No. 10 September 2009
An Examination of Market-Based Reforms for New Yorks Uninsured
Stephen T. Parente, Associate Professor of Finance, Carlson School of Management, University of Minnesota
Tarren Bragdon, Adjunct Fellow, Manhattan Institute for Policy Research
|MPR 10 (PDF)
Why Health Care Is So Expensive in New York Steve Parente and Tarren Bragdon, Wall Street Journal, 10-17-09
The New York Solution doesn't even work in New YorkSteve Parente and Tarren Bragdon, Albany Business Journal, October 9, 2009
New York shows how not to do health care reform, Paul Howard, Washington Examiner, 09-23-09
Stephen Parente and Tarren Bragdon talk to Paul Howard about "Healthier Choice: An Examination of Market-Based Reforms for New York's Uninsured", listen here.
IN THE PRESS
Failed experiments on healthcare,
Clive Crook, Financial Times Blog, 12-23-09
Health Bill Clears a Hurdle, Schenectady Daily Gazette, 10-14-09
The Lesson of State Health-Care Reforms Peter
Suderman, Wall Street Journal, 10-07-2009
Report Recommends Free-Market Reforms To Expand Health Coverage
in New York Gerald Silverman, Bureau of National Affairs, 09-23-09
If Big Government Health Care Doesn't Succeed,
Try Try Again, Philip Klein, The American Spectator, 09-22-09
Tarren Bragdon WIBA's "Upfront With Vicki McKenna", 9-22-09
Tarren Bragdon on WLOB's "The Ray and Ted Show", 9-29-09
Tarren Bragdon on WVOM's "The George Hale and Ric Tyler Show", 9-29-09
Tarren Bragdon on WTOP's "The Ray and Ted Show", 9-25-09
FROM THE BLOGOSPHERE
With Obamacare on the Ropes, Dems Go After the Insurers, David Gratzer, NewMajority.com, 9-25-09
New York Is No Model for the Nation, NRO Critical Condition Blog, 9-23-09
|About the Authors
| New Yorks Current Individual-Insurance
|The Impact of Regulations on Insurance
Premiums and Purchase Patterns: A Literature Review
|Summary of Findings from the Survey
and Focus Groups Composed of New Yorks Nonpoor Uninsured
|The Effects of Reform: Projected Take-Up
Rates for Plans within a Reformed New York Individual-Insurance
|Conclusions and Policy Recommendations
|Appendix I: Average Premium Differences
under Different Micro-Simulation Policy Choices
|Appendix II: A Note on the Micro-Simulation
Model Used in This Study: Comparing ARCOLA with Simulations
from Columbia University and the Urban Institute
|Appendix III: High-Risk-Pool Facts
Millions of Americans are living without health insurance. Congress
is currently considering a variety of insurance market reforms intended
to reduce their number. In New York, there are well over 2 million
uninsured adults, representing 14 percent of the non-elderly population,
a figure just below the national average. The goal of this paper
is to estimate the reduction in the number of uninsured New Yorkers
that would result from expanding access to unsubsidized, private
Bills before both houses of Congress contain provisions similar
to New York State laws that mandate guaranteed issue (which prohibits
denial of coverage on the basis of health status) and community
rating (which requires insurance companies to charge policyholders
the same premium, regardless of their age, gender, or health status).
Four other states have similar regulations. Yet New Yorks
individual-insurance market is unique in requiring insurers to offer
coverage to all individuals at all times at exactly the same price.
Although New Yorks guaranteed-issue and community-rating
laws were adopted with the best of intentions, they have not been
effective in substantially reducing the size of the states
uninsured population. In fact, as a result of a significant increase
in the cost of private-insurance coverage for individuals, the market
for individual health insurance in New York has nearly disappeared,
declining by 96 percent since 1994.
Uninsured New Yorkers of all income levels would benefit from access
to a reasonably priced private-insurance market. The existence of
such a market would ensure that scarce public dollars are reserved
for government programs like Medicaid that protect New Yorks
poorest and sickest citizens.
With data collected from a survey and three focus groups composed
of uninsured New Yorkers and conducted by Zogby International, the
authors of this study constructed a micro-simulation model to assess
the potential impact of four individual-insurance market reforms
on the level of premiums that individuals would pay for private-insurance
coverage and the potential willingness of the uninsured to purchase
coverage voluntarily. This model was first used by the U.S. Department
of Health and Human Services to simulate the effect of the Medicare
Modernization Act of 2003 on take-up rates of lower-premium, catastrophic-protection
health plans in the individual health-insurance market that were
compatible with Health Savings Accounts. Such accounts are not available
in New York State.
The market reforms that this paper proposes are:
- Repeal of community-rating and guaranteed-issue laws
- Approval of Health Savings Accounts for New Yorks individual-insurance
- Permission to individuals to shop for approved and affordable
health-insurance policies across state lines
- Approval of mandate-lite plans, which permit insurers
to offer plans with narrower coverage for sale
in New York
While each of these reforms would have some effect on reducing
the number of uninsured, repeal of New Yorks community-rating
and guaranteed-issue laws would have the greatest impact, potentially
reducing the price of individual insurance coverage by 42 percent
and encouraging up to 37 percent of the uninsured to buy coverage.
However, as the report also notes, a small portion of the uninsuredthose
with certain preexisting conditionscould be deemed uninsurable
or find individual insurance coverage too expensive. Therefore,
the authors recommend a modest assessment on policyholders in the
individual and small-group insurance markets, with the proceeds
used to fund a guaranteed-access, high-risk pool for this population.
The pool would offer portable private health insurance at a subsidized
price. Such a program would ensure that all New Yorkers had access
to health insurance.
About the Authors
Stephen T. Parente is director of the Medical Industry Leadership
Institute and associate professor in the finance department at the
Carlson School of Management, University of Minnesota. He specializes
in health economics, information technology, medical technology
evaluation, and health insurance. Parente has been the principal
investigator for a series of evaluations of consumer-directed health
plans since 2002. He was a health policy advisor for the McCain
2008 presidential campaign and served as legislative fellow in the
office of Senator John D. Rockefeller IV (DWV) during the 1990s
health reform initiatives. He received a Ph.D. in health finance
and organization from Johns Hopkins University.
Tarren Bragdon is an adjunct fellow at the Manhattan Institute.
A former two-term member of the Maine House of Representatives,
he served on the House Joint Standing Committee on Health and Human
Services and is the youngest person ever elected to the Maine House.
He was also health policy researcher for the president of the Maine
Senate. In 2007, he testified regarding health-insurance reform
before the U.S. Senates Small Business and Entrepreneurship
Committee. His articles on health issues have been published in
the Wall Street Journal, the New York Post, the Buffalo
News, and the Albany Times Union, among other publications.
Mr. Bragdon received a bachelors degree in computer science
from the University of Maine and a masters degree in business
from Husson University. Since January 2008, Bragdon has served as
chief executive officer of the Maine Heritage Policy Center, a nonprofit,
nonpartisan research and educational organization based in Portland,
Support for this work was provided by the New York State Health
Foundation (NYSHealth). The mission of NYSHealth is to expand health
insurance coverage, increase access to high-quality health care
services, and improve public and community health. The views presented
here are those of the authors and not necessarily those of the New
York State Health Foundation or its directors, officers, or staff.
In 2007, the Manhattan Institutes Empire Center for New York
State Policy published Rx NY: A Prescription for More Accessible
Health Care in NY (Rx NY). In that report, we
argued that a reasonably priced and widely available private-insurance
market should exist for uninsured but solvent individuals, both
for their own sake and to ensure that scarce public dollars are
reserved for government programs like Medicaid that protect New
Yorks poorest and sickest citizens.
This paper evaluates the practical impact of four individual-insurance
market reforms recommended in Rx NY:
- Repeal of community-rating and guaranteed-issue laws.
- Approval of Health Savings Accounts for New Yorks individual-insurance
- Permission to individuals to shop across state lines for affordable
health-insurance policies already licensed in nearby states.
- Approval of mandate-lite plans, which permit insurers
to offer narrower coverage by imposing fewer requirements on plans
for sale in New York.
We show that just a few policy changesin particular, the
repeal of New Yorks community-rating and guaranteed-issue
lawswould make private insurance more affordable and could
thereby reduce New Yorks uninsured population by up to 37
percent. Guaranteed issue requires health insurers to
enroll all individual-market applicants, regardless of their health
status. Community rating requires premiums to be uniform,
regardless of an applicants age and gender. We also note that
widespread uptake of insurance polices in a reformed market depends
on the cooperation of employers and policymakers, who must educate
the uninsured about their new options and then facilitate the adoption
of the ones chosen.
At the same time, we recognize that under this model, a small portion
of the uninsuredthose with certain preexisting conditionscould
be priced out of insurance coverage or deemed uninsurable. Therefore,
we recommend the imposition of a modest assessment on policyholders
participating in the individual-insurance market, with the proceeds
to be used to fund a guaranteed-access high-risk pool for excluded
individuals (see Appendix III). The pool would offer to such individuals
portable private health insurance at an affordable price.
New Yorks Uninsured: A Challenging Population to Reach
The uninsured are a diverse population, diverse in outlook, and
difficult to reach with any single form of insurance, public or
private. However, extending the reach of public plans or subsidies
places a substantial strain on public budgets, particularly in periods
like today, when the state is running deficits.
The goal of this paper is to estimate the reduction in the number
of uninsured New Yorkers that would result from expanding access
to unsubsidized private health insurance. Therefore, this paper
does not examine the merits of expanding government medical welfare
programs such as Family Health Plus.
Although more than 2 million New York residents are uninsured today,
they constitute a population that is largely young, healthy, without
dependents, and above the poverty level. Over 60 percent of the
uninsured earn over $25,000 annually, and one-third earn over $50,000.
Many are only temporarily uninsured. Having some disposable income
but a relatively small amount of it, the uninsured, as currently
constituted, are likely to be highly responsive to a reduction in
premium levels resulting from vigorous competition for their business.
What is a High-Risk
This paper shows that if New York policymakers reformed the
individual-insurance market, particularly by removing community-rating
and guaranteed-issue laws, so that it could start offering
more affordable choices, up to 37 percent of the currently
uninsured would buy private, unsubsidized insurance policies.
We recognize, however, that even a robust individual-insurance
market would not meet the needs of all applicants, particularly
those with a serious chronic illness that predates their
application for insurance. For these applicants, we
recommend the creation of a high-risk pool along the
lines of those maintained in many other states. A high-risk
pool is typically a state-chartered nonprofit that runs
a health-insurance program designed to serve the medically
uninsurable population by providing it access
to affordable private insurance. It does so with subsidies
of premiums, which are often financed by small assessments
imposed on persons with private health insurance in
the individual and small-group markets.
High-risk pools were first established in Connecticut and
Minnesota in 1976. Although only four states besides New York
mandate the sale of individual-insurance policies to all individuals,
regardless of health status, high-risk pools exist today in
thirty-five states where policymakers have recognized the
need for such an option. Eleven of them were started within
the last fifteen years.
The assessment on individual-insurance policies in New York,
we estimate, would be modest. If New Yorks highrisk
pool was of average size and cost, it would probably need
to raise just $58 million to underwrite its premium subsidy,
or $6 per member, per month (PMPM) from participants in a
reformed individual-insurance market. If the assessment were
extended to the 1.6 million ratepayers in the small-group
market, the monthly assessment would be just $2. (For more
detail, see Appendix III.)
The individual-insurance market is the primary unsubsidized private-insurance
option for the uninsured. Also known as the direct-pay market, it
is properly understood as a residual market for Americans who do
not obtain coverage from an employer or qualify for a public program.
Since it is a last resort for such people, it is critical that policymakers
in New York and other states ensure that this market is as flexible,
affordable, and accessible as it can be made to be. Nevertheless,
because of regulatory burdens and the costs they impose, the New
York market for such policies is atrophying.
This is best demonstrated by the markets dramatic contraction
since the early 1990s, attributable largely to a steep increase
in premiums, in contrast to the performance of the national market,
which has grown during this same period. As stated in Rx NY,
which was published in December 2007 (rates have only gone up since
In most regions of the Empire State, the monthly individual health-insurance
premium (not purchased through an employer) starts at $500 for an
individual policy and $1,400 for a family policy. The average premium
in the private market is roughly twice the national average. The
only cheaper option available to New Yorkers in the private market
has been the Healthy NY program, in which the state directly subsidizes
premium rates starting at $300. But eligibility for this plan is
limited to workers who earn less than $25,300 and have been uninsured
for at least a year or have recently lost employer-sponsored coverage.
Relatively few workers qualify, and the program has reached relatively
fewonly 147,000 have enrolled, or less than 0.8 percent of
the states population.
Many uninsured New Yorkers already qualify for existing public
insurance programs such as Medicaid or the State Childrens
Health Insurance Program (SCHIP). Almost all uninsured children
are eligible for Child Health Plus. About 800,000 uninsured adults
are eligible for Family Health Plus or Medicaid.
However, for the nonpoor adults who constitute the remaining 1.3
million of the uninsured, the lack of health-insurance plans that
reflect their particular needs and preferencesand the size
of their pocketbooksmay dissuade them from purchasing any
health-insurance policy at all. The stigma associated with dependence
on public programs may also drive away even those for whom such
programs are intended. Consequently, New York policymakers should
consider the potential of additional market reforms to expand coverage
more broadly than an expansion of public programs would alone.
Methods in Brief
To measure the potential market impact of our policy recommendations,
we retained the polling firm Zogby International to perform a survey
of more than a thousand uninsured New Yorkers (defined as those
who were uninsured at the time of the survey or who had been uninsured
in the two years preceding it) to examine the reasons they were
or had been uninsured, to explore their preferences among insurance
products, and to solicit their views on what kind of role the state
and federal government should play in providing information about
or access to health-insurance options.
To further explore the preferences of the individuals in our survey,
Zogby then conducted three focus groups with survey respondents
who discussed additional issues, such as their openness to interstate
insurance sales, their willingness to spend their limited financial
resources on health insurance, and whether it was an individuals
responsibility to obtain coverage or the state or federal governments
responsibility to provide it.
Building on the data obtained from the survey and focus groups,
we then conducted a micro-simulation that tested the effects of
the four market reforms on insurance take-up among the uninsured.
The simulation used the Adjusted Risk Choice & Outcomes Legislative
Assessment model (ARCOLA), which separately
analyzes recent insurance-product innovations such as high-deductible
health plans and limited-panel preferred-provider networks. By doing
so, the ARCOLA model distinguishes itself from recent models developed
at Columbia University and the Urban Institute, which compare private
insurance plans in aggregate with a set of public insurance options.
Both the ARCOLA and Urban Institute micro-simulation models were
initially funded by the U.S. Department of Health and Human Services.
For the purposes of this analysis, the ARCOLA model also was able
to predict the effects of premium changes on insurance take-up by
the uninsured in a reformed New York State individual-insurance
While most of our insurance reforms reduced the number of uninsured
by significant margins, the repeal of community rating and guaranteed
issue had the greatest impact, producing up to a 37 percent decline
in the number of uninsured.
New Yorks Current Individual-Insurance
Market surveys consistently report that New York has one of the
most expensive, highly regulated, and restrictive
individual-insurance (also known as direct-pay) markets in the country.
Indeed, New York is the only state individual-insurance market that
requires all insurers to guarantee issuance of all individual-insurance
products to all individuals at all times
bearing exactly the same premium (known
as community rating).
This individual health-insurance marketwhich is the only
place where New Yorkers can buy private, unsubsidized health insurance
if they are not sole proprietors, or have an employer that does
not offer coverage, or are unemployedallows just two kinds
of plans: a health maintenance organization (HMO) plan and a point
of service (POS) planboth of which operate under state mandates
specifying the minimum extent of coverage, the maximum deductible,
and the maximum co-payment. These mandates have not been significantly
modified since their enactment in 1996. The guaranteed-issue law
encourages an individual without employer-based coverage to wait
until he or she is sick before buying individual coverage, as insurers
are forbidden to deny coverage to any individuals applying, including
those already ill. However, even in New York, under certain circumstances,
individuals with preexisting conditions may be excluded from coverage
for up to one year.
(Until March 2003, New Jersey was the only other state with such
strict requirements, but its legislature instituted reforms effective
on that date and then adopted still-wider reforms that became effective
in January 2009 that made more affordable basic policies
available to residents, as explained further below.)
The experience of Healthy NY, which began in 2001, demonstrates
the significance of lower-cost alternatives, given the price sensitivity
of the uninsured. Healthy NY is a state-subsidized plan for sole
proprietors and low-income single adults and families. For single
adults, the monthly income limit in 2008 was $2,167. For families
of four, the monthly limit was $4,417. In 2008, the program covered
almost 155,000 New Yorkers. It offered two lower-cost plans in addition
to the traditional HMO plan: one with no prescription drug coverage;
and one that is a high-deductible Health Savings Account (HSA)-eligible
plan. The comprehensive Healthy NY HMO plan for single adults averaged
$252 in monthly premiums in 2008. The plan with no pharmacy coverage,
introduced in July 2003, costs 15 percent less and has attracted
about 20 percent of all Healthy NY enrollees. The HSA-eligible Healthy
NY plan, introduced in 2007, has a premium that is 22 percent lower
than the traditional HMO plan and in just seventeen months increased
its enrollment from 600 to more than 6,000 enrollees, representing
about 4 percent of the Healthy NY population. The program costs
New York taxpayers $122 million a year and has been estimated to
have reduced New Yorks uninsured population by one percentage
In addition to these laws, the New York legislature has enacted
fifty-one mandates dictating coverage of certain medical conditions
and inclusion of particular categories of providers in insurance
plans. The state average is forty-two mandates. Accordingly, the
cost of insurance in New York also exceeds the average. Certain
mandates, such as coverage of alcoholism treatment and provision
of emergency medical services, appear in almost every state.
Others, such as ambulatory cancer treatment and hormone-replacement
therapy, are found only in New York and two or three other states.
Premiums for New Yorks individualthat is, private and
unsubsidizedinsurance plans currently range from $753 to $2,655
a month for a single person and $2,205 to $6,770 for a family of
four living in New York City. Annualized,
New York Citys individual-insurance premiums cost at least
$9,036 for an individual or $26,460 for a family. Clearly, such
costs are out of reach for all but the wealthy or the very sick,
who would presumably choose to obtain coverage, if they could find
the money for it, only if they expected to incur costs at least
equal to their premium payments.
2002, sole proprietors have been able to buy into New Yorks
small-group market by paying no more than 20 percent more than the
average premium charged to businesses with two to fifty employees.
While sole proprietors are not included in New Yorks individual-insurance
market, as they are in most states, they are included in tables
comparing average individual-market premiums in the states. Despite
the inclusion of this relatively young and healthy group, New York
has a very expensive individual market with a high average age,
as shown below.
New Yorks individual-insurance market is small by any standard.
In 1994, the year after New Yorks community-rating and guaranteed-issue
laws were passed, its individual market had almost 752,000 policyholders,
about 4.7 percent of its non-elderly population.
Today, New Yorks individual market is just 34,246,16 about
0.2 percent of New Yorks total non-elderly population, and
a drop of 96 percent in the portion of the states population
covered by individual insurance.
In this respect, New York State and the nation have diverged. In
1994, about 10.45 million Americans nationwide, or about 4.5 percent
of the non-elderly population, were covered by individual insurance.
In 2007, that number had climbed to 14.35 million policyholders,
as had the portion of the non-elderly population it represented,
about 5.5 percent.
In Californias private individual-insurance market, the participation
rate in 2007 was even higher: its 2.6 million covered individuals
represented 8 percent of the non-elderly population, up from 6.6
percent in 2000.
While the individual market is growing nationally as a share of
the population, New Yorks well-meaning but costly laws, expansions
of mandated coverage, and increases in premiums to cover their cost
have effectively undermined the private-sector safety net by forcing
ever larger numbers of residents out of the market.
If New Yorks individual market were as big as it was in 1994
(4.7 percent of the non-elderly population), it would have 778,000
policyholders today, not the 34,246 it currently has. If its rate
of participation were as high as the average U.S. states (5.5
percent of the non-elderly population), New Yorks market would
be 27 times its current size and have 910,000 policyholders todayabout
36 percent of New Yorks 2.56 million uninsured non-elderly
Lessons from the Garden State
It is instructive to compare New Yorks individual-insurance
market with that of another large northeastern state. In August
1993, New Jersey began enforcing guaranteed issue and pure community
rating in its individual market, just as New York does currently.
Unlike New York, however, New Jersey permitted some variation among
its standard individual-insurance plans, including a range of deductibles.
Before enacting guaranteed issue and community rating, New Jersey
had 157,000 policyholders in its individual market. Despite New
Jerseys greater flexibility, this number had dropped to fewer
than 86,700 by the end of 2001.
Concerned about falling enrollment, the New Jersey legislature
in 2001 passed a law allowing Basic and Essential plans
to be sold in the individual market. These plans, which went into
effect in March 2003, may charge premiums that vary by a ratio of
up to 3:1 to reflect a policyholders age, gender, and place
of residence. Basic and Essential plans offer a limited benefit,
which covers only 90 days per year for hospitalization, $600
per year for wellness services, $700 per year for office visits
for illness or injury, $500 per year for out-of-hospital testing,
and limited benefits for mental health services, alcohol and substance
abuse treatment and physical therapy.
Carriers can sell a rider providing additional benefits.
At the end of 2002, before these Basic and Essential plans began
being sold, New Jerseys individual market had 79,870 policyholders,
almost all of them covered by pre-reform standard plans.
By the second quarter of 2009, individual-market enrollment had
increased to 105,158 (a gain of 32 percent). This increase was solely
a result of the popularity of these new Basic and Essential plans.
In fact, the number of people in the standard plan dropped from
78,698 at the end of 2002 to just 52,271 by the second quarter of
2009. The number of policyholders with Basic and Essential Plans
went from zero, pre-reform, to 52,645 by the second quarter of 2009.
Of note, more than 26,000 standard policyholders, a third of the
pre-reform market, switched to Basic and Essential plans during
this same period.
Surprisingly, New Jerseyans enrollment in the individual
market kept building during the current recession; 17,417 people
signed up for individual coverage (20 percent growth) from the end
of 2007 to the second quarter of 2009. During this same eighteen
month period, the size of New Jerseys small-group market dropped
by 66,000. In effect, for every four people previously employed
by small business who lost coverage, one voluntarily enrolled in
New Jerseys Basic and Essential plan instead of seeking government
assistance or going without coverage.
Unfortunately, New Yorks uninsured do not have this option.
New Jersey, with less than half (45 percent) of the population of
New York, now has three times as many policyholders
in its individual market. New Jerseys experience suggests
that regulationand its impact on insurance premiumsmatters
greatly and that many uninsured individuals will voluntarily buy
unsubsidized individual coverage when it becomes affordable.
The Impact of Regulations on
Insurance Premiums and Purchase Patterns: A Literature Review
To examine further the impact of regulations on insurance premiums
(and the price sensitivity of the uninsured), we conducted a review
of articles and scientific papers on the subject. Our review briefly
addresses three broad but key questions, and then provides a more
detailed summary of issues most relevant to the situation in New
Key Questions for New York State and Responses
The first and most critical question is: What are the effects of
regulationsand subsequent premium increaseson the rate
of insurance take-up among potential participants in the small-group
and individual-insurance markets? Do some actual participants lose
or drop their insurance as a result of premium increases?
The Pricing Impact of Guaranteed Issue, Community Rating, Any-Willing-Provider
Laws, and Individual Mandates
Insurance rules can increase the cost of insurance. Those having
the greatest impact fall into three broad types: community rating,
guaranteed issue, and any willing provider. In addition,
individual mandates require insurers to cover particular medical
services or conditions. Of the three major types, guaranteed issue
is the most costly for insurers and thus the regulation most responsible
for raising premiums and then driving down insurance take-up rates.
This finding is based on a comparison of the work of Congdon et
al. and Hadley and Reschovsky,
which allowed a distinction to be drawn between a guaranteed-issue
effect and a community-rating effect. Citing the experience of New
Jersey, the authors found that guaranteed issue can increase premiums
by as much as 100 percent.
The mandate having the next-largest effect on the size of premiums
is community rating (Congdon et al., Hadley
and Reschovsky). It is responsible for
a 2027 percent increase in premiums and is also accompanied
by a decline in consumer demand, as such increases usually are (Congdon
et al.). The higher premiums reflect actuarially
based projections of higher costs. Premium-support programs for
high-cost, chronically ill populations should, to some extent, be
able to reverse a price-sensitive decline in demand.
The latest work to examine the effect of community rating is by
Lo Sasso and Lurie (2009). Looking at data provided by all respondents
to the Survey of Income and Program Participation (SIPP), the authors
found that community rating had small effects on rates of individual
coverage. However, the data used were less recent than those used
by Congdon et al. (2005), Henderson et al. (2009), and New (2006),
having been drawn primarily from the 1990s. In view of the recent
growth of preferred-provider organizations (PPOs) offering lower
premiums, the age of Lo Sasso and Luries data is a concern,
even though their study was published more recently than those of
the other authors we have reviewed above.
Several researchers have examined any-willing-provider laws. These
laws require managed-care organizations, which favor bilateral negotiated
arrangements with selected providers, to compensate all providers
willing to accept their payment schedule, and found anywhere from
a 1.5 percent to a 10 percent increase in premiums following such
laws adoption. Congdon et al. and
Henderson et al. (2009) collected evidence
that was sufficiently credible for use in a micro-simulation analysis,
but they are the only two investigators to have done so, owing to
the difficulty of measuring the effect. The office of the Assistant
Secretary of Planning and Evaluation (ASPE), U.S. Department of
Health and Human Services, reviewed the quality of the studies.
We purposely did not model any-willing-provider laws, which New
York does not have, in order to produce a more conservative estimate.
Individual mandates can still have a large cumulative impact. Specifically,
researchers have shown that the average mandate, not counting the
three most burdensome ones, produces a premium increase of 0.5 percent
(Congdon et al., 2005; New, 2006). While this seems negligible when
compared with the impact of community-rating provisions (which may
add 2027 percent to premium price), many states have well
over twenty mandates and some considerably more. New York has more
than fifty. Forty of these mandates (adding 20 percent to the premium
price) can cumulatively equal the financial impact of community
rating (Congdon et al., 2005; New, 2006).
Beyond Regulation: Are There Tools to Encourage Insurance Take-Up?
Recognizing the low take-up rates in most individual-insurance
markets, policymakers, instead of instituting direct market reforms,
have extended subsidies to policyholders in order to bring down
their out-of-pocket costs. How have subsidy programs such as tax
credits and deductions affected insurance take-up rates in the various
individual markets where theyve been tried?
The health-economics literature shows that subsidies, if they go
directly to the policyholder, typically lower the cost of insurance
and thus increase take-up. The magnitude of the response will depend
on the size of the subsidy provided and whether it is offered in
combination with a requirement that individuals purchase insurance,
as it has been in Massachusetts. One simulation of California data,
by Marquis et al. (2004), showed that a 50 percent subsidy had little
effect on insurance take-up, the increase being between 4 and 6
percentage points. An empirical estimation of a subsidy program
in Washington State produced less anemic results (Long and Marquis,
2005), especially in enrollment rates for children. A more recent
subsidy simulation study (Marquis et al., 2007), using data from
the Community Tracking Survey, found that a 20 percent subsidy produced
a smaller response than the 50 percent subsidy in the earlier Marquis
One explanation offered for why premium subsidies are not more
effective is that those who would respond to the price changes have
already signed up. Those who have not signed up may need subsidies
nearly equal to the entire premium to respond, as the Marquis et
al. (2004) study suggested. Price-sensitive people sign up early;
the rest dont like the coverage offered or dont think
they need insurance badly enough to acquire it, even when it becomes
more affordable. Thus, marginal return on take-up diminishes, even
as subsidies increase. This effect was observed by Ken Thorpe in
the New York market seventeen years ago.
Finally, are there any market-based programs like Working Today,
an association health plan for freelancers, or eHealthInsurance,
an electronic market, that show consumers responding to the greater
variety of choices they represent and the wider range of premiums
There are fairly limited data on the results of these market-based
programs. The National Bureau of Economic Research working paper
by Joni Hersch (2003) is the best (though practically the only)
example of work in this area. Her paper found a large number of
freelance workers who were covered neither by any of the companies
for which they did work nor the employer of a family member. Therefore,
a program like Working Today, which, by pooling freelance workers,
brings rates closer to affordable levels, has promise. Empirical
studies of the effect of eHealthInsurance.com on coverage rates
do not exist, although that company favors the collection of evidence
on this question.
Implications for New York
In short, our literature survey shows very significant effects
of guaranteed-issue and community-rating regulations on premium
prices in individual-insurance markets, where the uninsured (who
are eligible for neither public health insurance nor employer-sponsored
coverage) are most likely to find themselves. Mandates probably
add additional costs to health insurance, but scholarly sources
are divided on the magnitude of the impact. Tax credits, another
policy tool that has been widely used to increase insurance take-up,
have had mixed effects, according to the literature, but their net
effect probably has been relatively minor. Finally, relatively little
empirical evidence demonstrates the efficacy of market-based mechanisms
like Working Today that bring uninsured individuals into insurance
pools. Again, this may be because the market for individual insurance
is one of last resort, spends less on marketing, and has higher
turnover rates than the large-employer and the small-group markets;
for example, individuals in this market may have less information
on plan pricing or design than participants in those other markets,
a possibility that finds some support in both our survey and focus-group
Accordingly, our review of the literature suggests that the cost
of coverage is one of the most important factors affecting the decision
of whether to obtain coverage. This review also helped determine
the questions we asked on our survey and posed to our focus groups.
These questions covered such topics as our subjects income
and health status and the extent of their knowledge of the health-insurance
market as it exists today in New York.
Summary of Findings from the
Survey and Focus Groups Composed of New Yorks Nonpoor Uninsured
Working with Zogby International, we surveyed 1,010 New Yorkers
likely to find themselves in the individual-insurance market about
the demographic niches they occupied, the health benefits that mattered
most to them, and other features they would hope to find in a health
plan. The survey firm also conducted focus groups to illuminate
issues raised during the survey or to confirm the reliability of
certain findings of the survey.
Of those surveyed, 69 percent were uninsured, and 31 percent had
been uninsured within the preceding two years but were now insured.
When queried about their preferences, the currently uninsured stated
the three features of a health plan that they most valued, in order
of importance: a) an ability to roll over the unused balance in
a health account to the following year; b) access to online tools
and resources; and c) an absence of co-payments. To the formerly
uninsured, the three most desirable features of a plan were, in
order of importance: a) an absence of co-payments; b) a small paycheck
deduction; and c) online tools and resources. These results suggest
that this population is most concerned about out-of-pocket costs.
Premium costs are thus sure to matter to it as well.
We also examined to what extent this population might be willing
to shop across state lines for coverage, which they cannot now do,
given the existence of a federal law, the McCarran Ferguson Act,
which prohibits it. Of the 1,010 individuals
surveyed, 52 percent said that they would not consider moving to
another state to get better health benefits, but almost a quarter
(23 percent) said that they were unsure whether they would, and
another quarter (25 percent) said that they would consider it. However,
most survey respondents were largely uninformed about restrictions
on the interstate purchasing of health insurance as well as rules
requiring community rating imposed by the Employment Retirement
Income Security Act (ERISA) on large self-insured firms. Most members
of the focus groups said that they did not know that they could
not purchase out-of-state policies. We also found that the likelihood
of purchasing was positively correlated to income. The income group
most willing to seek better health coverage encompassed those with
salaries between $50,000 and $75,000, low enough to reduce their
willingness if premiums rose. Ironically, individuals with lower
incomes might be less price-sensitive, since they would have started
out less disposed to incur the cost of coverage.
Other salient results of the survey include:
- Of those who were without health insurance in the past two years,
29 percent said that it was because they lost their job, and 15
percent said that it was because they had been covered by someone
elses policy but were no longer eligible. About 12 percent
said that they lost coverage because they switched jobs and their
new employer didnt offer health insurance. About 11 percent
canceled it to save money.
- Of those who had never had health insurance, a plurality (46
percent) said that it was because they could not afford it, and
26 percent said that it was because their employer never offered
- Almost 58 percent said that having an employer that provided
health insurance would make them more likely to sign up for it,
while 20 percent said that they would be more likely to obtain
coverage if the rates for private insurance were cheaper.
- Of those who did not have health insurance at the time of the
survey but did have it earlier, 40 percent said that they had
been without coverage for less than a year, while 31 percent said
that the length of the lapse had been between one and three years.
- The vast majority (82 percent) said that they did not search
for a health plan on eHealthInsurance.com in 2008.
- More than half (61 percent) said that they would rate their
overall health as positive, and 18 percent rated it
Nearly two-thirds (65 percent) said that neither they nor any
of their dependents had a chronic condition, but 32 percent said
that they or their dependents did suffer from a chronic condition
such as asthma, hypertension, diabetes, or arthritis.
- The health-plan feature that 90 percent of respondents wanted
most was coverage of preventive-care services, while 76 percent
objected most strongly to co-payments.
- About 69 percent were not sure whether they could buy a health-insurance
policy from an adjacent state, but 21 percent said that they believed
that they could not.
In sum, the survey reinforced the importance of employment as a
gateway to health insurance, and it confirmed the decisiveness of
cost in determining whether coverage was acquired. The population
surveyed is healthy as a whole, but significant numbers of the uninsured
and their dependents suffer from a chronic condition. The population
lacked knowledge of insurance laws and regulations affecting the
prices of plans in the group and individual markets, including those
governing interstate sales.
Given the lack of understanding about key aspects of health-insurance
plans, it seems apparent that any effort to reach the uninsured
must include outreach and education as well as attractively priced
The survey was designed not only to provide information on individuals
insurance preferences but to serve as the basis of a simulation
model that tested the impact of different health-insurance policy
reforms on take-up rates. To complete this task, attributes such
as age, gender, and the existence of a chronic condition in a persons
household were also weighed.
The survey firm also conducted three focus groups. These were quite
helpful in providing information unobtainable through the survey
or confirming the reliability of certain findings of the survey.
The focus-group report contains several key findings. First, steep
premiums were the most important factor in driving the decision
to go without coverage. Zogby summarized the focus-group findings:
Affordability was the clarion call that rang through all three
focus groups. It is the reason why many are without health insurance,
be it because they lost their jobs and cannot afford to pay COBRA
or other private rates, or simply because they choose not to buy
into plans that are available to them because they cost too much.
Given the state of todays economy, most of the participants
moved health insurance below paying bills and putting food on the
table on their list of priorities. If they could afford coverage,
most would like a few different plans to choose from, but they didnt
have much interest in plans that could be customized to meet their
particular needs. People just want affordable and basic coverage,
plain and simple.
This finding confirms much of the national economics literature
on the importance of premium level to health-plan choice and suggests
that the solution in New York State to low rates of coverage will
have to be some form of premium support (for the uninsured chronically
ill) as well as a redesign of the insurance market to lower consumers
cost of coverage.
The second key finding was consumers concern about the scarcity
of information on plan coverage and pricing available to them. Zogby
notes: While they may be aware of some government programs,
very few have a handle on health care and coverage in New York State,
and it was unanimous across all three groups that not enough information
is being made available to residents. Their complaint is consistent
with the survey finding that existing government and market-based
efforts have done an inadequate job of making consumers aware of
the coverage choices available. (Indeed, many in the groups blamed
the state and federal governments for their lack of knowledge in
this area.) A third finding was that many were unaware that they
could not buy insurance across state lines, and, when told that
they could not, they became upset, even though they had not previously
tried to obtain it. In the conversations, some interest in purchasing
insurance from neighboring states, where premiums may be lower,
Finally, all the groups thought that the U.S. health-care system
was flawed and called on policymakers to fix it, although there
was disagreement about the extent of the roles that employers and
government should play in the process.
The Effects of Reform: Projected
Take-Up Rates for Plans within a Reformed New York Individual-Insurance
The survey and focus-group results provided sufficient information
to permit the application of the ARCOLA national health-reform micro-simulation
model to New York State.
The primary variable altered in the ARCOLA model is premium price.
The focus-group results confirm that price is an appropriate lever
for modeling. We use the ARCOLA model to examine the likely impact
on insurance take-up rates of four policy options derived from Rx
NY. The four scenarios to be modeled are:
- Removing restrictions on underwriting. We model
the impact on plan premiums of no longer requiring community rating
or guaranteed issue.
- Allowing Health Savings Accounts into the market. Currently,
these high-deductible savings plans may not be sold in the New
York State individual market.
- Allowing the purchase of policies issued by insurers
based in and regulated by neighboring states. The ARCOLA
model gives the price of premiums and the impact of regulation
in all fifty states. The attractiveness of cross-state purchases
by the residents of three statesNew York, Pennsylvania,
and Connecticutof policies issued by insurance companies
based in them will be measured.
- Allow the sale of mandate-lite plans.
These plans do not require inclusion of as many types of services,
e.g., chiropractic services or alcohol-abuse treatment, as standard
policies usually do.
The primary output of these simulations will be a reduction in
the number of uninsured as a result of separately measured incremental
reductions in premium cost. These results will follow a description
of the bivariate findings derived from the survey data.
The methods used by the ARCOLA model are detailed below. The simulation
analysis was completed in three steps. First, we drew on the available
literature to characterize the regulatory framework of individual
states insurance markets and to identify its effect on the
level of health-insurance premiums. Second, we used empirical data
to develop premium estimates for the simulation that reflect case
mix as well as differences in the health-care markets of the various
states. Third, we used the survey data discussed earlier to complete
a set of simulations that identified the relative effectiveness
of four different scenarios in achieving New York State market reforms.
We summarize these steps below.
Step 1: Characterize Each States Individual-Insurance
The first step in this simulation was to describe the regulatory
environment of each state and its effect on health-insurance premiums.
Next, we identified the marginal cost of particular regulations,
including guaranteed issue, community rating, and any-willing-provider
laws, as well as other mandates.
- Mandates are state regulations or laws that require insurers
to cover particular services and reimburse certain categories
of provider. We decided to count the number of mandates in a state
rather than calculate the cost of each mandate. The number of
mandates by state was provided by Blue Cross/Blue Shield National
Association. Our decision follows the practice of a majority of
- Guaranteed issue requires insurers to sell insurance to all
candidates for coverage regardless of their state of health or
the presence of a preexisting condition. However, insurers are
not prohibited from inserting riders governing preexisting conditions
or raising premiums when they are present. Guaranteed-issue laws
can be broad (i.e., applying to all products and all consumers
at all times) or narrow (i.e., applying only to sharply defined
populations or during limited open-enrollment periods). Our coding
rules were biased toward states that had fairly broad guaranteed-issue
- Community rating requires insurers to limit the degree of variation
in the premiums that different individuals must pay. We coded
a state as having community rating if it was pure
(no premium differences are allowed) or adjusted.
We did not consider rating bands (whereby states allow variation
in groups premiums to be based on factors such as health
status or occupationtypically by +/-25 percent) to be a
form of community rating.
We reviewed the literature to identify the impact of these state
laws and regulations on health-insurance premiums. We drew only
on studies of the individual-insurance market. We ruled out studies
that focused on the relationship between regulations and premiums
in the small-group market (e.g., Simon, 2005).
We utilized estimates from the following four studies: Congdon
et al., 2005; Henderson et al., 2009; New, 2006; and Hadley and
Reschovsky, 2003. It should be noted that only the Henderson et
al. and Hadley and Reschovsky papers have been published in peer-reviewed
journals. The other two are working papers. We considered using
estimates appearing in only the peer-reviewed papers but found the
methods of the other papers sufficiently rigorous to justify including
in this analysis. Table 2 on page 13 summarizes the key findings.
To ensure the conservatism of our inferences, we chose to use values
at the twenty-fifth percentile of impact from regulation (i.e.,
we assumed a lower-end impact). Regulations and mandates are responsible
for important differences among states individual-insurance
markets, but other factors may also be important. We note three
in particular. First, with regard to regulations governing look-back
periods (to determine whether a claim arises from a preexisting
condition) and preexisting conditions generally, significant variation
exists among states. The impact of regulation on people with chronic
or acute illnesses will be similarly variable, with respect to coverage
value, prices, and take-up rates. Although we have information from
the various states on the permissible extent of look-back periods
and the particular preexisting conditions that are reviewable, we
know of no studies that model the effect of regulations in this
area on premiums. A second difference among states is the effect
of premium taxes on insurance take-up, although we have not attempted
to determine what the effect might be. Finally, provider costs,
plan types, and the market power of provider networks may vary by
state, as would their impact on premiums. Any-willing-provider laws
might, however, limit some of this variation.
Step 2: Calculate Adjusted Premiums
The second step in the analysis requires calculation of premiums
adjusted for the effect of state regulations. The basic idea behind
an interstate insurance sales market is that a person living in
heavily regulated State A will be able to buy insurance licensed
in less regulated State B. Suppose I live in State A, where the
premium is $100 per month. This price level reflects the influence
of the style of medical practice in my state, as well as the prices
charged by local health-care providers (which would not be different
if I bought insurance in State B) and the effect of regulation on
cost (which would). If I bought insurance in State B, the premium
would be $100 minus the effect of fewer regulations in State B.
To implement this step, we relied on the premiums reported by Congdon,
Kowalski, and Showalter (2005). These premiums were first adjusted
by age and sex to reflect standard actuarial differences in health-care
costs, and were then adjusted to reflect the effect of regulation.
The adjusted premiums were inputs in the insurance take-up simulation
Step 3: Simulation
In the third step, we simulated the effect of a local interstate
market (New York, Connecticut, and Pennsylvania) on the take-up
of individual health insurance. Adopting a simulation model developed
from previous analyses (Feldman, Parente, Abraham, et al., 2005;
Parente, Feldman, and Abraham, 2007), we used the New York State
survey data collected for this project to develop a set of New York
State estimates. The simulation model is capable of generating estimates
of health-insurance take-up within both the individual and employer-sponsored
(group) markets. For this analysis, we focused on the individual
One distinguishing attribute of the simulation model is the presence
of consumer-driven health plans (CDHPs). There are two types of
CDHPs: a low-option Health Reimbursement Arrangement (HRA) and a
high-option HRA. The low-option HRA is very similar in deductible,
coinsurance, and premium structure to a Health Savings Account (HSA)
plan. This similarity enabled us to model both HRA and HSA choices
in the simulation as well as high-, moderate-, and low-option Preferred
Provider Organizations (PPOs) and a Health Maintenance Organization
In the simulation, consumers in the individual market have five
choices: high-, moderate-, and low-option PPOs; a high-deductible
plan with an HSA; or no coverage. The insurance plans, as modeled,
are defined as:
- Direct Pay - PPO Low: Restrictive network, high co-pay, 15
- Direct Pay - PPO Medium: Lower co-pay and coinsurance than
the low PPO
- Direct Pay - PPO High: High option (i.e., open network, lowest
co-pay, no coinsurance)
- HSA: Self-paid HSA, no employer contribution
illness is modeled at the contract level in the simulations. That
is, the person choosing insurance, or someone covered by his or
her insurance contract, has a chronic illness. This assumption was
made because the data used to estimate the health-plan-choice model
could be attributed only to contract holders, not the person receiving
care. As a result, the chronic illness metric reflects a households
illness burden, rather than a single individuals, unless the
contract purchased is a single-coverage contract.
We used premium estimates for New York State for each of the plan
choices. These were based on our earlier work and are derived from
a combination of eHealthInsurance.com estimates and Kaiser/Commonwealth
estimates. Their price levels are given in terms of 2009 dollars.
The simulation results are presented in Tables 2 through 4. The
results show the projected impact of our regulatory reform proposals
on the individual market and thus New York States population
as it exists today. Each individual in the market can choose to
purchase one of four different types of health plans or go uninsured.
The Medicare and Medicaid markets are excluded from this population,
as well as those people who are offered insurance by large employers
or the Healthy NY program.
Table 2 presents the results of three regulatory changes represented
as columns. The impact of the regulations across the three columns
is additive. The first column represents the impact on the New York
individual-insurance market of rescission of guaranteed-issue laws.
In this instance, there would be a reduction of 18 percent in the
size of the uninsured population. Given the significant decrease
in premiums that result (see Appendix I), this reduction could occur
very rapidly, as consumers adjust to new market pricesperhaps
in as little as two years. The next column shows the impact of removing
both community rating and guaranteed issue. Dominating the growth
of the individual market would be high-option PPOs with low cost
sharing and higher premiums. The low-option
PPO with high cost sharing and lower premiums has the next-greatest
effect. The repeal of community rating would result in a 19 percent
reduction in the number of uninsured. Combined, the two policies
would result in up to a 37 percent reduction in the number of uninsured.
Finally, allowing HSAs into the market and encouraging their growth
has a very limited effect, reducing the number of uninsured by approximately
eight thousand. The HSAs, once introduced, also compete mostly with
the high PPO design. This is not surprising, given that people with
higher incomes tend to embrace both high-option PPOs and HSAs.
Table 2 presents the impact of several critical elements of the
Rx NY proposal, Table 3 shows the effect of allowing
New York consumers to cross state linesspecifically, Pennsylvanias
and Connecticuts. New Jersey was not included because of premium
levels for individual policies and a regulatory structure similar
to New Yorks.
The introduction of interstate health-insurance market competition
significantly reduces the number of uninsured in New York State.
In the simulation, we modeled two scenarios: one in which 25 percent
of the state market considers participating in out-of-state insurance
shopping; and one in which 100 percent does so. The survey results
indicated that approximately 25 percent of those surveyed would
consider crossing state lines to buy insurance. If 25 percent participated,
there would be a 17 percent reduction in the number of uninsured
New Yorkers. If the entire market participated, there would be a
26 percent reduction. Effective as such a policy would prove to
be, reforming New Yorks own market would be more so.
In Table 4, we show the impact of introducing a mandate-lite
plan similar to those offered in Massachusetts. We compare the effect
of two different options on the status quo. First, we examine the
impact of subtracting twenty mandates from the approximately fifty
mandates in force in the New York State market. Second, we look
at the impact of subtracting forty mandates. The mandate-lite health
plan in our analysis would be available only to adults aged 18 to
45 and is similar to plans offered in other states targeting younger,
healthier consumers, who are more price-sensitive.
Forty fewer mandates would reduce premiums 18 percent, and twenty
fewer mandates would reduce premiums 9 percent. A study by Parente
et al. of individual state mandates, funded
by the U.S. Department of Health and Human Services, came up with
most, the mandate-lite option would produce a 9 percent reduction
in the number of uninsured, a smaller impact than the other options
would have but by no means a negligible one. Getting rid of twenty
mandates reduces the number of uninsured by 3 percent; getting rid
of forty mandates reduces the number of uninsured by 9 percent.
Limitations of Our Micro-Simulation Analysis
We acknowledge certain limitations of our micro-simulation analysis.
Like any predictive statistical analysis, it uses a limited set
of assumptions to forecast an unknown future. Given the changing
character of the national health-care market due to reform efforts
and trends in spending and technology, our results must be viewed
as provisional. We note the following important limitations.
First, the ARCOLA model does not directly observe take-up by the
uninsured of particular policies. Thus, we make estimates calibrated
to existing market conditions. We do so by inputting published estimates
of New York States current number of uninsured. All subsequent
modeling shows a result that is different from the initial baseline
figure. This approach is quite similar to that used in other models.
Once policy changes are modeled, estimates of the number of uninsured
become internally consistent with the status quo. This approach
was validated in a study conducted by Feldman et al. (2005).
A second potential area of concern is that our model finds greater
responsiveness to premium-price changes than other micro-simulation
models, including those used by the Congressional Budget Office
(CBO) and several academic researchers. For instance, the CBO uses
Medical Expenditure Panel Survey (MEPS) data, which, unlike the
ARCOLA models, do not include more recent health-insurance
designs such as narrow-network PPOs and high-deductible health plans.
In one case, Glied et al. used a micro-simulation model that estimated
national take-up of HSAs not exceeding 1 million people by the end
of 2005, whereas the ARCOLA model correctly predicted a take-up
of 3 million people by then (Feldman et al., 2005). It is difficult
to judge the relative accuracy of our prediction of PPO take-up
volumes, since the other simulations, with the exception of Glied
et al.s analysis of high-deductible plans, compare only the
broad categories of public and private insurance and their success
at promoting take-up and do not investigate particular types of
private insurance offerings.
In particular, ARCOLAs findings in this paper contrast with
those of two recent models that have been used to examine the impact
of similar policy changes proposed for the New York marketincluding
reforms borrowed directly from the 2007 Rx NY report.
These models were used by Columbia University researchers Glied,
Tilipman, and Carrasquillo in Analysis of Five Health Insurance
Options for New York State as well
as in the recent report prepared by the Urban Institute on behalf
of the New York State Department of Health and Insurance: Achieving
Quality, Affordable Health Insurance for All New Yorkers: An Analysis
of Reform Options.
The Columbia model was used to estimate the size of the reduction
in the number of individual New Yorkers without health insurance.
The figure it arrived at was 100,000 to 130,000 individuals. The
Urban Institute estimated that the uninsured population would be
reduced by 15.4 percent, with approximately 400,000 new entrants
into the individual-insurance market, under market-oriented policy
changes. The Urban Institute estimate is much higher than Columbias,
but both estimates are lower than those generated by the ARCOLA
model, which foresees a reduction of up to 37 percent in the proportion
of uninsured, with 816,000 individuals purchasing insurance in the
Unlike the models used by the Urban Institute and Columbia University,
ARCOLA allows individuals to choose among several different private
insurance options with widely varying premiums, including newer
consumer-directed health plans such as Health Savings Accounts.
The Columbia and Urban Institute models also at least partly utilize
cell-based models that predict the behavior of groups rather than,
as in ARCOLAs case, individuals. If, as we predict, the uninsured
are highly sensitive to changes in the price of premiums, the differences
between ARCOLA and the other models explain why ARCOLA predicts
a greater take-up of private insurance plans among the uninsured.
(For additional details on the differences between these models
and ARCOLA, see Appendix II.)
Our third limitation is that we cannot control for the impact of
individual mandates. Clearly, every mandate has a different impact.
However, only two studies in the economics literature estimate the
effects of mandates, and neither provides any insight into how their
presence or absence affects premiums. An actuary could help with
the solution to this question, but only to the point of more fully
informing the assumptions underlying the estimates.
Finally, we have not modeled the interaction effects of our reforms,
such as employer crowd-out or their impact on enrollment in public
health-insurance programs. To model these interactions, we require
a fuller depiction of the New York State insurance market, both
small-group and large-employer, than we can obtain from our Zogby
survey and focus groups.
Conclusions and Policy Recommendations
New York State has a long history of providing generous health-care
benefits to poor and indigent residents. In recent years, Medicaid
has been expanded to cover as many as one in five New Yorkers. However,
the state must continue to cope with its substantial number of uninsured
people (approximately 14 percent of its population, slightly below
the national average) and rapidly rising budgets for its programs.
Health-care reform, which must entail both expanding access to
health care and restraining runaway costs, has been championed in
Albany as well as the nations capital. But recently, state
policymakers have acknowledged that although state spending is well
above average, New Yorkers health outcomes are not.
At the same time, legislators, with the best of motives, have expanded
regulation of private unsubsidized insurance marketsmost notably,
through the adoption of community rating and guaranteed issue, which
have significantly raised premiums and discouraged many young and
healthy residents from obtaining coverage. Unfortunately, individuals
forced to participate in those marketsbecause, unlike the
young invincibles, they are too old and sick to risk
forgoing coveragehave had to deal with skyrocketing costs.
Our study corroborates the economic literature on price sensitivity,
and our survey and focus-group findings highlight the challenges
confronting policymakers who seek to expand access to unsubsidized
private insurance. These are our three key recommendations:
- Allow underwriting of individual-insurance premiums.
Each policy reform that we identified (repealing community rating
and guaranteed issue, access to HSAs, cross-border sales, and
mandate-lite plans) would reduce the number of people without
insurance. But allowing underwriting to operate effectively in
the market by taking account of individuals particular risk
profiles would have the greatest impact, reducing the number of
uninsured by up to 37 percent.
- Broaden education and outreach efforts. Knowledge
of the substance of insurance policy coverage and the spectrum
of options available is very low among the uninsured. Improving
the level of understanding would probably promote insurance take-up.
- Create a separate insurance pool for the chronically ill
or the otherwise uninsurable. They or their dependents
will probably require some amount of subsidy to obtain private
insurance they can afford. We recommend that the legislature create
a high-risk pool (discussed in greater detail in Appendix III)
for the chronically ill in a reformed New York State individual-insurance
We believe that the findings of the survey and focus groups indicate
a need for innovative education, outreach, and enrollment programs
that are directed at the uninsured. Our suggestions are:
- A state health exchange, perhaps in combination with a market
actor like eHealthInsurance.com, where individuals could evaluate
prices and health-insurance options could be created.
- Community and civic organizations (unions, churches, health
clubs, chambers of commerce) could be offered a small finders
fee for enrolling members in creditable health-insurance
- Child Health Plus and Family Health Plus could offer individuals
premium quotes for unsubsidized coverage.
- Brokers should receive a residual annual commission for the
first three to five years of coverage maintained by previously
uninsured individuals. (The standard commission at present is
based on direct-pay customers first year of coverage.) Multiyear
commissions, paid by the insurer, are already built in to group
health coverage. Their availability to brokers selling individual
insurance would motivate them to promote such policies.
Connecting young, healthy New York residents to coverage could
offer them benefits beyond protection from crushing hospital bills
resulting from catastrophic illness. Because insurers will
want to hold on to these customers, these young, healthy people
should be able to keep their rates down by continuing their coverage
rather than periodically acquiring and dropping it as the need arises,
or waiting until they are older to obtain it for the first time.
An increase in both the number of holders of portable health insurance
and the duration of their coverage should also encourage insurers
to focus more on wellness and prevention efforts.
For the past several decades, New York policymakers have focused
on making publicly subsidized programs available to new segments
of the population rather than ensuring the affordability of private
plans and thus their availability. This approach may be tempting
in flush economic times, but it tests government budgets when, as
now, tax receipts are declining and the numbers of the needy are
rising. Public programs are an essential safety net, particularly
in difficult economic times, but when the economy is functioning
as it should, high-quality, affordable health insurance provided
by the private market should exist for all but the poor.
Public officials should not assume that public subsidies are the
only way to reduce the number of uninsured citizens. Rather,
as this report suggests, an effectively regulated private market
can serve the needs of a significant percentage of New Yorkers currently
without health insurance.
A Note on the Micro-Simulation Model used in This Study: Comparing
ArCOlA with Simulations from Columbia university and the urban Institute
This appendix explains the micro-simulation model used in this
study to estimate the effects of policy changes on New Yorks
individual-insurance market, as well as to compare it with other
similar models used in related work.
This study used the Adjusted Risk Choice & Outcomes Legislative
Assessment (ARCOLA) model to estimate the impact of health-policy
proposals. The model predicts individual adult responses to proposed
policy changes and can be used to generalize to larger population
groups (by state or nationally) the proposals impact on the
number of lives covered as well as the cost of that coverage.
ARCOLA is a predictive micro-simulation based on multivariate regression
analysis that predicts individuals plan choices under different
parameters such as income, health status, the extent of benefit
cost sharing (e.g., the size of co-payments and deductibles and
the presence of coinsurance), benefit design, and premium. Note,
especially, that it is the behavior of individuals under these varying
conditions that lies at the heart of this model.
ARCOLA Model Background
The model was developed for the Office of the Assistant Secretary
of Planning and Evaluation (OASPE) of the U.S. Department of Health
and Human Services (DHHS) and was used to simulate the effect of
the Medicare Modernization Act of 2003 (MMA) on take-up of high-deductible
health plans in the individual health-insurance market (Feldman,
Parente, Abraham, et al., 2005; Parente et al., Final Technical
Report for DHHS Contract HHSP233200400573P, 2005).
Later, the model incorporated the effect of prior health status
on health-plan choice, a refinement that strengthened its predictive
power. The latest version of the model also incorporates actual
claims data to develop premium estimates (i.e., claims expenses
multiplied by a loading factor) and then predicts choices again,
using the newly calculated premiums. The choice model then iterates
until premiums and choices converge at an equilibrium state. A subsequent
change to the model permitted state-specific predictions of the
impact of particular policy changes as well as the total national
Model Components and Data Sources
The three major components to the ARCOLA model are: 1) model estimation;
2) choice set assignment and prediction; and 3) policy simulation.
As illustrated in Figure A-1, often more than one database was required
to complete each task. Integral to this analysis was the use of
consumer-directed health-plan data from four large employers working
with the study investigators.
model estimation involved several steps. In the first step, pooled
data from four employers offering CDHPs were used to estimate a
conditional logistic plan-choice model. In the second step, estimated
choice-model coefficients were used to predict health-plan choices
for individuals in the Medical Expenditure Panel Survey Household
Component (MEPS-HC). In order to complete this step, it was necessary
to assign to each respondent in the MEPS-HC the number and types
of health-insurance choices that are available. For this purpose,
ARCOLA used the smaller but more detailed MEPS Household ComponentInsurance
Component linked file, which contained the needed information. The
third step was to generate HSA premiums and benefit designs. The
final step was to apply plan choice model coefficients to the MEPS
data with premium information from independent sources such as eHealthInsurance.com. In
this last step, care was taken to make sure modeling of the tax
treatment of health insurance benefits reflected current regulations
in order to get final estimates of take-up and subsidy costs.
The econometric specification of the choice model driving the ARCOLA
simulations took the form of a conditional logistic regression model.
Here we consider utility to be a function of personal attributes
such as health status, health-plan attributes (such as the out-of-pocket
premium), and the interaction of premium and health status, formally
Uij = f(Zj,Yi,Xij), where i is the decision-making employee choosing
- j = health-plan choices
- Yi = employee personal attributes
- Zj = health-plan attributes
- Xij = interactions between alternative-specific constants and
Because any plan attribute relying on employer data that was used
in the plan-choice model also had to be available in the MEPS data,
its key variables were:
- SCALEDPREM After-tax premium paid by the employee
- CLB The amount of money in the employees HRA, if any
- CUB The difference between the employees plan deductible
and the HSAs
- COIN Coinsurance rate
- CHRONIC Employee or dependent has a chronic illness=1, else
- AGE Employees age (years)
- FEM Employees gender (1=female, 0=male)
- FAM Employee has a two-person or family contract=1, else =0
- INC Employees annual wage income
Also included in the regression were alternative-specific constants
(intercepts) for each of the possible health-plan choices. These
intercepts are used to capture plan-specific features not represented
by other identifiers of plan design. They are also included as interaction
terms, along with age, gender, family status, and income.
The simulation model adjusts premiums according to the tax treatment
of health insurance offered by employers in the group market. Specifically,
premiums are adjusted to take into consideration the federal marginal
tax rate as well as the Social Security tax burden. The capability
to adjust for state-tax effects may exist but was not exercised
in this model so that the pure effects of differences in insurance
regulations by state could be identified.
ARCOLA, used previously by the Congressional Budget Office and
by author Parente and his University of Minnesota colleague Roger
Feldman in published peer-reviewed scholarly work,
was used in this report as well. It found that up to 816,000 individuals
might buy into a reformed New York individual-insurance market.
Micro-Simulation from Glied et al. at Columbia University
Columbia Universitys Sherry Glied provides distributions
of costs across a wide range of population groups using cell-based
data. In order to increase the precision of her teams estimates,
Glied pooled Current Population Survey data for New York State for
200406. With this increased sample size, Glied was able to
estimate subpopulations by age and primary insurance coverage more
precisely and to further sort the population by additional factors,
such as work status, employer size, industry, and so on.
Estimates of national per-capita health expenditures stratified
by age, gender, and type of service were obtained by using the 2004
MEPS-HC. To obtain New York State health-expenditure estimates that
conform to the estimates from the Centers for Medicare and Medicaid
Services (CMS) National Health Accounts, Glied adjusted the
MEPS data using aggregate New York Statelevel expenditure
data from CMS.
Relying on these baseline data, Glied then calculated the effects
of each proposal for coverage and expenditures by the state on each
population group (age, insurance coverage, poverty level, and so
on). In addition, she applied assumptions and parameters uniformly
to each applicable policy proposal, permitting attribution of most
variations in the effects to the nature of the proposal. However,
the variationor outcomebeing measured was coverage outcome,
i.e., whether an individual obtained coverage. Glied did not examine
the type of coverage offered to individuals, which may affect the
decision of whether to purchase insurance.
The Urban Institutes TRIM3 Model
The Transfer Income Model, version 3 (TRIM3), is a comprehensive
micro-simulation model developed and maintained at the Urban Institute
under primary funding from the U.S. Department of Health and Human
Services, Office of the Assistant Secretary for Planning and Evaluation
(HHS/ASPE). TRIM3 simulates the major governmental tax, transfer,
and health programs that affect the U.S. population, and it can
produce results at the individual, family, state, and national levels.
It is also a cell-based model but, like ARCOLA, is capable of making
individual predictions. Since the first TRIM model became operational
in 1973, TRIM models have been used to generate potential outcomes
of public-policy changes in the areas of welfare reform, tax reform,
national health-care reform, and so forth.
TRIM3s annual baseline simulations (simulations
of actual program rules) are used to correct for the underreporting
of transfer program benefits in the survey files used as input to
TRIM3 and to create other variablessuch as program eligibility
indicatorsunavailable in the input data. Registered public
users can access the micro-level variables produced by the models
baseline simulations. They can also access the models historical
library of program rules.
A baseline simulation applies the actual program rules that were
in effect in a particular year to the input data for that year.
A baseline simulation is performed for almost every year that the
simulated programs examined. In simulating transfer programs, TRIM3
identifies units eligible for assistance under each program and
selects participants who will enable it to match its administrative
targets as to size and composition of the caseload. In simulating
tax programs, TRIM3 calculates each units taxes under the
rules in effect in the year being simulated and assumes that the
unit pays the full amount of taxes that are due.
Baseline simulations serve three main functions. First, they augment
the input data (usually each years March CPS data) by creating
additional micro-level variables that are not present in the input
data. For example, the model adds variables that indicate whether
a given individual or household is eligible for each of the main
governmental transfer programs and whether a given tax unit is eligible
for various types of tax credits. Second, the baseline simulations
correct for the underreporting of transfer-program income that is
prevalent in survey files. By simulating for each transfer program
a caseload that matches the actual caseload in size and other key
characteristics, the model creates a data set that can be used in
place of the input data, when underreporting would pose a problem
for a particular analysis. Third, baseline simulations serve as
the comparison point for alternative simulationssimulations
of proposed or hypothetical program rules.
ARCOLA in Comparison
The primary differences between the Columbia and ARCOLA micro-simulations
- ARCOLA uses 2006 insurance plan-choice information from data
in which actual individual health-plan benefit choices have been
made. The Glied model uses cells and data from 2004 for plan choices.
- ARCOLA interacts premium information with other household attributes
to fashion the basic econometric model used for prediction.
- ARCOLA breaks down private insurance coverage to more granular
levels by offering different benefit designs, including: HMOs;
PPO high, medium, and low options; Health Savings Accounts; and
Health Reimbursement Arrangements. The Glied model identifies
private insurance only as a single coverage category. This difference
may be significant because of the substantial premium and cost-sharing
differences between benefit designs as expensive as high-option
PPOs and as economical as HSAs and HMOs.
- The Glied model uses specific CPS estimates from New York State;
the ARCOLA model does not. This is an asset of the Glied model.
The ARCOLA model compensates by calibrating its baseline estimates
to published New York data on the uninsured and direct-pay populations,
as well as by using a 2009 New Yorkspecific Zogby survey
targeted at the individual-insurance market in the state and providing
New Yorkspecific statistical weights for common socio-demographic
factors such as age and gender
The primary differences between ARCOLA and TRIM are:
- ARCOLA uses data on actual individual health-plan benefit choices;
TRIM uses cell data. This means that ARCOLA can take a survey
respondent and specifically assign him or her a probability based
on a set of consumer attributes as well as a set of consumer-attribute
interactions. A cell-based approach does not have to assign specific
person-level probabilities and account for their interactions.
This distinction is important because the individual effects of
premium price and chronic illness might be quite different from
the effects of chronic illness interacted with premium price.
Someone with a chronic illness may be much more sensitive to premium
price because he or she needs some sort of coverage. A regression
model like ARCOLA will pick up these effects automatically.
- ARCOLA interacts premium information with other household attributes
as part of the basic econometric model that it uses for prediction.
The Relative Utility of the ARCOLA, Columbia, and Urban Institute
Each of the models depends upon assumptions about the plan choices
offered in the individual-insurance market. The primary difference
between ARCOLA and the Columbia / Urban Institute models is the
ARCOLA models ability to observe and predict the response
of individuals faced with four types of private insurance contracts
widely varying in premium price.
The Columbia / Urban Institute models are well suited to estimate
the impact of reforms on public insurance programs. But we believe
that the ARCOLA model is best suited for analyzing our area of interest:
a private, unsubsidized individual-insurance market for New York.
We believe that that model will equip policymakers to evaluate how
reform of New Yorks existing insurance regulations, which
substantially increase costs in the individual-insurance market
and limit the variety of insurance plans available, might positively
affect New Yorkers decisions about whether to purchase insurance
or to remain uninsured.
High-Risk-Pool Facts and Function
This paper shows that up to 782,600 more New Yorkers would buy
individual health insurance in a market featuring greater choice
and more flexible regulationspecifically, one no longer burdened
by community rating and guaranteed issue and allowing the introduction
of Health Savings Accounteligible plans.
However, a flexible and robust individual-insurance market is not
necessarily open to all, inasmuch as not all individuals are insurable,
particularly those with serious chronic illness that predates their
application for insurance.
How Many People Could Be Refused Coverage in New Yorks
New, Flexible Market?
According to a recent study of 1.9 million individual-insurance
applicants undergoing medical underwriting, 89 percent were offered
coverage (with 79 percent of applicants offered coverage at or below
standard rates and just 10 percent offered coverage at higher than
standard rates). Of the 89 percent offered coverage, about one in
twelve had a condition waiver or exclusion for a specified
condition. One in twenty-five of those offered coverage faced a
condition waiver and a higher premium.
In sum, 11 percent of all applicants were denied coverage, and up
to another 10 percent faced higher premiums and/or condition waivers.
Thus, some individuals seeking coverage in the individual market
for high-cost medical conditions may require subsidies if they are
to afford private health insurance.
To provide for the so-called uninsurables, most states with competitive
individual-insurance markets have created subsidized high-risk insurance
pools. If New York is to have a competitive individual-insurance
market, it, too, should sponsor a high-risk pool for the excluded.
What Is a High-Risk Pool?
A high-risk pool is typically a state-chartered nonprofit that
runs a health-insurance program designed to serve the medically
uninsurable population by providing it access to affordable
private insurance. It does so by subsidizing premiums. These subsidies
are often financed by assessments of insurers.
According to the National Association of State Comprehensive Health
Insurance Plans (NASCHIP), high-risk pools have two primary purposes:
they provide a means for guaranteed access to insurance, which
enables individuals to protect themselves from catastrophic medical
bills; and they are increasingly recognized for the role they play
in helping to keep the individual insurance markets viable for companies
to continue to compete in.
Who Is Served by a High-Risk Pool?
NASCHIP has collected actual cases of people who fell through the
Joanne is a Redmond, Oregon, resident and a leukemia patient.
She faced an uncertain future when she learned last April that
her health coverage was ending the day before she was scheduled
to enter the hospital for treatment.
Representatives from the Oregon Medical Insurance Pool (OMIP)
enrolled Joanne in the states high-risk insurance pool.
OMIP, which became operational in 1990, was designed for residents
turned down by private insurers. She pays a monthly insurance
premium of $577 for a plan with a $500 annual deductible and a
maximum out-of-pocket expenditure of $1,000, after the deductible
has been paid.
Currently, Joanne is waiting for a donor so that she can undergo
a stem-cell transplant, which costs $250,000. If she had not been
enrolled in Oregons high-risk pool, she could not have afforded
After being employed for fifteen years at various companies that
provided health-insurance benefits, Betty became self-employed
and lost her coverage. She applied for an individual health-insurance
policy and was turned down because she had diabetes.
Fortunately, Betty lives in Minnesota, where there is a high-risk
insurance pool: the Minnesota Comprehensive Health Association
(MCHA). She secured coverage through MCHA and has been insured
since 1988. She has MCHAs federally qualified High Deductible
Health Plan, which is a qualified plan for a Health Savings Account.
Bettys premium payment is $447.31 a month, and she must
discharge a $3,000 annual deductible before she receives 100 percent
Without MCHA, I am not sure what I would have done,
said Betty. By having MCHA, I have been able to have peace
of mind that I will not be financially ruined and have therefore
been able to work and pay taxes. In those twenty-plus years, I
have only missed a few days of work. The high-risk insurance pool
insurance has permitted me to keep my diabetes under control.
How Large Are High-Risk Pools, and How Large Would New Yorks
According to 2007 data available from the Kaiser Family Foundation,
thirty-three states with a high-risk pool open to new enrollees
reported that an average of 1.9 percent of all those in the individual-insurance
market had enrolled in the high-risk pool. (Specifically, 10.6 million
individuals participate in the individual market in these states,
199,320 of whom inhabit the high-risk pools.) The percentages by
state range from 0.5 percent to 8.4 percent.
If it were to incorporate the reforms that we suggest, New Yorks
individual-insurance market could have up to 816,000 enrollees.
Therefore, it is reasonable to assume that a New York high-risk
pool would have between 15,520 (at 1.9 percent, the average size)
and 68,616 enrollees (at 8.4 percent, the largest pool). Healthy
NY, by comparison, covers 157,000 individuals.
Texas, with its 23.7 million residents, has 1.2 million individuals
who obtain coverage in its individual-insurance market. Since 1998,
Texas has operated a high-risk pool that had 27,733 individuals
in it as of December 2007.
How Expensive Are High-Risk Pools, and How Expensive Would New
Again, according to 2007 data available from the Kaiser Family
Foundation, thirty-three states with a high-risk pool open to new
enrollees reported costs for premium subsidies totaling $742.2 million
to cover 199,320 lives. This works out to a subsidy of $3,742 per
enrollee, reflecting a cost to the 10.6 million individuals in the
individual-insurance markets of these states of $70 per individual,
per annum, or $4.34 per individual if all 171 million people in
these states were assessed. Minnesota, with the highest percentage
of the individual market and the general population enrolled in
these pools, reported a subsidy of $4,282 per enrollee in the high-risk
pool; an assessment of $360 per enrollee in the individual market;
and $24 per state resident.
On the basis of the aforementioned information, if New York had
a high-risk pool that was of average size and cost, it would need
to raise over $58 million to underwrite its premium subsidy. This
sum could be reached by imposing an assessment of about $6 per member,
per month (PMPM) on those in the reformed individual-insurance market.
If the assessment were extended to the 1.6 million in the small-group
market, the per-month assessment would be just $2.
If New York had a high-risk pool that was the countrys largest
and highest-cost, it would require over $453 million in premium
subsidy, which could be collected by assessing those in the new
individual market about $46 per month. If the assessment were extended
to the 1.6 million in the small-group market, the per-month assessment
would be just $15.63.
Healthy NY costs taxpayers $122 million a year, covers 155,000
people, and is estimated to reduce the proportion of uninsured by
one percentage point. It is estimated that a New York high-risk
pool would cost between $58 million and $453 million if it were
to cover between 15,500 and 105,900 people and would be part of
a policy reform agenda that would reduce the number of uninsured
by up to 37 percent.
It is possible for any state to set up a high-risk pool. It should
have a board of directors representing health cares stakeholders:
citizens, legislators, the insurance industry, employers, and the
medical community. Following Minnesotas example, the board
would contract with insurers to cover plan participants. Insurers
perhaps should be required to cover innovative programs designed
to reduce individuals costs, such as disease-management programs,
individual case management, and health and wellness programs. The
high-risk program would offer a choice of competing plans charging
premiums no greater than 150 percent of those charged by the comparable
standard-risk plans sold in the state.
The high-risk pool would receive premiums, the proceeds of fines
for insurers noncompliance, and other receipts associated
with operation of the plans. Eligibility would result from outright
denial of coverage or restriction of the applicant to coverage that
falls well short of meeting the cost of a serious medical condition.
The state should also establish timely procedures for confirming
denial of coverage and making a referral to the high-risk pool.
The process must allow for the adjudication of claims of inappropriate
denial of coverage, which, if found to have occurred, should result
in a fine.
What Criticisms or Problems Are Associated with High-Risk Pools?
A frequent criticism of high-risk pools is that the premiums charged
in such pools, although capped, may still be a significant financial
burden or simply unaffordable. (That is why we recommend capping
premiums at 125150 percent of standard market rates, with
state funding to subsidize these lower premiums.) Also, when health-care
costs reach a certain level, some states may begin restricting eligibility
to their high-risk pools, limiting the time that individuals may
remain the pool, capping claims payments, or increasing policyholders
share of costs. Floridas high-risk pool, for example, is supported
by dollars from the states general funds and has been closed
since 1991. Indeed, critics have argued that this form of funding
is insufficiently stable and broad-based to meet the financial challenges
of the chronically ill.
These are legitimate concerns. Policymakers should carefully consider
disbursing additional subsidies to those individuals of limited
means who do not qualify for public programs such as Medicare and
Medicaid. But the long-term health of the individual market depends
on well-functioning high-risk pools receiving adequate funding.
As we have proposed here, funding for high-risk pools should come
from a flexible PMPM assessment that can be adjusted to meet demand
and not from general revenues, which are subject to wide fluctuations.
Any other funding stream considered should be just as stable and
long-term. Federal policymakers should also consider increasing
funding of high-risk pools that meet minimum criteria of affordability,
access, and program scope.
- Tarren Bragdon, Rx NY: A Prescription for More Accessible
Health Care in NY, Empire Center for New York State Policy
(December 2007), p. 8. Available at: http://www.empirecenter.org/Documents/PDF/Rx_11_07.pdf
(accessed July 2, 2009).
- Ibid., p. 7.
- Ibid., p. 8.
- New York Department of Health, Uninsured New Yorkers
Urged to Sign Up for Health Insurance during Cover the Uninsured
Week (March 23, 2009). Available at:
(accessed July 22, 2009).
- As noted, there are 2.1 million uninsured in New York State,
of which approximately 800,000 are eligible for public programs.
However, if the policy changes modeled in our report are enacted,
they would reduce the uninsured population, possibly allowing
some of the eligible population to receive private, unsubsidized
- ARCOLA is a micro-simulation model designed to estimate the
impact of health-policy proposals at the federal and state level.
The model predicts individual adult responses to proposed policy
changes and suggests what the impact on the number of people covered
and the premiums they pay would be if those changes were applied
nationally. This model was first used by the Office of the Assistant
Secretary of Planning and Evaluation (OASPE) of the Department
of Health and Human Services (DHHS) to simulate the effect of
the Medicare Modernization Act of 2003 (MMA) on take-up of high-deductible
health plans in the individual health-insurance market (Feldman,
Parente, Abraham, et al., 2005; Parente et al., Final Technical
Report for DHHS Contract HHSP233200400573P, 2005). The model
was later refined to incorporate the effect of prior health status
on health-plan choicea necessary step if one wants to predict
enrollment more accurately. The latest model also used insurance
expenditures derived from actual claims data to reset premium
levels and then predicted their impact on take-up rates. The model
then iterated consumers choice of plan until consumers got
the plan they wanted at a price they could afford. A further change
to the model permitted state-by-state predictions of consumer
behavior per state and nationwide.
- New Study Shows Half of Individual Health Insurance Policy
Holders Paid Less Than $130 per Month, eHealth (November
12, 2008). New York is cited as the highest average individual-insurance
premium, at $388 per month. Available at: http://www.marketwire.com/press-release/Ehealth-Inc-NASDAQ-EHTH-919521.html
(accessed July 2, 2009).
- Kaiser Family Foundation, Individual Market Guaranteed
Issue (Not Applicable to HIPAA Eligible Individuals), 2008
(data as of December 2008). Available at: http://www.statehealthfacts.org/comparetable.jsp?ind=353&cat=7
(accessed July 2, 2009).
- Kaiser Family Foundation, Individual Market Rate Restrictions
(Not Applicable to HIPAA Eligible Individuals), 2008 (data
as of December 2008). Available at: http://www.statehealthfacts.org/comparetable.jsp?ind=354&cat=7
(accessed July 2, 2009). New Jersey allows Basic and Essential
individual insurance plans with premiums that vary based on age,
gender, and geography at a rating band of 3.5:1. Carriers began
offering these plans in New Jersey in March 2003.
- Navigant Consulting, 2008 Annual Report on Healthy NY
(January 2009), pp. I-1 through I-4. Available at: http://www.ins.state.ny.us/website2/hny/reports/hnynav2008rep.pdf
(accessed July 2, 2009).
- Victoria Craig Bunce and J. P. Wieske, Health Insurance
Mandates in the States 2009, Council for Affordable Health
Insurance (May 2009). Available at: http://www.cahi.org/cahi_contents/resources/pdf/HealthInsuranceMandates2009.pdf
(accessed July 2, 2009).
- Premium Rates for Standard Individual Health Plans: July
2009 for New York County, New York State Insurance Department.
Available at: http://www.ins.state.ny.us/hmorates/html/hmonewyo.htm
(accessed July 2, 2009).
- N.Y. Gov. Pataki Signs Sole Proprietor Health Bill into
Law, Insurance Journal (September 23, 2002). Available at:
(accessed July 2, 2009).
- Cost and Benefits of Individual and Family Health Insurance
Plans, Forrester Consulting on behalf of eHealthInsurance.com
(November 2008), p. 18. Available at: http://www.ehealthinsurance.com/content/expertcenterNew/eHealthCBreport2008FINAL.pdf
(accessed July 2, 2009).
- Private Health Insurance: Millions Relying on Individual
Market Face Cost and Coverage Trade-Offs, General Accounting
Office (November 1996), p. 71. Available at: http://www.gao.gov/archive/1997/he97008.pdf.
- Geoffrey C. Sandler, New York Individual Market Rules
(February 19, 2009), slide 4. Available at: http://www.nhpf.org/library/handouts/Sandler.slides_02-19-09.pdf
(accessed July 2, 2009).
- Private Health Insurance: Millions Relying on Individual
Market Face Cost and Coverage Trade-Offs, p. 72.
- Kaiser Family Foundation, Health Insurance Coverage of
Nonelderly 064, States (20062007), U.S. (2007).
Available at: http://www.statehealthfacts.org/comparetable.jsp?typ=2&ind=126&cat=3&sub=39
(accessed July 2, 2009).
- California HealthCare Foundation, Snapshot: Californias
Uninsured 2008 (December 2008), p. 4. Available at: http://www.chcf.org/documents/insurance/UninsuredSnapshot08.pdf
(accessed July 2, 2009).
- State of New Jersey Department of Banking and Insurance, Individual
Health Coverage Program: Historical Comparison of Enrollment
(June 2, 2009). Available at:
(accessed July 2, 2009).
- Idem, NJ Individual Health Coverage Program Buyers
Guide. Available at: http://www.state.nj.us/dobi/division_insurance/ihcseh/ihcguide/benefits.html#be
(accessed July 2, 2009).
- Idem, Individual Health Coverage Program: Historical Comparison
of Enrollment. Available at: http://www.state.nj.us/dobi/division_insurance/ihcseh/enroll/2q09historical.pdf
(accessed September 2, 2009)
- There was a small number of people in hold-over plans from
before Standard plans were first introduced.
- Kaiser Family Foundation, Total Number of Residents,
States (20062007), U.S. (2007). Available at: http://www.statehealthfacts.org/comparemaptable.jsp?ind=1&cat=1
(accessed July 22, 2009).
- W. J. Congdon, A. Kowalski, and M. H. Showalter, State
Health Insurance Regulations and the Price of High-Deductible
Policies, unpublished manuscript (2005). Accessed at http://www.byu.edu.
- J. Hadley and J. D. Reschovsky, Health and the Cost of
Nongroup Insurance, Inquiry 40, no. 3 (2003): 23553.
- Congdon, Kowalski, and Showalter, State Health Insurance
Regulations and the Price of High-Deductible Policies.
- Hadley and Reschovsky, Health and the Cost of Nongroup
- Congdon, Kowalski, and Showalter, State Health Insurance
Regulations and the Price of High-Deductible Policies.
- J. W. Henderson et al., Estimating the Impact of State
Health Insurance Mandates on Premium Costs in the Individual Market
Using the Community Tracking Survey, Journal of Insurance
Regulation (June 2009).
- K. E. Thorpe et al., Reducing the Number of Uninsured
by Subsidizing Employment-Based Health Insurance: Results from
a Pilot Study, JAMA 267, no. 7 (1992): 94548.
- In 1944, in United States v. South-Eastern Underwriters Association,
the Supreme Court held that while insurance companies are certainly
engaged in interstate commerce, which confers federal jurisdiction,
federal law need not preempt state regulation. In response, Congress
soon passed the McCarran Ferguson Act in 1945, which explicitly
granted to the states regulatory authority over the insurance
industry, including the providers of health insurance, which the
states had already been exercising. If policies were sold across
state lines, the federal government would have jurisdiction over
them, which the statute does not authorize. ERISA, enacted in
1973, and the Gramm-Leach-Bliley Act, enacted in 1999, curtailed
the scope of McCarran Ferguson somewhat.
- We used several secondary sources for this description, including
Blue Cross / Blue Shield for state mandates; the Georgetown University
Health Policy Institute for guaranteed issue and community rating;
and Thomson-Wests Netscan / Health Policy Tracking Service
(Major Health Care Policies, 50 State Profiles, 2003/2004)
for any-willing-provider laws.
- Although the New York market does not currently offer PPO plans
(POS, or Point-of-Service plans, are offered), there are minimal
differences in plan designs between PPOs and POSs, with most of
the difference in premiums attributable to cost sharing of out-of-network
services and the size of the base network of physicians and hospitals.
However, many PPOs are effectively structured as POSs so that
they can provide coverage for out-of-network services. Consequently,
we have used baseline enrollment in existing POS plans for our
PPO estimates. None of these differences, however, would affect
our estimates of take-up rates or changes in the price of premiums.
- It may seem surprising that consumers gravitate to plans with
higher premiums. The ARCOLA model and our prior research show
that, given a discount (as in this case), individuals prefer richer
benefit plans (with lower out-of-pocket costs) even after lower-priced
plans become available. This reflects the strong preference of
most consumers for low cost-sharing plans, once they become more
affordable as the result of the repeal of guaranteed issue and
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resulting from a reduction in the number of mandates is nonlinear,
i.e., a reduction in the number of mandates from forty to twenty
has a bigger effect on take-up than a reduction in the number
of mandates from twenty to zero.
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