Aims
Consumer Valuation of
Medicare Part D Plans
• How much social welfare has been created by this new
government program?
– consumer surplus, producer surplus, and net social surplus
• What features of the drug plans were valued by consumers,
and by how much?
max uijt = α ( yi − p jt ) + X 'jt β + ξ jt + ε ijt
j∈{0,..., J t }
Claudio Lucarelli
Dept of Policy Analysis and Management, Cornell University
Jeff Prince
Dept of Applied Economics and Management, Cornell University
Kosali Simon (presenter)
Dept of Policy Analysis and Management, Cornell University and NBER
Motivation
• Plan features are heavily directed by policy, thus relevant to
know how they are valued by consumers.
• Important to know whether the cost of the program is less than
the surplus created, and the split between industry and
consumers, to know whether the program is a “give-away” to
the industry.
Method-Discrete Choice Analysis
Prior Work Using Similar Method
• Demand estimation in health care markets
– Town and Liu (RAND, 2003) find substantial net
social welfare from M+C, particularly from the
drug benefits.
•
Follows Berry (1994)
•
By studying aggregate market shares, we recover parameters
of the underlying utility function
Allows us to estimate the value derived from plan
characteristics, the price (and other features) elasticity of
demand (including cross price elasticities of demand), CS, PS
•
• Forecasts that Medicare Part D will create similar
surplus
•
Data and Method
• Data: CMS landscape files, plan enrollment file
web queries
– Enrollment reported by CMS includes those who did
not chose a plan but were auto-enrolled
– We calculate the number of enrollees who voluntarily
selected into a plan
– In addition to plan characteristics from the CMS
landscape file, we calculate OOP for set of drugs
N=1,429
• Method: Final model
Scaled market share= log(mktshare) log(outsidemktshare)=
f(premium,gap, Oop, deductible, top100 on formulary,
PA, cs under $20), with insurer fe, region fe
– utility function U=g(x,e)
– Behavioral process that leads to a choice is y=f(x,ε)
– ε is unobserved
[1] P(y | x) = P(ε s.t. f(x, ε) = y)
If errors are iid type 1 extreme value, [1] has a logistic distribution; we
estimate a logit for transformed market shares, y), includes insurer f.e.,
region f.e.
This implies all plans independent of each other. Means that all cross price
elasticities depend only on prices and market shares of the two plans
In reality, demand may be nested (i.e. people decide whether to go into
basic or enhanced plans), implies a nested logit
Supply side: We assume Bertrand pricing by firms,
Descriptive Statistics
Variable
Mean
St.Dev
Unadjusted enrollment
10,932
25,389
Adjusted enrollment
7,010
20,687
Scaled market share
0.025
0.065
# top 100 drugs on
formulary
93
6.62
Monthly premium
$37.4
12.85
Annual OoP top 5
$753.08
339.53
Annual deductible
$92.24
115.79
1
Estimates from Simple Logit
Variable Coefficient
Interpretation of coefficients:
Gap coverage
0.50 ***
Out of pocket
index
-0.0007 **
Premium
-0.07***
Deductible
-0.009***
Top 100 drugs
on formulary
0.095 ***
Prior auth top
100
0.008
Top 100 under
$20
0.01226 ***
-.07*y*(1-y)=% change in scaled market share
as my own price increases 1%
-.07*y1*y2=cross price elasticity
.5/.07=$7.14 is value placed on gap coverage pm
Other specifications
• Without insurer fixed effects
• With different OOP measures
• With nest (gap coverage or not)
Includes fe for region and insurer,
* Indicates statistical significance at te 10% level,
** at the 5% level and *** at the 1% level
Selected Estimates from Nested Logit
vs Simple Logit
Simple logit
Nested logit
Variable
Coefficient
Coefficient
Gap coverage
0.50 ***
--
OOP1
-0.0007 **
-0.0007**
Premium
-0.07***
-0.085***
Deductible
-0.009***
-0.0097 ***
Top 100 drugs on
formulary
0.095 ***
0.099**
Prior auth top 100
0.008
-0.001
Top 100 under $20
0.01226 ***
0.012***
Very preliminary: Consumer
welfare with and without enhanced
plans
With: $8,954,700
• Without: $5,786,100
• % Difference: Approx. 35% lower without
gap coverage
Includes fe for region and insurer,
* Indicates statistical significance at te 10% level, ** at the 5% level and *** at the 1% level
Conclusions
• The value of each characteristic is shown
by coefficients from logit estimation
– When own premium increases by 1%, own
demand (enrollment) decreases by roughly 34%
• Importance of demand estimation vs OLS
– OLS coef on regression ms=f(X) is interpreted
as difference in enrollment from x increasing
but cannot tell us about structural parameters
of utility
2