THE VALUE OF CONSUMER CHOICE AND

THE VALUE OF CONSUMER CHOICE AND

THE DECLINE IN HMO ENROLLMENTS

Gerard J. Wedig William E. Simon Graduate School of Business Administration

University of Rochester, Rochester NY 14627

-ABSTRACT-

Health insurance contracts may restrict consumers’ choice of medical provider (e.g., hospital) in order to minimize moral hazard inefficiencies. In this paper I assess the economic value of this strategy by comparing the estimated “option value” that consumers assign to provider choice to the negotiated discounts that insurers can achieve by negotiating with a restricted set of providers (i.e., volume discounts). Using a panel of federal employees’ health plan choices from 1999-2003, I show that the practice of selective contracting (SC) with a limited set of hospitals reduced HMO plans’ expected utility by $62-$118, on average, for a standard reduction in the provider choice set. I also conduct simulations that show that by 2003 health plans using selective contracting were theoretically unable to achieve sufficiently large volume discounts from hospital providers to fully compensate for the associated utility losses. My results help to explain the flight from HMO enrollments that occurred in the early 2000’s. I would like to thank Sanjog Misra, Bill Schwert, Jerry Zimmerman, seminar participants at the Simon School, the editor and two anonymous referees for useful comments on earlier drafts of this paper. Keywords: Health care markets, information, product quality, insurance JEL Classifications: I10, I11, L15, D83, D12

2

1. Introduction and Overview

Health insurance contracts may restrict consumers’ choice of medical provider in

order to minimize moral hazard. For example, consumers who are insured for their

marginal health care expenses have no incentive to enforce price discipline on their own.

In this case, insurers may impose this discipline by restricting consumers’ choice of

medical provider to those who offer competitive prices1. This practice, known as

“selective contracting” (SC), may have other benefits as well, including channeling

consumers to efficient providers and achieving other efficiencies associated with (partial)

vertical integration. Selective contracting has been utilized by insurers both in the U.S.

and abroad2. However, it is not a universal feature of health insurance contracts.

In part, this is because reducing the choice set of medical providers imposes costs on

consumers to the extent that consumers assign “option value” to choice (Capps et al.,

2003). For example, SC was widely employed in the U.S. by health maintenance

organizations (HMOs) and other insurers in the 1990s and appears to have been an

important cost control mechanism (Robinson and Phibbs, 1989, Cutler et al., 2000).

However, most private U.S health insurers moderated their use of SC during the past

decade, at the same time that health care costs accelerated. Anecdotally, a consumer and

regulatory “backlash” against provider choice restrictions forced insurers to expand

choice and largely abandon the practice (Blendon et al., 1998, Vita, 2001).

Simultaneously, HMO enrollments declined.

1 Restricting the provider network allows the insurer to promise higher volumes to those providers included in its network. Bargaining for lower fees can be an important cost control mechanism. Anderson et al., 2003, argue that prices for medical services explain much of the international difference in U.S. health care costs and that higher prices can be traced to the highly fragmented supplier side of the U.S. health system. 2 Internationally, several countries have introduced selective contracting in recent years in an effort to promote efficiency in their health care sectors. See Willcox, 2002, Schlett and Blum, 2009 and Westert, et al., 2009.

3

The purpose of this paper is to explore the economic tradeoffs of SC with a particular

focus on directly measuring the value that consumers assign to choice in medical

markets. I am interested in assessing whether the magnitude of any utility loss from

choice restrictions can be offset by volume-based price discounts insurers may receive

from hospitals. My results bear on the larger question of whether an economic linkage

can be drawn between consumers’ demand for choice and their apparent rejection of what

is called “tightly managed care”. They also have implications for many health care

reform proposals that necessarily imply restrictions on the consumers’ choice sets.

I analyze health plan’s selective contracting of hospitals in the state of Florida

between 1999 and 2003. Hospital selective contracting restricts consumer choice in

multiple ways including their choice of hospital. In addition, hospital choice restrictions

imply restrictions over the use of specific physicians3. I start by constructing hospital

networks for each of a set of plan choices and then estimate a discrete choice model of

health insurance demand, conditional on each plan’s restricted network. For these

purposes I use the Capps et al. (2003) measure of selective contracting disutility that

focuses on the “option value” of choice. I employ several different discrete choice

estimators to estimate the model, including models with plan-MSA fixed effects designed

to control for unobserved plan attributes. This provides me with estimates of disutility

resulting from choice restrictions. Subsequently, I project the estimated plan effects onto

an instrumented value of plan price in order to estimate the price elasticity and also

convert my disutility estimates into dollars.

My results show that consumers associated restrictive hospital networks with plan

disutility during each of the years of the study period with choice elasticities that range 3 This is because physicians practice in and admit to specific hospitals.

4

from (-.10) to (-.32) in pooled estimates (greater effects are obtained in 2003 using

models where I allow the estimates to vary by year). Using my instrumental variables

methodology to recover unbiased price coefficients, I show that consumers associated a

one standard deviation reduction in the size of a plan’s hospital network with a reduction

in plan value of between $62 and $1184. Finally, I compare these valuations with

simulations of maximum negotiated discounts achievable from SC and conclude that it

would have been infeasible for insurers to realize sufficient hospital discounts to offset

the associated utility losses.

To date, research documenting the value that consumers place on provider choice has

been limited. Most notably, Ho (2006) estimates consumers’ valuations of hospital

networks using aggregate (market shares) data. She uses her estimates of the value of

hospital choice to do a welfare assessment of choice restrictions, choosing not to estimate

the supply-side advantages that insurers may gain from such restrictions. Ho calls for

“further research…to both confirm the results of this paper and to pull together the

demand and supply side of the market”5.

My work is similar to Ho (2006) in that I estimate the value of provider choice in a

health insurance contract and use instruments for price to convert utils to dollars. I gain

certain advantages relative to Ho by using micro data that allow me to avoid potential

biases and estimate key demographic interactions. Crucially, by using data that span the

period 1999-2003, I am also able to track changing consumer attitudes towards choice

during the period of the managed care backlash. I find that consumer valuations of

4 In my sample, a one standard deviation reduction in hospital choice is equivalent to a health plan moving from a position of full contracting with all hospitals in the market to excluding hospitals that account for 37% of the market share of the hospital market. 5 See Ho (2006), page 1041.

5

choice increased during the interval from 1999 to 2003, when estimated elasticities

increased by up to a factor of two. Finally, I also bring in the supply side of the market

by explicitly comparing my estimates of choice valuation to simulations of feasible

discounts from selective contracting.

My paper makes several contributions to the topic of selective contracting and the

value of choice in health care markets. First, I confirm that choice in health care markets

“matters” while providing a point estimate of its value. I also show how the effect varies

with income, race and gender. Moreover, I conduct my tests within a sample (federal

employees) whose choices are not endogenously filtered by an employer whose

objectives may differ from those of its individual employees. This results in an arguably

less biased estimate of the value of choice than in prior work. Second, I am able to obtain

a reasonably precise estimate of the price elasticity within my sample and subsequently

use this estimate to compute the dollar-equivalent value of choice. Third, I am able to

trace consumers’ changing valuation of choice, showing how it increased between 1999

and 2003 at the same time that consumers abandoned HMO products. This provides

unique evidence of the role that provider choice played in the decline in HMO

enrollments. Previously, this linkage has been largely anecdotal and unproven even as it

has become part of the conventional wisdom of evolving health care markets.

Finally, fourth, I am able to show that, under reasonable assumptions, losses in

consumer utility outweigh any volume discounts that health plans may hope to achieve

through selective contracting with hospitals. This result has important implications for

the viability of selective contracting strategies. Thus, while of obvious historic interest,

my results also have general relevance to our understanding of the health economy.

6

Many strategies for controlling health care costs and addressing the rising costs of health

care entitlements envision a return to specific managed care strategies. The viability of

these strategies is fundamentally tied to consumers’ valuation of choice.

The paper is organized as follows. In Section 2, I describe my data, including their

advantages and limitations. Section 3 provides discrete choice model estimates of the

utility of choice using the Capps et al. (2003) “option value” framework. In Section 4, I

use an instrumental variables strategy to recover estimates of the dollar value of choice.

In Section 5, I consider the costs and benefits of HMO enrollment from a consumer

perspective. I also conduct my simulations of feasible volume discounts relative to utility

losses from selective contracting. The paper concludes in Section 6 with a brief

discussion.

2. The Data

I focus my analysis on an individual consumer’s choice of a health plan as it relates to

one important and measurable dimension of provider choice: the set of available hospitals

that the consumer (and her physician) may use in the event of illness or pregnancy. As

noted above, choice of hospital also has implications for choice of physician. To conduct

my analysis, I require a data set that includes both individual health plan choices as well

as the characteristics of health plans in the choice set of insurance options, including the

hospital networks of each plan.

2.1 Individual Data on Health Plan Choices

I collect data on the health plan choices of a set of federal employees residing in the

state of Florida between the years 1999 and 2003, and participating in the Federal

7

Employees Health Benefits Plan (FEHBP)6. Plan choices of federal employees are

obtained from the Office of Personnel Management (OPM). The OPM’s data set also

includes detailed demographic information for each employee and allows me to track a

single individual’s choices across years, using a set of anonymous identifiers.

These data have a number of important advantages. Federal employees are offered a

menu of health plan options that varies by geographic region. Unlike private employers,

the choice set includes all health plans that adhere to a set of bureaucratic rules and the

federal government makes a formulaic contribution to the premium cost of each plan. As

a result, the transaction price of the health plan to the consumer is unaffected by

endogenous contributions of employers to the employee’s heath benefits or the

employer’s decision to offer a specific plan to their employees. Second, the demographic

data can be used to estimate interactions between demographic variables and measures of

yearly plan cost and selective contracting disutility. Third, the time series of plan

choices allows me to estimate changes in the perceived value of choice over time.

I restrict the sample to individuals choosing single coverage to ensure that plan

choices are unaffected by (unobserved) characteristics of the employee’s spouse or

family. From this larger set, I further restrict the sample to: a) individuals residing in

counties in which at least 100 federal employees made a plan choice; b) individuals who

6 The FEHBP is a system through which employee health insurance is provided to government employees through a menu of private insurance options. Premiums vary from plan to plan and are paid in part by the employer (the government) and the remainder by the employee. The employer pays an amount up to 72 percent of the average plan premium for self-only or family. In 2010 about 250 plans participated in the program. About 20 plans are nationwide. There are about 230 locally-available plans, almost all HMOs (Francis, 2009). The FEHBP is open to all federal employees, including members of Congress.

8

selected either an HMO or the Blue Cross PPO product7; and c) individuals who selected

a plan for which I have complete data. I track 16,410 plan choices made by 10,349

unique individuals, 1,758 of whom are in my sample for all three years. The sample

grows significantly over time because my data set omits data for the Miami/Dade area in

1999 and because the number of federal employees grew by 2,131 individuals, statewide,

in 2003. Table 1 provides unweighted means of individual characteristics for the full

sample of data. I observe that median real wage is $38,226. The median enrollee is 47

years old and has been working for the federal government for 12 years. About 68% of

the sample reports their race as “white” and 39% is male. Finally, 60% of the sample has

a high school degree or less education, 24% have a college degree and slightly more than

15% have a graduate degree.

2.2 Health Plans Data

I require data on several characteristics of the health plans available to federal

employees including their cost to employees, the scope of their hospital networks, their

market locations, the characteristics of their markets and finally their perceived quality

(based upon claims processing service, etc.).

I use data from the Checkbooks Guide to Health Insurance Plans to provide actuarial

estimates of the total yearly costs of plan membership (i.e., “yearly plan cost” or “YPC”)

as well as survey data on plan quality. The Checkbooks’ actuarial estimates of

7 The Blue Cross PPO plans also engaged in selective contracting during this period.

9

Table 1 - Individual Characteristics of the Sample

N Mean Median Standard Deviation 25th Perc

75th Perc

Wage (1999 $) 10335 $43,999 $38,226 $20,579 $28,333 $54,284 Age 10349 45.66 47 10.99 37 54 Tenure 10349 13.02 12 9.56 4 20 Education < HS 10349 0.0144 HS Grad 10349 0.59 College Grad 10349 0.244 Grad Degree 10349 0.152 White 10349 0.678 Male 10349 0.393

10

plan costs are obtained by matching plan benefits, deductible and co-pays with actuarial

estimates of health care utilization. I obtain information on additional plan characteristics

from Health Leaders/Interstudy (HLI), including plan age, plan type, national affiliation

and several features of the plan’s market environment, including market HMO

penetration, number of HMOs in the market, Hirschman-Herfindahl index of the HMO

market and physicians per capita in the market. Many of the market environment

variables are used as instruments to obtain unbiased estimates of the price elasticity (as

described in Section 4).

I obtain data on the scope of each insurer’s hospital network from HLI’s Hospital

Contracting Files. These files, obtained from a proprietary survey of health plans, match

existing contracts between health insurers and each of the hospitals in a county. To

provide a precise estimate of the desirability of the network, I also need measures of the

market shares of each hospital, computed separately for distinct diagnostic categories. I

obtain data on hospital market shares using the Florida Discharge Data, obtained from the

state of Florida. The Florida Discharge Data identify the patient’s county of residence as

well as several other features of each admission, including the principle reason for the

admission. Using these data, I am able to construct hospital market shares by county of

patient origin within (6) broad diagnostic categories for the census of non-HMO

admissions for these years. The combination of the discharge data with the hospital

contracts data allows me to create a theoretical measure of the utility of each health

plan’s hospital network, as discussed below in Section 3.

11

Tables 2 and 3 provide summary information about the plans that comprise the choice

set of HMOs and PPOs that I analyze during the time period from 1999-2003. The full

universe of choices available to federal employees (not depicted here) includes

several indemnity plans that permit unrestricted hospital choice, plus 6 random HMOs for

which I could not obtain contracting data (i.e., did not respond to the contracting survey).

HMO plans responding to the contracting survey do not differ in any measurable way

from excluded HMO plans so that the sample conforms to a “sampling of a random

subset of alternatives.” This practice does not impart bias on the estimated coefficients

(Train, 2003)8. It is understood that results obtained from this sample apply to the

population of individuals selecting managed care health plans9.

Table 2 shows that many plans enter or exit the FEHBPs choice set during this period

and just five plans are available for all three years. The set of HMO/PPO choices was

largest in 1999, when 14 such plans were offered statewide (some of which are not

included in Table 2). The choice set was smallest in 2003, when only 8 such plans were

offered statewide. In 2001, 9 HMO/PPO plans were offered statewide.

Plans tend to be offered in a subset of the 14 counties that comprise the individual

markets in my study. For example, while Blue Cross is offered in every county and Av-

Med in all counties except for one, Capital Health Plan (a staff-style HMO) is offered in

only one county. The result is that the choice set varies across enrollees, depending upon

8 That is, I select plans that report contracting data and limit my sample of individuals to those who selected one of the plans for which I have contracting data. These criteria yield a large sample of individual choices. Moreover, the resulting insurance plan choice sets contain a manageable number of choices for estimation purposes (i.e., always less than ten choices per individual) and because all choices are managed care plans, the empirical model does not require a nested specification. 9 The marginal effect of selective contracting on plan choice is arguably greater for the set of individuals not selecting managed care plans, since one reason that they do not choose a managed care plan is that they value choice more highly than those who do select a managed care plan. For this reason, my estimates of the value of choice are arguably a lower bound of its value in the full population of federal employees.

12

Pla

n N

ame

Pla

n T

ypeC

oun

ties

Yea

rs A

vail

able

Dat

a A

vail

able

Plan

Cos

tO

vera

ll Q

uali

tyS

amp

le D

isu

tili

ty19

99 M

kt S

hr

Blu

e C

ross

Blu

e C

ross

-Bas

icP

PO14

All

All

$1,2

8571

.40

0.18

0474

.63%

Blu

e C

ross

- P

rem

ium

PPO

14A

llA

ll$1

,881

75.6

00.

1794

7.12

%

HM

Os

Av-

Med

HM

O13

All

All

$1,0

6662

.80

0.55

807.

34%

Hu

man

aH

MO

11A

ll20

03$8

5751

.20

0.24

68N

AFo

und

atio

nH

MO

6A

ll19

99, 2

001

$744

47.5

00.

2746

1.21

%P

rud

entia

lH

MO

819

99, 2

001

1999

$846

58.6

01.

2643

7.09

%H

IPH

MO

519

99, 2

001

1999

, 200

1$8

8052

.50

1.06

482.

04%

Bea

con

HM

O3

1999

, 200

119

99, 2

001

$742

NA

0.39

810.

08%

Cap

ital H

ealth

Pla

nH

MO

119

99, 2

003

1999

, 200

3$1

,013

75.6

00.

9123

1.49

%A

etn

aH

MO

719

9919

99$9

40N

A0.

9976

2.35

%H

ealth

Opt

ion

sH

MO

519

9919

99$1

,102

51.0

00.

1479

3.22

%U

nite

dH

MO

419

9919

99$9

80N

A0.

1430

0.82

%

Mea

n V

alu

e$1

,235

69.3

20.

530.

4150

Tab

le 2

- L

ist o

f P

lan

Ch

oice

s an

d C

har

acte

rist

ics

This

tabl

e pr

ovid

es d

escr

iptiv

e in

form

atio

n ab

out a

subs

et o

f the

hea

lth p

lan

choi

ces

of fe

der

al e

mpl

oyee

s in

the

stat

e of

Flo

rid

a du

ring

the

year

s 19

99, 2

001

and

2003

. Fe

e-fo

r -s

ervi

ce p

lan

choi

ces a

re n

ot d

epic

ted

her

e. M

easu

res o

f pla

n c

ost a

re in

clu

siv

e of

exp

ecte

d ou

t of p

ocke

t cos

ts a

nd a

re

an a

vera

ge a

cros

s th

e th

ree

year

s. T

he m

easu

re o

f pla

n qu

ality

ind

icat

es th

e pe

rcen

tage

of s

urv

ey r

espo

nden

ts r

atin

g th

e qu

alit

y of

car

e in

the

plan

as

"exc

elle

nt" o

r "v

ery

good

." "

Sam

ple

disu

tilit

y" r

efer

s to

the

aver

age

valu

e of

the

plan

's se

lect

ive

cont

ract

ing

disu

tilit

y m

easu

re, a

cros

s mar

kets

an

d ye

ars,

for

fem

ales

bet

wee

n 46

and

65

year

s of

age

. T

he m

easu

re is

des

crib

ed a

nd fo

rmal

ly d

efin

ed in

Sec

tion

3.

13

county of residence. For example, in 1999 Miami residents could choose among 13

HMOs and PPOs, while residents of Tallahassee had just 5 HMO/PPO choices.

The second half of Table 2 and all of Table 3 provide data on plan-specific

characteristics. Table 2 shows that individual plans exhibit significant variation in yearly

plan costs and surveyed quality. Average annual costs of enrollment for single coverage

(including out of pocket costs) range from a high of $1,881 (for the Blue Cross Premium

PPO) to a low of $742 for Beacon’s HMO product. Reported satisfaction with the quality

of medical care also varies significantly. The percentage of enrollees reporting the

quality of care as “very good” or “excellent” ranges from a high of 75.6% for the Blue

Cross Premium PPO (as well as the Capital Health Plan) to a low value of 47.5% for

Foundation’s HMO. The final column of Table 2 shows the percent of the sample that

selected each plan in 1999. Blue Cross accounts for the majority of plan enrollments,

owing in part to the wide spread availability of its plans. About 20% of the sample select

an HMO instead of a Blue Cross plan. The leading HMO competitors are Av-Med and

Prudential.

Table 3 provides a summary of additional plan-specific variables also used in the

analysis. This table shows that 8 of the 12 plan choices are national plans10. The average

age of each plan is 17 years. Table 3 also provides summary information about each

plan’s market environment, with each such variable given an “Mkt” prefix. Each of these

plan-specific variables is formed as a weighted average of market-level values in the

markets in which the plan operates, with the weights equal to the plan’s non-FEHBPs

HMO enrollments in each market. I am able to track market-level values for HMO

10 In addition, 6 of 10 HMOs are IPA models. An IPA is an “independent practice association.” In an IPA-style HMO, the HMO contracts with individual physicians (as opposed to groups of physicians). Furthermore, physicians contracting with an IPA can and commonly do contract with other health plans.

14

Plan

Nam

eN

atio

nal P

lan

Plan

Age

Mkt

HM

O P

enM

kt N

um H

MO

sM

kt C

nty

IOC

Mkt

MD

s C

apit

aM

kt S

pec

Cap

ita

Blue

Cro

ss -B

asic

Yes

160.

2108

9.02

0.69

830.

0023

80.

0007

7Bl

ue C

ross

- Pr

emiu

mY

es16

0.21

089.

020.

6983

0.00

238

0.00

077

HM

Os

Aet

naY

es11

0.27

3710

.86

0.82

240.

0024

0.00

082

Av-

Med

No

210.

3121

13.2

90.

8288

0.00

297

0.00

1Be

acon

No

50.

244

10.8

90.

8595

0.00

241

0.00

084

Cap

ital H

ealth

Pla

nN

o17

0.35

76

0.28

900.

0022

80.

0004

9Fo

unda

tion

Yes

130.

3676

16.4

30.

8674

0.00

228

0.00

1H

ealth

Opt

ions

No

190.

2641

10.7

90.

7754

0.00

249

0.00

083

HIP

Yes

NA

NA

NA

NA

NA

NA

Hum

ana

Yes

260.

2715

11.7

0.73

790.

002

0.00

077

Prud

entia

lY

es14

0.28

1410

.99

0.82

470.

0023

40.

0007

9U

nite

dY

es29

0.30

612

.81

0.84

620.

0025

0.00

086

Mea

n17

0.28

1711

.07

0.74

980.

0024

00.

0008

1St

and

Dev

6.72

0.05

2.67

0.16

0.00

0.00

Coe

ff V

aria

tion

0.40

0.18

0.24

0.22

0.10

0.17

Tab

le 3

- M

ark

et E

nvi

ron

men

t an

d O

ther

Sel

ecte

d C

har

acte

rist

ics

for

Eac

h P

lan

This

tabl

e pr

esen

ts se

lect

ed h

ealth

pla

n ch

arac

teri

stic

s, b

oth

conc

erni

ng th

e pl

an it

self

as w

ell a

s the

mar

kets

in w

hich

it e

nrol

ls it

s be

nefic

iari

es.

"Nat

iona

l pla

ns" s

ell p

lans

in a

t lea

st o

ne o

ther

stat

e.

"Pla

n ag

e" re

pres

ents

pla

n ag

e as

of 1

999.

"M

kt H

MO

Pen

" ref

ers t

o th

e av

erag

e p

erce

ntag

e of

the

popu

latio

n en

rolle

d in

an

HM

O in

the

plan

's m

arke

ts a

s of 2

003.

"M

kt N

um H

MO

s" is

the

aver

age

num

bero

f H

MO

s in

the

plan

's m

arke

ts a

s of 2

003.

"M

kt C

nty

IOC

" is d

efin

ed a

s the

ave

rage

val

ue o

f (1-

HH

I) in

the

plan

's m

arke

ts a

s of b

oth

2001

and

20

03.

Fina

lly, "

Mkt

MD

s Cap

" and

"Mkt

Spe

c C

ap" r

efer

to th

e av

erag

e nu

mbe

r of p

hysi

cian

s per

cap

ita a

nd sp

ecia

lists

per

cap

itain

the

plan

's m

arke

ts a

s of 2

003.

15

penetration, number of HMOs in the market, index of competition (equal to (1- the

Hirschman-Herfindahl index of the market)), physicians per capita and specialist

physicians per capita. Using these measures I can say, for example, that Aetna operated

its HMO in markets where HMO penetration was on average 27%, whereas the Blue

Cross PPO operated in markets where the average HMO penetration was 21%.

In summary, these data allow me to: a) associate plan choices with an exogenous

measure of price that is not filtered by employer objectives; b) precisely measure the

scope of choice provided for one key dimension of choice, the hospital network11; c)

estimate the interaction of demographic with plan characteristics in order to test, for

example, whether higher income individuals place a greater value on choice; and d)

instrument for price with a set of variables drawn from each plan’s market setting, thus

facilitating unbiased estimates of the price elasticity.

3. Estimates of the Utility of Hospital Choice

I start my analysis by estimating the utility that consumers associate with choice,

where choice is measured by the scope of a health plan’s hospital network. I conduct a

joint test of whether consumers value choice and whether they are sufficiently forward

looking to consider this health plan characteristic at their time of enrollment. In selecting

a health plan consumers, in effect, assess the utility of the health plan’s network and then

select a plan based upon this utility as well as the plan’s price and other characteristics.

To motivate the tests and formulate a specification, I start with the theory that choice in

medical markets has option value (Capps et al., 2003).

3.1 The Option Value of Choice

11 Other plan characteristics are controlled for by including an extensive set of plan or plan-MSA dummy variables in the models. This is discussed below.

16

Capps et al., (2003) provide a useful framework for modeling the utility of a hospital

network, where consumers learn their idiosyncratic preferences, ex post. The utility

obtained from a specific hospital depends upon systematic factors (e.g., observable

hospital amenities such as attractive facilities) as well as an idiosyncratic, random

element that is revealed to the consumer at the time of their illness (e.g., previously

unknown dimensions of quality that the consumer must learn). The hospital with the

greatest revealed utility is chosen by the consumer at the time of any illness.

Prior to illness, consumers must assess the utility of an entire network at the time that

they select an insurer and ex ante to revelation of their idiosyncratic preferences. Under

these circumstances, consumers evaluate the expected maximum utility that will be

realized from the set of hospitals in the network. Assuming that the random components

are independently distributed as extreme value, the expected maximum utility can be

expressed as the log sum of the exponentiated systematic utilities of the individual

hospitals in the network12.

Using this result, Capps et al. show that the incremental contribution of hospital g to

any network, G’s, total utility, conditional on illness z, is,

]);,(1

1ln[)( ,,

,,

zxhsgU igzig

ziG

(1)

where )(,, gU ziG is the incremental utility provided by hospital g to patient i with illness

z and );,(,, zxhs igzig is the probability that patient i selects hospital g for illness z out of

the network of hospitals, G13 (a function of hospital characteristics, gh and patient

12 See Capps et al. (2003, page 742) who note that this is a standard mathematical result for choice sets that contain both systematic utility and a random component that is distributed as extreme value. 13 Note that this probability may be estimated by hospital g’s market share within the network G for the illness category z for patients of type i.

17

characteristics, ix ). Given (1), the overall contribution of hospital g to network G’s

utility for patients of type i can be found by integrating )(,, gU ziG over the illness

distribution, z.

This approach to estimating incremental utility suggests a simple method for

estimating the overall utility of a partial hospital network. Because plan choices are

based on differences in utility across the choice set, it is sufficient to compute differences

in hospital network utility, relative to the case of full contracting. Specifically, consider

the following expression for the utility of a plan’s (partial) hospital network conditioned

on illness z:

]);,(1

1ln[

',,',,,,

zxhsUU

iGziGziFullziG

(2)

where G’ is the set of local hospitals excluded from the health plan’s network. Within a

market and demographic, i, ziFullU ,, will not vary across the choice set, so that the

investigator may apply the normalization that its utility is zero. Hence, the value of a

hospital network in treating condition z for demographic i may be proxied by the

disutility of its hospital exclusions, as measured by the term: ]);,(1

1ln[ ',,' zxhs iGziG

. The

overall value of a hospital network may be estimated by aggregating this term across

illness categories, weighted by their probability of occurrence. The expected sign of this

term’s coefficient is negative with units measured in utils. Furthermore, ziGs ,,' may be

proxied by the market share the excluded hospitals enjoy from consumers of type i whose

choice of hospital is not limited. For simplicity, I will refer to the second term on the

right side of equation (2), aggregated across illness categories, as my measure of

“selective contracting disutility” or “SCD”, for short.

18

I use these results to estimate the utility of choice. For these purposes, I use the

measure of SCD in equation (2). I estimate discrete choice regressions with the

following utility specification. Formally, for a given health plan j, operating in market m,

mjiimjmjmjimjmjiimjimji ICPIBPDIADU ,,,,,,,,,,,, ''' (3)

where mjiD ,, is an observable measure of SCD that varies across plans and, within plans,

across markets and individuals, iI is a vector of measurable demographic characteristics,

mjP , is observed plan costs, mj , is an unobserved scalar measure of other plan attributes

that contribute to utility and finally, mji ,, is an unobserved, individual-level, iid

component of utility. The parameters of this model are ),,,,( CBA , where A, B and C

are vectors14.

Results of these regressions are interpreted as follows. A negative and significant

estimated effect for the SCD variable implies that consumers assign positive (option)

value to choice15. In addition, if the coefficient on SCD grows in absolute value (i.e.,

becomes more negative) over time, I will conclude that consumers place greater value on

choice over time. Choice value may grow over time if consumers perceive that there is

increased heterogeneity in provider utility (intuitively, more uncertainty leads to greater

option value). For example, consumers may learn about the extent of provider

heterogeneity over time as they gain experience utilizing restricted choice sets.

3.2 Variables and Methods

Estimation of equation (3) requires an observable measure of SCD (in addition to

YPC). To construct SCD, I employ a 3-step process that utilizes three data sets. In the

14 I also estimate a random coefficients version of this specification as discussed below. 15 The estimated effect of SCD for a given demographic is given by the summed estimates of and A.

19

first step, I make use of the Florida Discharge data to define market shares for each

hospital across a set of 6 diagnostic categories16. I use individual counties to define

markets. Thus, for example, I measure hospital g’s share of all pregnancy-related

admissions that emanate from county m in a specific year17. Intuitively, this step

provides me with a measure of hospital g’s importance to the residents of county m.

In the second step, I use the plan-hospital contracting files to form plan-specific

disutility measures at the county and diagnosis level. For a specific diagnosis and health

plan, I aggregate the county-specific market shares of hospitals with which the plan does

not have a contract. The “non-contracted” hospital market shares are inserted into

equation (3) to define the diagnosis-specific disutility for residents of a specific county

for a specific plan choice. Finally, in step three, I form a weighted average of the

disutility measures across the six diagnosis categories, where the weights correspond to

the relative frequency of each diagnosis. In this step, I allow the weights to vary by

gender and age categories. The result is a plan-specific SCD measure that also varies

with the observable demographics of age, gender as well as county of residence.

I also include a comprehensive set of plan-specific dummy variables to provide

estimates of mj , . I use two sets of dummy variables for these purposes. One set of

dummies varies by plan only. The second set varies by both plan and market area, to the

extent that the plans use different enrollment codes for each market. My panel data

16 The six clinical categories are cancer, mental health, circulatory, respiratory, pregnancy-related and other admissions. These categories are major divisions in the ICD -9-CM International Classification of Diseases, 9th revision. I select these divisions so that they are sufficiently homogeneous within clinical category to be considered as a single group by consumers. For example, a consumer is likely to rate a hospital’s pregnancy-related services as a single group. 17 In measuring these market shares, I exclude HMO-related admissions that may be influenced by selective contracting. Thus I estimate the relative systematic utility (market share) of a given hospital using a sample of non-HMO consumers and assume that this relative utility is the same for my sample of HMO enrollees. This is the assumption and approach used in Ho (2006).

20

specification also includes interactions of each plan-MSA dummy with year dummies, so

that the mj , are estimated separately, by year. These time-plan dummy interactions are

included to guard against biases in the estimated effect of changes in SCD over time,

which are potentially correlated with changes in mj , .

My plan/MSA-level fixed effects capture elements of plan quality that vary at the

MSA level. As a result, estimates of the effects of SCD are subject to bias only to the

extent that there is unmeasured, within-plan and MSA quality variation that is correlated

with within-plan and MSA variation in SCD. Existing literature suggests that managed

care plans differentiate themselves on the basis of national and local affiliation (Dranove

et al., 2003), their ability to control costs (moral hazard) (Pauly, 1988) as well as specific

aspects of their service provision such as customer service and claims processing. None

of these dimensions of quality is likely to vary within a plan’s MSA. For example, plan

operations, related to service provision, tend to be centralized and not vary within MSA.

Furthermore, a plan’s success at controlling moral hazard should largely be reflected in

its plan premiums, which do not vary within MSA in my data. For these reasons, a

comprehensive set of plan-MSA dummies should be an effective strategy for controlling

for unmeasured plan quality in this study.

Consistent with equation (3), I also interact the SCD, yearly plan cost and plan

dummies with a series of observable individual characteristics, including age, income,

gender, race and education (see Section 2.1 for means and definitions).

I estimate several discrete choice models to provide a range of estimates for the

disutility effect. Because the sample comprises the set of federal employees that elected

coverage, my reference choice is the set of employees that choose standard Blue Cross

21

coverage, the most popular choice. In each case the model is estimated using a maximum

likelihood algorithm, assuming an extreme value distribution for the error. Finally,

because I interact a comprehensive set of demographic variables with my main study

variables, I assume that my models do not contain unobserved individual heterogeneity

(i.e., I assume that random coefficients are unnecessary). I test (and validate) this

assumption by also estimating several mixed logit models which include a heterogeneity

parameter for both the SCD and YPC variables, in addition to the other coefficients18. I

report results of these tests below.

3.3 Results

3.3.1 Distribution and Trends in SCD

Table 4 provides distributional information on my key study variable, SCD, by year.

Two types of information are provided, at the diagnostic and overall SCD level. The first

six rows under each year heading provide summary data for the individual diagnostic

components of SCD. Here, the “N” in each row of the component measures refers to the

number of plan-county combinations that contributed to the distribution. I note that there

is significant statistical variation within the individual diagnostic measures as evidenced

by standard deviations that routinely exceed the means. Furthermore, the median values

of the individual disutility components fall over time, as the plans in my sample engage

in more complete contracting with hospitals. For example, by 2003, median values for

all components are below (.10), with the exception of mental health admissions.

18 Another concern with ordinary discrete choice models is that they may give rise to implausible substitution patterns if choices differ along horizontal dimensions not reflected in relative market shares. However, this problem does not apply to my data, because all of the plan choices may be characterized as managed care health plans.

22

1999

Variable N Mean Median Standard Deviation25th Perc 75th Perc

Cancer Admissions 72 0.462 0.2 0.695 0.064 0.532Mental Health Admissions 72 1.026 0.7 0.868 0.323 1.401Circulatory Admissions 72 0.451 0.202 0.691 0.067 0.568Respiratory Admissions 72 0.497 0.198 0.763 0.095 0.591Pregancy-Related Admissions 72 0.547 0.311 0.874 0.032 0.535Other Admissions 72 0.466 0.232 0.686 0.082 0.519SCD - Offered 30560 1.0733 0.376 1.566 0.193 0.955SCD - Selected 3882 0.427 0.271 0.452 0.144 0.418

2001


Cancer Admissions 48 0.436 0.185 0.806 0.026 0.532Mental Health Admissions 48 0.807 0.47 0.954 0.201 1.031Circulatory Admissions 48 0.477 0.229 0.881 0.044 0.466Respiratory Admissions 48 0.502 0.218 0.995 0.054 0.442Pregnancy-Related Admissions 48 0.501 0.164 1.064 0.005 0.37Other Admissions 48 0.461 0.201 0.877 0.033 0.43SCD - Offered 23400 0.5908 0.226 1.0691 0.0799 0.5207SCD - Selected 5198 0.2165 0.0799 0.3874 0.0227 0.3561

2003


Cancer Admissions 46 0.143 0.036 0.296 0.0002 0.181Mental Health Admissions 46 0.479 0.265 0.729 0.133 0.296Circulatory Admissions 46 0.171 0.014 0.427 0.001 0.149Respiratory Admissions 46 0.189 0.035 0.51 0.014 0.163Pregnancy-Related Admissions 46 0.167 0.0005 0.524 0 0.082Other Admissions 46 0.166 0.02 0.422 0.002 0.209SCD-Offered 27529 0.171 0.0679 0.398 0.0143 0.1502SCD - Selected 7329 0.1051 0.0409 0.1594 0.0105 0.13

Table 4 - Distribution of SCD Measures - By Year

This table provides the distribution of my key study variable by year, including the individual components of the selective contracting disutility measure by plan and county as well as individual-level measures of SCD.

23

The last two rows under each year provide summary information for the overall SCD

measure. These data are presented for both offered and selected plans. Thus, for

example, I find that the median value of SCD for an offered plan in 1999 was (.376)

while the median value of SCD for a selected plan was lower, at (.271). As in the case of

the individual diagnostic components, the average and median values of overall SCD fall

over time. For example, for offered plans, the median value of SCD declines from (.38)

to (.23) between 1999 and 2001 and then falls further to (.07) in 2003 as plan networks

become increasingly comprehensive19. The median SCD values of selected plans

declines significantly from 1999 (.27) to 2001 (.08) to 2003 (.04). These patterns of data

indicate that plans were offering greater choice and individuals were selecting plans with

greater choice, over time.

Finally, I note that the sample of plans providing the data in Table 4 changes over

time. Thus, it is not clear whether reductions in SCD for offered plans reflects changes in

individual plans’ contracting behavior or entry (exit) by plans offering more (less)

complete networks. To address this question, I also examine the distribution of SCD for

a consistent set of plans between 1999 and 2001 and between 2001 and 2003 (results

available from the author). I find, within the time consistent sample, that median offered

SCD changed little between 1999 and 2001 (.336 to .356), but declined significantly (to

(.03)) by 2003. This suggests that some plans changed their selective contracting

strategies after consumers started migrating towards plans with greater choice in 2001.

3.3.2 Pooled Discrete Choice Estimates

19 As a point of reference, SCD values of (.38), (.23) and (.07) correspond to market shares of hospitals omitted from the plan’s network of (.32), (.21) and (.07).

24

Table 5 provides pooled estimates of employees’ health plan selections in the years

1999, 2001 and 2003, using a sample of 16,410 individual plan choices. The table

provides (6) specifications. The first two specifications include a single (state-wide)

dummy variable for each plan as well as the Yearly Plan Cost (YPC) and selective

contracting disutility (SCD) measures. Model 1 does not interact these variables with

individual demographic measures while Model 2 does. Models 3 and 4 include a more

comprehensive set of plan effects that correspond (approximately) to the plan-MSA level

(for convenience I will simply refer to these as “plan-MSA” dummies20.) Because YPC

does not vary within this level of disaggregation, I am unable to estimate a price effect

within these more complete dummy variable specifications. Finally, specifications 5 and

6 also interact the plan-MSA dummies with the demographic measures. This enables me

to control for differences in omitted plan effects that may exist across demographic

groups. Within these final two specifications, the effect of plan disutility on choice must

be estimated from differences in the disutility measure that occur within an MSA for a

given demographic profile21. Finally, in all specifications the plan or plan-MSA

dummies are estimated separately, by year, so that plan effects are allowed to change

over time.

The most important findings in Table 5 are the negative and statistically significant

coefficients for selective contracting disutility, which imply elasticities of plan choice

20 I include a different plan effect (dummy) for each separately-listed plan code in the FEHBPs guide. For most plans, a separate code is provided for each MSA in which the plan is marketed. However, for some plans, a single code is used across multiple MSAs. A plan’s premium does not vary within its listed plan code but does vary across separate plan codes. 21 In pooling the data in this manner, I assume that the model is stable across years (coefficients, other than plan effects, do not change) and that the purely random component of individuals’ utility is not correlated across years. This assumption is relaxed below where I estimate time interactions with key measures.

25

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Variable

Yearly Plan Cost 0.00077 -0.00388(3.90) *** -(13.13) ***

* Age 6.32E-05 6.22E-05(10.93) *** (10.80) ***

* Income 4.29E-08 4.27E-08(13.14) *** (13.14) ***

* Male -0.00072 -0.00075-(5.67) *** -(6.01) ***

* White 0.000473 0.00052(3.54) *** (3.90) ***

* Education 0.000553 0.000545(5.87) *** (5.84) ***

SC Disutility -0.4955 -0.3078 -1.0769 -0.67 -1.1139 -1.2794-(11.84) *** -(2.01) ** -(21.40) *** -(4.35) *** -(20.49) *** -(5.86) ***

* Age 0.006057 0.000402 -0.0052(1.71) * (0.11) -(0.97)

* Income -8.60E-06 -5.45E-06 2.30E-06-(3.57) *** -(2.29) ** (0.90)

* Male 0.1639 0.1347 0.0126(2.23) ** (1.91) * (0.11)

* White -0.1615 -0.1441 0.1579-(2.08) ** -(1.91) * (1.20)

* Education 0.0503 0.0206 0.0388(0.81) (0.34) (0.50)

Plan Dummies Yes YesPlan Dummies Interacted No NoPlan-MSA Dummies Yes Yes Yes YesPlan-MSA Dummies Interacted No No Yes Yes

Observations 16410 16387 16410 16387 16387 16387Cases 77123 77017 79791 77017 79681 79681Log Likelihood -13343 12954 3203 -12815 -12353 -12350Likelihood Ratio 21827 22540 23045 22816 24678 24683Likelihood Ratio Index 0.4499 0.4652 0.466 0.471 0.4997 0.4998Price Elasticity 0.0767 0.2611 NA NA NA NADisutility Elasticity -0.1308 -0.0969 -0.2843 -0.2400 -0.2941 -0.3156

Table 5: Discrete Choice Estimates for Pooled Data

Coefficient(t Statistic)

This table presents discrete choice regression results of a pooled sample of individual federal employees' choices of their health plans in years 1999, 2001 and 2003. Plan cost is inclusive of both employee premium contributions as well as expected out of pocket expenses. Disutility refers to selective contracting disutility as defined in Section 3.1. Demographic variables are self-explanatory with the exception of "education" which refers to a 4-part categorization of education corresponding to "less than high school", "high school graduate", "college graduate" and "graduate degree". Price and disutility elasticities refer to the elasticity of the probability of plan choice with respect to the variable in question. *** indicates significant at the (.01) confidence level, ** indicates significant at the (.05) confidence level and * indicates significant at the (.10) confidence level.

26

with respect to SCD that range from (-.10) to (-.32) (see the bottom of Table 5)22. These

effects persist across all (6) model specifications and are robust to the source of

data variation that is used to identify them. The effect is also largest in the specification

with the most complete set of plan-MSA controls. In addition, there are significant

demographic-disutility interaction coefficients in Models 2 and 4 (the SCD interactions

are imprecisely estimated in Model 6 due to insufficient data variation within MSA and

demographic). The estimates in Models 2 and 4 are consistent across models and take

on plausible signs, in cases where they are significant. For example, the income-disutility

interaction is negative, implying that individuals with higher income place greater value

on choice (i.e., choice is a normal good). In addition, the male-disutility interaction is

positive (males place less value on choice perhaps because they have less frequent

interaction with the health care system).

I also find positive price elasticities in Models 1 and 2, indicating that single, state-

wide plan dummies are insufficient to control for all omitted plan characteristics that may

be correlated with plan costs. I address the issue of obtaining an unbiased estimate of the

price effect below. Finally, the interactions between YPC and demographic measures, in

Models 2 and 4, are each highly significant with the expected signs. For example,

individuals who are older and have greater income have a lower (absolute) price elasticity

of demand for health insurance services23.

22 That is, I employ the formula xjijij

j Pxx

P)1(

log

log,

,

where P is the probability (at the mean) that

plan j is selected, ijx , is the value of an independent variable (e.g., SCD) and x is the estimated

coefficient on x. Here, x is evaluated at a value of (.264) and P is set equal to (.10) (i.e., a plan with a 10% market share). 23Finally, I also estimate several mixed logit specifications that allow the disutility and cost parameters to vary randomly (These results are available from the author). I am interested in whether my models with

27

3.3.3 Pooled Results with Time Trends

In order to test the stability of the disutility effect over time, I also estimate

regressions in which both the cost and SC disutility measures are interacted with time

dummies for the years 2001 and 2003. The estimated time trends for SCD are presented

in summary fashion in Table 6, where I present the fitted effects and elasticities of choice

with respect to SCD by year. The first three columns of Table 6 show the year-specific

fitted effects of SCD for all six models from Table 524. T statistics and statistical

significance levels (obtained using the variance/covariance matrix of estimates) are

displayed as well. The final two columns show the differences of the 2001 and 2003

SCD effects compared to the 1999 effect. Again, tests of statistical significance are

provided.

Turning to the results, I find that the estimated effect of SCD in 2003 increased

relative to 1999. The difference in the SCD effect is statistically significant in each of the

six models (see column 5). The increase in the absolute value of the elasticity of choice

with respect to SCD ranges from (.10) to (.19) with four of the estimated elasticity

changes clustering between (.10) and (.11). Thus, there is robust evidence that

consumers placed a greater value on choice in 2003 compared to 1999.

Between 1999 and 2001, three of the models indicate a positive change and three of

the models indicate a negative trend in the SCD effect across these two years (see

demographic controls adequately control for heterogeneity in the effects of disutility on plan choice. Low and insignificant values for the estimated heterogeneity parameter support this hypothesis. According to my results, there is no evidence of heterogeneity for the years 1999 and 2003 independent of whether the models include demographic controls. On the other hand, in 2001 there are significant estimates for the heterogeneity parameter that are sharply reduced in models that control for demographics. It appears, therefore, that unmeasured heterogeneity is not a significant problem in my data, conditional on the use of demographic controls. 24 The fitted effects of SCD include the interacted effects of SCD with demographic variables used in Models 2, 4 and 6.

28

Difference: Difference:1999 2001 2003 2001-1999 2003-1999

Model 1 -0.585 -0.187 -1.227 0.398 -0.642(10.95) *** (2.92) *** (5.95) *** (2.28) ** (2.92) ***-0.155 -0.098 -0.324 0.056 -0.170

Model 2 -0.816 -0.297 -1.232 0.519 -0.416(6.14) *** (2.41) ** (4.95) *** (5.14) *** (1.76) *-0.215 -0.078 -0.325 0.137 -0.110

Model 3 -0.892 -1.196 -1.614 -0.304 -0.720(11.33) *** (14.72) *** (7.15) *** (2.66) *** (2.69) ***-0.235 -0.316 -0.425 -0.081 -0.190

Model 4 -0.915 -0.584 -1.310 0.321 -0.398(5.91) *** (3.14) *** (4.34) *** (1.92) * (1.68) *-0.240 -0.150 -0.340 0.090 -0.100

Model 5 -0.982 -1.225 -1.390 -0.243 -0.408(11.36) *** (14.14) *** (16.05) *** (2.01) ** (1.67) *-0.259 -0.323 -0.367 -0.064 -0.108

Model 6 -0.996 -1.283 -1.411 -0.287 -0.415(5.67) *** (7.55) *** (4.97) *** (2.22) ** (1.66) *-0.263 -0.339 -0.370 -0.076 -0.107

Table 6: Time Trends of Selective Contracting Disutility Effect

Changes

Table 6: Time Trends in SCD Effects: Regression Results With Time Interactions

SCD Effect at the Mean(t statistic)

SCD Elasticity at the Mean

This table presents partial results of discrete choice regressions in which both the SCD and YPC (yearly plan cost) measures are interacted with time dummies for 2001 and 2003. The table presents summary results for the aggregated SCD effect evaluated at the mean values of demographic characteristics. t statistics and significance levels of this effect are reported as well. Changes in the aggregate SCD effect, evaluated between 1999-2001 and 1999-2003, are reported in the final two columns as well as t statistics and signifcance levels of the difference. The regression models (listed in the left column) and their specifications correspond to Models 1-6 described in Table 5 and have identical specifications plus the addition of time interactions with the SCD and YPC measures. *** indicates significant at the (.01) confidence level, ** indicates significant at the (.05) confidence level and * indicates significant at the (.10) confidence level.

29

column 4). Assuming that Model 5 and 6 estimates are most reliable (because they

contain the most comprehensive set of plan-MSA effects), the change in the SCD effect

between 1999 and 2001 is also negative and significant25.

4. The Dollar-Equivalent Value of Hospital Choice

The results in Section 3 provide a range of disutility estimates, each of which support

the hypothesis that choice matters in the selection of a health plan. However, the results

do not provide a dollar-equivalent value of hospital choice. Assigning a dollar-

equivalent value to choice is important, because the strategic use of selective contracting

is tied to the dollar loss in utility relative to the contractual savings that can be achieved

from negotiated volume discounts.

Dollar–equivalent utility computations require an unbiased price coefficient estimate.

The inverse of the price coefficient must be multiplied by the estimated SCD parameter

to give the required dollar-equivalent value of choice. The difficulty in identifying the

price effect is as follows. In Models 1 and 2, the plan effects fail to adequately control

for unmeasured dimensions of plan quality that may vary across markets. Evidence of

the resulting endogeneity is found in the implausibly positive price coefficient estimates.

On the other hand, Models 3-6, which include plan-MSA effects, do not permit

identification of the price effect because the YPC measure exhibits no variation within

plan and MSA26.

25 I also obtain regression estimates for Models 1-4 for a limited sample of individuals (1,758) who remain in my sample for all three years (thus tracking the behavior of a consistent set of individuals). In each of the models I find that the SCD elasticity increases in absolute value (more negative) between 1999 and 2003. Moreover, the changes are substantially larger than for the full sample, increasing by a factor of two to five times. The differences between 1999 and 2003 SCD elasticities for the time-consistent sample are as follows: Model 1: (-.32), Model 2: (-.32), Model 3: (-.48) and Model 4: (-.29). 26 There is variation in prices over time. However, the plan-MSA effects vary by year as noted above.

30

One way to address this identification problem is to regard the plan effects estimated

in Models 3-6 as “data” and to regress them on a properly instrumented measure of YPC.

Recovering underlying model structure in this fashion is discussed, for example, in Nevo

(2001). Thus, my approach to estimating the effects of price on plan choice amounts to a

2-step process: 1) collect a set of instruments for plan cost and regress YPC on the

instruments, yielding a predicted value of plan cost that is correlated with plan cost but

uncorrelated with plan quality; and 2) regress the estimated plan effects (from Section 3)

on the predicted values of plan cost plus other controls.

For these purposes I use characteristics of the other markets in which the plan

competes to form instruments of the plan’s YPC in a given market. My identification

strategy is based, first, on the assumption that each of a plan’s individual market settings

influences the plan fees that it charges in all of the markets in which it operates. For

example, I assume that if a plan operates in both Miami and Orlando, then market factors

that increase the costs of all plans operating in Miami will affect the fees that the plan

charges in Orlando as well. Because plans operate in different markets, this assumption

allows me to develop instruments that differ across plans in a given market. The

assumption, moreover, can be justified by noting that within the FEHBPs program,

several plans choose to offer a single premium across their various markets, leading to a

mechanical linkage of a plan’s individual market fees with its overall market

environment. Identification of firm conduct through attributes of the firm’s other

markets is a commonly-used identification strategy. The resulting instruments are

orthogonal to the plan’s unmeasured quality in a given market (as captured by plan-MSA

fixed effects) provided that a plan’s market-specific quality is uncorrelated with the

31

attributes of other markets in which the plan does business. For example, market j

customer service should be uncorrelated with the level of HMO penetration in other

markets, k, in which the plan operates.

4.1 Instrumental Variables Regressions of YPC

The results of the regressions of YPC on market-level instruments are provided in

Table 7, where I show the results of stepping in the instruments, one at a time. Most of

the instruments in Table 7 have a statistically significant effect on yearly plan costs, with

plausible signs. Market HMO penetration leads to lower costs, as would be expected if

HMO penetration results in a more “cost-efficient” style of medical practice in a market.

Similarly, more competitive markets also result in lower plan costs, which is consistent

with competition reducing margins. Conditional on competition, a larger number of

HMO competitors increases plan costs. Finally, more physicians per capita increase costs

while more physician specialists per capita reduce plan costs. More physicians, overall,

may increase access to the health care system, thus raising costs. However, conditional on

access to the system, more specialists may help to lower the costs of specialist services.

Finally, individual year effects for 1999 and 2001 are negative relative to 2003, reflecting

inflation in overall health care costs. The adjusted R squared for the most complete

model is (.64).

4.2 Estimated Price Elasticities

Table 8 depicts regressions of the plan effects from Models 4 and 6 on the predicted

values of YPC, as well as other controls. The top half of Table 8 provides the regressions

of Model 4 effects while the bottom half provides the regressions of Model 6 effects27.

27 For these purposes, I use only the main plan effects and disregard the plan effects that are interacted with demographic measures.

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Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Variable

Intercept 2214.98 2941.45 3961.71 3565.86 3665.131 3428.046(7.32) *** (8.22) *** (7.83) *** (6.53) *** (6.75) *** (6.39) ***

Market HMO Penetration -4031.3 -4201.9 -7894.3 -8236.6 -12074 -13276-(4.01) *** -(4.72) *** -(4.88) *** -(5.20) *** -(3.70) *** -(4.20) ***

County Index of Competition -864.28 -2525 -2539.8 -2147.106 -1552.164-(3.06) *** -(3.71) *** -(3.84) *** -(3.01) *** -(1.82) *

Market Number of HMOs 114.063 95.2273 238.8516 282.1657(2.64) *** (2.19) ** (2.07) ** (2.49) **

Market MDs Per Capita 278298 1335953 1875192(1.66) * (1.65) (2.23) **

Market Specialists Per Capita -4299325 -6179669-(1.34) -(1.84) *

Year 1999 -156.4351-(1.30)

Year 2001 -266.7109-(2.13) **

Observations 32 32 32 32 32 32Adjusted R Sq 0.3278 0.4744 0.5638 0.5893 0.601 0.6399

F Statistic 16.12 *** 14.99 *** 14.35 *** 12.12 *** 10.34 *** 8.87 ***

Table 7: Regressions of Yearly Plan Costs on Market Aggregates


This table presents regressions of plan costs (premium plus expected out of pocket costs) on a set of market aggregates that are specific to each plan. Market values are formed as weighted averages of values in the markets in which the plan operates. The weights are defined as the plan's non-FEHBP's HMO enrollments in the market. IOC is defined as (1-Hirschman-Herfindahl Index) for the market, so that higher values indicate more competitive markets. *** indicates significant at the (.01) confidence level, ** indicates significant at the (.05) confidence level and * indicates significant at the (.10) confidence level.

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Model 1 Model 2 Model 3 Model 4

Variable

Intercept 2.01083 0.2593 1.5844 4.4162(1.37) (0.15) (0.80) (2.00) **

Predicted Cost -0.0032 -0.0034 -0.004 -0.005-(2.25) ** -(2.48) ** -(2.88) *** -(3.55) ***

Plan Age 0.1068 0.1239 0.0994(1.71) * (1.97) ** (1.67) *

Plan is an IPA -1.153 -2.3533-(1.40) -(2.54) **

Plan is national plan -1.8791-(2.29) **

Observations 32 32 32 32Adjusted R Sq 0.17 0.195 0.302


Variable

Intercept 3.14459 -0.1387 2.2776 3.708(1.52) -(0.06) (0.88) (1.18)

Predicted Cost -0.0038 -0.0042 -0.006 -0.0062-(1.91) * -(2.28) ** -(2.97) *** -(3.06) ***

Plan Age 0.2003 0.2314 0.219(2.36) ** (2.80) *** (2.59) ***

Plan is an IPA -2.102 -2.7081-(1.95) * -(2.06) **

Plan is national plan -0.9492-(0.82)

Observations 32 32 32 32Adjusted R Sq 0.078 0.2003 0.2705 0.2617

Table 8: Regressions of Estimated Plan Effects on Predicted Values of YPC and Other Controls


Model 4 Plan Effects

Model 6 Plan Effects (Interactions Not Included)


This table presents regressions of estimated plan fixed effects on the predicted values of yearly plan costs (YPC) plus other controls. *** indicates significant at the (.01) confidence level, ** indicates significant at the (.05) confidence level and * indicates significant at the (.10) confidence level.

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Turning to the results, the fitted effect of predicted price is negative and significant in all

of the estimated models, with values ranging from -.003 (Model 1, in the top half of

Table 8) to -.006 (Model 4 in the bottom half of the table). I use the midpoint of these

values in the simulations reported below. The midpoint of these values corresponds to an

elasticity of choice with respect to price (at the mean) of about (-.45), from an employee

perspective. This elasticity value assumes that the plan enjoys a 10% market share and

the annual consumer costs are $1000. This elasticity estimate compares favorably with

several other plan choice-premium elasticities reported in the literature providing

validation of the IV approach employed here28.

4.3 The Value of Choice

Table 9 shows the results of a simple simulation of the dollar value (to the consumer)

of a one standard deviation change in the SCD of a health plan. The result is simulated

for a variety of demographic groups. For these purposes, I use the interactions

of demographic factors with SCD estimated in Model 4 of Table 5. Within my sample, a

one standard deviation change in SCD corresponds to a value of (.46). This difference

arises, for example, if a plan transitions from excluding hospitals with about 15% of

market share from its network to excluding hospitals with about 45% of market share.

Table 9 shows how this value varies across age, gender and income. Each of these

demographic categories has a significant interaction with SCD in the pooled results.

28 See, for example, Ho, 2006, Buchmueller, 2005, Strombom et al., 2002, Royalty and Solomon, 1998 and Cutler and Reber, 1996. In many prior papers the elasticity value is reported from the insurer perspective, using the fees received by the insurer. This results in reported values that exceed 1 in absolute value. Were I to report the price elasticity from an insurer perspective, the value would be several times higher and also lead to a reported value that exceeds 1. However, the correct approach for estimating employee utility is to use the elasticity from the employee perspective.

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Income Income White/ White/ Non-White/ Non-White/Percentile Male Female Male Female

$24,117 10th -$76.69 -$90.46 -$61.96 -$75.73

$29,420 25th -$79.64 -$93.41 -$64.91 -$78.68

$40,757 50th -$85.96 -$99.72 -$71.23 -$84.99

$57,876 75th -$95.49 -$109.25 -$80.76 -$94.52

$74,466 90th -$104.72 -$118.49 -$89.99 -$103.76

Table 9: Valuations of Selective Contracting Disutility Across Demographic Profiles

This table provides the dollar-denominated value of a one standard deviation increase in the selective contracting disutility measure. A one standard deviation increase, equal to .46 in my data, would arise if a plan went from excluding hospitals with a 15% market share to a more restrictive network in which hospitals with 45% of market share were excluded. The values are indicated for a set of 20 different demographic profiles that vary by income, gender and race.

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The dollar magnitudes in Table 9 range from low value of about $62 (low income, non-

white males) to a high value of about $118 (high income white females)29. These figures

are important in light of the fact that the YPC of most plans lay within $100-$200 of one

another.

5. SCD Versus Offered Premiums and Achievable Volume

Discounts

In this section I investigate the tradeoff between SCD and the financial savings that

may be achieved from this practice.

5.1 SCD and the Overall Cost of Plan Membership

I start by investigating the tradeoff between a plan’s SCD and its premiums. I

compute the sum of each plan’s YPC and the dollar value of its median-offered SCD30.

This sum represents the “all in” cost of plan membership. I regress this “all-in” cost on

the plan’s SCD and other controls. If there is a favorable tradeoff between SCD and

overall plan costs, the coefficient on SCD should be negative and vice versa. The

inclusion of other controls helps to insure that the SCD effect does not include other

drivers of plan costs that may correlate with the plan’s selective contracting behavior.

Results of 4 different regression models are provided in Table 10. I sequentially enter

sets of controls that include, first, market measures and then plan dummies. In Model 4, I

include an interaction of median SCD with a combined indicator for the years 2001 and

29In assessing this magnitude, it is impossible to make reliable comparisons with prior work in this area. Ho (2006) conducts an experiment in which she estimates the welfare gain of eliminating SC entirely across several markets, obtaining a median increase in consumer welfare (ceteris paribus) of $17 (and a mean increase of $60). However, it is impossible to make a comparison of the results obtained here without further information about the distribution of selective contracting in the two samples. 30 I form separate observations for each plan code.

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Variable

Intercept 898.62 1926.31 915.04 945.09

(21.28) *** (3.73) *** (7.80) *** (8.37) ***

Median SCD 142.53 99.48 163.76 88.44

(4.12) *** (2.89) *** (5.50) *** (1.73) *

Median SCD * Year = 2001 or 2003 105.43

(1.77) *

Market HMO Penetration -5157.64

(1.91) *

County Index of Competition -1144.22

(1.82) *

Market Number of HMOs 159.67

(1.10)

Market MDs Per Capita 638864

(1.38)

Market Specialists Per Capita -2404579

(0.92)

Year 2001 -27.51 -69.00 -31.22 -96.87

(0.46) (1.27) -(0.56) (1.49)

Year 2003 354.5013 241.495 349.2257 290.4925

(4.37) *** (3.50) *** (4.57) *** (3.64) ***

Plan Dummies No No Yes Yes

Observations 35 31 35 34

Adjusted R Sq 0.4 0.625 0.695 0.723

F Statistic 10.06 *** 7.47 *** 6.95 *** 7.34 ***

Table 10: Regressions of Full Plan Costs on SCD and Other Controls


This table presents regressions of total plan costs, inclusive of the dollar value of selective contracting disutility, on median plan values of SCD and other controls. The regressions test the tradeoff between imposing SCD on enrollees versus any associated plan discounts. Market values are formed as weighted averages of values in the markets in which the plan operates. The weights are defined as the plan's non-FEHBP's HMO enrollments in the market. IOC is defined as (1-Hirschman-Herfindahl Index) for the market, so that higher values indicate more competitive markets. *** indicates significant at the (.01) confidence level, ** indicates significant at the (.05) confidence level and * indicates significant at the (.10) confidence level.

38

2003 in order to test whether the relationship between SCD and overall plan costs

changes (e.g., deteriorates) over time.

Turning to the results, each of the model estimates shows that higher SCD is

associated with higher “all in” costs. In model 4, the effect of SCD on “all-in” costs is

also greater during 2001-2003. The unfavorable tradeoff between SCD and overall plan

cost is consistent with the flight away from plans engaging in selective contracting during

the study period. These results do not show whether plans were able to negotiate lower

hospital rates as a result of selective contracting. They may have negotiated lower rates

and retained the savings as profit instead of reducing premiums. I turn to the issue of

feasible hospital discounts presently.

5.2 Simulations of Available Discounts Compared to the Cost of SCD

Another perspective on selective contracting may be obtained by comparing dollar

values of SCD with simulated volume discounts that may be achieved from hospitals

included in the insurer’s network. Ultimately, the feasibility of selective contracting

depends upon whether the (negative) value of SCD imposed on enrollees can be matched

or exceeded by volume discounts provided by hospitals. The previous section shows that

this balance was not achieved in the premiums offered to potential enrollees. In this

section I simulate feasible discounts, given my estimated parameters for SCD and given

strong assumptions about the extent of possible volume discounts. The simulations are a

simple exercise. In the top half of Table 11 I divide the average value of SCD per

enrollee by aggregate hospital expense per enrollee. This provides a measure of required

hospital discounts needed to balance SCD disutility. I also show how these required

39

Table 12a - Required Margin Discount

Market Share Foregone10% 20% 30% 40%

Estimated SCD Parameter

Table 11a - Required Hospital Rate Discounts In Order to Balance Value of SCD

SCD Parameter 10% 20% 30% 40% 50%

1999 - Hi 5.0% 10.6% 16.9% 24.3% 32.9%1999 - Low 3.0% 6.3% 10.1% 14.5% 19.6%

2001 - Hi 5.5% 11.6% 18.5% 26.6% 36.0%2001 - Low 1.2% 2.6% 4.2% 6.0% 8.1%

2003 - Hi 6.8% 14.5% 23.2% 33.2% 45.0%2003 - Low 5.2% 11.0% 17.6% 25.2% 34.2%

Key AssumptionsHospital Cost/Enrollee - 2003 $552Hospital Cost/Enrollee - 2001 $511Hospital Cost/Enrollee - 1999 $459$/Util $222

Table 11b - Maximum Discounts Available From Selective Contracting

10% 20% 30% 40% 50%Full Contracting Margin

5% 0.5% 1.0% 1.5% 2.0% 2.5%

10% 1.0% 2.0% 3.0% 4.0% 5.0%

15% 1.5% 3.0% 4.5% 6.0% 7.5%

20% 2.0% 4.0% 6.0% 8.0% 10.0%

25% 2.5% 5.0% 7.5% 10.0% 12.5%

30% 3.0% 6.0% 9.0% 12.0% 15.0%

Note: Algorithm for max discount is : (Initial margin * % market share selectively contracted)

Table 11: Simulations of the Feasibility of Selective Contracting

Market Share Selectively Contracted

Market Share Selectively Contracted

This table presents simulations of: 1) percentage discounts in hospital rates needed to balance the aggregate disutility imposed on each plan enrollee from selective contracting (Table 12a); and 2) the maximum discounts that health plans would receive from hospitals under the assumption that they could negotiate volume discounts in which hospitals receiving incremental admissions price the incremental admissions "at cost". Table 12a presents the required discounts under a range of assumptions about the amount of selective contracting and the dollar equivalent loss of selective contracting to enrollees. Table 12b presents the maximum percentage reductions in hospital costs to plans under a range of assumptions about the amount of selective contracting and the margins paid over cost prior to selective contracting (i.e., under full contracting).

40

discounts vary by the amount of SCD and year31. Table 11 shows that the required

hospital discount increases with the amount of selective contracting. In addition, the

necessary discount increases over time, consistent with estimated increases in the

disutility of SCD (see results in Table 6). For each year, I show a range of results

corresponding to high and low estimated SCD parameters, as previously reported in

Table 6. For example, in 1999 I estimate that a health plan excluding 10% of the local

hospital market would require hospital rate discounts of between 3% - 5% in order to

achieve savings sufficient to compensate its enrollees for SCD.

In the bottom half of Table 11, I estimate the maximum discounts achievable from

selective contracting. I define maximum feasible discounts in the following way.

Suppose that a health plan engaging in selective contracting moves a given percentage of

its hospital admissions from a set of excluded hospitals to another set of hospitals

included in its network. The included hospitals provide volume discounts in return for

the incremental business. I assume that the largest achievable volume discount forces

hospitals receiving incremental volume to serve the incremental volume “at cost.” (i.e.,

zero profit margin). In achieving this discount, the health plan saves the margin over cost

that the insurer would otherwise pay hospitals if it engaged in full contracting.

Table 11 shows a range of resulting savings that depend upon the amount of selective

contracting and the level of the margin paid over cost under full contracting (the full

contracting margin determines how much can be saved by selective contracting).

31 To obtain average hospital expenses per enrollee, by year, I use the following assumptions: a) I assume that hospital days per thousand enrollees in Florida was 266/1000 during 2002 (HLI Florida Data for 2002); b) I assume that hospital costs per day were $1,285 in 2002 (Florida Hospital Association estimate for 2002); and c) I estimate that hospital outpatient costs were 38% of hospital inpatient costs in 2002 (American Hospital Association chart for 2002). I use the outpatient costs to scale up my inpatient cost estimates to get overall hospital costs per enrollee in 2002. I then inflate or deflate the 2002 hospital costs per enrollee by national trends in hospital expenses as reported by the Centers for Medicare and Medicaid Services (CMS, Office of Actuary, 2009).

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Maximum discounts increase with the level of selective contracting, as expected.

Assuming selective contracting of 30% of the hospital market and an initial margin over

cost of 20%, insurers could save at most 6% on their overall hospital expenses32.

Finally, a comparison of Tables 11a and 11b shows that in 1999 and 2001, maximum

achievable discounts from engaging in selective contracting would have been barely

sufficient to balance the costs of SCD to enrollees, provided that one uses the low-end

estimates of SCD and assumes full contracting hospital margins of around 30%. Under

high-end estimates of SCD, maximum feasible discounts would have been insufficient to

compensate for SCD during these years. Moreover, by 2003, selective contracting no

longer yields sufficient savings to compensate for SCD. These results provide yet

another way to view the demise of selective contracting.

6. Conclusion

The previous decade witnessed both a decline in HMO enrollments and many of the

practices that HMOs used to contain costs, including selective contracting. The

conventional wisdom used to explain these trends has focused, in part, on consumers’

negative reactions to constraints on their choice of health care provider. To date,

however, there has been little formal analysis of why consumers value choice and how

much they are willing to pay to preserve it.

My results focus on a measurable dimension of choice, the hospital network

available to the enrollee. I find that restrictions in the choice set caused sufficient

disutility to sway individuals away from choosing restrictive health plans, given offered

premiums. The magnitudes of disutility made it difficult (if not impossible) for plans to 32 Private insurers typically pay hospitals higher margins than do public insurers. For example, in 2002, the American Hospital Association (AHA) reports that average private insurer margins over cost were 19% (American Hospital Association table for 2002).

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achieve the level of volume discounts needed to compensate enrollees for the associated

disutility.

My methodology takes steps to ensure that the measured effects of selective

contracting are not biased by exclusions of other plan characteristics correlated with a

plan’s level of selective contracting. I include year-specific, plan-MSA dummies for

these purposes. The dummies control for all factors that do not vary within the MSA in a

given year, including plan reputation and operational features of service (e.g., related to

information technology, efficiency in claims processing, etc.) that are centralized by the

health plan.

It should also be noted that restrictions on hospital choice may correlate with

restrictions on physician choice as well. For example, it may be that hospital exclusions

prevent individuals who need a medical procedure from using physicians who do not

practice within the restrictive hospital network. To the extent that this is true, my results

measure the effects of general restrictions on provider choice, including the choice of

both physician and hospital.

As health care costs continue to grow, some of the tactics of managed care are again

under consideration. For example, some proposals focus on using sub-groups of

providers to manage the health of well-defined populations for a single, capitated fee.

This, in turn, implies restrictions on choices of providers. My results suggest that it may

be difficult to successfully market these approaches.

43

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