Long-term Care Insurance Policy Dropping in the U.S. from 1996 to 2000: Evidence and Implications for Long-term Care Financing

Transcription

1 The Geneva Papers on Risk and Insurance Vol. 29 No. 4 (October 2004) Long-term Care Insurance Policy Dropping in the U.S. from 1996 to 2000: Evidence and Implications for Long-term Care Financing by Paul E. McNamara and Nayoung Lee* While the market for private long-term care insurance in the U.S. has grown dramatically, consumer advocates have argued for increased regulatory attention and for broadened consumer education programs concerning long-term care insurance. We analyse Health and Retirement Survey data from 1996, 1998, and 2000 using a zero-inflated negative binomial regression model of the counts of consecutive periods of long-term care insurance coverage. We find that while a significant proportion of Americans over the age of 50 purchase long-term care insurance, many of these purchasers drop their coverage within a five-year period. This finding raises questions for long-term care insurance researchers and it contains implications for market regulators, public policy makers interested in financing long-term care, as well as for insurance companies and consumer advocates. Private long-term care insurance simply cannot be taken seriously as a mechanism for financing nursing home and home care if half or more of initial purchasers end up without coverage when it comes time to use its services. (Wiener, Illston, and Hanley, 1994, p. 107) 1. Introduction The challenge of financing long-term care in the U.S. continues to face the public sector as well as consumers, and, while long-term care insurance coverage is growing, coverage remains at relatively low levels. Nonetheless, many individuals purchase longterm care insurance and some observers feel that privately issued long-term care insurance is poised to play a more prominent role in the future in the financing of long-term care services. Indeed, some analysts feel that privately issued long-term care insurance products offer a means to help shore up the Medicaid system s finances as well as offering greater financial security to retirees (Cohen, 2003). While the private long-term care insurance market appears to be growing, it is characterized by high selling-costs and a low (though increasing) percentage of policies purchased through groups. Despite this growth and interest, little economic research exists that describes and analyses the dynamic behavior of long-term care policy purchasers in the U.S. This paper presents an analysis of the dynamics of long-term care insurance coverage over the period 1996 through We find a high rate of policy dropping (a policy purchase followed by a period without coverage) in our sample. As the above quotation suggests, this finding raises the question as to how far the U.S. private long-term care insurance market has progressed towards being a serious mechanism for financing long-term care expenses of the elderly. We argue that the existence of * University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, 1301 West Gregory Drive, 437 Mumford Hall, Urbana, Illinois 61801, U.S. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK.

2 LONG-TERM CARE INSURANCE POLICY DROPPING IN THE U.S. 641 significant levels of policy dropping contains implications for researchers, public policy, and for insurance companies and consumer advocates. The next section of the paper provides an overview of the previous research related to the issue. This is followed by a discussion of the study s data and a presentation of descriptive statistics on long-term care insurance policy dropping over the period. The following section presents the zero-inflated negative binomial regression model used in this analysis, along with an argument for its application in this case. Presentation of the count regression model results appears in section 5. The paper concludes with a distillation of the implications of the analysis for the public policy debate and research surrounding the structure of long-term care financing in the U.S. 2. Background and previous research While many observers concentrate on the relatively small size of the market for longterm care insurance in the U.S., this market has actually experienced remarkable growth over the past 20 years. In December ,000 policies had been sold and by December 1992 over 2.9 million policies had been sold (Wiener et al., 1994). By the end of 1993 about 3.4 million private policies were sold, representing coverage for between 5 to 6 per cent of the U.S. elderly population (Norton, 2000). For 1995, Cohen (1998) states that 4.3 million policies were held by individuals. By the end of 2001, 8.26 million long-term care insurance policies had been sold in the U.S., and the market experienced an average annual growth rate of 18 per cent over the time period of 1987 to 2001 (HIAA, 2003). While 8.26 million longterm care insurance policies sold by 2001 may seem high relative to the roughly 58 million Americans aged 55 and older in that year (U.S. Census Bureau, 2004), not all of these policies were in force in In 1992, for example, Wiener et al. (1994) state that 2 million policies were in force compared to 2.9 million sold. Previous economic research into the demand for long-term care insurance has explored a number of explanations for the limited size of the U.S. long-term care insurance market. One strand of previous research has emphasized the critical role that the presence of the Medicaid program has on the incentives for consumers to purchase long-term care insurance, and the crowding out role of Medicaid (Sloan and Norton, 1997). Pauly (1990) argues that private long-term care insurance serves primarily to protect bequests and, on the margin, the value of protecting bequests is not worth the trade-off from current consumption, given the price for the insurance contract. Pauly (1990) also suggests that bargaining factors within the household and the possibility that children might use formal care to substitute for informal care provided by them serve as a limiting factor in the market. However, empirical research has provided mixed evidence concerning the importance of the existence of substitute sources of care and the effect of asymmetric information on the overall size of the long-term care insurance market. Mellor (2001) examined data from the Asset and Health Dynamics survey and the Panel Study of Income Dynamics to measure the effect of the availability of potential caregivers on the demand for long-term care insurance. Her analysis found no statistically significant effect of these care-giving substitutes on the demand for long-term care insurance. A study of asymmetric information in the long-term care insurance market finds that, while individuals may hold private information about their risk type, they also vary by risk preferences (Finkelstein and McGarry, 2003). They find evidence of a case of multiple forms of private information leading to a market equilibrium that is economically inefficient, although it might appear to be one of symmetric information by some economic diagnostic approaches.

3 642 MCNAMARA AND LEE However, another explanation for the limited size of the private long-term care insurance market is that consumers face unique challenges in understanding the nature of the financial risk posed by long-term care and in evaluating insurance products and other strategies to help manage these risks. Indeed, many observers see lack of information as a critical dimension in explaining the current size of the market. For instance, Cohen (1998) states: Even as the long-term care insurance market grows at an annual rate of about 25 per cent, only 4.3 million individuals purchased policies by The reasons why individuals choose to buy or not to buy policies reflect the price of products, attitudes about insurance and the role of government, views about family responsibility and, perhaps most importantly, a lack of information about the risk, current coverages, and the availability of the insurance. A market characterized by a lack of consumer information and consumer-side difficulties in understanding the risk of long-term care and insurance policy alternatives in bearing the risk would lead to at least two observable traits characterizing the demand for long-term care insurance. First, a lack of consumer information might lead to an environment where marketing costs are high, as insurers seek to inform consumers about risks and to influence them to purchase a policy. The long-term care insurance market is characterized by this, as a high percentage of the first year premium (sometimes above 60 per cent) is allocated to sales, marketing, and administrative costs (Cutler, 1993). Wiener et al. (1994) report that first-year commissions to the selling agent range from 45 to 70 per cent of the total premium (p. 109). A second characteristic would be consumer search behavior as consumers seek out information about the product, test it, and perhaps switch or drop the use of the insurance product. This might be observable in the dynamic behavior of long-term care insurance consumers, if consumers buy policies and then learn that the policies are too expensive given their benefits and fail to make their premium payments, thereby dropping their policies. With regard to lapsing or policy-dropping behavior, little published research is available and, to our knowledge, no studies exist that examine the economics of policy dropping. Writing in 1994, Wiener et al. (p. 105) state that the insurance industry commonly assumes that, not counting the insured who die, approximately half of all insured people will drop their policies within the first five years of purchase and approximately 70 per cent will drop their policies within 15 years. From the consumer s perspective, policy dropping appears to be a particularly expensive means to gather data on the suitability of long-term care insurance as a means to achieve financial security goals, such as leaving a bequest, assuring access to quality long-term care, and maintaining income and assets for a spouse or partner. The inefficiency of this strategy is highlighted when one considers that with level premiums and the increasing risk of long-term care usage as a function of age, a consumer who drops his or her coverage has overpaid for the value of the coverage during the period the policy was in force. 3. Data This study uses data from the Health and Retirement Study (HRS), which is conducted by the University of Michigan under the sponsorship of the National Institute on Aging (NIA). Moreover, the Social Security Administration, the Assistant Secretary for Planning and Evaluation (ASPE) in the U.S. Department of Health and Human Services (DHHS), and

4 LONG-TERM CARE INSURANCE POLICY DROPPING IN THE U.S. 643 the Pension and Welfare Benefit Office also supported the original HRS. The HRS is a longitudinal survey that follows 22,000 Americans over the age of 50 in order to understand the interrelationships between health status, financial and economic well-being, family structure and family supports, retirement planning behaviors, and labor market participation. The creation of HRS was to achieve follow-ups for qualified individuals including their spouses. Data were collected as they progressed from being actively employed personnel to the post-retirement period of their later life. The HRS seeks to explain the cause and consequences of retirement, and the fluctuating nature of health and wealth over periods of time. It examines the lifecycle from gathering wealth to consumption of wealth, and from work capability to disability. It also examines the various distributed resources regarding economic status and family program data that will affect the outcomes of retirement planning, health declines, and institutionalization. Finally, it presents a rich source of data that has been utilized by economists, sociologists, gerontologists, health services researchers, and others interested in health and retirement themes in the U.S. For this analysis, we examine responses to the question in the survey about long-term care (LTC) insurance, which states, Not including government programs, do you have any insurance which specifically pays any part of long-term care, such as personal or medical care in your home or in a nursing home? We have dropped observations with a response of other, don t know, and refused. Working with the 1996, 1998, and 2000 survey waves, we followed individuals and their responses to this question over the five-year period. The number of consecutive periods with LTC insurance coverage forms the dependent variable for this study. Table 1 provides the variable names and definitions for the dependent variable and the independent variables used in this analysis. While we do not directly observe the price of the LTC insurance policy and the terms of the policy, we do have information about criteria that are relevant in the underwriting process, such as age, self-reported health status, sex and employment status. In addition, information is reported in the HRS on income and assets, educational attainment, race/ethnicity, and marital status. To help control for variation in financial risk tolerance we use the ownership of a life insurance policy as a proxy variable. For the independent variables, the values used in our analysis all come from the 1996 survey. (We have run regressions with variables from the 1998 and 2000 survey, particularly to control for changes in the income and wealth variables. However, those change variables were not significant in the regression analyses.) Table 2 shows the distribution of the dependent variable as well as the spread of independent variable values across the LTC insurance coverage values. Note that 6,220 useable observations were available for this analysis. A primary cause for lost observations was missing income and asset values. However, missing values for other variables as well as sample attrition and death also contributed to the dropping of some observations. As Table 2 shows, after a large cluster of zero observations, a strong decay pattern exists in the number of consecutive periods with LTC insurance coverage. While 5,520 (88.95 per cent) of the observations did not have any periods with LTC insurance coverage, only 2.6 per cent of the sample had LTC insurance in each of the survey years. A strong association exists between the number of consecutive periods covered by long-term care insurance and variables such as income, asset levels, educational attainment, health status, employment status and sex. The average household income for a person without long-term care insurance in 1996 is U.S.$ 47,930, while the average income for a person who had long-term care insurance coverage in 1996 and maintained it through 2000 is U.S.$ 71,070. The difference in asset levels is even larger, with an average household asset

5 644 MCNAMARA AND LEE Variable Table 1: Variable names and definitions Definition Ltcyrs 0 ¼ doesn t have LTC insurance from 1996 to ¼ has LTC insurance in ¼ has LTC insurance in 1996 and ¼ has LTC insurance in 1996, 1998 and 2000 Age96 Age in 1996 AgeSquared96 Age-squared in 1996 Income96 total household income in 1996 (U.S.$1,000) IncomeSquared96 (total household income) 2 in 1996 (U.S.$1,000) Assets96 total household assets in 1996 (U.S.$1,000) AssetsSquared96 (total household assets) 2 in 1996 (U.S.$1,000) Dlessthanhs 1 ¼ less than high school, 0 otherwise Dhs 1 ¼ high school, 0 otherwise Dsomecollege 1 ¼ some college, 0 otherwise Dcollegeormore 1 ¼ college graduates and graduate school grads, 0 otherwise Dwhite 1 ¼ White non-hispanic, 0 otherwise Dblack 1 ¼ Black non-hispanic, 0 otherwise Dhispanic 1 ¼ Hispanic, 0 otherwise Dotherrace 1 ¼ other race, 0 otherwise Dhealthexcellent 1 ¼ excellent health, 0 otherwise Dhealthverygood 1 ¼ very good health, 0 otherwise Dhealthgood 1 ¼ good health, 0 otherwise Dhealthfairpoor 1 ¼ fair and poor health, 0 otherwise Dmarriedorlivingwpartner 1 ¼ someone who is married or living with someone, 0 otherwise Dwork 1 ¼ someone who is currently working, 0 otherwise Dfemale 1 ¼ female, 0 ¼ male Dlife 1 ¼ having life insurance in 1996, 0 no life insurance in 1996 level of U.S.$ 159,260 for people with no long-term care insurance in 1996, as compared to U.S.$ 379,770 for people who maintained coverage over the entire period. A similar pattern held with respect to education where roughly 25 per cent of individuals who maintained coverage over the three survey waves held graduate degrees and only 10.6 per cent of persons without long-term care insurance in 1996 held a graduate degree. The differences between characteristics of people without long-term care insurance coverage in 1996 and those who held coverage over the five-year period also hold for employment status, sex and health status. A greater percentage of people with long-term care insurance for three consecutive periods reported having excellent health (33.33 per cent) compared to those with no long-term care insurance coverage in 1996 reporting excellent health (19.46 per cent). Also, per cent of people with no long-term care insurance coverage in 1996 were working, while per cent of people with LTC insurance coverage for all three periods worked. Note that the people who purchased LTC insurance coverage and then dropped it after one or two periods were more likely to be

6 LONG-TERM CARE INSURANCE POLICY DROPPING IN THE U.S. 645 Table 2: Descriptive statistics for LTC insurance coverage and other study variables LTC year Dependent variable Freqency (%) 5520 (88.75) 442 (7.11) 96 (1.54) 162 (2.60) Independent variables Mean (#obs) Age (5520) (442) (96) (162) Total income (U.S.$, 000) (5520) (442) (96) (162) Total assets (U.S.$, 000) (5520) (442) (96) (162) Education Freq.(%) Less than high school 1329 (24.08) 62 (14.03) 10 (10.42) 6 (3.70) High school 2033 (36.83) 157 (35.52) 32 (33.33) 47 (29.01) Some college 1047 (18.97) 117 (26.47) 28 (29.17) 40 (24.69) College graduated 526 (9.53) 45 (10.18) 14 (14.58) 29 (17.90) Post college 585 (10.60) 61 (13.80) 12 (12.50) 40 (24.69) Race White 4379 (79.33) 352 (79.64) 77 (80.21) 152 (93.83) Black 583 (10.56) 64 (14.48) 14 (14.58) 5 (3.09) Hispanic 454 (8.22) 17 (3.85) 3 (3.13) 3 (1.85) Others 104 (1.88) 9 (2.04) 2 (2.08) 2 (1.23) Health Excellent 1074 (19.46) 108 (24.43) 18 (18.75) 54 (33.33) Very good 1892 (34.28) 155 (35.07) 35 (36.46) 66 (40.74) Good health 1562 (28.30) 143 (25.79) 31 (32.29) 36 (22.22) Fair and poor 992 (17.97) 65 (14.71) 12 (12.50) 6 (3.70) Marital status Living together 4521 (81.90) 371 (83.94) 78 (81.25) 132 (81.48) Living alone 999 (18.10) 71 (16.06) 18 (18.75) 30 (18.52) Current employment Working now 3084 (55.87) 272 (61.54) 69 (71.88) 73 (45.06) Not working 2436 (44.13) 170 (38.46) 27 (28.13) 89 (54.94) Sex Female 2508 (45.43) 227 (51.36) 50 (52.08) 98 (60.49) Male 3012 (54.57) 215 (48.64) 46 (47.92) 64 (39.51) Life insurance Having life insurance 4229 (76.61) 382 (86.43) 84 (87.50) 135 (83.33) Not having life insur (23.39) 60 (13.57) 12 (12.50) 27 (16.67) employed than those who did not have LTC insurance coverage in 1996 or those who maintained it for three periods. The group of people who maintained LTC insurance coverage for all three periods consisted of more females (60.49 per cent) than males (39.51 per cent). Overall the descriptive statistics reveal strong associations between variables which an economic model will highlight as important, including income and asset levels

7 646 MCNAMARA AND LEE (related to ability to pay and having resources to bequest), and variables related to pricing and underwriting criteria (health status, employment status, age and sex). The previous economic analyses of the demand for LTC insurance have focused on why people do not purchase the policies. Economists have advanced a number of explanations for the relatively small size of the market. However, explanations such as the bequest motive, the crowding out effect of Medicaid, the intrafamily bargaining dimension, which serve to explain the lack of demand for long-term care insurance, do not extend to explaining why consumers would purchase coverage and then drop it. While it may be possible to generate such policy dropping behavior in a neo-classical rational consumer model of insurance demand, more plausible explanations for the policy dropping behavior appear to rest with factors such as lack of consumer information about LTC risks and future premiums and the difficulties of shopping for LTC insurance. Furthermore, while holding a long-term care insurance policy for a relatively short period (say four or five years) might play a role in a Medicaid planning strategy, the Medicaid planning rationale fails to explain why people would purchase a policy and then drop it within two years. A two-year period is less than the Medicaid look back period concerning asset transfers. Moreover, as the descriptive statistics show, the quantitative importance of policy dropping behavior is important. In 1996, the HRS data show that about 11 per cent of sampled people (700 observations) reported having LTC insurance. Of these 700 observations, 442 individuals (or 63.1 per cent of people who had coverage in 1996) had dropped their LTC insurance coverage by An additional 96 individuals dropped their coverage by the year 2000 (or 13.7 per cent of those with coverage in 1996). Of those with LTC insurance in 1996, only 23.2 per cent maintained their coverage over the five-year period. This represents a very high level of policy dropping and turnover. Instead of focusing solely on the relatively low levels of LTC insurance policy purchases, economists and other analysts might look to explanations arising from the consumer demand side of the market for some explanation as to why the market is relatively small and how its growth might be assisted. 4. Econometric approach To estimate the likelihood of having long-term care insurance coverage for a specific number of periods, which is a count variable, we utilize a zero-inflated negative binomial regression model (ZINB). We use the ZINB since the data on consecutive periods of coverage exhibit a preponderance of zero values (88.75 per cent of the observations). The ZINB extends the negative binomial regression model and introduces a mixing process which allows for the excess zeros in the data-generating process (Long and Freese, 2001). Essentially, relative to the negative binomial formulation, a zero-inflated negative binomial model introduces two separate processes for generating zero observations in the model (Mullahy, 1986). An economic rationale exists for allowing two separate (unobservable) processes for generating zero observations in the econometric model. Some people never become aware of private LTC insurance and its availability. Indeed, many consumers mistakenly assume that Medicare will cover all of their LTC expenses. Another economic rationale for a separate process to generate zero observations is that, as a result of medical underwriting, some people could not qualify for a LTC insurance policy even if they applied. In today s market, about 10 to 20 per cent of LTC insurance applications are denied and we cannot observe these coverage denials. A third contributing reason for a separate process to generate zeros is that some low-income consumers simply cannot afford the premiums, and

8 LONG-TERM CARE INSURANCE POLICY DROPPING IN THE U.S. 647 the budget constraint forces them to zero consumption. For these economic reasons, a zeroinflated count regression model makes good sense as a specification for the functional form of the regression. The specification of the model s explanatory variables included terms to capture measures associated with LTC insurance underwriting and pricing (Age, AgeSquared, Dhealthverygood, Dhealthgood, Dhealthfairorpoor, Dfemale, Dwork). To control for the effects of income and assets on demand, we include a measure of total household assets (Assets96, AssetsSquared96) and total household income (Income96, IncomeSquared96). Consumers differ in their ability to understand complex economic phenomena, and LTC insurance products are widely acknowledged to represent a complicated product for consumers to consider. To control for the effect of education levels upon the ability of consumers to appreciate the risks of LTC (independent of the effect of assets and income), we include a set of dummy variables that measure educational attainment (Dlessthanhs, Dhs, Dsomecollege), with college or graduate school being the omitted category. Risk attitudes also will influence the demand for LTC insurance, and we include Dlife (whether or not the individual has life insurance coverage) as a measure of attitudes towards future financial risks. The presence of a spouse or partner might influence the demand for LTC insurance, since there might exist a desire to protect a couple s assets so that a spouse can maintain his or her standard of living while the partner receives LTC services. Alternatively, some have postulated an effect where single people might desire LTC insurance since they will have less support because no spouse is present to help with LTC assistance. To control for this effect we include a dummy variable, Dmarriedorlivingwpartner. We include three dummy variables (Dblack, Dhispanic, Dotherrace) to control for potential differences in how families of different races and ethnic groups provide for and utilize LTC services. 5. Econometric results Table 3 presents the ZINB regression results. Overall, we see that coefficients for income, assets, Dhispanic, Dhealthfairorpoor, Dmarriedorlivingwpartner, Dlessthanhs, Dhs, Dfemale, Dlife, Dwork are all statistically significant. Overall, taken as a group, the estimated coefficients display a high degree of statistical significance, as the chi-squared statistic (19 degrees of freedom) of implies. Consistent with both an ability-to-pay effect and the asset protection motive is the positive and statistically significant effect found for income and assets. Figure 1 shows the slight non-linearity in the income and asset effect when they are considered together. In the case of this hypothetical person who is 65 years old, works, has life insurance, is in the other race ethnicity category, is in fair or poor health, is married or lives with a partner, is a male, and has an educational level of some college, we see the predicted count range from around 0.1 (at an income of U.S.$ 9k and assets of U.S.$ 30k) to about 0.24 (with an income of U.S.$ 120k and assets of U.S.$ 400k). In Figure 1, the difference between a male and a female with identical characteristics ranges from about 0.02 (at low income levels) to 0.06 at high income levels. While the estimated age effects are not statistically significant at conventional confidence levels, the estimated effects are consistent with other reports about age and the decision to purchase LTC insurance. Figure 2 shows a plot of the inverted U-shaped age effect for a hypothetical person. The peak of the U is found at about 75 years of age. Other important effects are the health status effect, where people in fair or poor health are estimated to have a lower number of consecutive years of LTC insurance coverage

9 648 MCNAMARA AND LEE Table 3: Zero-inflated negative binomial regression results, years of continuous long-term care insurance coverage ( ) Variable Coef. Std. Error t-stat. p value Negative Binomial Equation Income IncomeSquared Assets AssetsSquared Age AgeSquared Dblack Dhispanic Dotherrace Dhealthverygood Dhealthgood Dhealthfairorpoor Dmarriedorlivingwpartner Dlessthanhs Dhs Dsomecollege Dfemale Dlife Dwork Constant Inflation equation _cons /lnalpha alpha Number of obs ¼ 6220 Nonzero obs ¼ 700 Zero obs ¼ 5520 Inflation model ¼ logit LR chi2(19) ¼ Log likelihood ¼ Prob. chi2 ¼ compared to people in excellent health, holding other effects constant. Also, married people or people living with a partner will have a lower expected count of number of consecutive years of LTC insurance coverage. In terms of education, people with an education level of less than high school or of high school will have a lower predicted count of consecutive periods of LTC insurance coverage. 6. Conclusions and implications for long-term care finance policy The descriptive data displayed in Table 2 shows the dramatic policy dropping rates for LTC insurance policy holders over the period 1996 through While economic models

10 LONG-TERM CARE INSURANCE POLICY DROPPING IN THE U.S. 649 Figure 1: Predicted effects of income and assets for males and females Note: As income increased from U.S.$3k to U.S.$120k, assets increased from U.S.$10k to U.S.$400k. Also, the hypothetical person in this example is 65 years old, working, has life insurance, other race, in fair or poor health, is married or lives with a partner, and has an educational level of some college. that emphasize the role of protecting assets and bequests or that raise issues of intrafamily bargaining may be able to rationalize this behavior, an alternative and more direct economic explanation is limited consumer information on LTC risks and consumer difficulties in assessing the complex LTC market. The high level of purchase and dropping behavior may be part of a learning experience or search behavior process where consumers learn about LTC insurance and many decide it is not well suited to their situation. While the empirical analysis highlights the economic factors associated with the policy dropping behavior, it cannot conclusively explain the sources and rationales for the observed policy lapses. Future research should include qualitative studies of policy purchasers and policy droppers to learn about their views on why they maintain their policies or allow them to lapse. Additionally, quantitative research that explores the association between policy dropping and changes in policy premiums, consumer information and marketing practices, shopping behaviors and subsequent purchases of long-term care insurance coverage would help explain the observed level of dropping. If the lack of information explanation is correct, then several implications for policy might follow. First, policymakers should consider a designated structure for at least a basic set of LTC insurance policies. Such a structure could mandate all policies fit into a prescribed form described by a law or regulation. A less restrictive approach, but one that would still lead to significant informational economies, would be one where insurers would be required to at least offer some of the designated policies as a condition to market all of

11 650 MCNAMARA AND LEE Figure 2: Predicted effects of age on the number of periods of consecutive LTCI coverage for a hypothetical female Note: Income is U.S.$35k, assets are U.S.$200k, not working has life insurance, in very good health, lives alone, and has an educational level of some college. their policies. The current LTC insurance landscape in the U.S. appears to be characterized by high selling and marketing costs and by significant consumer difficulties in comparing policies and choosing the most appropriate. If companies were required to offer at least some identical policies, it would foster price competition and facilitate comparisons. A second implication is that publicly funded consumer education programs need to be further explored and, if their effectiveness can be documented, supported and expanded. Third, given the public interest in long-term care financing in general (with Medicaid as an insurer of last resort) and in a well-functioning long-term care insurance market, more information concerning lapse rates, policyholder demographics and economic information is needed to assess the performance of the private market. One avenue for consideration would be a unified reporting mechanism for all companies offering tax qualified policies so that such information could be collected efficiently with a minimum of burden on the respondent companies. Another implication is for additional research on consumer-level dynamics of LTC policy purchase. While a number of researchers have analysed the LTC questions present in the HRS, there is no specific research that we are aware of concerning the validity and reliability of these self-report questions. Another issue for researchers to consider is whether the ability of consumers to make choices in this market has improved over time from the time period examined in this paper. This research raises the prospect that consumer demand for LTC insurance may be best modeled as a search process, where learning occurs and

12 LONG-TERM CARE INSURANCE POLICY DROPPING IN THE U.S. 651 where some informational economies due to regulation might assist consumers, and potentially move the private market closer to what economists consider an efficient outcome. Furthermore, the finding of significant lapses may well indicate that the relatively small size of the current U.S. long-term care insurance market has more to do with the consumer side of the market and the complexity of the consumer s decision to find appropriate long-term care insurance coverage than has previously been emphasized. REFERENCES COHEN, M.A., 1998, Emerging trends in the finance and delivery of long-term care: Public and private opportunities and challenges, Gerontologist, 38, 1 (February), pp COHEN, M.A., 2003, Private Long-Term Care Insurance: A Look Ahead, Journal of Aging and Health, 15, 1 (February), pp CUTLER, D.M., 1993, Why Doesn t the Market Fully Insure Long-Term Care? National Bureau of Economic Research Working Paper No FINKELSTEIN, A. and McGARRY, K., 2003, Private Information and Its Effect on Market Equilibrium: New Evidence from Long-Term Care Insurance, National Bureau of Economic Research Working Paper No GREENE, W.H., 2000, Econometric Analysis. Upper Saddle River, N.J.: Prentice Hall. HEALTH INSURANCE ASSOCIATION OF AMERICA, 2003, Long-Term Care Insurance in Accessed online at on 6 July LONG, J.S. and FREESE, J., 2001, Regression Models for Categorical Dependent Variables Using Stata. College Station, TX: Stata Press. MELLOR, J.M., 2001, Long-term Care and Nursing Home Coverage: Are Adult Children Substitutes for Insurance Policies?, Journal of Health Economics, 20, pp MULLAHY, J., 1986, Specification and Testing of Some Modified Count Data Models, Journal of Econometrics, 33, pp NORTON, E., 2000, Long-term Care, in A.J. Culyer and J.P. Newhouse (eds), Handbook of Health Economics, Vol. 1, Chapter 17. Elsevier Science. PAULY, M.V., 1990, The Rational Nonpurchase of Long-Term Care Insurance, Journal of Political Economy, 98, No. 1 (February), pp SLOAN, F.A. and NORTON, E.C., 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, pp WIENER, J.M., ILLSTON, L.H. and HANLEY, R.J., 1994, Sharing the Burden: Strategies for Public and Private Long-term Care Insurance. Washington, DC: The Brookings Institution.