Adverse selection in health insurance markets? Evidence from state small-group health insurance reforms
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Journal of Public Economics 89 (2005) 1865 – 1877
www.elsevier.com/locate/econbase
Adverse selection in health insurance markets?
Evidence from state small-group health
insurance reforms
Kosali Ilayperuma Simon*
Department of Policy Analysis and Management, MVR Hall, Cornell University, Ithaca, NY 14853, United States
Received 6 August 2003; received in revised form 11 June 2004; accepted 1 July 2004
Available online 28 September 2004
Abstract
The past decade witnessed major changes in state laws governing the sale of health insurance to
small employers. States took measures to restrict insurers’ ability to distinguish between high and
low-risk customers because of concern about the low rate of coverage among workers in small firms,
the high prices in the small-group market and the absence of federal health reform. Using both
individual-level and employer-level data, I test predictions about the effect of reforms on the
employer-provided health insurance market. I estimate these effects for small firms and their workers
using large firms and their workers in the same states, as well as large and small firms and their
workers in non-reform states, as comparison groups. I find the reforms decreased the rate of
employer coverage on average for workers in small firms by less than two percentage points. Within
small firms, low-expenditure individuals experienced a larger decline in the rate of coverage through
their employer, while the coverage rate of high-expenditure individuals rose slightly in some
specifications. There is also evidence that comprehensive reforms increased premiums slightly for
small employers, and that most of this increase was passed on to workers through higher employee
contributions.
D 2004 Elsevier B.V. All rights reserved.
JEL classification: I18; I11; J32
Keywords: Adverse selection; Health insurance; Small firms
* Tel.: +1 607 255 7103.
E-mail address: kis6@cornell.edu.
0047-2727/$ - see front matter D 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.jpubeco.2004.07.0031866 K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877
Fearing that low health insurance coverage in small firms could be partly due to
experience rating and redlining,1 many states introduced laws that restricted these practices
in the small-group market. Inability to price and issue policies in accordance with risk
could worsen informational asymmetry and resulting adverse selection relative to the
unregulated market. Adverse selection is thought to generally reduce the insurance
consumption of the low-risk groups, to transfer resources from the low-risk group to the
high-risk group in cases where subsidized equilibria are sustained, or to result in a market
failing to exist altogether. A simple model of insurance where employers buy policies on
behalf of their heterogeneous workforce suggests that small group reforms may decrease
coverage for the low-risk and increase coverage for the high-risk (see Simon, 2004), while
the predicted changes are ambiguous for the market on average. This comprehensive
empirical analysis of state attempts to standardize the terms of insurance across different
risk types suggests that reforms have not succeeded in increasing coverage in small firms
as a whole. Instead, they may have inadvertently led to a small overall decrease in
coverage through an increase in premiums and employee contributions. This analysis also
suggests that certain low-cost populations have suffered larger declines in coverage than
others.
1. Government involvement in small-group health insurance
Starting in the early 1990s, state legislators took steps to bpromote the availability of
health insurance coverage to small employers regardless of their health status. . .and to
improve the overall. . . efficiency of the small-group health insurance marketQ.2 From 1991
to 1996, 47 states implemented some combination of the small-group reforms described
below. Rating restrictions limited the insurers’ ability to use certain predictors of health
care use in setting premiums, while guaranteed issue laws banned denial of policies. Some
states allowed insurers to market a guaranteed dbare bonesT plan to first-time insurance
buyers. Pre-existing conditions exclusion laws and portability laws improved continuity of
access to health insurance while working for small firms.3
Most states followed the language of model laws published by the National Association
of Insurance Commissioners (NAIC) (1998). The area in which most variation exists is
rating reforms. Most states allowed premiums to vary by certain demographic factors
called dcase characteristicsT and permitted variation around average prices through drate
bandsT within which group-specific information could be used. The language of most
rating statutes is not straightforward, and many allow insurance commissioners to decide
1
Redlining is the practice of systematically refusing to insure groups in certain high-risk industries or
occupations.
2
This quote is taken from Section 2 of the 1992 National Association of Insurance Commissioners (NAIC)
Small Employer Health Insurance Availability Model Act.
3
The regulatory information used in this analysis was gathered through a careful primary investigation of state
statutes and bills checked against all available secondary data sources, and from personal communication with
almost all state insurance departments (Simon, 2000). See Hall (1999) for detailed discussions of these reforms.K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877 1867
Table 1
Timing and nature of state reforms: 1991–1996
State Full reform Partial reform Bare bones State Full reform Partial reform Bare bones
plan laws plan laws
AK 1994–1996 MT 1994–1996 1992–1996
AL NC 1992–1996 1993–1996
AR 1992–1996 1993–1996 ND 1995–1996 1994–1992 1992–1996
AZ 1994–1996 1992–1996 NE 1995–1996 1992–1994 1992–1996
CA 1994–1996 NH 1996 1994–1995
CO 1996 1995 1992–1996 NJ 1995–1996 1992–1996
CT 1992–1996 1992–1996 NM 1996 1992–1995 1992–1996
DC NV 1992–1996
DE 1994–1996 1992–1993 1994–1996 NY 1994–1996
FL 1994–1996 1992–1993 1994–1996 OH 1993–1996
GA 1992–1996 1994–1996 OK 1995–1996 1993–1994 1991–1996
IA 1993–1996 1992 1992–1996 OR 1992–1996 1992–1996
ID 1994–1996 1996 PA
IL 1995–1996 1992–1994 RI 1993–1996 1991–1996
IN 1993–1996 SC 1996 1992–1995
KS 1993–1996 1992 1993–1996 SD 1996 1992–1995
KY 1996 1991–1996 TN 1994–1996 1994–1996
LA 1995–1996 1992–1994 TX 1995–1996
MA 1992–1996 1992–1996 UT 1996
MD 1995–1996 1992–1996 VA 1994–1996 1991–1996
ME 1994–1996 1991–1993 VT 1993–1996
MI WA 1994–1996 1993–1996
MN 1994–1996 1994–1996 WI 1993–1996 1993–1996
MO 1995–1996 1994 1992–1996 WV 1992–1996 1992–1996
MS 1996 1993–1996 WY 1993–1996 1993–1996
Source: Simon (2000).
on the specific restrictions upon reviewing the pricing structure submitted by insurers. The
definition of a small firm usually varies from one with 2–25 employees to ones with 1–50
employees. Most states fit naturally into a three-category definition of reform. dFull
reformT refers to states with the two most binding laws, guaranteed issue and rating reform,
together with the weaker portability and pre-existing conditions laws. dPartial reformT
refers to states with rating reforms that did not guarantee the issue of health insurance, and
states with no issue or rating laws are called dno reformT states.4 Table 1 describes the
regulatory regime in each state from 1991 to 1996.
2. Previous studies of small-group reform
The existing literature can be separated into studies using individual or employer level
data. Cross-sectional studies on the effect of reforms on the employer’s decision to
purchase insurance find some positive effects (Jensen and Morrisey, 1999; Hing and
4
No state enacted guaranteed issue laws without rating reforms. Since bare-bones plan laws were treated as
distinct from the other small-group laws by legislators, they constitute a separate reform category in my analysis.1868 K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877
Jensen, 1999), but this may reflect pre-existing differences. Marquis and Long (2001/
2002) use employer data from 1993 and 1997 to analyze the effects of reform in nine
separate states. Compared with 11 states that did not enact reform, no systematic pattern
emerges in these nine to suggest that reforms have had clear effects on offer rates,
enrollments or premiums, even when comparing small firms to medium firms. The papers
using individual-level data generally find no discernible impact of reform on insurance
coverage rates. Zuckerman and Rajan (1999) use national CPS data to look for general
effects on insurance rates; thus we do not know whether these reforms have had a
differential effect on workers in small firms, the group we expect to be affected. Sloan and
Conover (1998) find that, as a result of age rating bans, insurance coverage improved for
older workers in small firms in New York and New Jersey. They use national CPS data at
the individual level through the March 1994 survey, thus their findings pertain to the early
reform period. Kaestner and Simon (2002) find some effects of small group reform on
firms with under 100 workers, but their study does not examine firms with fewer than 25
workers separately or use a with-state control group of large firms since the focus of their
study is the impact of state insurance mandates, which affect all commercially insured
firms. Two individual level analyses look at the effect of reform in small firms, using large
firms as a further control group. Using the March CPS, Buchmueller and DiNardo (2002)
construct a three-state (PA, NY and CT) case study of the effects of community rating. The
authors find statistically insignificant effects of reform on insurance coverage rates.5
Monheit and Schone (2004) use household level surveys from a period years before
reforms (the 1987 National Medical Expenditure Survey, NMES) and a period just after
them (the 1996 Medical Expenditure Panel Survey, MEPS) to look at the effect of reform
on the health insurance status of workers.6 They find no evidence that coverage was
affected on average. When the authors look at workers by risk status, they find mixed
evidence. In the main specification, the only statistically significant effect is an unexpected
negative impact on coverage for high-risk workers as a result of one of two definitions of
stringent reform. In an alternative specification, there is a negative effect on high-risk
workers from one stringent reform but a positive impact as a result of the other stringent
reform. Overall, the sign of the coefficients on the effect of reform on offer rates in small
firms is negative, but the sign corresponding to the effects on being covered by that
employer’s policy is positive.
These studies do not paint a clear picture of the comprehensive effects of small-group
reform. My study adds to the literature in many ways. First, no nationally representative
study to date has analyzed the effect of reform on the employer and employee prices of
health insurance and other outcomes at the employer level which we expect to be directly
impacted by reforms.7 Studies to date that look at indirect outcomes, such as whether
5
This is somewhat at odds with the findings in Sloan and Conover (1998) for New Jersey and New York
combined.
6
Both Buchmueller and DiNardo and Monheit and Schone use the same DDD method as in this paper,
although their definitions of small firms include some sizes not characterized as intended targets in some states’
statutes.
7
There are descriptive comparisons of premiums and offer rates by state, adjusted for plan characteristics, in
Buchmueller and Jensen (1997) and Marquis and Long (2001/2002).K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877 1869
workers in small firms are insured, could benefit from additional analysis for a number of
reasons. They either use aggregate data and do not identify a separate effect on small firms,
or are not nationally representative, or use data from time periods separated by almost a
decade. The identification strategy in this paper differs from Monheit and Schone (2004)
in that I exploit important variation in the timing of reforms in different states. The policy
details such as content and effective dates were collected from primary sources and
checked rigorously. Between 1987 and 1996, all but three states enacted reforms, and these
states are likely to differ from the rest of the nation in many ways. A further difference is
the definition of the treatment group. In their study, workers are defined to be in small
firms if in establishments with fewer than 26 workers, or in single-location establishments
with 26–50 workers. This classification may not completely accurately measure the
treatment group as defined in these regulations to the extent that establishments with fewer
than 26 workers belong to larger firms, and to the extent that many states defined a small
firm as one with fewer than 25 workers, not 50 workers.8
3. Methods and data
I estimate the effect of reform by comparing the changes in insurance outcomes for
workers in small firms before and after reform, to changes in outcomes for workers in non-
reform states, controlling for health insurance outcomes of workers in large firms. This
method produces consistent results if the within-state control group picks up exogenous
factors that simultaneously affect small firms, and if policies are not endogenously
adopted. The data best suited to conduct tests about coverage at the individual level are the
March Current Population Surveys (CPS) from 1992 to 1997.9 I code a state as having a
certain reform if it is effective by the last day of year t2 in order to apply to health
insurance sold in t1 and reported in the CPS of t.
Using these data, the effect of treatment can be identified through the following probit
equation:
!
X
3
Yi ¼ U b1 þ b2 Xi þ b3 Si þ b4 Tt Ai þ b5 Ai Si þ b6 Tt Si þ pj Rj POSTijt Si ð1Þ
j¼1
In this equation, U stands for the normal distribution function, Yi represents the probability
that a full-time worker i received health insurance from his/her own employer. R j =1 if a
state ever had full reform ( j=1) partial reform ( j=2), or bare bones laws ( j=3). Each of
these three indicator variables is 0 otherwise. S i =1 if worker i is employed in a small firm
8
Although most states shifted to defining a small firm as one with fewer than 50 workers by 1996, 11 states
continued to use the cutoff of 25 workers to define small firms in their regulations. One state used 35 and another
used 40, while the rest almost all used 50 (Simon, 2000).
9
I select full-time private-sector workers who are between 17 and 65 years of age. I exclude information on
workers in firms with 25–99 employees since some states considered them to be small-firm workers and others
did not. Hawaii is excluded due to its employer mandate. Further details on the data set construction are provided
in Simon (1999).1870 K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877
(less than 25 workers), and 0 otherwise. POSTijt =1 if reform j is effective that year in that
state, and 0 otherwise. The regression specification includes state by year fixed effects
T t A i (as well as first-level effects). The coefficient on the three-level interaction term, p j ,
represents the effect of reform on small-firm workers.10
The March CPS does not contain information on prices and employer decisions to offer
coverage, thus I draw on two employer-level surveys to test other predictions about the
effects of reform. These employer data also contain independent measures of coverage of
workers that allow for a second test of the predictions to compare with CPS results. The
data best suited to answer questions about employer level effects of reform are the 1996
Medical Expenditure Panel Survey Insurance Component and the 1993 National Employer
Health Insurance Survey (NEHIS) that collected information about health insurance
provision and other employer characteristics for 23,000 and 34,604 private employers,
respectively. This is the only pair of surveys designed to produce bnational and state
estimates of the supply of private health insurance available to American workers and to
evaluate policy issues pertaining to health insurance.Q (Sommers, 1999, p. iii). The
empirical method followed in this analysis is similar to that used in the CPS analysis,
except that now the method of estimation is a linear probability model even for binary
outcomes because confidentiality restrictions prevented the two surveys from being
analyzed simultaneously.11 I estimate regression models of the following outcomes: the
premium paid by the firm per worker, the employee contribution paid by the worker,
whether the firm offers health insurance, and the fraction of workers who are covered.12
4. Results of individual level analysis
To address the adequacy of the control groups, I first rule out policy endogeneity and
ensure that the trends picked up through reform variables did not exist prior to reform.
10
The vector X includes controls for the following explanatory variables, with excluded categories where
appropriate to accommodate an intercept in the model: age, age2, indicators for gender, marital status (five
categories), and interactions between gender and marital status, education (nine categories), two indicators of poor
health of a family member, hours worked per week, log weekly wage, number of people in the household,
categorical variables for firm size, occupation (13 categories), and industry (13 categories).
11
The surveys were carried out by two separate federal agencies (the MEPISC by the Census Bureau funded by
AHRQ and NEHIS by NCHS) under guarantees of confidentiality, which prevent any transfer of micro data
between the two locations that are 25 miles apart. Despite the fact that this prevents analysis by conventional
means, I am able to compute regression coefficients, standard errors, and F tests statistics using the basic matrix
algebra properties (e.g. partitioning) of the OLS estimator, while still abiding by the confidentiality protocols.
This procedure involves creating the X’X matrix components necessary from the first data set at one location,
showing that the results contained cross products, which prevent confidentiality breaches, and transferring them to
the second site to be combined with similar cross product matrices that could then produce the vector of
coefficients, etc. For further details on the estimation method and the data sets used, see Simon (2004).
12
Since 25–50 full-time worker firms are in the treatment group in some states and control in others, I remove
them from my sample. I used 30–60 total workers as an approximation to the 25–50 full time worker category
because I know only the total number of workers in the firm. The control variables in these regressions are ones
appropriate for explaining an establishment’s insurance outcome such as industry, wage distribution, age of the
business, etc. A full list of control variables appears in footnote 16.K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877 1871
Table 2
Effect of full and partial reform on coverage rates: DDD calculations from means, 1991 and 1996
Effect of full reform
Before reform After reform
Reform Small 39.36 37.39
Large 75.79 73.71
No reform Small 47.18 47.04
Large 79.61 77.36
DDD estimate = 2.01 percentage points
Effect of partial reform
Before reform After reform
Reform Small 40.56 0.40949
Large 76.41 0.7486
No reform Small 47.18 47.04
Large 79.61 77.36
DDD estimate=0.18 percentage points
With additional data going back to the March 1988 CPS, I test whether the trend in the
small-firm vs. large-firm gap in coverage rates is different in reform vs. non-reform
states13 in the period before reform, and found no statistically significant differences. I also
tested whether a state’s adoption of reform in a certain year was influenced by differences
in the insurance time trend between small and large firms prior to that year and found no
statistical evidence that this was the case. As a further check, states separated into
categories based on the date and the type of reform enacted, and coverage rates plotted
from the 1988 through 1997 CPS by keeping the groups of states fixed. There was no
obvious difference in the pattern between small and large-firm coverage related to the date
of enactment. Thus, even though on a national scale insurance reform may have been
prompted by a deterioration of the small-group insurance market, it does not seem to
explain the pattern of reform adoption across states, which is the source of variation in this
study.
Second, I present an empirical exercise in Table 2 corresponding to the univariate DDD
estimator. This shows the CPS mean coverage rates for workers in small and large firms,
before and after reform, in states that enacted reforms and in states that did not, and
indicates that full reform decreased the probability that a full-time worker in a small firm
received health insurance by over two percentage points. Partial reforms have no
discernible impact. If one simply uses 1996 data to compare the coverage rate for small
firms in states with full reform to that in states without reform, it would appear that full
reform decreased coverage by 10 percentage points. However, looking at the correspond-
ing coverage rates for large firms suggests that part of this difference is due to underlying
level differences between the two groups of states. These numbers underline the usefulness
of these control groups in accounting for secular trends and level differences between
states that arise for extraneous reasons.
13
I define reform/non-reform states according to the state’s status in 1993, 1995 and 1997 separately. More
details on these tests are available upon request.1872 K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877
Table 3
Probit results, individual level (dependent variable=1 if worker received health insurance from employer)
Sample Observations Sample Small*Full*Post Small*Partial*Post Small*BBP*Post
mean Probit Marginal Probit Marginal Probit Marginal
coefficient effect coefficient effect coefficient effect
Whole sample 222,032 0.64 0.0566 0.0185 0.0057 0.0019 0.0225 0.0074
[0.0263] [0.0086] [0.0285] [0.0094] [0.0251] [0.0082]
Never married 23,256 0.51 0.2305 0.0635 0.0054 0.0016 0.0179 0.0050
males [0.0765] [0.0201] [0.0875] [0.0252] [0.0745] [0.0207]
b35 years
Married women 20,837 0.53 0.1008 0.0308 0.0666 0.0194 0.0570 0.0171
b41 years [0.0930] [0.0291] [0.0983] [0.0281] [0.0875] [0.0259]
with kids
Standard errors in parentheses. Marginal effects are calculated by first establishing a baseline at 1991 by changing
all the variables involving time, then setting the relevant three level interaction to 1, and 0, and computing the
average difference in predicted probability of the dependent variable over all small-firm workers. Standard errors
for marginal effects are calculated using a Taylor Series approximation (the ddelta methodT). Bold font indicates
significance at least at the p=0.10 level.
Other variables included in this regression for which results are not reported are: worker’s age, age squared,
gender, marital status (five categories), interactions between gender and marital status, education (nine
categories), indicators of poor health of a family member, hours worked per week, log weekly wage, number of
people in the household, firm size, occupation (13 categories) and industry (13 categories), state effects (49), year
effects (6), state by year interactions (300), year by small firm size (6), and state by small firm size (50).
I next turn to the multivariate estimates that control for other determinants of
insurance coverage. Table 3 shows the CPS probit coefficients, the associated marginal
effects for the three variables of interest, and standard errors for both the coefficients and
the marginal effects.14 Most findings are consistent with the theoretical predictions of the
effect of reform on coverage rates. On average, full reform carries a coefficient of 0.057
with a standard error of 0.026. This translates into a statistically significant marginal effect
of 1.9 percentage points on the coverage rate of small-firm workers.15 Given that 39% of
full-time workers in small firms receive health insurance from their employer, this means
that full reforms caused on average a 5% decrease in the rate of employer-provided
coverage. Coverage rates declined by a larger magnitude for workers whom insurers
considered low risk, while those considered high risk were not significantly affected and
thus, fared better than the average small-firm worker. Partial reform has an insignificant
effect in almost all cases, and a marginal effect that is generally smaller in magnitude than
14
Removing potentially endogenous variables such as hours worked and wages did not affect the results
substantially. The control variables in the model showed plausible effects, and their coefficients and standard
errors are available from the author upon request.
15
Marginal effects are calculated to simulate the effect on coverage rates for small-firm workers if all states
reformed, relative to remaining without reform. I chose 1991 as the baseline year at which to evaluate the effects
of reform by changing all the appropriate year by state and year by small-firm worker interactions. I calculated the
predicted probability of coverage under each type of reform for each small-firm worker by first setting the
appropriate three-level interaction to one, and then calculated the probability of coverage under no reform by
setting the relevant three-level interaction(s) to zero. The average difference between these two predicted
probabilities over all small-firm workers is the marginal effect reported in Table 3. The appropriate standard errors
are computed using a Taylor Series approximation.K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877 1873
full reform. Although states hoped that bare bones plans would provide low-cost
alternatives to uninsured small firms by removing mandated benefits, the empirical results
here suggest that they had a statistically insignificant impact on the small-group market.
Ideally, one would categorize people based on specific health risks and prior medical
use that insurers could detect through experience rating but are unable to use because of
rating reform. Since the CPS does not contain such detailed variables, I obtained medical
utilization information from the 1996 Medical Expenditure Panel Survey (MEPS) to
investigate the link between demographic factors and expected health costs. These data
show that married women of childbearing age (between 16 and 41 years) are more than
five times as likely to be admitted to hospital, have almost three times the number of
doctors visits and spend twice as many nights in hospital as never-married men under 35
years of age. Absent reforms, there is evidence that insurers sometimes refused to cover
businesses employing a high proportion of childbearing-aged women for this reason
(Zellers et al., 1992). It is unfortunate that the CPS lacks detailed health data because these
are demographic characteristics that about half of all states did not restrict through reform.
Only one state fully banned the use of age rating (New York).
When I limit my sample to young never-married men under 35 years of age, a
relatively low-cost demographic group, the marginal effect of full reform is 6.4
percentage points and statistically significant at p=0.01. The effects of partial reform and
of bare bones laws are negative but statistically insignificant. However, when I use the
relatively high expenditure sample, married female full-time workers of childbearing age
(17–41 years) with young children, the effect of full-reform is positive but statistically
insignificant. For similar reasons, I expect that those men likely to include women of
childbearing age under their policies should also be affected in a similar way. When I
restricted the sample to married men under 45 years of age with children, I found that
the effect due to full reform was similar to the effect for married women with children in
unreported results.
Since many states allowed pricing according to characteristics that define the high and
low expenditure groups, part of the result above is likely due to forces other than price
differentials caused by reform. One possibility is that since these groups differ in their
need for health care, they also may differ in their need for health insurance and
sensitivity to premium changes. Unfortunately, the employer level data used are not rich
enough to discern the differential premium increases for these groups. Even though
married individuals would in general have better outside options (and thus be more price
sensitive), those in the high expenditure group who had insurance in their name may
have been less likely to have a readily available source of spousal coverage given that
dual coverage is not very common. I explicitly tested the hypothesis that reforms lead to
a greater reliance on spousal health insurance by the high-risk group, and found no
evidence of this in unreported results. In summary, the differential in results for the two
groups is larger than expected, and further work using better health data is needed to
explore this issue.
I have attempted to reconcile my findings with other individual level studies. When I
limit my sample to just Pennsylvania, New York and Connecticut, I find insignificant
effects of reforms, consistent with the findings in Buchmueller and DiNardo (2002). To
test the stability of my findings, I undertook several specification checks. I first establish1874 K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877
Table 4
OLS results, establishment level: the impact of full reform
N Mean Small*Full*Post
Premiums 26,651 181.1 7.8 (4.2)
Employee contribution 28,052 32 5.1 (2.4)
Decision to offer 50,485 0.66 0.01 (0.01)
Coverage rate 47,598 42.9 2.12 (1.29)
Standard errors in parentheses. Bold font indicates significance at least at the p=0.10 level. See footnote 16 for a
full explanation of control variables included.
that the results are not driven by the control group by investigating whether small-group
reforms had any bearing on large-firm worker coverage rates. I found no apparent effects
of the treatment on the control group. I then changed the definition of a small firm to one
with fewer than 10 workers. I estimated this alternative model because smaller groups
were considered higher risk pre-reform and might have been able to increase coverage
(or not drop coverage as much) by being grouped together with somewhat larger firms. I
find that effects of reform are smaller in magnitude and statistically insignificant for
workers in smaller firms. I re-estimated the main models using data just on the outgoing
rotation group to see whether the re-appearance of the same individuals in my sample, as
they rotate through CPS, affects the estimates. The results indicate that the coefficient on
full reform is slightly smaller when I focus on the outgoing rotation group. When using
the complement of the outgoing rotation group, the coefficient is slightly larger and the
standard error is the same as for the outgoing rotation sample. I also separated the sample
into odd and even year observations, and found that the coefficients did not change
substantially as a result of this sample split. As a further specification check I
investigated whether part-time workers (working b20 and b15 h/week separately) who
are generally not eligible for health benefits were affected by reform. I find that as
expected, the coefficients are smaller and statistically insignificant for these workers.
Within the group I call full-reform states, there is variation in the number of plans that
insurers were required to issue on a guaranteed basis, and in the btightnessQ of the rating
reform measured by the health band. Unreported estimates suggest that the strength of
the guaranteed issue law was important, and that the variation in rating reform is
insignificant when entered as a continuous variable, but significant when coded as above
and below the median value.16 In states with all plans guaranteed issue (as opposed to
select plans issued on a guaranteed basis), the effect of full reform was a two percentage
point decline in coverage rates on average, statistically significant at the 1% level. The
effect of this type of reform on the low-risk group was a statistically significant decline in
coverage rates of 6.2 percentage points. The most interesting result from this exercise is
that guaranteeing the issue of all plans in full reform states had a weak but positive and
statistically significant (at the 10% level) effect of 6.7 percentage points on the high-risk
coverage rates.
16
This measure is highly correlated with the variable measuring the strength of full reform, hence I use just the
latter.K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877 1875
5. Results of establishment level analysis
Table 4 shows the effect of reform on establishment level outcomes utilizing the matrix
algebra explained earlier.17 Given the smaller sample size and shorter time period available
in the establishment data compared to the CPS, I look at the effect of full reform, including
no reform and partial reform in the omitted category. This specification indicates that full
reform increased premiums on average by a statistically significant US$7.80 a month per
person, that employee contributions rose a statistically significant US$5.10, that employer
decisions to offer coverage were not affected, but that the percent of workers covered at
the firm fell by a statistically significant 2.12 percentage points, a magnitude that is very
close to that from the individual level analysis. Further investigations testing whether there
is a differential impact on firms by predicted workforce characteristics, or by whether the
industry is red-lined or not (Zellers et al., 1992) showed results that are not statistically
significant, but whose magnitudes are generally consistent with predictions of an increase
of premiums, employee contributions and decreases in coverage rates for lower-risk firms.
6. Discussion and conclusion
The empirical results in this paper suggest that stringent small-group reforms have
spread the costs associated with health risks more evenly across the market and may have
unintentionally reduced insurance coverage through increased premiums and employee
contributions. The analysis of both employer and individual data yields fairly consistent
answers. Given that employers pass on about 75% of the premium hike as increased
employee contributions, it appears that employee contributions serve the role of allowing
employers to compensate workers efficiently. The results also suggest that young single
men were particularly sensitive to premium changes in their take-up decisions, although
the magnitude is larger than expected since the combination of gender, age and marital
status were observable to insurers in half the reform states. Further research is needed with
more detailed health risks than available in the CPS to fully understand the impact of
reform by risk level.
Specification checks indicate that the relationships emerging from my analysis
represent more than chance correlation. Economic theories of insurance markets warn
17
The explanatory variables included in the model include: a linear and quadratic term for the number of
employees at the firm, 10 industry indicators, an indicator variable for the presence of unionized workers at the
establishment, age of the firm and its square, the fraction of low-wage workers at the establishment level (defined
as earning below US$6.50 an hour in 1996 and below US$5 an hour in 1993), the fraction of high wage workers
(defined as earning above US$15 an hour in 1996 and in 1993). In regressions that involve premiums and
employee contributions, I also control for the type of plan (whether HMO, conventional, or a mixed plan, and
whether self insured in the case of large firms), the amount of the total deductible, whether there is a lifetime
maximum benefit and its amount, the co-payment required for outpatient treatment, and the coinsurance rate for
inpatient and outpatient treatment to proxy for the quantity of coverage provided by the plan. Additionally, I
include 14 indicators for specific covered services such as outpatient prescription drugs. When control variables
contain missing values, I create separate indicator variables that take a value of 1 for a valid number and 0 for a
missing value, and I replace missing values by zero.1876 K. Ilayperuma Simon / Journal of Public Economics 89 (2005) 1865–1877 us that preventing insurers from distinguishing between different risk groups may worsen the availability of insurance for healthier individuals but not for those who are considered medically expensive. This analysis of small-group health insurance reform reveals the complexity of regulating a market subject to adverse selection. By changing the rules of conduct for insurers, states did not directly address what many argue is the single most important reason why small firms are uninsured—high prices. Several questions about the effects of government intervention in health insurance markets remain unanswered. If forcing insurers to treat all customers alike does not produce optimal results, then what will? Should government policy encourage small employers to band together in purchasing pools to take advantage of size as large firms do? The optimal design of insurance regulations that takes adverse selection behavior into account, and other solutions to reducing the medical uninsurance problem remain areas of high priority for future research. Acknowledgements I am grateful to William Evans, Judith Hellerstein, Steve Coate and Jonathan Gruber for valuable suggestions. I would also like to thank seminar participants at several universities and conferences for helpful comments. I am grateful to the National Center for Health Statistics for allowing me to conduct a portion of this research at their offices. Part of this work was conducted while the author was a Research Associate at the Census Bureau. Dissertation support from the former Health Care Financing Administration is graciously acknowledged for a portion of this research. All errors and opinions expressed are mine. References Buchmueller, T., Jensen, G., 1997. Small group reform in a competitive managed care market: the case of California, 1993–1995. Inquiry 34, 249 – 263. Buchmueller, T., DiNardo, J., 2002. Did community rating induce an adverse selection death spiral? Evidence from New York, Pennsylvania, and Connecticut. American Economic Review 92 (1), 280 – 294. Hall, M., 1999. An Evaluation of Health Insurance Laws. Wake Forest University School of Medicine. Various reports available at www.phs.wfubmc.edu/insure (Access date 2003). Hing, E., Jensen, G., 1999. Health insurance portability and accountability act of 1996: lessons from the states. Medical Care 37 (7), 692 – 706. Jensen, G., Morrisey, M., 1999. Small group reform and insurance provision by small firms, 1989–1995. Inquiry 36 (2), 176 – 187. Kaestner, R., Simon, K., 2002. Labor market consequences of state health insurance reforms. Industrial Labor Relations Review 56 (1), 136 – 160. Marquis, M.S., Long, S., 2001/2002. Effects of dSecond GenerationT small group health insurance market reforms, 1993–1997. Inquiry 38, 365 – 380. Monheit, A., Schone, B., 2004. How has small group market reform affected employee health insurance coverage? Journal of Public Economics 88 (1–2), 237 – 254. National Association of Insurance Commissioners (NAIC), 1998. Model Laws and Regulations. Kansas City, MO. Simon, K. 1999 bThe Impact of Small-Group Health Insurance Reform.Q Dissertation. Department of Economics, University of Maryland.
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