Embed Size (px)
Citation preview
NBER WORKING PAPER SERIES
REGULATORY REDISTRIBUTION IN THE MARKET FOR HEALTH INSURANCE
Jeffrey Clemens
Working Paper 19904http://www.nber.org/papers/w19904
NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue
Cambridge, MA 02138February 2014
I am grateful to the editor and four anonymous referees, from whose suggestions the paper has improvedmarkedly. Further thanks go to Kate Baicker, Prashant Bharadwaj, Raj Chetty, Julie Cullen, MartinFeldstein, Alex Gelber, Ed Glaeser, Roger Gordon, Joshua Gottlieb, Bruce Meyer, Emily Oster, KellyShue, Monica Singhal, Stan Veuger, and participants in seminars at CBO, Harvard, Purdue, the StanfordInstitute for Economic Policy Research, UC San Diego, UCLA’s School of Public Affairs, the Universityof Maryland, and the US Naval Postgraduate School for comments and discussions which have movedthis project forward. Final thanks go to David Cutler and Larry Katz for their comments, advice, andguidance throughout the execution of this project. I acknowledge financial support from the Institutefor Humane Studies, the Rumsfeld Foundation, and the Taubman Center for the Study of State andLocal Governments during the writing of this paper. The views expressed herein are those of the authorand do not necessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies officialNBER publications.
© 2014 by Jeffrey Clemens. All rights reserved. Short sections of text, not to exceed two paragraphs,may be quoted without explicit permission provided that full credit, including © notice, is given tothe source.
Regulatory Redistribution in the Market for Health InsuranceJeffrey ClemensNBER Working Paper No. 19904February 2014, Revised July 2014JEL No. H51,H53,I13,I18
ABSTRACT
Community rating regulations equalize the insurance premiums faced by the healthy and the unhealthy.Intended reductions in the unhealthy’s premiums can be undone, however, if the healthy forgo coverage.The severity of this adverse selection problem hinges largely on how health care costs are distributedacross market participants. Theoretically, I show that Medicaid expansions can combat adverse selectionby removing high cost individuals from the relevant risk pool. Empirically, I find that private coveragerates improved significantly in community rated markets when states expanded Medicaid’s coverageof relatively unhealthy adults. The effects of Medicaid expansions and community rating regulationsare fundamentally linked.
Jeffrey ClemensDepartment of EconomicsUniversity of California, San Diego9500 Gilman Drive #0508La Jolla, CA 92093and [email protected]
Many health policy instruments serve social insurance objectives. Promi-nent examples include state Medicaid programs, which provide benefi-ciaries with public insurance, and community rating regulations, whichequalize the private insurance premiums faced by the healthy and the un-healthy. This paper shows that the effects of these policy measures, both ofwhich play important roles in the Patient Protection and Affordable CareAct (PPACA), are fundamentally linked.
Community rating regulations prevent insurance companies from ad-justing premiums on the basis of pre-existing conditions.1 Their intent isto generate “within-market” transfers from the healthy to the sick. Suchefforts may be undone by adverse selection, however, since the healthy canescape these transfers by reducing or dropping their coverage (Rothschildand Stiglitz, 1976; Buchmueller and DiNardo, 2002).
While the adverse selection problem is well known, the determinantsof its severity are under explored. The severity of adverse selection hingeslargely on the distribution of health care costs across potential insurancepurchasers. This paper’s central insight is that the relevant cost distribu-tion depends, in turn, on the structure and size of states’ Medicaid pro-grams. By altering the distribution of health costs across private insurancepurchasers, state Medicaid programs may significantly influence the per-formance of community rated markets.
A novel implication of the interplay between state Medicaid programsand community rating regulations is that Medicaid expansions may crowdin private coverage. This contrasts with Medicaid’s usual tendency to par-tially crowd private coverage out (Cutler and Gruber, 1996). Crowd-in canoccur when Medicaid expansions disproportionately cover the unhealthy.2
If the healthy prove more likely to take up Medicaid than the sick, however,
1These regulations are typically accompanied by guaranteed issue requirements,which prevent insurers from denying coverage on similar bases. For the sake of brevity,I use the term “community rating” to reference regulatory regimes incorporating bothcommunity rating and guaranteed issues rules.
2Medicaid can be targeted at the unhealthy through eligibility via disability or throughincome thresholds that explicitly take household medical spending into account. Thelatter approach is known as a medically-needy income concept.
2
Medicaid expansions can worsen the relevant risk pool and exacerbate ad-verse selection pressures. The effect of Medicaid expansions on communityrated markets thus depends on their design and implementation.
I use the experience of the U.S. states to investigate the empirical rel-evance of the forces described above. Many states imposed some form ofpremium regulations on their insurance markets during the early 1990s. Aset of New England and Mid-Atlantic states implemented relatively strictcommunity rating regimes. Later years ushered in substantial Medicaidexpansions, driven in part by the 1997 authorization of the State Children’sHealth Insurance Program (SCHIP). Beyond SCHIP, several states withcommunity rating regimes obtained waivers that unlocked federal fund-ing for coverage of low-income and medically-needy adults. Medicaid’scoverage of the disabled also increased significantly during this period.
I find that community rated markets initially experienced fairly severeadverse selection pressures. Within 3 years of adopting community rating,I find that coverage rates had fallen by around 8 percentage points in thecommunity rated markets relative to control markets. The analysis revealsthat coverage declines escalate over these initial years, with medium-rundeclines much larger than short-run declines. This should not be surpris-ing, as even the unraveling of a single insurance plan can take multipleyears to unfold (Cutler and Reber, 1998).
I next analyze the Medicaid expansions of the late 1990s and early2000s. In states with community rating regulations, Medicaid expansionscovered more adults, and in particular more unhealthy adults, than in mostother states. I find that Medicaid expansions were associated with privatecoverage increases in the community rated markets, but not elsewhere.Consistent with the proposed mechanism, community rated markets ex-perienced their strongest recoveries in states where Medicaid expansionsmost disproportionately covered unhealthy adults.
These findings contribute to both the theoretical and empirical litera-tures on equilibrium in health insurance markets. Recent work demon-strates the possibility of multiple equilibria in community rated markets(Scheuer and Smetters, 2014), and studies the severity of adverse selection
3
under fixed health cost distributions (Handel, Hendel and Whinston, 2013;Hackmann, Kolstad and Kowalski, 2012). This paper shows that equilib-rium depends crucially on additional features of the policy environment.The size and structure of public insurance programs are particularly im-portant, as they shape the risk pool from which the privately insured aredrawn. Adverse selection pressures can thus be alleviated (exacerbated) byexpansions in public coverage of high (low) cost populations.
Empirically, the paper revisits the conclusions of the literature on com-munity rating regulations. In contrast with the existing literature (see, e.g.,Buchmueller and DiNardo (2002), Simon (2005), and LoSasso and Lurie(2009)), I find that community rating initially resulted in significant de-clines in private coverage.3 The contrast is driven primarily by the dynam-ics of community rating’s effects. Past work reports the average of commu-nity rating’s impact over several years. This paper’s analysis reveals thatcoverage declines escalated over this time period.
More inventively, the analysis links the previously parallel literatures onMedicaid expansions and community rating regulations. Studies of Medi-caid expansions (see, e.g., Cutler and Gruber (1996) and Gruber and Simon(2008)) and the aforementioned literature on community rating make scantmention of one another. I show that these health policy instruments arefundamentally linked and that they mediate one another’s performance.Looking forward, accounting for the relationship between community rat-ing regulations and state Medicaid programs may be essential for under-standing the evolution of U.S. insurance markets.
The paper proceeds as follows. Section 1 develops the theoretical link-age between community rating regulations and state Medicaid programs.Section 2 characterizes the evolution of the relevant policies over the pe-riod under analysis. Section 3 lays out my empirical strategy and section4 describes the data used in its implementation. Section 5 characterizesthe evolution of insurance coverage in figures, while Section 6 does so us-
3For additional papers finding near-zero coverage impacts see, Zuckerman and Rajan(1999), Herring and Pauly (2006), Davidoff, Blumberg and Nichols (2005), Monheit andSchone (2004) and Sloan and Conover (1998).
4
ing Section 3’s regression framework. Section 7 presents estimates of therelationship between Medicaid expansions and private coverage rates andsection 8 concludes.
1 Why Public Insurance Matters for Community-
Rated Markets
This section characterizes the relationship between public insuranceprograms, community rating regulations, and equilibrium health insurancecoverage rates. Like the subsequent empirical work, the model focuses onthe effect of community rating on the extensive margin of the insurancepurchasing decision. Because the supply side of insurance markets mayalso respond on the intensive margin of coverage generosity, I consider theempirical relevance of such responses in Appendix A.7.4
1.1 Community Rating Regulations and Coverage Rates
The outcome of interest is an individual’s decision between purchasinga standardized insurance product and remaining uninsured. By standard-ized I mean that I assume there are no margins along which insurers are
4Recent work by Handel, Hendel and Whinston (2013) shows that the relevant regula-tions will likely induce substantial selection on the intensive margin of coverage generos-ity; they predict that most individuals will purchase the minimum coverage allowed byregulation. The current paper can be viewed as focusing on the margin of the decision topurchase this minimum coverage rather than remain uninsured. Ericson and Starc (2012)provide further analysis of choice in the context of insurance exchanges. In the context ofannuity markets, Finkelstein, Poterba and Rothschild (2009) find that changes in contractofferings undid roughly half of the redistribution otherwise implied by restrictions ongender-based pricing. Earlier work on health insurance rating regulations similarly findsa role for intensive-margin adjustments in the form of increases in the prevalence of cov-erage through Health Maintenance Organizations (HMOs) (Buchmueller and DiNardo,2002; Buchmueller and Liu, 2005; LoSasso and Lurie, 2009). Absent these intensive mar-gin adjustments, declines in coverage rates would likely have been even larger than thoseestimated in this paper’s empirical work. Appendix A.7 shows, however, that changes inHMO coverage are not a plausible explanation for variation in the long-run performanceof community rated markets. The data used in this section was found with guidance fromPinkovskiy (2014).
5
allowed to differentiate the product. I further assume a perfectly compet-itive market so that, in equilibrium, premiums are driven to the insurer’sexpected costs.
Individual i has two relevant characteristics, namely the plan’s expectedpayout on i’s health care, ci, and net insurance value, vi. Net insurancevalue reflects a variety of underlying characteristics, including risk aversionand the uncertainty to which type i is exposed. Individuals may also differin the value they place on the care made accessible by the plan.5 The char-acteristics are defined such that i’s total willingness to pay is Di = ci + vi.Market-wide demand for insurance is determined by the joint distributionof ci and vi, denoted F(c, v) with probability density function fc,v(c, v).
Insurers’ costs have two components. These include the aforementionedexpected payout, ci, and a loading cost that is constant across consumers,l. While insurers are assumed to observe ci, community rating regulationsmay restrict their use of this information in determining type i’s premium.Absent such regulations, premiums may be “experience rated,” meaningdifferentiated across cost types. It follows from perfect competition that theexperience rated premium offered to type i is pi = ci + l. With such pre-miums, any individual for whom vi is greater than or equal to the loadingcost will purchase insurance. The market-wide coverage rate is thus
Cov. Rateexp. rating =∫
c
∫v
1{vi ≥ l} fc,v(c, v)dvdc, (1)
where 1{vi ≥ l} is an indicator equal to 1 when net insurance value isgreater than or equal to the loading cost.
Community rating regulations prevent insurers from differentiating pre-miums on the basis of expected health care costs. This non-trivially compli-cates the characterization of market equilibrium, which may not exist andmay be non-unique (Scheuer and Smetters, 2014). When an equilibriumexists, it must satisfy two conditions. First, the stability of consumer choice
5Rich individuals may have relatively high willingness to pay for mid-life cancerscreenings, for example, while consumption of such services may be a manifestation ofmoral hazard from the perspective of the poor. Such differences are conceptually relatedto “moral hazard types” as analyzed by Einav, Finkelstein and Ryan (2013).
6
requires that, at the prevailing premium p∗, Di ≥ p∗ for all purchasers andDi < p∗ for all non-purchasers. Second, perfect competition requires thatthe prevailing premium equal the loading cost, l, plus the average of the ex-pected payouts across the insurance purchasers, c = E(ci|Di ≥ p∗). Givena c and p∗ = c + l for which these conditions are satisfied, the equilibriumcoverage rate is
Cov. Ratecomm. rating =∫
c
∫v
1{vi ≥ l + c− ci} fc,v(c, v)dvdc. (2)
The difference between type i’s experience- and community-rated premi-ums is c− ci, which I interpret as a within-market transfer from type i toother insurance purchasers.
Community rating’s effect on coverage depends largely on two factors.The first is the distribution of ci and the second is the relationship betweenci and vi. Regarding the latter, past work notes that if healthy individualsare highly risk averse, they may value insurance sufficiently to preventadverse selection from posing a problem (Cutler, Finkelstein and McGarry,2008; Einav and Finkelstein, 2010). This paper focuses on the distributionof ci. Specifically, I emphasize that community rating will significantlyincrease the premiums of the relatively healthy when the market containsa non-trivial number of very high cost individuals.
Analyses of community rating regulations often take the distributionof health risks as given (Handel, Hendel and Whinston, 2013; Hackmann,Kolstad and Kowalski, 2012). This paper emphasizes that the relevant dis-tribution depends crucially on other features of the policy environment.Public insurance programs are particularly relevant, as they alter the poolof potential market participants.
1.2 Medicaid’s Implications for Community-Rated Markets
This section walks through a numeric example that illustrates how pub-lic insurance programs can influence community rated insurance markets.Table 1 contains counts for each type, {ci, vi}, in the example population.There are three cost types and three insurance value types. Expected health
7
Table 1: Population Counts for Stylized Example
Net Insurance Value Type: vi
Type
$1,000 $2,000 $3,000
Expected Health Cost Type: ci
$3,000
60 65 75
$5,000
30 20 30
$10,000
10 5 5
Note: Cell entries are population counts for the cost/insurance value types associated with the example discussed in Section 2.2. For example, there are 60 individuals with c i = $3,000 and vi = $1,000 and 65 individuals with ci = $3,000 and vi = $2,000.
costs are $3,000, $5,000, or $10,000. Insurance value is $1,000, $2,000, or$3,000. The loading cost, l, for the insurance policy is $1,300.
Recall that under experience rating types with vi > l purchase insur-ance. In the present example, types with vi = $1, 000 opt out of coverage.The resulting coverage rate is 67 percent.
Suppose now that community rating regulations are introduced. Werethe full population to purchase insurance, c = $4, 000 and p = $5, 300.At this premium, types {$3000, $1000} and {$3000, $2000} exit the market.Absent these individuals, c rises just above $4,700 and the premium above$6,000. An adverse selection spiral continues until only types {$10000, $2000}and {$10000, $3000} are in the market, implying a 3 percent coverage rate.Community rating thus generates no redistribution across health typeswhile reducing the welfare of the newly uninsured.
Now consider a public insurance program that covers all types withvi = $1, 000, which could be a proxy for low income. If types with vi =
$1, 000 are removed from the market, the community-rated equilibrium isfor types {$5000, $3000}, {$10000, $2000}, and {$10000, $3000} to purchaseinsurance at p = $7, 550. The implied private coverage rate is 13 percent. Inthis example, public insurance improves the community-rated equilibriumdespite covering individuals who were not in the initial pool of purchasers.This occurs because type {$10000, $1000} was near the margin of purchas-ing insurance in the community-rated market. This type’s presence among
8
the uninsured breaks the pooling equilibrium in which type {$5000, $3000}enters the market.
Finally, consider a public insurance program that covers all types withci = $10, 000. This could be associated, for example, with coverage targetedat the disabled or other individuals with chronic conditions. With thesetypes removed from the market, equilibrium is for type {$3000, $3000}and all types with ci = $5, 000 to purchase insurance. The private cov-erage rate is 52 percent. Note that the effect of public insurance worksthrough coverage of both the initially insured and uninsured types withci = $10, 000. Had public insurance only covered types {$10000, $2000}and {$10000, $3000}, the equilibrium purchasing pool would include types{$5000, $3000} and {$10000, $1000}. As in the previous example, the pres-ence of type {$10000, $1000} among the uninsured significantly influencesthe equilibrium. This reflects the fact that, among the uninsured, high costtypes will tend to be relatively close to the margin of purchasing insurance.
2 Developments in Community Rating Regula-
tions and Adult Medicaid Coverage
Over the last quarter century, U.S. states have engaged in substantial ex-perimentation with their health insurance regulations and Medicaid pro-grams. This section characterizes these developments, with an emphasison their implications for the subsequent empirical analysis.
2.1 The Evolution of Insurance Regulations
As the Clinton Administration’s health plan stalled during the early1990s, many states adopted some form of community rating and/or guar-anteed issue regulations. Different sets of regulations apply across threedistinct purchasing settings. Some regulations apply to the non-groupmarket, where individuals purchase plans directly from insurers. Otherregulations apply to the small-group market, where small firms, typically
9
meaning those with 50 or fewer workers, purchase insurance on behalf oftheir employees. As described by Buchmueller and DiNardo (2002), thecoverage provided by firms that directly finance their employees’ insur-ance plans (i.e., firms that self insure) are regulated through the federalEmployee Retirement Income and Security Act (ERISA), which preemptsstate legislation. Firms with more than 50 employees, plus smaller firmsthat self insure, are thus unaffected by state regulation. I refer to sucharrangements as existing in the large-group market.
Community rating regulations restrict insurers from adjusting premi-ums on the basis of an individual’s health status (and, to degrees thatvary across states, on the basis of age and other demographic characteris-tics). Guaranteed issue rules prevent insurance companies from rejectingbeneficiaries or limiting their coverage on similar bases. As defined moreexplicitly below, I refer to regulatory packages involving both communityrating and guaranteed issue rules as community rating regimes.
Table 2 highlights states with at least some experience in communityrating regulations. As defined for this paper’s purposes, a state adopteda community rating regime if it meets two criteria. First, it must havemodified or pure community rating rules, as defined by GAO (2003), inboth its non- and small-group markets. Under pure community rating,premiums are only allowed to vary on the basis of family composition andgeography; premium variations due to pre-existing conditions and age aredisallowed. Modified community rating allows (limited) premium varia-tions on the basis of age, but disallows the use of pre-existing conditions.Many states allow premiums to vary within prescribed bounds (known as“rating bands”) on the basis of pre-existing conditions. In practice, thesebands significantly reduce the adverse selection pressures associated withcommunity rating, hence I do not consider such states to have communityrating regimes.6
6Most state rating bands allow premiums to vary by at least 100 percent. For a typicalfamily policy in the non-group market in 2002, this implies premium variation on the or-der of $4,400 (see, e.g., Bernard and Banthin (2008)). The within-market transfers impliedby the rating laws in such states are negligible in comparison with states that adopt pure
10
Tab
le 2
: Sta
te I
mp
lem
en
tati
on
of
Co
mm
un
ity R
ati
ng
Reg
ula
tio
ns
(1
) (2
) (3
) (4
)
(5)
(6
)
Sta
te
Co
mm
un
ity
Rat
ing
G
uar
ante
ed I
ssue
P
ub
lic I
nsu
ran
ce
No
n-G
roup
Y
ears
Eff
ecti
ve
Sm
all-
Gro
up
Yea
rs E
ffec
tive
Str
ict?
Hig
h R
isk P
oo
l b
y 2002
Pan
el A
: C
ore
sam
ple
of s
tate
s w
ith
com
mun
ity
rating
reg
imes
Ken
tuck
y C
om
m. R
atin
g 1996-1
998
Co
mm
. R
atin
g 1996-1
998
Y
es
N
o
Mai
ne
Co
mm
. R
atin
g 1993-
Co
mm
. R
atin
g 1993-
Y
es
N
o
Mas
sach
use
tts
Co
mm
. R
atin
g 1997-
Co
mm
. R
atin
g 1992-
Y
es
N
o
New
Ham
psh
ire
Co
mm
. R
atin
g*
1995-2
002
Co
mm
. R
atin
g 1994-2
002
Y
es
N
o
New
Jer
sey
Co
mm
. R
atin
g 1993-
Co
mm
. R
atin
g 1994-
Y
es
N
o
New
Yo
rk
Co
mm
. R
atin
g 1993-
Co
mm
. R
atin
g**
1993-
Y
es
N
o
Ver
mo
nt
Co
mm
. R
atin
g 1993-
Co
mm
. R
atin
g**
1993-
Y
es
N
o
P
anel
B: A
dditio
nal st
ates
with
less
ful
ly r
egul
ated
ins
uran
ce m
ark
ets
C
olo
rad
o
No
ne
n/
a C
om
m. R
atin
g 1995-
Y
es
Y
es
Co
nn
ecti
cut
No
ne
n/
a C
om
m. R
atin
g 1992-
Y
es
Y
es
Mar
ylan
d
No
ne
n/
a C
om
m. R
atin
g 1992-
Y
es
N
o
No
rth
Car
olin
a N
on
e n
/a
Co
mm
. R
atin
g 1992-
Y
es
N
o
Ore
gon
C
om
m. R
atin
g 1996-
Co
mm
. R
atin
g 1992-
N
o**
*
Yes
Was
hin
gto
n
Rat
ing
Ban
d*
1993-
Co
mm
. R
atin
g 1994-
Y
es
Y
es
No
tes:
*W
ash
ingt
on
an
d N
ew H
amp
shir
e (p
ost
rep
eal o
f co
mm
un
ity
rati
ng)
hav
e re
lati
vel
y ti
ght
rati
ng
ban
ds,
allo
win
g p
rem
ium
s to
var
y b
y o
nly
20%
wit
hin
dem
ogr
aph
ic
gro
up
s o
n t
he
bas
is o
f h
ealt
h. T
his
co
mp
ares
wit
h t
he
rem
ain
der
of
the
rati
ng
ban
d s
tate
s th
at a
llow
pre
miu
ms
to v
ary
anyw
here
fro
m 5
0-2
00%
on
th
e b
asis
of
hea
lth
. **
New
Yo
rk a
nd
Ver
mo
nt
hav
e p
ure
co
mm
un
ity
rati
ng,
wh
ich
co
mp
lete
ly p
roh
ibit
s ri
sk-r
atin
g o
n t
he
bas
is o
f d
emo
grap
hic
ch
arac
teri
stic
s as
wel
l as
hea
lth
sta
tus.
***O
rego
n is
the
on
ly s
tate
th
at f
ails
to
mak
e th
e co
re s
amp
le o
f re
gula
ted
sta
tes
due
to i
ts lac
k o
f a
stri
ct g
uar
ante
ed iss
ue
requir
emen
t in
its
no
n-g
rou
p m
arket
(L
o
Sas
so a
nd
Luri
e, 2
009)
and
its
sm
all-
gro
up
mar
ket
(Sim
on
, 2005).
So
urc
es: In
form
atio
n o
n s
tate
in
sura
nce
mar
ket
reg
ula
tio
ns
com
es f
rom
GA
O (
2003),
Sim
on
(2005),
Lei
da
and
Wac
hen
hei
m (
2008),
an
d C
BO
(2005).
11
The second requirement is that the state must have guaranteed issuerules that go beyond the federal requirements in the 1996 Health InsurancePortability and Accountability Act (HIPAA) and the 1985 ConsolidatedOmnibus Budget Reconciliation Act (COBRA). This typically involves re-quiring shorter exclusion periods for pre-existing conditions and longerperiods of continuation coverage for those who lose health insurance due,among other reasons, to loss of employment. Guaranteed issue rules canalso vary in terms of the range of products to which they apply, with thestrictest regulations requiring guaranteed issue of all insurance products.For categorizing the stringency of a state’s guaranteed issue requirementsin the small- and non-group markets, I rely on Simon (2005) and LoSassoand Lurie (2009) respectively.
I designate 7 states as having adopted community rating regimes. Or-dered chronologically, they are Maine, New York, Vermont, New Jersey,New Hampshire, Kentucky, and Massachusetts. Two of the states, namelyKentucky and New Hampshire, abandoned their community rating regimesduring the sample period.7
I separately estimate the effects of community rating regimes as adoptedby two groups of states. The first group includes New York, Maine, andVermont. I characterize these states as early adopters of “stable” commu-nity rating regimes. Their regulatory regimes have two empirically notablecharacteristics. First, they regulated their non- and small-group marketssimultaneously, which aids in examining the dynamics of community rat-ing’s effects. Second, their regulatory regimes were maintained in essen-tially the same form for the duration of the period under analysis, hencetheir designation as stable.
The second group of states includes Kentucky, Massachusetts, New
community rating. Even in the minority of states with relatively tight rating bands (e.g.,Washington), the implied transfers are on the order of $1,000 less than they would beunder a community rating regime.
7Wachenheim and Leida (2007) report that insurance companies exited Kentucky’smarket in large numbers and repeal of the law began a mere two years after its enactment.New Hampshire’s law was similarly repealed amidst fears of declining coverage.
12
Hampshire, and New Jersey. I characterize these states as staggered adoptersof relatively “unstable” community rating regimes. Kentucky and NewHampshire’s regimes were unstable in that they repealed community rat-ing during the period under study. Massachusetts was unique in terms ofthe gap between its regulation of the individual and small group markets(regulated in 1992 and 1997 as shown in Table 1). Within this group, NewJersey’s community rating regime most closely resembles those of Maine,New York, and Vermont. Its regulatory instability lies in its substantialchanges to other insurance market regulations during the sample period.
2.2 The Evolution of Adult Medicaid Coverage
Table 3 presents characteristics of states’ Medicaid programs as theypertained to the coverage of adults. Columns 1 and 2 indicate whether andwhen states acquired what are known as Section 1115 coverage expansionwaivers. These waivers were necessary to obtain federal financing for cov-erage of groups not traditionally eligible for Medicaid.8 Column 1 showsthat the community rating states were disproportionately likely to obtainwaivers for this purpose.
Columns 3 and 4 show the extent to which Medicaid eligibility comesthrough the “Medically Needy” income concept. This pathway to eligi-bility was available in all 7 of the community rating states as comparedwith two-thirds of all states. The degree of reliance on this form of eligi-bility varies substantially within the set of community rating states, withNew York and Vermont substantially above the national average and theremaining states around or below the national average.
Columns 5 and 6 show that the community rating states had rela-tively generous Medicaid eligibility thresholds for adults with children.Throughout the sample period, most states maintained the relatively low
8Since waivers were required to pass an ex ante test of federal budget neutrality, cov-erage expansions were typically linked to cost-saving efforts elsewhere in the Medicaidprogram. Shifts from traditional Medicaid towards Medicaid Managed Care were oftencredited as sources of substantial savings.
13
Tab
le 3
: F
eatu
res
of
Med
icaid
Exp
an
sio
ns
for
Ad
ult
s in
Co
mm
un
ity R
ati
ng
Sta
tes
(1
) (2
) (3
) (4
) (5
) (6
)
Sta
te
Pre
-2007 C
over
age
Exp
ansi
on
W
aiver
* M
edic
ally
Nee
dy
Pro
gram
(2009)*
* E
ligib
ility
Thre
sho
lds
for
Lo
w-I
nco
me
Par
ents
Duri
ng
AC
A T
ransi
tio
n
(Per
cen
t o
f P
over
ty L
ine)
***
In E
ffec
t?
Yea
r In
itia
ted
In E
ffec
t?
Per
cen
t o
f T
ota
l E
nro
llmen
t Jo
ble
ss P
aren
ts
Lo
w-I
nco
me
Wo
rkin
g P
aren
ts
Pan
el A
: C
ore
sam
ple
of s
tate
s w
ith
com
mun
ity
rating
reg
imes
Mai
ne
Yes
2002
Yes
1.7
200
200
Mas
sach
use
tts
Yes
1995
Yes
2.3
133
133
New
Jer
sey
No
n/
a Y
es
0.6
200
200
New
Yo
rk
Yes
1997
Yes
14.9
150
150
Ver
mo
nt
Yes
2005
Yes
9.4
185
191
Ken
tuck
y Y
es
1993
Yes
3.1
33
57
New
Ham
psh
ire
No
n/
a Y
es
5.8
38
47
P
anel
B: U
.S. A
vera
ges
U.S
. M
edia
n
No
n/
a Y
es
n/
a 37
64
U.S
. M
ean
0.3
5
n/
a 0.6
7
4.5
73
88
No
tes:
*Def
ined
Usi
ng
Ap
pen
dix
Tab
le A
of
Kai
ser
(2011).
Sta
tes
are
coun
ted
as
hav
ing
imp
lem
ente
d s
uch
exp
ansi
on
s if
th
ey f
all un
der
any
of
the
Kai
ser
tab
le's
pan
els,
w
ith
th
e ex
cep
tio
n o
f th
e "O
ther
" p
anel
, an
d if
the
tab
le in
dic
ates
th
at t
he
rele
van
t exp
ansi
on
was
im
ple
men
ted
pri
or
to 2
007. M
any
earl
y M
edic
aid e
xpan
sio
n w
aiver
s w
ere
linked
to
th
e ad
op
tio
n o
f M
edic
aid
Man
aged
Car
e b
ecau
se s
avin
gs c
red
ited
to
Med
icai
d M
anag
ed C
are
pro
po
sals
en
able
d t
he
wai
ver
s to
sat
isfy
th
e cr
iter
ion
of
fed
eral
bud
geta
ry n
eutr
alit
y w
hile
sti
ll ex
pan
din
g co
ver
age.
**T
aken
fro
m T
able
3 o
f K
aise
r (2
012).
***
Tak
en f
rom
Kai
ser
(2013).
14
eligibility thresholds associated with Medicaid’s historical linkage to re-ceipt of cash welfare assistance. For jobless parents, the median across allstates was just 37 percent of the federal poverty line. With the exceptionof Kentucky and New Hampshire, which repealed their community ratingregimes during the period under analysis, the remaining community rat-ing states had pushed their eligibility thresholds to or above 133 percent ofthe poverty line.
2.3 Illustrating the Link between Medicaid and Commu-
nity Rating
This section uses recent health expenditure data to illustrate the po-tential link between Medicaid and the performance of community ratedmarkets. A simple method for gauging adverse selection pressures is tocompare the average health spending of potentially relevant populationgroups. Using the 2011 Medical Expenditure Panel Survey (MEPS), I con-sider the health care costs of privately insured adults whose premiumswould be affected by standard community rating regimes. Among theseadults, average total health costs were $4,300. For those in the top twothirds of the distribution of self-reported health, average expenditures were$3,200.9 Those with lower self-reported health averaged $6,800. The fullpool’s average thus exceeds the average across the healthiest two thirds by34 percent.
Adults on Medicaid have higher health costs than those with privatepolicies. In the 2011 MEPS, costs for the full population of non-elderly,adult Medicaid beneficiaries averaged $8,300. Medicaid’s coverage of theseindividuals thus reduces average costs among the potentially privately in-sured. Pooling these adult Medicaid beneficiaries with the privately in-sured, for example, pushes average costs from $4,300 to $5,100. BecauseMedicaid pays lower rates than most private plans, average costs would
9This corresponds to individuals with self-reported health status of “Excellent” or“Very Good.”
15
be closer to $6,000 if all were privately insured.10 This is nearly twice the$3,200 associated with the healthiest two thirds of private policy holders.Healthy individuals would have to place a remarkably high value on in-surance’s risk-reducing benefits to remain in such a risk pool.
3 Estimating the Effects of Community Rating
This section presents my framework for estimating the effect of com-munity rating on insurance coverage. Non- and small-group markets gov-erned by community rating are the treated markets of interest. Equivalentmarkets in less tightly regulated states serve as controls within a difference-in-differences framework. The existing literature on community rating’seffects includes estimates of this form as well as triple-difference estimatesin which the large-group markets in all states serve as within-state controlgroups (Buchmueller and DiNardo, 2002).
The difference-in-differences framework is presented below:
COVi,s,t = β1Reg. States × Posts,t + β2s States + β3tYeart + Xi,s,tγ + εi,s,t.(3)
COVi,s,t is an indicator for the coverage status (e.g., has private coverage)of individual i who resides in state s in year t. Equation (3) contains thestandard features of difference-in-differences estimation, namely sets ofyear (Yeart) and state (States) indicator variables. The primary coefficient ofinterest is β1, which is an estimate of coverage changes in community ratedmarkets net of coverage changes in other markets. I estimate the standarderror on β1 using the block bootstrap method with clusters drawn at thestate level (Bertrand, Duflo and Mullainathan, 2004).
β1 will be an unbiased estimate of the effect of community rating oninsurance coverage under a standard “parallel trends” assumption. Thatis, conditional on Xi,s,t, it must be the case that the treatment and control
10See work by Zuckerman et al. (2004) and Clemens and Gottlieb (2013) for more onthe relationship between public and private payments for health care services.
16
states would have experienced the same changes in coverage had the treat-ment states not adopted community rating. This assumption faces stan-dard threats. It could be violated, for example, if treatment and controlstates experienced significantly different changes in economic conditions.It could also be violated if treatment states differentially adopted additionalpolicies that are likely to affect insurance coverage.
I take several steps to account for threats to the parallel trends assump-tion. First, the baseline covariates in Xi,s,t provide a set of controls forthe economic circumstances of the households in the sample. The full setof controls includes a set of 2-digit occupation dummy variables, regiondummy variables interacted with family income as a percent of the povertyline, an indicator for having a single mother as the household’s head, andan indicator for being black, additional indicators for having two or morefull time workers in the household and for the education levels of house-hold adults, an indicator for home ownership, and age group indicators. Inadditional analysis I control directly for state-level economic covariates andchanges in health expenditures. I further explore the relevance of threats tothe parallel trends assumption by applying matching criteria for selectingthe individuals and/or states included in the control group.
An additional check on the validity of the estimates is to construct awithin-state control group for use in a triple-difference framework. Thisframework appears below:
COVi,s,t = β1Posts,t × Reg. States × Small Firmi
+ β2s States × Small Firmi + β3s,t States ×Yeart
+ β4tYeart × Small Firmi + β5s States + β6Small Firmi + β7tYeart
+ Xi,s,tφ + εi,s,t. (4)
Equation (4) augments the fixed effects from equation (3) with an indicatorfor being on the non- and small-group insurance markets (Small Firmi).It further incorporates two-way interactions between Small Firmi and thestate and year fixed effects. These interactions control for differential trendsacross treatment and control states as well as across the large- and small-
17
group markets. They also allow the difference between large- and small-group markets to differ at baseline across the states. The primary coeffi-cient of interest is again β1.
In the triple-difference framework, unbiased estimation relies on threeassumptions that are not wholly satisfied in practice. First, it requires cor-rect assignment of units to the within-state treatment and control groups.In practice, the small- and large-group markets cannot be cleanly segre-gated using the firm-size information in the analysis data described inthe subsequent section. This misclassification will tend to attenuate thetriple-difference estimates towards 0. Second, triple difference estimationrequires that the policy not have spillover effects on the within-state con-trol group. This may be violated, for example, if regulation-induced losseslead an insurer to raise premiums in all markets. Third, for the within-statecontrol group to effectively control for state-specific shocks, these shocksmust similarly influence insurance coverage in the within-state treatmentand control groups. This is likely violated due to the stability of cover-age offerings by large firms relative to coverage offerings by small firms.For these reasons I estimate equation (4) as a check on the robustness ofestimates of equation (3) rather than as the baseline specification.
4 Description of the Analysis Data Set
I estimate equations (3) and (4) using samples of individuals from theMarch Supplements of the Current Population Survey (CPS) for years 1988-2007. The data provide information on insurance status and key householdeconomic and demographic characteristics for years 1987-2006. The sampleexcludes households in which the oldest member is either younger than 20
or older than 60 years of age. More substantively, I focus on individualsin households with at least one child and at least one full-time employedadult.11 This places attention on the market segments that were most di-
11Previous studies similarly restrict the sample population to the employed. AppendixFigure A4 presents coverage tabulations in which the unemployed are included in the
18
rectly affected by changes in Medicaid eligibility over this time period.12
Since these sample selection margins could, in principal, be affected by thepolicies under study, Appendix Figures A3 and A4 display the evolutionof coverage for samples in which these restrictions have not been imposed.
The summary statistics in Table 4 point to an important pre-communityrating difference between baseline insurance coverage in the treatment andcontrol states. Treatment states had relatively high private coverage rates(78.7% vs. 70.1%), translating into relatively small fractions of the popula-tion who lacked coverage altogether. Households in treatment states alsohad moderately higher incomes and education levels than households incontrol states.
The summary statistics highlight non-trivial differences between thecommunity rating states, which are concentrated in New England andthe Mid-Atlantic, and other states. As a result of these differences, con-trol group selection is non-trivial. The setting is such that no one methodfor selecting the control group is obviously preferred to all others. Whilethe paper’s baseline estimates of equations (3) and (4) include all non-community rating states in the control group, robustness to a range ofalternative approaches has been checked. One of these, namely a synthetic
sample. The principal rationale for excluding the unemployed involves the assumptionsunderlying the triple-difference framework of Equation (4). The triple difference frame-work assumes that the insurance coverage of workers at large firms are subject to the sameshocks as workers at small firms. As noted previously, this assumption may not hold forseveral reasons. Importantly, the insurance coverage of workers at large firms tends tobe more stable than coverage of workers at small firms. The assumption would becomeincreasingly implausible if the treatment group included the unemployed. A related issueis that essentially all unemployed adults with children will be eligible for Medicaid for atleast some portion of the calendar year. Their participation in private insurance marketsmay thus tend to be limited.
12The exclusion of childless households is a difference between the current study andprevious work on community rating regulations. I focus on households with children dueto my emphasis on the interplay between community rating regulations and subsequentMedicaid expansions. Community rating regulations treat households with and withoutchildren as separate market segments. By design, Medicaid expansions covered very fewchildless adults over this time period. The market for coverage of single adults wouldthus not have been affected by contemporaneous public insurance expansions. AppendixFigure A3 presents coverage tabulations in which childless households are included in thesample.
19
Table 4: Baseline Sample Characteristics in Community Rating States and Control States: 1987-1992
(1) (2)
States with
Community Rating Control States
Variable Mean Mean
(St. Dev.) (St. Dev.)
Private Insurance 0.788 0.701
(0.409) (0.457)
Uninsured 0.155 0.236
(0.361) (0.424)
On Medicaid 0.075 0.071
(0.263) (0.256)
Income as % of Poverty Line 326 286
(246) (234)
Household Size 4.2 4.25
(1.26) (1.34)
College Educated Adult 0.574 0.518
(0.572) (0.459)
Black 0.065 0.059
(0.244) (0.235)
1 Worker Household 0.72 0.667
(0.453) (0.471)
Observations 18309 89185 Sources: Baseline summary statistics were calculated by the author using data from the March Economic and Demographic Supplement to the Current Population Survey for years 1987-1992.
Note: Samples consist of individuals in households with at least one child and one full-time working adult, but with no adults working at a firm that has more than 100 employees. For the otherwise unrestricted samples shown in columns 1 and 2, CPS person weights are applied. For the samples selected using the matching methods described in the text, shown in columns 3 through 6, equal weights are applied. The community rating states include New York, Maine, Vermont, Kentucky, Massachusetts, New Hampshire, and New Jersey. The “Control” states in Column 2 incluce all other states. A household is defined as having a college educated adult if one of the adults in the household has at least some college education. The samples in columns 3 and 4 are restricted to matched pairs, constructed as described in the text. The samples in columns 5 and 6 are further restricted to exclude individuals in households with incomes less than 133% of the poverty line or headed by single mothers.
20
cohort approach to matching, is presented graphically in the main text.13
Results from additional robustness checks can be found in the Online Ap-pendix.
5 A Graphical View of Coverage Changes
Panel A of Figure 1 shows the evolution of the fraction of individ-uals that has neither private nor public insurance in the treatment andcontrol groups. In this figure, the treatment group consists of the statesthat contemporaneously adopted community rating in both their non- andsmall-group markets, namely New York, Maine, and Vermont. New Yorkaccounts for roughly 75 percent of the underlying observations in this sam-ple. Panel A provides a stark depiction of two of this paper’s central em-pirical findings. In large-group markets everywhere and in the small- andnon-group markets of the control states, Medicaid expansions and declinesin private coverage have roughly offset one another. The fraction of in-dividuals without insurance changed little in these markets throughoutthe sample (1987-2006). In contrast, the community rated small- and non-group markets followed quite different paths. After New York, Maine, andVermont adopted community rating in 1993, the fraction uninsured in theunrestricted sample (Panel A) increased by around 80%, from 0.16 to 0.29
in 1997. This erosion reversed in subsequent years, with the fraction unin-sured declining to 0.17 by 2006. Panel B shows that the coverage changesobserved in Panel A are robust to selecting the sample using synthetic co-
13The synthetic cohort analysis proceeds as follows. On a sample restricted to yearsincluding or prior to 1993, I estimate the relationship between private insurance coverageand the covariates used as controls in estimating equations (3) and (4). I then use thecoefficients from this regression to estimate each individual’s propensity to have privateinsurance coverage. I estimate these propensities for individuals from all years, includingyears subsequent to the adoption community rating regulations. Within each year, I thenuse the propensity scores to form nearest neighbor matches (without replacement) be-tween observations from treatment and control states. I arrive at the final synthetic cohortsample by dropping all matches for which the propensity score estimates differ by morethan 0.0025. This final step, which drops a very small number of observations involvingrelatively poor matches, can be characterized as an application of the “caliper” method.
21
hort methods.Panels C and D of Figure 1 are similar to Panels A and B, but report
the fraction of the population covered by private insurance. Private cov-erage rates turn sharply for the worse in the community rated marketsbetween 1993 and 1996. After 1997 these markets show signs of recoveries,with the recoveries appearing to be complete in the unrestricted sampleand partial in the matched samples. In Panel D, for example, coveragerates are roughly equal prior to the implementation of community ratingand decline by an excess of 12 percentage points in the regulated mar-kets between 1993 and 1996. They remained down by an excess of 4 or 5
percentage points over the last four years of the sample.A look across the panels of Figure 1 highlights an important point. Fol-
lowing 1997, the community rated markets experienced sharp declines inthe fraction of individuals without insurance. The sharpness of this declineresults from the fact that both the fraction of individuals covered by Med-icaid and the fraction of individuals with private insurance increased inthese markets relative to other markets. This positive correlation is uniqueacross time periods and market types, as Medicaid’s tendency to crowdout private insurance typically dominates the relationship between theseforms of coverage (see, e.g., Cutler and Gruber (1996) and Gruber and Si-mon (2008)).
Figure 2 presents tabulations in which the treatment states are Ken-tucky, Massachusetts, New Hampshire, and New Jersey. These states dif-fer from Maine, New York, and Vermont in that their implementation ofinsurance market regulations was both staggered and less stable. Becausetheir regulatory activity included potentially confounding policy changes,it is less clear that mid-1990s coverage movements can be interpreted ascausal effects of community rating regulations.
A similar pattern of decline and recovery is apparent in this secondgroup of community rating states. Notably, there appears to have been adecline in coverage prior to the adoption of community rating. This de-cline is driven primarily by New Jersey, where insurance markets werebeing strained by the state’s system for financing uncompensated hospital
22
.04.11.18.25.32Fraction Uninsured
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
A: F
ract
ion
Uni
nsur
ed:
.14.185.23.275.32Fraction Uninsured
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Syn
thet
ic C
ohor
t Sam
ple
Pan
el B
: Fra
ctio
n U
nins
ured
.56.65.74.83.92Fraction w/ Private Insurance
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
C: F
ract
ion
w/ P
rivat
e In
sura
nce
.52.59.66.73.8Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Syn
thet
ic C
ohor
t Sam
ple
Pan
el D
: Fra
ctio
n w
/ Priv
ate
Insu
ranc
e
Insu
ranc
e S
tatu
s O
ver
Tim
e: S
tabl
e R
egul
ator
y R
egim
es
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
trol
Sta
tes
Sm
all-
and
Non
-Gro
up M
arke
ts in
Tre
atm
ent S
tate
s
Larg
e-G
roup
Mar
kets
in C
ontr
ol S
tate
sLa
rge-
Gro
up M
arke
ts in
Tre
atm
ent S
tate
s
Figu
re1:
Insu
ranc
eSt
atus
Ove
rTi
me:
Hou
seho
lds
wit
hC
hild
ren.
The
figur
eco
ntai
nsta
bula
tion
sco
nstr
ucte
dus
ing
the
Mar
chD
emog
raph
icSu
pple
men
tsof
the
Cur
rent
Popu
lati
onSu
rvey
(CPS
).A
llsa
mpl
esco
nsis
tof
the
adul
tsan
dch
ildre
n(a
ged
18
and
low
er)
inho
useh
olds
wit
hat
leas
tone
child
and
atle
asto
nefu
ll-ti
me
wor
king
adul
t.Th
esa
mpl
esin
Pane
lsB
and
Dar
ere
stri
cted
tom
atch
edpa
irs,
cons
truc
ted
asde
scri
bed
inth
ete
xt.T
hetr
eatm
ents
tate
sin
allp
anel
sin
clud
eN
ewYo
rk,M
aine
,and
Verm
ont,
whi
chw
ere
the
thre
est
ates
toad
optt
heir
com
mun
ity
rati
ngre
gim
esin
1993
(as
indi
cate
dby
the
vert
ical
blac
klin
esin
each
pane
l).I
nPa
nels
Aan
dC
,whi
chdi
spla
yco
vera
gera
tes
inst
ates
’lar
ge-g
roup
mar
kets
asw
ell
asin
thei
rno
n-an
dsm
all-
grou
pm
arke
ts,i
ndiv
idua
lsar
ede
fined
asha
ving
acce
ssto
the
larg
e-gr
oup
mar
keti
fatl
east
one
adul
tin
the
hous
ehol
dis
empl
oyed
ata
firm
wit
hm
ore
than
100
empl
oyee
s(a
ndto
the
non-
and
smal
l-gr
oup
mar
kets
othe
rwis
e).
Insu
ranc
est
atus
ista
ken
dire
ctly
from
the
Min
neso
taPo
pula
tion
Cen
ter’
sSu
mm
ary
Hea
lth
Insu
ranc
eva
riab
les,
whi
char
eim
pute
dus
ing
the
CPS
.C
PSpe
rson
wei
ghts
are
appl
ied
inta
bula
tion
sin
volv
ing
the
unre
stri
cted
sam
ples
(Pan
els
Aan
dC
),w
hile
obse
rvat
ions
are
equa
llyw
eigh
ted
inpa
nels
usin
gob
serv
atio
nsse
lect
edth
roug
hth
em
atch
ing
proc
edur
e(P
anel
sB
and
D).
23
.04.11.18.25.32Fraction Uninsured
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
A: F
ract
ion
Uni
nsur
ed:
.1.14.18.22.26Fraction Uninsured
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Syn
thet
ic C
ohor
t Sam
ple
Pan
el B
: Fra
ctio
n U
nins
ured
.56.65.74.83.92Fraction w/ Private Insurance
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
C: F
ract
ion
w/ P
rivat
e In
sura
nce
.65.7.75.8.85Fraction w/ Private Insurance
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Syn
thet
ic C
ohor
t Sam
ple
Pan
el D
: Fra
ctio
n w
/ Priv
ate
Insu
ranc
e
Insu
ranc
e S
tatu
s O
ver
Tim
e: U
nsta
ble
Reg
ulat
ory
Reg
imes
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
trol
Sta
tes
Sm
all-
and
Non
-Gro
up M
arke
ts in
Tre
atm
ent S
tate
s
Larg
e-G
roup
Mar
kets
in C
ontr
ol S
tate
sLa
rge-
Gro
up M
arke
ts in
Tre
atm
ent S
tate
s
Figu
re2:
Insu
ranc
eSt
atus
Ove
rTi
me:
Hou
seho
lds
wit
hC
hild
ren.
The
figur
eco
ntai
nsta
bula
tion
sco
nstr
ucte
dus
ing
the
Mar
chD
emog
raph
icSu
pple
men
tsof
the
Cur
rent
Popu
lati
onSu
rvey
(CPS
).A
llsa
mpl
esco
nsis
tof
the
adul
tsan
dch
ildre
n(a
ged
18
and
low
er)
inho
useh
olds
wit
hat
leas
tone
child
and
atle
asto
nefu
ll-ti
me
wor
king
adul
t.Th
esa
mpl
esin
Pane
lsB
and
Dar
ere
stri
cted
tom
atch
edpa
irs,
cons
truc
ted
asde
scri
bed
inth
ete
xt.T
hetr
eatm
ents
tate
sin
allp
anel
sin
clud
eN
ewJe
rsey
,New
Ham
pshi
re,K
entu
cky,
and
Mas
sach
uset
ts,
whi
chw
ere
the
four
stat
esto
cond
uct
stag
gere
dim
plem
enta
tion
ofth
eir
com
mun
ity
rati
ngre
gim
esbe
twee
n1992
and
1997
(as
indi
cate
dby
the
dash
edve
rtic
albl
ack
lines
inea
chpa
nel)
.Th
eye
ars
inw
hich
each
indi
vidu
altr
eatm
ent
stat
eim
plem
ente
dit
sre
gula
tion
sca
nbe
foun
din
Tabl
e2.
InPa
nels
Aan
dC
,whi
chdi
spla
yco
vera
gera
tes
inst
ates
’lar
ge-g
roup
mar
kets
asw
ell
asin
thei
rno
n-an
dsm
all-
grou
pm
arke
ts,i
ndiv
idua
lsar
ede
fined
asha
ving
acce
ssto
the
larg
e-gr
oup
mar
ket
ifat
leas
ton
ead
ult
inth
eho
useh
old
isem
ploy
edat
afir
mw
ith
mor
eth
an100
empl
oyee
s(a
ndto
the
non-
and
smal
l-gr
oup
mar
kets
othe
rwis
e).
Insu
ranc
est
atus
ista
ken
dire
ctly
from
the
Min
neso
taPo
pula
tion
Cen
ter’
sSu
mm
ary
Hea
lth
Insu
ranc
eva
riab
les,
whi
char
eim
pute
dus
ing
the
CPS
.CPS
pers
onw
eigh
tsar
eap
plie
din
tabu
lati
ons
invo
lvin
gth
eun
rest
rict
edsa
mpl
es(P
anel
sA
and
C),
whi
leob
serv
atio
nsar
eeq
ually
wei
ghte
din
pane
lsus
ing
obse
rvat
ions
sele
cted
thro
ugh
the
mat
chin
gpr
oced
ure
(Pan
els
Ban
dD
).
24
care.14 New Jersey altered its financing of uncompensated care along withthe wave of reforms that ushered in community rating regulations. Ter-minating its uncompensated care arrangement should, all else equal, haveimproved New Jersey’s coverage rates. Establishing an appropriate coun-terfactual is thus quite difficult. The institutional background suggests thataveraging 1988-1992 as a “pre” community rating period is a more reason-able approach to the data than one might conclude from the figure. Thatsaid, it seems prudent to place more weight on the evidence associatedwith the states that adopted relatively stable regulatory regimes. AppendixFigure A2 presents the evolution of private coverage in each treatment statein separate panels.
6 Regression Analysis of Coverage Rates
This section presents estimates of equations (3) and (4). I first estimatethe medium-run declines in coverage experienced by the community ratedmarkets relative to other markets. I do this by estimating equations (3) and(4) on samples in which 1988-1992 constitute the pre-community ratingperiod and 1996-1997 capture the low point following community rating’sadoption. I then present estimates of coverage recoveries from the lowpoint of 1996-1997 through an end period covering 2003-2006.
Table 5 presents the estimates. The results in Panels A (difference-in-differences specifications) and B (triple-difference specifications) show thatcommunity rating resulted in substantial coverage declines. In states thatadopted relatively stable community rating regimes (columns 1 and 3) pri-vate coverage declined by around 9.5 percentage points. In states that
14“All-payer” regulations explicitly required that uncompensated care costs be fi-nanced through surcharges on payments from insurers to hospitals. This surchargespiked when, in 1988, the federal government canceled a waiver through which Medi-care had been generously subsidizing New Jersey hospitals’ provision of uncompensatedcare (Siegel, Weiss and Lynch, 1990). The resulting increase in private payers’ costs re-sulted in significant premium increases and thus declines in coverage. Coverage declinesincreased the ranks of the uninsured and thus the cost of uncompensated care, resultingin additional surcharge increases. New Jersey was thus experiencing an “uncompensatedcare spiral.”
25
Table 5: Coverage Changes in Community Rated Markets
(1) (2) (3) (4)
Dependent Variable: Coverage Status Private Private Uninsured Uninsured
Panel A: Effects of Implementing Community Rating Comm. Rating State x Post Regulation -0.0962*** -0.0666* 0.0955*** 0.0698
(0.0206) (0.039) (0.0350) (0.0611)
Sample of States Stable Regs Unstable Regs Stable Regs Unstable Regs
Estimation Framework D-in-D D-in-D D-in-D D-in-D
Observations 127,554 126,498 127,554 126,498
Panel B: Effects of Implementing Community Rating Small Firm x Comm. Rating State x Post Regulation
-0.0945*** -0.0421 0.0856*** 0.0426
(0.0181) (0.0272) (0.0282) (0.0532)
Sample of States Stable Regs Unstable Regs Stable Regs Unstable Regs
Estimation Framework D-in-D-in-D D-in-D-in-D D-in-D-in-D D-in-D-in-D
Observations 491,787 495,674 491,787 495,674
Panel C: Post-1997 Recoveries of Community Rated Markets Comm. Rating State x Post 2002 0.0628 0.0323 -0.108** -0.0231
(0.0526) (0.0208) (0.0432) (0.0160)
Sample of States Stable Regs Unstable Regs Stable Regs Unstable Regs
Estimation Framework D-in-D D-in-D D-in-D D-in-D
Observations 141,817 139,845 141,817 139,845
Panel D: Post-1997 Recoveries of Community Rated Markets Small Firm x Comm. Rating State x Post 2002
0.0582* 0.0154 -0.0807** -0.0121
(0.0296) (0.0200) (0.0347) (0.0157)
Sample of States Stable Regs Unstable Regs Stable Regs Unstable Regs
Estimation Framework D-in-D-in-D D-in-D-in-D D-in-D-in-D D-in-D-in-D
Observations 512,139 512,317 512,139 512,317 ***, **, and * indicate statistical significance at the .01, .05, and .10 levels respectively. Standard errors, reported beneath each point estimate, were calculated using a block bootstrap approach with clusters drawn at the state level. The samples in Panels A and B consist of individuals in households with at least one child and one full-time employed adult from the March Current Population Survey (CPS) in years 1987-1992 and 1996-1997. The samples in Panels C and D consist of similarly situated households from 1996-1997 and 2003-2006. CPS person weights are applied. Panels A and C report point estimates of β1 from equation (3). Panels B and D report point estimates of β1 from equation (4). In all cases the vector X includes a set of 2-digit occupation dummy variables, region dummy variables interacted with family income as a percent of the poverty line, an indicator for having a single mother as the household's head, and an indicator for being black, additional indicators for having two full time workers in the household and for the education levels of household adults, an indicator for home ownership, and age group indicators. The "Stable Regulations" group includes Maine, New York, and Vermont, while the "Unstable Regulations" group includes Kentucky, Massachusetts, New Hampshire, and New Jersey.
26
adopted less stable community rating regimes (columns 2 and 4) cover-age declines were less severe, with the estimates averaging 5.5 percentagepoints. The experience of these states was relatively varied, resulting inlarger standard errors. Note that because Massachusetts only completedthe enactment of its community rating regulations in 1997, it does not enterinto treatment-state status during this time period.
The results in Panels C and D show the evolution of coverage from thelows of 1996-1997 through the period covering 2003-2006. In states withstable community rating regimes, the fraction uninsured recovered essen-tially all of the losses experienced during the earlier period. For this group,the increase in standard errors from Panels A and B to Panels C and D re-flects an increase in variability across these states’ experiences. The stateswith less stable community rating regimes appear to have recovered little,if at all. The remainder of this paper’s analysis attempts to understand thisvariation in the long run performance of community rated markets.
7 Evidence on the Interplay between Medicaid
and Community Rating
This section empirically assesses the link between the performance ofcommunity rated markets and the structure and size of states’ Medicaidprograms. As emphasized in Section 2, Medicaid expansions can improvethe performance of community-rated markets if they draw high cost typesout of the pool of potential private market participants. Table 3 highlightedthe community rating states’ use of channels through which Medicaid canbe targeted at high cost populations. In this section I begin by using theCPS to gauge the extent to which states’ Medicaid coverage was, in prac-tice, taken up disproportionately by relatively unhealthy adults. I thenrelate the observed variation in coverage of unhealthy adults to the evolu-tion of private coverage rates.
27
7.1 The Evolution of Adult Medicaid Coverage in the Cur-
rent Population Survey
Using CPS data, Figure 3 displays the fraction of low-income adultson Medicaid both in total (Panels A and B) and by health status (Pan-els C and D). Because the CPS did not include the required health statusquestions in the sample’s initial years, the latter panels first display datafor 1995. Although the community rating states had more extensive adultMedicaid coverage throughout the sample period, coverage rates movedon roughly parallel trends during the first half of the sample. Medicaidcoverage rates diverge just before 2000. Between 1999 and 2006, coveragerates for adults with incomes less than 300% of the poverty line rose by 17
percentage points in Maine, New York, and Vermont and by 6 percentagepoints elsewhere (Panel A).
Figure 3’s Panels C and D present Medicaid coverage separately for un-healthy adults and healthy adults. I define unhealthy adults as those witha work-limiting disability or with self-reported health status worse than“very good.” This definition of unhealthy accounts for the bottom thirdof the self-reported health distribution. I find that Medicaid expansions incontrol states covered similar fractions of the healthy and unhealthy adultpopulations. In Maine, New York, and Vermont, coverage of unhealthyadults with low incomes rose by 25 percentage points while coverage ofhealthy adults with low incomes rose by roughly 12 percentage points.
7.2 Framework for Relating Public and Private Coverage
The data allow me to take an additional step towards linking public andprivate coverage in community rated markets. I use the following specifi-cation to descriptively estimate the relationship between private coverageand public coverage for unhealthy adults:
28
.05.15.25.35.45Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Low
Inco
me
Adu
lts: S
tabl
e R
egul
atio
nsP
anel
A: F
ract
ion
on M
edic
aid
.05.15.25.35.45Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Low
Inco
me
Adu
lts: U
nsta
ble
Reg
ulat
ions
Pan
el B
: Fra
ctio
n on
Med
icai
d
Evo
lutio
n of
Med
icai
d C
over
age
for
Low
Inco
me
Adu
lts
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
trol
Sta
tes
Sm
all-
and
Non
-Gro
up M
arke
ts in
Tre
atm
ent S
tate
s
.05.15.25.35.45Fraction On Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Low
Inco
me
Adu
lts: S
tabl
e R
egul
atio
nsP
anel
C: F
ract
ion
On
Med
icai
d
.05.15.25.35.45Fraction On Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Low
Inco
me
Adu
lts: U
nsta
ble
Reg
ulat
ions
Pan
el D
: Fra
ctio
n O
n M
edic
aid
Hea
lthy
Adu
lts in
Con
trol
Sta
tes
Hea
lthy
Adu
lts in
Tre
atm
ent S
tate
s
Unh
ealth
y A
dults
in C
ontr
ol S
tate
sU
nhea
lthy
Adu
lts in
Tre
atm
ent S
tate
s
Figu
re3:
Evol
utio
nof
Med
icai
dC
over
age
for
Adu
lts.
The
figur
eco
ntai
nsta
bula
tion
sco
nstr
ucte
dus
ing
the
Mar
chD
emog
raph
icSu
pple
men
tof
the
Cur
rent
Popu
lati
onSu
rvey
(CPS
).A
llsa
mpl
esco
nsis
tso
lely
ofad
ults
(age
d21
and
high
er)
inho
useh
olds
wit
hat
leas
ton
ech
ildan
dat
leas
ton
efu
ll-ti
me
wor
king
adul
t,bu
tw
ith
noad
ults
wor
king
ata
firm
wit
hm
ore
than
100
empl
oyee
s.A
llsa
mpl
esar
efu
rthe
rre
stri
cted
toad
ults
inho
useh
olds
wit
hin
com
ele
ssth
an300%
ofth
efe
dera
lpo
vert
ylin
e.Th
etr
eatm
ent
stat
esin
pane
lsA
and
Cin
clud
eN
ewYo
rk,
Mai
ne,
and
Verm
ont,
whi
leth
etr
eatm
ent
stat
esin
Pane
lsB
and
Din
clud
eK
entu
cky,
Mas
sach
uset
ts,N
ewH
amps
hire
,and
New
Jers
ey.H
ealt
hyad
ults
are
defin
edas
thos
ew
ith
self
-rep
orte
dhe
alth
stat
usof
Exce
llent
orVe
ryG
ood
and
wit
hno
self
-rep
orte
dw
ork
disa
bilit
y.U
nhea
lthy
adul
tsar
eth
eco
mpl
emen
tto
the
sam
ple
ofhe
alth
yad
ults
,whi
chac
coun
tsfo
rro
ughl
yon
eth
ird
ofth
esa
mpl
epo
pula
tion
.In
sura
nce
stat
usis
take
ndi
rect
lyfr
omth
eM
inne
sota
Popu
lati
onC
ente
r’s
Sum
mar
yH
ealt
hIn
sura
nce
vari
able
s,w
hich
are
impu
ted
usin
gth
eC
PS.
29
COVi,s,t = β1Unhealthy Frac. Medicaids,t × Reg. States,t
+ β2Unhealthy Frac. Medicaids,t
+ β3Healthy Frac. Medicaids,t × Reg. States,t
+ β4Healthy Frac. Medicaids,t
+ β5s States + β6tYeart + β7tReg. States,t ×Yeart + Xiγ + εi,s,t. (5)
The last terms in equation (5) are the components of a standard difference-in-differences framework, where the coefficients of interest would be theβ7t . In estimating equation (5), I think of the set of Reg. States × Yeart in-dicators as controls for changes common to the set of community ratedmarkets. Their inclusion allows me to estimate the relationship betweenMedicaid and private coverage within this set of community rated mar-kets. The principal coefficient of interest is β1, which describes the rela-tionship between private coverage and Medicaid’s coverage of unhealthyadults (Unhealthy Frac. Medicaid =
# of Unhealthy Adults on Medicaid# of Adults in the Population ) in a com-
munity rating state relative to other states.β1 should be given a predictive, rather than causal, interpretation be-
cause variation in Unhealthy Frac. Medicaids,t may be correlated with theerror term. Estimates of equation (5) thus provide suggestive evidenceof the proposed mechanism’s plausibility. Absent a partial correlationbetween private coverage and Medicaid’s coverage of unhealthy adults,it would be difficult to argue that Medicaid shapes the performance ofcommunity-rated markets. Notably, a positive correlation must overcomethe most obvious sources of bias, which produce negative correlations be-tween private coverage and Medicaid coverage of any kind.15
I also estimate the relationship between private coverage and the extentto which states’ Medicaid programs target unhealthy adults as follows:
15I do not pursue a “simulated instruments” approach (Currie and Gruber, 1996; Cutlerand Gruber, 1996) because Medicaid coverage less tightly tracks changes in eligibility rulesduring this period than during earlier periods.
30
COVi,s,t = β1Unhealthy Shares,t × Reg. States,t + β2Unhealthy Shares,t
+ β3Total Frac. Medicaids,t × Reg. States,t + β4Total Frac. Medicaids,t
+ β5s States + β6tYeart + β7tReg. States,t ×Yeart + Xiγ + εi,s,t. (6)
The primary variable of interest is Unhealthy Shares,t =Unhealthy Frac. Medicaids,t
Total Frac. Medicaids,t,
with Total Frac. Medicaids,t = # of Adults on Medicaid# of Adults in the Population . Unhealthy Shares,t
quantifies the fraction of a state’s adult Medicaid population that is un-healthy. Although we must again give β1 a predictive rather than causalinterpretation, potential sources of bias are less obvious here than in esti-mating equation (5).16 The test that β1 > 0 is this paper’s most direct testfor the relevance of the mechanisms described in Section 2. I estimate equa-tion (6) with and without controlling directly for Total Frac. Medicaids,t
and its interaction with the Reg. States,t indicator.
7.3 The Relationship between Medicaid Expansions and
the Performance of Community-Rated Markets
I conclude the analysis with estimates of equations (5) and (6). Sincethe primary explanatory variables of interest are new to these specifica-tions, I present summary statistics characterizing their state-level varia-tion in Table 6. The means in Table 6 confirm the nature of the Medicaidexpansions shown in Figure 3; community rating states expanded theirMedicaid programs to a greater degree than other states and their expan-sions disproportionately swept up unhealthy individuals. Magnitudes aresmaller than those seen in Figure 3 because the samples used to constructthe figure were restricted to adults in households with incomes less than
16There are standard reasons to worry that the fraction of the population on Medicaidis driven by unobservable economic factors. It is also possible that unobservable factorswould be correlated with the fraction of Medicaid beneficiaries who are unhealthy. Thereare no obvious reasons, however, to expect such unobservables to differ systematicallyacross states that did and did not adopt community rating.
31
300 percent of the poverty line. The standard deviations of the changes inUnhealthy Shares,t and in Medicaid’s coverage of unhealthy adults revealsubstantial variation in both the size and composition of adult Medicaidexpansions.
Table 7 presents estimates of equations (5) and (6). Public coverage ofunhealthy adults has a significant, positive partial correlation with privatecoverage in community rated markets. Columns 1 and 2 show that a 7
percentage point expansion in public coverage, exclusively reaching un-healthy adults, was associated with a 6 percentage point improvement inprivate coverage. This is roughly the size New York’s public coverage ex-pansion. Coverage of unhealthy adults has a modestly negative associationwith private coverage in the control markets. Coverage of healthy adultshas a negative, statistically significant relationship with private coveragerates in both market types.
Columns 4 through 6 report estimates of equation (6), in which the ex-planatory variable of interest is Unhealthy Shares,t =
Unhealthy Frac. Medicaids,tTotal Frac. Medicaids,t
.While there is no partial correlation between Unhealthy Shares,t and pri-vate coverage rates in the control markets, there is a strong, positive par-tial correlation between Unhealthy Shares,t and private coverage in thecommunity-rated markets. Controlling for the total fraction of the pop-ulation receiving coverage through Medicaid has little effect on this result.The coefficients on these variables show that increases in the total size ofstate Medicaid programs were associated reductions in private coverage inthe control states, but not in the community rated markets.
In columns 3 and 6 I restrict the sample to the years 2000 through 2006
and exclude the states that repealed their community rating regulationsbefore the end of the sample, namely Kentucky and New Hampshire. Theestimates are thus produced using a sample of community rating statesthat maintained community rating for the duration of the sample. Thesample begins in 2000 so that community rating had been in place for atleast 3 years in all states. This restriction helps to ensure that the estimatescharacterize the relationship between Medicaid and the long run perfor-mance of the community-rated markets. The estimate of equation (6) is
32
Tab
le 6
: In
sura
nce C
ove
rag
e R
ate
s (S
tan
dard
Devi
ati
on
s):
1995-1
997 L
eve
ls a
nd
Ch
an
ges
fro
m 1
995-1
997
to 2
003-2
006
(1
) (2
) (3
)
(4)
(5)
(6)
Var
iab
le
Sta
tes
wit
h
Co
mm
un
ity
Rat
ing
Co
ntr
ol
Sta
tes
Dif
fere
nce
Sta
tes
wit
h
Co
mm
un
ity
Rat
ing
Co
ntr
ol
Sta
tes
Dif
fere
nce
L
evel
s fr
om 1
995-1
997
C
hang
es fro
m 1
995-1
997 to
200
3-2
006
Hea
lth
y F
rac
Med
icai
d =
0.0
32
0.0
28
0.0
04
0.0
31
0.0
13
0.0
18
#
of
Hea
lth
y A
dult
s o
n M
edic
aid
/#
of
Adult
s (0
.016)
(0.0
15)
(0.0
36)
(0.0
23)
U
nh
ealt
hy
Fra
c M
edic
aid
=
0.0
41
0.0
36
0.0
05
0.0
31
0.0
07
0.0
24
#
of
Un
hea
lth
y A
dult
s o
n M
edic
aid
/#
of
Adult
s (0
.028)
(0.0
18)
(0.0
39)
(0.0
22)
To
tal M
edic
aid
=
0.0
73
0.0
64
0.0
09
0.0
62
0.0
20
0.0
42
#
of
Ad
ult
s o
n M
edic
aid
/#
of
Ad
ult
s (0
.040)
(0.0
29)
(0.0
71)
(0.0
38)
U
nh
ealt
hy
Sh
are
=
0.6
34
0.5
92
0.0
42
-0
.04
-0.0
45
0.0
05
U
nh
ealt
hy
Fra
c M
edic
aid
/T
ota
l M
edic
aid
(0
.232)
(0.1
59)
(0.2
45)
(0.2
18)
#
of
Pri
vat
ely
Co
ver
ed A
dult
s/#
of
Adult
s 0.7
08
0.6
98
0.0
10
-0
.032
-0.0
62
0.0
30
(0
.074)
(0.0
97)
(0.0
50)
(0.0
51)
#
of
Un
insu
red
Adult
s/#
of
Adult
s 0.2
31
0.2
45
-0.0
14
-0
.021
0.0
40
-0.0
61
(0
.074)
(0.0
96)
(0.0
52)
(0.0
55)
O
bse
rvat
ion
s 7
44
7
44
So
urc
es: Sum
mar
y st
atis
tics
wer
e ca
lcula
ted
by
the
auth
or
usi
ng
dat
a fr
om
th
e M
arch
Eco
no
mic
an
d D
emo
grap
hic
Sup
ple
men
t to
th
e C
urr
ent
Po
pula
tio
n
Surv
ey (
CP
S)
for
year
s 1995-1
997 a
nd
2003-2
006.
No
te: Sam
ple
s co
nsi
st o
f ad
ult
s in
ho
use
ho
lds
wit
h a
t le
ast
on
e ch
ild a
nd
on
e fu
ll-t
ime
wo
rkin
g ad
ult
, b
ut
wit
h n
o a
dult
s w
ork
ing
at a
fir
m t
hat
has
mo
re t
han
100 e
mp
loye
es. C
PS p
erso
n w
eigh
ts w
ere
app
lied
in
est
imat
ing
all n
um
ber
s re
po
rted
in
th
e ta
ble
. T
he
Co
mm
un
ity
Rat
ing
stat
es a
re K
entu
cky,
Mai
ne,
M
assa
chuse
tts,
New
Ham
psh
ire,
New
Jer
sey,
New
Yo
rk, an
d V
erm
on
t.
Th
e “C
on
tro
l” s
tate
s in
clud
e al
l o
ther
sta
tes.
T
able
val
ues
incl
ud
e m
ean
s an
d s
tan
dar
d
dev
iati
on
s, t
aken
acr
oss
th
e st
ates
wit
hin
eac
h g
rou
p. I
nd
ivid
ual
s ar
e d
efin
ed a
s b
ein
g un
hea
lth
y if
th
ey h
ave
a se
lf-r
epo
rted
hea
lth
sta
tus
of
"Go
od
," "
Fai
r,"
or
"Po
or,
" o
r if
th
ey r
epo
rt a
wo
rk-l
imit
ing
dis
abili
ty. I
nd
ivid
ual
s n
ot
mee
tin
g th
ese
crit
eria
are
def
ined
as
hea
lth
y.
Acr
oss
th
e fu
ll sa
mp
le, ro
ugh
ly 3
5 p
erce
nt
of
adult
s ar
e cl
assi
fied
as
un
hea
lth
y.
33
Tab
le 7
: Im
pact
of
Med
icaid
Exp
an
sio
ns
on
Pri
vate
Co
vera
ge R
ate
s (1
995-2
006)
Dep
enden
t V
aria
ble
: Pri
vat
e C
over
age
(1
) (2
) (3
) (4
) (5
) (6
)
Un
hea
lth
y Sh
are
x C
om
m. R
atin
g Sta
te
0.1
40**
* 0.1
31**
* 0.1
35**
*
(0.0
374)
(0.0
377)
(0.0
419)
Un
hea
lth
y Sh
are
-0
.00487
0.0
0119
-0.0
0532
(0.0
119)
(0.0
120)
(0.0
161)
To
tal M
edic
aid x
Co
mm
. R
atin
g Sta
te
0.3
74*
0.6
28**
(0
.192)
(0.2
11)
To
tal M
edic
aid
-0.3
02**
* -0
.386**
*
(0
.0988)
(0.1
14)
Un
hea
lth
y F
rac
Med
icai
d x
Co
mm
. Rat
ing
Sta
te
0.9
22**
* 0.9
39**
* 1.1
21**
*
(0
.246)
(0.2
98)
(0.2
71)
U
nh
ealt
hy
Fra
c M
edic
aid
-0.1
84
-0.1
46
-0.3
75**
*
(0
.128)
(0.1
45)
(0.1
35)
H
ealt
hy
Fra
c M
edic
aid x
Co
mm
. R
atin
g Sta
te
-0.2
11
-0.1
29
-0.0
836
(0.3
11)
(0.3
64)
(0.3
28)
H
ealt
hy
Fra
c M
edic
aid
-0.4
66**
* -0
.383**
-0
.413**
(0
.162)
(0.1
86)
(0.1
80)
Sta
te F
ixed
Eff
ects
and Y
ear
Eff
ects
Y
es
Yes
Y
es
Yes
Y
es
Yes
Yea
r E
ffec
ts x
Co
mm
un
ity
Rat
ing
Yes
Y
es
Yes
Y
es
Yes
Y
es
Add
itio
nal
Co
ntr
ols
Y
es
Yes
Y
es
Yes
Y
es
Yes
Ch
ildre
n?
Yes
N
o
Yes
Y
es
Yes
Y
es
New
Ham
psh
ire
and K
entu
cky?
/1990s?
Y
es/Y
es
Yes
/Y
es
No
/N
o
Yes
/Y
es
Yes
/Y
es
No
/N
o
Ob
serv
atio
ns
291,4
36
141,5
74
206,1
77
291,4
36
291,4
36
206,1
77
***,
**,
an
d *
in
dic
ate
stat
isti
cal si
gn
ific
ance
at
the
.01, .0
5, an
d .10 lev
els
resp
ecti
vel
y.
Sta
nd
ard
err
ors
, re
po
rted
ben
eath
eac
h p
oin
t es
tim
ate,
wer
e ca
lcula
ted
usi
ng
a b
lock
bo
ots
trap
ap
pro
ach
wit
h c
lust
ers
dra
wn
at
the
stat
e le
vel
. I
n c
olu
mn
s 1 t
hro
ugh
3, th
e re
po
rted
est
imat
es a
re o
f th
e β
1 t
hro
ugh
β4 f
rom
equat
ion
(5).
In
co
lum
ns
4 t
hro
ugh
6, th
e re
po
rted
est
imat
es a
re
of
the
β1 t
hro
ugh
β4 f
rom
equat
ion
(6).
Sam
ple
s w
ere
taken
fro
m t
he
Mar
ch E
con
om
ic a
nd
Dem
ogr
aph
ic S
up
ple
men
t to
th
e C
urr
ent
Po
pula
tio
n S
urv
ey (
CP
S)
for
year
s 1995-2
006. T
hey
co
nsi
st o
f m
emb
ers
of
ho
use
ho
lds
wit
h a
t le
ast
on
e ch
ild a
nd
on
e fu
ll-t
ime
wo
rkin
g ad
ult
, b
ut
wit
h n
o a
dult
s w
ork
ing
at a
fir
m t
hat
has
mo
re t
han
100 e
mp
loye
es. T
he
sam
ple
s in
Co
lum
ns
1,
3 a
nd
4 t
hro
ugh
6 in
clud
e al
l m
emb
ers
of
thes
e h
ouse
ho
lds
wh
ile
the
sam
ple
in
co
lum
n 2
exc
lud
es c
hild
ren
. T
he
resu
lts
are
fro
m O
LS r
egre
ssio
ns
that
in
clud
e co
ntr
ols
fo
r st
ate
and
yea
r fi
xed
eff
ects
, a
set
of
year
dum
my
var
ible
s in
tera
cted
wit
h a
n in
dic
ato
r fo
r in
-fo
rce
com
mun
ity
rati
ng
regi
mes
, a
set
of
2-d
igit
occ
up
atio
n d
um
my
var
iab
les,
reg
ion
dum
my
var
iab
les
inte
ract
ed
wit
h f
amily
in
com
e as
a p
erce
nt
of
the
po
ver
ty lin
e, a
n in
dic
ato
r fo
r h
avin
g a
sin
gle
mo
ther
as
the
ho
use
ho
ld's
hea
d, an
d a
n i
nd
icat
or
for
bei
ng
bla
ck, ad
dit
ion
al in
dic
ato
rs f
or
hav
ing
two
full
tim
e w
ork
ers
in t
he
ho
use
ho
ld a
nd
fo
r th
e ed
uca
tio
n lev
els
of
ho
use
ho
ld a
dult
s, a
n in
dic
ato
r fo
r h
om
e o
wn
ersh
ip, an
d a
ge g
rou
p in
dic
ato
rs. T
he
var
iab
les
rep
ort
ed in
th
e ta
ble
are
def
ined
an
d d
escr
ibed
in
Tab
le 6
.
34
unaffected by these sample restrictions. The primary coefficient of interestin the estimate of equation (5) is moderately strengthened.
7.4 A Check on the Plausibility of the Estimates
To assess the plausibility of the estimated effects, Appendix 6 presentsa calibration, conducted using data from the Medical Expenditure PanelSurvey (MEPS), of the potential effect of public insurance expansions onpremiums in community rated markets. The calibration shows that post-1993 public insurance expansions had the potential to hold community-rated premiums down by around $1,700 for a family of 4, with most of thisimpact coming from coverage of unhealthy adults.
This premium impact can be translated into a coverage change by ex-pressing it as a percent of the relevant premiums and multiplying by theextensive-margin elasticity of demand for insurance. Bernard and Banthin(2008) estimate that, in 2002, the average non-group premium for familieswas around $4,400 while the average small group premium was $8,500.$1,700 is roughly 25 percent of the average family premium in the non-and small-group markets. Chernew, Cutler and Keenan (2005) estimatethat the elasticity of insurance take-up with respect to premiums is approx-imately -0.1, while Marquis and Long (1995) estimate an elasticity of -0.4.Elasticities inferred from survey data by Krueger and Kuziemko (2013) arecloser to -1. With an elasticity on the order of -0.4, the Medicaid expansionsof community rating states could explain increases in private coverage ofroughly 7 percentage points on a baseline coverage rate of 70 percent.
8 Conclusion
This paper studies the relationship between two prominent instrumentsof health policy: community rating regulations and public insurance throughstate Medicaid programs. The effects of these policies are fundamentallyconnected. Community rating regulations risk substantial adverse selec-tion when large numbers of unhealthy individuals remain on the private
35
market. When targeted at relatively high cost populations, Medicaid ex-pansions can combat this adverse selection. Medicaid expansions are thusamong a suite of policies that mediate the performance of community ratedinsurance markets. Medicaid’s coverage of the unhealthy can reduce thesize of the subsidies and/or tax penalties required to stabilize such mar-kets. It can similarly be viewed as a complement to risk adjustment pro-grams.
The 2010 Patient Protection and Affordable Care Act (PPACA) containsregulatory measures including community rating rules, guaranteed issuerequirements, and an individual mandate to purchase insurance. Three ofPPACA’s features are designed to go farther than previous regulations toinduce pooling of the healthy and sick.17 First, it taxes healthy individu-als who forego insurance. Second, it limits adjustment along the intensivemargin of insurance generosity. Specifically, it expands minimum cover-age requirements and tightens limits on out-of-pocket spending. Third, itsguaranteed issue requirements are more stringent than those typically inplace across the states.
PPACA’s regulations may result in significant pressure to shift the costof unhealthy individuals out of the insurance exchanges. The law wouldgenerously finance such efforts, as the federal government will reimbursemore than 90 percent of the cost of its associated Medicaid expansions.18
Both the implementation of these expansions and their impact on states’insurance markets remain uncertain. These issues will be ripe for study asPPACA’s implementation unfolds.
References
Baicker, Katherine, Jeffrey Clemens, and Monica Singhal. 2012. “The rise
17See Kaiser (2010) for a detailed summary of the PPACA’s provisions.
18The federal government’s use of these levers provides a prominent example of thestate-federal interactions driving the rise of state spending on redistributive programsover the last half century (Baicker, Clemens and Singhal, 2012).
36
of the states: U.S. fiscal decentralization in the postwar period.” Journalof Public Economics, 96(11 - 12): 1079 – 1091.
Bernard, D., and J. Banthin. 2008. “Premiums in the Individual Health In-surance Market for Policyholders under Age 65: 2002 and 2005.” Agencyfor Healthcare Research and Quality 202, Rockville, MD.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004.“How much should we trust differences-in-differences estimates?” TheQuarterly Journal of Economics, 119(1): 249–275.
Buchmueller, Thomas, and John DiNardo. 2002. “Did community ratinginduce an adverse selection death spiral? Evidence from New York,Pennsylvania, and Connecticut.” American Economic Review, 92(1): 280–294.
Buchmueller, Thomas C., and Su Liu. 2005. “Health insurance reform andHMO penetration in the small group market.” Inquiry, 42(4).
Chernew, Michael, David M. Cutler, and Patricia S. Keenan. 2005. “In-creasing health insurance costs and the decline in insurance coverage.”Health Services Research, 40(4): 1021–1039.
Clemens, Jeffrey, and Joshua Gottlieb. 2013. “Bargaining in the Shadowof a Giant: Medicare’s Influence on Private Payment Systems.” NationalBureau of Economic Research Working Paper 19503.
Currie, Janet, and Jonathan Gruber. 1996. “Health insurance eligibility,utilization of medical care, and child health.” Quarterly Journal of Eco-nomics, 111(2): 431–466.
Cutler, David M, Amy Finkelstein, and Kathleen McGarry. 2008. “Pref-erence Heterogeneity and Insurance Markets: Explaining a Puzzle ofInsurance.” American Economic Review, 98(2): 157–62.
Cutler, David M., and Jonathan Gruber. 1996. “Does public insurancecrowd out private insurance.” Quarterly Journal of Economics, 111(2): 391–430.
37
Cutler, David M., and Sarah J. Reber. 1998. “Paying for Health Insurance:The Trade-Off between Competition and Adverse Selection*.” QuarterlyJournal of Economics, 113(2): 433–466.
Davidoff, Amy, Linda Blumberg, and Len Nichols. 2005. “State healthinsurance market reforms and access to insurance for high-risk employ-ees.” Journal of Health Economics, 24(4): 725–750.
Einav, Liran, Amy Finkelstein, and Stephen P Ryan. 2013. “Selectionon Moral Hazard in Health Insurance.” American Economic Review,103(1): 178–219.
Einav, Liran, and Amy Finkelstein. 2010. “Selection in insurance mar-kets: theory and empirics in pictures.” Journal of Economic Perspectives,25(1): 115–138.
Ericson, Keith M Marzilli, and Amanda Starc. 2012. “Pricing Regulationand Imperfect Competition on the Massachusetts Health Insurance Ex-change.” National Bureau of Economic Research Working Paper 18089.
Finkelstein, Amy, James Poterba, and Casey Rothschild. 2009. “Redis-tribution by insurance market regulation: Analyzing a ban on gender-based retirement annuities.” Journal of financial economics, 91(1): 38–58.
GAO. 2003. “Federal and State Requirements Affecting Coverage Offeredby Small Businesses.” Government Accountability Office GAO-03-1133.
Gruber, Jonathan, and Kosali Simon. 2008. “Crowd-out 10 years later:Have recent public insurance expansions crowded out private health in-surance?” Journal of Health Economics, 27(2): 201–217.
Hackmann, Martin B, Jonathan T Kolstad, and Amanda E Kowalski.2012. “Health Reform, Health Insurance, and Selection: Estimating Se-lection into Health Insurance Using the Massachusetts Health Reform.”National Bureau of Economic Research Working Paper 19149.
38
Handel, Ben, Igal Hendel, and Michael D Whinston. 2013. “Equilibria inHealth Exchanges: Adverse Selection vs. Reclassification Risk.” NationalBureau of Economic Research Working Paper.
Herring, Brad, and Mark V. Pauly. 2006. “The Effect of State CommunityRating Regulations on Premiums and Coverage in the Individual HealthInsurance Market.” National Bureau of Economic Research Working Pa-per.
Kaiser. 2010. “Summary of New Health Reform Law.” The Henry J. KaiserFamily Foundation 8061, Menlo Park, CA.
Kaiser. 2011. “FIVE KEY QUESTIONS AND ANSWERS ABOUT SECTION1115 MEDICAID DEMONSTRATION WAIVERS.” The Henry J. KaiserFamily Foundation 8196, Menlo Park, CA.
Kaiser. 2012. “The Medicaid Medically Needy Program: Spending and En-rollment Update.” The Henry J. Kaiser Family Foundation 4096, MenloPark, CA.
Krueger, Alan B, and Ilyana Kuziemko. 2013. “The demand for healthinsurance among uninsured Americans: Results of a survey experimentand implications for policy.” Journal of health economics, 32(5): 780–793.
LoSasso, A. T., and I. Z. Lurie. 2009. “Community rating and the marketfor private non-group health insurance.” Journal of Public Economics, 93(1-2): 264–279.
Marquis, M. Susan, and Stephen H. Long. 1995. “Worker demand forhealth insurance in the non-group market.” Journal of Health Economics,14(1): 47–63.
Monheit, Alan C., and Barbara S. Schone. 2004. “How has small groupmarket reform affected employee health insurance coverage?” Journal ofPublic Economics, 88(1-2): 237–254.
39
Pinkovskiy, Maxim L. 2014. “The Impact of the Political Response to theManaged Care Backlash on Health Care Spending: Evidence from StateRegulations of Managed Care.”
Rothschild, Michael, and Joseph Stiglitz. 1976. “Equilibrium in compet-itive insurance markets: An essay on the economics of imperfect infor-mation.” Quarterly Journal of Economics, 90(4): 629–649.
Scheuer, Florian, and Kent Smetters. 2014. “Could a Website Really HaveDoomed the Health Exchanges? Multiple Equilibria, Initial Conditionsand the Construction of the Fine.” National Bureau of Economic Re-search Working Paper 19835.
Siegel, Bruce, Anne Weiss, and Diane Lynch. 1990. “Setting New Jerseyhospital rates: a regulatory system under stress.” U. Puget Sound L. Rev.,14: 601.
Simon, Kosali. 2005. “Adverse selection in health insurance markets? Evi-dence from state small-group health insurance reforms.” Journal of PublicEconomics, 89(9-10): 1865–1877.
Sloan, Frank A., and Christopher J. Conover. 1998. “Effects of state re-forms on health insurance coverage of adults.” Inquiry, 35(3): 280–293.
Wachenheim, Leigh, and Hans Leida. 2007. “The impact of guaranteedissue and community rating reforms on individual insurance markets.”Americas Health Insurance Plans Center for Policy and Research.
Zuckerman, Stephen, and Shruti Rajan. 1999. “An alternative approach tomeasuring the effects of insurance market reforms.” Inquiry, 36(1): 44–56.
Zuckerman, Stephen, Joshua McFeeters, Peter Cunningham, and LenNichols. 2004. “Trends: Changes In Medicaid Physician Fees, 1998-2003:Implications For Physician Participation.” Health Affairs.
40
Appendix (For Online Publication)
A.1: Alternative Selection of Control and Treat-
ment States
Tables A1 and A2 explore the results obtained when estimating equa-tion (3) using alternative criteria for selecting the sample of either the treat-ment or control states. Table A1 focuses on the initial effect of implement-ing community rating (following Panels A from Table 4) while Table A2
focuses on the recoveries of community rated markets during the early2000s (following Panels C from Table 4).
Rows 1 through 6 of Tables A1 and A2 display results that involve pool-ing the full set of community-rating states and using alternative criteria forrestricting the sample of control states. When no states are excluded fromthe analysis, the estimated decline in coverage is 8 percentage points, con-sistent with results reported in Table 4. Restricting the control group tothe non-community rating states that voted for Al Gore in the 2000 Pres-idential election slightly increases the estimated coverage decline. Thisrestriction has essentially no effect on the estimated size of the subsequentcoverage recoveries. The same can be said for the results obtained using4 samples selected on the basis of estimates of each state’s propensity toadopt community rating regimes. Propensity score 1 was estimated on thebasis of state-level economic and demographic characteristics and an in-dicator for whether or not the state voted for Al Gore. Propensity score2 is based solely on state-level economic and demographic characteristics.Propensity scores 3 and 4 are equivalent to 1 and 2, but are based on theeconomic and demographic characteristics of households on the non- andsmall-group insurance markets (as opposed to the entire state population).
Rows 7 through 13 of Tables A1 and A2 investigate the results’ sensitiv-ity to excluding any one of the community rating states from the sample.New York emerges as an important contributor to the magnitudes of theresults in both tables. Estimates of both the initial coverage declines andlater coverage recoveries decline by around 2 percentage points when New
41
Table A1: Effects of Adopting Community Rating on Private Insurance Coverage:
Robustness Across Alternative Samples of Control and Treatment States
(1) (2) (3) (4)
Row Sample Selection Point Estimate Std. Error
1 All Treatment and Control States -0.0845*** (0.0128)
2 Sample Restricted to Goree 2000 States -0.0967*** (0.0108)
3 P-score 1 Control Sample -0.0847*** (0.0136)
4 P-score 2 Control Sample -0.0865*** (0.0132)
5 P-score 3 Control Sample -0.0890*** (0.0123)
6 P-score 4 Control Sample -0.0947*** (0.0116)
7 All States But NY -0.0647*** (0.0152)
8 All States But NJ -0.0862*** (0.0152)
9 All States But ME -0.0860*** (0.0126)
10 All States But VT -0.0851*** (0.0128)
11 All States But MA -0.0853*** (0.0129)
12 All States But NH -0.0896*** (0.0101)
13 All States But KY -0.0849*** (0.0135)
14 Only Treatment States that Maintened Their Regs. (MA, ME, NY, NJ and VT)
-0.0907*** (0.0102)
15 Only Treatment States with Pure Community Rating (NY and VT)
-0.0995*** (0.00796)
16 Augmented Treatment Group Including States with Weak Regs. (All from Table 2)
-0.0404 (0.0241)
***, **, and * indicate statistical significance at the .01, .05, and .10 levels respectively. Standard errors, reported beneath each point estimate, allow for clusters at the state level. The reported estimates are of β1 from equation (3). The sample consists of members of households from the March Current Population Survey in years 1987-1992 and 1996-1997 that have at least one child aged 18 and lower at least one full-time working adult. Each reported coefficient comes from a separate OLS regression. Additional sample inclusion criteria are described concisely in Column 2 and in greater detail in the main text. In the "P-Score" samples, control states were selected on the basis of cutoffs associated with estimates of each state's propensity to adopt community rating. The cutoffs were chosen to keep the number of control states in the neighborhood of 15-20.
42
Table A2: Post-1997 Private Coverage Recoveries of Community Rated Markets:
Robustness Across Alternative Samples of Control and Treatment States
(1) (2) (3) (4)
Row Sample Selection Point Estimate Std. Error
1 All Treatment and Control States 0.0481*** (0.0179)
2 Sample Restricted to Goree 2000 States 0.0523** (0.0236)
3 P-score 1 Control Sample 0.0465** (0.0195)
4 P-score 2 Control Sample 0.0488** (0.0203)
5 P-score 3 Control Sample 0.0513** (0.0203)
6 P-score 4 Control Sample 0.0547** (0.0202)
7 All States But NY 0.0241 (0.0151)
8 All States But NJ 0.0572*** (0.0165)
9 All States But ME 0.0515*** (0.0171)
10 All States But VT 0.0499*** (0.0177)
11 All States But MA 0.0526*** (0.0185)
12 All States But NH 0.0489*** (0.0181)
13 All States But KY 0.0438** (0.0197)
14 Only Treatment States that Maintened Their Regs. (MA, ME, NY, NJ and VT)
0.0445** (0.0200)
15 Only Treatment States with Pure Community Rating (NY and VT)
0.0700*** (0.0105)
16 Augmented Treatment Group Including States with Weak Regs. (All from Table 2)
0.00109 (0.0242)
***, **, and * indicate statistical significance at the .01, .05, and .10 levels respectively. Standard errors, reported beneath each point estimate, allow for clusters at the state level. The reported estimates are of β1 from equation (3). The sample consists of members of households from the March Current Population Survey in years 1996-1997 and 2003-2006 that have at least one child aged 18 and lower at least one full-time working adult. Each reported coefficient comes from a separate OLS regression. Additional sample inclusion criteria are described concisely in Column 2 and in greater detail in the main text. In the "P-Score" samples, control states were selected on the basis of cutoffs associated with estimates of each state's propensity to adopt community rating. The cutoffs were chosen to keep the number of control states in the neighborhood of 15-20.
43
York is excluded from the sample. New Jersey pushes the estimated sizeof the recovery down by roughly 1 percentage point. The New York andNew Jersey outcomes are important drivers of the results presented in Ta-ble 7. New York was the most aggressive of the community rating states inits expansion of Medicaid for unhealthy adults, while New Jersey was theleast.
The final rows of Tables A1 and A2 explore differences in the effects ofcommunity rating across groups of states that may objectively be expectedto have different experiences. Row 14 excludes states that abandoned theirregulations during the sample (i.e., New Hampshire and Kentucky). As acheck on the plausibility of the public insurance mechanism, it is essentialthat these states do not drive the results, and indeed they do not. Row 15
restricts the treatment group to New York and Vermont, which were theonly states to implement pure (as opposed to modified) community rat-ing laws in both their non- and small-group markets. Finally, I considerthe effect of adding less-tightly regulated states to the treatment group.Specifically, I define the treatment group to include all states describedin Table 1; this includes 6 additional states which either had weak guar-anteed issue requirements or which enforced community rating in theirsmall-group markets, but not in their non-group markets. The additionof these less-tightly regulated states significantly reduces the estimated ef-fects of community rating. In both tables the estimates become statisticallyinsignificant, suggesting that relatively modest regulations cause much lessdisruption in insurance markets than community rating regimes as definedthroughout this paper.
These last results suggest that regulating both of the markets to whichhouseholds have access has much greater effects than regulating one ofthem. It is also relevant that 4 of the 6 less tightly regulated states utilizedhigh risk pools during the 1990s. High risk pools provide subsidized cov-erage for high cost types who would otherwise put upward pressure oncommunity-rated premiums. As indicated in the last column of Table 1,none of the community rating states made use of such pools as means tolimit adverse selection pressures during the sample-period.
44
A.2: Presentation of State-Level Variation
Appendix Figure A1 presents state-level, regression adjusted changesin insurance coverage. These state-level observations display variation atthe level at which observations were re-sampled for purposes of block-bootstrap estimation of the standard errors. The variation displayed isthus the variation underlying the estimates reported in Table 5, Table A.1,and Table A.2. Figures A2, A3, and A4 present unadjusted tabulations, ofprivate coverage, the fraction uninsured, and Medicaid’s coverage of lowincome adults, in which the evolution of coverage rates is presented on atreatment state by treatment state basis.
Panel A of Figure A.1 shows changes in private coverage across theset of community-rating states from the base period of 1988-1992 through1996-1997. Each of the early-adopting states with stable regulatory regimes,namely Maine, New York, and Vermont, experienced declines well in ex-cess of the average change experienced by other states. The point estimateassociated with this group is little affected by excluding any one of them.The similarity of the experience of these states underlies the relatively smallblock-bootstrapped standard errors reported in column 1 of Table 4’s Pan-els A and B.
Because coverage in Maine and Vermont is estimated using smallernumbers of underlying observations, year-by-year tabulations of coverageare, of course, noisier for these states than for New York. New York isunique in that there is sufficient data for estimates of its annual coveragerates to move smoothly. Following are the number of observations asso-ciated with each of the community rating states for 1996 and 1997: NewYork, 7,216; Maine, 1,049; Vermont, 1,176; New Hampshire, 1,113; NewJersey, 3,623; Massachusetts, 2,410; Kentucky, 1,447. When restricted to thenon- and small-group market samples, the numbers of observations are:New York, 2,040; Maine, 305; Vermont, 358; New Hampshire, 238; NewJersey, 874; Massachusetts, 554; Kentucky, 320.
Among the states with relatively staggered adoption of their commu-nity rating rules, New Jersey and Kentucky experienced substantial cover-
45
KY
VT
NJ
ME
NH
MA
NY
-.2-.15-.1-.050.05Change in Private Insurance
Unr
eg. S
tate
sR
eg. S
tate
sT
reat
men
t Sta
tus
Priv
ate:
198
8-19
92 to
199
6-19
97
KY
VT
NJ
ME
NH
MA
NY
-.1-.050.05.1.15Change in Fraction Uninsured
Unr
eg. S
tate
sR
eg. S
tate
sT
reat
men
t Sta
tus
Uni
nsur
ed: 1
988-
1992
to 1
996-
1997
KY
VT
NH
ME
MA
NJ
NY
-.15-.1-.050.05Change in Private Insurance
Unr
eg. S
tate
sR
eg. S
tate
sT
reat
men
t Sta
tus
Priv
ate:
199
6-19
97 to
200
3-20
06
KY
VT
NH
ME
MA
NJ
NY
-.15-.1-.050.05.1Change in Fraction Uninsured
Unr
eg. S
tate
sR
eg. S
tate
sT
reat
men
t Sta
tus
Uni
nsur
ed: 1
996-
1997
to 2
003-
2006
Reg
ress
ion
Adj
uste
d C
hang
es in
Insu
ranc
e S
tatu
s by
Sta
te
Figu
reA
.1:
Reg
ress
ion
Adj
uste
dC
hang
esin
Insu
ranc
eSt
atus
bySt
ate.
The
figur
esco
ntai
nsth
est
ate
leve
l,re
gres
sion
adju
sted
chan
ges
inin
sura
nce
stat
usth
atun
derl
yth
ees
tim
ates
pres
ente
din
Tabl
e4.
Mar
ker
size
refle
cts
the
sum
ofth
eC
PSsa
mpl
ew
eigh
tsas
soci
ated
wit
hth
eob
serv
atio
nsus
edto
prod
uce
each
stat
e’s
esti
mat
e.
46
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
A: N
ew Y
ork
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
B: M
aine
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
C: V
erm
ont
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
D: N
ew J
erse
y
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
E: M
assa
chus
etts
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
F: N
ew H
amps
hire
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
G: K
entu
cky
Evo
lutio
n of
Priv
ate
Cov
erag
e by
Tre
atm
ent S
tate
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
trol
Sta
tes
Sm
all-
and
Non
-Gro
up M
arke
ts in
Tre
atm
ent S
tate
s
Figu
reA
.2:
Una
djus
ted
Tabu
lati
ons
ofPr
ivat
eC
over
age.
The
figur
eco
ntai
nsta
bula
tion
sco
nstr
ucte
dus
ing
the
Mar
chD
emo-
grap
hic
Supp
lem
ents
ofth
eC
urre
ntPo
pula
tion
Surv
ey(C
PS).
Sam
ples
cons
ist
ofth
ead
ults
and
child
ren
(age
d18
and
low
er)
inho
useh
olds
wit
hat
leas
ton
ech
ildan
dat
leas
ton
efu
ll-ti
me
wor
king
adul
t.In
each
pane
l,th
e“C
ontr
ol”
stat
eta
bula
tion
sre
plic
ate
that
show
nin
Pane
lC
ofFi
gure
1.
Tabu
lati
ons
for
trea
tmen
tst
ates
are
show
non
ast
ate-
by-s
tate
basi
s,as
indi
cate
din
each
Pane
l’sti
tle.
For
New
York
,Mai
ne,a
ndVe
rmon
t,th
eve
rtic
allin
eat
1993
indi
cate
sth
eyye
arin
whi
chal
lnon
-an
dsm
all-
grou
pco
mm
unit
yra
ting
regi
mes
wer
eim
plem
ente
d.Fo
rN
ewJe
rsey
,M
assa
chus
etts
,an
dN
ewH
amps
hire
,th
elin
esin
dica
teth
eye
ars
over
whi
chco
mm
unit
yra
ting
regu
lati
ons
wer
een
acte
d.Fo
rN
ewH
amps
hire
and
Ken
tuck
y,th
eda
shed
vert
ical
line
indi
cate
sth
eye
arin
whi
chco
mm
unit
yra
ting
regu
lati
ons
wer
ere
peal
ed.
47
0.08.16.24.32Fraction Uninsured
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
A: N
ew Y
ork
0.08.16.24.32Fraction Uninsured
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
B: M
aine
0.08.16.24.32Fraction Uninsured
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
C: V
erm
ont
0.08.16.24.32Fraction Uninsured
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
D: N
ew J
erse
y
0.08.16.24.32Fraction Uninsured
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
E: M
assa
chus
etts
0.08.16.24.32Fraction Uninsured
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
F: N
ew H
amps
hire
0.08.16.24.32Fraction Uninsured
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
G: K
entu
cky
Evo
lutio
n of
Fra
ctio
n U
nins
ured
by
Tre
atm
ent S
tate
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
trol
Sta
tes
Sm
all-
and
Non
-Gro
up M
arke
ts in
Tre
atm
ent S
tate
s
Figu
reA
.3:
Una
djus
ted
Tabu
lati
ons
ofth
eFr
acti
onU
nins
ured
.Th
efig
ure
cont
ains
tabu
lati
ons
cons
truc
ted
usin
gth
eM
arch
Dem
ogra
phic
Supp
lem
ents
ofth
eC
urre
ntPo
pula
tion
Surv
ey(C
PS).
Sam
ples
cons
ist
ofth
ead
ults
and
child
ren
(age
d18
and
low
er)
inho
useh
olds
wit
hat
leas
tone
child
and
atle
asto
nefu
ll-ti
me
wor
king
adul
t.In
each
pane
l,th
e“C
ontr
ol”
stat
eta
bula
tion
sre
plic
ate
that
show
nin
Pane
lC
ofFi
gure
1.
Tabu
lati
ons
for
trea
tmen
tst
ates
are
show
non
ast
ate-
by-s
tate
basi
s,as
indi
cate
din
each
Pane
l’sti
tle.
For
New
York
,Mai
ne,a
ndVe
rmon
t,th
eve
rtic
allin
eat
1993
indi
cate
sth
eyye
arin
whi
chal
lnon
-an
dsm
all-
grou
pco
mm
unit
yra
ting
regi
mes
wer
eim
plem
ente
d.Fo
rN
ewJe
rsey
,M
assa
chus
etts
,an
dN
ewH
amps
hire
,th
elin
esin
dica
teth
eye
ars
over
whi
chco
mm
unit
yra
ting
regu
lati
ons
wer
een
acte
d.Fo
rN
ewH
amps
hire
and
Ken
tuck
y,th
eda
shed
vert
ical
line
indi
cate
sth
eye
arin
whi
chco
mm
unit
yra
ting
regu
lati
ons
wer
ere
peal
ed.
48
0.1.2.3.4Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
A: N
ew Y
ork
0.1.2.3.4Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
B: M
aine
0.1.2.3.4Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
C: V
erm
ont
0.1.2.3.4Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
D: N
ew J
erse
y
0.1.2.3.4Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
E: M
assa
chus
etts
0.1.2.3.4Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
F: N
ew H
amps
hire
0.1.2.3.4Fraction on Medicaid
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Unr
estr
icte
d S
ampl
eP
anel
G: K
entu
cky
Evo
lutio
n of
Med
icai
d C
over
age
for
Low
Inco
me
Adu
lts
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
trol
Sta
tes
Sm
all-
and
Non
-Gro
up M
arke
ts in
Tre
atm
ent S
tate
s
Figu
reA
.4:
Una
djus
ted
Tabu
lati
ons
ofM
edic
aid
Cov
erag
eof
Low
Inco
me
Adu
lts.
The
figur
eco
ntai
nsta
bula
tion
sco
nstr
ucte
dus
ing
the
Mar
chD
emog
raph
icSu
pple
men
tsof
the
Cur
rent
Popu
lati
onSu
rvey
(CPS
).Sa
mpl
esco
nsis
toft
head
ults
(age
d21
and
over
)in
hous
ehol
dsw
ith
atle
ast
one
child
,at
leas
ton
efu
ll-ti
me
wor
king
adul
t,an
dho
useh
old
inco
me
belo
w300
perc
ent
ofth
epo
vert
ylin
e.In
each
pane
l,th
e“C
ontr
ol”
stat
eta
bula
tion
sre
plic
ate
that
show
nin
Pane
lC
ofFi
gure
1.
Tabu
lati
ons
for
trea
tmen
tst
ates
are
show
non
ast
ate-
by-s
tate
basi
s,as
indi
cate
din
each
Pane
l’sti
tle.
For
New
York
,M
aine
,an
dVe
rmon
t,th
eve
rtic
allin
eat
1993
indi
cate
sth
eyye
arin
whi
chal
lnon
-an
dsm
all-
grou
pco
mm
unit
yra
ting
regi
mes
wer
eim
plem
ente
d.Fo
rN
ewJe
rsey
,Mas
sach
uset
ts,
and
New
Ham
pshi
re,t
helin
esin
dica
teth
eye
ars
over
whi
chco
mm
unit
yra
ting
regu
lati
ons
wer
een
acte
d.Fo
rN
ewH
amps
hire
and
Ken
tuck
y,th
eda
shed
vert
ical
line
indi
cate
sth
eye
arin
whi
chco
mm
unit
yra
ting
regu
lati
ons
wer
ere
peal
ed.
49
age declines. It is perhaps not surprising that Massachusetts experienceda modest decline since it did not enact community rating in its non-groupmarket until 1997 (its small group market had been regulated since 1992).New Hampshire was, similarly, not one of the earliest movers.
The lower panels figure A1 show the state-level, regression adjustedrecoveries experienced from 1996-1997 through 2003-2006. This period’scoverage changes vary significantly across the set of community ratingstates. Over this period, New York performed much better than Maineand Vermont. New York thus drives what we see in the coverage tabula-tions presented in Panels C and D of Figure 2. The variation within thisgroup is reflected in the relatively large standard error associated with col-umn 1 of Panel D in Table 4. The variation presented in Panels C and Dof Figure A1 is precisely the variation found to be strongly correlated withchanges in Medicaid’s coverage of relatively unhealthy adults in Section 7.
A.3: Robustness to Supplementing Controls with
State Economic Aggregates
Appendix Tables A.3 and A.4 explore the robustness of the baselinedifference-in-differences estimates to controlling for economic aggregatesand growth in state-level health expenditures. Table A.3 reports the ro-bustness of the estimates of initial coverage declines. Table A.4 reports therobustness of the estimates of subsequent coverage recoveries.
Controlling directly for economic aggregates (specifically the employ-ment to population ratio and income per capita) has essentially no impacton the results. The estimated coefficients on these variables are statisticallyindistinguishable from 0. I take this as evidence that the individual- andhousehold-level economic and demographic controls proved sufficient ascontrols for the state of the economy. This was not guaranteed; aggregateeconomic activity could very well influence insurance offerings for reasonsbeyond its implications for household-level economic conditions. In prac-tice, however, this does not appear to be the case.
50
Tab
le A
3:
Ro
bu
stn
ess
of
Base
lin
e o
n t
he E
ffects
of
Imp
lem
en
tin
g C
om
mu
nit
y R
ati
ng
to
In
clu
sio
n o
f S
tate
Eco
no
mic
Ag
gre
gate
s
Dep
. V
aria
ble
: P
rivat
e C
over
age
(1
) (2
) (3
) (4
) (5
) (6
) (7
) (8
) C
om
m. R
atin
g Sta
te x
Po
st R
egula
tio
n
-0.0
962**
* -0
.0960**
* -0
.0956**
* -0
.0929**
* -0
.0666**
* -0
.0630**
* -0
.0628**
* -0
.0641**
*
(0.0
0974)
(0.0
167)
(0.0
112)
(0.0
0990)
(0.0
164)
(0.0
175)
(0.0
171)
(0.0
162)
Hea
lth
Sp
end
ing
Per
cap
. (1
000s)
-0
.000349
-0
.0155
(0.0
230)
(0
.0211)
Inco
me
Per
cap
. (1000s)
-0.0
0138
-0
.00425
(0
.00653)
(0
.00557)
E
mp
loym
ent
to P
op
ula
tion
0.1
01
0.1
59
(0.1
85)
(0
.181)
Ob
serv
atio
ns
127,5
54
127,5
54
127,5
54
127,5
54
126,4
98
126,4
98
126,4
98
126,4
98
***,
**,
an
d *
in
dic
ate
stat
isti
cal si
gnif
ican
ce a
t th
e .0
1, .0
5, an
d .10 lev
els
resp
ecti
vel
y.
Sta
nd
ard
err
ors
, re
po
rted
ben
eath
eac
h p
oin
t es
tim
ate,
wer
e cl
ust
ered
at
the
stat
e le
vel
. C
olu
mn
1 r
eplic
ates
th
e p
oin
t es
tim
ate
fro
m c
olu
mn
1 o
f P
anel
A in
Tab
le 5
. C
olu
mn
5 r
eplic
ates
th
e p
oin
t es
tim
ate
fro
m c
olu
mn
2 o
f P
anel
A
in T
able
5. A
s in
dic
ated
, ad
dit
ion
al s
pec
ific
atio
ns
con
tain
co
ntr
ols
fo
r st
ate
level
hea
lth
sp
end
ing
per
cap
ita
(in
1000s
of
do
llar
s), in
com
e p
er c
apit
a (i
n 1
000s
of
do
llars
), a
nd
th
e em
plo
ymen
t to
po
pula
tio
n r
atio
. S
tate
hea
lth
sp
end
ing
was
tak
en f
rom
th
e N
atio
nal
Hea
lth
Exp
end
iture
Acc
ou
nts
, w
hile
sta
te p
erso
nal
in
com
e, e
mp
loym
ent,
an
d p
op
ula
tio
n w
ere
taken
fro
m t
he
Bu
reau
of
Eco
no
mic
An
alys
is.
51
Tab
le A
4:
Ro
bu
stn
ess
of
Base
lin
e o
n P
riva
te C
ove
rag
e R
eco
veri
es
to I
nclu
sio
n o
f S
tate
Eco
no
mic
Ag
gre
gate
s
Dep
. V
aria
ble
: P
rivat
e C
over
age
(1
) (2
) (3
) (4
) (5
) (6
) (7
) (8
) C
om
m. R
atin
g Sta
te x
Po
st 2
002
0.0
628**
* 0.1
10**
* 0.0
638**
* 0.0
717**
* 0.0
323**
0.0
604**
* 0.0
374**
0.0
400**
*
(0.0
158)
(0.0
153)
(0.0
172)
(0.0
155)
(0.0
154)
(0.0
200)
(0.0
183)
(0.0
133)
Hea
lth
Sp
end
ing
Per
cap
. (1
000s)
-0
.0699**
*
-0.0
655**
*
(0.0
155)
(0
.0146)
Inco
me
Per
cap
. (1000s)
-0.0
00833
-0
.00240
(0
.00391)
(0
.00401)
E
mp
loym
ent
to P
op
ula
tion
-0
.501
-0
.657
(0.4
03)
(0
.405)
Ob
serv
atio
ns
141,8
17
141,8
17
141,8
17
141,8
17
139,8
45
139,8
45
139,8
45
139,8
45
***,
**,
an
d *
in
dic
ate
stat
isti
cal si
gnif
ican
ce a
t th
e .0
1, .0
5, an
d .10 lev
els
resp
ecti
vel
y.
Sta
nd
ard
err
ors
, re
po
rted
ben
eath
eac
h p
oin
t es
tim
ate,
wer
e cl
ust
ered
at
the
stat
e le
vel
. C
olu
mn
1 r
eplic
ates
th
e p
oin
t es
tim
ate
fro
m c
olu
mn
1 o
f P
anel
C in
Tab
le 5
. C
olu
mn
5 r
eplic
ates
th
e p
oin
t es
tim
ate
fro
m c
olu
mn
2 o
f P
anel
C
in T
able
5. A
s in
dic
ated
, ad
dit
ion
al s
pec
ific
atio
ns
con
tain
co
ntr
ols
fo
r st
ate
level
hea
lth
sp
end
ing
per
cap
ita
(in
1000s
of
do
llar
s), in
com
e p
er c
apit
a (i
n 1
000s
of
do
llars
), a
nd
th
e em
plo
ymen
t to
po
pula
tio
n r
atio
. S
tate
hea
lth
sp
end
ing
was
tak
en f
rom
th
e N
atio
nal
Hea
lth
Exp
end
iture
Acco
un
ts, w
hile
sta
te p
erso
nal
in
com
e, e
mp
loym
ent,
an
d p
op
ula
tio
n w
ere
taken
fro
m t
he
Bu
reau
of
Eco
no
mic
An
alys
is.
52
State health spending emerges as a strong predictor of private coveragechanges during the latter sample (i.e., the “subsequent coverage recoveries”sample). Growth in health spending per capita is negatively related tocoverage in all specifications, and the relationship is strongly statisticallydistinguishable from 0 for the latter period. Including this control results inmoderate increases in the estimated size of the recoveries in the communityrating states. This reflects the fact that the community rating states werestates in which health expenditures grew relatively rapidly.
A.4: Age Composition of Coverage Movements
If community-rating regulations induce adverse selection, one wouldexpect to observe a shift in the composition of the covered towards pop-ulations with relatively high expected health spending. Unfortunately theCPS contains no information on health status until 1995, which comes twoyears after the implementation of community rating in Maine, New York,and Vermont. Nonetheless, expected health spending is positively corre-lated with age. Appendix Figure A5 thus presents a breakdown of theevolution of private coverage rates by age group.
The year community rating went into place, namely 1993, saw a diver-gence in coverage rates for adults aged 21 to 45 relative to adults aged 46
to 60. In that year the coverage rate rose by around 8 percentage points forthose aged 46 to 60 and declined by around 5 percentage points for thoseaged 21 to 45. Coverage declines between 1994 and 1997, as estimated inthe main text, were of similar size for both groups. For those aged 45 to60, the net coverage change was from just over 70 percentage points to justunder 70 percentage points. For younger adults, the coverage rate declinedfrom 73 percentage points in 1991 to a 1997 low of 56 percentage points.While sub-group tabulations are somewhat noisy, in particular for the 46
to 60 group during the pre-regulation period, the data appear consistentwith adverse selection along the age margin.
53
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
acty
ear
21 to
45
Yea
r O
lds
Pan
el A
: Fra
ctio
n w
/ Priv
ate
Insu
ranc
e
.5.6.7.8.9Fraction w/ Private Insurance
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
acty
ear
46 to
60
Yea
r O
lds
Pan
el B
: Fra
ctio
n w
/ Priv
ate
Insu
ranc
e
Insu
ranc
e S
tatu
s B
y A
ge G
roup
: Sta
ble
Reg
ulat
ory
Reg
imes
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
tol S
tate
sS
mal
l- an
d N
on-G
roup
Mar
kets
in T
reat
men
t Sta
tes
Figu
reA
.5:
Insu
ranc
eSt
atus
byA
geG
roup
:St
able
Reg
ulat
ory
Reg
imes
.Th
efig
ure
cont
ains
tabu
lati
ons
cons
truc
ted
usin
gth
eM
arch
Dem
ogra
phic
Supp
lem
ent
ofth
eC
urre
ntPo
pula
tion
Surv
ey(C
PS).
The
sam
ple
inpa
nel
Aco
ntai
nsad
ults
aged
21
thro
ugh
45,w
hile
the
sam
ple
inpa
nelB
cont
ains
adul
tsag
ed45
thro
ugh
60.T
heTr
eatm
ent
Stat
esin
clud
eM
aine
,New
York
,and
Verm
ont.
54
A.5: Coverage Movements with Alternative Sam-
ple Inclusion Criteria
The analysis in the main text focuses on households with at least onechild and one full time employed adult. Appendix Figures A6 and A7
present tabulations of the samples that include childless households (A6)or households in which all adults are unemployed (A7). As can be seen inFigure A6, inclusion of childless households has very little impact on thecore features of the evolution of coverage. The data suggest that coveragerates in markets for “singles” coverage were relatively stable over the fullsample period. This may be because the market for singles coverage wasrelatively adversely selected to begin with. This market segment is hometo the “young invincibles” whose coverage decisions are much discussedin the context of the Affordable Care Act (ACA).
Figure A7 presents tabulations of coverage for samples augmented toinclude households in which no adult is employed. A household with chil-dren and no employed adults will almost invariably be eligible for Medi-caid at some point during the calendar year. Unsurprisingly, inclusion ofthis group significantly shifts up the Medicaid coverage rates associatedwith in-sample households. Medicaid’s counter-cyclicality (with respect tothe business cycle) is also readily apparent. The insurance status of theunemployed tells us little about the effects of community rating regula-tions because these households are unlikely participants in private mar-kets. Their inclusion in the sample serves primarily to compress realizedfluctuations in the fraction uninsured (see Panel A).
55
.04.11.18.25.32Fraction Uninsured
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Sta
ble
Reg
ulat
ion
Sta
tes
Pan
el A
: Fra
ctio
n U
nins
ured
:
.52.62.72.82.92Fraction w/ Private Insurance
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Sta
ble
Reg
ulat
ion
Sta
tes
Pan
el B
: Fra
ctio
n w
/ Priv
ate
Insu
ranc
e
0.1.2.3.4Fraction on Medicaid
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Uns
tabl
e R
egul
atio
n S
tate
sP
anel
C: F
ract
ion
on M
edic
aid
.04.11.18.25.32Fraction Uninsured
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Uns
tabl
e R
egul
atio
n S
tate
sP
anel
D: F
ract
ion
Uni
nsur
ed:
.52.62.72.82.92Fraction w/ Private Insurance
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Uns
tabl
e R
egul
atio
n S
tate
sP
anel
E: F
ract
ion
w/ P
rivat
e In
sura
nce
0.1.2.3.4Fraction on Medicaid
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Uns
tabl
e R
egul
atio
n S
tate
sP
anel
F: F
ract
ion
on M
edic
aid
Insu
ranc
e S
tatu
s O
ver
Tim
e: In
clud
ing
Chi
ldle
ss H
ouse
hold
s
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
tol S
tate
sS
mal
l- an
d N
on-G
roup
Mar
kets
in T
reat
men
t Sta
tes
Larg
e-G
roup
Mar
kets
in C
onto
l Sta
tes
Larg
e-G
roup
Mar
kets
in T
reat
men
t Sta
tes
Figu
reA
.6:
Insu
ranc
eSt
atus
Ove
rTi
me:
Incl
udin
gC
hild
less
Hou
seho
lds.
The
figur
eco
ntai
nsta
bula
tion
sco
nstr
ucte
dus
ing
the
Mar
chD
emog
raph
icSu
pple
men
tof
the
Cur
rent
Popu
lati
onSu
rvey
(CPS
).A
llsa
mpl
esco
nsis
tof
the
adul
tsan
dch
ildre
n(a
ged
18
and
low
er)
inho
useh
olds
wit
hat
leas
ton
efu
ll-ti
me
wor
king
adul
t.In
Pane
lsA
,B,a
ndC
,the
Trea
tmen
tSt
ates
incl
ude
Mai
ne,N
ewYo
rk,a
ndVe
rmon
t.In
Pane
lsD
,E,a
ndF,
the
Trea
tmen
tSt
ates
incl
ude
Ken
tuck
y,M
assa
chus
etts
,New
Ham
pshi
re,a
ndN
ewJe
rsey
.In
divi
dual
sar
ede
fined
asha
ving
acce
ssto
the
larg
e-gr
oup
mar
ket
ifat
leas
ton
ead
ult
inth
eho
useh
old
isem
ploy
edat
afir
mw
ith
mor
eth
an100
empl
oyee
s(a
ndto
the
non-
and
smal
l-gr
oup
mar
kets
othe
rwis
e).I
nsur
ance
stat
usis
take
ndi
rect
lyfr
omth
eM
inne
sota
Popu
lati
onC
ente
r’s
Sum
mar
yH
ealt
hIn
sura
nce
vari
able
s,w
hich
are
impu
ted
usin
gth
eC
PS.
56
.04.11.18.25.32Fraction Uninsured
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Sta
ble
Reg
ulat
ion
Sta
tes
Pan
el A
: Fra
ctio
n U
nins
ured
:
.42.54.66.78.9Fraction w/ Private Insurance
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Sta
ble
Reg
ulat
ion
Sta
tes
Pan
el B
: Fra
ctio
n w
/ Priv
ate
Insu
ranc
e
0.1.2.3.4Fraction on Medicaid
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Uns
tabl
e R
egul
atio
n S
tate
sP
anel
C: F
ract
ion
on M
edic
aid
.04.11.18.25.32Fraction Uninsured
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Uns
tabl
e R
egul
atio
n S
tate
sP
anel
D: F
ract
ion
Uni
nsur
ed:
.42.54.66.78.9Fraction w/ Private Insurance
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Uns
tabl
e R
egul
atio
n S
tate
sP
anel
E: F
ract
ion
w/ P
rivat
e In
sura
nce
0.1.2.3.4Fraction on Medicaid
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Yea
r
Uns
tabl
e R
egul
atio
n S
tate
sP
anel
F: F
ract
ion
on M
edic
aid
Insu
ranc
e S
tatu
s O
ver
Tim
e: In
clud
ing
the
Une
mpl
oyed
Sm
all-
and
Non
-Gro
up M
arke
ts in
Con
tol S
tate
sS
mal
l- an
d N
on-G
roup
Mar
kets
in T
reat
men
t Sta
tes
Larg
e-G
roup
Mar
kets
in C
onto
l Sta
tes
Larg
e-G
roup
Mar
kets
in T
reat
men
t Sta
tes
Figu
reA
.7:I
nsur
ance
Stat
usO
ver
Tim
e:In
clud
ing
the
Une
mpl
oyed
.The
figur
eco
ntai
nsta
bula
tion
sco
nstr
ucte
dus
ing
the
Mar
chD
emog
raph
icSu
pple
men
toft
heC
urre
ntPo
pula
tion
Surv
ey(C
PS).
All
sam
ples
cons
isto
fthe
adul
tsan
dch
ildre
n(a
ged
18
and
low
er)
inho
useh
olds
wit
hat
leas
ton
ech
ild.
InPa
nels
A,B
,and
C,t
heTr
eatm
ent
Stat
esin
clud
eM
aine
,New
York
,and
Verm
ont.
InPa
nels
D,
E,an
dF,
the
Trea
tmen
tSt
ates
incl
ude
Ken
tuck
y,M
assa
chus
etts
,N
ewH
amps
hire
,an
dN
ewJe
rsey
.In
divi
dual
sar
ede
fined
asha
ving
acce
ssto
the
larg
e-gr
oup
mar
ket
ifat
leas
ton
ead
ult
inth
eho
useh
old
isem
ploy
edat
afir
mw
ith
mor
eth
an100
empl
oyee
s(a
ndto
the
non-
and
smal
l-gr
oup
mar
kets
othe
rwis
e).
Insu
ranc
est
atus
ista
ken
dire
ctly
from
the
Min
neso
taPo
pula
tion
Cen
ter’
sSu
mm
ary
Hea
lth
Insu
ranc
eva
riab
les,
whi
char
eim
pute
dus
ing
the
CPS
.
57
A.6: Calibration of the Potential Effect of Public
Insurance Expansions on Premiums in Regulated
Markets
The potential effect of public insurance expansions on community-ratedpremiums can be approximated using the observed expenditures and healthstatus of those who are newly eligible for, and participating in, public in-surance.19 Table A5 calibrates the effect of all post-1993 public insuranceexpansions on the community-rated premium of a family with 2 adults and2 children. From 1993 to 2004, the number of Medicaid (or SCHIP) ben-eficiaries expanded by around 10 million children, 5 million non-disabledadults, and 3 million disabled persons. States with community rating ac-counted for roughly 1 million of these children, 1.1 million non-disabledadults, and 500,000 disabled persons while accounting for roughly 11 per-cent of the nation’s population. The vast majority of the expanded coverageof unhealthy adults and the disabled (roughly four fifths) drew from thepool of non- and small-group market participants.20
I examine the expenditures of newly eligible individuals using healthspending data from the 2004 Medical Expenditure Panel Survey (MEPS).
19Two important caveats arise in this context. Health spending will reflect the reim-bursement rates offered to providers by public programs, which are typically lower thanthose offered by private insurers. The calibration accounts for this using an estimate fromZuckerman et al. (2004) that Medicaid reimbursement rates are roughly 30 percent lowerthan reimbursement rates that prevail under Medicare (for comparable services). Largeemployer plan typically pay 40 percent more than Medicare’s rates Clemens and Gottlieb(2013). Non- and small-group plans likely pay rates between those of Medicare and large-employer plans. Spending on Medicaid beneficiaries will also reflect difficulties in ob-taining care due to physician (un)willingness to see Medicaid patients. Pregnant womenand the disabled were explicitly covered by Medicaid on account of their high health ex-penditures. The Medical Expenditure Panel Survey (MEPS) confirms that (non-disabled)adults on public insurance have higher health expenditures than the typical adult on pri-vate coverage. (It may still be the case, of course, that observed differences understate realdifferences in what the publicly insured would spend if they were on private insurance.)Children, however, were not made eligible on account of their health.
20This result does not stem directly from evidence presented earlier in the paper, butcan be seen quite readily in the data when comparing Medicaid coverage of unhealthyadults with and without access to insurance through the large group market.
58
Tab
le A
5:
Cali
bra
tio
n o
f P
ote
nti
al
Imp
act
of
Pu
bli
c I
nsu
ran
ce E
xp
an
sio
ns
on
Co
mm
un
ity R
ate
d P
rem
ium
s
Co
ver
age
Gro
up
New
B
enef
icia
ries
in
C
om
mun
ity
Rat
ing
Sta
tes
(mill
ions)
Ass
um
ed
fro
m N
on
- an
d S
mal
l-
Gro
up
(m
illio
ns)
Aver
age
Co
sts
in
Exc
ess
of
Pri
vat
ely
Co
ver
ed
Indiv
idual
s
Exc
ess
Co
sts
Per
Pri
vat
ely
Insu
red
Indiv
idual
Exc
ess
Co
sts
Co
ver
ed b
y P
rivat
e In
sura
nce
Exc
ess
Co
sts
Acc
oun
tin
g fo
r L
ow
er
Med
icai
d
Rei
mb
urs
emen
t R
ates
C
hild
ren
1
0.5
$1
00
$25
$17
$17
No
n-D
isab
led
Ad
ult
s 1.1
0.8
8
$1,3
25
$233
$156
$223
Dis
able
d A
dult
s 0.5
0.4
$8
,000
$640
$429
$613
T
ota
l E
xces
s C
ost
s fo
r F
amily
wit
h 2
Adult
s an
d 2
Ch
ildre
n
$1
,706
So
urc
es: C
alcu
lati
on
s use
dat
a fr
om
th
e C
ente
r fo
r M
edic
are
and
Med
icai
d S
ervic
es (
CM
S),
Mar
ch D
emo
grap
hic
Sup
ple
men
ts t
o t
he C
urr
ent
Po
pula
tio
n
Surv
ey (
CP
S),
an
d t
he
2004 M
edic
al E
xpen
dit
ure
Surv
ey a
s des
crib
ed in
th
e n
ote
bel
ow
.
No
tes:
Num
ber
s o
f n
ew b
enef
icia
ries
co
me
fro
m C
MS a
dm
inis
trat
ive
dat
a.
Th
e fr
acti
on
of
new
ben
efic
iari
es c
om
ing
fro
m t
he
no
n-
and
sm
all-
gro
up
mar
ket
s is
est
imat
ed o
n t
he
bas
is o
f d
iffe
ren
ces
in e
ligib
ility
an
d p
arti
cip
atio
n b
etw
een
th
ese
gro
up
s an
d t
ho
se in
lar
ge-g
roup
mar
ket
s as
ob
serv
ed in
th
e C
PS. A
ver
age
cost
s o
f p
ub
licly
in
sure
d in
div
idual
s in
exc
ess
of
pri
vat
ely
insu
red
in
div
idual
s w
ere
esti
mat
ed u
sin
g d
ata
fro
m t
he
2004 M
edic
al E
xpen
dit
ure
Pan
el S
urv
ey
(ME
PS).
T
he
ME
PS s
amp
le w
as r
estr
icte
d t
o in
div
idual
s in
ho
use
ho
lds
wit
h a
n e
mp
loye
d a
dult
in
ord
er t
o f
ocu
s o
n n
ewly
elig
ible
rec
ipie
nts
of
pu
blic
in
sura
nce
. E
xces
s sp
end
ing
for
pub
licly
in
sure
d a
dult
s is
ro
ugh
ly s
imila
r w
het
her
th
e co
mp
aris
on
sam
ple
in
clud
es a
ll o
ther
ad
ult
s o
r o
nly
ad
ult
s w
ith
pri
vat
e in
sura
nce
. T
he
exce
ss s
pen
din
g fo
r th
e d
isab
led
is
the
rep
ort
ed $
8000 w
hen
th
e sa
mp
le in
clud
es a
ll o
ther
ad
ult
s, a
nd
ris
es t
o $
14,0
00 w
hen
th
e sa
mp
le is
rest
rict
ed t
o o
nly
in
clud
e th
ose
wit
h p
rivat
e in
sura
nce
. E
xces
s co
sts
wer
e co
nver
ted
in
to c
ost
s p
er p
riv
atel
y in
sure
d in
div
idual
by
mult
iply
ing
aver
age
exce
ss
cost
s b
y th
e ra
tio
of
new
ben
efic
iari
es f
rom
th
e n
on
- an
d s
mal
l-gr
oup
mar
ket
s to
th
e b
asel
ine
num
ber
of
pri
vat
e in
sura
nce
ho
lder
s.
Alt
ho
ugh
no
t al
l n
ew
pub
lic in
sura
nce
rec
ipie
nts
wo
uld
hav
e b
een
on
pri
vat
e in
sura
nce
, in
div
idual
s w
ith
th
e h
igh
est
cost
s w
ould
hav
e b
een
lik
ely
to o
bta
in p
rivat
e co
ver
age
in t
he
com
pre
hen
sivel
y re
gula
ted
mar
ket
s si
nce
th
ey s
too
d t
o b
enef
it t
he
mo
st f
rom
co
mm
un
ity
rate
d p
rem
ium
s.
Fo
r ex
amp
le, ev
en if
on
ly 0
.5 o
f th
e 1.1
millio
n
adult
s h
ad p
revio
usl
y h
eld
pri
vat
e co
ver
age,
th
ese
wo
uld
lik
ely
hav
e b
een
rel
ativ
ely
hig
h c
ost
ad
ult
s, m
akin
g th
e ab
ove
con
vers
ion
in
to e
xces
s co
sts
per
p
rivat
ely
insu
red
in
div
idual
ap
pro
pri
ate.
T
ota
l ex
cess
co
sts
wer
e co
nver
ted
in
to e
xces
s co
sts
cover
ed b
y p
rivat
e in
sura
nce
by
mult
iply
ing
by
two
-th
ird
s.
Tw
o-
thir
ds
is t
he
typ
ical
sh
are
of
hea
lth
exp
end
iture
s co
ver
ed b
y p
rivat
e in
sura
nce
am
on
g n
on
- an
d s
mal
l-gr
ou
p p
olic
y h
old
ers
in t
he
ME
PS. T
he
fin
al a
dju
stm
ent
acco
un
ts f
or
the
fact
th
at M
edic
aid
rei
mb
urs
emen
t ra
tes
are
low
er t
han
pri
vat
e re
imb
urs
emen
t ra
tes.
E
stim
ates
fo
r n
on
-dis
able
d a
dult
s an
d d
isab
led
ad
ult
s,
wh
ich
are
bas
ed o
n t
he
actu
al e
xpen
dit
ure
s o
f p
ub
licly
in
sure
d in
vid
ual
s, w
ill u
nd
erst
ate
the
exp
end
iture
s th
at w
ould
be
incu
rred
un
der
pri
vat
e in
sura
nce
un
less
an
ap
pro
pri
ate
adju
stm
ent
is m
ade.
W
ork
by
Zuck
erm
an e
t al
. (2
004)
sugg
ests
th
at M
edic
aid
rei
mb
urs
emen
t ra
tes
are,
on
aver
age,
30%
lo
wer
th
an
reim
burs
emen
t ra
tes
that
pre
vai
l el
sew
her
e.
59
To proxy for newly-eligible status, I use household employment informa-tion. Specifically, I focus attention on those who are in households with atleast one full-time employed adult. The vast majority of those eligible forMedicaid prior to the 1990s expansions were in households in which therewere no full-time employed adults. These expansions were designed totarget the working poor, i.e., low income households in which at least onefamily member works regularly. In this sample, the typical non-disabled,publicly insured adult spends roughly $1,325 (standard error of $611) moreper year than the typical privately insured adult. The typical publicly in-sured disabled individual spends roughly $8,000 (standard error of $671)more than the typical adult. Finally, I estimate that, if privately insured,newly eligible children would have spent roughly $100 more than the typ-ical privately insured child.
The potential premium impacts of expanded coverage for adults andthe disabled are much larger than that associated with children. Therewere approximately 5 million adults with private insurance in the non-and small-group markets of the community rating states in 2004. I assumethat four-fifths, or 880 thousand, of the newly covered, non-disabled adultscame from the non- and small-group markets.21 Their excess spending of$1,325 per person thus amounts to roughly $233 per adult still on thesemarkets. The excess spending of the newly-covered disabled populationamounts to $640 per adult on these markets. If two-thirds of these ex-penditures would have been covered by private insurance (a typical sharefor the privately insured on the non- and small-group markets), the pre-mium impact would amount to nearly $585 per adult. A final adjustment,to account for Medicaid’s relatively low reimbursement rates (which willdepress observed spending by those on public insurance relative to whatthey would spend were they on private insurance), raises this estimate to$836 (see Zuckerman et al. (2004)). Similar calculations for expanded chil-dren’s coverage yields an estimate of roughly $17 per child. The post-1993
21This assumption is driven by CPS data suggesting that roughly one-fifth of newbeneficiaries came from families whose alternative source of insurance would have comethrough the large group insurance market.
60
public insurance expansions may thus have held down community-ratedpremiums by around $1,700 for a family of 4.
A.7: The Evolution of Health Maintenance Orga-
nizations in Community Rating States
The analysis in this paper’s main text focuses on the extensive mar-gin of the insurance purchasing decision. Adverse selection can also occuralong the intensive margin of insurance generosity. As discussed in themain text, past work estimated relatively small impacts of community rat-ing regulations on the extensive margin. Buchmueller and DiNardo (2002)hypothesize that these markets may have arrived at separating equilib-ria in the spirit of Rothschild and Stiglitz (1976). In support of this view,Buchmueller and DiNardo (2002) and Buchmueller and Liu (2005) find evi-dence that the market share of Health Maintenance Organizations (HMOs)increased significantly in community-rated markets relative to other mar-kets.
In contrast with past work, this paper finds that community rating reg-ulations caused extensive margin declines in private coverage rates. Insome states coverage subsequently rebounded. By way of explanation, thispaper advances a hypothesis involving the interplay between communityrating regulations and public insurance programs. A natural alternativehypothesis, considered below, involves the separating equilibria proposedby past work. Specifically, coverage may have recovered because of sepa-rating equilibria made possible by adjustments on the supply side of theinsurance market.
To assess this alternative hypothesis, I assembled data on the evolutionof HMO penetration rates over the course of my sample period. Thesedata are reported annually in the Statistical Abstract of the United Statesfor 1995 through 2006, and in five year increments from 1980 through 1995.Table A.6 displays the evolution of HMO penetration rates for the indi-vidual community rating states and for the United States as a whole. As
61
with the main text’s analysis of coverage rates, I separately present initialchanges from the pre-regulation period through 1997 as well as the subse-quent evolution from 1997 through 2005.
Consistent with Buchmueller and DiNardo (2002) and Buchmueller andLiu (2005), I find that HMO penetration rates increased disproportionatelyin the community rating states relative to other states during the mid-1990s. From 1990 to 1997, the average change in HMO penetration wasroughly 9 percentage points for the United States as a whole and roughly18 percentage points among the community rating states. Combined withthe results from the main text, this suggests that community rated marketsexperienced significant coverage declines on both the extensive margin andthe intensive margin of coverage generosity.
I next consider how intensive margin adjustments relate to the patternof coverage recoveries shown in Panel C of Figure A1. The evidence sug-gests that intensive-margin adjustments cannot explain the observed re-coveries. Note first that both the community rating states and the U.S. asa whole experienced little change in HMO penetration rates from 1997 to2005. As proxied by HMO penetration, there is no evidence of differentialchanges in coverage generosity over this time period.
Second, I examine the variation in HMO coverage changes within theset of community rating states. This variation appears unable to explainvariation in the size of the coverage recoveries. The last column of Ta-ble A6 reveals significant variation in the changes in HMO coverage rateswithin the set of community rating states. Notably, HMO coverage de-clined in New York, while increasing in Maine and Vermont. As shown inPanel C of Figure A1, New York experienced a substantial extensive mar-gin recovery over this time period, while Maine and Vermont lagged theperformance of other community rating states. This is inconsistent with theview that HMO penetration enabled coverage to recover. If anything, theevidence suggests a positive correlation between intensive and extensivemargin recoveries.
62
Table A6: HMO Penetration Rates
State 1990 1997 2005 Change from 1990 to 1997
Change from 1997 to 2005
Panel A: States That Maintained Community Rating Regulations
Maine 2.6 15.9 25.9 13.3 10.0
Massachusetts 26.5 44.6 37.4 18.1 -7.2
New Jersey 12.3 27.5 25.0 15.2 -2.5
New York 15.1 35.7 24.0 20.6 -11.7
Vermont 6.4 0.0 16.1 -6.4 16.1
Group Mean 12.6 24.7 25.7 12.2 0.9
Panel B: States That Repealed Their Community Rating Regulations
Kentucky 5.7 27.4 10.2 21.7 -17.2
New Hampshire 9.6 23.9 21.9 14.3 -2.0
Group Mean 7.7 25.7 16.1 18.0 -9.6
Panel C: U.S. Averages
U.S. Median 8.5 17.1 16.4 8.6 -0.7
U.S. Mean 10.3 19.9 17.9 9.5 -2.0 Source: HMO penetration rates for 2007 were taken directly from the statistical abstract of the United States. HMO penetration rates for 1990 and 1997 were gathered from historical issues of the Statistical Abstract by Facster (http://www.facster.com/Persons_Enrolled_Health_Maintenance_Organizations_State.aspx?t=997. Website accessed 1/19/2014).
63
