Aligning the Medical Expenditure Panel Survey to Aggregate U.S. Benchmarks 

September 21, 2007 

[Revised June 6, 2008] 

ABSTRACT

Household-level data are valuable for a range of research efforts, including health policy 

microsimulation analyses, distributional studies, and analyses of condition-specific spending. 

Household data, however, do not provide a complete picture of health care expenditures, because 

they exclude certain types of outlays, such as administrative costs, government payments to 

providers that are not linked to patient events, research, and public health. Household data also 

do not provide information on employer premium contributions or tax subsidies. This paper 

describes how data from the Medical Expenditure Panel Survey (MEPS) were aligned with 

aggregate benchmarks from the National Health Expenditure Accounts (NHEA) and 

supplemented with tax expenditure estimates to produce a database that will help support a range 

of health research initiatives that require comprehensive measures of medical expenditures. 

Thomas Selden, Ph.D. 

Agency for Healthcare Research and Quality 

Division of Modeling and Simulation 

Center for Financing, Access, and Cost Trends 

540 Gaither Road 

Rockville, MD 20850 

Thomas.Selden@ahrq.hhs.gov

Phone: (301)427-1677 

and 

Merrile Sing, Ph.D. 

Agency for Healthcare Research and Quality 

Division of Modeling and Simulation 

Center for Financing, Access, and Cost Trends 

540 Gaither Road 

Rockville, MD 20850 

Merrile.Sing@ahrq.hhs.gov

Phone: (301)427-1666

2
Introduction 

The Medical Expenditure Panel Survey (MEPS) is an annual household survey designed 

to yield nationally representative estimates of insurance coverage, medical expenditures, 

insurance premiums, and a wide range of other health-related and socioeconomic characteristics 

for persons in the civilian, noninstitutionalized population (Cohen et al., 1996, and Cohen, 1997). 

There are reasons, however, why no household expenditure survey can be expected to provide a 

complete picture of U.S. health care spending. First, household respondents cannot be expected 

to report administrative costs or payments to providers that are not linked to specific events. 

Second, household data can suffer from expenditure shortfalls due to under-reporting and 

differential attrition of high-cost cases. Third, household data must be augmented with tax 

simulations to measure the level and distribution of tax expenditures. 

MEPS household data are a vital national resource for policy analysis and have already 

been used in a large number of microsimulation studies of existing or proposed programs. 

However, we believe the value of MEPS for certain applications can be enhanced through the 

detailed alignment of MEPS with aggregate expenditure benchmarks – primarily those provided 

by the National Health Expenditure Accounts (NHEA).1

 In this paper, we provide details 

regarding our methodology for producing an aligned MEPS dataset. This paper serves in part as 

a companion piece to “The Distribution of Public Spending for Health Care in the United 

States.” The paper also serves as a background paper for other applications of these enhanced 

data. 

 

1

 See, for instance, CMS (2006) NHE Projections 2006-2016, Forecast Summary and Selected Tables (at 

http://www.cms.hhs.gov/NationalHealthExpendData/). 

3
Methods 

The starting point for our analysis is pooled MEPS data from 2002 and 2003. We 

combine two years of data to smooth year-to-year fluctuations in expenditures due in part to 

random sampling variation (Machlin et al., 2003). The resulting dataset contains 70,099 

positively-weighted observations. All health expenditures were inflation-adjusted to 2002 

dollars using the gross domestic product deflator for medical goods. This section describes how 

we align this pooled dataset with aggregate benchmarks for 2002.2 

NHEA Personal Health Care Benchmarks for MEPS 

There are numerous differences between NHEA Personal Health Care (PHC) and MEPS 

expenditures including: differences in population, differences in the scope of services, and 

definitional differences regarding service types and payment sources. Sing et al. (2006) provide 

a detailed analysis of the two sources, comparing the pooled 2002-2003 MEPS to NHEA 2002.3

 

National Health Expenditures in NHEA for 2002 totaled $1.603 trillion – approximately twice 

the MEPS total of $833 billion. A better comparison, however, is the NHEA estimate of $1.341 

trillion for PHC, which excludes spending on administrative costs, public health, research, and 

construction, none of which are captured by MEPS. As explained in Sing et al. (2006), part of 

the difference between NHEA PHC and MEPS is because MEPS excludes persons in institutions 

and the active duty military and because MEPS by its design misses a number of other spending 

types that household respondents would be unlikely to report accurately. After adjusting NHEA 

amounts to correspond as nearly as possible with the scope of MEPS, Sing et al. (2006) found a 

13.8 percent shortfall in MEPS. That analysis also found an uneven distribution of gaps 

between NHEA and MEPS, with very narrow gaps for Medicare-funded services contrasting 

 

2

 The alignment is conducted for 2002 to build on the analysis in Sing et al. (2006), which focused on 

2002 to take advantage of estimates from the quinquennial Economic Census. 

3

 See also Selden et al. (2001). 

4
with larger gaps for a number of service-payment combinations, such as Medicaid Hospital 

(facility) expenditures and Private Health Insurance Physician expenditures. 

To align MEPS with NHEA, we made several modifications to the MEPS-consistent 

NHEA PHC benchmarks in Sing et al. (2006). First, we used the most recent update to the 2002 

NHEA (Centers for Medicare and Medicaid Services, 2007). Second, we modified the NHEA 

allocation of capitated Medicaid payments across types of service using the MEPS expenditure 

distribution, rather than the fee-for-service Medicaid distribution used in the construction of 

NHEA.4 This adjustment shifted expenditures from Medicaid Hospital to Medicaid Physician. 

It also had the reverse effect on private expenditures, because these are calculated as residuals in 

NHEA. The result is a somewhat more even pattern of MEPS-NHEA discrepancies than 

reported in Sing et al. (2006). Third, we modified source of payment distributions for a number 

of the NHEA subtractions listed in Table 4 of Sing et al. (2006) These modifications were 

motivated in part by insights from a recently completed analysis of MEPS participants matched 

to Medicare claims data (Zuvekas and Olin, 2008). The combined effect of these changes 

slightly widened the gap between the adjusted NHEA PHC and MEPS to 14.4 percent, with a 

differentially large widening of the gaps for Medicare. Here again, the adjustments yield more 

uniform differences between MEPS and NHEA across payment sources and services. 

To obtain our final NHEA PHC benchmarks, we removed the NHEA adjustment 

described in Sing et al. (2006) pertaining to drug rebates for public insurance (so that the 

benchmarks, unlike MEPS, are net of such rebates). Finally, we employed MEPS data to 

increase the NHEA benchmarks to account for non-Medicare spending on ambulances. The 

resulting NHEA PHC benchmarks by payment source and service type are presented in Table 1. 

 

4

 Obtaining accurate NHEA estimates by service type for capitated public insurance poses a significant 

methodological challenge, and CMS researchers are currently exploring alternative estimation strategies. Our 

adjustment should be viewed as an interim approach. 

5
Aligning MEPS with MEPS-Consistent NHEA PHC Benchmarks 

We aligned MEPS with the MEPS-consistent NHEA PHC benchmarks in three steps. 

First, MEPS, like all household surveys, has fewer persons with coverage from Medicaid and the 

State Children’s Health Insurance Program (SCHIP) than are reported in administrative data. 

The gap is smaller if measured at a point in time than if one compares estimates of persons ever 

enrolled in the year. This may reflect the difficulty of unduplicating administrative estimates of 

annual enrollment to account for persons with multiple spells and persons moving between 

Medicaid and SCHIP or across state lines. Nevertheless, MEPS point in time enrollment falls 

short of administrative counts, which likely contributes to the 29.9 percent gap we observe for 

Medicaid between MEPS and the NHEA PHC benchmarks in Table 1. For this reason, the first 

step of our alignment was a 10 percent upweighting of Medicaid and SCHIP recipients, using a 

raking post-stratification to preserve the MEPS distribution of poverty level, age, sex, Medicare 

enrollment and uninsurance. One consequence of this adjustment is that it modestly reduces the 

share of the population with private insurance coverage – reflecting the growing evidence that 

household respondents have greater difficulty reporting their type of coverage than whether they 

are insured or uninsured.5

 

Second, we further modified the sampling weights to increase the prevalence of high-cost 

cases. Our motivation for doing so comes from unpublished AHRQ research showing attrition 

among such cases. Zuvekas, Cohen, and Pylypchuk (2005) and Zuvekas and Olin (2008) 

suggest the shortfall in high-cost cases might account for one-third to one-half of the MEPSNHEA gap. For this reason, we believe it may be better to implement a partial non-response 

adjustment rather than simply scaling all MEPS amounts to align with NHEA PHC benchmarks. 

Our upweighting strategy targets the top three percent of the expenditure distribution in each of 

 

5

 MEPS source of payment data help to reinforce this assumption. See also Call et al. (2008). 

6
four (hierarchically defined) coverage groups: ever on Medicare, ever on non-Medicare 

Medicaid and SCHIP, ever on Private, and full-year uninsured. A raking post-stratification was 

implemented to preserve MEPS distributions by age, sex, race/ethnicity, and poverty level (along 

with coverage). The average increase in weight was 18.1 percent, closing 40 percent of the 

remaining gaps in out-of-pocket, private health insurance, Medicare, and Medicaid.6

 Raking 

was used to preserve MEPS distributions by age, sex, race/ethnicity, poverty level, region, and 

insurance coverage. The impact of the reweighting MEPS is shown in Table 2. The left-most 

column shows the MEPS source of payment totals from the public use files, and the second 

column shows the reweighted estimates. 

Third, we scaled MEPS expenditure amounts to close any remaining gaps between the 

reweighted MEPS and the MEPS-consistent NHEA PHC benchmarks in Table 1. One area in 

which MEPS is particularly low is separately-billed laboratory tests, the number and financing of 

which are difficult to ascertain either from household respondents or from follow-back visits to 

providers ordering the tests. We allocated added spending on laboratory tests based on use of 

physician services. For most other type of service and source of payment differences, we simply 

scaled MEPS amounts to close the gap with the adjusted NHEA. The third column of Table 2 

shows the effect of this adjustment, bringing MEPS into alignment with the MEPS-consistent 

NHEA PHC benchmarks in Table 1. 

Augmenting MEPS with Additional NHEA PHC Spending 

The MEPS-consistent NHEA PHC benchmarks in Table 1 exclude PHC expenditures 

believed to fall outside the definition of medical care in MEPS. Many, however, would be 

within the scope of applications we envision for the aligned data, and thus we have allocated 

 

6

 We target these four payment sources because they align most directly between MEPS and NHEA and because the 

weighting adjustment was designed to preserve the coverage distribution. 

7
them within MEPS.7

 These adjustments are detailed in Table 3 under the subheading of PHC 

Additions. 

The single largest adjustment is for other personal care, including non-medical assistance 

with activities of daily living (such as housekeeping assistance), which were allocated in 

proportion to home health care by source of payment (most is paid by Medicaid). Another large 

group of adjustments are hospital subsidies not linked directly to patient care, such as Medicaid 

and Medicare disproportionate share payments and state and local funding for public hospitals. 

In each case, allocation to the person level was based on MEPS information regarding the receipt 

of uncompensated care (UC), which we calculate by comparing MEPS data on “full established 

charges” to actual payments. Medicare disproportional share (DSH) payments were allocated to 

low-income Medicare beneficiaries by UC. Medicaid DSH payments were allocated to poor 

non-Medicaid recipients by UC. Because MEPS UC estimates for these two groups roughly 

matched the DSH amounts, state and local funds for hospitals were allocated across all 

remaining cases by UC (regardless of income). Medicare hospital subsidies for graduate medical 

education were allocated to all patients in proportion to physician expenditures under the 

assumption that lower education costs lead to lower physician pricing. 

The result of these PHC additions is shown in the fourth column of Table 2. Note, 

however, that non-patient specific Medicaid and Medicare subsidies to hospitals appear in the 

Other Federal and Other State and Local expenditure categories, so that the Medicare and 

Medicaid lines refer only to payments linked directly to patient care. 

 

7

 For all NHEA-based additions to MEPS, care was taken to add in only those amounts attributable to the civilian, 

noninstitutionalized population. 

8
Augmenting MEPS with Other Non-PHC Amounts from NHEA 

For some applications, it is useful to expand MEPS to include NHEA estimates of 

expenditures on administration, public health, research and investment in capital and equipment. 

These amounts are detailed in Table 3, and their impact is shown in column five of Table 2. 

For Medicaid, Medicare, Workers’ Compensation, and other public programs, we 

allocated administrative costs in proportion to spending on care.8 In the case of private 

insurance, we do not directly apply estimates of loading. Rather, column five of Table 2 presents 

the national sum of private premiums for the civilian, non-institutionalized population, inclusive 

of amounts paid by households and by employers on their behalf (including TRICARE). The 

MEPS household data contain information on premiums paid by households, but not employer 

premium contributions. We filled this gap with regression-based imputations from employer 

data in the MEPS Insurance Component (IC). Private premiums were benchmarked to the 

MEPS IC, not to the NHEA, in part because the NHEA premium estimates include persons in 

institutions and active-duty military. 

Note, however, that Table 3 reveals a substantial gap between premiums paid and 

benefits received ($97.2 billion). Much of this difference can be explained by “loading,” defined 

here as the difference between premiums and total benefits paid out. For instance, applying the 

average loading factor in NHEA to the Private Health Insurance total in column four of Table 2 

explains $65.4 billion of this amount.9 In addition, premiums paid by members of the 

noninstitutionalized population may in part fund the $31.9 billion in NHEA PHC Private Health 

 

8

 A minor exception to this rule is that we allocated a portion of Medicaid administrative costs to new 

enrollees based on enrollment cost estimates from Fairbrother et al. (2004). 

9

 The NHEA PHC total for Private Health Insurance in 2002 is $482.396 billion, versus $552.478 in premiums. 

Thus, [($552.478 - $482.396)/ $482.396] * $450.5 = $65.4 (billion). 

9
Insurance expenditures that were made on behalf of persons in institutions.10 Thus, although we 

cannot rule out errors in our alignment or the underlying data sources, we do not view this gap as 

necessarily being evidence of inconsistency. 

Many of the remaining adjustments were broad-based in nature, such as research, 

spending on public health, and investments in plant and equipment. We allocated research 

spending to the full population in proportion to prescription drug expenditures. Investment in 

plant and equipment was allocated in proportion to hospital use. Public health dollars were 

allocated uniformly on a per capita basis. 

Having allocated these non-PHC elements of NHEA to MEPS, the resulting expenditure 

total in column 5 of Table 2 is $1.2906 trillion. (Column 6 of Table 2 is discussed below.) By 

construction, the remaining $312.8 million difference between the benchmarked MEPS and the 

2002 NHEA NHE total of $1.603 trillion is by or on behalf of persons outside the scope of 

MEPS (the institutionalized, activity duty military, and foreign visitors). 

Tax Expenditures 

The final step in our analysis is the simulation of a comprehensive array of tax 

expenditures. Marginal tax rates for our work were obtained by processing MEPS through the 

National Bureau of Economic Research’s TAXSIM web-based simulation package (Feenberg 

and Coutts, 1993; National Bureau of Economic Research, 2007). Estimates by type of subsidy 

are provided in Table 4. For tax subsidies regarding employer-sponsored insurance (ESI), we 

assume that the incidence of employer contributions falls on workers who enroll in coverage.11 

Thus, the tax subsidy on employer contributions equals the amount of taxes that would have been 

 

10 This is the difference between the NHEA PHC Private Health Insurance total of $482.4 and the civilian noninstitutionalized PHC Private Health Insurance total of $450.5 billion in the fourth column of Table 2. 11 Our analysis ignores the possibility that employers adjust cash wages across workers to alter the true incidence of 

employer premium concentrations across workers. For an analysis of how this might affect incidence of the tax 

subsidy, see Selden and Bernard (2004). 

10
paid if the worker instead received cash wages (holding total employer cost constant). Not 

surprisingly, the largest subsidy is the exclusion of premiums for ESI from federal income, 

Social Security and Medicare payroll, and state income taxation, which totaled $147.9 billion 

exclusive of subsidies for retiree coverage. This aligns well with estimates for more recent years 

if one takes into account rapid premium growth after 2002. For instance, Sheils and Haught 

(2004) estimate the federal income and federal Social Security and Medicare tax subsidies in 

2004 to be $101.0 billion and $66.4 billion, respectively (2004 dollars). Selden and Gray (2006) 

estimate the total (federal income tax, state income tax, and payroll tax) ESI subsidy for current 

workers in 2006 to have been $209 billion (in 2006 dollars). 

 Perhaps somewhat surprisingly, the second largest component is the exemption of 

medical care from state and local sales taxes. The average sales tax rate across states and 

localities in 2002 was approximately 5.9 percent (Fox and Murray, 2005). We assume that sales 

taxes, if levied on private medical care expenditures, would be borne by households and private 

insurance companies in proportion to their payments (rather than by providers).12 We also 

assume that absent the exemption, higher payments by insurers would translate into higher 

premiums (borne by households).13 

In most cases, tax expenditures do not represent a net increase in national health 

spending. Instead, they shift the burden of a given expenditure across payers. The sales tax 

exemption for medical care, however, is an exception to this rule, because national health 

spending would have been larger had sales taxes been levied on medical care. The same 

argument can be made for a number of smaller tax subsidies, such as tax exemptions for non-

 

12 Sales taxes, if levied on publicly funded care, would represent an intergovernmental transfer that we did not 

attempt to simulate. 

13 Note, however, that a portion of this increase would be borne by the public sector in the form of premium 

subsidies, and thus we reduced the sales tax subsidy accordingly. 

11
profit hospitals and insurers. Had such subsidies not been granted, it is likely that provider 

revenues would have risen to offset at least partially the higher cost.14 For simplicity, we count 

tax subsidies of this type as Other Federal and Other State and Local expenditures. Adding these 

amounts to Table 2 column five yields the fully benchmarked MEPS estimates in the last column 

of that table, with the benchmarked total being $1.3416 trillion. 

Sources of Funds 

Table 5 summarizes how we allocated each source of payment expenditure estimate from 

the adjusted MEPS to private and public sources of funds. The totals in the first row of this table 

repeat the source of payment totals for the fully adjusted MEPS expenditures from Table 2. The 

first two columns of Table 5 show how we re-allocated total estimated private out-of-pocket 

spending ($172.3 billion) and total estimated private health insurance premiums ($547.7 billion) 

to public sources to account for tax subsidies ($3.1 billion and $160.7 billion, respectively). For 

Medicare and Medicaid (columns 3 and 4), we shifted a portion of these public payments to 

private sources ($23.3 billion and $1.3 billion, respectively) to account for premiums paid by 

enrollees (in essence a “user’s fee”).15 We did not, however, account for the intergovernmental 

transfer that occurs when federally and state funded Medicaid pays federally-funded Medicare 

Part B premiums. Table 5 also shows our estimate that public sector spending for health care of 

the civilian, noninstitutionalized population, inclusive of tax expenditures and net of premiums 

paid for public coverage, was $752.9 billion in 2002. 

 

14 Of course, this reflects a “statutory” approach to measuring tax subsidies, ignoring the potential for economic 

actors to respond to taxes and subsidies and thereby shift the incidence of taxes throughout the economy. 

15 Note, however, that MEPS undercounts premiums paid by Medicaid beneficiaries. 

12
Discussion 

 Any effort to study health care benefit incidence or conduct microsimulation must 

necessarily rely on household-level data. Household data, however, by their design are unlikely 

to provide a complete picture of outlays on health care, and they miss tax expenditures entirely. 

For this reason, we believe that household data from MEPS aligned with aggregate benchmarks 

from NHEA and supplemented by tax expenditure estimates provide the nation’s best resource 

for conducting an analysis of these types. Nevertheless, our undertaking confronts challenges 

regarding differences in the scope of populations studied, differences in definitions for types of 

services and sources of payments, the fact that MEPS expenditures fall short of comparablydefined benchmarks from the NHEA, and the need to simulate tax expenditures. Our hope is that 

our alignment methodology does not impart biases that would affect mean public spending 

comparisons across broad population subgroups. However, we readily concede that differential 

under-reporting in MEPS, for instance by income category, would adversely impact the 

reliability of our estimates. 

 We estimate that public spending on health care on behalf of the civilian, 

noninstitutionalized population totaled $752.9 billion (2002 dollars) or 56.1 percent of total 

spending from all sources. Public spending on health care is estimated to have been $2,612 per 

capita. Of total public spending, more than a quarter ($214.8 billion) took the form of tax 

preferences, primarily tax subsidies to private insurance and the exemption of most medical care 

spending from state and local sales taxes. 

The data resource we have produced is a person-level file containing MEPS public use 

expenditures, as well as our adjusted and augmented amounts from each step of the analysis. 

13
We hope that that this file becomes a valuable national resource to those interested both in the 

incidence of expenditures and the use of household data for public policy microsimulation. 

14
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16
Acknowledgements 

The authors have benefited from the helpful comments of Jessica Banthin, Joel Cohen, Steven 

Cohen, Philip Cooper, Cathy Cowan, Micah Hartman, Stephen Heffler, Sean Keehan, Samuel 

Zuvekas, and two anonymous reviewers. All remaining errors are our own. The paper 

represents the views of the authors, and no official endorsement by the Agency for Healthcare 

Research and Quality or the Department of Health and Human Services is intended or should be 

inferred. 

17
Table 1: MEPS-Consistent Adjusted NHEA Personal Health Care Benchmarks, 2002

Sources of Payment (billions of 2002 dollars) 

Service Type 

Out-ofPocket 

Private 

Health 

Insurance Medicare

Medicaid 

& SCHIP Tricare 

Veterans’ 

Admin. 

Workers’

Comp. 

Other 

Federal 

Other 

State & 

Local 

Hospitala 13.192 154.890 113.150 46.407 1.750 11.383 5.762 0.437 0.817 Physicianb 23.553 128.663 53.159 19.217 3.841 1.097 9.451 1.056 0.983 Dental 31.783 34.719 0.071 4.196 0.000 0.013 0.000 0.117 0.209 Other Providers 14.031 30.037 11.711 4.886 0.000 0.093 4.959 0.212 0.192 Home Health 3.383 8.612 13.676 12.917 0.054 0.551 0.177 0.162 2.231 Prescriptions 37.881 77.586 2.238 34.290 1.842 1.820 2.088 0.178 0.373 Oth. Med. Equip. 18.128 3.167 8.294 0.000 0.000 0.000 0.329 0.017 0.084 Total 141.950 437.674 202.299 121.914 7.486 14.958 22.767 2.180 4.889 Source: Authors’ calculations based largely on unpublished details of the NHEA-MEPS reconciliation in Sing etype definitions are consistent with those in MEPS, subject to the caveats noted. Payment source definitions are of MEPS if Other Private is added to Private Health Insurance and if Other Public is added to Medicaid. a Facility expenditures only (for all hospital-based services). b Includes separately-billing physicians for hospital-based care. 
1

Table 2: Benchmarking Pooled MEPS (Billions of 2002 Dollars)

Pooled 2002-

2003 MEPSa

Post-stratified 

to upweight 

Medicaid/ 

SCHIP 

enrollees and 

high-cost 

cases b

Aligned to 

MEPSconsistent 

NHEA 

Personal 

Health Care 

benchmarks 

Augmented 

with 

additional 

NHEA 

Personal 

Health Care 

amountsc

Augmented 

with other 

NHEA (nonPHC) 

expendituresd

A

Out-of-Pocket 161.6 (4.4) 163.7 (4.5) 142.0 (4.0) 172.3 (4.6) 172.3 (4.6) Private Health Insurancef 358.0 (12.7) 372.8 (13.6) 445.2 (15.4) 450.5 (15.5) 547.7 (14.4) Medicare 174.6 (7.4) 182.8 (7.9) 202.3 (8.5) 203.9 (8.5) 211.0 (8.9) Medicaid/SCHIP 85.5 (4.1) 103.8 (5.7) 121.9 (6.7) 157.7 (7.6) 170.3 (8.1) Veterans’ Administration 20.2 (1.8) 21.8 (2.0) 15.0 (2.0) 16.4 (2.2) 16.4 (2.2) Workers’ Compensation 18.2 (6.6) 19.9 (7.6) 22.8 (4.0) 22.8 (4.0) 29.8 (5.3) Other Federal 2.2 (0.3) 2.3 (0.4) 2.2 (0.4) 19.6 (1.2) 57.9 (1.8) Other State and Local 4.9 (0.5) 5.5 (0.6) 4.9 (0.5) 21.0 (1.1) 77.2 (2.1) Other Sourcesg 8.0 (0.6) 8.2 (0.7) 8.0 (0.7) 8.0 (0.7) 8.0 (0.7) Total 833.2 (23.3) 880.7 (25.4) 964.1 (27.0) 1072.2 (29.3) 1290.6 (31.9) 1Sample size = 70,099 

Source: Authors’ calculations using pooled 2002 and 2003 MEPS aligned with 2002 NHEA and other national b

errors (in parentheses) are adjusted for the complex design of the MEPS sample, but do not reflect uncertainties r

adjustments to align MEPS with national benchmarks. 

a MEPS 2003 data adjusted downward to 2002 dollars by GDP deflator for medical goods. 
2

b Post-stratification upweighted persons with Medicaid and persons in the top 3 percent of the distribution of tota

insurance coverage. Raking was used to preserve MEPS control totals by age, sex, race/ethnicity, poverty status

insurance coverage. 

c Includes only spending amounts for the civilian, noninstitutionalized population. NHEA Personal Health Care 

from the MEPS-consistent NHEA benchmarks include personal care, non-prescription nondurable goods, and panot linked to patient care (Sing et al., 2006). These adjustments are detailed in Table 3. 

d Includes only spending amounts for the civilian, noninstitutionalized population. NHEA National Health Expe

added to MEPS include administrative costs, public health, research, and public spending on structures and equip

2006). These adjustments are detailed in Table 3. 

e Includes the exemption of health care spending from state and local sales taxes and tax subsidies for non-profit 

establishments. 

f Includes TRICARE. g Includes automobile, homeowner’s, and liability insurance, and other miscellaneous or unknown sources.
Table 3: Additions to MEPS after Calibrating to MEPS-Consistent NHEA Personal Health Care Benchma

billions) 

Public 

Description Allocation Method Private Federal 

St

NHEA Personal Health Care Additions  Personal Carea Medicaid and SCHIP In proportion to Home Health by 

payment source 

0 20.8 

 Veterans’ Administration In proportion to Home Health by 

payment source 

0 1.4 

 Other Federal, State, Local In proportion to Home Health by 

payment source 

0 4.6 

 Non-Prescription Nondurable Goods  Medicare In proportion to total prescription 

spending by payment source 

0 1.6 

 Private Out-of-Pocket In proportion to total prescription 

spending by payment source 

29.2 0 

 Miscellaneous Clinics, including Family Planning  Private Out-of-Pocket In proportion to physician spending by 

private payment source 

1.2 0 

 Private Health Insurance In proportion to physician spending by 

private payment source 

5.4 0 

3
Public 

Description Allocation Method Private Federal 

St

 Hospital payments not directly linked to patientsb Medicaid Disproportionate Share In proportion to uncompensated care 

among poor non-Medicaid or Medicare enrollees 

0 5.9 

 Medicare Disproportionate Share In proportion to uncompensated care 

among Medicare recipients 

0 0.6 

 Medicare retrospective  adjustments and capital pass  throughs 

In proportion to Medicare hospital expenditures 

0 2.8 

 Medicare Graduate Medical  Education 

In proportion to physician expensec 0 2.2 

 State and local subsidies to  public hospitals 

In proportion to uncompensated care (all income levels) 

0 0 

 Miscellaneous other public  NHEA PHC amounts 

In proportion to total spending among low-income persons 

0 1.3 

4
Public 

Description Allocation Method Private Federal 

St

Other NHEA National Health Expenditure Additions

Administrative cost  Private Insuranced Implicit difference between 

premiums and expenditures paid by private insurance 

97.2 0 

 Medicaid and SCHIP In proportion to Medicaid 

expenditurese

0 7.2 

 Medicare In proportion to Medicare 

expenditures 

0 7.2 

 Other Public Programsf In proportion to Other Public 

expenditures by program 

0 0.1 

Public Health In proportion to total expenditures 0 8.0 Public Research In proportion to total prescription 

drug expenditures 

0 24.6 

Public Investment in Structures and Equipment 

In proportion to hospital expenditures 

0 5.5 

Source: Spending amounts excluded from NHEA in the Sing et al. (2006) reconciliation with MEPS, adjusted to attributable to persons in institutions. a Includes non-medical assistance with activities of daily living. 

5
b Hospital payments by Medicare and Medicaid that are not linked to patient events are reported in the Other Fed

and Local categories in Tables 2 and 5, so that the Medicare and Medicaid/SCHIP estimates pertain solely to pay

and the administration thereof. 

c Medicare Graduate Medical Education subsidies are assumed to lower the prices physicians charge, by reducing 

expenses they must recoup. 

d Includes TRICARE. e A small proportion was allocated to cover the enrollment costs of new enrollees in Medicaid/SCHIP. f Includes Veteran’s Administration, Workers’ Compensation, and other pubic programs. 

6
7

Table 4: Simulated Federal, State, and Local Tax Expenditures, 2002 ($ in billions) 

Federal 

Income Tax 

Expenditures 

Social Security/ 

Medicare Tax 

Expenditures 

State & Local 

Tax 

Expendituresa

Total Tax 

Expenditures

Employer-Sponsored 

Insurance Exemption 

 

 Current Workers 76.3 

(2.2) 

55.2 

(1.5) 

16.4 

(0.6) 

147.9 

(4.2) 

 Retirees 7.1 

(0.3) 

0 1.5 

(0.1) 

8.6 

(0.4) 

Self-Employed Tax 

Deduction 

1.5 

(0.1) 

0 0.6 

(0.05) 

2.5 

(0.2) 

Medical Expense 

Deduction 

2.3 

(0.2) 

0 0.4 

(0.04) 

2.7 

(0.2) 

Sales Tax 

Exemption 

0 0 37.9 

(1.0) 

38.0 

(1.1) 

Otherb 7.1 

(0.3) 

0.8 

(0.03) 

7.3 

(0.4) 

15.2 

(0.7) 

Total 94.8 

(2.6) 

56.0 

(1.6) 

64.0 

(1.7) 

214.8 

(5.7) 

Source: Authors’ calculations using pooled 2002 and 2003 MEPS aligned with 2002 NHEA and 

other national benchmarks. Standard errors (in parentheses) are adjusted for the complex design 

of the MEPS sample, but do not reflect uncertainties regarding the adjustments to align MEPS 

with national benchmarks. 

a

 Includes state income tax expenditures, state and local sales tax expenditures, and local 

property tax expenditures. Local income taxes are not modeled. Local tax expenditures are 

captured only through our use of average state and local sales tax rates and through a national 

estimate of non-profit hospital exemptions (primarily for property taxes). 

b

 Included are tax subsidies for Flexible Savings Accounts, Medical Savings Accounts, charitable 

giving, non-profit hospitals, hospital bonds, and Blue Cross/Blue Shield.
Table 5: Allocating Adjusted Expenditures to Sources of Funds, 2002 ($ in billions) 

Out of 

Pocket 

Spending 

on Care 

Private 

Health 

Insurancea Medicare 

Medicaid, 

& SCHIP 

Other 

Publicb

Ot

Sou

Adjusted Expenditure Totals 172.3 (4.6) 547.7 (14.4) 211.0(8.9) 170.3(8.1) 232.3(8.1) 8.0(Private Sources 

Out-of-Pocket Spending on Care 169.2 (4.5) 0 0 0 0 0 Premiums 0 387.0 (10.1) 23.3 (0.8) 1.3 (0.2) 0 0 

 Private Sources Total 169.2 (4.5) 387.0 (10.1) 23.3 (0.8) 1.3 (0.2)d 0 0 Public Sources 

Tax Expenditures 3.1 (0.2) 160.7 (4.5) 0e 0e 51.0 (1.4) 0 Public Outlays 0 0f 187.7 (8.4) 169.0 (8.1) 181.3 (7.5) 0 

 Public Sources Total 3.1 (0.2) 160.7 (4.5) 187.7 (8.4) 169.0 (8.1) 232.3 (8.1) 0 Other Sourcesc 0 0 0 0 0 8.0 

Source: Authors’ calculations using pooled 2002 and 2003 MEPS aligned with 2002 NHEA and other national b

errors (in parentheses) are adjusted for the complex design of the MEPS sample, but do not reflect uncertainties r

adjustments to align MEPS with national benchmarks. 

a Private health insurance premiums (including TRICARE). 

8
b Includes Veteran’s Administration, Workers’ Compensation, the NHEA categories of Other Federal and Other 

spending, as well as Medicare and Medicaid payments to hospitals and tax expenditures arising from the state an

exemption and tax subsidies for non-profit providers. 

c Includes sources such as automobile, homeowner’s, or liability insurance, and other miscellaneous or unknown d MEPS undercounts premiums paid for public coverage through Medicaid and SCHIP. e In principle, out of pocket spending on Medicare (or Medicaid) premiums could be offset by tax expenditures th

expense deduction on federal (and many state) income taxes. Although we included premiums for public coverasimulation, all tax expenditures for medical expense deductions were attributed to private out-of-pocket spending spending on health insurance premiums. 

f We were unable to account for the small amount of private health insurance premiums paid by non-tax-related p

subsidy programs. 

9