HMGT 495 WK 2 PRO 2

Course Objectives for Assignment: 

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· Explain the basic terminologies of healthcare economics and analyze how healthcare supply and healthcare demand work to construct a model of the fundamental economic factors that impact the delivery of healthcare.

· Assess the key economic drivers that impact the delivery of healthcare in order to inform effective future decision-making.

You just started working as a Health Service Manager within one of the following health care industries.  First choose an industry below to discuss the questions that follow:

a. Ambulatory Surgery center

b. Pharmacy

c. Physician’s Office

d. Cosmetic Surgery Center

e. Laser Eye Center

f. Dental Office 

Your boss has asked you to write a report detailing how the demand for your product(s) is impacted by various economic factors.  In writing your memo, be sure to include your name and in the subject line identify the health care entity you chose above. In order for your boss to easily review your memo, please include section headers to correspond to the questions below.

Answer the following questions relying primarily on the course readings and other resource material presented in this class (do not cite any other outside sources).

1.
Describe a product or service your company provides to your patients

2.
Evaluate the demand curve for your product and relationship between the price of your service/product and the quantity demanded.

In this evaluation, be sure to 
identify:

· whether demand is sensitive (e.g. elastic) or less sensitive (e.g. inelastic) to changes in the price and

·
evaluate why this relationship might occur.

· include a 
discussion of how the existence of health insurance would impact the elasticity of demand.

3.
Define “substitute” goods and 
identify potential substitutes for your product/service. 
Evaluate how does the existence of a substitute impact the demand for your product/service.

4.
Define “complement” goods and 
identify potential complements for your product/service. 
Evaluate how does the existence of complement goods impact the demand for your product/service.

5.
Identify and discuss the economic factors that might lead to a shift in the demand curve for your product/service?

  

CHAPTER

8

1

5ADVERSE SELECTION

You know more about your likely use of health services than does your
typical insurance company. As a result, you have an incentive to use this
information to your best advantage. In particular, if you have some

health problem—say, heart disease—you might try to find an insurance plan
that is designed for healthier people. If you were successful, you would pay a
premium that was less than your expected claims experience. The insurer, on
the other hand, would probably lose money on you. As you might imagine,
insurers worry a good deal about this.

Adverse selection in health insurance exists when you know more
about your likely use of health services than does the insurer. Insurers deal
with the problem by trying to design risk classes that group similar risks
together. They then charge premiums that reflect this differential risk. The
same information that goes into defining risk classes can be used to identify
potential marketing opportunities for insurers. If one insurer can identify
an employer group that has lower claims experience, for example, it might
be able to quote a premium that will attract the group away from another
insurer.

The Affordable Care Act (ACA) seeks to remove adverse selection
concerns from the consumer by prohibiting the use of preexisting condi-
tions in setting insurance premiums. However, adverse selection does not
just go away because of federal law. In this and chapters 6 and 7, we will
discuss the classic adverse selection problem, see how insurers have dealt
with it, and begin to understand how the ACA addresses the problem
behind the scenes.

In this chapter, we explore some of the implications of adverse selec-
tion in the context of the reported differences in the utilization experience
of people enrolled in managed care plans (e.g., HMOs) and people enrolled
in conventional insurance plans (see Adverse Selection in Pension plans). We
discuss mechanisms whereby the differences could reflect efforts by the man-
aged care plan to reduce utilization and efforts it might make to attract lower
utilizers. This discussion leads to an examination of the insurance cost impli-
cations for employers who might begin to offer the HMO. We then review
the literature on the extent to which adverse selection and changes in use
patterns explain actual differences in HMO and conventional utilization. An

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Health Insurance82

exploration of adverse selection in employer-sponsored coverage follows and,
finally, we discuss some important selection issues that arise with the ACA.

Adverse Selection in Pension Plans

Adverse selection arises in many insurance markets. In the pension world,
for example, you can purchase an annuity that pays out monthly until you
die, or alternatively, you can buy an annuity that pays out monthly for a fixed
number of years, thereby leaving money to your heirs if you die early. In a
fascinating study of a large pension plan in the United Kingdom, Finkelstein
and Poterba (2002) found that those people who ultimately lived longer
disproportionately purchased pension plans that paid out until they died.
Those who died early disproportionately purchased fixed-term annuities.
The implication is that adverse selection is present in the pension markets,
and people appear to know more about their likely remaining length of life
than does the annuity seller.

Health Maintenance Organization Effect Versus
Favorable Selection

Much of the empirical research on adverse selection in healthcare was done in
the 1980s, as employers began to offer HMOs and other managed care plans.
The issue arose because of substantial differences in the utilization experience
of those enrolled in HMOs and those in conventional insurance plans. Miller
and Luft (1994) reviewed much of the literature on the differences in utiliza-
tion; see “HMO Performance” for a summary of their findings. Essentially,
Miller and Luft found that people enrolled in an HMO use considerably less
hospital care. The question is why.

One explanation is that HMOs do something to keep people out of
hospitals. This is the so-called HMO effect, which might be the result of
a number of strategies. For example, HMOs could substitute ambulatory
services for inpatient services at a much more aggressive rate than do con-
ventional insurers. HMOs could employ effective utilization management
techniques that are designed to limit hospital use to only those most likely to
benefit from it. HMOs may only affiliate with physicians who are conserva-
tive in their use of hospital services, and/or they may provide financial incen-
tives to physicians that lead the physicians to admit fewer patients. HMOs
may provide preventive services that identify harmful conditions at an early
stage and reduce hospitalizations.

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Chapter 5: Adverse Select ion 83

Health Maintenance Organization Performance

Compared to indemnity insurance, HMOs had the following:

• Admission rates: 26–37 percent lower

• Average length of stays: 1–20 percent lower

• Hospital days: 18–29 percent lower

• Office visits: Higher or equal

• Expensive services: Used less

Source: Data from Miller and Luft (1994).

Alternatively, HMOs may do nothing at all to lower the hospital
utilization experience of its members. Instead, they may attract members
who are low utilizers to begin with (favorable selection), which also could
be accomplished in many ways. HMOs could target their enrollment efforts
at younger or healthier groups by, for example, marketing to schoolteachers
rather than construction workers on the theory that schoolteachers, on aver-
age, are less likely to take risks in their daily lives. HMOs might contract
with physician groups and hospitals that are located in suburbs populated
by young, upwardly mobile professionals, believing that such proximity will
disproportionately attract the residents. HMOs could offer excellent mater-
nity and well-baby care in the hopes of attracting otherwise healthy young
families into their plans. Similarly, they could offer abundant preventive
services, expecting that those who value such services prefer to keep them-
selves healthy and out of the hospital. HMOs might offer a tie-in sale—for
example, enroll in the HMO and receive a substantial discount at a local gym.
Indeed, some HMOs have given health credits to members who undertake
healthy activities.

While these efforts could be to keep people out of the hospital, they
could also be designed to attract people with healthy lifestyles. Perhaps
those who are less prone to exercise will see these offers as wastes and not
join the plan. HMOs may choose their panel of providers such that there
is an abundance of primary care physicians but few specialists. The theory
may be that an individual with chronic health problems probably has an
ongoing relationship with a specialist, and if that specialist is not in the
HMO’s panel of providers, the consumer is less likely to join. In this con-
text, Goodman argues that insurance plans offered under the ACA in Texas
exclude MD Anderson Center and those in Minnesota exclude the Mayo

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Health Insurance84

Clinic as a means of limiting enrollment by people who have expensive
health problems (Goodman 2018).

Alternatively, HMOs may do none of these things. It may simply be
that the philosophy of health maintenance attracts people who do not like
to interact with the healthcare system. If so, even though HMOs may reach
out to all members of the community, they may still attract a favorable draw
of the population.1

Obviously, HMOs could seek to attract low utilizers and also to limit
their use of hospitals once they join the plan. To an employer considering
offering an HMO in addition to a conventional plan, however, appreciating
which effect dominates is critical. If the difference in utilization is largely
attributable to the HMO effect, then the plan can do something that will
lower healthcare costs for the employer and employees. Potential savings can
be had. On the other hand, if the difference in utilization is largely attribut-
able to favorable selection, then no savings occur. The best the employer
could hope for is writing two checks, one to the traditional plan and one
to the HMO.2 Moreover, the employer may conceivably be even worse off
because it has added the HMO.

Consider an employer that has long offered a conventional insurance
plan. It now adds an HMO that achieves its lower utilization by means of
favorable selection and attracts a disproportionate share of the employer’s
healthy workers. As Feldman and Dowd (1982) note, it is not at all obvi-
ous that the lower claims experience will be passed on to the employer and
employees in the form of lower premiums. The HMO may try to set the
premium just a shadow below the competitively priced conventional plan’s
premium. If so, the employer and employees will effectively pay a higher pre-
mium for the healthy employees than they were when only the conventional
plan was offered. To make matters worse, the conventional plan may find
that its claims experience now has increased and the plan will have to raise its
premiums! Thus, in the face of both favorable selection and shadow pricing,
the employer finds that its efforts to reduce insurance costs resulted in higher
costs. A solution to the shadow-pricing problem, as we will discuss in later
chapters, is competition in the HMO segment of the market.

Evidence of a Health Maintenance Organization Effect

The best evidence supporting the HMO effect is the RAND Health Insur-
ance Experiment (Manning et al. 1987). This study was designed to esti-
mate consumers’ price responsiveness to alternative coinsurance rates for
the use of clinical services. We will discuss this study at considerable length
in chapter 8. For current purposes, however, knowing that the experiment

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Chapter 5: Adverse Select ion 85

randomly assigned families to alternative health insurance plans is enough.
The random assignment has the advantage of largely overcoming the adverse
selection problem.

In one part of the experiment, people in Seattle, Washington, were
alternatively assigned to Group Health Cooperative of Puget Sound (called
Kaiser Permanente Washington since 2017)—a large, well-run staff model
HMO—or to a conventional health insurance plan that, like Group Health
at the time, had no out-of-pocket charges associated with the use of covered
services (a fee-for-service plan that had no cost sharing—hereafter, the Free
FFS). Thus, both plans covered an extremely wide range of health services,
and both required no copays or coinsurance for the use of the covered ser-
vices. Because people were randomly assigned to one plan or the other, any
difference in utilization should have arisen from an HMO effect of keeping
people out of the hospital.

The results are summarized in exhibit 5.1. Those in the Free FFS plan
had an 85 percent chance of interacting with the healthcare system. Those
randomly assigned to Group Health (this group was referred to as HMO
Assigned) had an 87 percent likelihood of any use. The results are virtually
identical. In contrast, the probability of one or more hospital admission was
lower for the HMO-Assigned group. They had a 7 percent chance of being
hospitalized, while the Free FFS group had an 11 percent chance. This sta-
tistically significant difference suggests that the HMO did something to keep
people out of the hospital.

The row in exhibit 5.1 titled “HMO control” reflects the experience of
a group of longtime Group Health Cooperative members with demographic
characteristics similar to those in each of the randomly assigned groups. This
addition allows a comparison of whether the newly assigned individuals have
experience different from that of longtime enrollees. The answer is that the
longtime enrollees have an even lower probability of using hospital care
(although the difference is not statistically significant), and they are more
likely to interact with the healthcare system. This suggests that the long-term
enrollees may see more substitution of ambulatory for inpatient services.

EXHIBIT 5.1
HMO EffectLikelihood of Any Use (%) One or More Admissions (%)

HMO assigned 87 7

HMO control 91 6

Free FFS 85 11

Note: HMO = health maintenance organization, FFS = fee-for-service plan.

Source: Data from Manning et al. (1987).

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Health Insurance86

While this study is the best evidence of an HMO effect, like all stud-
ies it is not without limitations. The key question in this case is the extent
to which differential participation rates introduced some selection bias into
the study. The participation rate for those Seattle residents participating in
the Free FFS plan was 93 percent, while the participation rate in the HMO-
assigned plan was only 75 percent (Davies et al. 1986).

Evidence of Favorable Selection

The evidence for favorable selection into HMOs comes from a series of
natural experiments that have the following framework: Suppose everyone
in an employer group is enrolled in a conventional health plan that collects
detailed information on employees’ use of covered health services. Then,
at some open enrollment period, employees can choose to take a newly
offered HMO or to remain in the existing plan. Once people have made
their choices, the researcher goes back into the preceding year’s claims data
and compares the health services use of those ultimately choosing the HMO
with those ultimately choosing to stay in the conventional plan. If favorable
selection into the HMO is present, we should see that, prior to having a
choice, those who ultimately chose the HMO had lower claims experience.
In contrast, if the conventional plan retained the low utilizers, the prior
claims experience of its ultimate enrollees should be lower. If no favorable
selection exists, then no difference in the reported levels of prior utilization
should exist.

Exhibit 5.2 reports the results of one of the first of these sorts of
studies. Jackson-Beeck and Kleinman (1983) reported on the experience of
11 employers who first offered an HMO in the early 1980s. They found that
those ultimately enrolling in a newly offered HMO had much lower claims
experience in the year prior to the choice than did those who remained in
the conventional plan. The difference was $23.14 per member per month

EXHIBIT 5.2
Favorable
Selection

Total Expense
Institutional
Expense

Professional
Expense

FFS $57.35 $40.45 $16.45

HMO $34.17 $22.23 $11.45

Difference $23.14* $18.22* $ 5.00*

Notes: *Significant at the 99% confidence level. FFS = fee-for-service plan; HMO = health
maintenance organization.

Source: Data from Jackson-Beeck and Kleinman (1983).

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Chapter 5: Adverse Select ion 87

(about $58 in today’s dollars). This difference was largely attributable to
institutional (i.e., inpatient) services, but professional services (i.e., ambula-
tory services) were also lower.

Wilensky and Rossiter (1986) reviewed the findings of a score of stud-
ies that examined the issue of patient favorable selection into HMOs pub-
lished between 1974 and 1986. Of the dozen most recent studies, beginning
with the Jackson-Beeck and Kleinman study in 1983, eight found evidence of
favorable selection into HMOs, three were inconclusive, and only one found
no evidence of selection bias.

While the RAND study does offer some strong evidence of an HMO
effect—at least in one large, well-run staff model HMO—the research litera-
ture suggests that typically substantial favorable selection into HMOs exists.
The evidence with respect to other forms of managed care plans is less defini-
tive. However, limited but strong evidence suggests that preferred provider
organizations (PPOs) get a less favorable draw of the population than HMOs
and, in fact, appear to have become the conventional plan of the 2000s, at
least with respect to selection bias (see Morrisey, Jensen, and Gabel 2003).

Favorable Selection in the Medicare Program

Adverse and favorable selection are not just concerns of private insurers; they
are also significant issues for the federal Medicare program for the elderly.
Since the 1970s, the Medicare program has allowed beneficiaries to be in
traditional Medicare or to join a Medicare HMO. (See chapter 22 for a more
complete discussion of the Medicare program and of Medicare managed care
options, called Medicare Advantage.)

Until 2006, the program allowed Medicare beneficiaries to transfer
to or from traditional Medicare each month. As with most HMOs, Medi-
care HMOs provide a limited panel of physicians and hospitals. Traditional
Medicare covers virtually all providers. However, many Medicare HMOs
offer broader coverage, including prescription drug coverage and annual
physicals, which were particular advantages in the days prior to Medicare Part
D prescription drug coverage. The Medicare program paid its participating
HMOs on a capitated basis for each covered beneficiary. The payment was
essentially 95 percent of the average Medicare cost of care in the local com-
munity. Medicare HMOs are required to accept all beneficiaries who choose
to enroll, but if a Medicare HMO can somehow attract sufficiently low utiliz-
ers of care, it could reap substantial profits.

A congressional advisory commission tasked with researching the
costs to Medicare of those who choose Medicare HMOs, relative to those in
traditional Medicare, used the same methodology as did the Jackson-Beeck

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Health Insurance88

and Kleinman (1983) study discussed earlier to look at Medicare claims data
from 1989 to 1994. It identified those who newly enrolled in a Medicare
HMO, then examined their Medicare claims experience in the six months
prior to switching to the HMO and compared it with the average claims
experience of all those in traditional Medicare in those months. Those who
ultimately switched to a Medicare HMO had total covered claims experience
that was only 63 percent of average. This finding suggests substantial favor-
able selection, to say the least!

The study also examined the claims experience of those Medicare
HMO enrollees who switched back to traditional Medicare. In the six
months following their switchback, they had claims experience that was 160
percent of the average. It is easy to speculate that the HMOs sought out low
utilizers, encouraged them to join the plan, and if they had health problems,
somehow pushed them out of the plan.

However, much less pernicious scenarios also are consistent with
these data. Consider a reasonably healthy, elderly, Medicare-eligible
woman. She joins a Medicare HMO, perhaps because of its coverage of
an annual physical or its encouragement of preventive services. Unfor-
tunately, her hip has deteriorated, and she discovers that she needs a hip
replacement. Her primary care doctor refers her to the plan’s orthopedic
surgeon, but her children want her to see the surgeon they consider the
best in town. That surgeon is not in the HMO’s panel. Under the terms
of the Medicare program, the woman could disenroll from the HMO, be
immediately covered by traditional Medicare, and have her surgery. Once
she has recovered, she could even switch back to the HMO. If stories of
this sort are common, they could explain the lower claims experience prior
to joining the HMO and the higher experience after disenrollment. Other
scenarios, such as the one in “An Urban Legend,” are also possible, but
unethical at best.

An Urban Legend

Medicare HMOs must enroll any Medicare-eligible person who wants to enroll.
The legend, alternatively described as occurring in Florida or New York, has it
that the Medicare HMO sets up its enrollment office in a third-floor walkup.
Any senior who can walk up three flights of stairs is enthusiastically enrolled!

More recently my colleagues and I have explored the extent of favor-
able selection in the Medicare Advantage program over a ten-year period.
Our results are summarized in exhibit 5.3. Using a methodology similar
to that used by the Jackson-Beeck and Kleinman (1983) and Prospective

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Chapter 5: Adverse Select ion 89

Payment Assessment Commission (1994) studies, we found that in the six
months prior to joining a Medicare Advantage plan, patients’ claims costs
were only about 80 percent as large as those of people who remained in
traditional Medicare. This relationship has been fairly stable over the entire
ten-year period. Those who dropped out of a Medicare Advantage plan and
returned to traditional Medicare had claims experience, in the six months
after they returned, of approximately 136 percent of those who never left
the traditional plan. Moreover, the relative costs of those who returned to
traditional Medicare have been increasing since the early 2000s. We will have
more to say about this in chapter 7.

Persistence of Favorable Selection over Time

The research strongly suggests that HMOs attract a healthier draw of the
population. This trend raises the important managerial and policy question
of whether favorable selection continues over time. If the experience persists
over time, then all an HMO must do to remain successful is attract some
low utilizers and keep them happy enough to stay in the plan (and work to
prevent the entry of new HMOs into its market). On the other hand, if low
utilizers quickly become average utilizers or worse, this suggests that the
plan must continuously turn over its enrollment or do something to keep the
enrollees healthy. From a policy perspective, if favorable selection is endur-
ing, we might consider efforts to promote competition or regulate insurer

EXHIBIT 5.3
Relative Claims
Experience
of Those
Enrolling in or
Disenrolling
from Medicare

1.5

1.25

1

0.75

0.5

19

99

20
00

20
01

20
02

20
03

20
04

20
05

20
06

20
07

20
08

1.75

1.5

1.25

1

0.75
19

99

20
00

20
01

20
02

20
03

20
04

20
05

20
06

20
07

20
08

Newly DisenrollingNewly Enrolling

Source: Data from Morrisey et al. (2013).

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Health Insurance90

practices. If the selection bias is fleeting, we might worry more about plan
turnover and the quality of care provided.

Obviously, the foregoing discussion of using prior utilization as an
indicator of favorable selection rests on a presumption of some persistence
of behavior. In the absence of changes in incentives, two factors are likely
to influence the persistence of healthcare usage. The first has to do with the
chronic-versus-random nature of personal health status. If a person’s illnesses
or injuries are largely random, that would suggest that particularly high or
low utilization in any one year is an unusual event and that the individual
would quickly revert to the average level of utilization. If the conditions are
chronic, it suggests that utilization will continue at an elevated level for some
time. The second factor is behavioral. For a given health condition and set of
prices, one individual may seek care, and another may not. The former will
be a persistently higher utilizer; the latter will be a persistently lower utilizer.

Only a handful of studies have examined healthcare utilization over
more than two years. One of the problems with undertaking such an analysis
is finding several years’ worth of data on a large, identifiable cohort of people
who have unchanged health insurance coverage over the period. Garber,
McCurdy, and McClellan (1999) undertook such a study using Medicare
beneficiary data from 1987 to 1991 and 1991 to 1995. While the study
focused only on those who had traditional Medicare over the period, it was
not able to control for differences in supplemental coverage that the benefi-
ciaries may have obtained, dropped, or changed over the years. Exhibit 5.4
summarizes the findings for the more-recent cohort.

EXHIBIT 5.4
Persistence of
Expenditures
for Surviving

Medicare
Beneficiaries

$45,000

$40,000

$35,000

$30,000

$25,000

$20,000

$15,000

$10,000

$5,000

0

Ex
pe

nd
it

ur
es

Year

1991 1992 1993 1994 1995

High Mean Middle Low

Source: Data from Garber, McCurdy, and McClellan (1999).

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Chapter 5: Adverse Select ion 91

Garber, McCurdy, and McClellan (1999) had Medicare claims data
on a cohort of 37,000 Medicare beneficiaries who were alive in 1989. They
divided this group into three subgroups based on their 1993 Medicare
spending. The low utilizers were those in the 0 to 50th percentiles. Their
average Medicare expenditure in 1993 was $211. The middle subgroup was
composed of those in the 51st to 95th percentiles; they had average expendi-
tures of $5,758. The high utilizers were those in the 96th+ percentiles; they
had average expenditures of $41,921. (This figure shows the typical health
insurance experience. A very small proportion of the covered individuals
incur the vast majority of the expenditures.)

The pattern of results was clear. First, those with low expenditures in
the base year (1993) had unusually low expenditures for that year; however,
in the two prior years and two subsequent years, they had much higher
expenditures. Analogously, those who were high utilizers in the base year had
unusually high expenditures that year. Their experience was much lower in
the prior and subsequent years. Second, even though their respective claims
experience did revert toward the mean, low utilizers continued to be low
utilizers, and high utilizers continued to be high utilizers. In short, while
healthcare utilization has a large random component, sizable persistence in
use exists. Selection bias tends to be enduring.

We should note that this pattern of results was observed in the 1989
to 1991 cohort as well. The study also noted the effects of deaths among the
sample in the two latter years (1994 to 1995 and 1990 to 1991, respectively).
Exhibit 5.4 only includes survivors in the last two years; however, the same
pattern of results occurs if we include the decedents in the analysis.

Selection Bias in Employer-Sponsored
Health Insurance

This chapter has focused on evidence of adverse selection in the HMO versus
conventional coverage decision because that is where most of the empirical
research has been conducted. The extent of any selection bias is always an
empirical question and is not limited to the managed care setting. In some
early work, Ellis (1985) examines the extent of selection bias in an employer
group that offered a single conventional plan in 1982 but three conventional
plans with differing deductibles and stop-loss features in 1983. Ellis con-
cludes, “The results presented here suggest that the self-selection effects in
these settings may be enormous, with high-coverage plans attracting enrollees
who are as much as four times as expensive as enrollees choosing the low-
coverage option.”

Recently Bundorf, Herring, and Pauly (2010) explored adverse
selection in employer-sponsored health insurance plans. They used data on

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Health Insurance92

demographics, health status, employment, and insurance coverage from the
1996–2002 Medical Expenditure Panel Survey to examine the extent of
adverse selection among low-, medium-, and high-income households and
coverage in small-, medium-, and large-employer-sponsored insurance plans.
They concluded that, “in aggregate, the likelihood of obtaining employer-
sponsored coverage nearly always increases with expected health expen-
ditures. The positive relationship between insurance status and expected
expenditures is generally consistent across the large group, medium group
and small group markets. . . . [This] is consistent with a moderate amount of
adverse selection.”

On a related note, it is not at all unheard of for families and indi-
viduals to “save up” their use of dental services and obtain dental cover-
age only when they expect to use the services. Such actions constitute
adverse selection.

The last 15 years have seen the rapid growth of consumer-directed
health plans. These products encompass a high-deductible health plan and a
tax-sheltered health savings account. Proponents argue that such plans give
consumers strong incentives to be value-conscious purchasers of health ser-
vices because they must spend their own, albeit tax-sheltered, dollars on the
first $2,000 or $3,000 of services used. Consumers are expected to forego
services that are not viewed as worth the cost and to shop around for provid-
ers who will give them good quality at a lower price. One might expect that
there could be substantial favorable selection into these plans, at least in an
employer-sponsored context where people have multiple options. In a care-
ful review of the empirical literature, Bundorf (2016) concludes that there is
such selection based on health status, age, or both.

Adverse Selection and the Affordable Care Act

Adverse selection is also one of the key reasons why the ACA mandates that
everyone above a certain income threshold must buy health insurance or
pay a penalty. Under the provisions of the law, preexisting health conditions
cannot be used to determine one’s insurance premium. As a consequence,
people have an incentive to forgo health insurance coverage generally, and
only buy it when they are sick.

Little rigorous empirical evidence can be found on the extent of
adverse selection in the ACA exchanges. However, it is widely believed to
have been a serious impediment to the success of insurers in the ACA market-
places. Field work in five states suggest that the presence and magnitude of
adverse selection varied significantly across markets. Reports from California
and Michigan suggested that that they encountered few problems. California

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Chapter 5: Adverse Select ion 93

explained this as the result of the efforts to manage risk and premiums by
CoverCalifornia, the state-based insurance exchange. Michigan attributed
it to the presence of regional providers. In contrast, the Florida and Texas
reports suggested major problems. Claims costs in Florida exceeded premi-
ums by 99 percent, 108 percent, and 256 percent for Aetna, UnitedHealth,
and Cigna, respectively. One insurer in Texas reported that it expected claims
experience to be 135 percent of standard, but in fact, the claims were 170
percent of standard morbidity assumptions (Morrisey et al. 2017).

Summary

• Adverse selection arises when there is asymmetric information. One
party, usually the consumer, knows more about his likely use of health
services than does the other.

• Enrollees in HMOs have substantially lower utilization experience than
do enrollees in traditional plans. The difference is largely attributable
to differences in the use of hospitals.

• The difference in utilization can be attributable to favorable selection
into HMOs, an HMO effect (whereby HMOs do something to keep
people out of the hospital), or both. While evidence exists on both
sides of the debate, the preponderance of data supports the favorable
selection argument.

• The available evidence also suggests that the propensity to be a high
or low utilizer of services regresses toward the mean over time but
nonetheless persists.

• Adverse selection is a potentially large problem for insurers and has
implications for Medicare, the private individual market, the employer-
sponsored market, and consumer-directed health plans.

• Adverse selection in the ACA marketplaces has been a substantial
problem in setting premiums, at least in some states.

Discussion Questions

1. Suppose that the difference in utilization experience between
conventional insurance and managed care is attributable to favorable
selection. If so, would an employer save any money if it required all of
its workers and their dependents to join a managed care plan?

2. When they began offering multiple health plans instead of a single
plan, employers often found that their total health insurance costs
increased. How could this occur?

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Health Insurance94

3. If insurers of dental services understand that high utilizers are
disproportionately likely to join their plan, what actions would you
expect them to take to deal with this condition when they design their
insurance plan?

4. If penalties are insufficient to keep people from forgoing required
coverage under the ACA, what else might the government do to
encourage people to buy coverage?

For the Interested Reader

Morrisey, M. A., A. M. Rivlin, R. P. Nathan, and M. A. Hall. 2017. Five-State Study of
ACA Marketplace Competition. Brookings Institution and Rockefeller Insti-
tute of Government. Published February. www.brookings.edu/wp-content/
uploads/2017/02/summary-report-final .

Newhouse, J. P., and the Insurance Experiment Group. 1993. “Results at the Health
Maintenance Organization: Use of Services.” In Free for All? Lessons from the
RAND Health Insurance Experiment. Cambridge, MA: Harvard University Press.

Wilensky, G. R., and L. F. Rossiter. 1986. “Patient Self-Selection in HMOs.” Health
Affairs 5 (1): 66–80.

References

Bundorf, M. K. 2016. “Consumer Directed Health Plans: A Review of the Evi-
dence.” Journal of Risk and Insurance 83 (1): 9–41.

Bundorf, M. K., B. Herring, and M. V. Pauly. 2010. “Health Risk, Income, and
Employment-Based Health Insurance.” Forum for Health Economics and
Policy. Published September 4. https://www.degruyter.com/view/j/fhep.
2010.13.2/fhep.2010.13.2.1159/fhep.2010.13.2.1159.xml.

Davies, A. R., J. E. Ware, Jr., R. H. Brook, J. R. Peterson, and J. P. Newhouse.
1986. “Consumer Acceptance of Prepaid and Fee-for-Service Medical Care:
Results from a Randomized Controlled Trial.” Health Services Research 21
(3): 429–52.

Ellis, R. P. 1985. “The Effect of Prior-Year Health Expenditures on Health Coverage
Plan Choice.” In Advances in Health Economics and Health Services Research,
vol. 6, edited by R. M. Scheffler and L. F. Rossiter, 149–70. Greenwich, CT:
JAI Press.

Feldman, R., and B. Dowd. 1982. “Simulation of a Health Insurance Market with
Adverse Selection.” Operations Research 30 (6): 1027–42.

Finkelstein, A., and J. Poterba. 2002. “Selection Effects in the United Kingdom
Individual Annuities Market.” Economic Journal 112 (476): 28–55.

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Chapter 5: Adverse Select ion 95

Garber, A. M., T. E. McCurdy, and M. B. McClellan. 1999. “Persistence of Medi-
care Expenditures Among Elderly Beneficiaries.” Frontiers of Health Policy
Research (2): 153–80.

Goodman, J. C. 2018. “ObamaCare Can Be Worse than Medicaid.” Wall Street
Journal, June 17, A17.

Jackson-Beeck, M., and J. H. Kleinman. 1983. “Evidence of Self-Selection Among
Health Maintenance Enrollees.” Journal of the American Medical Association
250 (23): 2826–29.

Manning, W. G., J. P. Newhouse, N. Duan, E. B. Keeler, A. Leibowitz, and M. S. Mar-
quis. 1987. “Health Insurance and the Demand for Medical Care: Evidence
from a Randomized Experiment.” American Economic Review 77 (3): 251–77.

Miller, R. H., and H. S. Luft. 1994. “Managed Care Plan Performance Since 1980.”
Journal of the American Medical Association 271 (19): 1512–19.

Morrisey, M. A., G. A. Jensen, and J. R. Gabel. 2003. “Managed Care and Employer
Premiums.” International Journal of Health Care Finance and Economics 3
(2): 95–116.

Morrisey, M. A., M. L. Kilgore, D. J. Becker, W. Smith, and E. Delzell. 2013.
“Favorable Selection, Risk Adjustment and the Medicare Advantage Pro-
gram.” Health Services Research 48 (3): 1039–56.

Morrisey, M. A., A. M. Rivlin, R. P. Nathan, and M. A. Hall. 2017. Five-State Study of
ACA Marketplace Competition. Brookings Institution and Rockefeller Insti-
tute of Government. Published February. www.brookings.edu/wp-content/
uploads/2017/02/summary-report-final .

Prospective Payment Assessment Commission (PPAC). 1996. “Risk Selection and
Risk Adjustment in Medicare.” Washington, DC: Prospective Payment
Assessment Commission.

Wilensky, G. R., and L. F. Rossiter. 1986. “Patient Self-Selection in HMOs.” Health
Affairs 5 (1): 66–80.

Notes

1. Given the somewhat unseemly assertions about HMO behavior in this section,
the author is compelled to disclose that he has been a member by choice of
one or another HMO for virtually all the past 35 years.

2. This scenario abstracts from the case in which some or all of the employees pre-
fer the HMO. If that is the case, the employer may be able to give employees
the HMO and somewhat lower wages than they would have, had the employer
offered the traditional health plan. We defer the discussion of compensating
differentials in employer-sponsored health insurance until chapter 14.

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CHAPTER

65

4THE DEMAND FOR HEALTH INSURANCE

Life is a gamble. Suppose we were to flip a coin. If it comes up heads, you
lead a healthy, normal life. If it comes up tails, you become seriously ill.
Medical science can return you to a healthy state, but medical science is

not cheap. Treatment will cost you $20,000, plus some associated pain and
suffering. Are you willing to buy a health insurance policy to attenuate the
financial consequences of your potential bad luck?

The correct response is “maybe.” It depends on the price of the policy
and the nature of the coverage. In this chapter, we present the theory of
insurance and develop four hypotheses about the conditions under which we
would be willing to buy coverage. We also use these hypotheses to begin to
explain some of the data on health insurance coverage that we examined in
chapter 3. At first blush, the theory of insurance appears inconsistent with
real-world experience. This is largely because the simple theory abstracts from
real-world complexities. In particular, it ignores adverse selection, employer-
sponsored health insurance, and the special tax treatment of health insurance.
We will anticipate future chapters by introducing these topics and the roles
they play in the demand for health insurance.

The Theory of Insurance

Friedman and Savage (1948) and Ehrlich and Becker (1972) viewed the
demand for insurance as reflecting the maximum we would pay, over and
above the expected loss, to avoid the consequences of the loss. The expected
loss is the amount we would expect to pay, on average, if the event occurred
many times. Thus, if we would have to pay $20,000 every time we flip a coin
and heads occurs and pay $0 whenever tails appears, then the expected loss
for 100 flips of our coin is $10,000 on each flip. Sometimes, we will have to
pay nothing; we win. Sometimes, we will have to pay $20,000; we lose. On
average, we will pay $10,000 per flip.

Again, consider the question of insurance against the financial conse-
quences of the coin flip. Are you willing to pay more than $10,000 to avoid
the coin flip? If so, you are like most of us and are risk averse. You are willing
to pay more than the expected loss to avoid the consequences of the loss.
Stated somewhat differently, you are willing to pay some loading fee over and
above the actuarially fair premium to avoid the consequences. Insurance exists

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Account: s4264928.main.eds

Health Insurance66

because there are enough of us who feel that way. The extra amount we are
willing to pay, often called a risk premium, means that the potential exists for
someone to come in and get 100 or more of us to buy an insurance policy
from her. Her claims costs will be $10,000 on each policy, on average. The risk
premiums we are willing to pay will compensate her for running the program.

Our simple insurance model suggests that many of us would pay a risk
premium (plus the expected loss) to avoid the consequences of the coin flip.
What is the maximum amount you would be willing to pay? It depends on
three factors: how “chicken” you are, how much you would lose if the bad
outcome occurred, and how great the chances are that the bad outcome will
actually occur. How chicken you are is merely a reflection of your unwilling-
ness to bear risk. The more chicken—that is, the more risk averse—you are,
the larger will be the risk premium and the more you are willing to pay to
get coverage. This fact raises an important point. Everyone does not have
the same demand for insurance. Some will prefer broader or deeper cover-
age. Others will prefer to buy much less. Some may prefer to buy none at all.

We need to formalize this discussion a bit. When we say that someone
is risk averse, what we mean is that the loss of $1 reduces their well-being
by more than the gain of $1 increases it. This is just another way of saying
that risk-averse individuals have diminishing marginal utility of wealth. Each
dollar of wealth makes them better off, but each additional dollar is not as
satisfying as the one before. This idea is no different than the discussion you
undoubtedly had in an introductory economics class, except that in that class,
the discussion revolved around the diminishing marginal utility of beer, or
pizza, or ice cream cones consumed at a single sitting.

Exhibit 4.1 illustrates this idea. The curve depicts total utility of
wealth. The individual whose utility of wealth is graphed here receives 4,727
units of utility from $20,000 and 8,000 units of utility from $40,000. Each
additional dollar increases total utility, so the curve is upward sloping. How-
ever, each additional dollar gives less additional utility than the previous dol-
lar, so the curve increases at a decreasing rate.

Now consider the coin-flip problem. If it comes up heads, the person
represented in Exhibit 4.1 with an initial wealth position of $40,000 will
have to pay $20,000. If it comes up tails, he pays nothing. The endpoints of
the straight line in exhibit 4.1 reflect these outcomes. He could end up with
$40,000 or $20,000. The midpoint of the line reflects the expected loss of
many coin flips. The expected loss is $10,000, so he would move from a wealth
position of $40,000 to one of $30,000. How much does he value the $30,000
wealth position? If he had $30,000, it would give him 7,090 units of utility.
However, he doesn’t have $30,000; he has $40,000 and a 50/50 chance of
losing $20,000. How much utility does that provide? The answer is 6,364 units
of utility. According to exhibit 4.1, the individual gets just as much utility from
a 50/50 chance of losing $20,000 as he does from having a certain $26,150.

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Chapter 4: The Demand for Health Insurance 67

This individual is willing to pay up to $13,850 to avoid the coin flip: $10,000
reflects the expected loss, and $3,850 is the risk premium. Two points are
important here. First, the risk premium is the measure of our willingness to pay
for insurance. It is the amount over and above the expected loss that we are will-
ing to pay to avoid the consequences of the loss. This risk premium is the reason
insurance can exist. Insurers must pay to settle claims; claims are the expected
losses. If insurers are to cover administrative and marketing costs and make at
least a normal profit, they must collect something over and above the expected
loss. The presence of a (big enough) risk premium allows this to occur.

Second, the risk premium reflects the most that we are willing to pay.
If the insurance market is competitive, we may end up paying much less than
what we are willing to pay for coverage, just as we often pay much less than
what we are willing to pay for a cold beer.

Not everyone has the same degree of risk aversion. Most of us are at
least somewhat uncomfortable dealing with risk, others are very uncomfort-
able, and some love it. Thus, in principle, each of us has his own unique
total utility curve like that shown in exhibit 4.1. The box How Risk Averse
Are You? gives you an opportunity to determine your personal degree of
risk aversion. Answer enough of the questions to allow you to plot four or
five points on your own total utility curve and see how much you would be
willing to pay to avoid this gamble. But be warned: Although the questions
themselves are not hard, coming up with honest answers is!

EXHIBIT 4.1
The Risk
Premium

Utility of
Wealth Total utility

Wealth

8,000

6,000

4,000

2,000

0

7,090

$10,000 $20,000 $30,000 $40,000 $50,000

$26,150

Risk
premium,

$3,850

Expected
loss,

$10,000

4,727

6,364

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Health Insurance68

How Risk Averse Are You?

To determine how risk averse you are, consider the following exercise. First,
choose two dollar amounts—say, $40,000 and $20,000—as we did in exhibit
4.1. Next, assign arbitrary utility values to each. The only requirement is
that the utility of $40,000 be greater than that of $20,000. In exhibit 4.1, we
chose the utility of $40,000 to be 8,000 [U($40,000) = 8,000] and the utility
of $20,000 to be 4,727 [U($20,000) = 4,727]. You choose whatever you like,
and plot the two points on a graph like exhibit 4.1.

Now you are faced with a series of coin flips. Here is the first: If the
coin comes up heads, you win $40,000. If it comes up tails, you win $20,000.
What is the minimum amount you would accept to sell your right to this
single flip of the coin? Your answer is $X. We now need to know the utility
value associated with your answer. To do this, we compute the expected
utility (EU):

EU = .5[U($40,000)] + .5[U($20,000)] = U($X)

That’s simply the probability of getting heads (.5) times the utility if
heads occurs U($40,000) plus the probability of tails (.5) times the utility if
tails occurs U($20,000). Substituting what we already know (from the example
in the chapter discussion):

EU = .5[8,000] + .5[4,727] = U($X),

EU = 4,000 + 2,364 = U($X), and

EU = 6,364 = U($X).

If you said that the minimum you would accept was $26,150 (as we
did in exhibit 4.1), then X = $26,150, and the U($26,150) is 6,364. Plot the
point that emerged from your answer on your graph.

Now consider a second gamble. If heads occurs on your single coin
flip, you get the value you chose for $X ($26,150 was our choice in exhibit
4.1), and if tails occurs, you get $40,000. What is the minimum amount you
would accept to sell your right to this coin flip? Choose your answer and
redo the math:

EU = .5[U($X)] + .5[U($40,000)] = U($Y),

EU = .5[U($26,150)] + .5[U($40,000)] =

U($Y) (in the case in exhibit 4.1),

EU = .5[6,364] + .5[8,000] = U($Y), so

EU = 7,182 = U($Y).

(continued)

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Chapter 4: The Demand for Health Insurance 69

Hypothesis I: The Degree of Risk Aversion

Your mother is likely to have a different tolerance for risk than you do. You
know how she worries! Suppose your utility curve was shown in exhibit 4.1.
Because your mother is less willing to take chances than you are, she is more
risk averse. Her total utility will lie above yours over the relevant range
shown in exhibit 4.2. When we again play out the 50/50 chance of losing
$20,000, we see that her risk premium is $5,380. She is willing to pay $5,380
to avoid the consequences of the coin toss. This reflects the first hypothesis
that emerges from the theory: As the degree of risk aversion increases, the
size of the risk premium increases, and the probability that we will buy insur-
ance increases. Because your mother is more risk averse than you are, she
is more likely to buy insurance than you are, other things being equal. The
other equal things are the conditions of the coin toss: the fair coin, the same
possible outcomes, and the same initial wealth positions. This rather obvious
hypothesis begins to give us some insight into the mix of people who do and
do not have health insurance.

In the context of auto safety, the National Highway Traffic Safety
Administration (2003) says, “The apparent disregard for one’s own personal
safety appears to be a defining element of youth.” If this is true, it suggests
that young people are less risk averse than older folks. As such, they are will-
ing to pay smaller risk premiums and, therefore, are less likely to buy insur-
ance. This tendency could begin to explain why more than 30 percent of
those in the 21–24 age group did not have health insurance prior to the ACA
(see chapter 3). This difference in tastes for uncertainty would also imply that
even if young folks had the same size and probability of loss, they would be
less likely to buy coverage in the ACA.

If your answer is $30,770 (as was ours in exhibit 4.1), then the utility
of $30,770 is 7,182. Plot the utility associated with your answer for $Y. From
here on, simply set up similar gambles of two known dollar amounts, identify
your minimum acceptance price, and then compute the utility value and plot
it. Once your individual curve has been plotted out, you can consider losses
as we did in the chapter discussion and determine your risk premium for a
relevant potential loss.

Note, however, that your answers may differ greatly from those in the
example. You have different tastes for risk than others do. As a consequence,
your graph may look different from the one in exhibit 4.1. In fact, if you are
a risk lover, your curve will be convex from below rather than concave. If so,
the model predicts that you will not be buying any insurance!

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Health Insurance70

Hypothesis II: The Size of the Potential Loss

The size of the possible loss is also relevant. If heads in the coin toss only implied
a $200 loss, you might be willing to pay only $10, plus the $100 expected loss,
to avoid the consequences. At $20,000, you might be willing to pay $3,850,
plus the $10,000 expected loss; at $200,000, you might be willing to pay
$10,000, plus the expected loss of $100,000, to avoid the consequences. As
the size of the possible loss increases, the risk premium we are willing to pay
increases. Exhibit 4.3 demonstrates this effect. It reproduces exhibit 4.1 but
includes the circumstance where heads on the coin flip yields a $30,000 loss
instead of just $20,000. A very risk-averse individual is willing to pay a risk
premium of $4,614 to avoid this risk, rather than the $3,850 risk premium to
avoid the smaller risk. Thus, as the size of the possible loss increases, the risk
premium gets larger, and we are more likely to buy insurance.

This hypothesis predicts, for example, that other things being equal,
people will be more likely to buy hospital insurance than dental insurance.
It also suggests that coverage for big-ticket, or catastrophic, loss is more
valuable to consumers than is coverage for first-dollar losses. Thus, if health
insurance were to become more expensive, we would expect consumers to

Utility of
Wealth

Your total
utility

Wealth

8,000

6,000

4,000

2,000

0
$10,000 $20,000 $30,000 $40,000 $50,000

Your mother’s
risk premium,

$5,380

Expected loss,
$10,000

Your mother’s
total utility

EXHIBIT 4.2
Effect of Change
in Risk Aversion

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Chapter 4: The Demand for Health Insurance 71

shift away from coverage for physician office visits or prescription drug cov-
erage but retain coverage for hospital care. They may do this by switching
to a policy that has a higher deductible or one with larger copays associated
with ambulatory service use. Note that this hypothesis provides some of the
rationale for catastrophic health insurance plans and health savings accounts
(HSAs). A catastrophic plan only provides coverage after a relatively large
deductible, perhaps $3,000 or $4,000, has been met. An HSA is a form
of health insurance that ties a catastrophic health insurance plan to a tax-
sheltered bank account on which you can draw to satisfy the deductible. We
discuss these plans in chapter 17.

Hypothesis III: The Probability

of Loss

The size of the risk premium also depends on the probability of the loss
occurring. If instead of a 1 in 2 chance of a bad outcome, suppose the chance
were only 1 in 10. Then we would be willing to pay only a very small risk
premium, perhaps only $400 in addition to the $2,000 expected loss (0.1 ×
$20,000 + 0.9 × $0 = $2,000) to avoid the gamble. Surprisingly, the model
also suggests that we would not pay much above the expected loss for a
policy that insured against an event that was virtually certain to occur. This

Utility of
Wealth

Wealth

8,000

6,000

4,000

2,000

0
$10,000 $20,000 $30,000 $40,000 $50,000

Risk premium
with possible
$30,000 loss

Risk premium
with possible
$20,000 loss

Source: Data from Health Insurance Association of America (1990).

EXHIBIT 4.3
Effect of
Change in the
Magnitude of
Possible Loss

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Health Insurance72

outcome is demonstrated in exhibit 4.4. Here, we again reproduce exhibit
4.1, but now we shift the probability of loss. A 50/50 chance of a loss was
characterized in the original exhibit as bisecting the straight line between the
two possible outcomes. If the chance of a loss is only 1 in 10, however, then
the expected loss appears one-tenth of the way from our initial wealth posi-
tion, and the risk premium in exhibit 4.4 is only a few hundred dollars. As
the probability of a loss increases, the expected loss line in the exhibit shifts
further to the left. As it does so, the risk premium continues to increase in
size, reaches some maximum, and then starts to decrease. This is the third
hypothesis. As the probability of the loss increases, the size of the risk pre-
mium initially increases but then declines, and the probability of buying
insurance initially increases but then declines.

This hypothesis is the least intuitive, but it is clear with a little
thought. We do not buy insurance for very small-probability losses because
the expected loss is very small and the risk premium associated with a small
expected loss is even smaller. But as the probability of loss increases, cover-
age is more attractive. However, we also do not buy coverage for very likely
events. If you knew that the cost of some medical procedure was $20,000
and that you had a 95 percent chance of needing this procedure, then the
expected loss would be $19,000. How much more than $19,000 would you
pay to avoid the consequences of paying $20,000? The answer is “not much.”
Thus, the theory says we do not buy coverage for virtually certain events.

Utility of
Wealth

Wealth

8,000

6,000

4,000

2,000

0
$10,000 $20,000 $30,000 $40,000 $50,000

Risk premium,
9 in 10 chance

Risk premium,
1 in 2 chance

Risk premium,
1 in 10 chance

EXHIBIT 4.4
Effect of

Change in the
Probability

of Loss

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Chapter 4: The Demand for Health Insurance 73

But you say, “Suppose I know that my probability of loss is 95 per-
cent, but the insurer doesn’t know this. Surely, I would buy coverage under
this circumstance.” The answer, of course, is yes, you would. The problem
you raise is called adverse selection. You know more about your likely use of
health services than does the insurer, and you use this knowledge to your
best advantage when buying insurance. This issue is fundamental for insurers,
and we will spend chapters 5, 6, and 7 dealing with it. The insurer tries to
reduce this problem by putting you in a risk class that reflects your expected
claims experience. In our simple insurance model, you and the insurer have
the same (complete) information. So, you might like to buy the coverage
designed for others but no insurer would sell it to you. Under the terms of
the ACA, however, an insurer may not charge you a higher premium based
on your higher expected loss. This law gives those with knowledge of their
higher utilization an incentive to enroll in the insurance plan.

Hypothesis IV: The Wealth Effect

Finally, the maximum amount we are willing to pay depends on our wealth
position. People with greater wealth are able in some sense to self-insure
against losses that the rest of us might buy insurance to protect against.
Exhibit 4.5 shows the effect of higher wealth. It takes the individual in
exhibit 4.1 with the same 50/50 chance of losing $20,000. However, here

Utility of
Wealth

Wealth

8,000

6,000

4,000

2,000

0
$10,000 $20,000 $30,000 $40,000 $50,000

Risk premium,
$40,000 wealth

Risk premium,
$50,000 wealth

EXHIBIT 4.5
Effect of Change
in Wealth

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Health Insurance74

he has an initial wealth position of $50,000 instead of $40,000. The risk pre-
mium associated with the expected loss of $10,000 is $2,307, less than the
risk premium of $3,850 in exhibit 4.1. As wealth increases, the risk premium
declines, and we are less likely to buy insurance.

This could also be called the Best Buy hypothesis. Whenever you pur-
chase an electronic device or electrical appliance from Best Buy, the clerks ask
if you wish to buy the extended warranty. They are asking if you want to buy
insurance. We could test the wealth hypothesis by simply knowing the zip
codes in which customers reside and whether they purchased the extended
warranty. From the zip codes, we could go to recent census data and deter-
mine the average household income in the zip code; this serves as a proxy for
wealth. Insurance theory predicts that those in the more-affluent zip codes
will be less likely to buy the extended warranty

Your reaction to this hypothesis is likely to be simple disbelief,
because all the empirical data suggest that more-affluent people are more
likely, not less likely, to have health insurance. This discrepancy between the
theory and our real-world observations has to do with the complexity of the
real world. Recall from chapter 1 that one of the key reasons for the growth
of health insurance in the twenty-first century is the tax-exempt status of
employer-sponsored health insurance. Factors such as this are excluded in
our simple model.

To summarize: This simple model is the basis of the demand for health
insurance. In the absence of employers, tax subsidies, and the like, we expect
to see four sorts of behavior:

• People who are more risk averse will buy more health insurance.
• People will be more likely to buy insurance for events that have large

financial consequences.
• People will be less likely to buy insurance for events that are very

unlikely or very likely to occur.
• People will be less likely to buy insurance as their wealth position

increases.

“Health Insurance: The Access Hypothesis” provides an additional hypothesis.

Taxes and Employer-Sponsored Health Insurance

Analysis of the demand for health insurance is complicated by the fact that
most people in the United States get their insurance through their workplace.
The reason for this is twofold: Workers value health insurance, and it is less
costly when purchased through an employer. Both points are important.

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Chapter 4: The Demand for Health Insurance 75

Workers do value health insurance. A Harvard Business Review study
(Jones 2017) reported that when choosing between a high-paying job and
a lower-paying one with better benefits in 2016, 88 percent of respondents
said better medical benefits would get some or serious consideration. More
flexible hours got the same consideration; all other options were less appeal-
ing. Thus, because many people value health insurance, they are willing to
trade some of their compensation for health benefits. (This willingness to
trade wages for benefits is key to understanding employer-sponsored health
insurance; we consider it in chapter 14.)

Health insurance also tends to be less expensive when purchased
through an employer for three reasons. The first has to do with “favorable
selection,” the flip side of adverse selection. Employed people tend to be
healthier, on average, than those who are unemployed. Employment serves as
a good signal of lower expected claims costs, and consequently, an employer
group can usually purchase coverage at a lower price than can an individual.

Health Insurance: The Access Hypothesis

In addition to the four classic rationales for the purchase of health insurance
presented here, Nyman (1999) argued for a fifth consideration: the access
motive. The argument is straightforward. Some health conditions, should they
occur, are so expensive that they exhaust your wealth. Because you could not
pay for such treatment in the first place, under the traditional rationales, you
would not buy insurance to avoid the consequences of the event occurring.
Nyman argued that health insurance may be the only mechanism whereby
you could obtain such treatment and that people do buy coverage to have
such treatments available to them, should they need them.

The second reason for lower costs has to do with the nature of the
existing tax laws. Health insurance is not taxed as federal or state income,
nor is it subject to Social Security and Medicare payroll taxes. Thus, if an
employee values a dollar of health insurance as equivalent to a dollar of take-
home pay, an employer need only spend a dollar on health insurance rather
than a dollar plus tax on money compensation. Third, there are economies
of scale in the marketing and administration of employer group plans, relative
to individually purchased insurance.

Tax advantages have provided a significant incentive for employer pro-
vision of health insurance. As discussed in chapter 1, employer contributions
to group health insurance are exempt from federal and state personal income
taxes. They are also exempt from federal payroll taxes for Social Security and

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Health Insurance76

Medicare. This tax treatment can be viewed as a subsidy for the provision of
health insurance (Feldstein and Allison 1974; Miller 2003). Workers in the
25 percent federal income tax bracket, paying 5 percent state income tax and
7.65 percent in Social Security and Medicare taxes, would find that an extra
dollar of employer-sponsored health insurance effectively cost them less than
63 cents. If workers are in a higher tax bracket, the tax subsidy for employer-
sponsored health insurance is even greater.

This tax incentive is likely to explain why we observe that more-
affluent people have more health insurance. With a progressive tax system as
in the United States, higher incomes imply higher tax rates. Higher tax rates
reduce the effective price of employer-sponsored health insurance, and at
these lower effective prices, people buy more coverage. Thus, the tax subsidy
provides an incentive for broader and deeper coverage.

In the simple insurance market discussed earlier, someone may not
purchase dental coverage because the size of the potential loss is relatively
low. The tax subsidy reduces the effective price, encouraging workers to press
their employers to include dental coverage in the benefit package. Similarly,
the tax subsidy encourages the coverage of events with low expected losses,
such as well-baby care and preventive services.

The purchase of health insurance through the employer is a complex
issue. It involves not only the premium charged but also the tax rates of
workers and the relative costs across firms. (We will examine the empirical
literature on the effects of tax law changes in chapter 15.)

The tax incentives also complicate the business decision to change
the coverage of health benefit plans. Suppose, for example, that a benefits
manager discovers the cost-saving implications of implementing greater cost
sharing in the form of larger out-of-pocket payments for health services. The
firm implements this change in a new health insurance plan. As expected,
claims costs decline. However, workers correctly view this change in benefits
as a diminution of their compensation. To keep the best workers from leaving
for other firms, the employer decides to raise wages. Indeed, if full-coverage
insurance caused workers to consume units of healthcare that were only of
minimal extra value to them, the cost savings from reduced claims should be
enough to make the workers whole and have something left to enhance firm
profits. That is, the employer has to add something to the compensation bas-
ket to make up for the reduced health insurance coverage, thus “making the
worker whole.” As a result, benefits changes have to not only save money, but
save enough money to make workers whole—after tax considerations. This
hurdle is a high one to cross.

The tax treatment of employer-sponsored health insurance also plays
a role in the ACA. The law requires that in 2018 (later changed to 2022),
any employer-sponsored plan that provides individual coverage greater than

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Chapter 4: The Demand for Health Insurance 77

$10,200 or family coverage of greater than $27,500 will face an excise tax of
40 percent of the amount in excess of these thresholds. Based on our frame-
work in this chapter, we should expect that this tax will result in reductions in
the generosity of health benefits in plans subject to the tax. We should expect
that the plans will reduce benefits that are least valuable to workers, subject
to the limits imposed by essential benefits provisions of the ACA.

Summary

• Insurance exists because enough people are willing to pay something
over and above the expected loss to avoid the consequences of the loss.
This willingness to pay is called the risk premium.

• The greater the risk premium, the more likely we are to buy insurance.
• The greater the extent of risk aversion—that is, the greater the level of

discomfort with uncertain outcomes—the larger the risk premium we
are willing to pay.

• The greater the size of the potential loss, the larger the risk premium.
• The risk premium increases with the probability of a loss, reaches some

maximum, and then declines with higher probabilities of loss.
• The risk premium declines with greater wealth.
• The tax treatment of employer-sponsored health insurance serves to

reduce the price of health insurance and may outweigh the effects
described by the pure theory of insurance.

Discussion Questions

1. Suppose that health insurance premiums have increased substantially
in the past year. You are a member of your firm’s fringe benefits
committee and have been charged with reducing the cost of health
insurance. Based on the analysis in this chapter, what sort of changes to
the benefits package would you recommend? Why?

2. Suppose that Congress was successful in reducing the marginal income
tax rates on money wages. What effect would you expect this to have
on the nature of health insurance benefits offered by employers? Why?

3. One of your high school buddies has just graduated with her master’s
in business administration and accepted a great job in a small
consulting firm. However, the firm does not offer health insurance.
Over dinner one night, she asks you whether she should buy some
health insurance, and if so, what kind. What do you say? Why?

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Health Insurance78

4. How would your answer to question 3 have changed prior to 2019,
when the ACA coverage mandate penalty was eliminated?

5. Prior to the ACA, some insurers offered “mini–medical plans.” These
plans cover routine services, provide little hospital coverage, often cap
payouts at $10,000 or less, and can cost as little as $40 per month.
Ignoring ACA issues, is an insurance plan of this sort consistent with
the hypotheses developed in this chapter? Why? What might people be
buying with a mini–medical plan if they are not buying insurance?

For the Interested Reader

Friedman, M., and L. C. Savage. 1948. “The Utility Analysis of Choices Involving
Risk.” Journal of Political Economy 56 (4): 251–80.

Nyman, J. A. 1999. “The Value of Health Insurance: The Access Motive.” Journal
of Health Economics 18 (2): 141–52.

References

Ehrlich, I., and G. Becker. 1972. “Market Insurance, Self-Insurance and Self-
Protection.” Journal of Political Economy 80 (4): 623–48.

Feldstein, M. S., and E. Allison. 1974. “Tax Subsidies of Private Health Insurance:
Distribution, Revenue Loss, and Effects.” In The Economics of Federal Subsidy
Programs, 977–94, Washington, DC: US Government Printing Office.

Friedman, M., and L. C. Savage. 1948. “The Utility Analysis of Choices Involving
Risk.” Journal of Political Economy 56 (4): 251–80.

Jones, K. 2017. “The Most Desirable Employee Benefits.” Harvard Business Review.
Published February 15. https://hbr.org/2017/02/the-most-desirable-
employee-benefits.

Miller, T. 2003. “How the Tax Exclusion Shaped Today’s Private Health Insurance Mar-
ket.” Report of the Joint Economic Committee, US Congress, December 17.

National Highway Traffic Safety Administration. 2003. Traffic Safety Facts, 2003: A
Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting
System and the General Estimates System. US Department of Transportation.
Washington, DC: National Center for Statistics and Analysis.

Nyman, J. A. 1999. “The Value of Health Insurance: The Access Motive.” Journal
of Health Economics 18 (2): 141–52.

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CHAPTER

121

7RISK ADJUSTMENT

In chapter 6, we considered a variety of measures that insurers could use
to place people into risk classes that reflected their likely claims experi-
ence. In this chapter, we look at some empirical evidence about the pre-

dictive power of alternative measures. In particular, we examine the extent to
which demographic, health status, and prior utilization measures predict
individual use of health services.

Three key points emerge from this discussion. First, even the most
complete set of measures explains only a small proportion of the variance
in an individual’s use of health services. If utilization was wholly predictable
based on readily available measures, there would be no role for insurance.
Instead, we would borrow and lend to even out the peaks and troughs of
our spending patterns. It should not be surprising that models have limited
predictive power. If our health status has a large random component to it,
then by definition it is not predictable.

The second key point is that some sets of measures are better predic-
tors of health services use than are others. Demographic characteristics per-
form surprisingly poorly. Prior utilization is the best predictor, and various
measures of health status fall somewhere in between. However, dismissing
the predictive abilities of these other measures would be a mistake. The abil-
ity to predict even a couple of percentage points better than others can yield
a substantial competitive advantage, provided it can be done at relatively low
cost.

Third, statistical modeling has its limits. The presumption in risk
adjustment is that statistical methods will eliminate the least costly efforts to
attract low utilizers and avoid high utilizers. This may be so. But it may be
that other approaches implicitly contain more or better information on the
future use of health services than those contained in the statistical models.
It may be, for example, that our urban legend from chapter 5, in which a
Medicare managed care plan was willing to enroll anyone who could walk
up three flights of stairs, is a better predictor than the best statistical model.

Risk adjustment methodology has at least two other uses besides
directly determining premiums based on expected utilization. First, the
healthcare exchanges required by the Affordable Care Act (ACA) must risk-
adjust the enrollment in exchange plans. Although individual consumers
are not to face premiums based on their health status, the exchanges must

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Health Insurance122

determine whether some plans obtain a more or less healthy draw of enrollees
and reduce payment to plans with disproportionately healthy enrollees while
raising payments to those with less healthy subscribers. Second, proposals
to convert Medicare into a “premium support” or voucher program often
require that the voucher be based in part on the health status of the Medicare
beneficiary (see chapter 23).

In this chapter, we consider risk adjustment measures in the context of
the payment system that Medicare uses to pay Medicare Advantage plans, the
managed care plans that provide care to approximately one-third of Medicare
beneficiaries. Although this risk adjustment system is used by a payer rather
than an insurer, it has the great advantage of being publicly available. It also
highlights the key issues.

Medicare Adjusted Average per Capita Costs

Because HMOs do not have a claims database, they were at a disadvantage
in participating in Medicare when it was introduced in 1965. After a number
of largely unsuccessful efforts, in 1985 Medicare implemented the Adjusted
Average Per Capita Costs (AAPCC) payment methodology under authoriza-
tion from Congress in the Tax Equity and Fiscal Responsibility Act of 1982
(TEFRA). See Zarabozo (2000) for a history of Medicare’s approaches to
paying managed care plans in its first 35 years.

Under TEFRA, Medicare essentially paid participating HMOs a fixed
dollar amount for each beneficiary that chose to join the plan. Because
HMOs were thought to be more efficient than traditional care providers
(recall the “HMO effect” from chapter 5), the legislation prescribed that
the capitated rate should be 95 percent of the average Medicare Part A (i.e.,
hospital) plus Part B (i.e., ambulatory) expenditures per beneficiary. As we
speculated in chapter 6, claims experience likely varies by location. Congress
appreciated this as well and ordered that the average expenditures be com-
puted and applied for each county. These rates were then adjusted by the
mix of beneficiaries the plan enrolled, taking into account their age, gender,
Medicaid status, whether they were in a nursing home, and whether the ben-
eficiary was an active worker with coverage through an employer. Thus, the
AAPCC paid 95 percent of the county average Medicare Part A and Part B
expenditures adjusted for age, gender, Medicaid, institutional, and active
worker status. This method is analogous to a simple manual rating system.

As we saw in chapter 5, the Medicare payment system appears to
provide Medicare Advantage plans with substantial incentives for enrolling
people with lower-than-average expected claims and avoiding people with
above-average claims. One government study found that early Medicare
HMO enrollees had expenditures that were only 63 percent of the average

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Chapter 7: Risk Adjustment 123

of all beneficiaries in the six months prior to joining the HMO (Physician
Payment Review Commission [PPRC] 1994). If this was so, Medicare was
consistently overpaying for the services provided by Medicare Advantage
plans, and higher-cost beneficiaries may have been effectively denied access
to a form of healthcare delivery that they may have preferred.

Improving the Adjusted Average Per Capita Costs
Payment Methodology

Almost immediately, Medicare funded research to try to improve its payment
system. Such research requires data on the demographic, health status, and
healthcare utilization characteristics of a relatively large number of heteroge-
neous people over time. Moreover, these people should face the same finan-
cial incentives for the use of health services; otherwise their use of services
will be distorted.

The RAND Health Insurance Experiment (RAND-HIE) provided a
data set that mostly satisfied these conditions. We discuss this experiment
in some detail in chapter 8, but for current purposes, it is enough to know
that the study randomly assigned people from six sites across the country
into different health plans and monitored their use of health services over
the four to five years of the experiment during the 1970s. It also recorded
demographic and health status characteristics of the participants at baseline.
In fact, much of the current knowledge about the measurement of health
status had its genesis with this study. Thus, the study is well suited to exam-
ine alternative predictive models of utilization based on demographic char-
acteristics, subjective and physiological measures of health status, and prior
utilization (Newhouse et al. 1989). See Measures of Potential Risk Factors
Used in the RAND-HIE Study for a summary of the measures available for
consideration.

Measures of Potential Risk Factors Used in the
RAND-HIE Study

Demographic Measures (AAPCC Variables)
• Age

• Gender

• Location (indicator for each of the six sites in the study)

• Eligible for welfare at baseline

(continued)

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Health Insurance12

4

Although the analysis was more complicated than suggested by the side-
bar, the study team essentially ran a series of regressions in which total inpatient
and outpatient expenditures of each individual in year t were the dependent
variables and were explained by alternative sets of potential risk characteristics.
When prior-year utilization measures were included, these were expenditures
in year t − 1. Because the overall RAND study was concerned with the effects
of insurance copayment arrangements on expenditures, the regressions also
controlled for the health plan in which the person was enrolled. Exhibit 7.1
reports the R2, or percentage of explained variation, for many of the regressions
the study team ran. Because their interest was in improving the AAPCC model
Medicare used, all of the models include the demographic or AAPCC factors.
The AAPCC variables by themselves explain 1.6 percent of total expenditures,
0.7 percent of inpatient expenditures, and 7.2 percent of outpatient varia-
tion. Notice that, in general, outpatient expenditures were more predictable
than inpatient spending. This probably reflects the greater extent to which

Subjective Health Status Measures
• Physical health (based on self-reported measures of role and

personal limitations)

• Mental health (based on self-reported measures of psychological
distress, behavioral and emotional control, and positive affect)

• General health (based on self-reported measures of general
well-being)

• Disease count (based on the presence of any of 32 chronic
conditions)

Physiological Health Status Measures
• Dichotomous measures

• Continuous measures (based on 27 measures, including such items
as elevated cholesterol, hypertension, diabetes, electrocardiogram
abnormalities, active ulcer, anemia, dyspepsia, abnormal thyroid
function, and so on)

Prior Utilization
• Outpatient expense in prior year

• Inpatient expense in prior year

Note: AAPC = adjusted average per capita costs.

Source: Data from Newhouse and the Insurance Experiment Group (1993) and Newhouse

et al. (1989).

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Chapter 7: Risk Adjustment 125

behavioral and chronic factors influence ambulatory use. Notice, too, that the
percentage of explained variation is quite small. Age, gender, location, and
welfare status explained less than 2 percent of total expenditures.

In principle, Medicare, or any insurer, could ask its subscribers to
report their health status and use the responses to assign the subscribers to
appropriate risk classes. This study had rather extensive measures of subjec-
tive health. When these were added to the AAPCC measures, the model
explained 2.8 percent of total expenditures—a 75 percent improvement!
Operationally, however, self-reported health status is likely to be problematic
for Medicare. Beneficiaries (and the health plans that they wish to join) may
have an incentive to report poorer health status in the hope that a higher cap-
itation payment will be forthcoming. Confirming that the information that
beneficiaries provide is truthful could become a serious and costly challenge.

Alternatively, Medicare could obtain relatively simple dichotomous
physiological measures of health status, such as measures from a clinical
record that indicate whether the beneficiary has hypertension, diabetes, and
so on. These measures were added to the AAPCC measures and are reported
in the third row of exhibit 7.1. Together with the demographic factors, they
explained 3.8 percent of total expenditures. This finding is a substantial
improvement over simply using the demographic measures, but obtaining
even simple clinical data is expensive both for Medicare and for the beneficiary.

We could go further and use even more detailed clinical information.
For example, we could collect and use data on actual blood pressure, instead
of a simple measure of whether the beneficiary has hypertension. We could
use a measure of elevated glucose, rather than a simple measure of whether
the beneficiary has diabetes. Such continuous measures of physiological health
are reported in the fourth row of exhibit 7.1. Together with the AAPCC
measures, they explained 4.2 percent of variation in total expenditures. Thus,

EXHIBIT 7.1
Percentage
of Explained
Variation in
Healthcare
Expenditures
Yielded by
Alternative
Specifications

Note: AAPC = adjusted average per capita costs.

Source: Data from Newhouse et al. (1989).

Total Inpatient Outpatient

AAPCC 1.6 0.7 7.2

AAPCC + Subjective health status 2.8 1.2 11.1

AAPCC + Dichotomous physiological
health status

3.8 2.0 13.5

AAPCC + Continuous physiological
health status

4.2 2.6 13.0

AAPCC + Prior utilization 6.4 2.8 21.2

All 9.0 5.0 25.1

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Health Insurance12

6

more detailed clinical measures do provide more predictive power, but the
costs of collecting such detailed data are probably prohibitive.

Alternatively, Medicare (or another insurer) could use prior utilization
data. These measures were added to the AAPCC demographic factors and are
reported in the fifth row of exhibit 7.1. This approach explained 6.4 percent
of total expenditures, 2.8 percent of inpatient claims, and 21.2 percent of
outpatient expenditures. Relative to the other approaches, prior utilization
has substantially greater explanatory power. This result probably explains
why health insurers tend to focus on prior claims experience when setting
insurance premiums. The data exhibit relatively strong predictive power.
Moreover, once insurers have a set of subscribers and their claims experience,
using those data to predict future use is relatively inexpensive.

We could go further and combine various sets of health status mea-
sures. The final row of exhibit 7.1 presents the percentages of explained varia-
tion when all the measures of subjective and physiological health and prior
utilization were included with the AAPCC measures. The model explained
9.0 percent of variation in total expenditures. Thus, using all these data does
improve ability to predict expenditures, but routinely collecting such infor-
mation is very expensive.

Implications of Better Risk Adjustment

The final exercise the RAND study team undertook was to estimate the
potential profit that an HMO could achieve if it could somehow better
predict future Medicare expenditures of potential enrollees than the existing
AAPCC formula. The admittedly unrealistic assumption is that the HMO
could do this costlessly and would use the information to enroll only profit-
able beneficiaries. The results are presented in exhibit 7.2, inflated to 2018
dollars using the Consumer Price Index (all categories of the index included).

EXHIBIT 7.2
Profits

from Better
Prediction of

HMO Medical
Expenditures

Source: Adapted from data in Newhouse et al. (1989).

Additional Variance
Explained by HMO

Profit per Enrollee,
1988 Dollars

Profit per Enrollee,
2018 Dollars

0 percentage points $0 $0

1 percentage point $630 $1,313

5.5 percentage points $1,170 $2,438

7.5 percentage points $1,320 $2,751

13 percentage points $1,530 $3,189

18.5 percentage points $1,650 $3,439

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Chapter 7: Risk Adjustment 127

Obviously, if HMOs cannot predict expenditures any better than can
Medicare’s AAPCC formula, there is no extra profit. However, if they could
predict 1 percentage point better and use this information to enroll only
healthier people, they would gain profits of $1,313 per enrollee because their
costs would be lower than the Medicare payment rate. If HMOs could do
5.5 percentage points better, the profit per enrollee would be $2,438. Notice
in exhibit 7.2 that, as we move to greater and greater additional explana-
tory power, higher profits are garnered. But notice, too, that the extra profit
gets smaller with each increment. One additional point yields $1,313 in
profit, but the next 4.5 percentage points only result in an additional $1,125
($2,438−$1,313). Two additional percentage points beyond that yield only
an extra $313. The modeling reported in exhibit 7.1 makes it clear how dif-
ficult it would be to get an additional 4.5 percentage points of explanatory
power. Using all the information available to the study, the RAND team could
only get 7.4 percentage points greater predictive power than the AAPCC.

This finding had important implications for Medicare. If Medicare
could improve its AAPCC model enough to predict just a few percentage
points better than it currently did, it could remove the easy opportunities for
favorable selection that the managed care plans seemed to enjoy. To do bet-
ter than, say, the AAPCC plus prior utilization would likely require managed
care plans to incur considerable costs of improved predicting for rather mod-
est increases in profits. Even plans bent on taking full advantage of favorable
selection would find that their efforts were likely to be unremunerative.

Generalizing the RAND Findings

Your first reaction to the RAND findings might be to say: “Surely, one can
do better than predicting only 6.4 or 9.0 percent of total expenditures!”
Van de Ven and Ellis (2000) provide a detailed summary of the research on
risk adjustment in their chapter in Handbook of Health Economics. Exhibit
7.3 reproduces a table from their work that summarizes six major studies of
risk adjustment, beginning with the RAND study. With the exception of the
study of US HMO enrollees (column 3), all of the results are remarkably
similar, with age and sex variables explaining 0.7 to 3.8 percent of variation,
and all variables explaining 7 to 9 percent. More recent work by Behrend
and colleagues (2007) examined risk adjustment strategies using German
sickness fund data from 1997 and 1998. They found that age, gender, and
“invalid status” explained 5.1 percent of concurrent healthcare spending.
However, adding inpatient hierarchical coexisting conditions explained a
total of 37 percent of concurrent expenditures and 12 percent of prospec-
tive expenditures. These condition codes, which we discuss next, were able
to substantially increase the ability to predict future healthcare expenditures.

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Health Insurance128

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Chapter 7: Risk Adjustment 129

Medicare’s Current Approach to Risk Adjustment

The Balanced Budget Act of 1997 (BBA) required Medicare to phase in a new
risk adjustment methodology beginning in 2000 (Ingber 2000). The new meth-
odology was to better incorporate health status into the capitation rates. Medicare
implemented a transitional risk adjustment system based only on inpatient data in
2000 and a full model based on both inpatient and ambulatory data in 2004. In
addition, because a risk-adjusted payment system is based on patient health status
measures, the BBA requires Medicare Advantage plans and other providers to
provide encounter data to the Centers for Medicare & Medicaid Services (CMS).
For a detailed discussion of what is now called the CMS Hierarchical Condition
Categories (CMS-HCC) model, see Pope and colleagues (2004).

CMS and its contractors developed the payment system by running a
series of regression models not unlike those used in the earlier RAND study.
In essence they ran a model something like the following:

Expendituresit = a1 * Age (65–69) + a2 * Age (70–74) +
a3 * Age (75–79) + . . . + a6 * Male +
a7 * Medicaid eligible +
a8 * Condition1 + a9 * Condition2 +
a10 * Condition3 + . . . +
a60 * Condition52 + εit.

The estimated coefficients—the a’s in the equation—tell CMS how
much the associated variable contributed to Medicare expenditures, on aver-
age. CMS experimented with how age, sex, and other demographic factors
were specified and with how alternative measures of the clinical conditions
explained contemporaneous expenditures and subsequent expenditures.
This experimentation continued until CMS was satisfied that its final model
reflected an acceptable compromise across the ten principles summarized in
Guiding Principles in Medicare’s Risk Adjustment Approach. As a reading
of the principles makes clear, considerable experimentation and judgment is
required to develop such a risk-adjusted payment system.

Guiding Principles in Medicare’s Risk Adjustment
Approach

The new risk adjustment system was designed to meet ten guiding principles
(Pope et al. 2004). These principles relate to insurance underwriting issues,
understanding and acceptance by users, and minimization of opportunities
to game the system. Briefly, the ten principles are the following:

(continued)

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Health Insurance130

1. The health status–related measures should be clinically meaningful.
This principle means that the measures should make sense to a
knowledgeable observer and be sufficiently clinically specific to make
it difficult for plans to assign a beneficiary with a vaguely defined
condition into a higher payment group.

2. The measures should predict both current and future medical
expenditures. Thus, a transitory condition, such as an ankle sprain,
would not be a useful measure.

3. The measures should be based on large enough sample sizes that
they yield accurate and stable predictions. Thus, as we saw in chapter
6, Medicare, as with any insurer, may have to sacrifice some risk
categories to gain reduction in variance.

4. Related clinical conditions should be treated hierarchically, while
unrelated conditions should increase the level of payment. Thus,
someone identified as having had a recent acute myocardial
infarction (i.e., a heart attack) and having unstable angina would only
be counted as having the more severe condition rather than both.
However, someone with unstable angina and lung cancer would be
counted as having both.

5. Vague measures should be grouped with low-paying diagnoses to
encourage specific coding of health conditions.

6. The measures should not encourage multiple reporting of the same
or closely related diagnoses. Thus, the hierarchy of related conditions
should be used and only the most severe condition coded.

7. Providers should not be penalized for reporting many conditions.
Thus, no condition should have a negative payment associated with
it, and a more severe condition must pay at least as much as a less
severe manifestation.

8. Transitivity must hold. If condition A results in a greater payment than
condition B, and if B is paid more than C, then A should be paid more
than C.

9. All of the diagnoses that clinicians use have to map onto the payment
system.

10. Discretionary diagnostic codes should be excluded to prevent
intentional or unintentional gaming of the system.

The chosen model would be used to determine the annual payment
that a Medicare Advantage plan would be paid on behalf of a Medicare ben-
eficiary living in a particular county. For example, the base payment for a
woman, aged 75–79, living in the community might be $2,475 per month.

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Chapter 7: Risk Adjustment 131

If in the last year she had diabetes without complications, that might add
$1,024. If she also had unstable angina, that might add $1,785. In this case,
the Medicare HMO would be paid $5,284 on her behalf.

The model that Medicare ultimately adopted contains 12 age cat-
egories × 2 sex categories × 2 site (community vs. institution) categories
for a total of 48, plus 6 Medicaid categories, plus 70 hierarchical condition
categories (i.e., the condition codes), plus another 6 condition code inter-
actions. In some instances, the costs associated with having two conditions
are greater than simply the sum of the costs of each; diabetes together with
cerebrovascular disease is an example. The interactions allow Medicare to pay
a Medicare Advantage plan more for the care of such patients. There are also
categories that relate to the Medicare disabled.

Age and sex explain approximately 1.0 percent of the variation in
Medicare expenses. The CMS-HCC model explains 11.2 percent. Exhibit
7.4 compares the predictive ratio of the age and sex model and the CMS-
HCC model for the quintiles of Medicare expenditures. The predictive ratio
is just the predicted costs of a group divided by the actual cost. If the value
is greater than 1, it means that Medicare would be overpaying for the care of
people in that group. If the value is less than 1, Medicare would be under-
paying. The first quintile of expenditures (i.e., the least expensive one-fifth of
Medicare beneficiaries) is shown in the first row. On average, just using age
and sex as adjusters (the first column) leads to an overpayment of 266 percent
of actual costs. In contrast, an HMO caring for Medicare beneficiaries in the
most expensive one-fifth of the distribution would be paid only 44 percent
of what their care would cost, on average.

Clearly, the CMS-HCC model is an improvement. While it still over-
pays for the less costly quintiles, the overpayments are drastically reduced.
Similarly, while it underpays for the most expensive fifth quintile, the pay-
ment is much closer to actual costs. These findings have led some researchers

EXHIBIT 7.4
Predictive
Ratios for
Alternative Risk
Adjustments

Source: Data from Pope et al. (2004).

Quintiles of Expenditures Age/Sex CMS-HCC Model

First 2.66 1.23

Second 1.93 1.23

Third 1.37 1.14

Fourth 0.95 1.02

Fifth 0.44 0.86

Top 5% 0.28 0.77

Top 1% 0.10 0.69

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Health Insurance132

to suggest that future risk adjustment models should continue to employ a
CMS-HCC–like model but incorporate a mechanism directly to pay some
share of costs for particularly high-cost beneficiaries (Ellis and McGuire
1993; Newhouse 1996).

CMS began phasing in risk-adjusted payments to Medicare Advantage
plans beginning in 2000. In 2007, payment rates were based entirely on
the CMS-HCC methodology. As with the original AAPCC formula, CMS-
HCC continues to establish the basic payment level based on Medicare’s
expenditures in geographic regions, usually counties. However, instead of
being simple averages as in the AAPCC, the payments are now based on the
risk-adjusted mix of beneficiaries in the county. The sidebar Sample Annual
Medicare Advantage Payment Under the CMS-HCC Model, Lake County,
Illinois, 2018 presents the payment that a Medicare HMO in a county just
north of Chicago would receive in 2018 for a 72-year-old woman who is
on Medicaid and who has diabetes and congestive heart failure. The base
rate for her county of residence is $10,078 per year. The factors for her sex,
Medicaid status, and health conditions are added and then multiplied by the
base rate to determine the payment to be made to the HMO each month
on her behalf.

Sample Annual Medicare Advantage Payment Under
the CMS-HCC Model, Lake County, Illinois, 2018

Basic Lake County, Illinois, rate: $10,078

Female, age 70–74: .406

HCC15 diabetes without complications: .118

HCC87 congestive heart failure: .368

Total payment is: $10,078 (1 + .406 + .118 + .368) = $19,067.58

While this format is less intuitive than the dollar-based formats
discussed earlier in the chapter, it has the administrative advantage that
CMS need not recompute each value every year. New data on average risk-
adjusted expenditures and any congressionally mandated across-the-board
increases or decreases can simply be applied to the base rates. The relative
values of the person-specific components are unaffected. The CMS-HCC
is used to risk adjust the payments to Medicare Advantage plans, but
the Congress has made the determination of the basic county rate more
nuanced (see Current Medicare Advantage Payment Plans Include a Bid-
ding Mechanism).

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Chapter 7: Risk Adjustment 133

How Well Did the Centers for Medicare & Medicaid
Services Hierarchical Condition Categories Model Work
in Reducing Favorable Selection?

The more sophisticated risk adjustment mechanism was intended to reduce
favorable selection into Medicare Advantage plans by determining payment
levels that were closer to actual expenditures. Plans would have reduced
incentives to spend resources seeking healthier subscribers. Moreover, if the
payment levels are closer to actual patient-specific costs, plans would also
have reduced incentives to try to disenroll expensive subscribers (see Current
Medicare Advantage Payment Plans Include a Bidding Mechanism).

Current Medicare Advantage Payment Plans Include
a Bidding Mechanism

As discussed in more detail in chapter 22, the Medicare Modernization Act
of 2004, which provided for prescription drug coverage for Medicare benefi-
ciaries, also modified the way Medicare Advantage plans are paid. The man-
aged care plans proffer a bid per enrollee per month to Medicare to provide a
basic set of benefits consistent with traditional Medicare. If this bid is below
the CMS-established benchmark for the county (or region, if applicable), the
managed care plan keeps 75 percent of the difference to apply to reduced
cost sharing or expanded benefits for enrolled beneficiaries. If it is above the
benchmark, the plan charges enrollees an additional premium. However,
the CMS-HCC model is used in all cases to adjust the payments for beneficia-
ries enrolled by the plan to reflect their demographics and health status. The
ACA also made some changes to the payment for Medicare Advantage plans.
However, these do not affect the risk adjustment mechanism and, therefore,
we defer that discussion to chapter 22.

In recent work (Morrisey et al. 2013) we found mixed effects. We
used 10 years of Medicare claims data on more than 3 million beneficiaries
from 1999 through 2008 and followed the modeling used by the PPRC
(1994) that was discussed in chapter 5. We identified people who switched
from traditional Medicare to a Medicare Advantage plan. Then we computed
their claims costs in the six months prior to the switch and compared these to
the costs of people in the same county, in the same six-month interval, who
did not switch from traditional Medicare. We made an analogous calculation
for the six months after disenrollment of those switching back to traditional
Medicare. In a regression model that accounted for payment levels, a time

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Health Insurance134

trend, and county-level fixed effects, we found no effect of the shift to CMS-
HCC on the extent of favorable selection into Medicare Advantage plans.
On the other hand, we did find that the CMS-HCC reduced the number
of disenrollees; these were concentrated among the most costly tail of the
distribution. Thus, if one returns to exhibit 7.4, it appears that the reduced
disenrollments were concentrated in the fourth quintile. These are the sub-
scribers that Pope and colleagues (2004) estimated to shift from net “losers”
for a managed care plan to net “winners.”

Brown and colleagues (2011) found a more nuanced story. They
examined data on 55,000 Medicare beneficiaries over the period 1994–2006.
They found that after risk adjustment using the CMS-HCC, the risk scores of
those switching to a Medicare Advantage plan rose relative to those remaining
in traditional Medicare. However, given the risk score, Medicare’s expendi-
tures for those switching to Medicare Advantage plans actually fell. As Brown
and his colleagues characterize it, the plans devoted less selection effort along
the beneficiary characteristics that were included in the HCC and more along
dimensions that were unmeasured. In their estimates, “After risk-adjustment,
those switching into Medicare Advantage plans were over $1,200 ‘cheaper’
than the risk-adjustment formula predicts them to be” (Brown et al. 2011, 2).

This is not to say that risk adjustment practices or underwriting more
generally are bad ideas. Rather, it reinforces a point we made in chapters 5 and 6.
People know more about their likely use of health services than an insurer does,
and they should be expected to use their greater knowledge to their advantage.
If an insurer gets the underwriting wrong or if a government program gets the
risk adjustment wrong, their costs will be higher than if they got it right.

Risk Adjustment in the Affordable Care Act

The ACA established three forms of adjusting for risk in the exchanges: a
permanent risk-adjustment program, a three-year temporary reinsurance pro-
gram, and a risk corridor program. The risk adjustment program is modeled
on the Medicare Advantage approach that was just described. However, it uses
Truvan Health Analytics data on privately insured individuals typically under
65 rather than Medicare data to establish HCCs that apply to the ACA popula-
tion. Data provided by insurers on their ACA enrollees allow CMS to compute
the average risk level of those enrolled by each insurer, and these are used to
adjust payments. If one insurer has a “sicker” enrollment mix it receives a risk-
adjustment payment. However, if the insurer has a less “sick” enrollment pool,
it makes a payment into the program. This is a zero-sum process. The amounts
paid into the program by those with less sick enrollees is equal to the amounts
paid out to other insurers. This zero-sum model generated problems for some
insurers (see An ACA’s Risk Adjustment Implication).

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Chapter 7: Risk Adjustment 135

The reinsurance program was designed to mitigate the costs of
extremely expensive enrollees. Fees were collected from essentially all insur-
ers, including self-insured large employers. These fees were $63, $44, and
$27 per enrollee, respectively, in 2014, 2015, and 2016. Insurers selling
plans through the ACA that had high-cost enrollees received monies to com-
pensate them partially for these unusually high costs.

The definition of high cost is determined by the “attachment point”
established by CMS. In 2014 and 2015, insurers would receive compensation
for those with costs above $45,000. In 2016, the attachment point was set
at $90,000. There were also limits on the amount that an insurer could draw
from the reinsurance fund. See Cox and colleagues (2016) for a more detailed
description. Several analysts have argued that a meaningful way to reduce the
effects of adverse selection in the ACA would be for the federal government
or the individual states to implement a reinsurance pool. As of the summer
of 2018, Alaska, Minnesota, Oregon, Wisconsin, Maine, and Maryland have
obtained waivers to establish ACA reinsurance programs in their states.

The risk corridor program was designed to mitigate some of the pre-
mium concerns faced by insurers who were uncertain about their risks under
the ACA. Insurers that had profits that exceeded 3 percent of a threshold
were assessed a fee, and those who had profits 3 percent below the threshold
received payments.

Summary

• In general, risk adjustment models have been able to predict about
12 percent of total claims. Ambulatory use is easier to predict than
inpatient use, perhaps because it has a larger behavioral component.

• Demographic characteristics, such as age and gender, are only
modestly predictive of future claims experience. While subjective and
physiological measures of health status are more predictive, prior
utilization provides the most predictive power.

An ACA Risk Adjustment Implication

The ACA risk adjustment process had important implications for insurers, as
one Texas insurer told me. In 2014, the organization set its premiums high,
attracted high morbidity enrollees, and lost money. The next year, it set lower
premiums, attracted lower morbidity enrollees, and made money—then came
the risk adjustment fee, and the insurer lost money overall. It dropped out
of the ACA.

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Health Insurance136

• The Medicare AAPCC is a manual rating program by which Medicare
paid Medicare HMOs based on the average costs in the county,
adjusted for the age, gender, and Medicaid, institutional, and active-
worker status of the beneficiary.

• Medicare currently pays Medicare Advantage plans on the basis of the
CMS-HCC model. This manual rating program uses approximately 70
clinical conditions, in addition to demographic and location factors,
to determine the amount Medicare will pay HMOs for the care of its
beneficiaries.

• The ACA uses an HCC approach to adjust payments to insurers that
have enrollment that is above or below average risk.

Discussion Questions

1. How would you describe the CMS-HCC risk adjustment system? Does
it use prior utilization, physiological, and demographic information to
determine payment rates? How?

2. Suppose a Medicare Advantage plan had been aggressively using
some method to attract low utilizers into its plan. In what ways
would you expect it to change its behavior, if at all, as a result of the
implementation of the new CMS-HCC model?

3. How would a CMS-HCC type model apply to people newly eligible
for Medicare?

4. If Medicare Advantage plans must provide Medicare with encounter
data on the healthcare utilization of their subscribers, what would you
predict about the nature of the underwriting that managed care plans
will use when negotiating future contracts with private employers?

5. How might an insurer offering coverage in the state individual
insurance exchanges be able to influence the mix of healthy people
enrolled in its plan even in the face of a prohibition on using health
status?

For the Interested Reader

Cox, C., A. Semanskee, G. Claxton, and L. Levitt. 2016. “Explaining Health
Care Reform: Risk Adjustment, Reinsurance, and Risk Corridors.” Kaiser
Family Foundation. Published August 17. www.kff.org/health-reform/
issue-brief/explaining-health-care-reform-risk-adjustment-reinsurance-and-
risk- corridors/.

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Chapter 7: Risk Adjustment 137

Morrisey, M. A., M. L. Kilgore, D. J. Becker, W. Smith, and E. Delzell. 2013.
“Favorable Selection, Risk Adjustment, and the Medicare Advantage Pro-
gram.” Health Services Research 48 (3): 1039–56.

Weiner, J. P., E. Trish, C. Abrams, and K. Lemke. 2012. “Adjusting for Risk Selec-
tion in State Health Insurance Exchanges Will Be Critically Important and
Feasible, but Not Easy.” Health Affairs 31(2): 306–15.

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“Risk-Adjusted Capitation Payments: How Well Do Principal Inpatient
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Brown, J., M. Duggan, I. Kuziemko, and W. Woolston. 2011. “How Does Risk
Selection Respond to Risk Adjustment? Evidence from the Medicare Advan-
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Cox, C., A. Semanskee, G. Claxton, and L. Levitt. 2016. “Explaining Health
Care Reform: Risk Adjustment, Reinsurance, and Risk Corridors.” Kaiser
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issue-brief/explaining-health-care-reform-risk-adjustment-reinsurance-and-
risk- corridors/.

Ellis, R. J., and T. McGuire. 1993. “Supply Side and Demand Side Cost Sharing in
Health Care.” Journal of Economic Perspectives 7 (4): 135–51.

Fowles, J. B., J. P. Weiner, D. Knutson, A. M. Tucker, and M. Ireland. 1996. “Taking
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Ingber, M. J. 2000. “Implementation of Risk Adjustment for Medicare.” Health
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Lamers, L. M. 1999. “The Simultaneous Predictive Accuracy for Future Health
Care Expenditures of DCGs, PCGs, and Prior Costs.” Paper presented at the
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“Favorable Selection, Risk Adjustment and the Medicare Advantage Pro-
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Newhouse, J. P. 1996. “Reimbursing Health Plans and Health Providers: Efficiency
in Production Versus Selection.” Journal of Economic Literature 34: 1236–63.

Newhouse, J. P., and the Insurance Experiment Group. 1993. Free for All? Lessons
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Pope, G. C., J. Kautter, R. P. Ellis, A. S. Ash, J. Z. Avanian, L. I. Iezzoni, M. J. Ing-
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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.

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EBSCO Publishing : eBook Collection (EBSCOhost) – printed on 1/12/2023 10:58 AM via UNIVERSITY OF MARYLAND GLOBAL CAMPUS
AN: 2459482 ; Michael A. Morrisey.; Health Insurance, Third Edition
Account: s4264928.main.eds

Health Insurance142

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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Health Insurance144

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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Health Insurance146

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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Health Insurance148

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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Health Insurance150

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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Health Insurance152

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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Health Insurance154

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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Health Insurance156

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

(continued)

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Chapter 8: Moral Hazard and Pr ices 157

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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Health Insurance158

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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Chapter 8: Moral Hazard and Pr ices 159

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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Health Insurance160

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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Chapter 8: Moral Hazard and Pr ices 161

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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Chapter 8: Moral Hazard and Pr ices 163

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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Health Insurance164

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

(continued)

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Chapter 8: Moral Hazard and Pr ices 165

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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Health Insurance166

– 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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CHAPTER

37

3AN OVERVIEW OF THE HEALTHCARE
FINANCING SYSTEM

Learning Objectives

After reading this chapter, students will be able to

• explain why health insurance is common,
• use standard health insurance terminology,
• identify major trends in health insurance,
• describe the major problems faced by the current insurance system, and
• find current information about health insurance.

Key Concepts

• Insurance pools the risks of high costs.
• Moral hazard and adverse selection complicate risk pooling.
• About 91 percent of the US population has medical insurance.
• Consumers pay for most medical care indirectly, through taxes and

insurance premiums.
• Most consumers obtain coverage through an employer- or government-

sponsored plan.
• Managed care has largely replaced traditional insurance.
• Managed care plans differ widely.

3.1 Introduction

3.1.1 Paying for Medical Care
Consumers pay for most medical care indirectly, through insurance. In 2016
insurance paid for 78 percent of healthcare spending (Centers for Medicare
& Medicaid Services [CMS] 2017). Healthcare managers must therefore
understand the structure of private and public insurance programs because
much of their organizations’ revenues are shaped by insurance.

Lee.indd 37 1/2/19 3:15 PM

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Economics for Healthcare Managers38

Managers must also be aware that consumers ultimately pay for
healthcare products, a key fact obscured by the complex structure of the
US healthcare financing system. When healthcare spending invokes higher
premiums or taxes, consumers are forced to spend less on other goods and
services. Some consumers may drop coverage, some employers may reduce
benefits, and some plans may reduce payments. This reaction need not occur
if a consensus has emerged in support of increased spending, but even then,
managers should be wary of the profound effects that changes to insurance
plans can cause for their firms. Finally, managers must consider more than
insurance payments. Even though the bulk of healthcare firms’ revenue
comes from insurers, consumers pay directly for some products. Consumers
directly spent more than $352 billion on healthcare products in 2016 (CMS
2017). No firm should ignore this huge market.

3.1.2 Direct Spending
Despite its large amount, direct consumer spending accounts for only a frac-
tion of total healthcare spending. Exhibit 3.1 depicts a healthcare market in
which consumers directly pay the full cost of some services and part of the
costs of other services. Consumers’ direct payments are often called out-of-
pocket payments. For example, a consumer’s payment for the full cost of a
pharmaceutical product, her 20 percent coinsurance payment to her dentist,
and her $25 copayment to her son’s pediatrician are all considered out-of-
pocket payments. Insurance beneficiaries make out-of-pocket payments for

out-of-pocket
payment
Money a consumer
directly pays for a
good or service.

coinsurance
A form of cost
sharing in which
a patient pays a
share of the bill,
not a set fee.

copayment
A fee the patient
must pay in
addition to the
amount paid by
insurance.

Consumers

Providers

Third parties

Premiums and taxes

Out-of-pocket payments

EXHIBIT 3.1
The Flow

of Funds in
Healthcare

Markets

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Chapter 3: An Over view of the Healthcare F inancing System 39

services that are not covered, for services in excess of their policy’s coverage
limits, and for deductibles (amounts consumers are required to spend before
their plan pays anything). Another name for out-of-pocket payments is cost
sharing. Economics teaches us that a well-designed insurance plan usually
incorporates some cost sharing. We will explore this concept in detail in the
discussion of demand in chapter 7.

Insurance payments continue to be the largest source of revenue for
most healthcare providers. In 2016, they represented 88 percent of pay-
ments to hospitals, 81 percent of payments to physicians, and 66 percent of
payments to nursing homes (CMS 2017). Because insurance affects most
healthcare purchases, its structure has a profound influence on the healthcare
system and healthcare organizations.

The extent of insurance distinguishes the healthcare market from most
other markets. Insurance has three important effects on patients:

• It protects them against high healthcare expenses, which is the main
goal.

• It encourages them to use more healthcare services, which is a side
effect.

• It limits their autonomy in healthcare decision making, which is not a
goal.

Nonetheless, the advantages of insurance continue to exceed its disad-
vantages. As discussed in chapter 2, the share of direct payments for health-
care has steadily fallen during the past 15 years.

3.1.3 Sources of Insurance
Nearly 300 million Americans had some health insurance coverage in 2016
(US Census Bureau 2017). Only 1 percent of those older than 65 lacked cov-
erage, only 5 percent of those younger than 18 lacked coverage, and 12 per-
cent of those aged 18 to 64 lacked coverage. Although 27 percent of those
older than age 65 had employment-based insurance, 93 percent had Medi-
care coverage (meaning that many had duplicate coverage). Employment-
based insurance was the most common form of coverage for those younger
than 65. Fifty-six percent of children had employment-based insurance, and
39 percent had Medicaid. Sixty-three percent of those aged 18 to 64 had
employment-based insurance, and only 15 percent had Medicaid. In section
3.2 we will explore why employment-based insurance is so prevalent.

3.1.4 The Uninsured
For many years the share of the population without medical insurance rose
steadily, even as insurance payments rose as a share of total spending. Since

deductible
The amount a
consumer must
pay before
insurance covers
any healthcare
costs.

cost sharing
The general
term for direct
payments
to providers
by insurance
beneficiaries.
(Deductibles,
copayments, and
coinsurance are
forms of cost
sharing.)

Medicare
An insurance
program for
the elderly and
disabled, run
by the Centers
for Medicare &
Medicaid Services.

Medicaid
A collection
of state-run
insurance
programs that
meet standards
set by the Centers
for Medicare &
Medicaid Services
and serve those
with incomes low
enough to qualify
for their state’s
program. Medicaid
enrollment has
increased by more
than 20 percent
as a result of state
expansions under
the Affordable
Care Act.

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Economics for Healthcare Managers40

the enactment of the Affordable Care Act (ACA), the percentage of the
population without health insurance has fallen sharply. The share of those
younger than age 65 without insurance was 18.2 percent in 2010. By 2016
it was 10.1 percent (US Census Bureau 2017).

Uninsured consumers enter healthcare markets with three significant
disadvantages. First, they must finance their needs from their own resources
or the resources of family, friends, and well-wishers. If these funds are not
adequate, they must do without care or rely on charity care. The uninsured
do not have access to the vast resources of modern insurance companies
when large healthcare bills arrive. Second, unlike most insured customers,
uninsured customers may be expected to pay list prices for services. Most
insured consumers are covered by plans that have secured discounts from
providers. None of the major government insurance plans and few private
insurance plans pay list prices for care. Although uninsured patients could
negotiate discounts, this practice is not routine. Third, the uninsured tend
to have low incomes. In 2016, 11.9 percent of those with annual household
incomes below $25,000 did not have health insurance, compared with only
5.5 percent of those with annual household incomes above $75,000 (US
Census Bureau 2017).

The combination of low income and no insurance often creates access
problems. For example, in 2016, 23 percent of uninsured adults reported
going without care when they had a medical problem (Kaiser Family Foun-
dation 2017a). This rate was more than six times that of well-insured adults.
Delaying or forgoing care can lead to worse health outcomes.

3.2 What Is Insurance, and Why Is It So Prevalent?

3.2.1 What Insurance Does
Insurance pools the risks of healthcare costs, which have a skewed distri-
bution. Most consumers have modest healthcare costs, but a few incur
crushing sums. For example, in 2014, 1 percent of the noninstitutionalized
population spent 23 percent of the total, averaging more than $107,000
(Berk and Fang 2017). Insurance addresses this problem. Suppose that one
person in a hundred has the misfortune to run up $100,000 in healthcare
bills and no one else spends anything. Consumers cannot predict whether
they will be lucky or unlucky, so they may buy insurance. If a private firm
offers insurance for an annual premium of $1,040, many consumers would
gladly buy insurance to eliminate a 1 percent chance of a $100,000 bill.
(The insurer gets $4,000 per 100 people to cover its selling costs, claims
processing costs, and profits.)

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Chapter 3: An Over view of the Healthcare F inancing System 41

3.2.2 Adverse Selection and Moral Hazard
Alas, the world is more complex than the preceding scenario, and such a sim-
ple plan probably would not work. To begin with, insurance tends to change
the purchasing decisions of consumers. Insured consumers are more likely to
use services, and providers no longer feel compelled to limit their diagnosis
and treatment recommendations to amounts that individual consumers can
afford. The increase in spending that occurs as a result of insurance cover-
age is known as moral hazard. Moral hazard can be substantially reduced
if consumers face cost-sharing requirements, and most contemporary plans
have this provision.

Another, less tractable problem remains. Some consumers, notably
older people with chronic illnesses, are much more likely than average to
face large bills. Such consumers would be especially eager to buy insurance.
On the other hand, some consumers, notably younger people with healthy
ancestors and no chronic illnesses, are much less likely than average to face
large bills. Such consumers would not be especially eager to buy insurance.
This situation illustrates adverse selection: People with high risk are apt to
be eager to buy insurance, but people with low risk may not be. Wary of this
phenomenon, insurance firms have tried to assess the risks that individual
consumers pose and base their premiums on those risks, a process known
as underwriting. Of course underwriting drives up costs, making coverage
more expensive, which further reduces the share of consumers who are will-
ing to pay for insurance. In the worst case, no private firm would be willing
to offer insurance to the general public.

In the United States, three mechanisms reduce the effects of adverse
selection: employment-sponsored medical insurance, government-sponsored
medical insurance, and health insurance subsidies. In 2016, 91 percent of
the population had health insurance. About 37 percent had government-
sponsored medical insurance, and 56 percent had employer-sponsored insur-
ance (US Census Bureau 2017). Ninety-four percent of Americans aged 65
years or older have coverage through Medicare or Medicaid. Ninety percent
of those younger than 65 years have coverage, with 63 percent having
employer-sponsored coverage and 27 percent having government-sponsored
coverage (14% of these younger Americans bought insurance themselves, but
for some this purchase was in addition to other insurance).

Why is the link between employment and medical insurance so
strong? First, insurers are able to offer lower prices on employment-based
insurance because they reduce their sales costs and adverse selection risks by
selling to groups. Selling a policy to a group of 1,000 people costs only a
little more than selling a policy to an individual; thus the sales cost is much
lower. And because few people take jobs or stay in them just because of the
medical insurance benefits, adverse selection rarely occurs (i.e., most of the

moral hazard
The incentive to
use additional
care that having
insurance creates.

adverse selection
A situation that
occurs when
buyers have
better information
than sellers. For
example, high-
risk consumers
are willing to pay
more for insurance
than low-risk
consumers are.
(Organizations
that have difficulty
distinguishing
high-risk from low-
risk consumers
are unlikely to be
profitable.)

underwriting
The process of
assessing the
risks associated
with an insurance
policy and setting
the premium
accordingly.

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Economics for Healthcare Managers42

employees get the insurance, regardless of whether they think they’ll need
it soon). Second, insurance can benefit employers. If coverage improves the
health of employees or their dependents, workers will be more productive,
thereby improving profits for the company. Companies also benefit because
workers with employment-based medical insurance are less likely to quit. The
costs of hiring and training employees are high, so firms do not want to lose
employees unnecessarily. Third, employers’ contributions to insurance premi-
ums are excluded from their employees’ Social Security taxes, Medicare taxes,
federal income taxes, and most state and local income taxes. Earning $5,000
in cash instead of a $5,000 medical insurance benefit could easily increase an
employee’s tax bill by $2,500.

This system is clearly advantageous for insurers, employers, and
employees. From the perspective of society as a whole, however, its desir-
ability is less clear. The subsidies built into the tax code tend to force tax
rates higher, may encourage the use of insurance for costs such as eyeglasses
and routine dental checkups, and give employees an unrealistic sense of how
much insurance costs.

3.2.3 Medicare as an Example of Complexity
The health insurance system in the United States is so complex that only a
few specialists understand it. Exhibit 3.2 illustrates the complexity of health-
care financing by examining the flow of funds in traditional Medicare. Many

Understanding Health Risks and Insurance

Adverse selection is one reason for governments to intervene in health
insurance markets. A persistent fear is that people with low risks will
not buy insurance, pushing up premiums for people with higher risks.
Once premiums go up, additional people with low risks will drop out.
This sequence is called a death spiral because it will ultimately result
in no one buying insurance. To prevent this outcome, governments
subsidize insurance or mandate that it be bought.

Little evidence suggests that people understand health risks or
insurance well. Yet to make a good choice, consumers must compare
many different products with varying attributes and forecast what
their risks will be (Ericson and Starc 2016). Not surprisingly, many
find insurance choices difficult. A recent survey of Americans who
might seek insurance through the ACA marketplace found that many
struggled to understand basic concepts, such as a premium, a provider
network, or covered services (Long et al. 2014).

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Chapter 3: An Over view of the Healthcare F inancing System 43

Medicare beneficiaries pay for supplemental policies that cover deductibles,
coinsurance, and other expenses that Medicare does not cover. Like many
insurers, Medicare requires a deductible. In 2017, the Medicare Part A
deductible was $1,316 per year, and the Medicare Part B deductible was
$183. The most common coinsurance payments spring from the 20 percent
of allowed fees Medicare beneficiaries must pay for most Part B services. For
simplicity, exhibit 3.2 focuses on supplemental policies that reimburse ben-
eficiaries rather than pay providers directly. Beneficiaries with these sorts of
policies (and many without supplemental coverage) must make required out-
of-pocket payments directly to providers. Beneficiaries must also pay the Part
B premiums that fund 25 percent of this Medicare component. Like other
taxpayers, beneficiaries must also pay income taxes, which cover the other 75
percent of Part B costs.

Medicare Part A
Coverage for
inpatient hospital,
skilled nursing,
hospice, and home
health services.

Medicare Part B
Coverage for
outpatient services
and medical
equipment.

Medicare
beneficiaries

Premiums

Part B
premiums and
income taxes

Providers

Government

Employees

Wages

Employers

Payroll and
income taxes

Medicare payments

Supplemental
insurers

Out-of-pocket payments

EXHIBIT 3.2
The Flow
of Funds in
Medicare

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Economics for Healthcare Managers44

Employers and employees also pay taxes to fund the Medicare system.
The most visible of these taxes is the Medicare payroll tax, which is levied on
wages to fund Part A (which covers hospital, home health, skilled nursing,
and hospice services). In addition, corporation and individual income taxes
help fund the 75 percent of Part B costs that premiums do not cover. CMS,
the federal agency that operates Medicare, combines these tax and premium
funds to pay providers. Not surprisingly, few taxpayers, beneficiaries, or pub-
lic officials understand how Medicare is financed.

3.3 The Changing Nature of Health Insurance

Traditional, open-ended fee-for-service (FFS) plans (of which pre-1984
Medicare was a classic example) have three basic problems. First, they
encourage providers and consumers to use covered services as long as the
direct cost to consumers is less than the direct benefit. Because the actual
cost of care is much greater than the amount consumers pay, some consum-
ers may use services that are worth less than they actually cost. In addition,
open-ended FFS plans discourage consumers from using services that are not
covered, even highly effective ones. Finally, much of the system is unplanned,
in that the prices paid by consumers and the prices received by providers do
not reflect actual provider costs or consumer valuations.

Given the origins of traditional medical insurance, this inattention to
efficiency makes sense. Medical insurance was started by providers, largely
in response to consumers’ inability to afford expensive services and the
unwillingness of some consumers to pay their bills after services had been
rendered. The goal was to cover the costs of services, not to provide care in
the most efficient manner possible nor to improve the health of the covered
population.

Managed care is a varied collection of insurance plans with only one
common denominator: They are different from FFS insurance plans. Tradi-
tionally, FFS plans covered all services if they were included in the contract
and if a provider, typically a physician, was willing to certify that they were
medically necessary. The FFS plans had no features that tried to influence
the decisions of patients or physicians (aside from the effects of subsidizing
higher spending).

Currently, insurance takes five basic forms: FFS plans, PPOs (pre-
ferred provider organizations), HMOs (health maintenance organiza-
tions), point-of-service (POS) plans, and high-deductible (HD) plans.
We will briefly describe each of the alternatives to FFS plans.

fee-for-service
(FFS)
An insurance plan
that pays providers
on the basis of
their charges for
services.

managed care
A loosely defined
term that includes
all plans except
open-ended
fee-for-service.
It is sometimes
used to describe
the techniques
insurance
companies use.

PPO (preferred
provider
organization)
Plan that contracts
with a network
of providers.
(Network providers
may be chosen
for a variety of
reasons, but a
willingness to
discount fees is
usually required.)

HMO (health
maintenance
organization)
Plan that provides
comprehensive
benefits to
enrollees in
exchange for
a premium.
(Originally, HMOs
were distinct from
other insurance
plans because
providers were
not paid on a
fee-for-service
basis and because
enrollees faced
no cost-sharing
requirements.)

point-of-service
(POS) plan
Plan that allows
members to see
any physician
but increases
cost sharing for
physicians outside
the plan’s network.
(This arrangement
has become so
common that POS
plans may not be
labeled as such.)

high-deductible
(HD) plan
Plan that has a
deductible of at
least $1,000 and
may be combined
with a health
savings account.

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Chapter 3: An Over view of the Healthcare F inancing System 45

Employers and employees also pay taxes to fund the Medicare system.
The most visible of these taxes is the Medicare payroll tax, which is levied on
wages to fund Part A (which covers hospital, home health, skilled nursing,
and hospice services). In addition, corporation and individual income taxes
help fund the 75 percent of Part B costs that premiums do not cover. CMS,
the federal agency that operates Medicare, combines these tax and premium
funds to pay providers. Not surprisingly, few taxpayers, beneficiaries, or pub-
lic officials understand how Medicare is financed.

3.3 The Changing Nature of Health Insurance

Traditional, open-ended fee-for-service (FFS) plans (of which pre-1984
Medicare was a classic example) have three basic problems. First, they
encourage providers and consumers to use covered services as long as the
direct cost to consumers is less than the direct benefit. Because the actual
cost of care is much greater than the amount consumers pay, some consum-
ers may use services that are worth less than they actually cost. In addition,
open-ended FFS plans discourage consumers from using services that are not
covered, even highly effective ones. Finally, much of the system is unplanned,
in that the prices paid by consumers and the prices received by providers do
not reflect actual provider costs or consumer valuations.

Given the origins of traditional medical insurance, this inattention to
efficiency makes sense. Medical insurance was started by providers, largely
in response to consumers’ inability to afford expensive services and the
unwillingness of some consumers to pay their bills after services had been
rendered. The goal was to cover the costs of services, not to provide care in
the most efficient manner possible nor to improve the health of the covered
population.

Managed care is a varied collection of insurance plans with only one
common denominator: They are different from FFS insurance plans. Tradi-
tionally, FFS plans covered all services if they were included in the contract
and if a provider, typically a physician, was willing to certify that they were
medically necessary. The FFS plans had no features that tried to influence
the decisions of patients or physicians (aside from the effects of subsidizing
higher spending).

Currently, insurance takes five basic forms: FFS plans, PPOs (pre-
ferred provider organizations), HMOs (health maintenance organiza-
tions), point-of-service (POS) plans, and high-deductible (HD) plans.
We will briefly describe each of the alternatives to FFS plans.

fee-for-service
(FFS)
An insurance plan
that pays providers
on the basis of
their charges for
services.

managed care
A loosely defined
term that includes
all plans except
open-ended
fee-for-service.
It is sometimes
used to describe
the techniques
insurance
companies use.

PPO (preferred
provider
organization)
Plan that contracts
with a network
of providers.
(Network providers
may be chosen
for a variety of
reasons, but a
willingness to
discount fees is
usually required.)

HMO (health
maintenance
organization)
Plan that provides
comprehensive
benefits to
enrollees in
exchange for
a premium.
(Originally, HMOs
were distinct from
other insurance
plans because
providers were
not paid on a
fee-for-service
basis and because
enrollees faced
no cost-sharing
requirements.)

point-of-service
(POS) plan
Plan that allows
members to see
any physician
but increases
cost sharing for
physicians outside
the plan’s network.
(This arrangement
has become so
common that POS
plans may not be
labeled as such.)

high-deductible
(HD) plan
Plan that has a
deductible of at
least $1,000 and
may be combined
with a health
savings account.

Oregon’s Coordinated Care
Organizations

In 2012 Oregon launched an ambitious redesign of its Medicaid pro-
gram. It created a statewide network of coordinated care organizations,
which are similar in some respects to accountable care organizations,
but these coordinated care organizations get global, risk-adjusted
budgets from the state, are responsible for a broad range of services
(behavioral, dental, and physical), and are governed by a broad range
of local stakeholders. The coordinated care organizations have imple-
mented a number of innovations, including the following:

• Locating behavioral health specialists in primary care settings

• Using community health workers

• Using emergency department navigators to connect patients with
primary care

• Emphasizing identification and brief treatment of substance
abusers

How has this program worked? Per-member per-month spending
for hospital care decreased sharply, and spending on primary care
increased sharply. Most of the quality measures with incentives
attached have improved. Most without incentives have not.

Discussion Questions
• Why should a state provide Medicaid to its citizens?

• Who is eligible for Medicaid in Oregon?

• How does this situation differ from eligibility in your state?

• How is Oregon’s Medicaid different from your health insurance?
From Medicare?

• What type of insurance is Oregon Medicaid?

• Why might community health workers improve outcomes and save
money?

• Why does Medicare not pay for community health workers?

• How might linking behavioral health and primary care improve
outcomes and save money?

accountable care
organization
Network of
providers that have
financial incentives
to reduce spending
and improve
outcomes.

global, risk-
adjusted budget
Payment of a fixed
amount per person
to the organization
responsible for
providing care to
a population. Risk
adjustment means
that the amount per
person is higher for
people with higher
risk of expensive
illnesses.

community health
workers
Local, nonclinical
workers who
help patients live
healthier lives and
help providers
understand
patients’ needs.

Case 3.1

(continued)

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Economics for Healthcare Managers46

PPOs are the most common form of managed care organization. All
PPOs negotiate discounts with a panel of hospitals, physicians, and other
providers, but their similarities end there. Some PPOs have small panels; oth-
ers have large panels. Some PPOs require that care be approved by a primary
care physician; some do not.

PPOs are far less diverse than HMOs, however. Some HMOs are
structured around large medical group practices and are called group model
HMOs. Group model HMOs typically make a flat payment per consumer
enrolled with the group. This practice is called capitation. Other HMOs,
called staff model HMOs, employ physicians directly and pay them salaries.
Both staff and group model HMOs still exist, but they are expensive to set
up and make sense only for large numbers of enrollees (because small HMOs
cannot negotiate favorable prices with hospitals).

HMO expansion largely has been fueled by the growth of indepen-
dent practice association (IPA) HMOs. These plans contract with large
groups of physicians, small groups of physicians, and solo-practice physicians.
These contracts assume many forms. Physicians can be paid per service (as
PPOs usually operate) or per enrollee (as group model HMOs usually oper-
ate). IPAs also pay hospitals and other providers in varied ways.

POS plans are another form of HMO. These plans are a combination
of PPO and IPA models. Unlike IPA HMOs, they cover nonemergency ser-
vices provided by nonnetwork providers, but copayments are higher. Unlike
PPOs, they pay some providers using methods other than discounted FFS
payments.

HD plans have a deductible of at least $1,000. These plans may be
combined with health savings accounts, which are nontaxable accounts that
employees and employers can contribute to and employees can use to pay
medical bills.

group model HMO
A plan that
contracts with a
physician group to
provide services.

capitation
Payment per
person. (The
payment does not
depend on the
amount or type of
services provided.)

staff model HMO
A plan that
employs staff
physicians to
provide services.

independent
practice
association (IPA)
HMO
A plan that
contracts with
independent
practice
associations,
which in turn
contract with
physician groups.

health savings
account
An account that
employees and
employers can
contribute to and
employees can
use to pay medical
bills. Employees’
contributions and
payments are not
taxable.

• How might connecting patients with primary
care improve outcomes and save money?

• Is there evidence that good primary care
improves outcomes and saves money?

• How might increasing treatment of substance abusers improve
outcomes and save money?

• Is reducing the hospitalization rate a good thing?

• Is reducing use of emergency departments a good thing?

Case 3.1
(continued)

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Chapter 3: An Over view of the Healthcare F inancing System 47

Health insurance continues to evolve in a disorderly fashion. Where
this development will lead is not clear. The belief that health insurance is
changing rapidly is widespread, and exhibit 3.3 seems to confirm this belief.
Since 2007, FFS plans have all but disappeared, and the market share of PPO
plans has fallen from 57 percent to 48 percent. HMO plans (which include
POS plans in exhibit 3.3) have fallen from 34 percent of the employer-based
market to 24 percent. HD plans have risen from 5 percent of the market in
2007 to 28 percent in 2017.

The patterns in other sectors differ from those in the employer-
sponsored market. More than 60 percent of Medicare Advantage beneficia-
ries are in HMOs (Kaiser Family Foundation 2017b). Likewise, more than
70 percent of Medicaid beneficiaries are in HMOs, and about half of those
buying ACA plans are as well.

Complicating this already complex picture are recent changes in
Medicare, Medicaid, ACA marketplace plans, and employment-based plans.
These innovations could have widespread effects, although only preliminary
evidence is available for most of them. We will explore these changes in detail
in chapter 6.

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
0%

10%

20%

30%

40%

50%

60%

70%
PPO
60%

HD
8%

HMO
24%

FFS
1%

PPO
48%

HD
29%

EXHIBIT 3.3
Enrollment
Patterns in
Employer-
Sponsored
Insurance

Source: Kaiser Family Foundation and Health Research & Educational Trust (2017).

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Economics for Healthcare Managers48

3.4 Payment Systems

In the past, most healthcare providers were paid on the basis of volume.
Today, insurers have begun to experiment with alternative payment models.

Geisinger’s Transformation

The conflicting incentives of FFS and capitation
present significant problems for integrated health

systems like Geisinger Health System of central Pennsylvania. Indeed,
after its merger with Penn State Health collapsed, many were con-
cerned about its viability. Geisinger faced losses in its hospitals and
physician group, and its health plan was not doing well (Goldsmith
2017).

Geisinger’s turnaround involved two strategies: increasing the
share of physician compensation in the form of FFS payments and
implementing a robust network of patient-centered medical homes to
limit low-value care (healthcare offering little or no clinical value or
even having potential harms greater than its benefits). The change in
physician compensation allowed the creation of a broader network and
rewarded higher volumes. In essence, Geisinger became a network
HMO (an HMO having a variety of contracts with physician groups,
IPAs, and individual physicians; it may also own hospitals and employ
physicians).

Geisinger had two major advantages. Its Medicare Advantage plan
was profitable, and its strong market position allowed it to negotiate
good rates with local insurance plans. Those high rates gave it the
resources necessary to transform its primary care practices.

Discussion Questions
• Why would it make sense to become a network HMO?

• Did it make sense for Geisinger to support the patient-centered
medical home transition?

• Could an independent practice afford to become a patient-centered
medical home?

• Why is Medicare sponsoring patient-centered medical home
demonstrations?

• How would a 6 percent reduction in hospitalization rates affect
hospitals?

low-value care
Care that has
been scientifically
evaluated and
found to be of
little or no clinical
value or to have
potential harms
greater than its
benefits.

network HMO
An insurance
plan that has a
variety of contracts
with physician
groups, IPAs,
and individual
physicians. A
network HMO
may also own the
hospitals that it
uses and employ
physicians.

Case 3.2

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Chapter 3: An Over view of the Healthcare F inancing System 49

These alternative payment models can change providers’ incentives, which
can change patterns of care. The power of changing incentives should not
be underestimated, and managers need to be wary of getting what they
pay for rather than getting what they want. When contracting with insurers
or providers, managers need to recognize the strengths and weaknesses of
different systems. The four basic payment methods—salary, volume-based,
value-based, and capitation—can be modified by the addition of incentive
payments, increasing the number of possible payment methods.

A salary is fixed compensation paid per defined period. As such, it is
not directly linked to output. Typically, physicians are paid a salary when their
productivity is difficult to measure (e.g., in the case of academic physicians)
or when the incentives created by payments per service are seen as undesir-
able (e.g., an incentive to overtreat that increases costs). As noted earlier,
most physicians in the United States have traditionally been paid on the basis
of volume, meaning providing more services increases revenue.

Volume-based payments can take a number of forms. Per-service
payments entail a payment for each separate service. For example, a physician
visit that involved 10 minutes talking to the doctor, an X-ray, and a labora-
tory test would result in a bill for three services. Case-based payments are
single payments for all covered services associated with an episode of care.
Medicare’s diagnosis-related group (DRG) system is a case-based system
for hospital care, although it does not include physicians’ services or posthos-
pital care. In essence, case-based payments are volume-based payments for a
bundle of services rather than separate payments for each individual service.
Value-based payments add a quality bonus or penalty to volume-based
payments. For example, Medicare reduces DRG payments to hospitals with
above-average 30-day readmission rates for pneumonia patients. Capitation is
compensation paid per beneficiary enrolled with a physician or an organiza-
tion. Capitation is similar to a salary but varies according to the number of
customers.

Each of the four basic payment methods has advantages and disadvan-
tages. Salaries are straightforward and incorporate no incentives to provide
more care than necessary, but they do not encourage efficiency or reward
exemplary service. In addition, salaries give providers incentives to use
resources other than their time and effort to meet their customers’ needs.
In the absence of incentives not to refer patients to other providers, salaried
providers may well seek to refer substantial numbers of patients to specialists,
urgent care clinics, or other sources of care.

Capitation incorporates many of the same incentives as a salary, with
two important differences. One is that capitation payments drop if customers
leave the practice, so physicians have more incentive to serve patients well.
The other is that capitation creates incentives to undertreat. Providing a

salary
Fixed
compensation per
period.

volume-based
payment
Payment that
increases if a
provider delivers
more services.

per-service
payment
Payment for each
billable service.
Providing an
additional service
increases the bill.

case-based
payment
A single payment
for an episode of
care, regardless
of the number of
services.

diagnosis-related
group (DRG)
The basis of
Medicare’s case-
based payment
system for
hospitals.

value-based
payment
Payment adjusted
on the basis of
quality measures.

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Economics for Healthcare Managers50

service increases costs but does not increase revenue, so profits rise if service
levels drop.

Volume-based payment encourages productivity and efficiency but
may create incentives to overtreat (especially for services with prices that
are much higher than production costs). Services that offer limited benefits
to patients can be profitable in volume-based payment systems as long as
the benefits exceed the consumer’s out-of-pocket cost. On the other hand,
incentives to undertreat can emerge if services are unprofitable. Getting
prices right is vital in volume-based payment.

Case-based payment combines features of per-service payment and
capitation. It is a form of volume-based payment, so it rewards productivity
and efficiency. However, case-based payment may encourage providers to
treat patients with highly profitable cases who should not be treated, or it
may create incentives for providers not to treat patients with less profitable
cases who should be treated. Like capitation, it can create incentives to skimp
on care. Costs can be reduced by improving efficiency, shifting responsibility
for therapy to other sources (e.g., the health department), avoiding complex
patients, and narrowing the definition of a case. The challenge is to keep
providers focused on improving efficiency, not on gaming the system.

Any of these four basic methods can be modified by including bonuses
and penalties, as value-based payment does. A base salary plus a bonus for
reducing inpatient days in selected cases is not a straight salary contract.
Similarly, a capitation plan with bonuses or penalties for exceeding or not
meeting customer service standards (e.g., a bonus for returning more than
75% of after-hours calls within 15 minutes) would not generate the same
incentives a plain capitation plan would. Most insurers are moving away from
volume-based payments toward value-based payments; however, insurers are
trying a variety of approaches because the best way to implement value-based
payment is not yet clear.

Capitation was previously expected to become the dominant method
of payment. Experience with capitation suggests, however, that few providers
(or insurers, for that matter) have the administrative skills or data that capita-
tion demands. In addition, the financial risks of capitation can be substantial.
Few providers have enough capitated patients for variations in average costs
to cease being worrisome, and capitation payments are seldom risk adjusted
(i.e., increased when spending can be expected to be higher than average).
These considerations have dampened most providers’ enthusiasm for capita-
tion. Insurers also have realized that capitation is not a panacea, recognizing
that providers have ways other than becoming more efficient to reduce their
costs. Volume-based payments remain the norm, but most major insurers are
seeking to switch to value-based payments (which include careful monitoring

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Chapter 3: An Over view of the Healthcare F inancing System 51

of quality). What compensation arrangements will look like in ten years
remains to be seen.

3.5 Conclusion

Health insurance is common because of the risks of unexpected costs, but the
days of traditional, open-ended insurance plans are over. Despite the ubiquity
of managed care and much discussion of value-based payment systems, most
consumers are enrolled in plans that are minimally managed, such as PPOs or
POS plans that pay providers in familiar ways. But more than half of the pro-
viders who responded to a recent survey are deeply involved with value-based
payment, and most expect to be shortly (KPMG 2017). Medicare, Medicaid,
and most private insurers have already begun to change how they pay provid-
ers. Changes in payment systems substantially increase the risks that managers
must face. The next chapter will introduce strategies for managing these risks.

The central challenge of cost remains. In 2016 median household
income was $59,039, meaning that half of the households in the country
made less than $59,039. The Milliman Medical Index, which tracks all
healthcare costs, shows that an average family of four spent $25,824 in 2016
(Girod, Weltz, and Hart 2016). Many families simply cannot afford this level
of spending.

Exercises

3.1 Why is health insurance necessary?
3.2 Explain how adverse selection and moral hazard differ, and give an

example of each.
3.3 Some consumers are overinsured, yet some are underinsured.

Millions are clearly uninsured. What do you think these concepts
mean?

3.4 Should health insurance continue to be employment-based for most
Americans?

3.5 A radiology firm charges $2,000 per exam. Uninsured patients are
expected to pay list price. How much do they pay?

3.6 A radiology firm charges $2,000 per exam. An insurer’s allowed
fee is 80 percent of charges. Its beneficiaries pay 25 percent of the
allowed fee. How much does the insurer pay? How much does the
beneficiary pay?

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Economics for Healthcare Managers52

3.7 If the radiology firm in exercise 3.6 raised its charge to $3,000,
how much would the insurer pay? How much would the beneficiary
pay?

3.8 A surgeon charges $2,400 for hernia surgery. The surgeon contracts
with an insurer that allows a fee of $800. Patients pay 20 percent of
the allowed fee. How much does the insurer pay? How much does
the patient pay?

3.9 You have incurred a medical bill of $10,000. Your plan has a
deductible of $1,000 and coinsurance of 20 percent. How much of
this bill will you have to pay directly?

3.10 Why do employers provide health insurance coverage to their
employees?

3.11 Your practice offers only a PPO with a large deductible, high
coinsurance, and a limited network. You pay $400 per month for
single coverage. Some of your employees have been urging you to
offer a more generous plan. Who would you expect to choose the
more generous plan and pay any extra premium?

3.12 What are the fundamental differences between HMO and PPO
plans?

3.13 Suppose that your employer offered you $4,000 in cash instead
of health insurance coverage. Health insurance is excluded from
state income taxes and federal income taxes. (To keep the problem
simple, we will ignore Social Security and Medicare taxes.) The cash
would be subject to state income taxes (8%) and federal income
taxes (28%). How much would your after-tax income go up if you
took the cash rather than the insurance?

3.14 How would the calculation in exercise 3.13 differ for a worker who
earns $500,000 and lives in Vermont? This worker faces a state
income tax rate of 9.5 percent and a federal income tax rate of 35
percent.

References

Berk, M. L., and Z. Fang. 2017. “Most Americans Have Good Health, Little Unmet
Need, and Few Health Care Expenses.” Health Affairs 36 (4): 742–46.

Centers for Medicare & Medicaid Services (CMS). 2017. “National Health Expen-
ditures by Type of Service and Source of Funds: Calendar Years 1960 to
2016.” Accessed November 3. www.cms.gov/Research-Statistics-Data-and
-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData
/Downloads/NHE2016.zip.

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Chapter 3: An Over view of the Healthcare F inancing System 53

Ericson, K. M., and A. Starc. 2016. “How Product Standardization Affects Choice:
Evidence from the Massachusetts Health Insurance Exchange.” Journal of
Health Economics 50: 71–85.

Girod, C. S., S. A Weltz, and S. K. Hart. 2016. “2016 Milliman Medical Index.” Milli-
man. Published May 24. www.milliman.com/insight/Periodicals/mmi/2016
-Milliman-Medical-Index/.

Goldsmith, J. 2017. “Geisinger’s Transformation: Balancing Growth and Risk.”
Health Affairs Blog. Published March 8. http://healthaffairs.org/blog/2017
/03/08/geisingers -transformation -balancing-growth-and-risk/.

Kaiser Family Foundation. 2017a. “Key Facts About the Uninsured Population.”
Updated November 29. www.kff.org/uninsured/fact-sheet/key-facts-about
-the-uninsured-population/.

. 2017b. “Medicare Advantage.” Published October 10. www.kff.org
/medicare/fact-sheet/medicare-advantage/.

Kaiser Family Foundation and Health Research & Educational Trust. 2017. Employer
Health Benefits: 2017 Annual Survey. Published September. http://files.kff.
org/attachment/Report-Employer-Health-Benefits-Annual-Survey-2017.

KPMG. 2017. “KPMG Value-Based Care.” Accessed September 10, 2018. https://
assets.kpmg.com/content/dam/kpmg/us/pdf/2017/01/VBC_Info
graphic .

Long, S. K., G. M. Kenney, S. Zuckerman, D. E. Goin, D. Wissoker, F. Blavin, L. J.
Blumberg, L. Clemans-Cope, J. Holahan, and K. Hempstead. 2014. “The
Health Reform Monitoring Survey: Addressing Data Gaps to Provide Timely
Insights into the Affordable Care Act.” Health Affairs 33 (1): 161–67.

US Census Bureau. 2017. “Annual Social and Economic Supplement (ASEC) of
the Current Population Survey (CPS).” Revised May 8. www.census.gov
/programs-surveys/saipe/guidance/model-input-data/cpsasec.html.

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