Income Measurement in the Medical Expenditure Panel Survey Jessica S. Banthin and Thomas M. Selden

Agency for Healthcare Research and Quality Working Paper No. 06005 July 2006

Suggested citation: Banthin JS, Selden TM. Income Measurement in the Medical Expenditure

Panel Survey. Agency for Healthcare Research and Quality Working Paper No. 06005, July

2006, http://gold.ahrq.gov.

AHRQ Working Papers provide preliminary analysis of substantive, technical, and methodological

issues. The papers have not undergone external peer review. They are distributed to share

valuable experience and research. Comments are welcome and should be directed to the

authors. The views expressed are those 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.
 

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Income Measurement in the Medical Expenditure Panel Survey (MEPS) 

Jessica S. Banthin and Thomas M. Selden, Division of Modeling and Simulation, Center 

for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality 

1. Introduction 

Accurate measures of individual and family income within health surveys are important 

for many types of health policy analyses. For example, analyses of trends in insurance 

coverage are more policy relevant when the groups who lack insurance can be 

characterized by income or poverty status. Effective policy responses often depend on 

income and poverty status. In particular, accurate income data are critical when using 

survey date to estimate who is eligible for public programs such as Medicaid and SCHIP 

and determining the associated take-up rates. Income also plays the crucial role of 

denominator when estimating the financial burden on families of out of pocket 

expenditures for health care. 

Health policy analyses can suffer from significant biases if they depend on poorly 

measured and underreported income data. When income levels are underreported then 

the extent of poverty will be overstated. Eligibility for public programs will be 

overestimated and the associated take-up rates will be biased downwards. Estimates of 

financial burdens as a function of family income will also be biased upwards if income 

levels are underreported. 

The Medical Expenditure Panel Survey (MEPS) is one of the major on-going household 

surveys used extensively to provide health policy analyses at the national level. 

Policymakers routinely depend on MEPS data to inform the policy process. In this paper 

we briefly describe how income data are collected in the MEPS based on a series of 

detailed questions about various possible sources of income. We compare MEPS and 

Current Population Survey (CPS) poverty status distributions overall and by important 

subgroups to show that MEPS income data benchmark relatively closely to estimates 

from the CPS. We then compare an experimental poverty status measure based on a 

single question recently added to the MEPS Round 1/3 instrument with the standard 

poverty status measure based on the more detailed MEPS income questions. 

Finally, we illustrate the usefulness of detailed income questions, such as those asked in 

the MEPS, for simulating tax-adjusted income. Tax-adjusted, or disposable, family 

income is a more appropriate measure of income for examining financial burdens. We 

show how estimates of financial burden based on disposable income differ significantly 

from estimates based on pre-tax income. 

The views expressed in this paper are those of the authors and no official endorsement by the 

Department of Health and Human Services or the Agency for Healthcare Research and 

Quality is intended or should be inferred. 
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2. Collection of Income Data in the Medical Expenditure Panel Survey 

The MEPS – Household Component is a survey of households designed to yield national 

estimates of insurance status, health care utilization and expenditures, employment and 

other important health and sociodemographic variables. The sample is a subset of the 

National Health Interview Survey (NHIS) sample and in 2002 included about 15,000 

households in 195 primary sampling units. Interviews are conducted with a single 

household respondent but detailed information, including income data, is collected for 

every member of the household. Respondents are interviewed five times over two and a 

half years, providing information referencing a two calendar year period. The income 

supplement is administered twice, once for each year of survey participation, in the first 

half of the year following the reference year. In other words, the income supplement for 

calendar year 2002 was administered sometime between January and June of 2003. 

Questions vary by the type of tax form filed and are asked at the level of the tax filing 

unit, either the individual or a married couple filing jointly. 

In the MEPS – Household Component data for each person in the household are collected 

about most types of taxable and non-taxable income including wages, interest, dividends, 

pensions, IRAs, unemployment compensation, workers compensation, social security, 

SSI, DI, public assistance, child support, cash transfers, royalties, rental income, food 

stamps, and veterans payments. In addition, long form filers are asked about tax refunds, 

alimony, business income (farm and non-farm), sales of assets, and trusts. 

Total person level income can be summed across various family members depending on 

what family definition is required to create a family or unit level income measure. For 

example, in addition to CPS family units, the MEPS also defines Medicaid eligibility 

units and health insurance eligibility units. These units are based on narrower definitions 

of a family than the CPS definition of a family. 

MEPS income data are carefully edited to fill in for missing and incomplete data prior to 

public release. Response rates for selected sources of income are provided in Table 4-a. 

The editing process relies on a sophisticated sequential hotdeck imputation program 

guided by regression analysis. The editing process makes use of other sources of 

information to guide the imputations and attempts to preserve key relationships between 

different sources of income. In particular, detailed questions about current employment 

status, current hours worked, number of weeks worked, wage and salary rates are asked 

of all household members age 16 in the employment section of the MEPS instrument. 

These data are used to fill in for missing data on annual earned income when necessary. 

Information on wages is missing from the annual income supplement in about 22 percent 

of all cases. When information collected in the employment section is used to fill in, then 

the rate of missing data falls to less than 4 percent of all cases. In addition, the link to 

NHIS allows information on previous years’ income to help guide imputations. For 

example, information collected in the NHIS on whether a person receives Social Security 

income (not the value of the Social Security payment) is used to impute for missing 

Social Security income in the MEPS. 
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3. Comparison of MEPS and CPS poverty distributions 

The final population weights that are released with the full year income data for MEPS 

are post-stratified to CPS poverty status categories. Thus, we make sure that MEPS 

income and poverty distributions match exactly those in the CPS. Table 1 provides 

MEPS and CPS estimates of the distribution of the civilian, non-institutionalized 

population by poverty category. Small differences occur because MEPS income is topcoded after the post-stratification and prior to public release in order to preserve data 

confidentiality. 

The post-stratification of MEPS data to CPS poverty categories does not accommodate 

all age and poverty groups. For example the youngest age cut includes persons age 0 and 

persons age 1 to 20. The top two poverty categories displayed in these tables are 

collapsed into one during the post-stratification. When we subset to several different 

policy relevant subpopulations the similarities in the distributions of MEPS and CPS 

poverty categories are slightly less exact but remain very similar (Tables 2-4). Among 

children under age 19, MEPS and CPS both report 17.2 percent living below the poverty 

line. For both children and the elderly, there are compensating differences in the 

distribution of these groups in the two highest poverty categories. MEPS estimates 

slightly more children living between 200 and 400 percent of poverty than the CPS (34.3 

vs. 32.2 percent), and slightly fewer children living above 400 percent of poverty than the 

CPS (27.3 vs. 29.3). Among persons aged 65 and older, MEPS and CPS estimate a 

similar percent living below poverty (10.9 vs. 10.5) and the difference in not statistically 

significant. MEPS estimates more elderly living at 400 percent or higher than the CPS 

(32.7 vs. 26.3). 

The uninsured are a very important subpopulation for both surveys and annual estimates 

of the number of uninsured from the CPS are reported widely every September as soon as 

they are released. The income distribution of this population is a key part of every 

analysis of the new estimates. Again we find that the surveys yield similar results and the 

small differences are not statistically significant. The MEPS, however, finds more 

uninsured between 100 and 400 percent of poverty than does the CPS. 

4. Comparisons of Single Income Question vs Detailed Income Questions 

An experimental question was added to the MEPS as of 2003 asking respondents to 

estimate their total household income within a range. Depending on the size of the 

reporting unit and the age of the household head, respondents were shown a card that 

provided family income ranges corresponding to five poverty status categories. The five 

categories included: 1. below poverty; 2. 100 to 150 percent of poverty; 3. 150 to 200 

percent of poverty; 4. 200 to 300 percent of poverty; and 5. 300 percent or more. 

Although a single income question for the entire family is unlikely to be as accurate as a 

series of more specific income questions, the single question was added to the MEPS 

because of the potential to publicly release a poverty status categorical variable much 
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earlier than the standard income measure based on the detailed questions. In Tables 5 

through 9 we show how well this single question income measure compares to the 

detailed questions income measure. For this part of the analysis, we subset to individuals 

in Panel 7, Round 3 who have positive responses for the single income question on 

family income. 

[Table 5 will be rerun with weighted frequencies.] As expected, the distribution of the 

population across poverty categories indicates that people underestimate their family 

income when asked a single question rather than a series of more detailed questions. The 

distribution is shifted towards the lower income levels based on the single question 

compared with the detailed questions.... 

Table 6 shows that 63.1 percent of individuals with positive responses for the single 

income question provided data that matched the more detailed information collected later 

for the same calendar year. But 26.2 percent underreported their family income while 

10.7 percent overestimated their family income. When we break down the comparisons 

by poverty category, we see that less than half of the middle categories are providing 

information consistent with the more detailed responses. For example, only 32.7 percent 

of those with income between 150 and 200 percent of poverty according to the detailed 

questions are providing responses to the single question that are consistent. 

One of the main reasons to include a single question on family income in the MEPS 

Rounds 1 and 3 instrument is to use it in conjunction with the MEPS estimates of 

insurance status in the so-called Point in Time files. These files are released much earlier 

than the full year files that include the detailed income variables. We compare insurance 

status by poverty status according to the two different measures in Table 7 for the entire 

population and in Table 8 for children. 

Taking into account the standard errors, the distributions by insurance and poverty status 

are not significantly different between the two income measures. Nonetheless, if we 

restrict our analyses to the point estimates, looking at the first row of each of the three 

panels in Table 7, we find that the single income question overestimates the percent of 

people in poverty who are covered by private insurance (24.3 vs 21.9), underestimates the 

percent who have public coverage (51.3 vs 55.6), and overestimates the percent who are 

uninsured (30 vs 27.9). The pattern is similar when we subset to children less than 18 as 

shown in Table 8. These differences are meaningful in terms of public policy. 

5. Comparisons of Financial Burdens by Reported Income vs. Tax-Adjusted Income 

In addition to providing more accurate estimates of individual and family income, the 

detailed series of income questions included in the MEPS income supplement also permit 

analysts to simulate tax payments and marginal tax rates. These simulated tax rates are 

useful in many research projects related to tax subsidies and health insurance. 

Furthermore, estimates of disposable family income (income less tax payments) are more 

appropriate when estimating the percent of family income spent on health care services 

and out of pocket premiums. 
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Table 9 shows how tax-adjusted income compares with reported income at the level of 

the health insurance eligibility unit. This definition of a family unit includes all members 

of family who would normally be eligible for coverage under a family insurance policy, 

including spouses and children under age 19 or full-time students up to age 24. Mean 

reported family income is $37,169 compared to a mean tax-adjusted family income of 

$30,267. As the distribution in the table indicates, the differences are greater at the 

higher end of the income distribution. 

Using tax-adjusted family income rather than reported family income in the denominator 

of a ratio results in different point estimates of people living with high financial burdens. 

In Table 10 we compute the ratio of family out of pocket expenditures as a percent of 

family income, using two measures of family income. Individuals living in families that 

spend 10 percent or more of total family income on out of pocket health care 

expenditures are deemed to have a high financial burden. The first row of Table 10 

indicates that 9.4 percent of the population has a high burden when we use reported 

family income in the denominator, while 10.2 percent of the population have a high 

burden when we use tax-adjusted family income in the denominator. These differences 

are larger among the older individuals, elderly, whites, and middle to upper income 

individuals. Although these differences are not statistically significant they do have 

policy significance. 

6. Discussion 

In a period of rapidly rising health care costs combined with continued high levels of 

uninsurance, health policy analysts will need to track changes over time by income and 

poverty status. Policymakers will likely need to devise various policy interventions 

based in part on income and poverty status. Health policy analysts continue to debate the 

success of existing public insurance programs and the possibility of future expansions of 

such programs as part of an incremental approach to policy change. For all of these 

reasons and others, accurate and timely income data are a critical component of major 

health surveys. 

Studying the impacts of SCHIP and Medicaid require researchers to identify those 

eligible for the programs and this depends critically in accurate income measurement. In 

addition, the new Medicare Part D has income-based subsidies, thus accurate income 

measurement will be critical in studying the impact of the MMA in the future. 

With respect to eligibility criteria for various public insurance programs such as Medicaid 

and SCHIP, in addition to complicated rules on what types of income are included or 

excluded, there are also complicated rules as to whose income is counted. Thus, income 

earned by an uncle, aunt, grandmother, or stepfather is not necessarily counted when 

determining eligibility of a child. Therefore a survey that collects income for each 

individual in the household or family is more flexible than a survey that collects 

information for the entire family unit. 
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The MEPS administers a detailed income supplement that produces income and poverty 

status estimates that are post-stratified to match the Current Population Survey’s 

distributions, the nation’s official source of poverty statistics. The flexibility of the 

MEPS design also supports formation of different family definitions depending on the 

analytic needs. The MEPS supports a simulation model that estimates tax payments and 

marginal tax rates. MEPS data is also used in another simulation model that estimates 

eligibility for public insurance programs, including Medicaid and SCHIP. 

On-going methodological research aims to improve the quality, timeliness, and utility of 

MEPS income data. Future improvement to the single income question may combine 

answers to the single income questions with simulations of earned income based on 

employment status data. 
 

 

 

 

 

 

 

 

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Table 1. Distribution of Total Population by Poverty Categories, MEPS vs. CPS, 2002
 

Percent of Poverty Line MEPS CPS 

0-<100 12.4 12.3 

100-<200 18.3 18.4 

200-<400 31.6 31.8 

400+ 37.8 37.6 

Table 2. Distribution of Children Under Age 19 by Poverty Categories, MEPS vs. CPS, 2002
 

Percent of Poverty Line MEPS CPS 

0-<100 % 17.2 17.2 

100-<200 % 20.5 21.2 

200-<400 % 34.3 32.3 

400% + 27.3 29.3 

Table 3. Distribution of Elderly Age 65 and Over by Poverty Categories, MEPS vs. CPS, 2002 

Percent of Poverty Line MEPS CPS 

0-<100 % 10.9 10.5 

100-<200 % 27.0 28.0 

200-<400 % 29.4 35.3 

400% + 32.7 26.3 

Table 4. Distribution of Uninsured by Poverty Categories, MEPS vs. CPS, 2002 

Percent of Poverty Line MEPS CPS 

0-<100 % 22.5 24.3 

100-<200 % 31.8 29.0 

200-<400 % 30.9 29.2 

400% + 14.9 17.4 

Table 4-a. Response Rates for Selected Sources of Income among all Primary Filers and Aged Primary 

Filers, MEPS, 2002 

All Primary Filers, 

18 and over 

Primary Filers, 

Age 65 and over 

Wages: w/o employment data 78.2% 74.8 

Wages: w/ employment data 96.3 97.5 

Interest 84.2 76.5 

Dividend 86.3 78.2 

Pension 90.8 82.6 

Social Security 90.0 72.4* 

SSI 97.3 93.5 

Welfare 98.0 95.7 

* NHIS information on recipiency of Social Security is used to guide imputations for missing Social 

Security income 
 

 

2002 

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Table 5. Comparing Poverty Distributions, Single Income Question vs Detailed Income Questions, MEPS, 

Percent of Poverty Line Single Question 

(SE) 

Detailed Questions 

(SE) 

0-<100 % 14.9 (.61) 11.8 (.48) 

100-<150 % 11.6 (.51) 8.6 (.42) 

150-<200 % 11.3 (.50) 9.1 (.44) 

200-<300 % 17.4 (.68) 18.7 (.73) 

300% + 44.9 (.93) 51.8 (.92) 

Table 6. Differences in Poverty Status, Single Income Question vs. Detailed Income Questions, MEPS, 

2002
 

Poverty Status Based on Single Income Question Compared to Poverty Status 

Based on Detailed Income Questions 

percent distribution (SE) 

Poverty Status based on 

Detailed Questions 

Lower Equal Higher 

All persons 25.9 (.78) 63.7 (.86) 10.5 (.48) 

0-<100 % 71.4 28.6 

100-<150 % 32.3 41.5 26.2 

150-<200 % 45.8 33.9 20.3 

200-<300 % 41.1 42.9 16.0 

300% + 21.7 78.3 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Table 7. Insurance Status by Two Measures of Poverty Status, All Persons, MEPS, 2002 

Using Detailed Questions Using Single Income 

Question 

Percentage of Persons with Private Insurance by Poverty Status

 <100% FPL 21.9 24.3 

 100-150% FPL 36.8 40.1 

 150-200% FPL 48.5 54.9 

 200-300%FPL 68.7 75.9 

 >300% FPL 86.5 89.6 

Percentage of Persons with Public Insurance by Poverty Status

 <100% FPL 55.6 51.3 

 100-150% FPL 43.0 40.9 

 150-200% FPL 35.1 29.0 

 200-300% FPL 22.3 20.9 

 >300% FPL 13.6 12.0 

Percentage of Persons without Any Coverage by Poverty Status

 <100% FPL 27.9 30.0 

 100-150% FPL 30.4 28.4 

 150-200% FPL 27.3 25.8 

 200-300%FPL 18.2 13.8 

 >300% FPL 8.5 7.0 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Table 8. Insurance Status by Poverty Status, Children less than18, MEPS, 2002
 

Using Detailed

Questions 

Using Single Income 

Question 

Percentage of Children with Private Insurance by Poverty Status

 <100% FPL 16.0 16.2 

 100-150% FPL 31.0 38.2 

 150-200% FPL 47.9 53.1 

 200-300%FPL 68.4 75.9 

 >300% FPL 89.8 92.1 

Percentage of Children with Public Insurance by Poverty Status

 <100% FPL 68.8 65.2 

 100-150% FPL 50.1 47.6 

 150-200% FPL 36.3 30.8 

 200-300% FPL 21.9 17.5 

 >300% FPL 5.4 3.9 

Percentage of Children without Any Coverage by Poverty Status

 <100% FPL 18.8 22.3 

 100-150% FPL 23.3 18.8 

 150-200% FPL 20.0 19.2 

 200-300%FPL 12.6 9.9 

 >300% FPL 6.8 5.7 
 

 

 

 

 

 

 

 

 

 

 

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Table 9. Comparing Tax-Adjusted Family Income with Reported Family Income, MEPS, 2002, 

Analyses at Family Level Reported Family Income, not 

adjusted for taxes 

Tax-Adjusted Family Income 

Mean $37,169 $30,267 

75th percentile $50,022 $41,419 

50th percentile (median) $25,000 $22,720 

25th percentile $11,094 $10,987 

Table 10. Comparing Rates of High Financial Burdens Calculated as Percent of Reported Family Income
 

vs. as Percent of Tax-Adjusted Family Income, MEPS, 2002 

Analyses at 

Individual 

Level 

Percent Living in Family with High Financial Burden 

Total out of pocket expenditures 

10 % or more than reported 

family income 

Total out of pocket expenditures 

10% or more than tax-adjusted 

family income 

All persons 9.4 10.2 

Age Categories 

0-18 5.9 6.4 

19-34 4.4 4.9 

35-44 5.5 6.2 

45-64 9.7 11.5 

65+ 28.4 30.0 

Gender 

Male 7.7 8.6 

Female 10.7 11.8 

Race/Ethnicity 

Black, non-Hispanic 7.7 8.2 

Hispanic 6.3 6.5 

White and all others 10.0 11.2 

Poverty Status 

< 100% 23.1 23.2 

100-<200% 16.9 17.0 

200-<400% 7.8 9.2 

400% + 2.2 3.5 

Health Status 

Excellent/VGood/Good 7.0 8.0 

Fair/Poor 27.3 28.7 

Insurance Status by Age 

Private, 0-64 4.7 5.8 

Any Public, 0-64 12.6 12.7 

Uninsured all year, 0-64 9.7 10.6 

Medicare only, 65+ 36.2 37.8 

Medicare, plus private, 65+ 24.5 26.2 

Medicare, plus public, 65+ 30.8 32.4