly growing but still rather small compared to its potential importance, there are several remaining gaps in the literature which we would like to spell out.

In terms of substance: (1) Analyses of some crucial aspects of LTC are inadequate or missing. For instance, not much has been published about nursing home residents in general, probably due to lack of data on this particular group. There is also little work on assisted living and continuing care facilities. Furthermore, there is much work to be done in industrial organization regarding the long-term care market (e.g., entry/exit of nursing homes and home healthcare providers). Another example is that the literature largely neglects regional economic issues for demographic aging and LTC. Moreover, there is little (theoretical) understanding of the behavioral mechanisms behind the emergence of LTC needs and means over the individual’s life-cycle or, indeed, how they are linked to different modes of LTC provision and funding as well as the resulting outcomes. (2) Many of the studied research questions are far from being answered; the puzzle of low demand for LTCI being one example. Also, little is known about inequality in the needs and means of LTC and how it could be addressed. (3) Even now, most of the empirical and calibrated work on LTC based on studies in and of the United States.

In terms of methodology and data: (4) The number of studies exploiting natural experiments to achieve results of highest credibility could be increased. (5) There is also more work to be done using administrative data covering individuals and/or nursing homes. (6) While there is a growing body of research that incorporates dynamics (see Sovinsky & Stern, 2016, for a review), the inclusion of dynamics in models and empirical analyses of long-term care is still in its infancy.

Other gaps in the literature might be attributable to a lack of concerted research efforts, leading to a highly disparate field of study. This is expressed in (7) strict separation between empirical reduced-form and structural models with few attempts to exploit the best of both worlds, (8) few overarching models and frameworks for the analysis of LTC.

The field of economics of long-term care is still developing. Future studies will address these gaps.

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Notes:

(1.) Many more papers analyze this matter. However, we would like to focus on those that use the most convincing exogenous variation in this part of the text.

(2.) As in the study of Løken et al. (2017), the results are also evidence for a substitution between formal and informal care as discussed in “The Demand for Long-Term Care.”

(3.) Michaud (2010) estimates a structural dynamic model but does not consider the long-run perspective.

(4.) How to adequately measure quality of care is another field of research that is outside the scope of this article.