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Cost and Coverage Impacts of the President’s Health Care Reform Proposal and a Congressional Tax Credit Proposal

Publication Date
May 18, 2008

Final Report

Prepared by:

The Lewin Group
May 19, 2008

Prepared for:
Office of the Assistant Secretary for Planning and Evaluation (ASPE)
U.S. Department of Health and Human Services (HHS)

This report was produced under the direction of Carrie Shelton, and Thomas Musco, Project Officers, Office of the Assistant Secretary for Planning and Evaluation (ASPE), Office of Health Policy. The findings and conclusions of this report are those of the authors and do not necessarily represent the views of ASPE or HHS.

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Introduction

The Office of the Assistant Secretary for Planning and Evaluation (ASPE), Department of Health and Human Services (DHHS) tasked Lewin to analyze health care reform proposals that aim to expand health insurance coverage by providing tax deductions and tax credits.   The two proposals Lewin analyzed were the President’s health care tax deduction proposal combined with an affordable choices health insurance program; and a Congressional tax credit proposal.

In this report, we describe the data and methods we used to simulate the impact on health insurance coverage and changes in government health spending under the two proposals.  We discuss the results at the national and state levels.  We begin by summarizing both health reform proposals.  More detail on the proposals is provided in the methodology section. 

The President’s Tax Deduction Proposal and State-Based Grant Funding Program

There are two major components to the President’s proposal: the tax deduction and a state-based grant funding program called Affordable Choices. 

Tax Deduction Proposal.  President Bush proposes to replace the existing tax exemption for employer-sponsored insurance (ESI) with a single tax deduction ($7,500 single; $15,000 family).  The tax deduction is available to people with private health insurance, regardless of whether it is ESI or non-group coverage. It is expected that allowing a deduction for non-group coverage will help people without ESI purchase insurance, thus reducing the number of uninsured.

Tax filers would count employer spending for ESI as taxable income for both income and payroll taxes, but would receive the full amount of the deduction as long as they have private health insurance.  Using a fixed deduction amount eliminates existing tax incentives that reward people for taking comprehensive coverage that encourages increased health spending. The current tax exclusion provides incentives for employees to purchase high cost plans with comprehensive benefits and low cost sharing so that most of their health care expenses are purchased with pre-tax dollars leaving only minimal copayments to be financed with after-tax dollars. The fixed deduction caps the amount of spending that can be financed with pre-tax dollars, which provides incentives for employees to purchase lower cost plans that do not exceed the amount of the deduction.  

Affordable Choices.  The President has proposed an Affordable Choices program.  Our report on this program is based on certain policy parameters and potential specifications provided to us by ASPE.  Under these draft specifications, the program would permit States to cover all people living below 150 percent of the federal poverty level (FPL).  States would be required to use private health insurance as the basis for health care coverage and delivery.  State residents must have access to at least one “basic” affordable private health plan that provides certain minimum required benefits.  The “basic” plan must also have a standard premium based on a certain percentage of the State median income that is expected to be accessible for its residents.  We were asked to assume that for a basic plan, a premium level at 6 percent of the median income in a State is required to make coverage affordable.  

Under Affordable Choices, states must also include provisions to increase access to basic private health insurance for high-risk individuals and provide a premium assistance program to subsidize coverage for specified low-income residents of the State.  The State must continue to provide the same level of funding that it currently provides for covering high-risk individuals in order to receive Federal grant money under the program. 

Also, some funding for Affordable Choices must be obtained through Medicaid Disproportionate Share Hospital (DSH) funds.  By the fifth year of the program, at least 50 percent of Medicaid DSH funds from the most recent fiscal year prior to implementation must be redirected into funding for Affordable Choices.    The State could also use other sources to offset the required DSH funding. 

Medicaid and State Children’s Health Insurance Program (SCHIP) eligible individuals are allowed to enroll in Affordable Choices to the extent that the State has submitted certain waivers for alternative coverage and that the State does not use Affordable Choices funds to supplant Title XIX (Medicaid) or XXI (SCHIP) funds to cover these populations. 

The grants to the States will be determined by a formula taking into account several factors such as the State’s number of uninsured and individuals under 150 percent of the FPL relative to the national totals, geographic differences in health care costs across States, and available DSH allotments.  Federal Affordable Choices funds cannot be used to reimburse care provided for Medicaid or SCHIP eligible individuals and a State must use no more than 10 percent of the grant money for administrative purposes. States are also required to adopt policies designed to prevent a shift of previously privately insured people to Affordable Choices.

Key findings on the impact of the President’s proposal include:

  • The proposal would reduce the number of uninsured – projected to be 48.8 million people in 2009 – by about 18.1 million people (37 percent);
  • Replacing the existing tax exclusion with the deduction would increase the federal deficit by approximately $73.8 billion in 2009 assuming the program was fully-phased in;
  • The initial increase in the deficit would decline to a net reduction in the deficit of $88 billion in 2018.  This is because the tax deduction is indexed with the Consumer Price Index (CPI), which grows at about 2.8 percent per year, while the tax exclusion that it replaces would be expected to grow with health care cost inflation at about 7.0 percent per year; and
  • The Federal impact of the program over the 2009 to 2018 period is estimated to be a net cost of $25.7 billion.

The Congressional Tax Credit Proposal

We also estimated the cost and coverage impacts of a Congressional tax credit proposal, which was modeled after Senate bill S.1019. This proposal would replace the current tax exclusion for ESI with a flat-tax credit of $2,000 per adult and $500 per child with a maximum of 2 children.

A key assumption in this analysis is that the tax credit, as well as the deduction explained in the previous section, would be “advanceable” through the withholding system so that people can get the subsidy as they pay their premiums rather than waiting until taxes are filed in the following year.  This means that people would be permitted to adjust their income tax withholding to reflect these tax subsidies, much as people now do when they purchase a home.

Because the tax credit is “refundable,” (i.e., the credit can exceed the amount of taxes owed) we assume that workers can obtain advance payments through withholding. Thus, low-income people could have an amount added to their check each month as an advance for the tax credit amount. We assume that non-workers would be able to apply separately for advance payments of the tax credit. 

Key findings on the impact of the Congressional tax credit proposal include:

  • The proposal would reduce the number of uninsured by about 21.1 million people (43 percent);
  • Replacing the existing tax exclusion with the tax credit would increase the federal deficit by approximately $36.1 billion in 2009 assuming the program was fully phased-in;
  • The initial increase in the deficit would decline to a net reduction in the deficit of $171.6 billion in 2018.  This is because the tax credit is indexed with the growth in the Gross Domestic Product (GDP), which grows at about 2.2 percent per year, while the tax exclusion that it replaces would be expected to grow with health care cost inflation at about 7.0 percent per year; and
  • The Federal impact of the program over the 2009 to 2018 period is estimated to be a net savings of $564.4 billion.

When comparing the impact of the President’s proposal to the Congressional proposal, there is a greater reduction of the uninsured and lower costs under the Congressional option.  More discussion of the impacts of the two proposals will be given following the methodology section below.   

Methodology and Assumptions

We used The Lewin Group Health Benefits Simulation Model (HBSM) to analyze the costs and coverage impacts of the proposals nationally and for each State.  The Health Benefits Simulation Model (HBSM) is a micro-simulation model of the U.S. health care system.  HBSM is a fully integrated platform for simulating policies ranging from narrowly defined Medicaid coverage expansions to broad-based reforms such as changes in the tax treatment of health benefits.  Below, we provide more detail on the HBSM and how it was used to model key aspects of both proposals.  More detailed documentation of the full model is available upon request.  We also describe the assumptions used to estimate impacts for each State. 

Description of the Lewin Health Benefits Simulation Model

HBSM was created to provide comparisons of the impact of alternative health reform models on coverage and expenditures for employers, governments and households.   HBSM facilitates comparisons of alternative health reform initiatives by using uniform data and assumptions.  For example, take-up rates for Medicaid and various tax credit/premium proposals are simulated using uniform take-up equations and modules.  Uniform methods are also used to simulate changes in health services utilization attributed to changes in coverage status and cost-sharing parameters.  This uniform approach assures that we can develop estimates of program impacts for very different policies using consistent assumptions and reporting formats. The use of uniform processes also enables us to simulate the impact of substantially different policy options in a short period of time.

The key to the design of the HBSM is a “base case” scenario depicting the distribution of health services utilization and expenditures across a representative sample of households under current policy for a base year such as 2009.  We developed this base case scenario based upon recent household and employer data on coverage and expenditures. We also “aged” these data to be representative of the population in 2009 based upon recent economic, demographic and health expenditure trends. The resulting database provides a detailed accounting of spending in the U.S. health care system for stakeholder groups.  These base case data serve as the reference point for our simulations of alternative health reform proposals.

The model first simulates how these policies would affect sources of coverage, health services utilization and health expenditures by source of payment (Figure 1).  For instance, the model simulates enrollment in voluntary programs, such as tax credits for employers and employees, based upon multivariate models of how coverage for these groups varies with the change in the cost of coverage (i.e., modeled as the premium minus the tax credit).  In addition, the model simulates enrollment in Medicaid and SCHIP expansions based upon a multivariate analysis of take-up rates under these programs, including a simulation of coverage substitution (i.e., “crowd out”).

Once changes in sources of coverage are modeled, HBSM simulates the amount of covered health spending for each affected individual, given the covered services and cost-sharing provisions of the health plan provided under the proposal.  This includes simulating the increase in utilization among newly insured people and changes in utilization resulting from the cost sharing provisions of the plan.  In general, we assume that utilization among newly insured people will increase to the level reported by insured people with similar characteristics. We also simulate the impact of changes in cost sharing provisions (i.e., co-payments, deductibles, etc.) on utilization.

Changes in employer costs are assumed to be passed-on to workers in the form of changes in wage growth over time.  For example, policies that increase employer costs would result in a corresponding reduction in wages for affected workers, with a corresponding reduction in income and payroll tax revenues.  Similarly, reductions in employer costs are assumed to be passed on to workers as wage increases.  HBSM includes a tax module that simulates tax effects due to these changes in wages as well. The model will simulate wage pass-through under varying assumptions on how long it would take for the labor markets to adjust.

The model includes a simulation of health insurance premiums in the private small group and individual markets using the range of rating practices permitted in each state.  This permits us to simulate the impact of options for implementing rate compressions proposals. It is also designed to simulate “adverse selection” that may result under policies that give employers and/or individuals a choice of alternative insurance pools with their own unique rating practices.  For example, some of the proposals analyzed in this study would give employers the option of enrolling in a public insurance pool at a community-rated premium.  This would tend to attract employers and individuals with high health care costs who find that the community-rated premium is less than the cost of an experience-rated plan for that group in the private market.

We present our summary of how the Lewin Health Benefits Simulation Model was used to estimate the cost and coverage impacts due to both proposals in the following sections:

  • Baseline (i.e. Current-Law) Development;
  • Key Modeling Assumptions for the Affordable Choices Health Insurance Program;
  • Key Modeling Assumptions for Tax Deduction and Tax Credit; and
  • Assumptions used for the State-level Estimates

Figure 1 Flow Diagram of the Health Benefits Simulation Model (HBSM)
Figure 1 Flow Diagram of the Health Benefits Simulation Model (HBSM)

Baseline (Current-law) Development

The key to simulating changes in the health care system is to develop a baseline database that depicts the U.S. health care system in detail.   Our HBSM baseline data is based upon the 1999 through 2001 Medical Expenditures Panel Survey (MEPS) data, which provide information on sources of coverage and health expenditures for a representative sample of the population. These data are adjusted to reflect the population and coverage levels reported in the 2006 Current Population Survey (CPS) data (with adjustments for under-reporting discussed below). We also statistically match workers in these data to the Kaiser/HRET survey of employers which provides additional detail on coverage provided through work. The methods used to develop baseline data for households and employers is presented in the following sections.

The Household Database.  The HBSM baseline data is derived from a sample of households that is representative of the economic, demographic and health sector characteristics of the population.  HBSM uses the 1999 through 2001 MEPS data to provide the underlying distribution of health care utilization and expenditures across individuals by age, sex, income, source of coverage and employment status. The use of data for three years substantially increases sample size, thus permitting us to develop more stable estimates of narrowly defined policy options.

We re-weighted the MEPS household data to reflect population control totals reported in the 2006 March CPS data.  The March CPS is used for the annual Census Bureau estimates of the number of uninsured in the US and each state.  While the CPS provides the most current data on insurance coverage, it under-reports the number of people covered under the Medicaid program, which causes these data to over-estimate the number of uninsured.  Consequently, we corrected the CPS data for under-reporting of Medicaid coverage to provide a more accurate count of the number of people without coverage. 

We corrected the CPS for under-reporting of Medicaid using the HBSM.  The model first allocates earnings over the number of weeks each individual worked during the prior year and creates information on income for each month of the year.  The model then simulates eligibility for Medicaid and SCHIP using these monthly income data to identify people who appear to be eligible for these programs based upon the income eligibility levels actually used in these programs for various categories of eligibility (e.g. children, parents, etc…).  The model does this in a way that accounts for changes in eligibility over the year as people move into and out of employment from month-to-month.  We then select a portion of the people who appear to be eligible for Medicaid or SCHIP and assign them to enrolled status so that these data report the correct number of people participating in these programs. 

Another issue to deal with is that the CPS reports the number of people who were without coverage from any source during all 12 months of the prior year.  However, this definition omits those who were uninsured for only a portion of the year.  This not only understates the number of uninsured, it would also lead us to under-estimate the cost of covering these people under various proposals to expand insurance coverage.  Thus, the most appropriate measure of the uninsured for policy purposes is the average monthly number of uninsured.

In order to estimate average monthly figures, we allocate reported coverage from each source over the 12 months of the year based upon employment and duration of enrollment data reported in the CPS.  We allocate employer wages and employer health insurance coverage over the periods of work reported in the CPS. We also allocated Medicaid and SCHIP coverage over the number of months they report (or are assigned) being enrolled for months where these individuals appear to be income eligible. We assume that people reporting coverage from Medicare, TRICARE or non-group coverage are insured by these sources all year. This enables us to estimate the number of people without insurance coverage in each month.

After adjusting the CPS data, we are able to develop the weights to adjust the MEPS household data to reflect the population control totals reported in the CPS data.  These weight adjustments were performed with an iterative proportional-fitting model, which adjusts the data to match approximately 250 separate classifications of individuals by socioeconomic status, sources of coverage and job characteristics in the CPS.  Iterative proportional fitting is a process where the sample weights for each individual in the sample are repeatedly adjusted in a stepwise fashion until the database simultaneously replicates the distribution of people across each of these variables in the state.1 This approach permits us to simultaneously replicate the distribution of people across a large number of variables while preserving the underlying distribution of people by level of healthcare utilization and expenditures as reported in MEPS.  

The health spending data are adjusted to reflect projections of the health spending by type of service and source of payment in the base year (i.e., 2009). These data are used to estimate health insurance premium costs for people with private health insurance. These spending estimates are based upon health spending data provided by the Centers for Medicare & Medicaid Services and detailed projections of expenditures for people in Medicare and Medicaid spending across various eligibility groups.2 The result is a database that is representative of the base year population by economic and demographic group, which also provides extensive information on the joint distribution of health expenditures and utilization across population groups.

The Employer Database.  HBSM includes a database of employers for use in simulating policies that affect employer decisions to offer health insurance.  We used the survey of employers conducted by the Kaiser Family Foundation and the Health Research and Educational Trust (Kaiser/HRET).  These data include about 2,000 randomly selected public and private employers with 3 or more workers, which provide information on whether they sponsor coverage and the premiums and coverage characteristics of the plans that insuring employers offer.

We statistically match each MEPS worker with one of the firms in the Kaiser/HRET data. Experience has shown that it is important that the individuals assigned to each firm be consistent with the employer’s workforce characteristics. The Kaiser/HRET data provide information on the distribution of workers by wage level.  However, additional information such as age of worker and marital status for insured people are not included in the database.  Thus, in order to use these data in our analysis, we statistically matched the Kaiser/HRET data with employers surveyed in the 1991 Health Insurance Association of America (HIAA) employer survey data, which provides detailed information on the characteristics of each employer’s workforce including number of workers by:3

  • Age;
  • Gender;
  • Full-time/part-time status;
  • Coverage status (eligible enrolled, eligible not enrolled and ineligible);
  • Policy type for covered people (i.e., single/family); and
  • Wage level.

The employer health plan eligibility data in the database is important to simulations of policies affecting employers.  One important consideration is that many of those who do not have employer coverage work for a firm that offers coverage to at least some of their workers.  About 81.5 percent of all workers are employed by a firm that covers at least some of their workers (Figure 2).  However, only about 75 percent of these people are eligible and enrolled.  About 10.2 percent are ineligible and about 14.3 percent are eligible but have declined coverage.4 Some of these workers have declined coverage because they receive insurance elsewhere (e.g., through their spouse’s employer or the individual market).


Figure 2 Workers by Employer Insurance Status (in millions)

Figure 2 Workers by Employer Insurance Status (in millions)

Source: Lewin Group Estimates using the Health Benefits Simulation Model (HBSM).


The model controls for the workforce characteristics for each firm in matching individuals to firms. While the firm data provide information on the number of people in the firm with these characteristics, they do not provide the “joint distribution” across these groups (e.g., by age, sex, income etc.).  We estimate the joint distribution for each firm using an iterative proportional fitting process.  In this approach, we begin with the joint distribution of workers across these variables as reported nationally in the CPS, and scale them in an iterative process so that in the aggregate they replicate the aggregate number of workers in the firm for each worker characteristic. Each non-zero cell of the joint distribution matrix for each firm is treated as an individual worker, who is matched to MEPS individuals based upon these individual characteristics.

Thus, if a firm reports that it employs mostly low-wage female workers, the firm tended to be matched to low-wage female workers in the MEPS data. This approach helps assure that Kaiser/HRET firms are matched to workers with health expenditure patterns that are generally consistent with the premiums reported by the firm. This feature is crucial to simulating the effects of employer coverage decisions that impact the health spending profiles of workers going into various insurance pools.  Controlling for the joint distribution of workers within firms is crucial to simulations of program impacts because premiums and behavioral responses vary widely by age, wage level, part time/full-time status, and the number of workers with dependents.

Key Modeling Assumptions for the Affordable Choices Program

State Participation.  Affordable Choices is an optional program to the states.  To participate, the state must redirect half of their DSH funds to the Affordable Choices program. The remainder of the program is financed with federal matching funds, at the SCHIP enhanced matching rates. While the enhanced matching rate is attractive, the state must be willing to pay for the state share of the program.

For illustrative purposes, we assume that all states adopt the program and cover all people through the maximum income eligibility level under the program of 150 percent of the FPL.  While it is difficult to know with certainty what individual states would do, state participation in Affordable Choices can be predicted based on their historical participation in SCHIP.  All states elected to implement an SCHIP program and nearly all cover children through 200 percent of the FPL.  Also, through waivers, some states have already covered adults below 150 percent of the FPL and in some cases have redirected DSH funds to expand coverage. This suggests that most, if not all states will be inclined to participate. We assume that states are required to maintain their current income eligibility levels under both Medicaid and SCHIP.

Benefit Packages.  The program requires states to establish an “affordable insurance” product. Based upon the guidance provided in the proposal, we assume that this will be defined as a package costing no more than 6 percent of state median household income for any individual. This implies a premium at the national level of about $2,820 per year, or $235 per-member per-month (PMPM). Thus, the “affordable premium amount (APA)” would be $235 PMPM.

The only way to have a given insurance product with a uniform APA for all applicants would be to use a community rated premium.  This is where the premium is the same for all applicants regardless of the risk characteristics of the individual. However, the proposal would not alter existing state regulations of rating practices.  Consequently the only way to meet the $235 APA requirement for all applicants will be to vary the benefits package itself by age and other risk characteristics that are typically used to set premiums in each state.

In this analysis, we assume that states would establish “affordable insurance” products where the benefits package itself varies with individual risk characteristics such as age, gender and health status. Thus, while a benefits package with a $200 deductible might be feasible at the APA for an adult under age 25, the deductible might need to increase to $5,000 for someone 59 years of age to hold the cost for that person to the APA.

In this analysis, we assumed that states would use a single benefits package with a cap on total benefits that varies with the risk characteristics of the individual, such that all eligible people have access to a package at the APA. We assume the typical plan would cover:

  • Inpatient/outpatient health facility or clinic services;
  • Inpatient and outpatient professional provider services by licensed professionals;
  • Diagnostic imaging, laboratory services, and other diagnostic and evaluative services;
  • Child and adult immunizations and preventive care;
  • Health education;
  • Prescription drugs subject to a formulary;
  • Mental health care;
  • Preventive dental care;
  • Blood and blood products;
  • Emergency care services;
    Substance abuse treatment; and
  • Dialysis.

Services that would not be covered by the program include:

  • Vision care including eyeglasses (assumed limit of one pair per year);
  • Hearing services including hearing aids;
  • Durable medical equipment;
  • Nursing home services;
  • Home health services;
  • Cosmetic surgery; and
  • Private hospital rooms.

Point-of-service Cost Sharing:

  • $200 Deductible;
  • $10 Co-payment per visit;
  • Prescription drug co-payment: $5 generics, $15 Brand.

This benefits package is designed to provide access to primary care services for all eligible individuals with relatively low point-of-service cost sharing, while using the overall benefits caps to keep costs to the APA levels. 

We estimated the premiums for this benefits package with HBSM by age, gender and health status. The HBSM premium estimates reflect the actual demographic and health status characteristics of the people who would become covered under the program.  This is important because the uninsured are on average younger than the commercially insured population.  Also, because nearly all states now cover pregnant women living below 150 percent of the FPL, none of the newly eligible women in this group will be pregnant.  We then used HBSM to estimate the benefits cap required in each age group to hold the premium to the APA amount.

Table 1 presents our premium and benefits cap amounts by age.  We assume that the APA in each state will vary in proportion to median household income.

Age Premium: No
Benefits CAP
Annual Benefit
Cap Amounts
Premium: With
Benefits Cap
Figure 3
Estimated Premiums PMPM by Age and Gender with and without the Age-Specific Benefits Caps
Less than 25 $90.83 no cap $55.16
25 – 34 $201.92 no cap $165.00
35 - 44 $221.17 no cap $179.33
45 - 54 $558.59 $12,000 $230.66
55 - 64 $693.17 $5,000 $235.00
Source: Lewin Group Estimates using the Health Benefits Simulation Model (HBSM).

Program Enrollment.  We simulated enrollment in Affordable Choices based upon a multivariate analysis of the likelihood that a person eligible for Medicaid enrolls in the program.  The model shows how enrollment rates vary with demographic characteristics, income, premium level (if any) and the availability of employer coverage.  Using this multivariate model, we estimated that about 75 percent of uninsured people eligible for Affordable Choices would enroll.

As discussed above, states are required to adopt policies designed to avert a shift of already privately insured people to public coverage under Affordable Choices (i.e., anti-crowd-out measures).  For illustrative purposes, we assume that state Affordable Choices plans impose a six-month waiting period prior to enrollment as an anti-crowd-out measure.  This means that individuals must be uninsured for six continuous months before they are eligible. However, we assume that there are two exceptions to this rule:

  • We assume that people who have lost coverage due to job change or a change in marital status would be eligible (an employer termination of coverage does not qualify for an exemption); and
  • We assume that some newly eligible people who currently purchase non-group coverage would shift to Affordable Choices if they lost coverage due to job change or a change in marital status.  

Using the multivariate model described above, we estimate that on average, about 39 percent of people in these circumstances would enroll.  

We assume that the program has no effect on enrollment in the existing Medicaid and SCHIP programs, with the exception that children of adults enrolling in Affordable Choices would be enrolled in SCHIP.  We estimate that about 10 percent of the uninsured are actually eligible for Medicaid or SCHIP but have not enrolled. We assume no change in enrollment for this population, although some of these people are assumed to enroll in private insurance due to the tax credit (discussed below).

Key Modeling Assumptions for Tax Deduction and Tax Credit

We modeled the effect of the President’s tax deduction proposal and the Congressional tax credit proposal on coverage using a uniform methodology.  We estimated the impact of these tax incentive methodologies in HBSM based upon a multivariate model of how the likelihood of taking coverage is affected by changes in the net cost of insurance to the individual. We assume that the value of the tax deduction or credit is seen by individuals as a reduction in the price of insurance. Our use of a uniform methodology assures that the difference in estimates for the tax deduction and tax credit proposals are due to differences in the design of these proposal rather than mere inconsistencies in assumptions.

Individual Insurance Premiums.  We simulated the premiums that individuals would pay for coverage in the non-group market using the HBSM individual insurance market sub-model under current law and under each proposal.  This part of the model estimates premiums for each person in the individual market, which includes people now purchasing non-group policies and the uninsured.  We simulated premiums for a uniform benefits package under the individual market rating rules now used in each state.  For illustrative purposes, we use as our uniform benefits package the Blue Cross/Blue Shield standard benefits option, which is estimated to be in the 75th percentile of all health plans on an actuarial value basis.

Neither proposal alters the premium rating methods used in the individual market.  Thus, under the tax credit proposal, the gross (i.e., before-tax) premium is the same as under current law.  For the President’s proposal, we adjusted the estimated premium under the proposal to reflect the elimination of state mandatory benefits requirements.  The model also adjusts premiums to reflect reduced provider cost-shifting for uncompensated care as the number of uninsured is reduced.

Multivariate Model of the Likelihood of Taking Coverage.  We estimated the impact of these proposals on coverage based upon a multivariate analysis of how the likelihood that an individual would take coverage varies with the amount of the premium. This estimate is based upon a pooled, time-series, cross-section analysis of private employer coverage reported in the Current Population Survey for the 1987 through 1997 period. 5 These analyses indicate an overall price elasticity of -0.34 percent, which means that on average, a one percent real (i.e., inflation adjusted) reduction in private employer premiums would result in a 0.34 percentage increase in the number of people with insurance.6

We estimated price elasticities by age, income and other demographic characteristics.  For example, the percentage increase in coverage resulting from a one percent reduction in premiums ranges from a high of 0.55 percent among people with incomes of $10,000 to 0.09 percent among people with incomes of $100,000 (Figure 4) (i.e. a price elasticity of –0.55 to –0.09).  Similarly, the percentage increase in coverage resulting from a one percent reduction in premiums ranges from 0.46 percent for people 20 years of age to 0.30 percent among people 60 years of age (Figure 5) (i.e. a price elasticity of –0.46 to –0.30).  Thus, the model shows that older people and people in higher income groups are less sensitive to changes in price than other population groups.


Figure 4 Percentage Change in Coverage Resulting from a One-Percent Reduction in Premiums by Income Level (in percentages) a/

Figure 4 Percentage Change in Coverage Resulting from a One-Percent Reduction in Premiums by Income Level (in percentages)

a/ Indicates a price elasticity ranging between –0.55 to -0.09 by income.

Source: Lewin Group estimates.


Figure 5 Percentage Change in Coverage Resulting from a One-Percent Reduction in Premiums by Age (in percentages) a/
Figure 5 Percentage Change in Coverage Resulting from a One-Percent Reduction in Premiums by Age (in percentages) 

a/  Indicates a price elasticity ranging between –0.46 and –0.30 by age.

Source: Lewin Group estimates.


Changes in Worker Enrollment.  We also used HBSM to simulate increases in the number of workers and dependents taking employer coverage when offered.  Up to 20 percent of uninsured workers are actually offered coverage through their job but have declined the coverage.  Because workers with ESI are eligible for the deduction and the tax credit, many of these individuals would take the coverage offered under one of the proposals.  For many, this has the effect of reducing the after-tax cost of insurance to the worker, which would result in an increase in the number of people taking coverage when offered.  We simulated this increase in take-up of employer coverage based upon the change in the after-tax cost of insurance to these individuals using the multivariate model discussed above.

Pre-emption of State Mandatory Benefits Law.  The President’s proposal would effectively pre-empt state mandated benefits laws in order for a state to offer an Affordable Choices benefit package similar to the one described above. Many states require coverage of selected services for all insurance policies sold in the state. The Affordable Choices benefits plan would require a reduced set of benefits and possible limits on benefits in to meet the definition of affordable (6 percent of state household median income). Thus, state mandatory benefit requirements would need to be eliminated for insurers and health plans to offer this product.

Employer Coverage Decision.  For each employer, we use the multivariate model to estimate the probability that an employer would offer coverage given the employer’s characteristics and the amount of the premium they would pay under current law. We then estimated the probability of offering coverage under the premiums they would pay under the proposal and simulated changes in employer coverage based upon the change in the probability of offering coverage.

We used the 1997 RWJF Survey of Employers which provides data on a representative sample of establishments.  These data include information on the size of the firm, industry and workforce characteristics of establishments.  Data include both firms that offer insurance and those that do not.  It also provides information on the characteristics of the health plans offered by each employer including premium costs and the share of the premium paid by the employer.

These data were used to develop a multivariate model to estimate price elasticities showing how the likelihood that a firm will offer coverage varies with wage level, workforce composition, firm size, industry, other firm characteristics and the price of health insurance.7 For example, the implicit price elasticity for firms with fewer than ten employees is -0.87.  This means that for each 1.0 percent reduction in price, there is an increase of 0.87 percent in the number of firms offering insurance.  The implicit price elasticity declines as firm size increases to -0.41 for firms with 10 to 20 workers and -0.22 for firms with 1,000 or more workers (Figure 6 ).


Figure 6: Employer Health Insurance Price Elasticity Estimates by Firm Size a/

Figure 6: Employer Health Insurance Price Elasticity Estimates by Firm Size

Firm Size

a/ Based upon multivariate analysis of the 1997 Robert Wood Johnson Foundation (RWJF) Survey of Employer Characteristics. “Health Benefits Simulation Model (HBSM),” The Lewin Group, August 2003.

Source: Lewin Group estimates using the Health Benefits Simulation Model (HBSM)


The model simulates the effect of employer premium subsidies using this multivariate model of the employer decision to offer coverage.  For each non-insuring employer in the data, we estimate the change in the price of insurance resulting from the premium subsidies.  The model then simulates the decisions to offer coverage based upon the predicted price elasticity for the employer.

The model reflects variations in firm price elasticity depending upon the characteristics of the firm and its workforce.  For example, the model shows that the firm price elasticity tends to decline as workers’ age and income rise, as shown in Figures 7 and 8. This results in a lower estimated price elasticity among currently insuring firms -- averaging about -0.56 for firms with 10 or fewer workers -- because the employers that offer coverage tend to have older and more highly compensated workers.


Figure 7 : Employer Health Insurance Price Elasticity Estimates for Firms with Under 10 Workers by Average Wages and Salaries per Worker a/

Figure 7 : Employer Health Insurance Price Elasticity Estimates for Firms with Under 10 Workers by Average Wages and Salaries per Worker

a/ Based upon multivariate analysis of the 1997 Robert Wood Johnson Foundation (RWJF) Survey of Employer Characteristics. “Health Benefits Simulation Model (HBSM),” The Lewin Group, August 2003.

Source: Lewin Group estimates using the Health Benefits Simulation Model (HBSM).


Figure 8: Employer Health Insurance Price Elasticity Estimates for Firms with Under 10 Workers by Age of Workers a/

bar chart

a/ Based upon multivariate analysis of the 1997 Robert Wood Johnson Foundation (RWJF) Survey of Employer  Characteristics. “Health Benefits Simulation Model (HBSM),” The Lewin Group, August 2003.

Source: Lewin Group estimates using the Health Benefits Simulation Model (HBSM).


Employer Premium Contribution.  We developed multivariate models predicting the percentage of the premium paid by the worker using the RWJF employer data.  These equations measure how premium shares vary with the characteristics of the firm, their workforce characteristics and the amount of the total premium.  These amounts are used to estimate the cost of insurance for workers in each firm selected to offer coverage in response to the program.

Worker Enrollment Decision.  Once firms are selected to offer coverage, we simulate enrollment among workers assigned to these plans.  The enrollment decision is simulated with a multivariate model of the likelihood that eligible workers would take the coverage offered to them based upon data reported in the 1996 MEPS data for people offered coverage through an employer. The model measures how take-up varies with the characteristics of the individual as well as the employee premium contribution required by the employer.

Impact of Individual Tax Deductions and Credits on Employer Coverage.   For both proposals, we used HBSM to estimate the number of employers who would discontinue coverage, as tax deductions and credits become available for non-group health insurance.  Using the synthetic firm database described above, we estimated the cost of covering each firm’s workforce under ESI with the tax credit or deduction and the cost to their workforce of purchasing coverage in the non-group market with the tax credit and deduction. In some of those firms that currently offer insurance, the after-tax cost of non-group coverage for their workforce would be less than the after-tax cost of continuing to provide ESI. We estimate that some portion of these firms would discontinue their ESI.

The underlying assumption in our analysis is that employers offer coverage because they need to in order to attract and retain workers. Thus, we do not expect employers to discontinue their coverage en-masse solely because the tax credits for non-group coverage become available. We assume that employers will make the decision to discontinue coverage only if it is more cost-effective for their workers to obtain the coverage on their own with the tax credit.

We simulated the employer decision to discontinue coverage using the synthetic firm data described above. These data include the family income and tax data required to determine the non-group premium and tax deductible and credit amounts for each worker in each firm.  Using these data, we are able to estimate the after-tax cost of coverage for each group under their current employer health plan, and the after-tax cost of coverage for these individuals if they all purchase coverage in the non-group market.

In firms where coverage in the individual market would be less costly, we assume that some portion would discontinue their health plans.  We simulated the employer decision to discontinue coverage as a shift to the less costly coverage alternative for the group (in this case non-group coverage).  To do this, we relied upon a study by Stombom et al. of the likelihood of shifting to another plan when a lower priced alternative is introduced.8 This study indicates that a 1.0 percent decrease in the price of an alternative source of coverage was on average associated with a 2.47 percent migration of enrollees to the lower cost health plan (i.e., a cross-price elasticity of -2.47). The likelihood of changing plans varies with age and health status as shown in Figure 9 .

Figure 9: Plan Switching Price Elasticity Estimates Used in HBSM

Age of Participant Low Risk High Risk a/
Under 31 -3.50 -2.78
31 to 45 -2.54 -2.54
Over 45 -2.07 -1.38

a/ People in the 90th percentile of health spending.

Source: Stombom, B., Buchmueller, T., Feldstein, P. “Switching Costs, Price Sensitivity and Health Plan Choice,” Journal of Health Economics, 21 (2002), 89-116.

To model the decision to offer or drop coverage, we calculated a “composite” plan-change price elasticity for each employer group based upon the average plan-change price elasticity for each group member.9 Firms were then simulated to discontinue coverage in proportion to the composite plan-change elasticity.

Tax Simulation Data.  HBSM is used to simulate changes in federal income and payroll taxes resulting from changes in the tax treatment of health benefits.  The CPS data provide information on tax payments and marginal income tax rates.  These data are used to impute average and marginal tax rates for households in MEPS, estimate the tax expenditure for health benefits, and estimate the value of tax deductions for health benefits.

Based upon an analysis of the CPS data on tax filings, we estimate that about 40 percent of all uninsured have no tax liability and are not required to file a tax return.  However, about half of these people file even though not required to do so, presumably so that they can obtain any refund they are entitled to (Figure 10).

Figure 10: Distribution of Insured and Uninsured Tax Filers by Marginal Tax rate in 2004
  All Tax Filing Units in the US Uninsured Tax Filing units in US
  With Earnings Without Earnings Total With Earnings Without Earnings Total
Total Potential Filers 119,981 39,367 159,348 23,004 5,016 28,020
Non-Filers 9,451 20,377 29,828 2,848 3,330 6,178
  All Filers by Marginal Tax Rate Uninsured Filers by Marginal Tax Rate
Figure 10: Distribution of Insured and Uninsured Tax Filers by Marginal Tax rate in 2004(continued)
0 18,855 11,203 30,068 5,982 648 6,630
10 15,679 2,470 18,149 4,992 354 5,346
15 43,914 3,447 47,361 7,389 484 7,873
27 25,537 1,394 26,931 1,424 140 1,564
30 4,437 359 4,796 242 43 285
35 870 60 930 60 9 69
39 1,235 54 1,289 67 7 74
Total Filers 110,530 18,990 129,520 20,156 1,686 21,842
Source: Lewin Group Estimates Using the 2005 Current Population Survey (CPS) Data.

Assumptions used for the State-Level Estimates

As mentioned earlier, aside from developing national estimates, we also modeled the President’s proposal and the Congressional tax proposal for each state.  Again, we used the HBSM to estimate the state impacts.  The policy options were modeled using similar assumptions and methods as described above for the national estimates.  This facilitates comparisons between the nation and states, as well as across states.  However, it was necessary to incorporate data from some different data sources in order to ensure that the model results are as relevant to each state as possible.

Baseline Data.  As with the national estimates, we begin by developing the current-law estimates of health spending in each State.  This process is similar to the national process.  We used state-specific population and expenditure data to develop baseline estimates for each state.  We used the state-specific sub-samples of the CPS data for 2004 through 2006 in order to estimate the population figures needed, which were described above.

Unfortunately, no single entity maintains a detailed accounting of all health expenditures by state.  A major reason is that our current multi-payer system does not require the kind of centralized systems for the payment of health care services that would be conducive to collecting and evaluating overall health expenditures.  For example, private employer health plans generally maintain separate health data systems that are not conducive to tracking health expenditures for individual geographic areas such as states (e.g. some workers are employed in firms where the corporation and its health plan are headquartered in a different state).  

Our approach to developing state health expenditure accounts is to piece together estimates of health spending by source of payment and type of service from the limited data that are available.  One source is the Centers for Medicare & Medicaid Services, which has developed estimates of total health spending, as well as Medicare and Medicaid spending by type of service for each state between 1980 and 2004.10

We also used a report prepared for the National Association of State Budget Officers to get expenditures for State and Local programs, such as state-run AIDS treatment programs, chronic disease programs, and funding for services provided at safety net clinics.11 This data was used to estimate the proposals’ impacts on spending for the existing other State and Local government health programs that focus on funding services for the uninsured.   It may be possible for savings in these other public programs to be incurred as the uninsured become covered because of the proposals.   

While data on spending for government programs in the state are available, comparable information on health spending under specific types of private insurance and household out-of-pocket spending generally is not available for individual states.  We estimated these spending amounts using data from MEPS household and employer surveys.   The employer survey contains information representative at the state level such as average health insurance premiums, including splits for employer and employee shares, by firm-size.  We use this data as the basis for our private health insurance spending estimates by state. 

As mentioned above, the household survey provides nationally representative information on the sources and uses of funds.  We use the CPS-based, state-specific population estimates to re-weight the MEPS household data in order to develop state estimates for out-of-pocket spending and uncompensated care.  We also use the household data to distribute source of funds by type of service.   

Information from all of these sources were incorporated into our analysis to develop a detailed accounting of health spending by state.  This process required converting some of the health spending data from a government fiscal year basis to a calendar year basis.  We also needed to project all health spending estimates to 2009.  Our projections were based on the trends in national health care expenditure projections estimated by CMS, with some adjustments for differential spending across states based on historical spending trends. 

After estimating baseline spending for all fifty states, we calibrated the state estimates to ensure that the sum of the state estimates is equivalent to our national estimates.  This is necessary as different data sources were used to model the national and state baselines. 

Impact on Coverage

Below we describe the impact of the President’s proposal and the Congressional tax credit proposal on health insurance coverage nationally and by state.

Impact on the Uninsured

President’s Proposal.  We project that under current policy there will be about 48.8 million people without health insurance in 2009.  Under the President’s proposal, about 10.9 million people would become newly covered under the tax deduction, 8.6 million under Affordable Choices, and another 0.8 million children under SCHIP (Figure 12).  Note that children would be automatically enrolled into the SCHIP program as their parents enroll in an Affordable Choices plan. 

We estimate a net reduction in the uninsured of 18.1 million after taking into account approximately 2.2 million that would be expected to lose coverage when their employer coverage is discontinued as a result of eliminating the tax preference for ESI (see discussion below). 


Number of People Affected (in millions)
Figure 11: Summary of Changes in Coverage Under the Presidents Proposal in 2009
Number of people who take the tax deduction Total 174.5
Previously uninsured 10.9
Previously Non-group Insurance 9.3
Previously Employer Coverage 154.3
Number of people who enroll in Affordable Choices Health Insurance Program (Affordable Choices) Total 9.7
Previously uninsured 8.6
Previously with employer or non-group insurance 1.1
Workers and dependents whose employer drops coverage Total 12.3
Take non-group coverage 8.5
Enroll in Affordable Choices 0.5
Enroll in Medicaid/SCHIP 1.1
Go uninsured 2.2
Take up Employer coverage Total 1.0
Currently decline ESI who take it 0.7
Firms who start offering coverage 0.3
Reduction in uninsured Total 18.1
Newly covered due to tax deduction 10.9
Newly covered adults due to Affordable Choices 8.6
Children newly covered in Public Program due to Parents
covered in Affordable Choices
0.8
Become uninsured from employer dropping coverage (2.2)
Source: Lewin Group estimates using the Health Benefits simulation model (HBSM).

Congressional Tax Credit proposal.  Under the tax credit proposal, about 22.7 million people would become newly covered (Figure 12).  We estimate a net reduction in the uninsured of 21.1 million. This takes into account approximately 1.6 million that would be expected to lose coverage when their employer coverage is discontinued as a result of eliminating the tax preference for ESI (see discussion below).

Figure 12: Summary of Changes in Coverage under the S.1019 Proposal in 2009

Number of People Affected (in millions)
Figure 12: Summary of Changes in Coverage under the S.1019 Proposal in 2009
Number of People who take the tax credit total 188.8
Previously uninsured 22.7
Previously non-group 10.0
Previously ESI coverage 156.1
Workers and dependents whose employer drops coverage Total 12.6
Take non-group coverage 10.6
Enroll in Medicaid/SCHIP 0.4
Go uninsured 1.6
Take up ESI coverage Total 2.8
Currently decline ESI who take it 2.1
Firms who start offering coverage 0.7
Reduction in uninsured Total 21.1
Newly covered 22.7
Become uninsured from employer dropping coverage (1.6)
Source: Lewin Group estimates using the Health Benefits simulation model (HBSM).

Figure 13 displays our estimates of the reduction in uninsured by state.  The average percent reduction in the number of uninsured is higher under the Congressional tax credit proposal (41 percent reduction) compared to the President’s proposal (35 percent reduction).  In fact, in all but one state (Arizona), the reduction in uninsured was greater under the Congressional proposal.  In Arizona, there was a rather high take-up of the tax deduction coupled with a relatively low take-up rate of the tax credit (see state specific tables in Appendix).  Also of note, North Dakota has the lowest reduction in the number of uninsured under either proposal. 

Figure 13. Summary of Proposal Impacts on State Uninsured and Cost Estimates
    President's Proposal Congressional Tax Credit Proposal
State Current Uninsured (in 1,000s) Reduction in Insured (in 1,000s) Net Federal Cost (in  $ millions) Net State Cost (in  $ millions) Net Total Public Cost (in  $ millions) Reduction in Insured (in 1,000s) Net Federal Cost (in  $ millions) Net State Cost (in  $ millions) Net Total Public Cost (in  $ millions)
Alabama 696 252 $1,446 $262 $1,709 286 $844 -$440 $404
Alaska 141 34 $84 $12 $97 40 -$42 -$13 -$55
Arizona 909 446 $2,022 $271 $2,293 299 $993 -$461 $531
Arkansas 520 197 $1,097 $168 $1,265 235 $764 -$203 $561
California 7,276 2,341 $12,220 $2,175 $14,394 2,830 $6,799 -$4,946 $1,853
Colorado 880 348 $1,724 $340 $2,065 396 $789 -$561 $228
Connecticut 449 138 $551 $68 $619 173 -$154 -$545 -$699
Delaware 103 28 $152 $28 $180 36 $0 -$110 -$110
Florida 3,596 1,432 $5,774 $52 $5,826 1,723 $3,526 -$190 $3,336
Georgia 1,615 625 $3,189 $422 $3,611 771 $1,758 -$939 $819
Hawaii 118 43 $425 $1 $426 47 $262 -$189 $73
Idaho 280 140 $494 $75 $569 148 $289 -$149 $140
Illinois 1,881 621 $3,451 $622 $4,074 745 $1,115 -$1,489 -$374
Indiana 975 311 $1,675 $292 $1,967 382 $542 -$747 -$205
Iowa 379 120 $1,124 $257 $1,382 144 $531 -$396 $135
Kansas 380 122 $947 $202 $1,148 136 $423 -$274 $149
Kentucky 666 217 $1,237 $218 $1,455 250 $718 -$393 $325
Louisiana 825 336 $1,229 $73 $1,303 387 $787 -$218 $569
Maine 149 27 -$23 $22 -$1 48 -$205 -$225 -$430
Maryland 778 259 $1,838 $306 $2,144 326 $639 -$698 -$59
Massachusetts NA NA NA NA NA NA NA NA NA
Michigan 1,224 395 $2,168 $377 $2,546 479 $173 -$1,159 -$985
Minnesota 609 223 $1,849 $517 $2,366 289 $689 -$1,191 -$501
Mississippi 516 186 $1,048 $124 $1,171 219 $771 -$246 $525
Missouri 655 201 $1,881 $276 $2,158 258 $998 -$607 $391
Montana 195 64 $322 $57 $379 72 $196 -$97 $99
Nebraska 258 75 $619 $111 $730 91 $267 -$196 $71
Nevada 464 166 $901 $39 $940 191 $603 -$17 $586
New Hampshire 168 50 $276 $2 $278 67 -$50 -$9 -$59
New Jersey 1,350 634 $1,724 $78 $1,802 736 -$24 -$1,428 -$1,453
New Mexico 300 111 $675 $83 $758 118 $361 -$141 $219
New York 2,483 1,053 $2,950 $488 $3,439 1,236 $315 -$2,333 -$2,018
North Carolina 1,496 541 $3,154 $501 $3,655 654 $2,176 -$822 $1,354
North Dakota 95 17 $229 $50 $278 23 $111 -$57 $54
Ohio 1,646 559 $3,082 $476 $3,557 704 $996 -$1,320 -$324
Oklahoma 696 284 $1,256 $215 $1,471 323 $831 -$345 $486

 

Figure 13. Summary of Proposal Impacts on State Uninsured and Cost Estimates (cont’d)
    President's Proposal Congressional Tax Credit Proposal
State Current Uninsured (in 1,000s) Reduction in Insured (in 1,000s) Net Federal Cost (in $ millions) Net StateCost (in $ millions) Net Total Public Cost (in $ millions) Reduction in Insured (in 1,000s) Net Federal Cost (in $ millions) Net State Cost (in $ millions) Net Total Public Cost (in $ millions)
Oregon 609 303 $744 $126 $871 314 $93 -$445 -$352
Pennsylvania 1,668 599 $2,787 $391 $3,179 683 $831 -$1,315 -$484
Rhode Island 141 41 $125 $41 $166 51 -$52 -$133 -$185
South Carolina 715 242 $1,122 $161 $1,283 292 $694 -$439 $254
South Dakota 117 28 $266 $17 $283 36 $143 -$6 $138
Tennessee 928 312 $1,769 $53 $1,821 370 $897 -$67 $830
Texas 5,511 2,014 $6,523 $344 $6,867 2,308 $4,109 -$227 $3,881
Utah 425 154 $790 $122 $912 186 $395 -$228 $167
Vermont 71 27 $14 $18 $32 33 -$126 -$86 -$212
Virginia 1,086 375 $2,746 $473 $3,219 466 $1,372 -$838 $534
Washington 1,009 492 $1,556 $17 $1,573 557 $521 -$86 $435
West Virginia 321 103 $548 $70 $618 116 $304 -$164 $140
Wisconsin 708 192 $926 $255 $1,181 241 -$399 -$709 -$1,108
Wyoming 93 22 $141 -$11 $130 29 $58 -$26 $32

NA – Estimates are not included for Massachusetts due to the transitioning of their reform plan.

Source: Lewin Group estimates using the Health Benefits simulation model (HBSM).

Changes in Private Insurance Coverage

President’s Proposal.  Under current law, the tax exclusion for employer-sponsored health benefits effectively reduces the cost of insurance to workers.   Creating a tax deduction of an equal amount for both employer and non-group coverage eliminates the relative tax advantage of providing coverage through the employer, resulting in a shift of people away from ESI to private non-group coverage.   With the elimination of the relative tax advantage of ESI, some employers are expected to discontinue their coverage, particularly in cases where their workforces can obtain individual coverage for less than what their employer would have to pay in the small group market.  This would typically occur among small firms with younger and healthier workers in states where insurers are permitted to provide greater discounts for age and health status in the individual market than are permitted in the small group market.12

Under current-law we projected there would be about 158.1 million workers and dependents with ESI in 2009.13 Using the assumptions discussed above, we estimate that about 12.3 million covered workers and dependents would be in firms that discontinue health insurance benefits.  This would be partly offset by approximately 0.3 million people in firms where the employer is stimulated to start to offer coverage.14 Also, we estimate that there are about 0.7 million workers and dependents that have declined the coverage offered to them at work who would now take ESI in cases where the value of the tax deduction is greater than the value of the existing tax exclusion.15 Thus, we estimate a net reduction in ESI enrollment of approximately 11.3 million. 

Of the 12.3 million that would lose their ESI, about 2.2 million (18 percent) would become uninsured.  We also expect about 0.5 million people who lose ESI coverage to enroll in Affordable Choices and about 1.1 million to be covered under Medicaid.  The vast majority would take individual coverage (8.5 million) with the tax deduction.

The number of people with individual coverage nearly triples under the proposal from approximately 10 million to 27.7 million.  Along with the 8.5 million who would lose their ESI coverage, an additional 9.3 million would remain in non-group coverage and 9.9 previously uninsured would take-up coverage as the tax deduction makes insurance coverage more affordable.   

Congressional Tax Credit Proposal.  Similar to the tax deduction described above, creating a tax credit of an equal amount for both employer and non-group coverage and removing the tax exclusion for employer sponsored insurance eliminates the relative tax advantage of providing coverage through the employer, resulting in a shift of people away from ESI to private non-group coverage. 

Under the tax credit proposal, we estimate that about 12.6 million covered workers and dependents would be in firms that discontinue health insurance benefits.  This would be partly offset by approximately 0.7 million people in firms where the employer is simulated to start to offer coverage.16 Also, we estimate that there are about 2.1 million workers and dependents that have declined the coverage offered to them at work who would now take ESI in cases where the value of the tax credit is greater than the value of the existing tax exclusion.  Thus, we estimate a net reduction in ESI enrollment of approximately 9.8 million. 

Of the 12.6 million that would lose their ESI, about 1.6 million (13 percent) would become uninsured.  We expect about 0.4 million to be covered under Medicaid or SCHIP.  The vast majority would take individual coverage (10.6 million).

The increase in the number of people with individual coverage is even greater for the Congressional proposal in comparison to the President’s proposal, as coverage reaches approximately 40.4 million.  In this case, along with the 10.6 million who would lose their ESI coverage, an additional 10.0 million would remain in non-group coverage and 19.9 previously uninsured would take-up coverage as the tax credit makes insurance coverage more affordable.   

Figure 14 displays the changes in the employer-sponsored and individual insurance markets by State under each proposal. 

Figure 14. Changes in the employer-sponsored and individual insurance markets by State under the President’s Proposal and Congressional tax credit proposal.
  Current Insured Change Under President's Proposal Change Under the Congressional Tax Credit Proposal
State ESI Non-Group Total Private ESI Non-Group Net Change Private ESI Non-Group Net Change
Private
Alabama 2,401 106 2,506 -209 261 52 -187 469 283
Alaska 331 18 350 -29 35 6 -26 65 39
Arizona 2,792 188 2,980 -241 614 373 -221 497 276
Arkansas 1,331 101 1,431 -113 176 63 -97 328 231
California 18,278 1,803 20,081 -1,606 2,126 520 -1,492 4,255 2,763
Colorado 2,623 223 2,847 -231 355 125 -211 603 393
Connecticut 2,130 111 2,241 -180 222 42 -172 342 169
Delaware 483 17 500 -42 49 8 -39 74 35
Florida 8,261 656 8,917 -731 1,368 637 -631 2,332 1,701
Georgia 4,751 226 4,976 -418 671 253 -363 1,123 760
Hawaii 745 30 775 -71 75 5 -70 115 45
Idaho 770 74 844 4 79 83 18 130 148
Illinois 7,315 368 7,683 -640 780 140 -598 1,326 728
Indiana 3,596 223 3,819 -296 368 71 -271 643 372
Iowa 1,738 146 1,884 -152 161 9 -145 286 141
Kansas 1,589 122 1,711 -143 149 6 -136 271 134
Kentucky 2,175 107 2,281 -187 224 37 -172 415 243
Louisiana 2,097 147 2,244 -190 274 84 -159 525 366
Maine 681 33 713 2 16 18 10 38 48
Maryland 3,330 148 3,479 -291 370 79 -271 590 319
Massachusetts NA NA NA NA NA NA NA NA NA
Michigan 5,946 239 6,184 -504 602 98 -482 946 464
Minnesota 3,346 243 3,589 -295 395 100 -288 569 281
Mississippi 1,359 87 1,445 -115 163 48 -97 313 216
Missouri 3,222 208 3,430 -279 330 51 -267 518 251
Montana 424 57 481 -45 55 11 -40 112 72
Nebraska 1,041 109 1,150 -91 96 5 -86 175 89
Nevada 1,329 73 1,402 -107 157 50 -97 283 186
New Hampshire 851 36 887 -75 92 17 -72 137 65
New Jersey 5,377 191 5,568 24 370 393 111 625 736
New Mexico 835 46 880 -72 108 35 -68 184 115
New York 10,206 497 10,703 37 560 597 176 1,060 1,236
North Carolina 4,320 326 4,646 -380 533 153 -335 979 643
North Dakota 356 45 400 -35 28 -8 -34 56 22
Ohio 6,693 264 6,957 -557 732 175 -517 1,208 691
Oklahoma 1,704 101 1,805 -153 255 101 -133 453 320
Figure 14. Changes in the employer-sponsored and individual insurance markets by State under the President’s Proposal and Congressional tax credit proposal (cont’d)
  Current Insured Change Under President's Proposal Change under the Congressional Tax Credit Proposal
State ESI Non-Group Total Private ESI Non-Group Net Change Private ESI Non-Group Net Change Private
Oregon 1,894 129 2,023 8 176 184 38 276 314
Pennsylvania 6,886 456 7,342 -593 722 129 -554 1,228 674
Rhode Island 617 26 643 -54 65 11 -51 101 50
South Carolina 2,108 131 2,239 -182 244 62 -157 444 287
South Dakota 403 53 456 -37 35 -2 -34 70 35
Tennessee 3,071 224 3,295 -255 325 71 -229 590 361
Texas 10,852 613 11,465 -889 1,493 605 -754 3,024 2,270
Utah 1,466 88 1,554 -129 178 48 -122 303 181
Vermont 344 19 362 1 16 17 4 28 33
Virginia 4,387 253 4,639 -379 489 110 -353 810 457
Washington 3,481 241 3,723 13 274 287 71 487 557
West Virginia 819 39 857 -70 92 22 -61 175 114
Wisconsin 3,332 189 3,521 -286 303 16 -275 508 233
Wyoming 267 25 293 -27 30 3 -25 54 29

NA – Estimates are not included for Massachusetts due to the transitioning of their reform plan.

Source: Lewin Group estimates using the Health Benefits simulation model (HBSM).

Impact on Health Spending

In this section we will discuss the impacts from both programs on Federal and State health spending.  We first do so based upon our single year estimates assuming that the programs are fully implemented in 2009.  We then present the estimated impacts over a 10-year projection period including an expected phase-in of the program over time. 

Single-Year Estimates of Federal Costs

President’s ProposalThe total amount of the federal tax revenue lost due to the new deduction would be $294.6 billion, including income and Social Security taxes (Figure 16).  These revenue losses would be largely offset by about $229.9 billion by requiring workers to count the value of their ESI coverage as taxable income.  After taking into account the redirected DSH funding, the net costs of the Affordable Choices program to the federal government would be $6.7 billion.  There would also be additional federal Medicaid spending of $2.3 billion due to increased enrollment in Medicaid or SCHIP of 1.1 million people.  Thus, the President’s proposal would increase the Federal deficit by about $73.8 billion.17

Figure 15: Summary of Public Program Costs Under the President’s Proposal in 2009

Public Program Costs (in $ billions)
    Federal State Total
Tax Deduction for Private Insurance   $294.6 $34.3 $328.9
Federal Income Tax Deduction $173.70      
Federal Payroll Tax Deduction $120.90      
State Income Tax Deduction $34.30      
Affordable Choices Health Insurance Program (Affordable Choices)
Figure 15: Summary of Public Program Costs Under the President’s Proposal in 2009(con't)
DSH funding redirected to Affordable Choices at current Federal matching rates a/ $4.6 $3.5 $8.1
Remaining Affordable Choices costs at enhanced Federal matching rates $6.7 $2.9 $9.6
Total Affordable Choices Spending $11.3 $6.4 $17.7
Savings to DSH Program b/ ($4.6) ($3.5) ($8.1)
Net Spending for Affordable Choices $6.7 $2.9 $9.6
Current Medicaid/SCHIP and other State Health Programs
Figure 15: Summary of Public Program Costs Under the President’s Proposal in 2009(con't)
Current Medicaid/SCHIP $2.3 $1.8 $4.1
Other State and Local Government Health Programs c/ $0.0 ($3.3) ($3.3)
Eliminate ESI Tax Exclusion
Figure 15: Summary of Public Program Costs Under the President’s Proposal in 2009(con't)
Eliminate ESI Tax Exclusion     ($229.9) ($25.4) ($255.3)
ESI Tax Exclusion Federal income tax $130.20      
Federal payroll tax $86.70      
State income tax $25.40      
Flexible Spending Accounts $5.00      
Health Care Tax Deduction $8.00      
Net Program Costs/Savings     $73.8 $10.3 $84.0

a/ Assumes states use half of their federal DSH funding for expanding coverage under the Affordable Choices Health Insurance Program. States are required to match these funds at the current Medicaid matching rates. (Data provided by CMS).
b/ Actual amount of state savings would vary depending on the method currently used by the state to fund DSH payments. The state’s share of DSH matching funds could consist of general revenues, provider taxes, intergovernmental transfers and certified public expenditures.
c/ Includes savings to state and local government health programs as users become covered by private insurance. Some portion of these savings has been counted as State’s share of DSH program savings.

Source: Lewin Group estimates using the Health Benefits simulation model (HBSM).


Congressional Flat Tax ProposalThe total amount of the federal tax revenue lost due to the new tax credit would be $265.0 billion, including income and Social Security taxes (Figure 16).  As with the President’s proposal, these revenue losses would be largely offset by about $229.9 billion by requiring workers to count the value of their ESI coverage as taxable income.  There is also additional federal Medicaid spending of $1.0 billion due to increased enrollment in Medicaid or SCHIP of 0.4 million.  Thus, the tax credit proposal would increase the Federal deficit by about $36.1 billion.18

Figure 16: Summary of Public Program Costs Under the S.1019 Proposal in 2009
Public Program Costs (in $ billions)
      Federal State Total
      $265.0 $0.0 $265.0
Tax Credits Tax Credit used for ESI $202.7      
Tax Credit used for Non-group $62.3      
Current Medicaid/SCHIP     $1.0 $0.8 $1.8
Other State Programs     $0.0 ($5.1) ($5.1)
Eliminate ESI Tax Exclusion     ($229.9) ($25.4) ($255.3)
ESI Tax Exclusion Federal income tax $130.2      
Federal payroll tax $86.7      
State income tax $25.4      
Flexible Spending Accounts $5.0      
Health Care Tax Deduction $8.0      
Total     $36.1 ($29.7) $6.4
Source: Lewin Group estimates using the Health Benefits simulation model (HBSM).

Single-Year Estimates of State Costs

Note that the net Federal, State and total public costs for both proposals are displayed by State in Figure 13.  Detailed estimates of the costs, similar to those displayed in Figure 15 and Figure 16 are displayed in the Appendix for each State. Below we describe the derivation of the net cost amounts for the States under each proposal. 

President’s Proposal.  The net cost to state governments under the President’s proposal is $10.3 billion (Figure 15).  The total amount of the state tax revenue lost due to the deduction would be $34.3 billion.   This would be offset by an increase in $25.4 billion in increased revenue due to taxing the value of ESI coverage.

We estimate that approximately $8.1 billion in DSH funding would be redirected to the Affordable Choices plan.   As described above, this represents at least 50 percent of each State’s current DSH spending. 19 The Federal and State share of this DSH funding is determined by the Federal Medical Assistance Percentage (FMAP).  The remaining Affordable Choices costs are split between State and Federal spending using the enhanced FMAP rate.  This brings the State share of total Affordable Choices spending to $6.4 billion. However, because the DSH funding amount is money the States currently spend, the net spending is equal to the remaining Affordable Choices spending split at the enhanced Federal matching rates, which amounts to $2.9 billion for States. 

The States also incur additional Medicaid and SCHIP spending from increased enrollment, but experience $3.3 billion in savings with their other State and Local health programs, such as State “safety net” clinics.  These other public programs essentially act as “safety net” programs for many of the uninsured who would become covered under the President’s proposal. 

Thus, the gross costs to the States amounts to the sum of the revenue lost due to the tax deduction ($34.4 billion), the State net Affordable Choices spending ($2.9 billion), and spending for additional Medicaid and SCHIP enrollees ($1.8 billion).  The costs are offset by the savings to other public programs ($3.3 billion) and revenue gained from the removal of the tax exclusions ($25.4 billion) amounting to the net cost to the States of $10.3 billion. 

Congressional Tax Credit Proposal.  The costs to State governments under the Tax Credit proposal are more straightforward in comparison to those under the President’s proposal (see Figure 15).   In this case the State does not experience any loss of tax revenue since they do not provide any tax deduction or tax credit. 

The States, due to increased enrollment, experience increased Medicaid and SCHIP costs of $0.8 billion.  This amount is more than offset by the savings to other public programs, $5.1 billion, and the revenue gained from the removal of the tax exclusions, $25.4 billion.  Thus, the Congressional proposal amounts to a net savings to States of $29.7 billion. 

10-Year Estimates

We projected the cost of the two proposals over the 10-year period, 2009 through 2018.  Note that when projecting program enrollment and costs, we assume a 40 percent phase-in for 2009 and an 80 percent phase in for 2010, with the program reaching ultimate enrollment beginning in 2011.  Therefore, the 2009 impact estimates in our projections differ from those discussed above, which assumed a fully-phased-in program in 2009. 

President’s Proposal.  In Figure 17 we display 10-year projections of the cost of the President’s program to the federal government.  The increase in the deficit during the initial implementation of the program would diminish in the following years because of the way the tax deduction amount is indexed.  The deduction amount (i.e., $7,500 single; $15,000 family) is indexed each year to the growth in general price levels as measured by the consumer price index, which is about 2.8 percent per year.  Because health care costs are projected to grow at about 7.0 percent per year, the revenue gain from taxing employer benefits would grow faster than the revenue loss due to the deduction. 20

Consequently, by 2015 the proposal would result in a net reduction in the federal deficit of about $15.4 billion.  The net effect of the proposal over a ten-year period from 2009 through 2018 would be a net increase in the federal deficit of about $25.7 billion.

The Congressional Tax Credit Proposal.  As with the President’s proposal, the increase in the deficit during the first years of the Congressional tax credit proposal would diminish in the following years because of the way the tax credit amount is indexed (Figure 18).  This is because the tax credit (i.e., $2,000 per adult and $500 per child up to a maximum of 2 children) is indexed with the growth in the chain-weighted Gross Domestic Product, which grows at about 2.2 percent per year, while the tax exclusion that it replaces would be expected to grow with health care cost inflation at about 7.0 percent per year.21 Consequently, by 2012 the proposal would result in a net reduction in the federal deficit of about $12.4 billion. The net effect of the proposal over a ten-year period from 2009 through 2018 would be a net increase in the federal revenue of about $564.4 billion.

  2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2009-2018
Figure 17: Federal Program Costs under the President’s Program: 2009-2018 (in $ billions) a/ b/
Health Care Tax Deduction for Private Insurance c/ $283.6 $299.7 $312.4 $321.6 $331.2 $341.1 $351.2 $361.7 $372.5 $383.8 $3,358.6
DSH funding redirected to Affordable Choices at current Federal matching rates d/ $1.8 $4.0 $5.4 $5.8 $6.3 $6.8 $7.4 $8.0 $8.6 $9.2 $63.3
Total Affordable Choices Spending e/ $4.5 $9.8 $13.2 $14.3 $15.5 $16.8 $18.2 $19.7 $21.1 $22.6 $155.6
Net Spending for Affordable Choices $2.7 $5.8 $7.8 $8.5 $9.2 $9.9 $10.8 $11.7 $12.5 $13.4 $92.3
Current Medicaid/SCHIP d/ $0.93 $2.02 $2.7 $2.9 $3.2 $3.5 $3.7 $4.0 $4.3 $4.7 $32.1
Eliminate ESI Tax
Exclusion f/
($229.9) ($250.3) ($272.3) ($296.2) ($322.3) ($350.5) ($381.0) ($414.3) ($450.5) ($489.8) ($3,457.2)
Net Program Costs/Savings $57.3 $57.1 $50.6 $36.8 $21.3 $4.0 ($15.4) ($36.9) ($61.1) ($88.0) $25.7
CPI Growth   2.8% 2.8% 2.8% 2.8% 2.8% 2.8% 2.8% 2.8% 2.8%  
Growth in ESI Coverage   0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%  
Medicaid Spending Growth   7.9% 8.1% 8.2% 8.2% 8.2% 8.3% 8.3% 7.2% 7.2%  
Personal Health Care Spending Growth   7.0% 6.9% 6.9% 6.9% 6.9% 6.9% 6.9% 6.9% 6.9%  
Tax Exclusion Growth over and above Medical Inflation   1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8%  

a/ We assume a 40 percent phase-in for 2009 and an 80 percent phase in for 2010, with the program reaching ultimate enrollment beginning in 2011.  Therefore the full impact on spending is not experienced until 2011.
b/ We assume that the relative value of the tax deduction decreases over time as medical price inflation increases faster than the health care tax deduction, which is indexed to the consumer price index.  This effect reduces the number of people expected to use the tax deduction.
c/ The growth in the tax deduction is indexed to the growth in the consumer price index, which is approximately 2.8 percent each year, plus growth in ESI coverage.   
d/ DSH funding and Medicaid spending are assumed to grow at a similar rate to total Medicaid expenditures as projected by the Office of the Actuary, Centers for Medicare & Medicaid Services (CMS).  The latest projections are available on the CMS website at: http://www.cms.hhs.gov/NationalHealthExpendData/03_NationalHealthAccoun….
e/ Affordable Choices spending is assumed to grow at a similar rate to Medicaid spending.
f/ The value of the ESI tax exclusion is assumed to grow similarly historical rates, which we measured as personal health care expenditures growth (CMS projections) plus about 1.8%. This additional amount is assumed to account for growth in personal incomes resulting in increased marginal tax rates over time.

Source: Lewin Group estimates using the Health Benefits simulation model (HBSM).

  2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2009-2018
Figure 18: Federal program costs under the S.1019 Proposal: 2009-2018 (in $ billions) a/ b/
Tax Credits c/ $245.9 $264.8 $277.0 $282.6 $287.5 $292.6 $298.2 $303.9 $309.7 $316.2 $2,878.3
Current Medicaid/
SCHIP d/
$1.0 $1.1 $1.2 $1.3 $1.4 $1.5 $1.6 $1.7 $1.9 $2.0 $14.5
Eliminate ESI Tax Exclusion e/ ($229.9) ($250.3) ($272.3) ($296.2) ($322.3) ($350.5) ($381.0) ($414.3) ($450.5) ($489.8) ($3,457.2)
Net Program Costs/Savings $17.0 $15.5 $5.9 ($12.4) ($33.4) ($56.4) ($81.3) ($108.7) ($138.9) ($171.6) ($564.4)
Chain-weighted GDP   2.6% 2.5% 2.3% 2.0% 2.0% 2.1% 2.1% 2.1% 2.1%  
Growth in ESI Coverage   0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%  
Medicaid Spending Growth   7.9% 8.1% 8.2% 8.2% 8.2% 8.3% 8.3% 7.2% 7.2%  
Personal Health Care Spending Growth   7.0% 6.9% 6.9% 6.9% 6.9% 6.9% 6.9% 6.9% 6.9%  
Tax Exclusion Growth over and above Medical Inflation   1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8% 1.8%  

a/ We assume a 40 percent phase-in for 2009 and an 80 percent phase in for 2010, with the program reaching ultimate enrollment beginning in 2011.  Therefore the full impact on spending is not experienced until 2011.
b/ We assume that the relative value of the tax credit decreases over time as medical price inflation increases faster than the tax credit, which is indexed to the change in the chain-weighted price index for the gross domestic product.  This effect reduces the number of people expected to use the tax credit.
c/ The value of the Tax Credit is assumed to grow at the chain-weighted Gross Domestic Product plus the growth in people with ESI coverage as projected by the Office of the Actuary, Centers for Medicare & Medicaid Services (CMS).  The latest projections are available on the CMS website at: http://www.cms.hhs.gov/NationalHealthExpendData/03_NationalHealthAccoun….
d/ Medicaid spending is assumed to grow at a similar rate to total Medicaid expenditures as projected by the Office of the Actuary, Centers for Medicare & Medicaid Services (CMS).  The latest projections are available on the CMS website at: http://www.cms.hhs.gov/NationalHealthExpendData/03_NationalHealthAccoun….
e/ The value of the ESI tax exclusion is assumed to grow similarly to historical rates, which we measured as personal health care expenditures growth (CMS projections) plus about 1.8%. This additional amount is assumed to account for growth in personal incomes resulting in increased marginal tax rates over time.

Source: Lewin Group estimates using the Health Benefits simulation model (HBSM).

Footnotes

  1. The process used is similar to that used by the Bureau of the Census to establish final family weights in the March CPS.
  2. A description of the CMS data is available at:  http://www.cms.hhs.gov/NationalHealthExpendData/02_NationalHealthAccoun…
  3. We controlled for worker wage levels, industry, firm size and other characteristics when matching these firms.
  4. HBSM baseline data based upon Lewin Group Analysis of the February and March CPS data for 1997.
  5. This required imputing premiums based upon employer survey data developed by the Kaiser Family Foundation (KFF) and the Health Research and Education Trust.
  6. See Sheils, J., Haught, R., “Health Insurance and Taxes:  The Impact of Proposed Changes in Current Federal Policy”, (report to The National Coalition on Health Care), The Lewin Group, October 18, 1999.
  7. While the RWJF data includes premium information for employers that offer coverage, no data is provided on the premiums faced by firms that do not offer coverage. To model the price effect we imputed premiums to non-insuring firms with a multivariate model of how premium levels vary with the workforce and firm characteristics that we estimated from the RWJF data on insuring establishments.
  8. Stombom, B., Buchmueller, T., Feldstein, P. “Switching Costs, Price Sensitivity and Health Plan Choice,” Journal of Health Economics, 21 (2002), 89-116.
  9. Stombom, B., Buchmueller, T., Feldstein, P. “Switching Costs, Price Sensitivity and Health Plan Choice,” Journal of Health Economics, 21 (2002), 89-116.
  10. Centers for Medicare & Medicaid Services.  “Health Expenditures by State.”  <Available as of June 9, 2007 at: http://www.cms.hhs.gov/NationalHealthExpendData/05_NationalHealthAccoun…;
  11. National Association of State Budget Officers.    2005.  2002-2003 State Health Expenditure Report.
  12. The process used is similar to that used by the Bureau of the Census to establish final family weights in the March CPS.
  13. A description of the CMS data is available at:  http://www.cms.hhs.gov/NationalHealthExpendData/02_NationalHealthAccoun…
  14. We controlled for worker wage levels, industry, firm size and other characteristics when matching these firms.
  15. HBSM baseline data based upon Lewin Group Analysis of the February and March CPS data for 1997.
  16. This required imputing premiums based upon employer survey data developed by the Kaiser Family Foundation (KFF) and the Health Research and Education Trust.
  17. See Sheils, J., Haught, R., “Health Insurance and Taxes:  The Impact of Proposed Changes in Current Federal Policy”, (report to The National Coalition on Health Care), The Lewin Group, October 18, 1999.
  18. While the RWJF data includes premium information for employers that offer coverage, no data is provided on the premiums faced by firms that do not offer coverage. To model the price effect we imputed premiums to non-insuring firms with a multivariate model of how premium levels vary with the workforce and firm characteristics that we estimated from the RWJF data on insuring establishments.
  19. Stombom, B., Buchmueller, T., Feldstein, P. “Switching Costs, Price Sensitivity and Health Plan Choice,” Journal of Health Economics, 21 (2002), 89-116.
  20. Stombom, B., Buchmueller, T., Feldstein, P. “Switching Costs, Price Sensitivity and Health Plan Choice,” Journal of Health Economics, 21 (2002), 89-116.
  21. Centers for Medicare & Medicaid Services.  “Health Expenditures by State.”  <Available as of June 9, 2007 at: http://www.cms.hhs.gov/NationalHealthExpendData/05_NationalHealthAccoun…;
  22. National Association of State Budget Officers.    2005.  2002-2003 State Health Expenditure Report.
  23. For example, some states restrict the amount by which premiums may vary with age and health status in the small group market, but permit insurers to vary premiums with age, health status, and other risk factors in the individual market. 
  24. This is an estimate of the average monthly enrollment in employer plans.
  25. We assume that the employer would start to offer coverage if employer coverage could be less costly that non-group coverage in cases where at least 75 percent of the workers in the firm would have taken non-group coverage. 
  26. Workers get the full amount of the deduction regardless of the actual premiums, while the value of the tax exclusion is limited to only the cost of insurance. 
  27. We assume that the employer would start to offer coverage if employer coverage could be less costly that non-group coverage in cases where at least 75 percent of the workers in the firm would have taken non-group coverage. 
  28. The net public cost per family would be $642 in 2009 ($563 in federal costs and $79 in state costs).
  29. The net public cost per family would be $49 in 2009 ($275 in federal costs and a $226 reduction in state costs).
  30. We should note that this total for each state is the minimum between 50 percent of the current DSH spending amount or the total funds
  31. Estimate based upon projections of health care cost growth developed by the Office of the Actuary at the Centers for Medicare & Medicaid Services. 
  32. Previously, the GDP index was fixed-weighted.  Under a fixed-weight measure, the contribution to GDP of what is produced in the current year would be determined based upon its worth in a base year.  Alternatively, chain weights move the base year along through time.  For example, under chain weights, a computer in 2000 is worth the average of what it cost in 1999 and 2000 and a computer in 2001 is worth the average of 2000 and 2001, etc….  This mitigates biases that may occur from valuing products based on their worth in a distant benchmark year. 
  33. For example, some states restrict the amount by which premiums may vary with age and health status in the small group market, but permit insurers to vary premiums with age, health status, and other risk factors in the individual market. 
  34. This is an estimate of the average monthly enrollment in employer plans.
  35. We assume that the employer would start to offer coverage if employer coverage could be less costly that non-group coverage in cases where at least 75 percent of the workers in the firm would have taken non-group coverage. 
  36. Workers get the full amount of the deduction regardless of the actual premiums, while the value of the tax exclusion is limited to only the cost of insurance. 
  37. We assume that the employer would start to offer coverage if employer coverage could be less costly that non-group coverage in cases where at least 75 percent of the workers in the firm would have taken non-group coverage. 
  38. The net public cost per family would be $642 in 2009 ($563 in federal costs and $79 in state costs).
  39. The net public cost per family would be $49 in 2009 ($275 in federal costs and a $226 reduction in state costs).
  40. We should note that this total for each state is the minimum between 50 percent of the current DSH spending amount or the total funds
  41. Estimate based upon projections of health care cost growth developed by the Office of the Actuary at the Centers for Medicare & Medicaid Services. 
  42. Previously, the GDP index was fixed-weighted.  Under a fixed-weight measure, the contribution to GDP of what is produced in the current year would be determined based upon its worth in a base year.  Alternatively, chain weights move the base year along through time.  For example, under chain weights, a computer in 2000 is worth the average of what it cost in 1999 and 2000 and a computer in 2001 is worth the average of 2000 and 2001, etc….  This mitigates biases that may occur from valuing products based on their worth in a distant benchmark year.