Post on 10-Jun-2020
College of the Holy Cross
Competition and Premiums Under the Affordable Care
Act:
A Study of How Insurer Participation in the Health Benefits Exchanges
Has Affected Premiums
Austin Barselau
Washington Semester, Spring 2017
Faculty Advisors: Professor Justin Svec and Professor Melissa Boyle
April 25, 2017
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Introduction
In the waning months of 2009, Congress passed the Patient Protection and Affordable
Care Act (ACA), better known as “Obamacare,” on a party-line vote after months of partisan
rancor and many failed attempts by previous administrations to reform the health care system
(Patient Protection and Affordable Care Act, 2010). In what amounted to the most extensive
overhaul of health insurance in over four decades, the ACA was the first breakthrough for a
national health insurance program since Lyndon Johnson’s Great Society, and the culmination of
the long drive for universal coverage since the progressive insurgency of Teddy Roosevelt and his
1912 Bull Moose platform. Obamacare was a historic victory for progressives, an ambitious
project which reinvented the individual insurance market by fundamentally transforming how
insurance is designed and purchased.
By completely redefining the relationship between the patient and the government, the
ACA gave the federal government the responsibility for providing care to millions of people for
the first time (Obama, 2016). One of the goals of the law was to replace the fragmented individual
insurance markets, where people not insured through their workplace or federal programs like
Medicare and Medicaid go to purchase health insurance, with centralized web-based
marketplaces. These marketplaces, known more formally as “health benefits exchanges” or
collectively as the “Marketplace,” would allow potential enrollees to shop and compare plans
from multiple different insurance providers. In 2013, around the time when the online portal was
first established, then-President Barack Obama proclaimed that visitors could shop and compare
insurance plans “the same way you’d shop for a plan ticket on Kayak or a TV on Amazon”
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(“Remarks by the President,” 2013). The results, he predicted, would be “more choices, more
competition, and in many cases, lower prices.”
The ACA’s architects believed that the exchanges would attract health insurance
companies looking to issue plans in the individual market. According to this reasoning, robust
insurer competition in the Marketplace would directly lead to lower rates. This paper seeks to
explain whether this association is true, namely whether the number of insurers offering plans in
an area is linked with premium levels. It also seeks to determine whether insurer competition is
associated with the growth in premiums between years. Because of the complexity and political
relevance of the issue, this paper uses multiple approaches to examine this relationship. It studies
premiums at the state level, while also delving deeper by looking at premiums in specific localities
and across different plan types.
Using data from the Department of Health and Human Services and Helathcare.gov, this
study finds that premiums are linked with the number of insurers offering plans in any given area.
More specifically, this paper finds that monthly premium levels would fall by about $4 for every
new insurer that enters a state marketplace—a two percent reduction in the cost of the average
plan. In addition, a new insurer in a state market would also reduce premium growth by the
same amount between years. Insurer competition was also found to lower premiums across all
plan types with different coverage requirements. This paper suggests that states should do more
to encourage more robust insurer participation in the Marketplace, as more competition would
lead to lower premiums and a greater selection of health plans.
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Revamping the Individual Health Insurance Market
The nongroup market before the ACA was extremely fragmented, and widely considered
to be discriminatory and inefficient (Claxton et al., 2016). Insurance companies could charge
enrollees higher prices, or prevent them from buying insurance altogether, based on their
medical history or health status. Premiums could be set with minimal oversight, and customers
could face benefits limits and increasingly higher deductibles. In general, most states before the
ACA did not have robust consumer protections. There was no “guaranteed issue” requirement
for insurers to issue health plans to enrollees regardless of health status or risk, and people with
preexisting conditions were frequently excluded from coverage. More than one-third, or nine
million, of enrollees were turned down, charged higher rates, or faced restricted coverage
because of their health status (Collins et al., 2011). The market was also largely unstable,
characterized by high turnover and frequent disruptions in coverage (Sommers, 2014).
Insurance carriers also utilized high-risk pools to control for risk. Thirty-five states offered
risk-pools to enrollees prior to the ACA, starting in 1976 and continuing until right before the ACA
was passed (Pollitz, 2017). Insurers used a practice called “medical underwriting” to discriminate
against people with preexisting conditions and charge them higher premiums. Many of these
enrollees faced premiums above the nongroup market average, lifetime and annual limits on
coverage, and high deductibles. However, not all states orchestrated risk-pools or allowed
medical underwriting. For example, New Jersey required guaranteed issue and community rating
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for all carriers starting in 1993 (Monheit et al., 2004).1 California and Indiana both prohibited
carriers from excluding certain benefits provisions in plans (“How accessible,” 2001).
The ACA ended this fragmented system of buying health insurance by establishing
universal coverage for everyone regardless of health status. To achieve this goal, and to revamp
the market more broadly, the ACA uses what health care experts call a “three-legged stool”
approach, which combines prohibitive measures with new incentives to purchase health
insurance (Gruber, 2011). The legs of the stool include guaranteed issue, an individual mandate
to purchase health insurance or instead pay a penalty, and income-based premium tax credits
and cost-sharing subsidies for those who are eligible.
The first leg prohibits all forms of discrimination by health insurers based on medical
status. Sections 2702-2705 of the law enshrine the “guaranteed availability of coverage” for all
participants in the individual and group markets, as well as the “guaranteed renewability of
coverage” without consideration of health status, medical conditions or history, genetic
information, or evidence of insurability. This section was designed to counteract the tendency of
insurers to select healthy patients at the expense of those who might consume a higher
concentration of health care expenditures. In addition, the law has several risk mitigation and
market stabilization programs that protect insurers against adverse selection in the individual
and small group markets, specifically by offsetting the expenses of high-cost patients and
spreading financial risk across the markets (Cox and Semanskee et al., 2016).
1 New Jersey’s program, the Individual Health Coverage Program (IHCP), mirrored the ACA in many ways. In addition to income-based subsidies, guaranteed issue/renewal of coverage, and a medical loss ratio for carriers, IHCP’s also contained risk adjustment mechanisms to encourage many small carriers to sell coverage. Many of these issuers entered the market, only to underprice their premiums and incur larger-than-expected losses. This created some market instability, as many of these small carriers exited the market.
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The second leg of the proverbial stool is the individual mandate to purchase health
insurance. The mandate was intended to minimize the problem of free riding, where some
individuals choose not to buy the minimum level of health insurance. This can amount to what
some health care experts call a “hidden health care tax,” where higher claims from the uninsured
are indirectly paid for by the insured though higher premiums (“Hidden health tax,” 2009). This
can lead to lower premiums: The Congressional Budget Office estimated that eliminating the
provision would increase premiums by 15 to 20 percent (“Effects of eliminating,” 2012).
Thirdly, the ACA provides subsidies to enrollees based on income. Both premium tax
credits, which reduce enrollees’ monthly premiums, and cost-sharing subsidies, which minimize
out-of-pocket costs, are offered to enrollees under Sections 1401-1415 of the law. Tax credit
eligibility ranges from 133 percent to 400 percent of the Federal Poverty Line (FPL), with those
under that range eligible to receive Medicaid in states that decided to expand the program under
the law. In states that elected not to expand Medicaid, tax credit eligibility goes as low as 100
percent of the FPL. Subsidies are calculated as a percentage of the cost of the second-lowest
silver plan (plans with an average cost sharing value of 70 percent). The government will cover
the remaining costs for enrollees with silver plans that exceed this benchmark. Cost-sharing
reductions are accessible to those with household incomes at or 250 percent of the FPL
(“Explaining health care reform,” 2016).
The Marketplace: A New Way to Buy Health Insurance
The ACA encourages individuals who do not currently receive coverage through their
employers or through federal programs like Medicaid or Medicare to visit the health benefits
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exchanges and purchase certified health plans. All plans offered on the exchanges must meet a
minimum standard of quality and coverage, and must offer what the law calls the “essential
benefits package.” Section 1302 of the ACA outlines the package, which includes among other
things ambulatory and emergency services, hospitalization, maternity and newborn care, mental
health treatment, and preventive and wellness services.
The ACA also allows for plans with different levels of coverage. Enrollees can choose
among several different “metal tiers,” each with a different actuarial value (AV), or percentage
of health care expenses the issuer will cover for all enrollees in that plan. The purpose of the tiers
is threefold. First, they establish the minimum level of coverage that individuals need to satisfy
in order be exempt from the penalty. Secondly, they define which insurance products can be sold
in the Marketplace. Finally, the tiers serve as benchmarks for the financial assistance measures
that enrollees receive when purchasing insurance (“What the actuarial values mean,” 2011).
Enrollees can select from four different coverage levels, including bronze (60 percent AV), silver
(70 percent AV), gold (80 percent AV), and platinum levels (90 percent AV). The law also offers
catastrophic plans, which have higher deductibles and lower monthly premiums than the metal
tier plans, for select individuals.
To provide enrollees with access to a choice of different health care plans, each state must
have an exchange established either by the state or facilitated by the federal government.2 From
the outset, many states elected not to establish and operate their own exchanges, and instead
handed the responsibility to the federal government. Only sixteen states at the onset of the law
2 The state-by-state model is derived from the original Senate bill, which envisioned all 50 states establishing and operating their own exchanges. The bill that was drafted in the House included a national exchange set up and run by the federal government (“House, Senate view health exchanges differently,” 2010).
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chose to run their own exchange, and seven more chose to partner with the federal government.
The Department of Health and Human Services (HHS) allowed states to use a partnership
platform as a “stepping stone” to an entirely state-run exchange in the future. HHS also allowed
states to adopt variants of the federally-facilitated exchange that allowed more state
involvement (“Health insurance exchanges,” 2013).3
The decision of whether a state should manage its own exchange, or let the government
manage its operations, was often politically-influenced. Most Republican governors have been
reluctant to sign on to the ACA’s sweeping reforms. In states that elected not to administer and
oversee their own Marketplace, the federal government established an exchange for customers
in that state. According to a New York Times article documenting how states were establishing
their own exchanges and hiring navigators to assist people in purchasing insurance, there was
stunning variation in how states were implementing the law (Goodnough, 2013). On one hand,
states like Maryland, Colorado, and New York—all states that administered their own
exchanges—were spending tens of millions of dollars on programs that would dispatch health
care navigators and assistors around the state. On the other, states like Virginia, where Gov. Bob
McDonnell rejected a state-based exchange, allocated very little money for outreach and
assistance.4
3 Seven states were granted permission to conduct plan management on behalf of the federal government, while the government oversaw the rest of the exchange. Utah was granted permission to use a “bifurcated exchange,” where the state oversaw both plan management and its small business exchanges while the federal government handled the rest. 4 Another recent example of how political decisions may affect coverage stems from the recent political turnover of the governorship in Kentucky. Kentucky’s state-established exchange, Kynect, was launched in 2013 and vigorously supported by then-Democratic governor Steve Beshear. In 2016, Kentucky’s newest governor, Republican Matt Bevin, expressed his desire to shut down the exchange and hand the reins to the federal government (Phillips, 2016). This decision might affect how Kentucky’s uninsured are able to receive coverage.
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On average, states that oversee their own exchanges have higher enrollment rates than
both federally-facilitated exchanges and partnered exchanges. (Polsky et al., 2016). In this vein,
states have more autonomy over their policy and outreach choices. As the Commonwealth Fund
writes, this model has allowed states to “capitalize on local knowledge and connections to reach
underserved populations, facilitate enrollment assistance services for consumers, and engage
with stakeholders” (Giovannelli and Lucia, 2015). Put simply, politics can contribute to how the
uninsured are served in the context of the ACA’s reforms.
The choice of whether to embrace the law has also led to different outcomes in terms of
insurer participation. Some states have even attempted to increase competition themselves. For
example, some states encouraged carrier participation by issuing waiting periods for insurers that
chose not to participate that year (Holahan, 2013). Maryland, for example, required insurers to
participate in the exchanges if they met an aggregate revenue threshold. In general, states that
manage their own exchanges have not drawn up exchange standards or regulations that disrupt
or limit competition. The result has been a disparity in competition in the exchanges between
states that have been hostile to the law and states that have accommodated it.
The Marketplace Goes Live: The First Few Years
The results of the first open enrollment period in 2013 seemed positive. In addition to
attracting insurers that held a larger share of the market in the years leading up to the exchanges,
many new insurers participated in both the individual and small group markets in 2014
(Houchens et al., 2013). An HHS issue brief found that nearly all consumers (95 percent) had a
choice of two or more insurers, with enrollees being able to select among an average of 53
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different qualified health plans in exchanges that were fully or partly run by the HHS (“Health
insurance Marketplace,” 2013). Paul Ginsburg, president of the nonprofit Center for Studying
Health System Change, commented at the time that the exchanges and subsidies “have created
a highly competitive environment for insurers” (qtd. in Farley, 2013).
The competition among insures in the first year of the Marketplace had a direct effect on
premiums. Premiums set in 2014 were even lower than initially forecasted. Premiums before the
application of tax credits were 16 percent lower than the CBO projected for that year (“Health
insurance Marketplace,” 2013). Ninety-five percent of all consumers lived in states with average
premiums below earlier estimates.
The tax credits and cost-sharing also drastically lowered premiums relative to the sticker
price. Enrollees in the federally-run exchanges had a post-tax credit premium that was 76 percent
less than the full premium, on average (Burke et al., 2014). Nearly seven in ten selected plans in
those exchanges with premiums less than $100 post tax credits. “Competition,” the HHS wrote
in an issue brief, “is associated with more affordable benchmark plans (the second-lowest cost
silver plan) for individuals and reduced costs for the federal government.” The competition
mechanism seemed to be successful.
In 2015, the number of participating insurers rose by 25 percent, while 86 percent of
eligible enrollees had access to at least three insurers—up from 70 percent in 2014. (Mangan,
2014; Sheingold et al., 2015). Only eight percent of all counties experienced a net loss of issuers.
Premiums, meanwhile, grew only two percent from the previous year, with the government
reporting many areas that experienced decreases in premiums. Most importantly, competition
among insurers was directly associated with lower premiums. The premium growth for a
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benchmark plan was lower in areas that saw a net gain in insurers over the period, and the HHS
quantified that each net gain of one insurer was linked with a 2.8 percentage point drop in the
rate of premium growth.
While the first two years of the exchanges were characterized by soaring enrollment,
robust insurer competition, and greater choices, the 2016 enrollment picture was more of a
mixed bag. Seven in 10 could still find plans for $75 or less after tax credits, and the average
premium was a little over $100—both of which were roughly in line with those of the previous
two years (Avery et al., 2015; “Health insurance marketplace premiums,” 2016). HHS reported
that the number of issuers in the average consumers’ state remained stable from 2015, and there
was even a net increase in carriers offering Marketplace plans. “The Marketplace continues to
offer more be competitive and dynamic, and issuers are continuing to compete and offer more
affordable options to consumers,” the HHS wrote in advance of the 2016 open enrollment
session.
By other measures, competition in the exchanges was dwindling. Two reports from right-
leaning sources suggested that fewer insurers participated in the 2016 market. Counting carrier
participation at the parent company level rather than at the subsidiary level, a report from the
Heritage Foundation calculated that there were fewer insurers offering exchange coverage in
each state and fewer unique carriers offering exchange coverage in one or more states.
(Haislmaier, 2016). It wrote that the number of exchange-participating insurers in 2016 dropped
by nearly 30 percent since the year prior to the ACA taking effect, and the number of unique
carriers in 2016 was fewer than in 2014. Many of the exchange exits, Heritage wrote, were due
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either to new state regulations barring select issuers from offering coverage or natural churn in
the insurance industry.
In addition, a report from the office of U.S. Sen. Ben Sasse (R-NE) studied exchange
competition at the county level. Using parent-company data, it found that the 2016 exchanges
hosted six percent fewer insurers than in 2015. What’s more, it found that only two or fewer
issuers offered coverage in more than one-third of all U.S. counties (36 percent) (“Sasse issues,”
2016). Compared to the pre-ACA individual insurance market landscape, competition declined by
77 percent across all states. The report projected a gloomy outlook for the health care law: “This
report shows that despite promises of increased competition and choice, the opposite is
occurring. The 2016 exchanges include fewer insurance companies than the previous year’s
exchanges and are far less competitive than the individual market was prior to the ACA’s
implementation.” These trends, it concluded, would leave consumers with dwindling choices and
steeper costs.
Actual enrollment numbers for the 2016 sign-up period were also lower than expected.
In 2015, the Congressional Budget Office estimated that 21 million customers would enroll in the
exchanges in 2016, but only 13 million ended up enrolling (“Insurance coverage,” 2015; “The
budget and economic outlook,” 2016).5 To make things worse, the individual market in 2016 had
an overall risk pool that was older and less healthy than expected. Of all Marketplace plan
5 In an April 2017 interview, Loren Adler, Associate Director of the Center for Health Policy at the Brookings Institution, said that initial enrollment projections missed the mark because there was no reference market to estimate the demand for insurance. Insurers referenced the small-employment market prior to the ACA to set premiums for the first few enrollment cycles. Insurers initially set lower premiums to lock in customers, only to underestimate the size of the uninsured pool. The market churn in the aftermath of the law was mostly due to the inherent uncertainty of a new market, with some firms succeeding and others failing. He argues that the recent premium increases are a one-time correction caused by insurers adjusting to the initial mispricing. (More: Garthwaite and Graves, 2017; Holahan, 2017; “The ACA individual market,” 2016).
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selections, only 28 percent were by people between the ages of 18-34—down from 35 percent
in 2015. (“Health insurance marketplaces,” 2016). Additionally, while only 18 percent of total
Marketplace enrollment was expected from individuals over the age of 55, the actual number
was 28 percent. (Blase et al., 2014). Low turnout and an adverse risk pool were startling signs for
insurers.
2017 and Beyond
In the months leading up to the 2017 enrollment, many health care experts suggested
that these trends would continue. As some insurers faced losses due to initial mispricing, they
were presented with two options: drop out or raise premiums to cover claims. This has led
industry leaders suggest that the Marketplace is entering a “death spiral,” characterized by
adverse selection and a poorly-structured premium model. (Johnson, 2017). “The industry is
clearly setting the stage for bigger premium increases in 2017,” said Larry Levitt, a health care
expert at the Kaiser Family Foundation (qtd. in Sullivan, 2016). The Kaiser Family Foundation
projected the number of Marketplace enrollees with access to just one insurer would increase
tenfold, from two percent of enrollees in 2016 to 19 percent in 2017. (Cox and Semanskee, 2016).
At least for the beginning of 2017, the health insurance Marketplace will continue to offer
prospective and returning enrollees a choice of plans at mostly stable prices. According to the
HHS, an overwhelming majority of consumers will have a choice of plans for less than $75 (72
percent) and $100 (77 percent) in monthly premiums, after tax credits (“Health plan choice,”
2017). In all, consumers are expected to have a choice of 30 plans, with 79 percent of returning
enrollees having the choice of two or more insurers. “The Affordable Care Act continues to
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promote access to affordable health insurance plans through the Marketplace,” the HHS report
wrote. Kevin Counihan, CEO of Healthcare.gov, similarly projected a more optimistic outlook for
2017 enrollment: “With higher consumer satisfaction, more people getting care, and an
improving risk pool, incoming data continue to show that the future of the Marketplace is strong”
(O’Donnell et al., 2016).
The HHS report then went on to describe a less promising insurance landscape. In total,
there would be a net loss of 73 insurers and 17 qualified health plans being offered through the
exchanges. Even worse, average premiums would soar by 25 percent. It attributed the markups
around the country to exchanges “maturing and approaching stable price points.” This appraisal
corroborates the hunch that insurers may have miscalculated premiums in the early years of the
Marketplace, only to issue adjustments when claims began to outstrip premiums.
The 25 percent premium increase figure, however, does not account for variation across
states. The median increase in premiums was significantly lower, reflecting more below average
increases. For example, Arkansas reported premium increases for the average second-lowest
cost silver plan before tax credits of only two percent, while premiums in Oklahoma increased by
69 percent.6 What’s more, the subsidies and tax credits mostly shielded consumers from the rate
increase. After financial assistance, premiums for a 27-year-old with $25,000 in income will have
an average monthly premium of $142, one dollar less than what they would have paid in 2016.
6 Low premium increases in Arkansas may be caused by several factors. First, while UnitedHealthcare exited the Arkansas exchange in 2017, many people were insulated from the market effects—only about 550 people had to switch to another carrier. Secondly, the state transitioned from a federally-run exchange platform to a partnership exchange. Thirdly, state regulators rejected proposed rate increases due to insufficient justification. On the other hand, premiums may have increased in Oklahoma because the state does not have an effective rate review program, or because the state had only one insurer with 95% of the market share in 2017 (Norris, 2017).
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While the 2017 open enrollment period has yet to close at the time of writing, initial
reports and projections suggest that the state of the law remains mixed. The new enrollment
session also comes amidst a political turnover in the presidency, namely in the election of
Republican Donald Trump. Trump, who frequently calls the health care law “catastrophic,” joined
a chorus of Republican lawmakers who have repeatedly attempted to repeal the law.
“Obamacare is collapsing, and we must act decisively to protect all Americans,” he said in an
inaugural address to a joint session of Congress (“Remarks by President Trump,” 2017). Trump
even supported the American Health Care Act (AHCA), a bill which would have repealed most of
the ACA’s regulations and coverage requirements. If the AHCA is any indication, Republicans will
be looking to scrap most, if not all, of the law’s mandates and subsidies—completely
disassembling the three-legged stool which is the heart of the law.7
Review of Existing Literature on Insurer Competition and Premiums Studies Finding a Negative Association between Insurer Market Competition and Premiums
There is robust evidence supporting the hypothesis that the number of insurers offering
plans health care markets, including the exchanges, is negatively associated with both the level
and growth of premiums. The literature evaluating this relationship starts before the onset of the
ACA and the individual market reforms, beginning in the mid-1990s. Researchers have studied
7 Further context for repeal and replace is gathered from conversations with Marc Goldwein, Senior Vice President and Senior Policy Director for the Committee for a Responsible Federal Budget (CRFB), and Tyler Evilsizer, Research Manager at CRFB. An event co-hosted by CRFB and Kaiser Family Foundation in January was also helpful in understanding the challenges of repeal, as well as possible options for replacing or repairing the ACA, including payment model reforms, expanding or modifying the subsidy design, and adjusting the individual mandate.
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whether the consolidation of health insurance companies led to higher premiums. Other findings
have confirmed the association between competition and premiums for Health Maintenance
Organizations (HMOs) and employer-sponsored insurance more generally.
Several studies (Wholey et al., 1995; Wickizer and Feldstein, 1995) show that changes in
the health care industry during the late-1980s and early-1990s had a clear effect on premiums.
Competition between HMOs—managed plans that facilitate the financing, management, and
delivery of care—was linked with lower premiums in that market area. The association was also
found for premium growth rates. In markets with increased HMO penetration, the real rate of
premium growth fell increased more slowly than it would have been without competition.
Other studies have determined the effect of large insurer mergers on the local costs of
coverage. Dafny et al., (2009) concluded that the 1999 merger between Aetna and Prudential
Healthcare raised premiums by seven percent relative to those in areas unaffected by the
merger, all else equal. While increases insurer concentration was not the driving cause of rate
increases, the authors found that it did contribute to the upward trajectory of health insurance
premiums at the time of the merger. Guardado et al., (2013) found a positive association
between premiums and the 2008 merger between UnitedHealth Group and Sierra Health
Services. The authors found that premiums in the small employer market in Nevada went up
approximately 14 percent in the wake of the merger.
Insurer competition has also been studied specifically in the employer-market. A recent
paper (Trish and Herring, 2015) used employer health benefits data to study whether large group
insurance premiums were affected by the level of concentration in local insurer markets.
Premiums, they found, were higher in plans in markets with more insurer concentration. More
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specifically, employers buying insurance in markets with higher levels of insurer concentration
had less leverage to negotiate lower premiums. This same relationship was also proven for
insurer negotiations with providers. Insurers could only bargain for lower rates where they had
more bargaining power over hospitals.
More recently, there have been studies of how competition on the individual market
exchanges has affected premiums. A research brief by (Burke, et al., 2014) studied the patterns
of premium levels across rating areas in the first year of Marketplace operations. They found that
the number of issuers in a rating area, as well as the ratio of insurers that had “established”
operations in that area prior to the ACA, was linked with premium levels.8 More specifically, an
increase of one issuer in a rating area was linked with a four percent decrease in the second-
lowest cost silver plan for a 27-year-old-individual. More insurer was not linked, however, with
premiums averaged across all metal levels. The authors attributed the findings to greater
dispersion in premiums across the market, implying that lower premiums were offset by
premiums that are higher in other parts of the market.
A similar study (Dafny, et al., 2015) likewise found a consistent relationship between
insurer competition and the mean and median benchmark silver plan premiums for rating area
data. What’s more, they also found that premiums are hypothetically affected when insurers
decide not to compete in the exchanges. Testing a hypothetical scenario where
UnitedHealthcare, the largest health insurer in the U.S., decided to participate in the exchanges,
the authors found that the second-lowest price silver premium would have decreased by 5.4
8 “Established” insurers may have additional knowledge of local provider markets, or a consistent customer base. Both may affect the insurer’s ability to set premiums.
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percent on average where UnitedHealthcare would have participated. Further extending these
conclusions, the authors found that premiums would have been 11 percent lower had every
insurer offering plans in a state’s individual insurance market in 2011 participated in the
Marketplace in 2014.
Other studies (Gabel, et al. 2015; Jacobs, et al., 2015) found that average premiums across
different metal levels also fell in response to more insurer competition. Average premiums for
the two lowest-cost bronze and silver plans fell when a new carrier entered the exchanges, both
at the county and rating area levels. Holahan, et al., (2017) studied the association specifically
during the last enrollment period. Using rating area data for 15 states, the authors of this study
concluded than silver premium levels and changes in premiums between enrollment periods
were lower in areas with more competing carriers. Specifically, the marginal addition of an
insurer to a rating region during the most recent enrollment period lowered the benchmark
premium level by about 17 percent. An increase in one insurer over this time also lowered the
rate of premium growth by five percentage points.
Holahan, et al., (2017) also analyzed premium changes across different states. “Premium
levels and premium growth vary considerably across states, indicating very different
experiences,” they wrote. “Markets that had lower premiums and lower premium growth are
characterized by a large number of insurers and intense competition.” States that experienced
larger increases in premiums in 2017 usually lost more insurers on the exchanges.9 The authors
also found that states with the lowest growth in premiums, or even a decrease, had strong insurer
9 For example, Arizona’s average lowest-cost silver premium increased 125 percent in 2017. The state saw nearly all its insurers leave from 2015, leaving only one insurer option in 2017. Premiums for 2017 in Oklahoma by 74 percent for the average lowest-cost silver plan, after all but one of insurers left the Marketplace.
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competition.10 Specifically, the presence of a Medicaid issuer in a state exchange lowered the
second-lowest-cost silver premium level by a whopping 58 percent—likely due to their
experience working with low-income enrollees.
Studies Finding a Positive or No Association between Insurer Competition and Premiums
There are some studies that have found either a negative or negligible impact in any
direction of insurer competition on premiums. Feldman et al., (1996) found that premiums for
non-Medicaid HMO’s between 1985 and 1993 were hardly affected by any of the HMO mergers
during the time, leading the authors to conclude that their research “does not support the
argument that consolidation of HMOs in local markets will benefit consumers through lower
premiums.” This implies that the economies of scale associated with the insurer merger did not
redound to customers, at least in the form of lower premiums.
Sheingold et al., (2015) found minimal to no effect on the average premiums of health
plans that were offered in areas where there were more insurers. While the authors found that
a county with a net gain in insurers had slightly lower premium growth rates than those that had
no growth in the number of participating insurers, they found that the marginal addition of one
insurer to a county had “minimal to no impact” on the growth of the average silver premium.
Like Burke et al., (2014), they concluded that average premiums might not be affected by the
level of insurer participation due to the dispersion of premium prices across the market. In other
10 Premiums in Washington state, Ohio, and Michigan had some of the lowest growth in premiums due to competition from Medicaid insurers and not-for-profits.
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words, any reduction in premium growth rates caused by competition would be offset by higher
premium growth rates by other issuers in the Marketplace.
Other studies have found that the concentration of health plans caused different results
in different states. Sheffler, et al., (2016) studied this association in two state-based
Marketplaces: Covered California and NY State of Health. The authors found that while
premiums in both states increased with greater hospital and medical group concentration, the
concentration of health plans exerted contradictory effects on premium growth. Greater insurer
market concentration was associated with higher premiums in New York, but lower premiums in
California. The authors suggested that the different outcomes may be caused by Covered
California’s selective contracting with health plans.11 This finding seems to indicate that states
may be able to offset the higher premiums that follow increased insurer market concentration
by contracting with insurers that set lower rates.
Finally, Cohen et al., (2015) found that premiums increased in areas of lower relative
insurer market concentration. Using premium data by rating area, the authors unexpectedly
found a slightly positive relationship between average monthly premiums and the number of
participating insurers in the market. Specifically, they found that monthly premiums rose by
approximately $6 for each new market entry. They also found that plan types did not matter;
identical plans were more expensive in competitive areas than noncompetitive areas. The
authors suggest that the results can be attributed to an underdeveloped market, where pricing
11 Section 1331 of the ACA allows state health benefits exchanges to sign with plans to negotiate lower premiums and additional plan benefits.
21
has yet to reach market equilibrium. In addition, the authors posit that insurers may have raised
premiums to compensate for higher overhead costs.
This Paper’s Contribution to the Literature
This paper seeks to elaborate and expand on the existing body of research studying the
association between the number of participating insurers in the ACA exchanges and premiums.
To do so, it will study both premium levels and growth rates at both the state and local levels. It
will also test the association at the local level by metal tier, which allows for comparison between
plans with different actuarial coverage requirements. This paper will also control for supply- and
demand-side determinants of premium fluctuations, as well as other political conditions which
affect the delivery and cost of care.
This paper adds to existing research by conducting state and local analyses of premium
levels and variations for all four years of the ACA exchanges, including 2017. First, this paper is
the most recent study of premiums in the ACA Marketplace, incorporating 2017 data on premium
levels and insurer concentration. Second, this study examines premiums by metal tier, testing
the association between metal level premiums and insurers offering qualified plans in those tiers.
All four metal levels and catastrophic health plans are studied across all four years of the
exchanges—the most comprehensive analysis of premiums and insurer competition to date.
This paper also tests whether competition lowers premiums by controlling for a set of
factors that affect premiums. Population density is used to indicate how much of a state is
22
classified as rural. Rural areas tend to have fewer providers and limited care networks.12 Due to
poorer health and a higher frequency of chronic conditions, rural populations also have higher
rates of health care utilization than urban populations (“Rural and urban health,” 2003). Both
factors may contribute to higher premiums.
To capture changes in the baseline cost of health care, this study utilizes the Hospital
Wage Index (HWI) to account for geographical differences in hospital costs. The index captures
local differences hospitals face in their respective factor markets (Edmunds, 2011). The index is
a reliable indicator of hospital costs, as wages typically account for about two-thirds of total costs.
The index also does not discriminate by occupation or pay rates.
This paper will also control for a selection of demand-side variables, including the percent
of enrollees receiving financial assistance with their premiums. The share of enrollees receiving
either cost-sharing subsidies or tax credits is instructive because it might factor into an insurer’s
calculus of whether to raise premiums. Tebaldi, (2016) has indicated that the ACA’s fixed, income-
based subsidy design might be causing insurers to raise their premiums.13 This paper uses the
12 Field research suggests that provider competition may be the reason for the discrepancy in premiums across states. In a February 2017 lecture at the Brookings Institution, Texas A&M health care researcher Michael A. Morrisey presented evidence for wide variation in premiums and competition down to the local level. Examining five states, Morrisey found that the predominant cause of divergence within insurance markets was between rural and urban areas. Rural areas were more likely to have a single hospital and limited specialist Insurers, by consequence, found it difficult to negotiate with monopolistic or duopolistic providers. Where there were more providers, Morrisey found that premiums were lower. Finally, Morissey argues that this dynamic is part of a decades-long trend toward consolidation among providers—a pattern that makes competition increasingly difficult (Also see Trish and Herring, 2015). 13 The author writes that the ACA’s subsidy model can cause higher markups, because price increases do not lead to equivalent premium increases, which in turn produces a less elastic demand response. In other words, if the benchmark silver premium rises, the subsidy amount, which is calculated as a percentage of the benchmark plan, rises as well. This may create incentivize insurers to raise their price points, relative those associated with a voucher system, because the federal government foots the premium increase. The author found that premiums would be 15 percent lower if the health care law replaced its current subsidy scheme with a voucher.
23
percent of a state’s enrollment population that receives financial assistance to measure demand
for subsidies and tax credits.
Secondly, this paper uniquely tests the number of people enrolled in a state exchange in
each open enrollment period. This variable is important to consider, as a more robust enrollment
pool would imply more balanced risks for the insurer. When older and sicker individuals
disproportionately buy coverage, they induce higher risks to the issuer that are not offset by the
typically lower relative risks of healthier enrollees. And the costs for this cohort are usually
exorbitant: The top one percent of spenders account for more than 20 percent of spending, and
the top five percent take up almost half of all health care spending (“The concentration of health
care spending,” 2012). This is also why this paper controls for state healthiness: A population
with better determinants of health will exhibit better health outcomes.
This paper uniquely accounts for the political affiliation of each state’s governor.
Republican governors have been reluctant to sign on to the ACA’s sweeping revisions to the
individual and non-group markets. In states that elected not to administer and oversee their own
Marketplace, the federal government will establish an exchange for customers in that state. State
politics may contribute to premium prices in the exchanges. Thus, it is important to account for
the politics of the sitting governor.
In addition, this study replicates previous research in its use of other covariates of
premium increases. Mirroring Burke et al, (2014) and Sen and DeLeire (2016), this paper controls
for a state’s decision whether to expand Medicaid eligibility, an option under the ACA reforms.14
14 Burke et al. (2014) is not the only paper that studies the effect of Medicaid on premiums in the exchanges. Holahan et al. (2013) studied the effect of Medicaid-only insurers and Medicaid managed care organizations
24
The expectation is that states that opt to expand Medicaid will see lower premiums in the
exchanges, as lower-income and potentially more sickly customers move out of the exchanges.
Finally, this paper uniquely tests whether the type of state exchange, namely whether a
state elected to run its own exchange, partner with the federal government, or cede all
operational and managerial responsibilities to the government, had an impact on Marketplace
premiums. Burke et al, (2014) tested whether the type of exchange platform affected premiums.
The authors found that the association varied across different metal tiers; premiums for bronze,
silver, and platinum plans rose on federally-run exchanges, but fell among gold metal level plans.
In an article published in Forbes, Josh Archambault (2013) found that average premiums for the
second-lowest cost plans were lower on federally-run exchanges, but increased more in dollar
and percent amounts relative to state-based exchanges. This paper also tests whether an
exchange is run by the state or federal government affects premium levels and changes between
years.
Data and Empirics The primary dataset for this paper is from Healthcare.gov, which tracks health plan data
on federally-facilitated exchanges for the years 2014 through 2017. The site’s qualified health
plan landscape file includes monthly premium data by rating area and county, as well as the
number of plans and health insurance issuers by area. For this paper’s study of premiums and
insurer competition by rating region, data from Healthcare.gov was sorted by plan type. Premium
on competition in the exchanges. Areas with more competition, they found, had lower premiums for unsubsidized enrollees.
25
data reflect the average monthly premium for an average 27-year-old nonsmoker who enrolled
on the exchanges. The issuer number represents the number of insurers offering plans of that
metal type in a rating area.15
To compute the effect of insurer competition on premiums at the state level, this paper
uses datasets published in research briefs and enrollment reports by the U.S. Department of
Health and Human Services’ Office for the Assistant Secretary for Planning and Evaluation (ASPE)
for the years 2014 through 2017. (Burke et al., 2014; “Health insurance Marketplace,” 2015;
“Health insurance marketplace,” 2016; “Health plan choice and premiums,” 2017) State premium
data varied in its accessibility depending on the type of exchange in that state. For the first few
years of the Marketplace, data for state-based exchanges were not accessible through ASPE files.
Instead, premiums for these states were computed using the average silver monthly premium
data for a 40-year-old nonsmoker in 2016, published by the Commonwealth Fund (Gabel et al.,
2017).16 All premium data were adjusted for inflation using the Office of Management and
Budget’s chained-GDP price index.
While ASPE did not report the number of insurers participating in a state’s health
insurance exchange, insurer data had to be gathered from elsewhere. The number of insurers
comes from the Heritage Foundation’s 2017 health care reform report of competition and
15 All issuer data are gathered at the parent-company level; company subsidiaries are aggregated under the parent company name only, as all subsidiary operations are ultimately controlled by the parent organization (Federal Register, 2014). 16 To be consistent with ASPE’s premium data for 27-year-olds, premiums were adjusted by age using a sample age-rating factor ratio (Actuarial memorandum, 2017). For states without public 2015 premium data, average change in silver plans between 2015 and 2016 was gathered from the Commonwealth Fund, which was then used to extrapolate backward from 2016 premium figures. For states without listed premium data for 2017, 2016 state-level premiums were multiplied by the average rate of silver monthly premium growth determined by ASPE from 2016 to 2017 (22 percent).
26
choices in the exchanges (Haislmaier and Senger, 2017), which lists the number of participating
insurers on the exchanges from 2014 through 2017. In addition, the Herfindahl-Hirschman Index
(HHI) is used to measure insurer competition in the individual market—both at the state and
rating area levels (Individual insurance market, 2014). This data source has its limitations. The
most recent dataset available was from 2014, the first year of the exchanges. While HHI is
commonly-used indicator of industry competition, there are no available data through the year
2017. The most recent figures are tested in this paper.
Health care inflation is computed using the hospital wage index (HWI), a geographically-
adjusted measure of the labor costs hospitals face in their factor markets. This dataset comes
from the Centers for Medicaid and Medicare Services’ wage index files, which includes the years
2014 through 2017. State-by-state public health rankings are expressed in an index calculated by
the UnitedHealth Foundation in their annual America’s Health Rankings.17 The index is based on
a variety of community and state health indicators like smoking and obesity rates, air pollution,
adolescent immunizations, and the number of primary care physicians and dentists per 100,000
people.
The two demand variables include total annual enrollment and the percent of enrollees
receiving financial help. Enrollment numbers are derived from the ASPE’s effectuated enrollment
reports from 2014 through 2017. While the 2017 enrollment period had not closed at the time
of writing, enrollment projections for 2017 were obtained from ACASignups.net, a trusted source
on ACA enrollment data. The percent of enrollees obtaining financial assistance is also derived
17 Data for 2017 were not yet publically available. The 2017 number was adjusted according to the three-year average change from the years 2014 through 2016.
27
from ASPE research briefs and enrollment reports from 2014 through 2016.18 Finally, population
density data is gathered from the U.S. Census Bureau in the most recent census report in 2010.
Main State and Rating Area Specification
This paper uses a fixed-effects model to estimate the following equation:
yi = b0 + b1 Insurer_Numberit + b2 HHIit + b3 HWIit + b4 %Ruralit + b5 Health_Rankingit +
b6 #Enrolledit + b7 %Received_Financialit + b8 Governor_Politicsit + b9 Medicaidit + b10
Federally-Facilitated Exchange + b10 State-Based Exchange + b11 Partnership Exchange ai + lt +
uit,
where ai is the area of premium sample fixed effect and lt is the time fixed effect by year.19 This
paper conducts two primary sets of regressions, each varying by the geographical area of
observation. The dependent variables, yi, includes premium levels and premium growth between
open enrollment periods, both of which will be tested by state and rating area. Premium growth
and levels will also be tested at the rating area level by metal tier and plan type.
Among the independent variables, insurer number tracks the number of insurers in each
state or rating area that sold plans in that year. The HHI covariate is a measure of the index in
18 Numbers for 2017 were computed using three-year average change of the share of enrollees receiving assistance from 2014 through 2016. 19 A fixed-effects regression is a way of accounting for omitted variables that, in this case, may be constant over time but vary across states or rating areas, while also accounting for omitted variables that may be constant across states or rating areas but vary over time. Both the entity and time fixed effects are treated as individual intercepts to be estimated, one for each entity and time frame (Stock and Watson, 2007).
28
2014; HWI includes the years 2014 through 2017; the %Rural term expresses how much of a
state (or the state in which a rating area resides) is rural according to the 2010 Census; the Health
Rankings indicator is a measure of state healthiness from 2014 through 2017. Demand-side
variables include both the #Enrolled and %Received Financial, which are the number and
financial conditions of enrollees in a state (or state in which the rating area resides) that enrolled,
respectively.
The final five independent variables are dummies equal to 1 or 0. The Governor Politics
variable is 1 if the current governor identifies as a Republican—otherwise, the governor is
Democrat or Independent. A 1 for the Medicaid dummy signifies that the state (or a rating area
in that state) expanded Medicaid eligibility as part of the ACA. A 1 for either of three Marketplace
indicators signifies a state’s exchange type. The exchange indicator variable is only included in
the state-level regressions.
Secondary State and Rating Area Specifications
The following two specifications are reduced-form regressions of the main specifications,
and will be tested at both state and rating area levels. These specifications include supply- and
demand-side factors that affect the equilibrium price of premiums. The two specifications
include:
yi = b0 + b1 Insurer_Numberit + b2 HHIit + b3 HWIit + b4 %Ruralit + ai + lt + uit,
and:
29
yi = b0 + b1 Insurer_Numberit + b2 HHIit + b3 HWIit + b4 %Ruralit + b5 Health_Rankingit +
b6 #Enrolledit + b7 %Received_Financialit + + ai + lt + uit,
The first specification regresses premiums on insurers and includes relevant supply-side
covariates such as an alternative measure of insurance industry concentration, hospital labor
costs, and the challenge of establishing and coordinating provider networks that are associated
with the population distribution. This regression is also run with both year and area size fixed
effects.
The second specification tests the supply dimension of health insurance with demand-
side factors, including the size and financial conditions of the enrollment base, and the potential
healthiness of that enrollment pool. This specification will also be tested with area size and year
fixed effects.
Testing for Differences in Exchange Platform
The final regression specification incorporates the effect of the exchange type (i.e. state-
based, federally-facilitated, or partnership) on premiums. This adjustment will only be made to
the regressions on state-specific premium data, not the rating area models. To do so, the entity
fixed effects will be converted from state effects to census division effects to account for regional
patterns in establishing certain types of exchange platforms. There are clear patterns of state-
based exchanges being more prominent in the North East, Atlantic Coast, and the Pacific West.
Federally-facilitated exchanges should be more common in the Deep South, Midwest, and
30
Mountain West. Both patterns reflect state political choices to be either accommodating or
hostile to the ACA and its insurance market reforms.
Description of Summary Statistics
Note: Complete tables of summary statistics from 2014-2017 are printed in the Appendix.
There is a wide dispersion of premium levels and insurer participation across different
states. For example, the lowest inflation-adjusted monthly silver premium for any state in 2017
is nearly $500 less than the average silver premium in the highest state. In 2017, monthly
premiums in states with Republican governors averaged $271, while states with Democrats for
governors had average monthly premiums of $301. Exchanges in red states were also slightly less
competitive, with an average of four insurers compared to five for blue states. Members of both
blue and red states were about equally likely to receive for financial assistance on the exchanges,
with 84 percent of enrollees in Republican states receiving tax credits or subsidies compared to
82 percent of enrollees in Democratic states.
Across all states, average monthly premiums rose consistently from 2014 through 2017.
The national average of monthly silver premiums in 2014 was $215, while the national average
in 2017 was $289, about 34 percent higher. As premiums have risen, more people are accessing
financial help to pay their premiums. Whereas 73 percent of enrollees were receiving some
financial help in 2014, 83 percent of enrollees were claiming financial help in 2017. Also, the
average number of insurers across every exchange nationwide fell from an average of six insurers
in 2015 and 2016 to four in 2017.
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States also varied in how their premiums changed from year to year. From 2016 to 2017,
average monthly premiums in Arizona rose by nearly $200, as the number of insurers offering
plans in the state exchange fell from eight to two. Some states saw their health insurance
premiums fall. In 2017, Arkansas, Indiana, and Massachusetts each had lower premiums than in
2016. On average, monthly premiums increased by about $54 in 2017, a higher increase than
what was calculated for 2015 (less than $1) and 2016 ($20). A typical Marketplace in 2017 had
fewer participating insurers and higher premiums than a typical Marketplace in 2014, on average.
Looking even more closely at premium changes, the highest monthly premiums in 2017
varied by state according to different plan types. The five highest premiums for bronze and silver
plans in 2017 were clustered in rating areas in Alaska and Arizona. All of Alaska’s rating areas had
the largest gold metal premiums for all states in 2017, followed by two areas in Tennessee. The
five highest monthly premiums for platinum plans were concentrated in Florida, while the top
four highest premiums for catastrophic plans were in Oklahoma. The lowest monthly premiums
were also concentrated in a few states. Rating regions in Kentucky, Michigan, New Mexico, and
Pennsylvania consistently had the lowest monthly premiums for all states in 2017. Indiana’s 16th
rating area had one of the lowest silver and gold plan monthly premiums, while New Hampshire’s
1st rating area sold the plan with lowest platinum premium.
There is also wide variation among rating areas and states more broadly in terms of
insurer competition. In 2017, most or all the rating areas in Alaska, Alabama, Arizona, Oklahoma,
South Carolina, Tennessee, and Wyoming were offering only one insurer option for health
insurance plans—a number that was consistent across most metal levels. On the other hand,
32
states like Michigan, Ohio, and Wisconsin played host to a handful of insurers in 2017. These
dynamics will have consequences for premiums in those states.
Results
This paper seeks to test the effect of insurer participation in the ACA exchanges on the
cost of the health insurance plans listed on the exchanges. Because of the complexity and
political relevance of the issue, this paper uses multiple approaches to examine this relationship.
For the first approach, this paper test whether the number of insurers offering plans within each
state is related to that state’s monthly premium level (and, in a separate series of regressions, its
growth rate) for the silver benchmark plan for an average 27-year-old, controlling for other
important factors that could influence the premium. The advantage of this regression is that
there are data on all 50 states and so the results are relatively more representative for the nation
as a whole. The disadvantage of this approach, however, is that the total number of insurers
offering plans in a state is not the same as the number of insurers offering contracts to any
particular individual. This is because some insurers might offer plans throughout the state, while
others may be more concentrated in specific localities.
Note: Complete regression tables for each regression are included in the Appendix.
Premium Levels and Insurer Number (State Level)
This regression tests the association between monthly premium levels for the silver
benchmark plan for an average 27-year-old on the number of insurers participating in each
state’s health insurance exchange. This regression controls for several controls for several
33
additional factors that could influence the average premium on health care plan, including the
distribution of insurer market share, the geographically-adjusted price of labor for hospitals,
population density, and the demand for insurance products in the exchanges. One might also
worry that the stance of the state government and its resulting policies could affect the premium.
To address this concern, this study controls for both the political affiliation of a state’s sitting
governor, as well as whether the state decided to expand Medicaid eligibility.
The results in Table 7 suggest that there is strong empirical support for the idea that a
greater number of insurers within a state reduces the average monthly premium on a state’s
second-lowest silver plan. The main coefficient of interest—the coefficient on the number of
insurers – is negative in all specifications, suggesting that premiums tend to be lower when there
are more insurance companies offering health insurance plans. The coefficient on the number of
insurers is statistically significant at the 5% level in all three of the specifications, and significant
at 1% in the second and third specifications.
Focusing on the coefficients that have the strongest significance, the size of the
coefficient is meaningful. Specifically, the coefficients in the second and third specifications
suggest that for each additional insurer that enters a state exchange, monthly premium levels in
that state will fall by about $4 for the benchmark silver plan. This is equivalent to a 2% reduction
in the cost of the average silver plan, after adjusting for inflation. The coefficient on insurer
number for the first specification suggests that monthly premiums will fall by about $3 for each
new insurer entry in a state exchange.
This regression also indicates that this combination of variables explains only a modest
amount of the variation in monthly premiums for the benchmark silver plan. The third
34
specification has an R-squared term, a commonly-accepted measure of how well the model fits
the data pattern, of only 0.33, meaning that only one-third of the annual changes in premium
levels is captured by the regression—the rest is unidentified. While the second specification has
roughly the same R-squared value as the third specification, the first specification with only the
insurer number, wage index, market concentration index, and the population density statistic
explains only 21% of the variation in premium levels.
Controlling for Marketplace Type
A state’s decision to establish its own exchange platform, or alternatively allow the
Department of Health and Human services to facilitate an exchange in that state, might also
affect the price of health insurance. Insurers may be less willing to participate in an exchange if
the state government is not entirely invested in the ACA, leading to less market competition.
States that manage and operate their own exchanges may also have prior experience overseeing
a health insurance marketplace, meaning that they could have greater expertise than other states
at courting insurers, sponsoring enrollment campaigns, or guaranteeing insurer reimbursements
to compensate for the risk of participation. The type of exchange platform a state chooses to
adopt—or not adopt, for that matter—is a key indicator of how knowledgeable it is about the
challenges of overseeing centralized insurance market, and how willing it is to work within the
ACA’s reforms.
The results in the table suggest that premiums do vary when a state chooses not to
manage its own exchange. A state that chooses to cede its exchange operations to the federal
government could see monthly premiums rise by about $35 compared to states that run their
35
own market. This statistic is significant at the 1% level, strongly suggesting that federally-
facilitated exchanges have higher prices for health insurance.
Annual Premium Growth and Insurer Number (State Level)
This paper also seeks to test whether the number of insurers participating in each state
exchange is associated with changes in monthly benchmark silver plan premiums between years.
Like the regressions on state-specific premium level data, these regressions will also control for
several covariates of premium changes, including insurer market concentration, the cost of
hospital care represented by the hospital wage index, population density, enrollment, and
political factors such as the political affiliation of the state’s governor and that state’s decision
whether to allow the Medicaid expansion under the law.
The results in Table 8 suggest very strong support for the idea that a state with more
insurers will experience lower changes in monthly silver premiums between years. The main
coefficient of interest—the coefficient on the number of insurers—is negative in all
specifications, and significant at the 1% level for every specification. The first specification is
significant at the 0.1% level, a strong indication that premiums grow more slowly where there is
more insurer competition.
Focusing on the size of the coefficient is meaningful. Specifically, the coefficients on all
three specifications suggests that monthly premiums between years will fall by $4 when a new
insurer enters an exchange—the exact same number for the regressions on insurer competition
and premium levels. Again, this strongly indicates that new entrants into the individual insurance
market reduce premiums.
36
Controlling for Marketplace Type
As suggested by the regression on premium levels and insurer number in the exchanges,
exchange type may be a determinant of premium levels. It may also be a determinant of changes
in premiums between years. The results suggest otherwise. The coefficient on the indicator for a
state with a federally-run exchange is positive, suggesting that this type of exchange raises
premium changes between years. In the third specification, premiums in a federally-facilitated
market type rose by $15 between years. However, the coefficient it is not statistically significant
at or below the 5% level. These results suggest that there is mixed evidence for the idea that a
federally-run exchange is associated with higher premiums.
In contrast, the coefficient on the indicator for a state-based exchange was negative,
reflecting the idea that states that operate their own market platforms contribute to lower
premium changes between years. The presence of a state-run exchange led to lower annual
changes in monthly premiums, suggesting that premiums would have been about $16 higher if
that state had handed control over its exchange to the federal government. The coefficient on
the indicator, however, was not significant at any of the tested levels.
Finally, the addition of the market type indicator minimally changed the coefficients on
the only significant variable, insurer number, by a few decimal places. Premiums in the third
specification would have risen by $5 fewer if an additional insurer entered the market. Premiums
in the first two specifications would have risen by $4 fewer for each marginal insurer entry. In
each of the specifications, the coefficients on insurer number were significant at the 1% level or
better.
37
Premium Levels, Premium Growth, and Insurer Number (Rating Area)
To provide a more detailed look at premium levels and insurer participation in the
exchanges, this paper also tests the association by rating area and across different metal tiers.
Insurers calculate premiums by rating area, which consist of demographically-similar counties or
metropolitan centers, by applying the same adjustment factor across all households in the area.
The next series of regressions will use premium and insurer data at the rating area level.
Using rating area level data is different for two reasons. First, it allows for a more detailed
look at where insurers are selling plans within states. The total number of insurers offering plans
in a state is not the same as the number of insurers offering contracts to any particular individual.
This is because insurers might offer plans throughout the state, while others may be
concentrated in specific localities. Secondly, in addition to publishing rating area data,
Healthcare.gov also publishes premium data by metal level. This is an important element that
state-level data misses. By separately testing how the number of insurers affects each tier’s
premium, these regressions also control for plans that attract different types of people. By using
plan data, one can control for the enrollees preferences in terms of deductibles, out-of-pocket
costs, and premiums.
Regressions on rating area data also come with a limitation. Healthcare.gov only publishes
local data for states that have decided not to manage their health benefits exchange. Thus,
premium and insurer data for state-based exchanges are excluded from the dataset. In total,
about one-fourth of all states are excluded. While not the most comprehensive look at premiums
and insurer competition, these data help understand how states with federally-facilitated
exchanges are doing under the ACA.
38
Bronze Metal Level: Premium Levels
The bronze metal level, which covers 60% of the cost of care, is the category with the
lowest level of coverage on the exchanges. Thus, premiums for bronze plans are the lowest of all
the metal categories. This regression will test the effect of insurer number on bronze premiums,
controlling for other relevant covariates of premium levels.
The results in Table 9(a.) suggest that there is very strong empirical support for the idea
that a greater number of insurers reduces the average monthly premium for the average bronze
plan offered in a rating area. The main coefficient of interest—the coefficient on the number of
insurers – is negative in all specifications, suggesting that monthly premiums tend to be lower by
$6-$8 when there are more insurance companies offering health insurance plans. The coefficient
on the number of insurers is statistically significant at the 0.1% level in all three of the
specifications, solidifying the link between insurer number and premiums.
There were a few surprises in this regression. The sign on the coefficient indicating a
Republican governor was negative, suggesting that red states have lower premiums than states
with Democrats or Independents as governors. The effect is also quite large: the presence of a
Republican governor lowered premiums by about $27, a result that was significant at the 0.1%
level. This is surprising, considering that Republican governors have been most hostile to the
health care law, and have mostly ceded control of their state exchanges to the federally
government. Even more surprising: a federally-facilitated exchange was found to be linked with
higher premiums at the state level. Since Republican governors have been more hostile to the
law, the sign on the coefficient is surprising.
39
The sign on the health ranking coefficient is also surprising. The index used to compute
state healthiness becomes more positive as a state’s health outcomes improve. The results of
this regression suggest that premiums will rise if a state become healthier. While the coefficient
is not statistically significant, it raises questions about whether the collective health of a state’s
enrollment pool mirrors that of the wider population. Evidence from the first few years of the
exchanges suggests that healthier people are choosing to forego insurance and instead pay the
penalty. This would suggest that a state’s health ranking does not matter, as the individual market
caters to a slice of the wider population.
Finally, each the specifications in this regression do a modest job at capturing all the
fluctuations in premium levels between years. The first model explains only 13% of the variation
in premium levels. The second and third models do not do much better; the second model
captures only 22% of the variation, and the third model explains only 25% of premium variation.
Like the state-specific models, this regression does a poor job accurately capturing the variation
in premiums.
Bronze Metal Level: Premium Growth
The results in Table 9(b.) suggest that the number of insurers offering bronze plans in a
market is associated with slower premium growth between years. The coefficients on insurer
number across all specifications indicate that more insurers in the market leads to lower
premium growth—about $5 less per month in the first and third specifications and $4 in the
second specification. All three coefficients are significant at the 0.1% level.
40
Silver Metal Level: Premium Levels
The silver metal level, which covers 70% of the cost of care, is the category with the
second-lowest level of coverage on the exchanges. The silver plan is the most popular metal level
on the exchanges, and is used as a benchmark for calculating premium tax credits. This regression
will test the effect of insurer number on silver premiums, controlling for other relevant covariates
of premium levels.
The results in Table 10(a.) suggest that there is very strong empirical support for the idea
that a greater number of insurers reduces the average monthly premium for the average silver
plan offered in a rating area. The main coefficient of interest—the coefficient on the number of
insurers – is negative in all specifications, suggesting that premiums tend to be lower when there
are more insurance companies offering health insurance plans. Like the regression results for
bronze plans, the coefficient on the number of insurers is statistically significant at the 0.1% level
in all three of the specifications, solidifying the link between insurer number and premiums. The
coefficient on insurer number indicates that monthly premiums will fall by $4-$6 for each new
entry into the market. This represents roughly a 2% decrease in silver premiums, after adjusting
for inflation.
Silver Metal Level: Premium Growth
The results in Table 10(b.) suggest that the number of insurers offering silver plans in a
market is associated with slower premium growth between years. The coefficients on insurer
number across all specifications indicate that more insurers offering plans lead to lower growth—
41
about $4 fewer per month in the first specification and $3 fewer in the second and third
specifications. All three coefficients are significant at the 0.1% level.
Gold Metal Level: Premium Level
The gold metal level, which covers 80% of the cost of care, is the category with the third-
lowest level of coverage on the exchanges. Gold plans typically have higher premiums than
bronze and silver plans, reflecting a higher percentage of health care costs that are covered. This
regression will test the effect of insurer number on gold premiums, after controlling for other
relevant covariates of premium levels.
The results in Table 11(a.) suggest that there is very strong empirical support for the idea
that a greater number of insurers reduces the average monthly premium for the average gold
plan offered in a rating area. The main coefficient of interest—the coefficient on the number of
insurers – is negative in all specifications, suggesting that premiums tend to be lower when there
is more insurer competition
Like the regression results for bronze plans, the coefficient on the number of insurers is
statistically significant at the 0.1% level in all three of the specifications, solidifying the link
between insurer number and premiums. The coefficient on insurer number indicates that
monthly premiums will fall by $8-$12 for each new entry into the market, a slightly higher range
than was found for bronze and silver plans. This reflects the higher base premiums for gold plans,
which cover a higher percentage of the costs of care than metal tiers with lower actuarial value
levels.
42
Finally, this regression has a slightly higher R-squared term among the second and third
specifications than the bronze and silver level regressions. The second model captures 28% of
the variation in premium levels, and the third model captures about 31% of the premium change.
While this term is higher than those of the lower actuarial value plans, it is still modest.
Gold Metal Level: Premium Growth
The results in Table 11(b.) suggest that the number of insurers offering gold plans in a
market is associated with slower premium growth between years. The coefficients on insurer
number across all specifications indicate that more insurers causes to lower premium growth—
about $11 less per month in the first specification, $8 per month less in the second specification,
and $9 in third specification. All three coefficients are significant at the 0.1% level.
Platinum Metal Level: Premium Level
The platinum metal level, which covers 90% of the cost of care, is the category with the
highest level of coverage on the exchanges. Thus, platinum plans tend to have the highest
premiums of all the metal levels, reflecting a higher percentage of health care costs that are
covered. This regression will test the effect of insurer number on platinum premiums, after
controlling for other relevant covariates of premium levels.
The results in Table 12(a.) suggest that there is a strong association between the number
of insurers offering platinum plans and growth in platinum plans between years. The main
coefficient of interest—the coefficient on the number of insurers– is negative in all specifications,
suggesting that premiums are lower when more insurers are competing in the market.
43
Specifically, monthly premiums would fall by $16 for every new insurer according to the first
specification, and $7 according to the second and third specifications. Each of the coefficients is
significant at the 0.1% level.
Overall, this model has very low explanatory power over the dependent variable. The
highest R-squared term is only 0.13, a low statistic reflecting the fact that this regression captures
almost none of the variation in premiums. Additionally, all the coefficients in all the specifications
are less than one, indicating that this combination of variables does not explain why premiums
for platinum level plans changed over time.
Platinum Metal Level: Premium Growth
Likewise, the results in Table 12(b.) suggest that the growth in premiums is associated
with the number of insurers offering plans. For every new insurer that enters an exchange,
monthly premiums will fall by $14 according to the first specification, and $9 according to the
second and third specifications. The main coefficient of interest is statistically significant at the
0.1% level in all three models.
Catastrophic Plans: Premium Level
Catastrophic health insurance plans are typically high-deductible plans exclusively
available for enrollees younger than 30 or those who qualify for an exemption. Thus, premiums
for catastrophic plans are generally lower than for the metal level plans. This regression will test
the effect of insurer number on premiums for catastrophic plans, after controlling for other
relevant covariates of premium levels.
44
The results in Table 13(a.) strongly suggest that new entrants into the insurance market
cause monthly premiums to fall. The main coefficient of interest—the coefficient on the number
of insurers – is negative in all specifications. This indicates that premiums fall when there is more
insurer competition. The decrease in monthly premiums ranges from $7-$9 across the three
specifications. Additionally, the coefficient on the number of insurers is statistically significant at
the 0.1% level in all three of the specifications, solidifying the link between insurer number and
premiums.
Catastrophic Plans: Premium Growth
The results in Table 13(b.) suggest that the number of unique carriers offering
catastrophic plans in a rating area is associated with monthly catastrophic premiums levels. The
main coefficient of interest—number of insurers offering plans in an area—is negative, indicating
that insurer competition is linked with lower premiums. Specifically, a net increase of one insurer
would cause monthly premiums to fall by $7-$9 across all of the specifications. Each of the
coefficients are significant at the 0.1% level.
Discussion and Conclusion This paper’s findings that premiums across all metal levels are lower where there is more
insurer participation in the exchanges suggests that states and localities should do more to
encourage insurer participation in the individual market. There are several supply- and demand-
side adjustments that can be made to shore up insurer competition by drawing more carriers
into the market and increasing the number of health insurance plans being offered.
45
In an April 2017 interview, Loren Adler, Associate Director of the Center for Health Policy
at the Brookings Institution, suggested that competition can be improved by renewing or making
permanent some of the risk adjustment programs that cushion insurers from losses.20 By
spreading risk around the market, with more profitable insurers cross-subsidizing less profitable
ones, these programs were designed to minimize insurer losses. Two of these programs,
however, were either never funded or had expired before 2017. The third, the risk adjustment
mechanism that redistributes funds based on the actuarial risk of a plan, is permanent but has
been sequestered in recent years (“Federal Register,” 2016). Adler suggests that funding these
programs can help to stabilize the insurance market, mitigate insurer withdrawal, and suppress
premium increases.
Another idea mentioned in conservative policy circles is adjusting the caps on insurer
profit margins established under the ACA. Section 1331 of the law requires insurers to use a
certain fraction on premium revenue, called the Medical Loss Ratio (MLR), to pay for medical
claims and activities that improve the quality of care. Under these provisions, insurers can only
spend 20 percent of their premiums on plan administration if they offer plans through the
exchanges. Critics of the MLR suggest loosening the caps to give insurers more breathing space
to establish operations in the Marketplace. Scott Gottlieb of the American Enterprise Institute
suggests allowing new carriers to retain a higher percentage of their premium revenue to pay for
20The transitional reinsurance program, which reimburses plans that enroll high-risk individuals to inhibit premium increases, expired in 2016. Without this provision, experts estimate that insurers would need to raise premiums by at least 26 percent to avoid losses (Blase et al., 2016). The ACA’s risk corridor program, which reimburses insurers on a sliding-scale if their costs exceed a target amount by a certain percentage, was never initially funded. Insurers filed claims seeking $2.87 billion in risk reimbursements, but only received $362 million—just 12.6 cents on the dollar (“Risk corridors,” 2015).
46
start-up costs (Gottlieb, 2016). He argues that this would allow insurers to enter and stay in the
market.
Others argue that the weakness of the tax penalty is partly to blame for weak enrollment
numbers. According to this reasoning, many young, less sickly people decided not to purchase
insurance and to instead pay the penalty. Insurers were left with enrollment pools that had
higher rates of health care service utilization, leading to higher premiums. “The penalty for
violating the individual mandate has not been very effective,” said Joseph J. Thorndike, a tax
expert at Tax Analysts, a nonprofit publisher of tax information. “If it were effective, we would
have higher enrollment, and the population buying policies in the insurance exchange would be
younger and healthier” (qtd. In Pear, 2016). Increasing the financial penalty for foregoing health
insurance would be one way to boost enrollment in the individual market.
Steps can also be taken to shore up enrollment rules to minimize adverse selection. For
example, the federal government can tighten special enrollment period standards (windows
which allow people who did not enroll during open enrollment to purchase insurance), and lock
individuals who delay enrollment out of the market for a span of time (Mendelson, 2016). States
can also increase enrollment by increasing funding for outreach, including airing television ads.21
Increasing awareness of the law and enrollment sessions is one way to bolster the enrollment
pool.
Another suggestion to improve the condition of the exchanges is to increase the financial
assistance measures, including the premium tax credits and the cost-sharing reductions. Adler
21 The Trump administration curtailed all outreach and advertising in the final days of the 2017 open enrollment session (Demko, 2017).
47
suggests increasing the income-related tax premiums, or adding to a flat-tax credit on top of the
existing scheme. He argues that this would allow more people to afford insurance. In addition,
Adler argued that President Trump’s discussed plan to eliminate the ACA’s cost-sharing
reductions, which reimburse insurers for payments that cover low-income enrollees’ out-of-
pocket costs, would cause premiums to increase by about one-fifth. Funding the cost-sharing
reimbursements and keeping or increasing the premium tax credits are two ideas to reduce the
pressure on insurers and attract more customers to the health insurance market.
Finally, some health care experts suggest that individual health insurance market could
be strengthened with the addition of a public option. These same policy experts argue that a
public backstop in the insurance market would not crowd out private insurance. According to the
Urban Institute, the introduction of a public plan would not lead to reduced private competition
in the market (Holahan and Blumberg, 2009). “Private plans would not disappear,” the report
wrote. “Private plans that offer better services and greater access to providers...would survive
the competition in this environment.” The paper also suggests that plans offering lower-cost
options could even find separate competitive niches in the market. In addition, the Congressional
Budget Office estimates that a public plan would have lower administrative costs than private
plans (“Add a ‘public plan,’” 2013). It also calculated that a public option would reduce subsidies
by $39 billion due to lower premiums.
To conclude, these results suggest that there are plenty of options for lawmakers at the
federal and local levels to improve the health care law. Having a robust selection of insurers
offering health plans in the Marketplace not only leads to greater consumer choices, but it also
convincingly leads to lower premiums. This paper suggests several improvements to the law that
48
target the conditions for both buying and selling health insurance. Implementing these policy
recommendations would lead to greater competition between carriers and, as one can expect,
lower premiums across the board¨
49
Appendix Table 1(a.): State-Specific Premium Levels, Second-Lowest Silver Monthly
Table 1(b.): State-Specific Premium Change, Second-Lowest Silver Monthly
Table 2(a.): Rating Area-Specific Premium Level, Average Bronze Plan Monthly
50
Table 2(b.): Rating Area-Specific Premium Change, Average Bronze Plan Monthly
Table 3(a.): Rating Area-Specific Premium Level, Average Silver Plan Monthly
Table 3(b.): Rating Area-Specific Premium Change, Average Silver Plan Monthly
51
Table 4(a.): Rating Area-Specific Premium Level, Average Gold Plan Monthly
Table 4(b.): Rating Area-Specific Premium Change, Average Gold Plan Monthly
Table 5(a.): Rating Area-Specific Premium Level, Average Platinum Plan Monthly
52
Table 5(b.): Rating Area-Specific Premium Change, Average Platinum Plan Monthly
Table 6(a.): Rating Area-Specific Premium Level, Average Catastrophic Plan Monthly
Table 6(b.): Rating Area-Specific Premium Change, Average Catastrophic Plan Monthly
53
Table 7:
Note: Regression was tested using Census division and year fixed-effects. Governor Politics, Medicaid expansion, Federally-facilitated exchange, and State-based exchange are dummies (0,1), with 1 representing a Republican governor, expanded Medicaid, a federally-facilitated exchange, and a state-based exchange, respectively. The partnership exchange indicator is excluded from the table. Figures in parentheses indicate standard errors.
54
Table 8:
Note: Regression was tested using Census division and year fixed-effects. Governor Politics, Medicaid expansion, Federally-facilitated exchange, and State-based exchange are dummies (0,1), with 1 representing a Republican governor, expanded Medicaid, a federally-facilitated exchange, and a state-based exchange, respectively. The partnership exchange indicator is excluded from the table. Figures in parentheses indicate standard errors.
55
Table 9(a.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
56
Table 9(b.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
57
Table 10(a.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
58
Table 10(b.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
59
Table 11(a.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
60
Table 11(b.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
61
Table 12(a.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
62
Table 12(b.)
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
63
Table 13(a.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
64
Table 13(b.):
Note: Regression was tested using rating area and year fixed-effects. Governor Politics and Medicaid Expansion are dummies (0,1), with 1 representing a Republican governor and expanded Medicaid, respectively. Figures in parentheses represent standard errors.
65
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