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  1. Table of Contents
  2. Increasing Health Insurance Costs and the Decline in Insurance Coverage
  3. Pick the Best Health Care Plan for You

In the study of states' health insurance plans, some plans stood out as offering higher value while charging lower premiums -- the lowest is less than half of the cost of the most expensive health plan identified in this study. Click through to see the 10 states where health insurance providers give customers better deals.

When to Use Your Emergency Fund. Idaho is one of the worst states for accessible and affordable healthcare, with 22 percent of people experiencing high out-of-pocket costs relative to income and rates of uninsured adults remaining high near 20 percent, according to data from the Commonwealth Fund. Because the state has opted not to expand Medicaid through federal funding, the state also has 78, residents who have incomes too high to qualify for subsidies or Medicaid but too low to afford monthly premiums, reported the Associated Press. The good news for Idaho residents is that their health insurance costs are already relatively low.

Flat fees for visits to emergency rooms and primary doctors also keep costs low. Several new healthcare laws went into effect Jan. The provisions require health insurance providers serving Oregon residents to cover a full year of birth control, 90 days' worth of most prescription medicines, and medical services provided via videoconference, reported Portland Business Journal. Despite the expansions to health insurance coverage for Oregonians in , the costs are still some of the lowest in the nation. Despite having one of the nations' highest costs of living, Hawaii's health insurance costs are some of the most reasonable.

Along with one of the better low-cost silver plans, Hawaii also has one of the best health systems in the nation, ranked as the third best in the nation by the CommonWealth Fund's study. It also charges flat copays for emergency care and visits to primary physicians, which help to limit plan holders' costs when they need to use their insurance. The District of Columbia has some of the best health insurance costs. These low costs are offset somewhat by Michigan's 0. Even with this tax, however, health insurance is relatively affordable in Michigan, and , state residents have signed up for health plans through the ACA exchange since Nov.

Competition between Pennsylvania's 19 health insurers selling individual plans on the ACA exchange has led to better prices and plans for residents. The state's lowest-cost silver plan from Independence Blue Cross, for example, has one of the lowest deductibles in this study: This deductible, paired with flat copays that make it easier to predict and control costs, are the top factors that put Pennsylvania at No. Health insurance plans offered on the healthcare exchange in Texas are some of the best in the nation.

Flat fees on health services also reduce the overall cost. Despite its low healthcare costs, Texas still has some catching up to do. Brown hopes to use to expand programs and services such as those for Californians with developmental disabilities. For Utah residents, low-cost health insurance is within reach even though the state has not adopted the federal Medicaid expansion.

Utah residents on this plan can also look forward to more affordable out-of-pocket costs. Despite low costs, many New Mexico residents have encountered issues getting their health coverage. Due to a high volume of health insurance applications, many applications submitted in December remained unprocessed even past the date when insurance coverage should have kicked in, reported the Santa Fe New Mexican.

Enrollees whose applications weren't processed quickly enough experienced problems such as being unable to visit a doctor or receive care using their new policy. Here is the full ranking of the 50 states and the District of Columbia from best to worst, according to their health insurance costs for the silver plan with the lowest monthly premium in each state. This study compared silver plans with the lowest monthly premiums offered through the national or state-level insurance exchanges administered through the Affordable Care Act.

Silver plans were used because these are the most popular plans, accounting for two-thirds of plans purchased through exchanges, according to data from the Department of Health and Human Services. Costs were estimated based on the following assumptions: Where the enrollee qualified for a tax credit, the costs cited in the study reflect the estimated deduction that would apply.

An alternative explanation is that coverage has dropped because the cost of insurance has risen. In contrast to substantial media coverage linking rising premiums to declining coverage rates, empirical evidence quantifying the relationship between premiums and coverage is limited. The studies that use multivariate techniques to examine the relationship between health care costs and coverage rates find support for the view that increasing costs decrease coverage Fronstin and Snider ; Kronick and Gilmer ; Cutler ; Glied and Jack Kronick and Gilmer rely on national measures of health care costs, relative to income, and generate most of the variance in the cost to income ratio from variation in income, not health care costs.

Fronstin and Snider analyze state-level data from to and include only one cost proxy, the price of a hospital day.

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  • Health Insurance Premiums!

Cutler uses national-level data on employee contributions. Glied and Jack use state-level Medicare per capita spending excluding home health, adjusted by the ratio of private spending per enrollee to Medicare spending per enrollee. Thus these studies do not directly measure the effects of rising premiums on coverage, nor do they attempt to adjust for potential reverse causality that arises because declining coverage may lead to higher premiums.

Further, existing studies typically focus on employer-sponsored coverage, which, although important, does not give a full picture of the effects of rising premiums on coverage because some individuals may substitute public for private coverage. Finally, these studies typically do not devote substantial attention to controlling for potential confounding explanations for the decline in coverage such as the expansion in Medicaid or changing tax policy. This paper explores the relationship between health care premiums and coverage rates. It takes advantage of wide geographic variation in changes in premiums and coverage rates.

Thus the variation in premiums that we use is broader than that used in existing literature and less likely to be confounded with other secular trends. In contrast to existing work, we also use instrumental variable IV techniques to address the potential for reverse causality between rising costs and coverage rates. The IV techniques also adjust for potential measurement error in our premium data.

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  8. We focus on coverage from any source, which gives a more complete picture of coverage because some individuals may switch from private to public coverage. We also focus only on the ultimate coverage decision, without attempting to explain the detailed set of decisions such as employer offer or employee take-up, which lead to coverage.

    Finally, we control for a wide range of factors associated with alternative explanations of coverage declines. We thus quantify the link between rising health insurance premiums and rates of insurance coverage, addressing limitations of the existing literature. The price of insurance can be measured in several ways. Economics textbooks define the price of health insurance as the loading fee , or the difference between the premium and expected payout Phelps ; Feldstein An alternate approach uses premiums or costs to measure price.

    In contrast to a price measure based on the load, the use of premiums as a measure of price captures the effects of rising medical expenditures on coverage rates. Interpreting rising premiums as an increase in the potential loss is reasonable because most research examining the causes for rising expenditures attributes spending increases to advances in medical technology. Therefore rising premiums may reflect services individuals value Newhouse ; Cutler ; Chernew et al. This analysis would lead one to expect coverage rates would increase as medical expenditures rise.

    For certain medical services this has certainly been true. For example, coverage rates for pharmaceuticals have risen as pharmaceutical expenditures have risen.

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    Why might rising premiums be associated with falling coverage? One possibility is that premiums reflect not only desired medical expenditures, but also moral hazard Pauly Although on average individuals may desire new medical technology, at the margin the premiums may reflect growing moral hazard. The magnitude of moral hazard is somewhat controversial Nyman , and very little work examines changes in moral hazard over time. Note that changing moral hazard does not require changing insurance contracts but may instead arise simply because of new technology even if insurance contracts stay constant.

    A growing body of work reports that many of the new medical advances offer substantial clinical benefit, on average, but notes that value is not uniform across all clinical areas Cutler and McClellan This leaves the potential for substantial moral hazard at the margin. Moreover, the s have been characterized by growing attempts to constrain health care costs, suggesting that moral hazard may be a growing or just a more recognizable concern.

    In fact, work by Fisher and colleagues suggests that satisfaction is not greater in areas that use medical resources more intensively Fisher et al. If technological progress brings with it more moral hazard, one would expect more individuals to decline options for coverage. Another possibility is that the relative value of health insurance compared with the medical care one would receive if uninsured is changing over time. Technological progress is widely considered to be responsible for driving up premiums Newhouse ; Cutler ; Chernew et al.

    The new technologies may also be incorporated to an extent into care provided to the uninsured, particularly for acute services. For some individuals, the value of additional services provided by health insurance coverage above the amount of care available if uninsured may not be worth the cost of the insurance package. A third possibility is that rising premiums increase the incentives for low risk individuals to separate from high risk individuals in the risk pool.

    If this is the case, adverse selection in the insurance market may increase over time and the market may have a tendency to unravel as costs rise. Thus rising premiums would lead to declining coverage rates. We examine changes in insurance coverage from — to — Because essentially all of the elderly have coverage through Medicare, we consider only the nonelderly population. We divide people into health insurance units HIUs reflecting coverage under typical health insurance policies Cutler and Gruber a.

    The initial sample size is , people. From these data, we keep people in the 64 large metropolitan statistical areas MSAs for which we have matching data on health insurance premiums. The resulting sample size is , Our primary data source for insurance coverage and individual level demographic variables is the Current Population Survey CPS. The CPS asks about insurance coverage in the previous year.

    To increase sample size, we pool data from the — samples for the early time period and from the — samples for the later time period. These surveys obtain information about the type of policies offered and their premiums. We pool surveys from and for the early years and and for the later years. We include metropolitan statistical areas in our sample if there are at least 10 premium observations in both the early and late time periods, to minimize sampling error. This yields a total of 64 metropolitan statistical areas. There are 2, premium observations in the early years and 4, observations in the later years.

    Premiums are for an individual policy offered in a group setting. To account for the differing nature of health insurance coverage, we adjust premiums using a regression model relating plan premium to type of plan HMO, PPO, POS plan, indemnity plan and several plan benefit characteristics.

    Specifically, we regressed the premiums on: The coefficients from this regression were generally reasonable, indicating that more generous benefits implied greater premiums, and the R 2 from this regression was 0. State spending is calculated from CMS state health accounts data and excludes Medicare spending. Our premium measure may be a more accurate reflection of costs for the nonelderly than Medicare spending because it is less affected by changing costs for services such as home care. It is likely a better measure than state health care spending because insurance markets are likely smaller than the state.

    State spending blends cost trends across multiple urban and rural areas. Undoubtedly, measurement error in the premium measure remains. If the associated bias is large, our IV models, discussed below, will provide a better measure of the relationship between rising premiums and coverage.

    In our models we include two sets of controls taken from the CPS. Demographic controls are intended to absorb some of the unobserved factors that could confound the analysis. The second set of explanatory variables is designed to control for competing explanations for declining coverage that have been proposed in the literature.

    We have tried to adopt the measurement approaches used in all the other work to better control for competing explanations.

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    We include the following demographic variables for each individual and the head of their HIU: We also include indicators of whether there are no workers or more than one worker in the HIU; interactions of being a spouse or a child in a family with multiple workers; binary variables for the income decile the HIU falls into, calculated separately for singles and married people, and interactions of income decile and marital status of the HIU head.

    We include interaction terms between these variables and a binary variable capturing observations in the later period to allow for the possibility that their effect changes over time. Several metropolitan area-level demographic factors are included based on CPS data.

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    These capture market-level effects and competing explanations for the decline in coverage. The MSA-level covariates include the share of the population that is foreign born, the share of the population in the metropolitan area that is nonwhite, the share that is elderly, average HIU income, and the share of women that are working. Unless otherwise indicated, we do not interact the MSA-level or policy covariates with a time period dummy. Two of the important potential explanations for declining coverage that have been explored in the literature are rising Medicaid eligibility and falling tax subsidies.

    We control for these explanations using the approaches followed by studies focusing on these explanations. Specifically, we generate measures of the generosity of Medicaid coverage of children following the approach of Cutler and Gruber a , using information from the Intergovernmental Health Policy Project ; ; and the National Governors' Association ; They measure Medicaid eligibility by the fraction of HIU health spending eligible for Medicaid, based on family composition, which captures the role of Medicaid eligibility in the context of family health insurance decisions.

    This is calculated by applying state regulations to CPS data to assess generosity at the state level and adding controls for the fraction of family health spending attributable to each child age. To control for changing taxes, we follow the methods of Gruber Specifically, we match average tax rates to individuals based on their state of residence, income decile, marital status, and year Gruber Gruber suggests that this IVs measure of taxes is preferable to calculating tax rates at the individual level to avoid endogeneity associated with the relationship between coverage, income, and taxes.

    We measure these variables only in to minimize reverse causality issues that may arise because these measures could be affected by insurance, rather than the reverse. We include this variable as an interaction with time period, to examine whether the effect of charity care availability changes over time. State regulations in the s established many restrictions on insurance pricing that could affect coverage Simon We indicate with a dummy variable whether the state has passed rating reforms, which limits the variability in prices across groups, or enacted guaranteed issue, which requires insurers to sell at least some policies to all groups, for the small-group insurance market.

    In our base specification we estimate probit regression models for the probability that an individual has health insurance coverage from any source. The regressions are weighted using the Current Population Survey sampling weights to reflect the national sample.

    Our predictions about the impact of insurance premiums and other factors on coverage are obtained from these probit regression estimates. We control for general time trends and time invariant area traits using a dummy variable equal to 1 for the late sample, and dummy variables for each metropolitan area. To project forward, we approximate the effect of rising premiums over GDP growth by inflating premiums by 1, 2, and 3 percent average annual growth for 10 years, holding income and all other variables constant.

    The nature of the insurance questions in the Current Population Survey changed somewhat over time Swartz ; Fronstin ; Mills Time trends in insurance coverage are thus subject to some uncertainty. These changes in question wording should not affect our analysis, however, because we control for national trends and rely on differential changes across areas to identify the effects of rising premiums and other covariates.

    Increasing Health Insurance Costs and the Decline in Insurance Coverage

    We were concerned about two potential sources of bias in our probit estimates. First, our premium data is measured with noise, both because of the small sample in the survey of employers and because of imprecision in our ability to adjust for all benefit traits. This measurement error will lead to an underestimate of the effects of premiums on coverage. Second, there is the potential for reverse causality because of selection, if providers in markets with more uninsured individuals raise prices or shift costs to private insurers.

    This bias would overstate the effects of premiums on coverage. To address these dual concerns we estimated linear probability models analogous to the probit model using IV techniques McClellan, Newhouse, and McNeil As an instrument for private premiums we used Medicare Part B and state level per capita spending. The joint significance test on the two spending variables from the first stage regression gives an F -statistic of In addition, the partial R 2 is 0. In the MSAs in our sample, health insurance coverage fell 3.

    This compares with 2. There was substantial variation across MSAs. In general, areas with larger premium increases experience greater declines in insurance coverage.

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    Change in Coverage and Change in Premiums. Table 1 reports descriptive statistics for our sample for the main covariates. The probit models confirm the impact of premiums on coverage Table 2 , column 1. Among the other policy variables, Medicaid eligibility increases are associated with increases in insurance coverage. Every 10 percentage point increase in Medicaid eligibility share for children leads to a 2. State insurance reforms were not statistically significantly associated with insurance coverage.

    Similarly, uncompensated care availability is not statistically significantly associated with declines in insurance coverage. Contrary to expectations, increases in the tax subsidy to health insurance are significantly associated with reductions in insurance coverage rates. We do not attach much importance to this result since there is little variation in changes in tax rates across metropolitan areas in our sample and the result is not very robust to changes in specification. Controls for individual-level traits and MSA demographics are included but not displayed. Individual-level economic and demographic changes not reported are associated with changes in coverage.

    Higher income, dual-headed families are more likely to have insurance coverage. Area-level economic and demographic changes, as expressed in the percentage of women that are working, average HIU income, unemployment rates, and the percentage of the population that is elderly, nonwhite, or foreign born, are not related to changes in coverage. Columns 2 and 3 of Table 2 show the results for regressions with private and public coverage as the outcomes. The results generally are consistent with theory, indicating variables that one would expect to operate primarily through their effects on private coverage do have their effect in that population, without affecting public coverage.

    For example, premiums and percent working spouses operate almost exclusively although private coverage.

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    This corresponds to a 0. The comparable figure reported by Glied and Jack is 0.