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Regulating the Medical Loss Ratio: Implications for the Individual Market
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Regulating the Medical Loss Ratio: Implications for the Individual Market

Jean M. Abraham, PhD; and Pinar Karaca-Mandic, PhD
This study investigates the potential impact of new medical loss ratio regulation on the individual market for health insurance in the United States.
Table 3 summarizes the 2009 data on the number of insurers and corresponding enrollment population potentially affected by the new minimum MLR requirement. Column 1 shows the number of health insurers with active operations by state. Columns 2 and 3 identify those insurers with MLRs under the 80% minimum (based on the historical MLR definition) and their corresponding enrollment. Columns 4 and 5 provide analogous estimates, but use the PPACA-adjusted MLR definition described above. Columns 6 and 7 provide upper-bound and lower-bound estimates of market enrollment potentially vulnerable to coverage disruption for medical reasons in the worst case that insurers who fail to meet the new MLR regulation choose to exit the market.

In Table 3, column 2, we note that 146 of the 371 company-state observations were below the 80% minimum MLR requirement. In 21 states, at least 50% of health insurers would not meet the minimum threshold. Allowing for the 5 percentage point upward adjustment to the MLR, the number of insurers who would not meet the minimum threshold declined to 106; only 9 of the 21 states (Arkansas, Illinois, Louisiana, Nebraska, New Hampshire, Oklahoma, Rhode Island, Wyoming, and West Virginia) would continue to have at least one-half of their health insurers below the threshold.

In 2009, health insurers filing with the NAIC reportedalmost 6.7 million member-years of enrollment (see Table 1, column 6). Approximately 3.3 million member-years were associated with insurers that had MLRs that fell under the 80% threshold, based on the historical MLR definition. Using the PPACA-adjusted MLR definition reduced the aggregate enrollment impact to 2.18 million member-years. Twelve states had at least 50% of their enrollment affiliated with health insurers under the 80% threshold (Arkansas, Arizona, Florida, Illinois, Indiana, New Hampshire, Nevada, South Carolina, Tennessee, Texas, Virginia, and West Virginia). Together, insurers within these states had 1.87 million member-years (28% of total reported US enrollment) concentrated among health insurers with MLRs below the minimum threshold, even after allowing for the 5 percentage point upward adjustment to the historical MLR definition.

Insurers with MLRs that fall below the minimum requirement may respond in a variety of ways, including cutting administrative expenses related to marketing or distribution, lowering premiums, using less aggressive medical management, dropping product lines that may contribute to lower MLRs, or exiting the market. (High-deductible health plans generally have much lower MLRs than traditional health maintenance organizations [HMOs] or preferred provider organizations [PPOs]. The interim final rule includes an additional adjustment in the calculation of the MLR for insurers that sell plans with high deductibles.) Should insurers pursue either product line elimination or market exit, consumers could experience coverage disruptions. For enrollees with recently diagnosed, serious medical conditions or high claims experience, these types of responses could make them particularly vulnerable. This is particularly true for states that permit medical underwriting. Of course, in 2014, guaranteed issue provisions will go into effect for plans offered within Exchanges, thereby mitigating some of these potentially adverse consequences.

Columns 6 and 7 in Table 3 provide an estimated range of enrollees vulnerable to coverage disruption because of health status, calculated using our results for enrollment among insurers that have MLRs below 80% and our estimated proportion of the individual market population considered “medically uninsurable” (.048). We estimated the range to be 104,624 to 158,736 member-years. Removing from consideration enrollment associated with states already having guaranteed issue reduced the upper bound by only 3041 member-years (fewer than 2%). (These estimates assumed a constant proportion of total enrollment with high claims across states. In the MEPS, we found little difference in the fraction of enrollees in excess of $9437 by census region [Northeast, Midwest, South, and West].) In absolute terms, states with the largest levels of vulnerable enrollment include Arizona, Florida, South Carolina, Texas, and Virginia—states that tend to have more limited consumer protections with respect to purchasing coverage within the individual market.10


The introduction of a minimum MLR regulation as part of the PPACA has the potential to significantly affect the functioning of the individual market. Our results suggest that in 9 states, at least 50% of actively operating health insurers would likely fail to meet the 80% minimum MLR even after allowing for a 5 percentage point upward adjustment to account for changes in the way the measure will be calculated relative to the historical definition. Furthermore, we observed that in 12 states, at least one-half of total member-years of enrollment were affiliated with health insurers failing to meet the minimum MLR using the PPACA-adjusted definition.

The implications of MLR regulation remain uncertain, since one cannot accurately predict insurers’ strategic responses. To the extent that insurers with low MLRs opt for terminating product lines or exiting the market completely, consumers could experience some coverage disruption. Individual market enrollees who are medically uninsurable due to a recent diagnosis and/or high claims experience may be particularly vulnerable, with the largest numbers in Arizona, Florida, South Carolina, Texas, and Virginia. Of course, the effects of the MLR regulation on enrollees’ experiences in the short run could vary extensively given differences across states in individual market regulations pertaining to guaranteed issue,  designated insurers of last resort, and premium rating, as well as individuals’ access to state-based high-risk pools. In the longer run, it will be important to investigate how MLR regulation interacts with the creation of state-based Exchanges, the latter of which are hypothesized to lower insurers’ administrative costs.

Two study limitations are worth noting. First, while the NAIC data are the most comprehensive available, insurers in California are not represented. In addition, coverage sold by other types of insurers, notably life insurers, are not included since they do not file the information necessary for estimating comparable MLRs at the state level. In eAppendix C, we present an indirect method for identifying the presence of these insurers using alternative NAIC filing data sources. Results from these supplemental analyses suggest that in certain states, these insurers may have a large market presence. As a result, Tables 1 and 2 underestimate the number of competing insurers and enrollment in each state. Moreover, the absence of data for these firms could lead to underestimation or overestimation of the impact of MLR regulation, depending on whether these firms have ratios that are above or below the 80% threshold.

Second, the NAIC data did not permit a precise accounting of the number of unique individuals in each state that are enrolled by health insurers operating with MLRs below the 80% threshold. Rather, we observed only total member years of coverage.

Policy Implications

For state regulators, it will be important to monitor insurers’ activities as they respond to the MLR regulation, particularly since compliance can be achieved in a number of ways, including reductions in administrative costs, profits, or net income; increases to claims costs; or premium reductions. Each has different implications for consumers.

As our analysis reveals, the individual market for health insurance in some states is already highly concentrated, with only a few active health insurers. For these states in particular, exit or threat of exit could be very disruptive to individuals in the short run. In such cases, state insurance commissioners could consider seeking transitional relief from the US Department of Health and Human Services, given the likelihood of market stabilization.

For federal policy makers, our analysis suggests that the potential impact of this new regulation on the individual market for health insurance could be quite large and that it will vary dramatically across states. Moreover, our analysis illustrates the significant challenges facing policy analysts and researchers in evaluating how insurance markets will be affected, given the introduction of this and other new PPACA regulations. The most notable challenge is not having a consistent, comprehensive source of data across all types of insurers writing health insurance across all US states.

Going forward, it will be important for additional investments to be made in the collection of information that can enable monitoring of both the individual and group markets with validated outcome measures. Finally, additional research is needed to better understand variation in insurers’ MLRs; the determinants of MLRs, including the role of population characteristics and provider market competition; and the impact that this new federal regulation will have on insurance market stability and long-run functioning.

We thank the Changes in Health Care Financing and Organization, an initiative of the Robert Wood Johnson Foundation, for financial support. We also thank David Vacca, CPA, Todd Sells, MBA, Dan Daveline, CPA, and Brian Webb, MPA, from the National Association of Insurance Commissioners for providing technical assistance regarding the data. Finally, we thank Jon Christianson, PhD, Roger Feldman, PhD, John Nyman, PhD, Amitabh Chandra, PhD, and Alshadye Yemane, MPP, for helpful advice.


Author Affiliations: From Division of Health Policy and Management (JMA, PK-M), School of Public Health, University of Minnesota, Minneapolis, MN.

Funding Source: Changes in Health Care Financing and Organization, Robert Wood Johnson Foundation.


Author Disclosures: The authors (JMA, PK-M) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.


Authorship Information: Concept and design (JMA, PK-M); acquisition of data (JMA, PK-M); analysis and interpretation of data (JMA, PK-M); drafting of the manuscript (JMA, PK-M); critical revision of the manuscript for important intellectual content (JMA, PK-M); statistical analysis (JMA, PKM); and obtaining funding (JMA, PK-M).


Address correspondence to: Jean M. Abraham, PhD, Division of Health Policy and Management, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 729, Minneapolis, MN 55455. E-mail:

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