Managed Competition in Health Insurance
Liran Einav and Jonathan Levin
(Stanford and NBER)
The Seventh Health Policy Workshop
June 5, 2015
1
Rising healthcare cost everywhere, but
especially in the US …
Health Spending (% of GDP)
Per Capita
(PPP Adj.)
Hope?
 Reasonably broad consensus, and scattered evidence, that healthcare
operates inside the efficiency frontier, so in principle we may be able to
cut costs and improve quality at the same time
5-7K per std.
enrollee
9-16K per
std. enrollee
3
Hope?
 Reasonably broad consensus, and scattered evidence, that healthcare
operates inside the efficiency frontier, so in principle we may be able to
cut costs and improve quality at the same time
But how?
 Many economists and policymakers advocate for various forms of
managed competition
 Examples exist in US private sector and in other countries (e.g.,
Netherlands …), but not a high degree of consensus on how to design the
market or on how well it has worked or can work
4
Outline for the rest of the talk
 Describe in detail one particular environment of managed competition
– “Medicare Advantage” – and its key ingredients: competitive pricing
and risk adjustments
 Short digression about risk adjustments
 Summarize some results from our study of Medicare Advantage
(based on recent work with Vilsa Curto, Jon Levin, and Jay Bhattacharya)
 End up with some open-ended thoughts about healthcare competition
5
Outline for the rest of the talk
 Describe in detail one particular environment of managed competition
– “Medicare Advantage” – and its key ingredients: competitive pricing
and risk adjustment
 Short digression about risk adjustments
 Summarize some results from our study of Medicare Advantage
(based on recent work with Vilsa Curto, Jon Levin, and Jay Bhattacharya)
 End up with some open-ended thoughts about healthcare competition
6
“Medicare 101”
 Provides health insurance coverage to essentially all Americans once
they turn 65
 “Part A”
 Inpatient spending; Free; (almost) Full coverage
 Hospitals get reimbursed a fixed amount for each admission, based on DRG
 “Part B”
 Outpatient spending; $105/month (in 2014); Residual financial risk exposure: 20%
coinsurance rate (plus $150 annual deductible)
 Fee for service (FFS)! Patient can see anyone, and providers bill Medicare
 “Part D” (recent, 2006 addition)
 Prescription drugs via private providers, who compete for Medicare beneficiaries
 Premiums heavily subsidized and plans receive capitated, risk adjusted rates
Medicare is big!!! (and growing …)
In 2014: 54M beneficiaries, $580B in spending, ~20% of US healthcare cost
7
Medicare Advantage (MA)
 Allows Medicare beneficiaries to opt out of traditional, fee-for-service
(FFS) Medicare, and enroll in private insurance plans
 Plans must provide at least the insurance benefits of standard Medicare
(parts A and B), and typically provide more generous financial coverage,
and additional benefits
 Medicare pays plans a fixed, risk adjusted amount to cover each
enrollee
8
Medicare Advantage (MA)
 Insurers differentiate themselves mostly through provider networks
 Once contracts/networks are approved; plans offered on top of it
 Many participants build off provider networks they offer in other markets
 Compared to FFS Medicare, MA could be cheaper or more expensive …
 Presumably lower Q due to more coordinated care
 Presumably higher P, especially when providers have market power
 Note: Benefits of managed competition could take a very different form
when compared to other (non-FFS) benchmarks
9
MA background
 Program started in the 1980s, but was relatively small and had “issues”:
Low take-up, significant risk selection, no cost savings
 Major reforms in the last decade, in the direction of what most
economists would be routing for:
– Annual (instead of monthly) plan choices
– Much improved risk adjustment
– Bidding system to set plan payments (instead of administratively-set rates)
 Nowadays the program is big and expanding, covering ~30% of
Medicare beneficiaries
10
Expansion of Medicare Advantage
30%
800
Administered
Capitation Rates
Managed
Competition
25%
700
600
20%
500
Number of Contracts
Enrollment
15%
400
300
10%
Data for talk
2006-2011
5%
0%
200
100
0
1985
1990
1995
2000
2005
2010
Our work on Medicare Advantage
 Great opportunity to assess how well managed competition can work
‒ Large program, many local markets, multiple years, extremely rich and granular data
‒ Universe of beneficiaries (>150M beneficiary years, >12K plan years), including location, demographics,
risk scores, and choice sets
‒ Every health claim made by FFS Medicare beneficiaries
‒ Choices (but not utilization) of MA enrollees
‒ Has the key ingredients of managed competition
‒ Runs in parallel to traditional Medicare, allows for appropriate benchmark that often
doesn’t exist in other settings
12
Our work on Medicare Advantage
 Great opportunity to assess how well managed competition can work
‒ Large program, many local markets, multiple years, extremely rich and granular data
‒ Has the key ingredients of managed competition
‒ Runs in parallel to traditional Medicare, allows for appropriate benchmark that often
doesn’t exist in other settings
 Specifically focused on where the action is in the US context
‒ Private (vs. Public) share of healthcare spending much greater in the US
100%
80%
60%
Private
40%
Public
20%
Czech
Slovak
Estonia
Poland
Denmark
Finland
Hungary
Portugal
Austria
Belgium
Netherlands
Korea
Germany
Switzerland
Canada
Slovenia
France
US
0%
Out-of-Pocket
13
How does MA bidding work?
 Medicare sets benchmark rate B for each county
 Plans submit bids to provide “standard” insurance for a “standard” individual
 Each individual chooses (once a year) a plan in her county, or standard Medicare
Benchmark
b
Number of Year-Plan Pairs
140
120
100
b
B
Enrollee pays 0
Medicare pays plan93%
rb
Plan Bid b ($)
7%
And Medicare pays a “rebate” of
80
¾*(B-b)
that is passed to consumers
60
as additional
benefits
Enrollee pays plan b-B
Plan receives rb
Medicare pays plan rb-(b-B)
Risk adj.
40
 Extra consumer premium or benefits depend on b-B
 Plan receives rb for enrolling individual with risk r:
20
0
‒ One r=3 enrollee generates same revenue (and cost) as three r=1 beneficiaries
b - B ($US)
‒ Easier to think about demand
and market shares in terms of risk units
14
Outline for the rest of the talk
 Describe in detail one particular environment of managed competition
– “Medicare Advantage” – and its key ingredients: competitive pricing
and risk adjustment
 Short digression about risk adjustments
 Summarize some results from our study of Medicare Advantage
(based on recent work with Vilsa Curto, Jon Levin, and Jay Bhattacharya)
 End up with some open-ended thoughts about healthcare competition
15
Risk selection
 The potential for risk selection might be the main concern in insurance markets:
 Adverse selection and associated inefficiencies
 Potential for “bad” competitive practices (“cherry picking” and “lemon dropping”)
 MA is not different:
0.25
MA Share
0.2
0.15
0.1
0.05
0
0
1
2
3
4
5
6
Risk Score
7
8
9
10
16
Risk adjustments
 Typical way to combat adverse selection is to price risk, and charge high-risk
people higher premiums
 Not a great solution in the context of health insurance:
‒ Regressive … We’d generally want to tilt pricing the other way
‒ May price out of the market exactly those we would like to cover the most
‒ May introduce large “reclassification risk”
 But managed competition is a “sponsored market,” making it possible to address
selection concerns in a better way, using risk adjustments
 This is a way to implement uniform (or constrained) premiums and still
compensate insurers for covering a (predictably) riskier consumer pool
 Progressive … A form of cross subsidy from healthy to sick
 When the sponsor subsidizes the entire market (as is often the case), take up is less of an
issue even for the healthy
 Reclassification risk not an issue anymore
17
Risk adjustment in MA
 In MA, risk score r is constructed using predictive models of annual
spending using data on health claims from previous year
– Basic idea is to map claims to health conditions, and distinguish persistent
conditions (e.g. diabetes) from transitory ones (e.g. broken arm)
 Current model uses gender, age, and 55 health conditions: use claims to
“turn on” relevant dummies, and add up corresponding “scores”
– Demographics alone explain a tiny fraction (1-2%), current model about 11%
18
Risk adjustment in MA
Annual Medicare Spending ($000)
200
180
99th pctile
160
140
95th pctile
120
90th pctile
100
80
75th pctile
60
Mean
40
Median
20
0
0
1
2
3
4
5
6
Risk Score
7
8
9
19
Risk adjustment in MA
 In MA, risk score r is constructed using predictive models of annual
spending using data on health claims from previous year
– Basic idea is to map claims to health conditions, and distinguish persistent
conditions (e.g. diabetes) from transitory ones (e.g. broken arm)
 Current model uses gender, age, and 55 health conditions: use claims to
“turn on” relevant dummies, and add up corresponding “scores”
– Demographics alone explain a tiny fraction (1-2%), current model about 11%
 Fully predictive model could explain ~25%, but constraints exist:
‒ “Gaming” and “Upcoding”
‒ Political/procedural constraints, requiring risk score coefficients to “make sense”
 Makes one wonder about the trade-off between transparency and efficiency
20
Risk selection in MA
 Some risk selection remains (although may be competed away):
0.4
0.35
Mortality Rate
0.3
0.25
FFS Medicare
0.2
0.15
0.1
MA
0.05
0
0
1
2
3
4
>5
Risk Score
 We’ll adjust for it later, but to a first order risk selection is not a huge
deal anymore
– (although in theory, better-yet-imperfect scoring may make things worse)
21
Risk score as an economic object
 Risk adjustment models are becoming common and important
 E.g., in the new “ObamaCare” Health Insurance Exchanges in the US and in many
employer-provided US settings
 This trend will continue with the advent of “big data”
 So far risk adjustment models are treated as “purely” predictive,
essentially trying to maximize R2, with only little input from economics
and economists
 This should probably change …
22
Example: Risk scores in Medicare Part D
(based on work with Amy Finkelstein, Ray Kluender, and Paul Schrimpf)
 Convex kink in Medicare Part D contracts should lead to “bunching” due
to “moral hazard” (as in Saez’s and Chetty’s work on labor supply)
Out-ofpocket
spending
Catastrophic covg.
“Bunching” at the kink
Deductible
“Donut hole”
Coins. arm
Total
spending
23
Example: Clear bunching at the convex kink
2.4%
2.0%
1.6%
1.2%
0.8%
0.4%
0.0%
-$2,000
-$1,500
-$1,000
-$500
$0
$500
$1,000
$1,500
Total Annual Drug Expenditure (relative to Kink)
$2,000
Example: Who is bunching and how they get scored?
 But those who bunch are younger healthier males, who would likely spend
significantly more under alternative coverages (e.g., in the absence of the kink)
78.5
0.68
78.0
0.67
Average Age
77.5
Fraction Died
Fraction Female
7.5%
6.5%
0.66
77.0
5.5%
0.65
76.5
4.5%
0.64
76.0
3.5%
0.63
75.5
75.0
-$2,000 -$1,500 -$1,000
-$500
$0
$500
$1,000
$1,500
0.62
-$2,000 -$1,500 -$1,000
$2,000
-$500
$0
$500
$1,000
$1,500
2.5%
-$2,000 -$1,500 -$1,000
$2,000
Total Annual Drug Expenditure (relative to Kink)
Total Annual Drug Expenditure (relative to Kink)
 While risk scores, by design, capture none of this:
Average Risk Score
1.15
1.10
1.05
1.00
0.95
0.90
0.85
0.80
0.75
-$2,000 -$1,500 -$1,000
-$500
$0
$500
$1,000
-$500
$0
$500
$1,000
$1,500
Total Annual Drug Expenditure (relative to Kink)
$1,500
Total Annual Drug Expenditure (relative to Kink)
$2,000
$2,000
10,000
10,000
9,000
9,000
Annual Spending under No-Deductible Contract
Annual Spending under Filled-Gap Contract
Perfect scoring in sample  Imperfect out of sample
8,000
7,000
6,000
5,000
4,000
3,000
Correlation = 0.964
2,000
1,000
8,000
Correlation = 0.992
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
0
0
1,000
2,000
3,000
4,000
5,000
Annual Spending under The Standard Contract
6,000
7,000
0
1,000
2,000
3,000
4,000
5,000
Annual Spending under The Standard Contract
6,000
7,000
Risk score as an economic object
1. Risk score in one plan may not be great for another, as incentives change
–
–
Why? We predict spending, which is a combination of (at least) two components:
health and a behavioral response to it (“moral hazard”)
For most contexts we would have probably wanted to predict only the health
component (if we only knew how)
2. Many good health spending predictors are endogenous to past behavior
–
–
–
•
Do we want to reward smoking behavior by scoring it high?
Or perhaps use the scoring model to penalize smokers and encourage them to quit?
To engage in this, an equilibrium model is needed, a clear objective function of the
policy, and within this context one can decide about the best scoring model; it is
unlikely to be the one that maximizes R2
Note: In very different contexts (Google AdWords, eBay), scoring algorithms are being
used, quite effectively, to tilt market operation toward the objective of the market maker.
No reason that healthcare markets won’t take advantage of this as well!
27
Outline for the rest of the talk
 Describe in detail one particular environment of managed competition –
“Medicare Advantage” – and its key ingredients: competitive pricing and
risk adjustment
 Short digression about risk adjustments
 Summarize some results from our study of Medicare Advantage
(based on recent work with Vilsa Curto, Jon Levin, and Jay Bhattacharya)
 End up with some open-ended thoughts about healthcare competition
28
Our work on Medicare Advantage
 Great opportunity to assess how well managed competition can work
‒ Large program, many local markets, multiple years, extremely rich and granular data
‒ Has the key ingredients of managed competition
‒ Runs in parallel to traditional Medicare, allows for appropriate benchmark that often
doesn’t exist in other settings
 Specifically focused on where the action is in the US context
‒ Private (vs. Public) share of healthcare spending much greater in the US
‒ Much of the US spending differential concentrated at the elderly population
 Basic goal:
‒ Are there “gains from trade” in allowing seniors to enroll in private plans?
‒ Who captures the gain? Beneficiaries, insurers, and/or tax payers?
‒ Potential effects of changes in market design
29
Gov’t Spends Extra on Private Plans
Avg. across
MA enrollees
MA Plan Payment
$670
Rebate Payments
$76
Tot. Gov’t Payment
As fraction of
predicted FFS
$746
Amounts are per enrollee month
So from now on, multiply each
number by ~15M*12 = 180M / year
Gov’t Spends Extra on Private Plans
Avg. across
MA enrollees
As fraction of
predicted FFS
MA Plan Payment
$670
99%
Rebate Payments
$76
12%
Tot. Gov’t Payment
$746
111%
Predicted FFS Cost
$675
100%
Amounts are per enrollee month
 We use a pure predictive model to account non-parametrically for
heterogeneity across county-year pairs
 Would not have been possible without having FFS Medicare running in parallel
 We adjust for risk selection using the differential mortality between
MA and FFS enrollees
Gov’t Spends Extra on Private Plans
Avg. across
MA enrollees
As fraction of
predicted FFS
MA Plan Payment
$670
99%
Rebate Payments
$76
12%
Tot. Gov’t Payment
$746
111%
Predicted FFS Cost
$675
100%
Amounts are per enrollee month
 Plans bid at the level of FFS cost => so cost likely lower?
 Government payments are well above matched FFS costs due to the
rebates, which presumably attract enrollees
 Enrollees get extra benefits but may not like restrictions => CS?
Market power
 Risk selection is at least partially “solved” by risk adjustment, but
a much more “traditional” concern remains
 Many MA plans available in each market, but markets highly
concentrated:
– In 66% of the markets, C2 > 75%
– In 94% of the markets, C3 > 75%
– In 60% of the market, C3 > 90%
 And “traditional” concerns call for “traditional” analyses …
Benchmarks and Bidding Incentives
 Plan demand depends on 𝑝𝑗 = 𝑏𝑗 − 𝐵
𝜋𝑗 = 𝑝𝑗 + 𝐵 − 𝑐𝑗 𝑄𝑗 𝑝1 , … , 𝑝𝑁
i.e. 𝜋𝑗 = 𝑏𝑗 − 𝑐𝑗 𝑄𝑗 , where 𝑄𝑗 is “risk-weighted” demand
 Benchmark plays the role of a subsidy
𝑑𝑝𝑗
𝑑𝑝𝑗
=
𝑑𝐵 𝑑 −𝑐
⇒
𝑑𝑏𝑗
𝑑𝑏𝑗
=1−
𝑑𝐵
𝑑𝑐
 Under “perfect” competition 𝑏𝑗 = 𝑐 ⇒
𝑑𝑏𝑗
𝑑𝐵
=0
34
Determinants of Plan Bids
 Estimate sensitivity of plan bids to benchmarks and FFS costs
 Incomplete (~40%) pass-through of benchmark changes
 market power is important
 Bids only weakly correlated with FFS costs in same locations:
 Related finding in the context of FFS vs Employer-Provided insurance
 Confirming the potential benefits of managed competition
 Suggest looking for alternative ways to estimate plan costs …
 We will thus follow the IO literature and use first order
conditions for optimal bidding to back out cost
Implied Plan Markups and Costs
 Estimate plan choice model to identify consumer price sensitivity,
and extent to which consumers benefit from MA plans
 Identify demand off changes in plan bids over time, and variation
in plan bids within the same contract
 Recall 𝑝𝑗 = 𝑏𝑗 − 𝐵 and
𝜋𝑗 = 𝑝𝑗 + 𝐵 − 𝑐𝑗 𝑄𝑗 𝑝1 , … , 𝑝𝑁
 First order condition:
𝑑 ln 𝑄𝑗
𝑏𝑗 = 𝑐𝑗 +
𝑑𝑏𝑗
−1
36
Sources of Competition
 Estimated mark-ups are  $55-140 for “standard” enrollee
 Competition comes both from FFS Medicare and other MA plans
 If plan j increases its bid by $20
‒ It loses about 20% of its enrollees
‒ About 1/2 of these go to other MA plans
‒ About 1/2 of these switch to FFS Medicare
 By the same logic, a decrease in the benchmark has the same
effect as an identical decrease in the bids of all rival plans
37
Putting the Pieces Together
Avg. across MA
enrollees 2006-10
Imputed FFS Cost
$675
Estimated MA Cost
$586
Plan Payment
$681
Tot. Gov’t Payment
$756
Program “surplus” and incidence
Cost savings from MA
$89
(“Disutility” from MA)
- $26
Gov’t Net Surplus
- $81
Plan Surplus
$95
Consumer Surplus
$49
38
Potential for market design
Actual
(2006-10)
Pred. FFS Cost
Est. MA Cost
Plan Payment
Tot. Gov’t Payment
$675
$586
$681
$756
MA share
18.2%
Benchmarks
100% FFS $50 Lower
Rebates
At 50% At 100%
Program “surplus” and incidence
Gov’t Net Surplus
Plan Surplus
Consumer Surplus
-$81
$95
$49
39
Potential for market design
Actual
(2006-10)
Benchmarks
100% FFS $50 Lower
Pred. FFS Cost
Est. MA Cost
Plan Payment
Tot. Gov’t Payment
$675
$586
$681
$756
$691
$598
$674
$731
$676
$587
$660
$716
MA share
18.2%
13.7%
15.4%
-$40
$76
$40
-$40
$73
$38
Rebates
At 50% At 100%
Program “surplus” and incidence
Gov’t Net Surplus
Plan Surplus
Consumer Surplus
-$81
$95
$49
40
Potential for market design
Actual
(2006-10)
Benchmarks
100% FFS $50 Lower
Rebates
At 50% At 100%
Pred. FFS Cost
Est. MA Cost
Plan Payment
Tot. Gov’t Payment
$675
$586
$681
$756
$691
$598
$674
$731
$676
$587
$660
$716
$672
$590
$722
$744
$676
$581
$654
$789
MA share
18.2%
13.7%
15.4%
12.2%
26.4%
-$40
$76
$40
-$40
$73
$38
-$72
$132
$17
-$113
$73
$77
Program “surplus” and incidence
Gov’t Net Surplus
Plan Surplus
Consumer Surplus
-$81
$95
$49
41
Market Design Summary
42
Room for more customization?
Implied MA Costs ($US)
1,400
1,200
1,000
800
600
400
Rural
Urban
200
0
0
200
400
600
800
1,000
1,200
1,400
Predicted FFS Costs for MA Enrollees ($US)
43
Outline for the rest of the talk
 Describe in detail one particular environment of managed competition
– “Medicare Advantage” – and its key ingredients: competitive pricing
and risk adjustment
 Short digression about risk adjustments
 Summarize some results from our study of Medicare Advantage
(based on recent work with Vilsa Curto, Jon Levin, and Jay Bhattacharya)
 End up with some open-ended thoughts about healthcare
competition
44
Can we rely on consumers?
 Our work on MA suggests that not-very-price-sensitive consumers give rise to
market power, and other recent work suggests other “behavioral” concerns that
may be consistent with it (inertia, inattention, “mistakes”)
 Informed consumer choices have (at least) two important roles:
 Drive competition to the right margin: downward pressure on prices, and upward pressure
on “value” per dollar
 Sort consumers to better and more efficient plans
 Creative market design (defaults, benchmarking, etc.) could help on the former,
but the latter would remain a concern
 One difficulty is that key choice made when consumers aren’t well informed, but
when stuff happens and they become informed, key choice cannot be changed
 perhaps there is room for some form of “secondary markets” or joint optimization, with a
split of the potential gain from trade?
45
The transactable unit?
 Typical unit is a person-year
 Too narrow?
 Leads to too much weight on short-run rather than long-run costs and benefits
 Some longer-run incentives are already built in, but not fully priced
 Too broad?
 May not take advantage of economies of scale, learning, and specialization
 Many forms of healthcare (but not all) are tradable goods and can be performed
(cheaper and/or better) elsewhere
 For some “separable” components of health (e.g., hip replacements?), could design
competition at the component level
46
The role of competition?
 Ideal goal: healthcare that is cheaper and better at the same time
 In the absence of the ideal, some possible answers:
1. Cheaper delivery of the “same” care
2. Better care at similar or not too expensive prices
3. Sorting people to “worse” but presumably cheaper care, which is still “good enough”
 Much focus on the first two
 Even in context where it makes less sense (e.g., Medicare part D)
 May want to encourage market designs that focus on the latter
 Could imagine private market having comparative advantage in facilitating money for
health/comfort transactions
47
Health vs. Healthcare
 Not the same thing …
 But very much bundled: in coverage, in measurement, and in incentives
 Better and more objective measures of health would allow us to contract on
outputs rather than inputs, and to separate amenities (Waiting time for the
doctor, Driving time to surgical or radiation facility, Quality of post-labor
recovery room) from “pure” health
 Reasonable argument to make that the government should do the best to
provide good health, but not so clear that it should mandate, subsidize, or
guarantee better-than-respectable amenities
 One concern is that at least in the US amenities are heavily subsidized, little
incentives for insurers to offer “economy service,” and all Americans are “flying
business” …
48
Final slide
 Healthcare costs keep going up, and cannot do this forever, so
something will need to happen
 Many are optimistic that Information Technology and various forms
of “big data” have the potential to completely transform production
of healthcare, in a way that would make it affordable and accessible
to many
 But once the healthcare version of Ford’s Model T shows up, are
markets designed to help it grow and get adopted? Perhaps, but very
slowly ... Healthcare markets should make this a primary goal
49