Consumer Valuation of Medicare Part D Plans VERY PRELIMINARY-Please do not quote Claudio Lucarelli Dept of Policy Analysis and Management, Cornell University Jeff Prince Dept of Applied Economics and Management, Cornell University Kosali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

Aims 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? 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. Motivation

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. Forecasts that Medicare Part D will create similar surplus

Method-Discrete Choice Analysis 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 –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,

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

Descriptive Statistics VariableMeanSt.Dev Unadjusted enrollment10,93225,389 Adjusted enrollment7,01020,687 Scaled market share # top 100 drugs on formulary Monthly premium$ Annual OoP top 5$ Annual deductible$

PRELIMINARY Estimates from Simple Logit VariableCoefficient Gap coverage 0.50 *** Out of pocket index ** Premium -0.07*** Deductible *** Top 100 drugs on formulary *** Prior auth top Top 100 under $ *** Includes fe for region and insurer, * Indicates statistical significance at te 10% level, ** at the 5% level and *** at the 1% level -.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 Interpretation of coefficients:

Other specifications Without insurer fixed effects With different OOP measures With nest (gap coverage or not)

Selected Estimates from Nested Logit vs Simple Logit Simple logitNested logit Variable Coefficient Gap coverage 0.50 ***-- OOP ** Premium -0.07*** *** Deductible *** *** Top 100 drugs on formulary *** 0.099** Prior auth top Top 100 under $ *** 0.012*** Includes fe for region and insurer, * Indicates statistical significance at te 10% level, ** at the 5% level and *** at the 1% level

Very preliminary: Consumer welfare with and without enhanced plans With: $8,954,700 Without: $5,786,100 % Difference: Approx. 35% lower without gap coverage

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 3- 4% 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