HMGT 495 WK 2 PRO 2

CHAPTER

141

8MORAL HAZARD AND PRICES

The first major challenge for insurers was adverse selection; the second is called moral hazard.1 The term comes from the casualty insurance mar- ket. A house may face a variety of fire hazards: it may be struck by

lightning, it may burn because of faulty wiring, or it may be destroyed because the owner set it on fire to collect the insurance. This last hazard is referred to as moral hazard. The term has carried over to health insurance in that it is assumed that individuals with a health insurance policy use more health services. Of course, unlike the casualty market, there is nothing immoral about using more health insurance when you have coverage. It is simply an application of the law of demand. The issues for insurers are how much people are going to increase their use of various health services when they pay less out of pocket and whether cost-effective strategies exist that can minimize the extra utilization.

In this chapter, we develop the concept of moral hazard in healthcare and examine the empirical evidence on the extent to which higher coinsur- ance, copays, and deductibles are successful in reducing use. In chapter 9, we will explore the effectiveness of utilization management techniques, such as preadmission certification and gatekeeping, as mechanisms to control moral hazard.

Price elasticity is the economist’s rigorous way of quantifying the effect of a change in price on the change in quantity demanded. It is simply the percentage change in quantity divided by the percentage change in price. It has the advantage of being independent of the units in which the price or the quantity is measured. Health services generally have a price elasticity of about −0.2. This means that a 10 percent increase in the out-of-pocket price reduces the use of services by about 2 percent. However, the effects of changes in price differ rather substantially across types of health services. Ambulatory mental health visits, for example, traditionally have been much more price sensitive than physician visits. Dental care exhibits a large transi- tory effect not seen with other services, and hospital care is much less price responsive than physician services. Moreover, people with different oppor- tunity costs of time have different responses to changes in out-of-pocket charges. These differences in elasticities explain much of the difference in the structure of health benefits.

C o p y r i g h t 2 0 2 0 . A U P H A / H A P B o o k .

A l l r i g h t s r e s e r v e d . M a y n o t b e r e p r o d u c e d i n a n y f o r m w i t h o u t p e r m i s s i o n f r o m t h e p u b l i s h e r , e x c e p t f a i r u s e s p e r m i t t e d u n d e r U . S . o r a p p l i c a b l e c o p y r i g h t l a w .

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The Nature of Moral Hazard

Moral hazard is nothing more than the law of demand. Consider exhibit 8.1, which shows a downward-sloping demand curve for physician visits. At $100, individuals might purchase X1 visits. At $25, they would buy more—X2. This is the law of demand: at a lower price, people buy more of a good.

Now suppose that the market price of physician office visits is $100, and that people buy a health insurance policy that covers such physician visits. Under the contract, subscribers only have to pay a small copay of $25 for each physician visit used. A copay is the amount the insurance contract requires the insured to pay for each unit of a covered service, regardless of either the actual price the provider charges or the actual amount the insurer pays. Copays may differ by type of service and according to which provider the subscriber uses. In exhibit 8.1, individuals purchased X1 physician visits when they had to pay the full $100 price, but now, because they only have to pay the $25 copay, they purchase X2 physician visits. This slide down the health services demand curve in response to the lower out-of-pocket payment is precisely what is meant by moral hazard. It is also precisely what is meant by the law of demand.

The nature of demand is that each additional unit of service is worth less to consumers than the preceding one. Our consumers in exhibit 8.1 stop buying at X1 because an additional physician visit is not worth the cost. Sup- pose they are not feeling well. At $100 a visit, they will wait and see if they feel better tomorrow. At $25 a visit, they may try to get a physician visit later this afternoon. Thus, they stop consuming when the price of the service is greater than what they perceive that unit to be worth.

Physician Visits

Price

X1 X2

Demand

$100

$25

EXHIBIT 8.1 Moral Hazard in

Healthcare

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Chapter 8: Moral Hazard and Pr ices 143

The problem with moral hazard is that the extra units of health ser- vices subscribers consume as a result of having insurance coverage are worth less to them than the price of care the insurer pays on their behalf. Consider exhibit 8.2. Again, the market price of a physician visit is $100, and the copay required of the insured is $25. For every visit between X1 and X2, the physi- cian is paid more for the visit than the consumer’s demand curve says it is worth. Yet subscribers rationally consume up to X2. The triangle marked “Z” is the loss associated with this extra consumption. It reflects the expenditure made on behalf of the insured over and above the value of the service.

If the insurer could find a low-cost way of pushing subscribers back up the demand curve, it could save $75 ($100 − $25) on each visit averted and easily compensate subscribers for giving up some low-valued visits to physicians. One way to achieve this is to raise the copay by $10 or $20 and lower the insurance premium. Another way is to establish a utilization man- agement program designed to identify and eliminate low-value visits. The utilization management program, of course, would have to cost less than the visits averted. This chapter and chapter 9 examine the extent to which health services use responds to price and utilization management techniques to push subscribers back up the demand curve.

Early Efforts to Estimate the Extent of Moral Hazard

One approach to estimating the magnitude of the moral hazard effect is to identify two groups of people—one with health insurance and one without—and then compare their use of health services. Eichhorn and

Physician Visits

Price

X1 X2

Demand

Market price

Z

$100

$25

EXHIBIT 8.2 Loss Associated with Moral Hazard

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Aday (1972) and Donabedian (1976) provide excellent reviews of these types of studies. The problem with this approach is that adverse selection is likely to confound the comparison. The group with coverage is likely to have acquired insurance because group members were more likely to use health services. Simply comparing use rates will overstate the effect of differences in the out-of-pocket price. If insurers followed this route, they would find that utilization was not reduced as much as they antici- pated. They would reduce their premiums too much, and they would lose money.

Scitovsky and Snyder (1972) undertook the classic early study of moral hazard. They analyzed a natural experiment in which Stanford Uni- versity employees faced the introduction of a 25 percent coinsurance rate on physician services when the rate previously had been zero. (A coinsurance rate is an insurance contract provision by which the subscriber pays a fixed percentage of the price of health services.) Scitovsky and Snyder compared use by the same employees in 1966, when care was “free,” with use in 1968, the year after the plan went into effect. They found that the physician office visit rate in 1966 was 33 percent higher when visits cost nothing out of pocket than it was in 1968. Ancillary services were 15 percent higher in the year prior to the change. Phelps and Newhouse (1972) also analyzed these data, and Scitovsky and McCall (1977) revisited the study with new data five years later. The results were confirmed.

A more recent example of a case study is work by Anderson, Dob- kin, and Gross (2012). Until the advent of the ACA, young adults often aged out of their parents’ health insurance plans when they reached 19 (or 22 if they were attending college). This study examined the effects of the abrupt age-related drop in coverage on the use of hospital services. Aging out resulted in a 5 to 8 percent drop in the probability of having health insurance, and those newly without coverage reduced their emer- gency department visits by 40 percent and their hospital admissions by 61 percent.

There are problems with case studies, even one as clean as the Stan- ford University experience (Scitovsky and Snyder 1972). For example, the Stanford study represents only one firm and one local area, it covers only a single small range of coinsurance, and it also attributes all of the change in use to the natural experiment. While there was no obvious reason to believe other factors were at play in the Stanford case, as Problems with Case Studies suggests, this is not necessarily the case in natural experiments. A number of other early studies attempted to estimate the extent of price sensitivity of health services. See Newhouse (1978) and Morrisey (2005) for reviews.

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Chapter 8: Moral Hazard and Pr ices 145

The RAND Health Insurance Experiment

While the early studies provided only limited information regarding the extent of price responsiveness, the RAND Health Insurance Experiment (RAND-HIE) provided considerable insight into the price responsiveness of consumers of health services. The study is particularly useful because it largely (but not entirely) avoided the adverse selection problem by randomly assigning families to health insurance plans. It investigated a wide range of coinsurance rates, allowing consideration of a broader set of price responses, and it was conducted over six sites chosen to be reflective of urban and rural communities in four census regions. See Manning and colleagues (1987) for a summary of the experiment and the major findings, and Newhouse and the Insurance Experiment Group (1993) for a systematic presentation of this seminal study.

You may legitimately ask about the relevance of a 40-year-old study. Clinical practice and insurance institutions have changed dramatically in the intervening years. However, the RAND-HIE is still the gold standard for examining price sensitivity of health services for three reasons. First, its meth- odology was very strong. It overcame the adverse selection problem in a way that no other study ever has. Second, it examined virtually the whole range of health services provided, and it did so in a consistent framework. Third, studies undertaken since the experiment have been able to look at the price sensitivity of selected health services and almost always find results consistent with the older RAND-HIE.

Problems with Case Studies

Scheffler (1984) examined the effects of the introduction of a 40 percent physician coinsurance requirement in the United Mine Workers healthcare plan. Prior to the introduction of the coinsurance requirement in 1977, the union had not had any cost-sharing features in the 30-year history of the benefit. In the first six months of the study, the probability of a physician office visit declined by 28 percent. The study was terminated at that point because the union went out on strike over its health benefits! Unfortunately, what to make of the results of Scheffler’s study is not at all clear. Was the changed behavior reflective of the new price? Was it some mixture of price and disgruntled union effects? Follow-up work by Roddy, Wallen, and Meyers (1986) suggested that many of the effects of cost sharing in this population disappeared in the subsequent year.

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Between 1974 and 1977, families in Dayton, Ohio; Seattle, Washing- ton; Fitchburg, Massachusetts; Franklin County, Massachusetts; Charleston, South Carolina; and Georgetown County, South Carolina, were enrolled in a health insurance program run by RAND under a federal contract. Partici- pating families were randomly assigned to one of 14 different fee-for-service health plans. In Seattle, some participants were enrolled in Group Health of Puget Sound, a health maintenance organization (HMO). The plans had coinsurance rates of 0, 25, 50, and 95 percent. Within each coinsurance group, families were assigned to stop-loss groups of 5, 10, and 15 percent of income to a maximum of $1,000. That is, out-of-pocket expenses for covered services could not exceed the percentage of income cap or $1,000, whichever was lower. While the $1,000 stop-loss feature appears low, in 2018 dollars, it would be approximately $5,143, or about $1,500 below the maximum out- of-pocket limit on a health savings account (HSA) in 2018 (see chapter 17). Virtually all medical services were covered.

One final point about the design of the RAND-HIE: You might ask what happened to people who already had health insurance. The answer is that they kept it. Because the RAND-HIE only lasted four to five years, there was some concern that a health event could make participants uninsurable, or uninsurable at the same prices, if they did not continue coverage. Also, by keeping the existing policies in force and assigning the benefits to the RAND-HIE, the study was able to pass on many of the claims expenses to the participants’ existing insurers.

Many participants received more generous coverage from the RAND- HIE than from their existing plans, but some were assigned to worse plans. Why did some people give up the coverage they had to take inferior coverage through the RAND-HIE? The answer is that the RAND-HIE paid them to participate. A lump-sum payment of this sort did not affect their incentives to use services in the context of the RAND-HIE plan to which they were assigned (Newhouse and the Insurance Experiment Group 1993). The sample of families was generally representative of a population that is under 65 and non- wealthy. It excluded those who would be eligible for Medicare over the course of the experiment, those with incomes above $25,000 ($128,600 in 2018 dollars), as well as those in the military and veterans with disabilities connected to service. Slightly more than 5,800 people were enrolled in the various fee-for- service plans, and data on 20,190 person-years of experience were collected.

Overall RAND Health Insurance Experiment Findings The major findings of the RAND-HIE with respect to the price responsive- ness of ambulatory and inpatient services are summarized in exhibit 8.3. When faced with a zero out-of-pocket price, people had an 86.7 percent annual probability of interacting with the healthcare system. They also used 4.6 physician visits per capita per year. In contrast, those who had to pay 95

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Chapter 8: Moral Hazard and Pr ices 147

percent of the bill (up to the $5,075 stop-loss in 2018 dollars) had only a 68 percent probability of using any healthcare and used only 2.7 visits per capita. Those who had to pay 25 cents on the dollar had a 78.8 percent probability of using any care and used 3.3 visits per year per capita. Relative to those who had to pay 25 percent, those with free care used nearly 37 percent more physician visits. Thus, the use of ambulatory services decreases with higher out-of-pocket prices. The difference between the free plan and any of the others is statistically significant at the 95 percent confidence level.2 Children’s care exhibited about the same price responsiveness for the use of ambulatory services as did adult care.

The results for hospital admissions also displayed evidence of price sensitivity. Those covered under a free care plan had 128 admissions per 1,000 persons. This was 29 percent greater than those with the 95 percent coinsurance plan. Similarly, inpatient expenditures were 30 percent higher for those who had a free plan (see Effects of Hospital Coinsurance on Appropri- ate Versus Inappropriate Admissions).

EXHIBIT 8.3 Various Measures of Estimated Mean Annual Use of Medical Services, by Plan

Note: Standard errors in parentheses.

Source: Adapted from data in Manning et al. (1987).

Coinsurance Rate

Likelihood of Any Use

(percentage)

Face-to-Face Physician Visits per

Capita

One or More Admissions

(percentage)

Medical Expenses

(2018 dollars)

0% 86.7 (0.67) 4.55 (0.17) 10.37 (0.42) $3,954 (166.9)

25% 78.8 (0.99) 3.33 (0.19) 8.83 (0.38) $3,205 (147.6)

50% 74.3 (1.86) 3.03 (0.22) 8.31 (0.40) $2,967 (165.8)

95% 68.0 (1.48) 2.73 (0.18) 7.74 (0.35) $2,712 (139.4)

Effects of Hospital Coinsurance on Appropriate Versus Inappropriate Admissions

Inappropriate admissions do not appear to have been disproportionately reduced as a result of the cost sharing. Siu (1986) and Lohr and colleagues (1986) showed that the same proportions of what they identify as appropri- ate and inappropriate admissions were found among those with free care and those with each of the coinsurance rates. Similarly, on comparing use of services in small geographical areas, Chassin and colleagues (1987) found that differences in hospital admission rates across areas were not attribut- able to differences in the rate of appropriate or inappropriate admissions.

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Unlike the ambulatory results, where reductions in use were seen across the range of coinsurance rates, with hospital use, the vast majority of the effect is found between the free and 25 percent plans. This result reflects the stop-loss features of the plans. Seventy percent of those hospitalized exceeded the maximum out-of-pocket limit imposed. Once this threshold was exceeded, care became free. Thus, the lack of additional reductions in hospital use as a result of higher coinsurance rates may merely reflect the fact that prices quickly became zero. Unlike adult care, children’s inpatient use showed almost no price responsiveness.

The final column of exhibit 8.3 is perhaps the most important. It indicates that, in 2018 dollars, those who faced no out-of-pocket costs had average annual total medical expenditures of $3,954. This was 23 percent more than those who had to pay 25 percent of the bill and nearly 46 percent more than those who had to pay 95 percent (up to the stop-loss). Thus, substantially higher out-of-pocket prices result in meaningfully lower medical care expenditures.

More formally, the RAND-HIE provided elasticity estimates of the extent of price responsiveness across types of medical care services and tried to put them in the context of the earlier literature. Essentially, the RAND- HIE estimates are in the lower range of the nonexperimental estimates. These results are summarized in exhibit 8.4. In the free-to-25 percent coinsurance range, hospital care had an elasticity of −0.17, as did overall ambulatory care. In the coinsurance range of 25 to 95 percent, ambulatory care had an overall elasticity of −0.31, while hospital care was estimated to be −0.14. The small response for hospital care at higher levels of out-of-pocket payment reflects the stop-loss in place in the insurance plans. The one-sentence summary of the RAND-HIE is that the price elasticity of health services is about −0.2. In other words, a 10 percent increase in out-of-pocket price reduces use by 2 percent.

EXHIBIT 8.4 RAND-HIE Elasticity

Estimates for Health Services

Source: Manning et al. (1987), “Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment.” American Economic Review 77: 251–77, Table 2. Reprinted with permission.

Ambulatory Hospital All Care

Coinsurance Range Acute Chronic Well All

0–25% –0.16 –0.20 –0.14 –0.17 –0.17 –0.17

25–95% –0.32 –0.23 –0.43 –0.31 –0.14 –0.22

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Chapter 8: Moral Hazard and Pr ices 149

Full Coverage Versus Inpatient-Only Coverage One component of the RAND-HIE examined the consequences of having insurance only for hospital services, rather than for both ambulatory and hospital care. At the time of the experiment, many people had generous hospitalization coverage but only limited coverage for ambulatory services. Some said that this policy was “penny wise and pound foolish.” They argued that people with only hospital coverage would forego relatively simple and inexpensive services because they had to pay the full price. The result, they said, would be that many people would be hospitalized and spend large amounts of money when timely and inexpensive ambulatory services would have avoided such costs.

The RAND-HIE set up an additional arm of the study in which people were randomly assigned to an individual deductible. In this arm, participants had free care if they were treated in a hospital but paid 95 percent of their bill if they obtained ambulatory services. This arm reflected the common hospi- talization coverage of the time. It was compared to the free-coverage arm, in which both inpatient and ambulatory services were free. The study found that those in the individual deductible arm did interact less with the health- care system: they had a 72.6 percent chance of using any care, compared to 86.7 percent for those with free care. However, they also had fewer hospital admissions (9.52 percent compared to 10.37 percent). Overall, those with the hospital-only coverage had total medical expenditures of $3,170 (in 2018 dollars), compared with $3,954 for those with full coverage. While this dif- ference lacks statistical significance at the conventional levels, it clearly does not support the “penny wise, pound foolish” argument. If anything, it sug- gests that ambulatory and inpatient care are complements, not substitutes.

This complementary relationship was also found in a 1996 study of increased access to primary care in the US Department of Veterans Affairs (VA). Weinberger, Oddone, and Henderson (1996) studied nearly 1,400 veterans who were hospitalized for diabetes, chronic obstructive pulmonary disease, or congestive heart failure in nine VA medical centers. They ran- domly assigned half of the veterans to an intensive intervention designed to increase access to primary care after discharge from the hospital and the other half to the usual postdischarge care. They found that the group with greater access to primary care had significantly higher, not lower, readmission rates.

Findings by Income Group The out-of-pocket money price is only one component of the full price of health services use. There is also a time component. You must go to the phy- sician’s office, wait to be seen, receive services, and return to other activities.

The full price of a visit includes both the money price and this time price. If you have a high opportunity cost of time, the time component can

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easily be the larger portion of the full price. We should expect, therefore, that, other things being equal, those individuals with higher opportunity costs of time will be less responsive to a given change in the out-of-pocket money price of care. A given change in money price is a smaller change in the full price for these individuals than for those with lower opportunity costs of time.

The RAND-HIE looked at the effect of differing coinsurance rates across income groups. This inquiry is effectively an examination of the time- price hypothesis. Exhibit 8.5 shows the effect of a 25 percent coinsurance rate relative to free care across low-, medium-, and high-income groups. Low-income (i.e., low opportunity cost of time) people had twice the price responsiveness as those with high opportunity cost of time. This finding implies that an insurer would have to use much higher copays or coinsurance rates to get higher-income subscribers to reduce their use of health services. Analogously, a small copay on, say, emergency department visits may be enough to encourage Medicaid recipients to not use the emergency depart- ment for routine care.

Some research has examined the effects of changes in copayments on the use of services by lower-income people with Medicaid or Children’s Health Insurance Program (CHIP); these are generally consistent with the RAND-HIE findings (Sen et al. 2012).

The RAND-HIE provided estimates of the price sensitivity for many types of health services. In addition, a number of more recent studies have independently estimated service-specific elasticities. In this section, we review the findings for a variety of services.

Hospital Services As noted earlier, the RAND-HIE found that free care, relative to the 95 percent plan, resulted in 29 percent more hospital admissions, as well as inpa- tient expenses that were 30 percent higher. Relative to the 25 percent plan,

Income Groups 0

–5

–10

–15

P er

ce nt

ag e

–13

Low income

Medium income

High income

–6

–8

EXHIBIT 8.5 Reduction in

the Probability of Any Health

Services Use, Free

Care Versus 25 Percent

Coinsurance Rate, by Income

Class

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Chapter 8: Moral Hazard and Pr ices 151

those with free care had 22 percent more admissions but only 10 percent higher expenses. These results suggest that the additional admissions in the free plan relative to the 25 percent plan were for short stays.

Since the RAND-HIE, only a small handful of studies have examined the effects of health insurance on hospital use. Buchmueller and colleagues (2005) reviewed the studies and concluded that having private health insur- ance increases adult inpatient use by 0.17 to 0.24 days per year and child- hood use by 3 to 4 percent. The trouble with these estimates is that they do not account for the size of out-of-pocket payments, and they often do not control for the adverse selection problem.

Hospital Emergency Department Services The RAND-HIE results for the use of hospital emergency departments (EDs) were generally similar to those for ambulatory care (O’Grady et al. 1985). Persons with free care had about 54 percent more ED visits than did persons in the 95 percent plan and about 27 percent more than persons with 25 percent cost sharing. Comparable estimates for ED expenses were 45 and 16 percent higher, respectively. In the ED, the type of services used also increased differently as prices were lowered. Relative to no coverage, free care increased the use for “less urgent” care by 90 percent and “more urgent” care by only 30 percent. Thus, the less serious services appeared to be the most price sensitive. Selby, Fireman, and Swain (1996) provided a detailed analysis of ED use in an HMO. At the request of some electron- ics and computer firms, in 1993 Kaiser-Permanente of Northern California introduced a $25 to $35 copay for ED use for members employed by these firms. Other Kaiser-Permanente members continued to have no copays for ED use. The study compared the change in the number of ED visits before and after the introduction of the copays for both this affected group and for two unaffected groups. Other services had no changes in copays. This is a classic “differences-in-differences” evaluation method. Comparison group 1 consisted of a sample of members selected by age, gender, and area of resi- dence to be similar to those facing the copay. Comparison group 2 consisted of members who were similarly selected by age, gender, and area but were also employed in the electronics and computer industries.

Exhibit 8.6 summarizes the findings. Overall ED visits per 1,000 per- sons declined by 14.6 percent among those facing the new copay relative to the change in either control group (statistically significant at the 95 percent confidence level). The investigators went further and looked at the severity of diagnosis. They found the largest relative reductions (20.8 to 29.2 percent, depending on control group) in visits deemed “often not an emergency.” Visits that were “always an emergency” showed the smallest relative change (a decline of 9.6 percent and an increase of 7.3 percent, depending on control group), but these differences were not statistically different from no change.

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Interestingly, office visits also declined as a result of the ED copays, though there was no change in copays for such visits. This result suggests that ED and office visits, on net, are complements rather than substitutes.

Work by Hsu and colleagues (2006) also used Kaiser-Permanente data, in their case for the 1999–2001 period, to examine the effects of higher copays on ED and hospital use and unfavorable outcomes. This analysis found that a $20–35 ED copay reduced ED visits by 12 percent, and a $50–100 copay reduced use by 23 percent, relative to no ED copays. Hos- pital admissions, intensive care unit use, and deaths did not increase among the affected groups. Relatively modest levels of copayment reduced visit rates and did not increase unfavorable events.

Physician Services The RAND-HIE found that people with free care had nearly 37 percent more physician visits per capita than did those facing a 25 percent coinsur- ance rate; their use was 67 percent higher than those who essentially paid their entire bill out of pocket.

Cherkin, Grothaus, and Wagner (1989) examined the effects of a $5 copay (about $12 in 2018 dollars) introduced in 1985 on the use of physician office visits for Washington State government and higher educa- tion employees enrolled in Group Health of Puget Sound, a staff model HMO. As a control group, Cherkin and colleagues used federal government

EXHIBIT 8.6 Adjusted Kaiser- Permanente ED

Use

Note: Values are adjusted for age, sex, socioeconomic status, and study group. Values in parentheses are the 95 percent confidence intervals of the change.

Source: Data from Selby, Fireman, and Swain (1996).

Overall ED Visits per 1,000 Persons

Visits in 1992

Copayment group 162

Control group 1 206

Control group 2 173

Visits in 1993

Copayment group 135

Control group 1 202

Control group 2 169

Percentage change in copayment group

Relative to percentage change in control group 1 –14.6 (–19.4 to –9.5)

Relative to percentage change in control group 2 –14.6 (–19.9 to –8.9)

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Chapter 8: Moral Hazard and Pr ices 153

enrollees who did not face the copay. In examining the utilization patterns of persons continuously enrolled for two years, they found that office visits for primary care decreased by an estimated 10.9 percent as a result of the copay. Specialty visits declined by 3.3 percent, optometry by 10.9 percent, and all visits by 8.3 percent. However, the effect on specialty visits lacked statistical significance at the conventional levels, perhaps because specialty visits required a primary care referral.

More recent studies have tried to examine the effects of having health insurance versus not having health insurance on physician visits. Of course, adverse selection issues are associated with such comparisons. See The Ore- gon Medicaid Experiment for a new study that randomly assigns people to insurance coverage. Buchmueller and colleagues (2005) provided a review of these studies. Overall, for adult ambulatory use, the studies found hav- ing coverage to be associated with one to two additional physician visits per year. This range is pretty narrow, clustered around the 1.85 additional visits reported by the RAND-HIE.

Interestingly, chiropractic services appear to be more price sensitive than physician visits (Schelelle, Rogers, and Newhouse 1996). The RAND- HIE found that free care relative to the 25 percent plan increased expen- ditures on chiropractic care by 132 percent. Higher coinsurance rates had essentially no additional effect.

The Oregon Medicaid Experiment

In 2008 the state of Oregon expanded its Medicaid program through a lottery open to individuals aged 19–64 with sufficiently low income who otherwise would not be eligible. Potentially eligible people were invited to participate in the lottery; 6,387 were randomly selected and another 5,842 were not selected but served as a control group. This lottery process overcomes the problem of adverse selection and, like the RAND-HIE, allows us to look at the effects of insurance on use of health services. Unlike the RAND-HIE study, however, we can look at differential use of services, but we cannot calculate elasticities because we do not know the prices faced by the control group.

After the first year, the study found that having new Medicaid cover- age “was associated with a 2.1 percentage point (30 percent) increase in the probability of having a hospital admission, an 8.8 percentage point (15 percent) increase in the probability of taking any prescription drugs, and a 21 percentage point (35 percent) increase in the probability of having an outpatient visit” (p. 1061). Emergency department visits increased by 0.41

(continued)

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Dental Services The RAND-HIE randomly assigned individuals to 0 (free), 25, 50, and 95 percent coinsurance rate insurance plans. It found that, in steady state (that is, after a transition period), participants in the free plan had 34 percent more dental visits and 46 percent higher expenses than did enrollees in the 95 per- cent coinsurance plan (Manning et al. 1985). Again, most of the effect was observed in the difference between free care and a 25 percent coinsurance. Also, nearly two-thirds of the response was attributed to number of visits per enrollee, the remainder to expenditures per user. Thus, cost sharing tended to affect the decision to seek treatment much more than the expenditure once treatment was sought. Preventive services were about as price sensitive as general dental visits; in contrast, prosthodontics, endodontics, and peri- odontics were more price sensitive.

Of particular note, dental care seems to be much more sensitive to a transitory effect of cost sharing than does medical care more generally. The RAND-HIE found that, in the first year of coverage, the difference in use between the free plan and the 95 percent plan was nearly twice as large as in the second year. However, in the second year (i.e., the steady state), dental care was less price sensitive than other health services.

Conrad, Grembowski, and Milgram (1987) and Grembowski, Conrad, and Milgram (1987) have analyzed survey data on the effects of coinsurance on the use of dental services among adults and children, respectively. Their population was covered by dental insurance (Pennsylvania Blue Shield in 1980), so the issue was the effects of differences in the coinsurance rate in an insured population. They found little money price (i.e., coinsurance) sensitiv- ity among this insured population. This result is consistent with the RAND- HIE because most of the price sensitivity was found between free care and a 25 percent coinsurance rate with little additional sensitivity at higher levels of cost sharing. These findings are also supported in work by Muller and Mon- heit (1987). Like the RAND-HIE, the Conrad, Grembowski, and Milgram studies found increased price sensitivity for more extensive (i.e., expensive) services. Children’s basic dental services also appear to be less price sensitive than adult care. As with the RAND-HIE, these researchers found substantial transitory effects on dental usage. Little new work on the price effects for dental care has been done since the mid-1980s.

visits per person (40 percent). The authors suggest that these broad effects are somewhat smaller than those found in the RAND-HIE experiment. They also have findings with respect to health outcomes and financial well-being that we will discuss elsewhere in the chapter.

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Chapter 8: Moral Hazard and Pr ices 155

A particularly interesting aspect of the Conrad, Grembowski, and Milgram (1987) study was a consideration of people with dental coverage through community-rated and experience-rated group dental plans. Recall from chapter 6 that community-rated plans are more likely to be subject to adverse selection because the single average price will overcharge low utiliz- ers and undercharge high utilizers. The results from Conrad, Grembowski, and Milgram indicated that expenditures were 37 and 90 percent higher, respectively, for insured workers and spouses in community-rated plans than in experience-rated plans.

Ambulatory Mental Health Services Ambulatory mental healthcare services are considerably more price sensitive than ambulatory medical services generally. In a natural experiment, Wal- len, Roddy, and Fahs (1982) found that the introduction of a $5 copay per visit reduced mental health visits from 110 to 60 visits per 1,000. McGuire (1981) was the first to use econometric techniques on individual-level data. He analyzed data from a survey of heavy users of psychiatric services and found a price elasticity of greater than −1.0 for actual and anticipated visits. This finding suggests that a 1 percent increase in price would result in a more than 1 percent reduction in the number of visits.

Horgan (1986) found that a 10 percent increase in the coinsurance rate led to a 2.7 percent reduction in the probability of any use. However, visits and expenditures, conditional on some use, were much more price responsive. A 10 percent increase in the coinsurance rate reduced visits by 4.4 percent and expenditures by 5.4 percent. These results suggest that the intensity of mental health use is more responsive to price than is simple use of services. This finding contrasts with general ambulatory medical services, where the probability of use is more responsive. Taube, Kessler, and Burns (1986) found similar results. No significant relationship existed between price and the probability of using mental health services. However, there was substantial price sensitivity among those who did use services. They reported a price elasticity of nearly −1.0 for those with some use. Thus, use of care, by those who used some care, would likely more than double if free care replaced full payment.

The RAND-HIE confirmed these results. It found that free care would result in a quadrupling of mental health care expenses, relative to no insur- ance. Further, the response between 50 percent and 95 percent out-of-pocket payment was about twice as price responsive as general medical care. The response between 25 percent coinsurance and free care was about equal to that of medical care (Keeler et al. 1986; Wells, Keeler, and Manning 1990).

The use of mental health care in the presence of expanded insurance coverage can be described as subject to a slow buildup. With dental coverage,

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an immediate burst of use was followed by a lower, sustained level. With men- tal healthcare, use increased over time from a relatively low initial level. Keeler and his colleagues (1986, 166) speculated about why mental health care is more price responsive: “The additional users may be better informed and simply want help only if the price is right. Alternatively, they may not know how mental healthcare would help them, or they may be deterred by real or imaginary stigmatization, until coverage legitimizes taking a chance on use.”

Ultimately, the high price sensitivity of mental health services suggests why insurance coverage for these maladies tends to be different from cover- age for medical conditions. Simple application of copays reduces the use of services substantially, relative to no coverage. Similarly, limitations on the number of mental health visits and more aggressive use of nonprice ration- ing devices are common in managed care plans as a means of reducing moral hazard. See Frank and Ellis (2000) for a discussion.

Prescription Drugs Early studies of prescription drugs found that the quantity demanded approx- imately doubled when drugs became free under a full coverage plan, appar- ently because of an inability to control for adverse selection. The RAND-HIE data did not bear out these early studies. In general, the RAND-HIE found that prescription drugs were about as price responsive as physician services. Leibowitz, Manning, and Newhouse (1985) found that prescription drug expenses per person were 76 percent higher for those in the free plan, rela- tive to those with 95 percent coinsurance. Relative to those in the 25 percent coinsurance plan, those in the free plan used 32 percent more. These results were largely driven by the number of prescriptions filled rather than differen- tial costliness of the drugs received. Those in the free plan filled 50 percent more prescriptions than did those in the 95 percent coinsurance plan and 23 percent more than those in the 25 percent coinsurance plan (but see The Effects of Health Insurance on Health).

The Effects of Health Insurance on Health

We have good evidence from the RAND-HIE and a number of smaller-scale studies that having health insurance increases the use of health services. But does having health insurance improve one’s health?

A large number of studies show that those with health insurance have better health compared with those without insurance. However, adverse selection is almost always a problem with these studies. Those more likely

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Pharmaceutical use is certainly one of the areas where clinical and insurance practice has changed the most since the RAND-HIE. Prescription drug coverage now often includes two-, three-, and even four-tier programs in which subscribers pay one low copay for generic drugs (perhaps $10), a higher copay (perhaps $30) for brand-name or “preferred brand-name” drugs, and a still higher copay for nonpreferred brand-name drugs. The fourth tier is reserved for expensive biotech drugs. Several studies have inves- tigated the effects of these copayment systems on prescription drug use.

Motheral and Fairman (2001) examined the effect of introducing a three-tier drug coverage program among employers who offered employees a preferred provider organization over the 1997–1999 period. In a differ- ences-in-differences model, they examined generic copay changes from $7 to $8 per prescription, $12 to $15 for preferred brands, and $12 to $25 for nonpreferred branded drug prescriptions. There was essentially no reduction

to improve their health by buying health insurance are the ones more likely to buy it. The vast majority of these insurance studies make no effort to account for this. So, at best they show an association but not causation and yield estimates of moral hazard that are too large.

A handful of studies have taken advantage of natural experiments in which coverage is provided through no effort on the part of the recipient and, of course, the RAND-HIE that randomly assigned people to coverage. Levy and Meltzer (2008, 2004) have carefully reviewed these studies. They conclude that the question of whether health insurance improves health largely remains unanswered. The evidence clearly shows that health insurance improves the health of vulnerable populations, such as children, infants, and people with AIDS. It can also improve specific measures of health status, such as corrected vision and high blood pressure, particularly for those with lower incomes. However, no convincing evidence exists that health insurance has or would raise the health status of the population generally.

More recently, the Oregon Experiment has made headlines reiterating these same conclusions. As the result of a lottery, nearly 6,500 low-income people were randomly selected to participate in an expanded Medicaid pro- gram in Oregon. After two years the study found no statistically significant improvements in the measured physical health outcomes. However, it did find higher rates of diabetes detection and treatment and lower rates of depres- sion. It is important to note that the coverage nearly eliminated catastrophic out-of-pocket medical expenditures. It is easy to forget that the purpose of health insurance, like all insurance, is to reduce financial uncertainty, not necessarily to affect health status (Baicker et al. 2013).

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in drug use in the first two tiers. In the third tier, the copay elasticity with respect to utilization was −0.21 with respect to utilization and −0.24 with respect to total tier-three pharmaceutical expenditures. This result is consis- tent with the RAND-HIE.

Joyce and colleagues (2002) also examined drug benefit copays over the 1997–1999 period but used an unnamed health benefits consult- ing firm’s data on 25 firms with more than 702,000 person-years of data. The study essentially compared those with one regime of copays relative to another, controlling for sociodemographic characteristics and chronic health conditions of the subscribers. The overall findings are summarized in exhibit 8.7. Higher copays did reduce overall drug spending substantially. Those in a one-tier plan (i.e., one with a single copay for all covered drugs) that had a $10 copay had expenditures that were 22.3 percent lower than those with only a $5 copay. Indeed, in every tier, for each drug type, those with higher copays had lower drug expenditures. The price elasticities ranged from −0.22 to −0.40, with the three-tier nonpreferred brand-name prescriptions being the most price sensitive. These results are nearly twice as price sensitive as those found in the RAND-HIE.

The Joyce and colleagues (2002) study also demonstrated expendi- ture reductions in moving from a one-tier to a two-tier, or from a two-tier to a three-tier drug plan. In exhibit 8.7, moving from a one-tier plan with a common $10 copay to a two-tier plan with $10 and $20 copays reduced average spending from $563 to $455, a 19 percent reduction. Moving from a two-tier to a three-tier plan with $10, $20, and $30 copays was estimated to reduce expenditures by an additional 4 percent.

EXHIBIT 8.7 Predicted

Average Annual Prescription

Drug Spending per Member

Note: All values in 1997 dollars. All horizontal comparisons within tiers are statistically significant at the 95% confidence level.

Source: Data from Joyce et al. (2002).

One-Tier Copay Two-Tier Copay Three-Tier Copay

$5 $10

$5 Generic,

$10 Brand

$10 Generic,

$20 Brand

$5 Generic, $10

Preferred, $15

Nonpreferred

$10 Generic, $20

Preferred, $30

Nonpreferred

All drugs $725 $563 $678 $455 $666 $436

Generic $91 $69 $71 $41 $81 $53

Preferred $571 $448 $534 $367 $518 $343

Nonpreferred $63 $46 $73 $47 $67 $40

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The shift from nonpreferred to preferred brands is one of the key objectives of three-tier pharmacy benefits plans. Rector and colleagues (2003) examined the use of angiotensin-converting enzyme inhibitors (i.e., ACE inhibitors), proton pump inhibitors, and statins in four health plans from 1998 to 1999. They found that the presence of an average $18 higher copay for nonpreferred drugs (approximately $28 in 2018 dollars) was asso- ciated with a 13.3, 8.9, and 6.0 percentage point increase, respectively, in the use of the preferred brands in each drug class.

Goldman and colleagues (2004) examined the effect of doubling the copay associated with the use of eight classes of therapeutic drugs. They examined the claims data from 30 employers with 52 health plans over the 1997–2000 period. Reductions in days of prescription use across the eight classes ranged from 45 percent for nonsteroidal anti-inflammatories to 25 percent for antidiabetics. They concluded, “The use of medications . . . which are taken intermittently to treat symptoms, was sensitive to co-payment changes. . . . The reduction in use of medications for individuals in ongo- ing care was more modest. Still, significant increases in co-payments raise concern about adverse health consequences because of large price effects, especially among diabetic patients.”

Goldman, Joyce, and Zheng (2007) undertook a thorough review of the literature on the effects of cost sharing on prescription drug use, on other health services, and on health outcomes. They concluded that the lit- erature showed that a 10 percent increase in cost sharing resulted in a 2 to 6 percent decrease in use, depending on the drug class and the condition of the patient. Evidence shows that these higher cost-sharing requirements are associated with increased use of other health services, at least for patients with particular health conditions, such as congestive heart failure and diabetes, among others.

The RAND-HIE also investigated the extent to which over-the- counter (OTC) drugs were substituted for prescription drugs. Given greater cost sharing, we might expect consumers to use OTC drugs as a substitute for prescriptions or physician visits. Leibowitz (1989) found no evidence of this. Based on biweekly reports filed by the insurance experi- ment participants, she found that OTC drug use was relatively low and was complementary with prescription drug use. Those with lower out-of-pocket insurance plans used more OTC drugs than did those facing higher out- of-pocket prices, though OTC drugs were generally not covered by the insurance experiment.

Few other studies have addressed this issue, probably because of the difficulty in getting OTC usage data. However, as part of their study of drug copays and chronic health conditions, Goldman and colleagues (2004) found that those drugs with close OTC substitutes had larger reductions in

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prescription drug use than did those without close substitutes. Doubling the prescription copay led to a 32 percent reduction in the days of drug treatment supplied for medications with close OTC substitutes, such as antihistamines, but only a 15 percent reduction for those with no close substitutes.

Value-Based Insurance Design

The finding that increased prescription drug cost sharing reduced adherence to treatment regimens in some important instances has led to experimenta- tion with value-based insurance design (V-BID). With this approach, cost sharing for prescription drugs generally continues to be in place. However, for certain drugs, such as beta-blockers, the insurance plan may set cost shar- ing to zero to encourage adherence. In more sophisticated versions, the cost sharing for beta-blockers may be set to zero for those with chronic heart dis- ease but not for all enrollees. Chernew, Rosen, and Fendrick (2007) provide a nice overview of the concept and early examples of ongoing experiments with these models. Fendrick, Martin, and Weiss (2011) summarize much of the empirical literature on value-based insurance experiments. That lower cost sharing improves adherence comes as no surprise. However, there are limits to this research. As Look (2015) points out, many studies do before- and-after comparisons but do not have a control group.

In more recent work, Gruber and colleagues (2016) examine the introduction of a V-BID program by a large public employer in Oregon in 2008. The intervention sought to increase the out-of-pocket prices of low-value health services. It increased the cost sharing for sleep stud- ies, upper gastrointestinal endoscopies, advanced imaging services, and potentially overused surgery services such as spinal surgery for pain. Out- of-pocket prices increased by $100–500 (46–159 percent). Comparing usage in 2008 and 2013 for the intervention group and other employers who did not adopt the program, the study found that the V-BID program reduced service use substantially. Overall, the use of the targeted services decreased by 11.9 percent, sleep studies and low-valued surgeries more than 20 percent, advanced imaging by 7.7 percent, and endoscopies by 12 percent.

Little analysis has been done of the private demand for nursing home and related long-term care services. Historically, this lack is understandable— much of nursing home care has been provided through Medicaid. While this continues to be the case, the advent of a large number of relatively affluent baby boomer retirees suggests that the price sensitivity of nursing home and other long-term care services will be increasingly relevant for long-term care management and policy decisions.

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Early work by Scanlon (1980) and Chiswick (1976) used metro- politan and state-level data. Only Scanlon examined private payers distinct from Medicaid-subsidized payers. However, both found substantial price sensitivity—elasticities of −1.1 and −2.3, respectively—implying that a 10 percent reduction in nursing home prices would increase volume by 11 to 23 percent. The work, however, can be criticized for its aggregated units of analysis and the potential that the results are overstated by a failure to account for requirements that financially better-off residents would have had to spend some of their assets on care before they were eligible for Med- icaid during the period. In an analysis of 1983 Wisconsin facility-specific data, Nyman (1999) also found substantial price sensitivity—an elasticity of −1.7.

In more sophisticated work, Reschovsky (1998) used the 1989 National Long-Term Care Survey to examine the effect of Medicaid eligibil- ity on nursing home use among older persons with disabilities. As part of this study, he estimated a series of private demand equations. Price elasticity among private payers was −0.98. Married people with disabilities had an elas- ticity nearly two and one-half times higher (−2.40), presumably because mar- ried individuals have access to relatively inexpensive informal care provided by a spouse. Unmarried individuals had much lower price sensitivity (−0.53), as did those with high levels of disability. In both cases, there are fewer viable substitute sources of care and, therefore, less price responsiveness. Care must be used in employing these estimates because the estimates often lacked sta- tistical significance at the conventional levels.

Mukamel and Spector (2002) used 1991 New York State data on for-profit nursing homes to impute a degree of price sensitivity derived from marginal cost estimates. They found firm-specific elasticities in the neigh- borhood of −3.46. A 10 percent decrease in price would increase demand at a given nursing home by nearly 35 percent. As with managed care plans’ demand for inpatient services from a specific hospital, we would expect firm-specific demand for nursing homes to be much larger than marketwide demand.

In one of the few efforts to look at price sensitivity for other types of long-term care services, Nyman and colleagues (1997) examined the extent to which long-term care service users substitute adult foster care for nursing home care. (Adult foster care is a program in which an older adult lives in a private home of an unrelated individual.) A simple regression of the number of foster-care residents in Oregon counties in 1989, controlling for other factors, indicated that a nursing home lost 0.85 residents for every additional foster-care resident. In addition, an analysis of the demand for foster care demonstrated substantial price responsiveness in the private market. A 1 per- cent increase in the average adult foster care price was associated with a 5.2 percent decrease in day care users.

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The number and rigor of the long-term care studies do not match those of other service areas, largely because of an inability to account for adverse selection and, certainly, the lack of a controlled experiment of the nature of the RAND-HIE study. Thus, these findings inevitably overstate the extent of price sensitivity. Nonetheless, by the standards of acute care services, the private demand for long-term care services appears to be very price sensitive.

Deductibles

With the passage of the Medicare Reform Act in late 2003 and its provi- sions for HSAs, attention again turned to the effects of higher deductibles on healthcare spending. Individuals and employers are able to establish tax- sheltered spending accounts that allow unused balances to be rolled over from one year to the next if they have an eligible health insurance plan. Among other requirements, an eligible health insurance plan must include a deductible (in 2012) of at least $1,200 per individual. This amount is to be adjusted annually for inflation under the terms of the legislation. HSAs and the evidence of their effects on use of services are discussed in detail in chapter 17.

The effect of a deductible depends on the nature of coverage once the deductible is satisfied. Suppose you have an annual deductible of $1,000 and must pay an out-of-pocket copay of $20 for each physician visit once the deductible is satisfied. If you knew with certainty that you would satisfy the deductible, then you would consume as though the price of a doctor visit was $20. If you knew you would not satisfy the deductible, then you would consume as though you had to pay the full price of the visit—perhaps $100 per visit. The higher the deductible, the less likely you are to satisfy it and the more likely you are to act as though you are paying the full price for medical services.

Very little empirical research has been done on the effects of deduct- ibles on medical usage, at least in the United States. The RAND-HIE is, again, the exception. As part of the experiment, some participants were enrolled in the 95 percent plan. In this plan, people paid 95 percent of every medical bill until they had spent 5, 10, or 15 percent of their family income (depending on the plan) or $1,000, whichever was lower. In this plan, participants faced a deductible of $1,000 and afterward paid nothing out of pocket. In 2018 dollars, this equals a deductible of approximately $5,000. The results of the RAND-HIE shown in exhibit 8.4 indicate that the pres- ence of a $4,734 family deductible followed by free care (i.e., the 95 percent plan) resulted in a more than 31 percent reduction in medical spending, rela- tive to the plan with free care.

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Brot-Goldberg and colleagues (2017) studied the conversion of a large employer’s single health insurance plan from a no-deductible, no–cost- sharing plan to one with a deductible of $3,000 to $4,000, a 10 percent coinsurance rate, and a maximum out-of-pocket limit of $6,000–7,000. The benefits and the network of providers were the same before and after the switch. This set-up is analogous to the RAND-HIE comparison of free care (the pre-period in the Brot-Goldberg study) to the 95 percent plan (in which, in the RAND study, people had to pay for care until the stop-loss).

The employees in this firm were well paid, with median income in the $125,000–150,000 range, well educated, and technologically savvy. As the authors note, this was perhaps the best-case scenario for examining changes in the use of health services. They expected to see reductions in the use of health services, consumer price shopping, and the substitution of some less expensive services for more expensive ones. In fact, what they found was an 11.8 to 13.8 percent reduction in total firmwide health spending. The change was entirely the result of reduced use of health services. There was little evidence of price shopping or service substitution.

The reduction in service use was generally across the board. They looked at the top 30 procedures by total spending in each year of the study and found that consumers reduced quantities in all areas rather than tar- geting specific kinds of services. They also examined services categorized as high value and low value. Consumers reduced both types of care. One might expect that consumers would learn over time and make different sorts of changes in the second year of the study, after appreciating the incentives through the first year. This learning did not happen; behavior was essentially unchanged between the first and second years of the high- deductible plan.

The economics of deductibles are a little bit complex. An economically astute consumer will realize that if she does not expect to meet her deduct- ible, she should view providers’ prices as the ones she will pay. Think of these as the spot prices of, say, a physician visit or a magnetic resonance imaging test. On the other hand, if the consumer anticipates that she will exceed the deductible over the year, then she should buy medical care all year as if she is only responsible for the 10 percent coinsurance (in this study) or at $0 if she is likely to satisfy the maximum out-of-pocket expense. In the study by Brot- Goldberg and colleagues, consumers consistently acted on the spot prices. Even those who were statistically expected to easily surpass the deductible or the out-of-pocket maximum still reduced their use of health services based on the spot prices. Thus, they underconsumed given the relevant prices.

We see here, as we saw in the premium-only driven enrollment in the ACA in chapter 2 and will again in examples in chapters 16 and 23, that decision-making in the context of health insurance is complex, and consum- ers are not (yet) very good at it. Much of this difficulty appears to arise from

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a focus on the current spot price (e.g., the premium only or the physician’s fee), rather than considering the longer-term view of out-of-pocket prices later in the year.

We should note that there is growing literature on the effects of consumer-directed health plans, with their high deductibles and linked tax- sheltered spending accounts, much like the study by Brot-Goldberg and col- leagues; we will examine these in chapter 18.

Effects of Large-Scale Increases in Insurance Coverage

With the coming of the ACA, an important question is the extent to which the RAND-HIE findings can be used to estimate the effects of expanded coverage on healthcare spending. Finkelstein (2007) suggests that the RAND estimates may substantially understate the likely effect (see also Challenges to the RAND Health Insurance Experiment). She examined the impact of the introduction of Medicare in 1965 on hospital spending over the ensuing five years. She argues that just prior to enactment, approximately 50 percent of the elderly had Blue Cross–type coverage and another 30 percent had more mod- est coverage. Substantial geographic variation existed in this private coverage, and Finkelstein used this variation to develop her estimates. She concluded that hospital expenditures increased by roughly 37 percent between 1965 and 1970 as a result of the introduction of Medicare. By comparison, the RAND- HIE results would imply an increase of less than 6 percent. Finkelstein argued that the principal reason for the difference in results is the marketwide nature of Medicare’s introduction compared to the modest market role that the RAND experiment played in its six sites. The broad-based Medicare coverage expansion gave hospitals an incentive to invest in plant and equipment and to expand their capacity dramatically, particularly in areas that had low coverage prior to the law. The implication for the ACA or other broad-based changes in coverage is that the impact of such a coverage expansion will depend on the ability of hospitals and physicians to expand or contract capacity.

Challenges to the RAND Health Insurance Experiment

We have given considerable weight to the RAND-HIE. A number of challenges to the methodology and generalizability of the RAND-HIE have arisen over the years. These have included concerns about the relatively short-term nature of the health follow-up and the changes in medical technology and

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Summary

• In general, health services exhibit modest price sensitivity with an elasticity of −0.2. A 10 percent increase in prices paid by the consumer reduces consumption by about 2 percent. By comparison, gasoline has a short-run price elasticity of −0.1 to −0.5 (Grabowski and Morrisey 2004).

• Large increases in the prices of healthcare, even given modest elasticities, will reduce consumption considerably.

• Price sensitivity for various health services, as estimated in the RAND- HIE, differs substantially: – Hospital services. Full coverage compared to no coverage increased

admissions by about 29 percent and total inpatient expenses by 30 percent. Almost all of this effect in the RAND-HIE was found in the difference in usage between 25 percent coinsurance and free care.

insurance design that have occurred since the experiment was conducted. Two of the most important recent challenges have to do with forward-looking behavior and the applicability of the findings of a small-scale experiment to a marketwide change. We highlight the forward-looking issue here. The scale issue is taken up in the next section.

Kowalski (2009) explores the forward-looking issue. She argues that the analysis in the RAND-HIE experiment employed “myopic prices” and that the true effects are larger by an order of magnitude if one uses forward-looking prices. The key issue is the point at which one faces a stop-loss feature in an insurance plan. When one’s spending exceeds the stop-loss, the marginal price of care is zero. A myopic purchaser only considers the price at the point of service. The forward-looking purchaser incorporates her expectation of exceeding the stop-loss. If she expects to do so, then she will make purchas- ing decisions throughout the insurance contract period as though she has exceeded the stop-loss. Taking forward-looking behavior into account, she finds an overall price elasticity of –2.3 over the middle range of expenditures.

Aron-Dine and colleagues (2012) also investigated the myopia issue with data from Alcoa, Inc., and two anonymous firms over the 2004–2007 period. Their key test was to examine the differences in the use of health services between yearlong employees and new hires who, because of the annual deductibles, face different year-end out-of-pocket prices for health services than do longtime employees. They found price elasticities of –0.4 to –0.6. These are substantially smaller elasticity estimates (in absolute value) than those implied by the fully forward-looking behavior of Kowalski, but much larger than those found in the RAND-HIE.

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– Hospital emergency department services. Full coverage relative to no coverage increased visits by 54 percent and expenses by 45 percent. Free care resulted in about a 90 percent increase in less urgent visits but only a 30 percent increase in visits for more urgent cases.

– Price sensitivity by income level. Higher-income groups were found to be less sensitive to price changes than were lower-income groups.

– Children versus adults. Children’s use of ambulatory services was about as price sensitive as was adults’ use. However, hospital services tended to be almost insensitive to differences in price, at least under the conditions of the RAND-HIE.

– Physician services. Full coverage increased both visits and expenditures by about two-thirds, controlling for other factors. The effect of moving from a 25 percent coinsurance rate to free care accounted for about one-half of the overall change.

– Dental services. A large transitory effect occurred when coverage was first introduced. The RAND-HIE found that the first year of coverage had price effects that were twice as large as subsequent use. In the steady state, full coverage increased visits by 34 percent and expenses by 46 percent.

– Mental health services. Greatest price sensitivity was found in outpatient mental health services. Full coverage relative to no coverage increased expenditures 300 percent. Evidence also showed that, unlike dental care, use of mental health services increased over time.

• Prescription drug price sensitivity is about the same as for physician visits. Most drug coverage now includes tiers of coverage, with lower out-of-pocket prices for generic drugs and higher prices for preferred drugs; the highest prices are for nonpreferred drugs.

• Value-based insurance design (V-BID) assigns lower out-of-pocket prices to prescription drugs and other health services when they are linked to specific health conditions and patient characteristics. Higher prices are assigned to less effective interventions.

• Very little rigorous research has been devoted to nursing home or assisted living care. Early work suggests that these services are very price sensitive.

• Recent empirical research on high deductible health plans suggests that the introduction of a $3,000–4,000 deductible with a 10 percent coinsurance rate and an out-of-pocket maximum of $6,000–7,000 reduced spending by approximately 12–14 percent. The entire change arises from reduced use of health services. Researchers have found no evidence of greater price shopping or of substitution of less expensive health services for more expensive ones.

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Chapter 8: Moral Hazard and Pr ices 167

Discussion Questions

1. Why do you think the moral hazard response for dental care was different than that for medical services more generally?

2. Prescription drug plans often have three tiers of increasing copayment. Given the results noted in the chapter, do you think the third tier saves enough to justify its presence?

3. Ambulatory mental health services appear to be among the most price sensitive. Some have argued that this area of healthcare has changed dramatically since the RAND-HIE was conducted in the 1970s. If mental health services are less price sensitive now than formerly, what evidence in the current market would you look for to support or refute this argument?

4. Suppose the RAND-HIE could be redone in 2019 for $50 to $75 million. What topics would you include that were not in the original 1974 study? To what topics would you give less attention? If you were a member of Congress, would you vote to fund a new study? Why or why not?

5. Do you think the results of the RAND study are likely to over- or underestimate the likely effects of the ACA on the use of health services?

For the Interested Reader

Aron-Dine, A., L. Einav, and A. Finkelstein. 2013. “The RAND Health Insurance Experiment, Three Decades Later.” Journal of Economic Perspectives 27 (1): 197–222.

Goldman, D. P., G. F. Joyce, and Y. Zheng. 2007. “Prescription Drug Cost Sharing.” Journal of the American Medical Association 298 (1): 61–69.

Levy, H., and D. Meltzer. 2008. “The Impact of Health Insurance on Health.” Annual Review of Public Health 29: 399–409.

Newhouse, J. P., and the Insurance Experiment Group. 1993. Free for All? Lessons from the RAND Health Insurance Experiment. Cambridge, MA: Harvard University Press.

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Notes

1. Parts of this chapter draw on chapter 3 of Michael A. Morrisey, 2005, Price Sensitivity in Health Care: Implications for Policy, 2nd edition, Washington, DC: NFIB Research Foundation. Used with permission.

2. The differences among the 25, 50, and 95 percent plans were not statistically different at this level.

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