Taxing Cadillac Health Plans May Produce Chevy ... - Health Affairs

By Jon Gabel, Jeremy Pickreign, Roland McDevitt, and Thomas Briggs

Taxing Cadillac Health Plans May Produce Chevy Results

It’s often assumed that high-cost health insurance plans— sometimes called “Cadillac” plans—provide rich benefits to plan subscribers. Health reform provisions that treat these plans like luxuries may be misguided. Only 3.7 percent of variation in the cost of family coverage can be explained by benefit design (actuarial value). Benefit design plus plan type (HMO, PPO, POS, or high-deductible plans) explains 6.1 percent of this variation. Industry type and medical costs in the region also play a role. Most variation in premiums, however, remains largely unexplained. ABSTRACT

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or more than sixty years, the U.S. tax code has regarded employers’ contributions for health insurance premiums as nontaxable income for employees. Presidents Ronald Reagan and George W. Bush attempted to amend the open-ended tax treatment of employer-based insurance. At the time this is published, President Barack Obama and Senate Democrats, as part of their health reform plan, are considering a 40 percent excise tax on high-cost health plans. A prime motivation is finding a revenue source to help pay for expanded coverage under health care reform, but some advisers see it as a costcontrol strategy. Altering the tax treatment of employer-based health insurance has long been popular with health economists, particularly “free-market” economists. In 1973, Martin Feldstein maintained that by treating employers’ contributions for health insurance as nontaxable income to employees, the government promoted “overinsurance.”1 At the margin, because income was taxed and employer contributions for health insurance were not, employees had the option of receiving a dollar in health insurance or $0.67 in income (for an employee whose marginal tax rate is 33 percent). Hence, rational employees would “overinsure” and purchase plans with rich benefits and limited employee cost sharing. This

would reduce employees’ sensitivity to the cost of health care and thereby increase the use of services and medical spending. Critics of the “employer exclusion” argue that not only does it promote inefficiency, but that also it is unfair. A recent analysis by the staff of the Joint Committee on Taxation estimates that households earning $200,000–$499,999 per year receive tax benefits of $4,728 on average, whereas households earning $10,000–$29,999 receive $1,952 on average.2 Proponents of the open-ended tax treatment assert that without the employer exclusion, many young and healthy workers, facing higher monthly contributions for premiums, would decline to participate. This would break apart the insurance pool for large employers—a pool that now includes both healthy and sick people. Employer-based insurance is a superior method for pooling risks than the alternative—individual insurance—which is characterized by heavy medical underwriting and high marketing expenses. Proponents also note that when measured as a percentage of income, the tax subsidy is greater for low-middle- and middle-income households than for high-income ones.3 Whether a cap, a credit, a fixed deduction, or an excise tax, these proposals presume that highcost plans are expensive because of rich benefits. This paper examines that assumption. We found J AN UARY 2 0 10

doi: 10.1377/hlthaff.2008.0430 HEALTH AFFAIRS 29, NO. 1 (2010): ©2009 Project HOPE— The People-to-People Health Foundation, Inc.

Jon Gabel (Gabel-Jon@NORC .org) is a senior fellow at the National Opinion Research Center (NORC) in Bethesda, Maryland. Jeremy Pickreign is a senior research scientist, Health Policy and Evaluation, for NORC, based in Albany, New York. Roland McDevitt is director of health care research for Watson-Wyatt Worldwide, a benefit consulting firm in Arlington, Virginia. Thomas Briggs was a research analyst, Health Policy and Evaluation, for NORC, based in Washington, D.C. He is now a senior analyst at the Association of State and Territorial Health Officials, in Arlington, Virginia.

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that other factors—notably, industry sector and the costs of delivering care in the region—are more likely to explain the higher costs of some health plans. Although the paper analyzes the Bush administration’s 2007 proposal to consider health premiums as taxable income, general findings apply to other proposals ending the open-ended deductibility of employer-based insurance. The Bush administration’s proposal in 2007 would have provided a standardized annual deduction in 2009 for anyone purchasing health insurance (regardless of the cost of the plan) up to $7,500 for single coverage and $15,000 for family coverage. When an individual or a family purchased a plan exceeding these limits, there would be no tax subsidy for the amount above the limit, similar to a tax cap. The proposed standard deduction would have pertained to the total cost of the plan, not just the employer’s contribution. The standard deduction would have applied whether the purchase was through the employer-based or individual insurance market. If high-cost plans are high cost because of plan design, then limits on the deductibility of benefits constitute a well-targeted mechanism. If high-cost plans are linked to factors other than plan design—such as characteristics of the workforce or the local medical and insurance market—then limits on deductibility for health insurance would constitute a poorly targeted instrument.

Study Data And Methods DATA The 2007 Kaiser Family Foundation/Health

Research and Educational Trust (KFF/HRET) Employer Benefits Survey public use file is the primary data source for the study analysis. Based on a stratified random sample of public and private employers with three or more workers, the KFF/HRET survey and its predecessors have been conducted annually for the past twenty years. The survey has been described elsewhere in greater detail.4 All weights in the paper are employee-based weights. At the plan level, these weights represent the number of employees enrolled in the plan times the employer weight. In statistical testing, we use 0.05 as the level of statistical significance. Throughout the paper, the unit of analysis is the individual plan, of which there are 2,654 in the full database. We have linked characteristics of the firm and the county of location to the plan. Both descriptive and multivariate analyses are limited to the 522 sample firms that offer only one plan at one geographic location (we return 2

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to this point later). IDENTIFYING PLANS OVER THE STANDARD DEDUC-

The Bush administration’s 2007 proposal set the standard deduction at $7,500 for single coverage and $15,000 for family coverage in 2009. This paper examines a hypothetical deduction of $6,675 (single) and $13,350 (family) in 2007. We estimated these 2007 standard deductions by assuming annual rates of increase of 6 percent for 2008 and 2009.5 Most analysis pertains to family rather than individual coverage. The KFF/HRET survey collects data on the total cost of coverage for a family of four.6 More people enrolled in these plans are likely to exceed the standard deduction for family than individual coverage simply because family plans cover two or more people, but the standard deduction is only twice as high for family as for individual coverage. ANALYTICAL APPROACH One objective of this analysis is to understand factors explaining whether a specific plan exceeds or is under the standard deduction in 2007. In discussing individual factors, we note the expected effect on claims expenses. Reduced claims expenses, in turn, are likely to reduce premiums. These factors are grouped into three categories. ▸▸ CHARACTERISTICS OF PLANS : For each plan in the KFF/HRET sample, we calculated actuarial values through simulated bill paying.7 The medical claims data are from the MarketScan Database, a proprietary database of approximately ten million persons built by Thomson/Medstat for the year 2006, and further developed by Watson-Wyatt Worldwide.We adjusted the claims database for medical inflation from 2006–2007. For each plan in the sample, we know key features such as deductibles, copayments, and outof-pocket limits, and we used those parameters to calculate actuarial value—the percentage of the bill the plan would pay for each person in the medical claims database. We then calculated the percentage of the total bill paid by insurance.8 Another important measure of a firm’s benefit package is the type of plan: health maintenance organization (HMO), preferred provider organization (PPO), point-of-service (POS), or highdeductible health plan with savings option (HDHP/SO). Because of limited sample size, we pooled health reimbursement arrangement (HRA) and health savings account (HSA) plans under high-deductible plan with savings option. Plan type is associated with employees’ ability to seek care from out-of-network providers and the requirement to use a primary care gatekeeper. Because actuarial value is difficult to intuitively understand, the descriptive analysis includes measures of the benefit package that unTION

derlie the actuarial value of the plan. Among the measures presented are deductibles, whether the plan has copayments or coinsurance for office visits, the size of copayments for use of nonpreferred drugs, and the size of copayments for primary care office visits. ▸▸ FIRM CHARACTERISTICS : Measures of firm characteristics include firm size, industry, percentage of the workforce earning $21,000 or less per year, and an indicator that the workforce includes union workers. Firm size may measure the purchasing power for the firm. Industry is a proxy for characteristics of the workforce. It may capture the effects of age, human capital, and workers’ health status. Percentage of workforce earning $21,000 or less per year might affect premiums in a number of ways. Other factors held constant, lower-income workers have poorer health status and should require more services. On the other hand, such workers are likely to be younger and less able to pay for services and, hence, use fewer services, than higherincome workers. Firms with union workers would be expected to have richer benefits and hence be associated with higher premiums than firms without union workers. ▸▸ CHARACTERISTICS OF THE LOCAL MEDICAL CARE AND INSURANCE MARKET : As a measure of purchasing power in the community, counties with higher per capita income should have higher-cost health insurance. Insurers in communities where the costs of inputs for medical care services are more expensive will need to price their plans higher. The analysis uses Medicare’s measure of the cost of a physician practice (geographic practice cost index) to characterize the cost of local medical inputs.9 To control for different patterns of care in the country, we also tested a Dartmouth Atlas reimbursement index, which is based on the relative Medicare per capita cost (Parts A and B) in the specific hospital service area where the employer is located compared to the national average. We presumed that often unexplainable differences in the cost and use of care in the Medicare population in different areas would occur similarly in the private sector. Lastly, employers located in high-costof-living areas are likely to face higher premiums than those in low-cost-of-living areas. Here we used regional variables to control for varying cost-of-living expenses across the nation. DESCRIPTIVE AND MULTIVARIATE ANALYSIS The descriptive analysis compared characteristics of plans over the standard deduction with plans under the standard deduction. Comparisons were categorized according to each of the three classifications of factors (plan, firm, and market characteristics). In multivariate analysis, we estimated both

ordinary least squares (OLS) with family premium as the dependent variable and logistic regression where the dependent variable was whether or not the plan exceeds the standard deduction. Results were similar for the two sets of equations but easier to interpret when family premiums were the dependent variable. For that reason, our discussion emphasizes OLS results. The analysis examined the explanatory power of three different sets of explanatory variables. First, we noted the explanatory power of one variable—the actuarial value of the plan. Then the type of plan was entered into the regression. Finally, characteristics of the local market and firm were included in the model. If plans are high-cost because of rich benefits, then actuarial value (and plan type) should explain most variation across plans. Plan type should capture the restrictiveness of the provider network, use of a primary care gatekeeper, and utilization management. If the explanatory power of the full equation is much greater than that with actuarial value plus plan type, we can reject the proposition that high-cost plans are expensive largely because of rich benefits. Because a single firm may offer more than one plan, there is concern about mapping workforce characteristics to the individual plan. Similarly, because the observation unit in the KFF/HRET survey is the firm rather than the establishment, there is concern about linking characteristics of local market characteristics to plans. In the KFF/ HRET questionnaire, characteristics of the workforce such as the percentage of employees who are low income applies to the firm rather than enrollees in a specific plan. Similarly, if a hypothetical multisite firm with twenty establishments nationwide is headquartered in Fairfax County, Virginia, the market characteristics linked to the firm are those of Fairfax County, Virginia. Thus, conceptually a single-site, singleplan firm would not be subject to the same measurement error as would multisite and multiplan firms. (See Technical Appendix.)10 To determine if limiting the sample to singlesite, single-plan firms had a serious effect on the analysis, we additionally estimated regressions (OLS and logistic) for all firms in the sample.We tested for whether there were significant differences in the coefficients for the major explanatory variables for the different sets of equations. We found no statistically significant differences in the coefficients between two sets of equations. Therefore, the subsequent discussion presents results from the restricted sample of 522 single-site firms offering one plan, from the total sample of 2,654 plans.11

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Study Results DESCRIPTIVE ANALYSIS

▸▸ CHARACTERISTICS

OF

PLANS

EXCEEDING

Among single-site, single-plan firms, the average premium for a family plan in excess of the standard deduction is $16,266—statistically larger than the figure for plans below the standard deduction of $9,874 (Exhibit 1). Despite substantial differences in premiums, differences in plan characteristics under and over the standard deduction are not so dramatic. The average actuarial value for firms over the standard deduction (0.810) is statistically different than firms under the standard deduction (0.793). In 2007, 67 percent of enrollment among plans exceeding the standard deduction was in PPOs, whereas PPOs constituted 56 percent of enrollment for plans under the standard deduction (this difference is not statistically significant). In contrast, highdeductible plans with savings option constituted

THE FAMILY STANDARD DEDUCTION :

8 percent of enrollment among plans under the standard deduction, compared to 1 percent of enrollment among plans over the cap (this difference is statistically significant).12 Single deductibles constitute the clearest difference in characteristics of plans exceeding the standard deduction ($319) compared to plans under the standard deduction ($473).13 ▸▸ MARKET - AREA CHARACTERISTICS FOR FIRMS EXCEEDING THE STANDARD DEDUCTION : Among plans from single-site, single-plan firms, there were no statistical differences in average county per capita income among plans exceeding and under the standard deduction (Exhibit 1). No statistical differences were found in the regional distribution of plans. The geographic practice cost index is not statistically different among firms over and under the standard deduction. Similarly, the Dartmouth Atlas reimbursement index is not statistically different.

EXHIBIT 1

Characteristics Of Plans And Markets Exceeding And Below The Cap For Family Coverage Of $13,350 In 2007 Category

Plans exceeding cap

SE mean

Plans below cap

SE mean

Actuarial value Average premium Total enrollment

0.810a $16,266b 31%

0.006 370

0.793 $9,874 69%

0.006 142

Plan type HMO PPO POS HDHP-SO Total

9% 67 22 1b 100

2.88 5.79 5.63 0.84

16% 56 21 8 100

2.80 3.56 2.65 1.98

Mean deductible amount—single coverage Percent of plans with copays Average copay for an office visit Average copay for nonpreferred drug

$319b 91% $19 $39

$41 2.46 $1.06 $3.10

$473 81% $19 $44

$44 2.73 $0.53 $1.15

Out-of-pocket maximum? Limit No limit Total

73% 27 100

4.77 4.77

70% 30 100

3.09 3.09

County per capita income Percent of enrollment in nonmetropolitan counties

$30,796 17%

1,177 3.56

$29,322 8%

523 2.04

Regional location Northeast Midwest South West Total

17% 27 31 25 100

3.83 4.51 4.96 6.51

21 22 36 21 100

2.99 2.52 3.77 2.91

Geographic practice cost index Dartmouth Atlas reimbursement index

98.3 98.7

0.79 2.98

96.6 96.8

0.43 1.50

SOURCE Henry J. Kaiser Foundation/Health Research and Educational Trust 2007 Employer Health Benefits Survey. NOTES N ¼ 522. All firms in the sample are located at one site and offer only one plan. SE is standard error. HMO is health maintenance organization. PPO is preferred provider organization. POS is point-of-service plan. HDHP-SO is high-deductible health plan with savings option. aDifference between plans exceeding and below the cap is statistically significant (p < 0:05). bDifference between plans exceeding and below the cap is statistically significant (p < 0:01).

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▸▸ CHARACTERISTICS EXCEEDING

THE

OF FIRMS OFFERING PLANS STANDARD

DEDUCTION :

Among single-site firms offering just one plan, the distribution of plan enrollment across firm size was not statistically different for plans under and over the standard deduction (Exhibit 2). Plans from firms in the health care industry disproportionately exceeded the standard deduction, while manufacturing and retail plans were disproportionately below it. MULTIVARIATE ANALYSIS With family premium as the dependent variable, actuarial value alone could explain only 3.7 percent of the variation in family premiums (Exhibit 3). When plan type was added to the regression model, that figure increased to 6.1 percent. The full model, which includes variables for firm and market characteristics, explains 15.5 percent of the variation in family premiums. Differences in the restricted regressions’ ability to explain variance were statistically different from the full model. Logistic regression models had lower explanatory power. Differences in the restricted and full logistic regressions were statistically significant according to a testing of likelihood ratio. The full model correctly predicts whether a plan will be over the family standard deduction of $13,635 in 2007 in only 34 percent of the cases.

The following summarizes findings about individual variables from the full regression model with family premium as the dependent variable (see Appendix Exhibit 1).10 The intent is to demonstrate that variables other than plan characteristics significantly and substantially affect family premiums, after plan characteristics are controlled for. ▸▸ PLAN CHARACTERISTICS : With high-deductible plans as the reference plan, premiums in PPOs, but not HMOs or POS plans, were significantly higher (14 percent). Actuarial value was statistically significant. A 1 percent increase in the actuarial value of the plan is associated with a 0.49 percent increase in the family premium.14 ▸▸ FIRM CHARACTERISTICS : Industry is a highly significant factor. Premiums in the agriculture/ mining, manufacturing, retail, finance, service, and state and local government industries were 22, 20, 14, 15, 12, and 10 percent lower than in the health care industry. ▸▸ MARKET CHARACTERISTICS : The gographic practice cost index variable, a measure of the cost of a physician’s practice, is highly significant (p < 0:01). At the mean family premium and index, a 1 percent increase in the index resulted in an increase in the family premium of 0.98 percent. Regional variables and county per capita

EXHIBIT 2

Firm Characteristics For Health Plans Exceeding And Below The Cap Of $13,350 For Family Coverage In 2007 Category

Plans exceeding cap

SE mean

Plans below cap

SE mean

Firm size 3–9 employees 10–24 employees 25–49 employees 50–199 employees 200–999 employees 1,000–4999 employees 5,000 or more employees

17% 17 13 19 15 11 9

6.63 3.62 3.22 3.88 2.69 2.34 3.78

14% 19 11 21 10 11 13

2.94 2.18 1.68 2.60 1.55 1.79 3.55

Industry Mining Construction Manufacturing Transportation/utilities/communications Wholesale Retail Financial Service Government Health care

1% 3 4b 10 5 3a 7 43 5 21a

0.75 1.50 1.26 5.08 1.99 1.20 2.40 5.93 1.07 4.39

1% 8 13 5 3 10 8 37 5 9

0.75 2.41 2.99 1.54 0.83 2.50 2.25 3.42 1.09 1.57

Presence of union workers in firm Percent of workforce earning less than $21,000

21% 12%

5.15 2.17

17% 16%

3.37 1.64

SOURCE Henry J. Kaiser Foundation/Health Research and Educational Trust 2006 Employer Health Benefits Survey. NOTES N ¼ 522. All firms in the sample are located at one site and offer only one plan. SE is standard error. aDifference between plans exceeding and below the cap is statistically significant (p < 0:05). bDifference between plans exceeding and below the cap is statistically significant (p < 0:01).

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EXHIBIT 3

How Well Plan Characteristics Alone Explain The Premium And Whether Or Not The Premium Is Over The Cap Limit, 2007 Family premium as dependent variable (OLS)

Did family premium exceed cap in 2007? (logit)

Amount of variation (R2 ) explained by actuarial value with dependent variable

0.037b

0.012

Amount of variation (R2 ) explained by actuarial value and type of plan with dependent variable

0.061b

0.031

Amount of variation (R2 ) explained by full specification including firm and market characteristics

0.155b

0.147

Percent of plans correctly predicted to be over the limit by the full model from logit regression

–a

0.339

Category

SOURCE Henry J. Kaiser Foundation/Health Research and Educational Trust 2006 Employer Health Benefits Survey. NOTES Summary of findings from ordinary least squares (OLS) and logistic regressions (see details in text and online Technical Appendix). N ¼ 522. All firms in the sample are located at one site and offer only one plan. aNot applicable. bStatistically significant (p < 0:01).

income were statistically insignificant. ▸▸ SENSITIVITY ANALYSIS : To determine the robustness of the regression analysis, we ran a series of regressions withdrawing one key individual explanatory variable at a time from the full model. When we removed the actuarial value of the plan, the percentage of variation explained by the model fell from 15.5 percent to 14.1 percent. When the geographic practice cost index was removed, the percentage of variation explained by the model fell from 15.5 percent to 13.0 percent. Removing plan type resulted in the model’s explaining 13.1 percent of the variation in family premiums. When the dummy variables for industry were omitted from the full model, the model could explain only 10.8 percent of the variation. Removing both actuarial value and plan type resulted in the R2 falling to 11.2 percent. All figures on variation are statistically significant from the full model (p < 0:01). In summary, at a minimum the actuarial value of the plan explains variation in family premiums no better than the geographic cost index or industry does. THE STRANGE CASE OF THE DARTMOUTH ATLAS

The Dartmouth Atlas variable described earlier was added to the regression to determine whether the unexplainable variations in per capita Medicare spending across hospital service areas (identified in the work of John Wennberg, Elliot Fisher, and their colleagues) also applied to the private sector.15 Areas where Medicare has higher utilization and reimbursement rates would seemingly have higher prices and utilization in the private sector, and thus higher premiums. Presumably, providers have similar practice styles for Medicare and private patients. Results were contrary to our hypothesis (Appendix Exhibit 2).10 When the Dartmouth Atlas reimbursement index was entered with the geo-

graphic practice cost index, it was statistically significant for the family premium regression but with a negative coefficient.16 A one-percentage-point increase in the Dartmouth Atlas index was associated with a 0.15 percent decrease in the cost of family coverage, and a one-percentage-point increase in the practice cost index was associated with a 1.21 percent increase in the cost of family coverage. What explains these counterintuitive results? Stuart Schmid in a 1995 article in this journal17 noted that HMO premiums for the Federal Employees Health Benefits (FEHB) program were uncorrelated with county-based Medicare per capita fee-for-service costs. Schmid’s explanation was that HMOs operate in a competitive market and Medicare fee-for-service does not. An alternative explanation is that where Medicare revenues are greater, hospitals and providers do not have as great a need to shift costs to private payers; consequently, charges and utilization are lower for private patients. Neither explanation is compelling, so the proverbial “more research is needed” applies.

REIMBURSEMENT INDEX

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Discussion An unspoken premise of limiting the deductibility of health benefits is that high-cost plans are expensive because of rich benefits. Descriptive analysis indicated that despite dramatic differences in family premiums for plans over and under the standard deduction, plan characteristics did not differ greatly. Plans under the standard deduction are more likely to be highdeductible plans with savings accounts, to have comparatively high deductibles, and to have statistically lower actuarial values (although absolute differences were small). Regression analysis found that plan character-

istics explain only a small percentage of the variation in family premiums. Actuarial value alone explains less than 4 percent of the variation, and when plan type is additionally entered into the model, this figure increases to 6 percent. The full regression model was able to explain 15.5 percent of the variation in family premiums. Two powerful variables explaining variation in premiums are industry and the geographic practice cost index. Industry may capture unmeasured characteristics of the workforce such as health status. The geographic practice cost index measures the relative cost of medical inputs. Because the health status of the workforce and cost of medical inputs are beyond a firm’s control, public policy efforts to limit deductibility of employee benefits should make adjustments for these two factors. The implication is that limiting deductibility of employee benefits is not the targeted policy mechanism advocated for more than thirty years. Alternatively, if policymakers are committed to limiting tax-advantaged benefits, the methodology needs to be more sophisticated (and complex) than what has been proposed. One limitation of this analysis is lack of information about the health status of the population covered by each health plan. All employer surveys suffer from this limitation. We used the usual proxies that researchers apply when analyzing employee benefit information from surveys of firms—industry, firm size, income of the overall workforce, and others—but these are less-than-ideal measures of the health of workers and dependents enrolled in a specific The authors thank the Commonwealth Fund for its financial support. They thank Sara Collins, Cathy Schoen, Heidi

plan. For small and midsize firms, one to five sick people may account for 50 percent of spending, and thus greatly affect the cost of coverage. If information on workers’ health status were available, regression analysis would explain far more variation in premiums. This paper did not assess the extent to which a limit on the open-ended deductibility of health insurance would redistribute tax benefits more progressively. Likewise, the analysis examined the baseline relationship between family premiums and plan benefits, not how employers’ and employees’ behavior would change if Congress changed the tax code. Nonetheless, because the cost of medical inputs and the health status of employees are beyond the employer’s control, inadequacies identified in this paper likely would exist after a limit was established. In conclusion, there are strong conceptual grounds to assert that limits on the deductibility of health insurance would improve the fairness of public subsidies for private health insurance. Similarly, there are persuasive arguments that the current tax treatment of health benefits encourages the purchase of high-cost health plans. Our inquiry suggests, however, that analysts should not equate high-cost plans with Cadillac plans, but that in fact other factors—industry and cost of medical inputs—are as important in predicting whether a plan is a high-cost plan. Without appropriate adjustments, a simple cap may exacerbate rather than ameliorate current inequities. ▪

Whitmore, Tom Rice, and Jim Reschovsky for their helpful suggestions

and comments. Special thanks are due to Shova KC for her research assistance.

method for calculating premium increases. Most large employers have many tiers of family coverage such as single plus one adult, single plus one child, two adults and a child, family of four, and other classifications. Actuarial value is the percentage of the bill paid by insurance for a large standardized population. Readers interested in a fuller description of the method for calculating actuarial value should refer to McDevitt R, Gabel J, Gandolfo L, Lore R, Pickreign J. Financial protection afforded by employersponsored health insurance: current plan designs and high deductible health plans. Med Care Res Rev. 2007. 64(2):212–28. The Medicare Payment Advisory Commission has constructed the

geographic practice cost index to adjust payments to physicians for the Medicare fee schedule in eightynine payment areas. One component measures incomes of college graduates in the area; another component measures practice expenses for physician practices in the area, and a third component measures professional liability costs. Medicare Payment Advisory Commission. Geographic practice cost indexes [Internet]. Washington (DC): MedPAC; updated 2003 Aug 12 [cited 2007 Oct 4];[about 2 p]. Available from: http://www .medpac.gov/publications/ other_reports/ Aug03_GPCI_2pgrKH.pdf 10 The Technical Appendix is online at http://content.healthaffairs.org/ cgi/reprint/hlthaff.2008.0430/DC1 11 We opted for the single-site, single

NOTES

1 Feldstein MS. The welfare loss of excess health insurance. J Polit Econ. 1973;81(2):251–80. 2 Staff of the Joint Committee on Taxation. Background materials for Senate Committee on Finance roundtable on health care financing, presented before the Senate Committee on Finance. 2009 May 12; p. 5. 3 Chollet D. Crafting a viable health insurance system. Health Aff (Millwood). 1991;10(2):224–8. 4 See Claxton G, Gabel J, DiJulio B, Pickreign J, Whitmore H, Finder B, et al. Health benefits in 2007: premium increases fall to an eight-year low, while offer rates and enrollment remain stable. Health Aff (Millwood). 2007;26(5):1407–16. 5 In 2007 premiums increased 6.1 percent, and in 2008 the Kaiser Family Foundation changed the

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plan sample because of the conceptual advantages discussed previously. In addition, in regressions with the full sample, dummy variables for single-site and single-plan firms were statistically insignificant. 12 High-deductible plans with savings option include employer contributions to the savings accounts in the family premium figure. 13 In the full sample of 2,654 plans, differences were statistically significant between plans over and under the cap for these variables: (1) PPO membership; (2) size of copayment

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for office visit; (3) size of copayment for nonpreferred drugs; and (4) the presence of an out-of-pocket limit. We believe that the smaller sample size is responsible for the differences in statistical significance from the single-site, single-plan firms and the full sample. 14 The 0.49 statistic is a point elasticity figure calculated at the mean. 15 For examples, see Wennberg J, Fisher ES, Skinner JS. Geography and the debate over Medicare reform. Health Aff (Millwood). 2002;21:w96–114.

16 The correlation between the geographic practice cost index and the Dartmouth Atlas reimbursement index was 51 percent and significant. We checked for high multicollinearity for both indices by determining the tolerance for each variable relative to all other variables in our equations. Both variables do not meet the common rule of thumb for high multicollinearity. 17 Schmid SG. Geographic variation in medical costs: evidence from HMOs. Health Aff (Millwood). 1995;14 (1):271–5.