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.

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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

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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,

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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.

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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

References 2010 census urban and rural classification and urban area criteria.

https://www.census.gov/geo/reference/ua/urban-rural-2010.html (accessed 14 Apr. 2017)

ACA private qualified health plan spreadsheet. 2017. http://acasignups.net/spreadsheet

(accessed 14 Apr. 2017) Actuarial memorandum. 2017. PacificSource Health Plans.

https://ratereview.healthcare.gov/files/792538_ACTUARIAL_MEMORANDUM_v.5_signed.pdf (accessed 18 Mar. 2017).

Add a “public plan” to the health insurance exchanges. 2013. The Congressional Budget Office.

https://www.cbo.gov/budget-options/2013/44890 (accessed 23 Apr. 2017). Adler, Loren. 2017. Informational Interview. Brookings Institution. America's health rankings report. UnitedHealth Foundation.

http://www.americashealthrankings.org/ Web. (accessed 18 Mar. 2017). Archambault, Josh. 2013. Do premiums differ In states with a state-based exchange versus

federal exchange under ObamaCare? Forbes https://www.forbes.com/sites/theapothecary/2013/11/12/do-premiums-differ-in-states-with-a-state-based-exchange-versus-federal-exchange-under-obamacare/#783c533959d8 (accessed 18 Apr. 2017).

Avery, Kelsey, Mathias Gardner, Emily Gee, Elena Marchetti-Bowick, Audrey McDowell, and

Aditi Sen. 2015. Health plan choice and premiums in the 2016 health insurance marketplace. Department of Health and Human Services. Office of the Assistant Secretary for Planning and Evaluation. https://aspe.hhs.gov/pdf-report/health-plan-choice-and-premiums-2016-health-insurance-marketplace (accessed 10 Mar. 2017).

Blase, Brian, Doug Badger, and Edmund F. Haislmaier. 2016. The Affordable Care Act in 2014:

significant insurer losses despite substantial subsidies. Mercatus Center. https://www.mercatus.org/publication/affordable-care-act-2014-significant-insurer-losses-despite-substantial-subsidies. (accessed 10 Mar. 2017).

Burke, Amy, Arpit Misra, and Steven H. Sheingold. 2014. Premium affordability, competition,

and choice in the health insurance marketplace. ASPE research brief https://aspe.hhs.gov/pdf-report/premium-affordability-competition-and-choice-health-insurance-marketplace-2014 (accessed 18 Mar. 2017).

66

Claxton, Gary, Cynthia Cox, Anthony Damico, Larry Levitt, and Karen Pollitz. 2016. Pre-existing conditions and medical underwriting in the individual insurance market prior to the ACA. Kaiser Family Foundation. http://kff.org/health-reform/issue-brief/pre-existing-conditions-and-medical-underwriting-in-the-individual-insurance-market-prior-to-the-aca/ (accessed 22 Apr. 2017).

Cohen, Jesse N, Alexander Coppock, Arnab K. Ghosh, and Benjamin P. Geislet. 2015. Do insurers

compete on the federal health insurance exchange? F1000Research, https://f1000research.com/articles/4-25/v1#f1 (accessed 14 Mar. 2017).

Collins, Sara R., Michelle M. Doty, Ruth Robertson, and Tracy Garber. 2011. Help on the

horizon: how the recession has left millions of workers without health insurance, and how health reform will bring relief. The Commonwealth Fund. http://www.commonwealthfund.org/publications/fund-reports/2011/mar/help-on-the-horizon (accessed 22 Apr. 2017)

Cox, Cynthia, and Ashley Semanskee. 2016. Preliminary data on insurer exits and entrants in

2017 Affordable Care Act marketplaces. Kaiser Family Foundation. http://kff.org/health-reform/issue-brief/preliminary-data-on-insurer-exits-and-entrants-in-2017-affordable-care-act-marketplaces/ (accessed 10 Mar. 2017).

Cox, Cynthia, Ashley Semanskee, Gary Claxton, and Larry Levitt. 2016. Explaining health care

reform: risk adjustment, reinsurance, and risk corridors." Kaiser Family Foundation. http://kff.org/health-reform/issue-brief/explaining-health-care-reform-risk-adjustment-reinsurance-and-risk-corridors/ (accessed 05 Mar. 2017).

Dafny, Leemore S., Mark Duggan, and Subramaniam Ramanarayanan. 2012. Paying a premium

on your premium? Consolidation in the U.S. health insurance industry. American Economic Review 102(2): 1161-85.

Dafny, Leemore S., Jonathan Gruber, and Christopher Ody. 2015. More insurers, lower

premiums: evidence from initial pricing in the health insurance marketplaces. American Journal of Health Economics 1(1): 53-81.

Demko, Paul. 2017. Trump White House abruptly halts Obamacare ads. Politico.

http://www.politico.com/story/2017/01/trump-white-house-obamacare-ads-234245 (accessed 24 Apr. 2017).

Edmunds, Margaret. 2013. Geographic Adjustment in Medicare Payment. National Center for

Biotechnology Information. https://www.ncbi.nlm.nih.gov/books/NBK190070/ (accessed 17 Mar. 2017).

Effects of eliminating the individual mandate to obtain health insurance. 2012. Congressional

Budget Office. https://www.cbo.gov/publication/43117 (accessed 05 Mar. 2017).

67

Explaining health care reform: questions about health insurance subsidies. 2016. Kaiser Family

Foundation. http://kff.org/health-reform/issue-brief/explaining-health-care-reform-questions-about-health/ (accessed 05 Mar. 2017).

Farley, Robert. 2013. ACA doesn't 'set prices' on exchanges. FactCheck.org.

http://www.factcheck.org/2013/12/aca-doesnt-set-prices-on-exchanges/ (accessed 05 Mar. 2017).

Federal Register, Vol. 79, No. 7 (January 10, 2014), p. 1961, http://www.gpo.gov/fdsys/pkg/FR-

2014-01-10/pdf/2013-31497.pdf (accessed 21 Apr. 2017). Federal Register, Vol. 81, No. 45. (March 8, 2016). P. 12216. Patient Protection and Affordable

Care Act; HHS notice of benefit and payment parameters for 2017. https://www.gpo.gov/fdsys/pkg/FR-2016-03-08/pdf/2016-04439.pdf#page=14 (accessed 23 Apr. 2017).

Feldman, Richard, Douglas Wholey, and Jon Christianson. 1996. Effect of mergers on health

maintenance organization premiums. Health Care Financing Review 17(3): 171-189. FFM QHP landscape files: health and dental datasets for researchers and issuers.

https://www.healthcare.gov/health-and-dental-plan-datasets-for-researchers-and-issuers/

Gabel, Jon R., Heidi Whitmore, Adrienne Call, Matthew Green, Rebecca Oran, and Sam

Stromberg. 2017. Modest changes in 2016 health insurance marketplace premiums and insurer participation. The Commonwealth Fund. http://www.commonwealthfund.org/publications/blog/2016/jan/2016-health-insurance-marketplace-premiums (accessed 18 Mar. 2017).

Gabel, Jon R., Heidi Whitmore, Matthew Green, Sam T. Stromberg, and Daniel S. Weinstein.

2015. In second year of marketplaces, new entrants, ACA ‘co-ops,’ and Medicaid plans restrain average premium growth rates. Health Affairs 34(12): 2020-2026.

Garthwaite, Craig, and John A. Graves. 2017. Success and failure in the individual insurance

exchanges. The New England Journal of Medicine 376: 907-910. Gillin, Joshua. 2017. Fact-checking Trump's claim about soaring Obamacare premiums. Politfact.

http://www.politifact.com/truth-o-meter/statements/2017/feb/28/donald-trump/fact-checking-donald-trumps-claim-about-soaring-ob/ (accessed 10 Mar. 2017).

Giovannelli, Justin, and Kevin Lucia. 2015. The experiences of state-run marketplaces that use

HealthCare.gov. The Commonwealth Fund.

68

http://www.commonwealthfund.org/publications/issue-briefs/2015/sep/state-run-marketplaces-use-healthcaregov (accessed 17 Mar. 2017).

Goodnough, Abby. 2013. The challenge of helping the uninsured find coverage. The New York

Times. http://www.nytimes.com/2013/08/15/us/politics/the-challenge-of-helping-the-uninsured-find-coverage.html?hpw (accessed 25 Apr. 2017).

Gottlieb, Scott. 2016. How to increase competition under the Affordable Care Act. American

Enterprise Institute. http://www.aei.org/publication/how-to-increase-competition-under-the-affordable-care-act/ (accessed 24 Apr. 2017).

Gruber, Jonathan. 2011. The impacts of the Affordable Care Act: how reasonable are the

projections? National Tax Journal 64(3): 893-908. Guardado, Jose R., David W. Emmons, and Carol K. Kane. 2013. The price effects of a large

merger of health insurers: a case study of UnitedHealth-Sierra. Health Management, Policy and Innovation 1(3): 16-35.

Haislmaier, Edmund. 2016. Insurer ACA exchange participation declines in 2016. The Heritage

Foundation. http://www.heritage.org/health-care-reform/report/insurer-aca-exchange-participation-declines-2016 (accessed 10 Mar. 2017).

Haislmaier, Edmund, and Alyene Senger. 2017. The 2017 health insurance exchanges: major

decrease in competition and choice." Heritage Foundation http://www.heritage.org/health-care-reform/report/the-2017-health-insurance-exchanges-major-decrease-competition-and-choice (accessed 18 Mar. 2017).

Health insurance exchanges and state decisions. 2013. Health Affairs http://healthaffairs.org/healthpolicybriefs/brief_pdfs/healthpolicybrief_96.pdf (accessed 22 Apr. 2017).

Health insurance marketplace 2015 open enrollment period: March enrollment report. 2015.

HHS. ASPE. https://aspe.hhs.gov/pdf-report/health-insurance-marketplace-2015-open-enrollment-period-march-enrollment-report (accessed 18 Mar. 2017).

Health insurance marketplace: summary enrollment report for the initial annual open

enrollment period. 2014. HHS. ASPE. https://aspe.hhs.gov/pdf-report/health-insurance-marketplace-summary-enrollment-report-initial-annual-open-enrollment-period (accessed 18 Mar. 2017).

Health insurance marketplace premiums for 2014. 2013. Department of Health and Human

Services. Office of the Assistant Secretary for Planning and Evaluation. https://aspe.hhs.gov/pdf-report/health-insurance-marketplace-premiums-2014. (accessed 05 Mar 2017).

69

Health insurance marketplaces 2016 open enrollment period: final enrollment report. 2016.

HHS. ASPE. https://aspe.hhs.gov/health-insurance-marketplaces-2016-open-enrollment-period-final-enrollment-report (accessed 18 Mar. 2017)

Health plan choice and premiums in the 2017 health insurance marketplace. 2017. HHS. ASPE. https://aspe.hhs.gov/health-plan-choice-and-premiums-2017-health-insurance-marketplace (accessed 18 Mar. 2017)

Hidden health tax: Americans pay a premium. 2009. Families USA.

http://familiesusa.org/sites/default/files/product_documents/hidden-health-tax.pdf (accessed 05 Mar. 2017).

Historical Tables. Office of Management and Budget.

https://obamawhitehouse.archives.gov/omb/budget/Historicals (accessed 18 Mar. 2017).

Holahan, John, and Linda J. Blumberg. 2009. Can a public insurance plan increase competition

and lower the costs of health reform? The Urban Institute. http://www.urban.org/research/publication/can-public-insurance-plan-increase-competition-and-lower-costs-health-reform (accessed 23 Apr. 2017).

Holahan, John, Rebecca Peters, Kevin Lucia, and Christine Monahan. 2013. Cross- cutting issues:

insurer participation and competition in health insurance exchanges: early indications from selected states. Urban Institute. http://www.rwjf.org/content/dam/farm/reports/issue_briefs/2013/rwjf406939 (accessed 05 Mar. 2017).

Holahan, John, Erik Wengle, Linda J. Blumberg, and Patricia Solleveld. 2017. What explains the

21 percent increase in 2017 marketplace premiums, and why do increases vary across the country? Urban Institute. http://www.urban.org/sites/default/files/publication/87021/2001052-what-explains-the-21-percent-increase-in-2017-marketplace-premiums-and-why-do-increases-vary-across-the-country_1.pdf (accessed 05 Mar. 2017).

Houchens, Paul R., Jason A. Clarkson, and Michael G. Sturm. 2013. 2014 federal insurance

exchange: evaluation of insurer participation and consumer choice. Milliman. http://us.milliman.com/insight/2013/2014-federal-insurance-exchange-Evaluation-of-insurer-participation-and-consumer-choice/ (accessed 05 Mar. 2017).

House, Senate view health exchanges differently. 2010. NPR.

http://www.npr.org/templates/story/story.php?storyId=122476051 (accessed 24 Apr. 2017)

70

How accessible is individual health insurance for consumers in less-than-perfect health? 2001. Kaiser Family Foundation. http://kff.org/health-costs/report/how-accessible-is-individual-health-insurance-for-2/ (accessed 22 Apr. 2017).

Individual insurance market competition. 2014. Kaiser Family Foundation.

http://kff.org/other/state-indicator/individual-insurance-market-competition (accessed 21 Apr. 2017

Insurance coverage provisions of the Affordable Care Act- CBO’s March 2015 baseline. 2015.

Congressional Budget Office. https://www.cbo.gov/sites/default/files/recurringdata/51298-2015-03-aca.pdf (accessed 10 Mar. 2017).

Jacobs, Paul D., Jessica S. Banthin, and Samuel Trachtman. 2015. Insurer competition in

federally run marketplaces is associated with lower premiums. Health Affairs 34(12): 2027-2035.

Johnson, Carolyn Y. 2017. Aetna chief executive says Obamacare Is in a ‘death spiral’." The

Washington Post. https://www.washingtonpost.com/news/wonk/wp/2017/02/15/aetna-ceo-says-obamacare-is-in-a-death-spiral/ (accessed 10 Mar. 2017).

Major considerations for repealing and replacing the ACA. 2017. The Committee for a

Responsible Federal Budget and the Kaiser Family Foundation. Joint panel discussion. Mangan, Dan. 2014. More insurers to join Obamacare in 2015. CNBC.

http://www.cnbc.com/2014/09/23/number-of-obamacare-insurers-to-rise-by-25-in-2015.html (accessed 08 Mar. 2017).

Mendelson, Dan. 2016. Experts predict sharp decline in competition across the ACA exchange.

Avalere. http://avalere.com/expertise/life-sciences/insights/experts-predict-sharp-decline-in-competition-across-the-aca-exchanges (accessed 24 Apr. 2017).

Monheit, Alan C., Joel C. Cantor, Margaret Koller, and Kimberly S. Fox. 2004. Community rating

and sustainable individual health insurance markets in New Jersey. Health Affairs 23(4): 167-175.

Morrisey, Michael. 2017. What worked and what didn’t in Obamacare insurance markets? The

Brookings Institution. Lecture. Norris, Louise. 2017. Arkansas health insurance marketplace: history and news of the state’s

exchange. Healthinsurance.org. https://www.healthinsurance.org/arkansas-state-health-insurance-exchange/ (accessed 22 Apr. 2017).

71

Norris, Louise. 2017. Oklahoma health insurance marketplace: history and news of the state’s exchange. Healthinsurance.org. https://www.healthinsurance.org/oklahoma-state-health-insurance-exchange/ (accessed 22 Apr. 2017).

Obama, Barack. 2016. United States Health Care Reform: Progress to Date and Next

Steps. Journal of the American Medical Association. 316(5):525-532. O'Donnell, Jayne, Ken Alltucker, Laura Ungar, and Tony Leys. 2016. As Obamacare choices

dwindle, feds face consumer, political backlash. USA Today. https://www.usatoday.com/story/news/politics/2016/08/29/obamacare-choices-dwindle-feds-face-consumer-political-backlash/89403698/

(accessed 12 Mar. 2017). Patient Protection and Affordable Care Act (ACA). 2010. Pub. L. No. 111-148, 124 Stat. 119. Pear, Robert. 2016. Health law tax penalty? I'll take it, millions say. The New York Times.

https://www.nytimes.com/2016/10/27/us/obamacare-affordable-care-act-tax-penalties.html?_r=0 (accessed 10 Mar. 2017).

Polsky, Daniel, Janet Weiner, Christopher Colameco, and Nora Becker. 2016. Health insurance

marketplace enrollment rates by type of exchange. Leonard Davis Institute of Health Economics. University of Pennsylvania. http://ldi.upenn.edu/sites/default/files/rte/state-narrow-networks.pdf (accessed 17 Mar. 2017).

Phillips, Amber. 2016. Kentucky, once an Obamacare exchange success story, now moves to

shut It down. The Washington Post. https://www.washingtonpost.com/news/the-fix/wp/2016/01/14/a-republican-governors-move-to-shutter-kentuckys-obamacare-exchange-explained/?utm_term=.4683e4fc9909 (accessed 17 Mar. 2017).

Pollitz, Karen. 2017. High-risk pools for uninsurable individuals. Kaiser Family Foundation.

http://kff.org/health-reform/issue-brief/high-risk-pools-for-uninsurable-individuals/ (accessed 22 Apr. 2017).

Risk corridors payment proration rate for 2014. 2015. Centers for Medicare & Medicaid Services. Center for Information & Insurer Oversight. https://www.cms.gov/CCIIO/Programs-and-Initiatives/Premium-Stabilization-Programs/Downloads/RiskCorridorsPaymentProrationRatefor2014.pdf (accessed 10 Mar. 2017).

Remarks by the President on the Affordable Care Act and the government shutdown. 2013.

https://obamawhitehouse.archives.gov/the-press-office/2013/10/01/remarks-president-affordable-care-act-and-government-shutdown (accessed 22 Apr. 2017).

72

Remarks by President Trump in joint address to Congress. 2017. https://www.whitehouse.gov/the-press-office/2017/02/28/remarks-president-trump-joint-address-congress (accessed 22 Apr. 2017)

Rural and Urban Health. 2003. Georgetown University Health Policy Institute.

https://hpi.georgetown.edu/agingsociety/pubhtml/rural/rural.html (accessed 18 Apr. 2017).

Sasse issues report on competition on ACA's 2016 exchanges. 2016. Office of Senator Ben Sasse. https://www.sasse.senate.gov/public/index.cfm/2016/3/sasse-issues-report-on-competition-on-aca-s-2016-exchanges (accessed 10 Mar. 2017).

Sen, Aditi P., and Thomas DeLeire. 2016. The effect of Medicaid expansion on marketplace

premiums. ASPE research briefhttps://aspe.hhs.gov/system/files/pdf/206761/McaidExpMktplPrem.pdf (accessed 18 Apr. 2017).

Sheffler, Richard M., Daniel R. Arnold, Brent D. Fulton, and Sherry A. Giled. 2016. Differing

impacts of market concentration on Affordable Care Act marketplace premiums. Health Affairs 35(5): 880-888.

Sheingold, Steven, Nguyen Nguyen, and Andre Chappel. 2015. Competition and choice in the

health insurance marketplaces, 2014-2015: impact on premiums. ASPE research brief, https://aspe.hhs.gov/basic-report/competition-and-choice-health-insurance-

marketplaces-2014-2015-impact-premiums (accessed 08 Mar. 2017) Sommers, Benjamin D. 2014. Insurance cancellations in context: stability of coverage in the

nongroup market prior to health reform. Health Affairs 33(5): 887-894. Stock, James H., and Mark W. Watson. 2007. Introduction to Econometrics. Boston:

Pearson/Addison Wesley. Print. 357-373. Sullivan, Peter. 2016. Insurers warn losses from ObamaCare are unsustainable. The Hill.

http://thehill.com/policy/healthcare/276366-insurers-warn-losses-from-obamacare-are-unsustainable (accessed 10 Mar. 2017).

Tebaldi, Pietro. 2016. Estimating equilibrium in health insurance exchanges: price competition

and subsidy design under the ACA. SSRN Electronic Journal. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2688894 (accessed 17 Mar. 2017).

The ACA individual market: 2016 will be better than 2015, but achieving target profitability will

take longer. 2016. Standard & Poor's Global Credit Portal. https://morningconsult.com/wp-content/uploads/2016/12/12-22-16-The-ACA-

73

Individual-Market-2016-Will-Be-Better-Than-2015-But-Achieving-Target-Profitability-Will-Take-Longer.pdf (accessed 10 Mar. 2017).

The budget and economic outlook: 2016 to 2026. 2016. Congressional Budget Office.

https://www.cbo.gov/publication/51129 (accessed 10 Mar. 2017). The concentration of health care spending. 2012. NIHCM Foundation data brief.

https://www.nihcm.org/pdf/DataBrief3%20Final.pdf (accessed 17 Mar. 2017). Trish, Erin E., and Bradley J. Herring. 2015. How do health insurer market concentration and

bargaining power with hospitals affect insurance premiums? Journal of Health Economics 42 (July): 104-14.

Wage Index. 2016. Centers for Medicare & Medicaid Services.

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/wageindex.html (accessed 17 Mar. 2017).

What the actuarial values in the Affordable Care Act mean. 2011. Kaiser Family Foundation.

http://kff.org/health-reform/issue-brief/what-the-actuarial-values-in-the-affordable/ (accessed 06 Mar. 2017).

Wholey, Douglas, Roger Feldman, and Jon Christianson. 1995. The effect of market structure on

HMO premiums. American Journal of Health Economics 14(1): 81-105. Wickizer, Thomas M., and Paul J. Feldstein. 1995. The impact of HMO competition on private

health insurance premiums, 1985-1992. Inquiry 32(3): 241-251.