Income Measurement in Federal Surveys 

John L. Czajka* 

Gabrielle Denmead** 

*Mathematica Policy Research 

jczajka@mathematica-mpr.com 

**Denmead Services & Consulting 

gabrielle.denmead@verizon.net 

Introduction 

Income is a critical variable in policy-related analysis and public program design. Major federal and state programs address 

the consequences of inadequate resources and the needs resulting from or correlated with low income, and income and 

poverty status often play key roles in the development of public policy. As a result, regardless of their primary purpose, most 

major household surveys collect some income data and provide measures of poverty status. Nonetheless, income is difficult 

to measure well in household surveys. Furthermore, poverty status depends not only on income but on how the family is 

defined, which differs across surveys. Despite many similarities, there are significant differences among the income and 

poverty measures in different surveys. More importantly, various federal surveys provide markedly differing estimates of 

income and poverty even when the same concepts are applied. 

A comprehensive and systematic assessment of the income data and their utility for policy-related analyses in eight major 

surveys was conducted by Mathematica Policy Research, Inc. and its subcontractor, Denmead Services & Consulting, under a 

contract with the Office of the Assistant Secretary for Planning and Evaluation in the Department of Health and Human 

Services. The surveys are all federal or federally-sponsored: the Survey of Income and Program Participation (SIPP); the 

Annual Social and Economic Supplement (ASEC) to the Current Population Survey (CPS); the American Community 

Survey (ACS); the Household Component of the Medical Expenditure Panel Survey (MEPS); the National Health Interview 

Survey family core (NHIS); the Medicare Current Beneficiary Survey Cost and Use files (MCBS); the Health and Retirement 

Study (HRS); and the Panel Study of Income Dynamics (PSID). 

The assessment was focused on three issues: 

• The quality and usability of each survey’s income and poverty data for policy-related analyses 

• The overall impact of different design and methodological approaches 

• Specific design and processing choices that may be related to the quality and utility of income and poverty data 

in each survey 

This paper summarizes key findings on income measurement in the five federal surveys of the general population: the CPS, 

ACS, SIPP, MEPS, and NHIS. For all surveys, the analysis was restricted to the civilian noninstitutional population, and the 

income reference period conforms as closely as possible to calendar year 2002. 

We begin by discussing differences among surveys, the development of comparable income estimates across surveys, and 

some limitations of the survey comparisons. Next we compare survey estimates of income, income by quintile, and earned 

and unearned income aggregates and quintiles. Following that we compare survey estimates of poverty rates, the number and 

composition of the poor and near poor, and the impact of family definition on measured poverty. Next we present 

comparative findings on several aspects of data quality, including overall response rates, allocation, internal consistency, and 

rounding. Finally, we discuss some implications for users of income data from federal surveys. 

Development of Comparable Estimates Across Surveys 

The assessment's empirical methodology was designed to yield, to the extent feasible, estimates of income with comparably 

defined universes, income, and families across the surveys. Comparability was based on CPS definitions of these concepts, as 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

the CPS is the official source of statistics on family income and poverty for the U.S. All analyses in the study use income 

data for calendar year 2002, except for the rolling reference period in ACS that spans 23 instead of 12 months. Also, since 

various surveys apply the same term to somewhat different concepts or measures, and may have different names for the same 

concepts or measures, the terminology used in the study and in this paper has been standardized to Census Bureau and CPS 

definitions. Since NHIS public use files have no dollar amounts, only brackets, all NHIS tabulations were performed on the 

internal file, on-site. 

Adjustments to Enhance Comparability 

Our estimates of income focus on calendar year 2002, but the surveys all differ in the point in time at which they collect 

income information. We did not attempt to adjust for differences due to differences in survey timing, although the study on 

which this paper is based explored the impact of survey timing on poverty rates in the light of dynamic changes in family 

composition, and also on response rates. We did adjust for universe differences that arose from differential treatment of 

specific subpopulations. Survey samples were restricted to approximately the same universe by removing any military and 

their families, unrelated children under 15, persons institutionalized or deceased by the end of the year, and persons residing 

outside the fifty states and the District of Columbia. Since the ACS in 2002 had not yet been extended to include group 

quarters, those living in group quarters and included in other surveys could not be included in 2002 ACS tabulations. ACS 

data for 2006, the first year covering group quarters, include 2.3 million students residing in college or university dormitories, 

treated as unrelated individuals, and excluded from ACS poverty estimates. 

In conformity with CPS income definitions, lump sums and irregular payments were removed where included in survey 

income—$12.7 billion from SIPP and $65.6 billion from MEPS. Missing components of income that could not be added 

include educational benefits in SIPP, and tax exempt interest for tax filers in MEPS. In addition, by referring respondents to 

their tax returns, MEPS implicitly uses tax concepts to define income, which implies that wages may exclude, for example, 

pre-tax deductions for contributions to 401(k) plans or other tax-advantaged retirement accounts, contributions to Medical 

Savings Accounts, and some health insurance premiums. For other surveys, whether there are differences in the income 

concepts depends heavily on respondent interpretation of questions asking about broadly-defined sources. 

Official poverty statistics incorporate the definition of a family that is used in the CPS—two or more persons related by 

blood, marriage, or adoption. The NHIS uses a broader family definition that includes unmarried partners and their relatives, 

and foster children. Those NHIS families that included unmarried partners or foster children were split into two or more CPS 

families, and the income of the original family was apportioned among the new families. MEPS uses both the CPS and the 

broader NHIS family concepts but identifies CPS families on the file; the study and this paper use MEPS income data for the 

CPS-type families. 

Limitations on Comparability 

While the goal of these efforts was to make the survey estimates of income as comparable as feasible, there remain a number 

of differences due to universe definition, survey design, or methodological features for which adjustments were not possible. 

While the assessment included a number of special studies of the impact of these design and methodological features, these 

are not discussed in this paper. 

First, our adjustments do not compensate for the fact that the surveys represent populations at different times. Family 

composition is dynamic, and the people living together as a family at the time the data were collected and whose income is 

summed to determine family income may not have lived together for the entire income reference year. Conversely, other 

individuals may have lived with the family for some or all of the income reference year but if they are no longer part of the 

family at the time data are collected, their income is not included in family income. The longer the lag between family 

composition and the income reference period, the more changes in family composition will occur, with possible bias. Both 

the ACS and MEPS fix family composition at the end of the income reference period while the CPS fixes family composition 

two-and-a-half months later, on average.1

 The NHIS collects family income for the prior calendar year from families 

interviewed over the course of the next calendar year, so family composition lags the end of the income reference period by 

one-half to 11-and-a-half months, or 6 months on average. 

1

 CPS ASEC interviews are conducted in February, March, and April in the week that includes the 19th. 
 

 

 

 

 

 

 

 

Second, our adjustments do not correct for the rolling reference period in ACS that spans 23 months instead of a calendar 

year. Other surveys represent income during the 12 months of a calendar year, with each month equally represented. In the 

ACS, respondents are asked to report their income for the past 12 months. For persons completing the survey at the beginning 

of the year, the past 12 months are January through December of the prior year; for persons completing the survey at the end 

of the year, the past 12 months are December of the prior year through November of the current year. Thus the 23 months 

spanned by the rolling reference period are unequally represented. Income data for an ACS "year" are centered on December 

of the prior year, with half of the income attributable to the seven- month interval from September of the prior year through 

March of the survey year. The remaining half of income for an ACS "year" is attributable to the 16 months of January 

through August of the prior year and April through November of the survey year.2 

Third, we could not compensate for or remove the unique income and poverty-related post-stratification used in generating 

MEPS weights. All five surveys calculate weights based on selection probabilities adjusted for non-response, then poststratify to CPS or other Census Bureau control totals by age, race/ethnicity, and sex of the person, family head, or family 

reference person, and sometimes other information such as household type, or geographic location. MEPS additionally poststratifies persons on the public use file to match CPS poverty rates by age, sex, race/ethnicity, Census region and MSA/nonMSA status for CPS-type families as of December 31 of the year, thus ensuring that the MEPS public use file yields the same 

poverty rates by demographic groups as the CPS. 

Fourth, the adjustments do not correct for the differential treatment of college students living away from home while 

attending school. SIPP, CPS and MEPS include college students living away from home in the parental family. NHIS treats 

college students living away from home as unrelated individuals, and includes them as such in poverty estimates. The 2002 

ACS did not interview in dormitories (group quarters), and also treated any students living in off-campus housing as 

unrelated individuals. These differences affect the size of the ACS population and the composition of ACS and NHIS 

families. 

Fifth, our adjustments do not compensate for differences in the timing or source of the population controls that were applied 

or how they were applied. The MEPS and SIPP estimates represent populations in December 2002 while the CPS represents 

a March 2003 population. Both the ACS and NHIS represent an average of populations over a calendar year. The 2002 ACS 

is weighted to July 1, 2002 while the four segments of the 2003 NHIS are weighted, separately, to February 1, May 1, August 

1, and November 1 of that year, then combined to create a single annual weight on the public use file, with an effective 

reference date of mid-June 2003. Also, some surveys use Census Bureau population estimates directly as control totals; 

others use CPS population estimates, or perform their own calculations of control totals based on CPS data. These alternative 

controls do not agree completely. 

Sixth, since the NHIS collects only a family-level income amount and person-level earnings, creating CPS families in the 

NHIS required assumptions about the allocation of family income other than earnings among the new families when a nonCPS family was split. Lastly, no attempt was made to address differences in how respondents interpreted what they were to 

include or not include in the income they reported. 

Income 

In comparing estimates of income across surveys, we look first at total income and income by quintile. We then divide total 

income into earned and unearned income. 

Total Income 

As a summary statistic, total or aggregate income is appealing for its simplicity and its use of all the income data in each 

survey. However, aggregate income is heavily dependent on the amount of income captured from the upper end of the 

income distribution, which holds the least interest for policy analysis. In presenting estimates of aggregate income, we 

include a breakdown by quintile, which enables us to compare the surveys with respect to their collection of income from 

different segments of the distribution. Table 1 presents estimates of aggregate income for the whole population and by 

2

 Income reported in ACS published data and on-line tables that are based on internal files is adjusted across the rolling 

reference period to the same real dollars, based on the Consumer Price Index (CPI). Income in ACS public use files is not 

adjusted for inflation, although an average inflation factor is provided. 
 

 

 

 

 

 

 

 

quintile of family income for the five surveys.3 Table 1 also shows these amounts as a percentage of the corresponding 

amounts for the CPS. There is no gold standard for estimates of income, nor do we mean to suggest that the CPS estimates

are the best. But the CPS is the official source of household income and poverty statistics for the U.S. and expressing other 

survey estimates of income as a percentage of the CPS provides a useful standardization. 

Aggregate income ranges from $5.77 trillion in the SIPP to $6.47 trillion in the CPS—a difference of nearly 11 percent, 

while estimates from the other three surveys lie within two to five percent of the CPS. Since aggregates in the top quintile 

may be affected by outliers and by differences in survey topcoding practices, we summed the survey aggregates through the 

four lower quintiles. For every survey, the four-quintile sum is closer to the CPS estimate than is the full aggregate, with the 

MEPS total exceeding the CPS by 2.5 percent. The SIPP total moves to within 1.5 percent of the NHIS total but is still six 

percent below the CPS. 

When income is examined by quintile, we find that SIPP obtains the largest amount of income for the lowest quintile, at 

105.6 percent of the CPS total, but this apparent success erodes noticeably above the lowest quintile. In the second quintile, 

SIPP collects 97 percent of CPS income, dropping to 92.5 percent by the third quintile, 90.3 percent by the fourth and 82.8 

percent in the top quintile. MEPS aggregates exceed the corresponding CPS amounts for quintiles two through four while the 

ACS aggregates lie within a percent of the CPS aggregates (both above and below) through the first three quintiles before 

dropping to 98 and 97 percent of the CPS in the fourth and fifth quintiles. 

The similarity of aggregate income across surveys with a vast range in the number and detail of income questions raises a 

fundamental issue for the collection of income data – how much do question number and detail matter? With a single 

question NHIS captures 95 percent as much total income as the CPS; with a simple instrument primarily filled out by 

respondents rather than a trained interviewer, the ACS approximates the CPS more closely than any other survey. SIPP, with 

far more income questions than the other four surveys, captures 11 percent less total income than the CPS and 6 percent less 

than the NHIS’s single question. It is not possible to determine what impact the post-stratification of MEPS to match CPS 

poverty rates has on MEPS estimates of aggregate income, but MEPS, with its SIPP-like panel design, might yield SIPP-like 

income estimates in the absence of this post-stratification, or alternatively, the use of retrospective annual versus monthly 

income questions may trump the panel design. 

Earned and Unearned Income 

Earnings (wages and salaries plus self-employment income) are the dominant income source in the U.S., and account for 82 

to 86 percent of total aggregate income. Employment is a major area of policy concern, as a source of self-support and of 

health insurance coverage. For both these reasons, accuracy in its measurement is key. Table 2 presents estimates of earned 

income and unearned income, showing these amounts as shares of total income by survey and as percentages of the 

corresponding amounts for the CPS. 

For SIPP, CPS and ACS, earned income accounts for 82.1 to 82.8 percent of total income, with MEPS slightly higher at 84.1 

percent and NHIS highest at 86.0 percent. The identical share of earnings in total income for CPS and SIPP is particularly 

striking, given the fact that total earned income in the SIPP is only 89 percent of the corresponding amount in the CPS. This 

implies that unearned income must be similarly lower in SIPP as compared to CPS, which is confirmed in the bottom panel 

of the table. The NHIS does not collect data on unearned income, but the difference between total income and total earnings 

for the NHIS suggests an amount of unearned income that is only 77 percent of the CPS total. This implied shortfall may 

indicate that the NHIS does not do as well in obtaining total income with its single question as it does in collecting earned 

income from all adults, or may be explained by inconsistencies between earnings and total income among NHIS families, 

discussed below. 

There are much larger differences among survey counts of persons self-employed or working for others and among amounts 

earned from wages and salaries and from self-employment, than among numbers of earners and total or average earnings. 

SIPP's higher estimates for self-employment help offset its apparent understatement of wage and salary income; SIPP 

captures only 82.4 percent as much aggregate wage and salary income as the CPS, but has 188 percent as much selfemployment income. The ACS captures four percent less wage and salary income than the CPS but 19 percent more self3

 Within each survey, each of the five quintiles contains the same number of people (weighted) except when the 

numbers are affected by heaping at quintile boundaries. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

employment income. (MEPS is not discussed due to classification issues that prevent accurate separation of wage and salary 

and self-employment earnings.) 

Table 3 compares survey estimates of earned income by quintile of family income, and Table 4 presents the same 

comparisons for unearned income. The ACS, SIPP, MEPS, and NHIS all find more earnings in the lowest quintile of family 

income than does the CPS, but the aggregate amounts are small. The ACS and SIPP find progressively less total earnings 

relative to the CPS as we move up the quintiles. For MEPS this is true after the first quintile. The NHIS, on the other hand, 

finds progressively more aggregate earnings relative to the CPS in quintiles two through four. By contrast, there are few 

consistent patterns for unearned income as one moves up the quintiles. The ACS finds progressively more unearned income 

than the CPS for higher quintiles, with 23 percent more in the top quintile. SIPP has 99.6 percent as much unearned income 

as the CPS through the first four quintiles but 35 percent less in the top quintile. MEPS, on the other hand, falls short of the 

CPS in all quintiles. In the NHIS, unearned income is a residual rather than a reported amount, as already noted, and is 

erratic. The difference between aggregate total and aggregate earned income in NHIS is as low as 60 percent of the CPS 

aggregate in one quintile and as high as 88 percent in an adjacent quintile. 

Poverty 

The income measures most often employed in policy-related analyses concern the lower end of the income distribution, and 

most frequently are classifications that compare family income to the family-size adjusted income amounts that comprise the 

set of official poverty thresholds. The poor are those persons with family incomes below these thresholds, and the poverty 

rate is the percentage they constitute of all persons in the poverty universe of each survey.4

 The composition of the poor and 

near poor (whom we define as those with family income between 100 and 200 percent of the poverty threshold)5

 and the 

poverty rates for specific demographic groups such as children or the elderly may also be of great interest. Marked 

differences across surveys in estimates of the poor and near poor would usually raise questions about income data quality, 

reliability and utility for policy-related research. 

Estimates of the Poor and Near Poor 

Table 5 presents total numbers of poor and near poor, and poverty rates, for all ages. The range of estimates—lowest to 

highest—of the number of poor and the poverty rate is 8.4 million people and 2.9 percentage points. SIPP obtains the lowest 

poverty rate among the five surveys at 11.8 percent, based on an estimate of 33.2 million poor. The CPS, ACS, and MEPS 

cluster very close to each other and slightly above SIPP with poverty rates between 12.2 percent and 12.5 percent. As we 

have noted, the MEPS sample weights are post-stratified to the CPS poverty distribution, so the poverty rates for the two 

surveys should be identical if not for the differential effect of our universe adjustments.6

 At the high end, the NHIS is an 

outlier with an estimate of 41.6 million poor and a poverty rate of 14.7 percent for persons in comparable CPS-like families. 

The NHIS poverty rate is more than two percentage points higher than any of the other four surveys and nearly three 

percentage points higher than the SIPP. 

Despite having the lowest poverty rate, SIPP exceeds the other four surveys in its estimate of the near poor. SIPP finds 20.0 

percent of the population to be near poor. This is nearly two percentage points above the CPS and MEPS, more than two 

percentage points above the ACS, and one percentage point above NHIS. SIPP’s estimate of 56.2 million near poor exceeds 

the ACS by 7.0 million and NHIS by 2.3 million. 

4

 CPS and SIPP exclude unrelated children under 15 from the poverty universe but not the survey universe. MEPS and 

NHIS exclude unrelated minors (usually under 18) from both survey and poverty universes. ACS excludes unrelated children 

under 15 from the poverty but not the survey universe, including those that would be in unrelated subfamilies in CPS or 

SIPP. After group quarters were added to ACS in 2006, students residing in college or university dormitories are excluded 

from the ACS poverty universe but not the survey universe 

5

 There is no standard definition of "near poor". Historically, the term referred to persons between 100 and 125 percent 

of poverty. We use it for those with low family income, below 200 percent of poverty, but not poor. 

6

 Also, our independently calculated poverty status differs occasionally from the status on the MEPS public use file due 

to an apparent error in the algorithm used in creating the recode for the public use file. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

When the low-income population of both poor and near poor is examined, SIPP is higher than all but NHIS in the number 

and percentage of persons who are low-income. For the SIPP, 31.8 percent or 89.5 million persons are low-income compared 

to 30.5 percent or 86.2 million persons for the CPS. MEPS is somewhat higher than the CPS on both measures while the 

ACS is lower. NHIS finds 33.7 percent of the population or 95.5 million persons to be low-income, when a comparable CPS 

family definition is used. The count of low-income persons in the NHIS is 6.0 million higher than in the SIPP and 9.3 million 

higher than in the CPS. 

Estimates of Poor and Near-Poor Children and Elderly 

Table 6 presents numbers of poor and near-poor children under 18, and their poverty rates. SIPP’s comparatively high 

estimates of near-poor and low-income persons in the general population extend to children as well. SIPP finds more nearpoor and low-income children than any of the other four surveys. While the estimates of children in low-income families 

from the CPS, ACS, and MEPS cluster between 27.4 and 28.0 million, or 38.2 to 38.9 percent, SIPP finds 30.5 million lowincome children or 42.7 percent of all children. NHIS is slightly lower than SIPP with 41.4 million low-income children or 

29.7 percent. Furthermore, unlike the general population, where SIPP had the lowest estimate of persons in poverty, SIPP’s 

estimate of poor children exceeds those of the ACS, MEPS, and CPS, if only marginally. NHIS finds the most poor children 

with a child poverty rate that exceeds the other surveys by 2 to 3 percentage points, but NHIS has no more near-poor children 

than CPS or MEPS. In fact, the estimates of near-poor children vary from only 14.9 to 15.4 million or 21.1 to 21.5 percent 

across the CPS, ACS, MEPS, and NHIS while SIPP finds 17.7 million or 24.8 percent. 

The living arrangements of poor, near-poor, and low-income children are generally similar across the five surveys. Poor 

children are much more likely to be living in single-parent than husband-wife families while near-poor children are more 

likely to be living in husband-wife than single-parent families. All low-income children divide almost equally between the 

two types of living arrangements in the CPS, SIPP, and MEPS, with single-parent families more prevalent in the ACS and 

husband-wife families more common in the NHIS. 

Table 7 presents numbers of poor and near poor elderly age 65 or over, and their poverty rates. SIPP’s comparatively high 

estimates of low-income persons in the general population do not extend to the elderly. SIPP finds fewer low-income elderly 

than the CPS, MEPS, or NHIS at 11.6 million compared to 12.9 to 13.6 million, or 34.1 percent compared to 37.6 to 39.7 

percent. The ACS finds the fewest low-income elderly at 11.2 million or 33.3 percent, but SIPP finds the fewest poor elderly 

at 3.0 million and the lowest poverty rate for the elderly at 8.9 percent. However, estimates of the number of poor elderly do 

not differ greatly among the five surveys, with the range among the CPS, MEPS, and NHIS being only 3.6 to 3.8 million or 

10.5 to 11.3 percent. 

Impact of the Family Definition 

Some surveys utilize family definitions that deviate from the CPS family concept incorporated into the official measure of 

poverty in the U.S., and surveys also differ in the level of detail with which they capture family relationships. We found that 

broadening the family concept as compared to the CPS measure produces major changes in family income and poverty rates. 

Lack of relationship information among persons unrelated to the household head, as is the case for the ACS, also affects 

family income and poverty rates. 

The response unit in the NHIS is the family, and families are defined to include unmarried partners of the same or opposite 

sex, relatives of unmarried partners, and foster children. Family income is collected as a single amount for the entire family. 

In developing the NHIS estimates of income for comparison with the other surveys, we separated unmarried partners and 

their relatives including children, and foster children, from the NHIS family. We assigned each person his or her reported 

earnings, then apportioned family income over and above individual earnings (if any) among the two or more CPS-type 

family units into which these NHIS families were split. By comparing the income and poverty estimates that we prepared 

using the CPS family definition with estimates obtained from the original data, we assessed the impact of using the NHIS 

rather than the CPS family definition to combine individuals into families for the purposes of estimating family income, 

numbers of poor, and poverty rates. 

MEPS also uses the family as its response unit and defines the family in the same way as the NHIS. However, in order to 

post-stratify MEPS sample weights to the CPS poverty distribution, the Agency for Healthcare Research and Quality (or its 

MEPS survey contractor) identifies CPS families within the broader MEPS families. Income, which is reported at the person 

level, can be aggregated to either family definition using alternative family identifiers on the public use file. We used the CPS 

family to prepare the income estimates reported earlier, but by preparing an alternative set of estimates based on the 

MEPS/NHIS family definition, we can assess the impact of using one versus the other family definition just as we did with 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

the NHIS. This process did not require us to make any assumptions in order to apportion income, since MEPS collects 

person-level income data. 

Table 8 shows our estimates of the impact of the broader NHIS family definition for both the NHIS and MEPS. The results 

for the two surveys are remarkably similar. In both surveys we find that the NHIS family definition reduces the number of 

poor by 2.6 million and reduces the poverty rate by 0.9 percentage points. There is no impact in either survey on the 

percentage of persons between 100 and 200 percent of poverty, which means that the number of people who were moved 

above the poverty line by the NHIS family concept is offset by the number of people who were moved beyond 200 percent of 

poverty. Most of the upward shift is observed in the top category—that is, among people above 400 percent of poverty, 

where the broader family concept adds 2.3 million to the number in the NHIS and 1.4 million to the number in MEPS. 

While not an issue of family definition, the limited relationship information in the ACS affects ACS estimates of the number 

of poor and the poverty rate. ACS determines relationship only to the household head and cannot identify unrelated 

subfamilies. As a result, members of these families, usually a single parent with one or more never-married children, are all 

treated in the ACS as unrelated individuals regardless of age. While one might expect this treatment to increase the number 

of poor and the poverty rate, it probably has the opposite effect. Examination of unrelated subfamilies in the CPS showed that 

almost half of the unrelated individuals that would be created if unrelated subfamilies were not identified were under 15 and 

would be dropped from the conventional poverty universe, and thus not counted as poor. Given the high poverty rate found 

for unrelated subfamilies in the CPS, the reduction in poor children under 15 due to eliminating them from the poverty 

universe more than offsets the increase in poor age 15 or older resulting from treating them as unrelated individuals. 

Aspects of Data Quality 

The assessment of income data was specifically focused on the quality and utility of income and poverty data for policyrelated analysis. A large number of factors affect data quality, including overall survey response rates, item non-response and 

allocation rates, the use or absence of internal consistency edits, and heaping or rounding. Policy analyses may be particularly 

sensitive to any upward or downward bias in income data, and to inconsistencies that could significantly change estimates of 

the poor and near poor. 

Non-Response and Allocation 

Non-response to household surveys is a serious issue, since high rates of refusal or overall survey non-response may lead to 

non-response bias. For longitudinal surveys, attrition is a lesser issue than initial non-response, because earlier interviews

provide information with which to complete or allocate income amounts and to measure and adjust for any attrition bias. But 

for income data, item non-response is also a serious problem. Income questions generally yield some of the highest rates of 

missing values,7

 and significant fractions of income data are in fact allocated or created during processing of the raw data. 

Table 9 presents initial survey response rates across surveys, and 2002 income data response rates after attrition for the two 

panel surveys. The highest initial response rate, over 97 percent, is for the only mandatory survey, the ACS. The lowest 

initial response rate, 70 percent, is for MEPS, which piggy-backs on the NHIS sample and only contacts NHIS respondents. 

SIPP and the NHIS have initial response rates of 88 and 89 percent; the CPS has a response of 92 percent for the underlying 

monthly labor force survey, but about 11 percent of persons with income in the CPS are whole imputes who have refused to 

answer the ASEC supplement. For the two panel surveys, the overall survey response after attrition for the interviews 

providing income data for 2002 was 72 percent for the SIPP and 65 percent for MEPS. 

Table 9 also presents measures of the fraction of total income, in dollars, for which missing values were filled in or allocated, 

by any method, and further indicates the proportions allocated with and without use of “partial” information on the missing 

amounts. Partial information could include prior wave values (SIPP), detailed income brackets reported in lieu of a dollar 

amount (MEPS and NHIS), or wage rates and hours worked (used for annual wages in MEPS). Allocation methods without 

7

 For surveys for which income data is not a high priority, this can become a rationale for limiting the length and detail

of income questions or relegating them to the end of interview. The latter minimizes impacts on other questions or early 

break-off of interviews, but may further reduce income question responses rates and adversely affect response quality, as 

many researchers have noted appears to be the case with CPS health insurance questions. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

the use of partial information could be “hot deck”8 imputations (CPS, ACS, SIPP and MEPS) or regression methods (NHIS 

and MEPS). Arguably, allocations that make use of partial information are qualitatively different from allocations that rely 

solely on covariates of the missing items. 

The percent of total dollars allocated ranges from 18 percent in the ACS to 43 percent in MEPS, with the CPS, SIPP and the 

NHIS all having 32 to 34 percent of total dollars allocated. When we divide allocated dollars of total income into allocations 

performed with or without partial information, we find that allocations with partial information dominate the allocations for 

SIPP and MEPS. In each of these surveys, allocations without partial information account for about 7 percent of total income 

while allocations with partial information account for 25 percent of total income in SIPP and 36 percent in MEPS. In the 

NHIS, allocations with partial information represent only 2 percent of total income, and allocations without partial

information account for 30 percent of total income.9

 Neither CPS nor ACS had allocations classified as using partial 

information, leaving 34 percent of total income in the CPS and 18 percent in the ACS as allocated without partial 

information. However, lacking a direct quality measure for allocated data with partial information, we hesitate to assert that 

non-response to the income questions is as much less of a problem in the SIPP and MEPS as these results might be read to 

suggest. 

Internal Consistency 

Consistency between total income and its sources or between reported employment and reported income from employment is 

an important indicator of data quality. Internal consistency can be achieved through questionnaire design, or through 

consistency edits during processing, and most of the surveys, like the CPS, do such edits or consistency checks. However, 

there are some exceptions. 

NHIS asks total income for families, and earnings (never negative) for persons, but does not determine whether the sum of 

earnings for persons in the family (family earnings) exceeds the family’s income. As shown in Table 10, for 61.7 million 

persons and 9.9 million poor, family earnings exceed family income. Family earnings are over $10,000 above family income 

for 27.6 million people and over $20,000 higher for 15.4 million, with the excess totaling about $290 billion. Earnings and/or 

family income were imputed for most NHIS families with total earnings in excess of total income. They were imputed for 71 

percent of all persons with family earnings greater than family income, 83 percent of those whose poverty status changes, and 

88 percent of those with a difference of more than $20,000. 

The lack of agreement between family earnings and income in the NHIS has dramatic consequences for numbers of poor and 

the poverty rate. When higher family earnings were substituted for family income, the poverty rate was reduced by 1.4 

percentage points on either the CPS or NHIS family definition, and the number of poor by 3.9 or 4.0 million for the CPS and 

NHIS family definitions, respectively. Using higher family earnings improved poverty status for another 12.3 million by 

shifting them from 100 to 200 percent of poverty to above 200 percent of poverty, or from 200 to 400 percent of poverty to 

above 400 percent of poverty. 

Other internal inconsistencies involve earnings and work activity in MEPS and the NHIS. CPS and ACS include consistency 

checks in data processing to ensure there is income associated with all work activity, and work activity associated with all 

reported earnings. But in NHIS, 4.3 million persons reported receipt of earnings but have no work activity or earnings in the 

same year, and another 4.0 million persons reported working, and amounts earned, but no receipt of earnings. Similarly, in 

MEPS, 6.6 million persons reported earned income for the year but no work activity on the detailed JOBS file of 

employment, and 2.6 million persons reported details of one or more jobs during the year but no earned income for the same 

time period. SIPP does not edit or impute monthly work activity against monthly earnings or monthly earnings against 

monthly work activity, yet finds less than one-half million persons with either work activity but no earnings or earnings but 

no work activity on an annual basis, compared to 8.3 million in NHIS and 9.2 million in MEPS. 

8

 Hot deck methods involve matching records with missing values to “donor” records based on a typically large number 

of characteristics, then assigning values from the donor records. Using other respondents as donors helps ensure that the 

allocated values are plausible and have an appropriate distribution. Often, multiple variables may be assigned from the same 

donor to ensure that there is some internal consistency among the allocated values. 

9

 Revisions to the NHIS income questions for the 2007 survey have increased the percentage of respondents providing 

brackets. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Rounding 

We cannot directly assess the accuracy of survey responses to income questions, but round numbers suggest inexact reporting 

or approximation of actual income amounts. Respondent rounding distorts the distribution of income by creating artificial 

spikes or heaping at the rounded values, and in fact, rounding is an accepted way to protect confidentiality in public use 

files.10 Further, distortions due to rounding in reporting will be exaggerated by hot-deck allocations that carry rounding over 

from donor records. We tested for rounding by determining the percent of persons with reported, unallocated, income 

amounts exactly divisible by $5,000 or $10,000 up to $52,500, which tests the frequency of rounding to $50,00011 or less. 

The test is most reliable for the NHIS and ACS, where the amounts tested were always responses to single questions. It is 

least applicable to the SIPP, where all annual amounts are built up from monthly values. 

Table 11 presents the percent of persons with reported (not allocated) earnings, wages and salaries, Social Security benefits, 

other retirement income, total person-level income, or total family income amounts exactly divisible by $5,000 or $10,000, 

across the five surveys. In SIPP, with detailed income questions and monthly data, virtually no one has rounded income 

amounts. In NHIS, with single annual amounts, 40 percent of earners and 36 percent of families report amounts divisible by 

$5,000, and 23 percent of earners and 21 percent of families report amounts divisible by $10,000. In CPS and ACS, 28 to 30 

percent of earners report amounts divisible by $5,000, and 16 to 17 percent report amounts divisible by $10,000. MEPS has 

less rounding—19 percent of earners report amounts divisible by $5,000, and 10 percent report amounts divisible by $10,000. 

In contrast to earnings, Social Security and retirement income have little rounding—less than 10 percent of recipients of 

either source reported amounts divisible by $5,000 in CPS, SIPP, ACS or MEPS, although the Social Security amounts 

would almost always be a response to a single question in CPS, ACS or MEPS. 

Implications 

Our findings from this comparison of income estimates across federal surveys have several implications for users of these 

data. Income and poverty as measured in different surveys are not equivalent, and there are also important differences in the 

estimated composition and distribution of income among surveys, as well as in the detail available on sources of income. 

Some surveys have high survey non-response rates, raising the possibility of non-response bias. All but ACS have high 

income allocation rates, but little is known about the quality of alternative approaches to allocation, and even less about the 

prevalence of rounding of reported incomes. It would seem that the quality and utility of income data for policy-related 

analyses varies substantially across surveys, and this is likely to reflect the different levels of importance assigned income 

data and its low priority for some surveys. Impacts of differences may be greatest for uses that involve detailed simulations of 

program eligibility, particularly when evaluating reform options that alter eligibility thresholds and benefit formulas. As a 

result, no one survey is ideal for policy use. 

At one extreme, the NHIS obtains family income only for families more broadly defined than in official statistics, has no 

person-level information with which to compute poverty rates consistent with official statistics or to test alternative filing 

units and eligibility, and has internal inconsistencies that changed the count of poor by four million persons. In addition, 

income information on the NHIS public use file is limited to $5,000- and $10,000-wide brackets, and policy users are 

unlikely to have the time or flexibility to get an analytic plan approved in order to use the internal file on-site, as was done by 

the assessment study whose data are used in this paper. 

While SIPP was specifically designed for policy use and has strong income and program participation information for the 

lower half of the income distribution, data quality overall has degraded in the last decade due to budget contractions, the files 

as currently structured are difficult to use, and changing family composition in conjunction with monthly income reporting 

means that only part-year income information is available in many cases. 

10 Limiting the number of significant digits in reported incomes reduces their uniqueness, making them less identifiable. 

The ACS applies a very well-defined rounding rule. 

11 On the ACS public use file, amounts of $50,000 or greater rounded to the nearest $1,000. 
 

 

 

 

 

 

 

 

CPS has strong income data, but its program participation and health insurance data are less credible and for decades have 

required complex modeling efforts for policy use. The basic CPS is a monthly labor force survey to which the ASEC is an 

annual supplement, and income data are subordinated to the needs of employment and labor force information. 

MEPS income data also make a strong showing, but the impact of post-stratifying the file, altering the distribution of reported 

incomes to match CPS poverty rates, is not understood. In addition, MEPS has the highest initial survey non-response rate, 

thus the highest risk of non-response bias, and has internal inconsistencies that are also not well understood. 

The ACS is a massive survey with exceptionally good response rates and surprisingly good income data, but has been so 

narrowly focused on demographics that the needs of policy users have yet to be acknowledged. The ACS has major 

definitional and methodological differences from the other surveys and from the Decennial Long Form that it has replaced, 

but these differences have not been fully explored, since most such work requires use of internal Census Bureau data files.

12 

The impact of these differences on income measures, especially on the income distribution and poverty rates for 

subpopulations, is also not fully understood. 

12 The Census Bureau produced, pro bono, an extensive set of tabulations on the internal ACS files with which the study 

could examine some aspects of the unique ACS rolling sample, rolling reference period and the within-year inflation 

adjustments. 
 

TABLE 1 

AGGREGATE INCOME BY QUINTILE OF FAMILY INCOME: FIVE SURVEYS 

Income Estimate CPS ACS SIPP MEPS NHIS 

Billions of Dollars 

Aggregate Income, All Persons 6,468.4 6,346.3 5,766.2 6,257.7 6,116.2 

Family Income Quintile 

Lowest 370.5 368.7 391.4 360.0 313.7 

Second 774.1 778.4 750.8 808.4 717.7 

Third 1,090.2 1,087.4 1,008.8 1,144.7 1,058.4 

Fourth 1,446.8 1,415.8 1,307.2 1,461.8 1,420.7 

Highest 2,786.7 2,696.0 2,308.0 2,483.0 2,605.8 

Sum through Four Quintiles 3,681.7 3,650.3 3,458.2 3,774.7 3,510.4 

Percent of CPS 

Aggregate Income, All Persons 100.0 98.1 89.1 96.7 94.6

Family Income Quintile 

Lowest 100.0 99.5 105.6 97.2 84.7 

Second 100.0 100.6 97.0 104.4 92.7 

Third 100.0 99.7 92.5 105.0 97.1 

Fourth 100.0 97.9 90.3 101.0 98.2 

Highest 100.0 96.7 82.8 89.1 93.5 

Sum through Four Quintiles Sum through Four Quintiles 100.0 99.1 93.9 102.5 95.3 

Source: Mathematica Policy Research, from tabulations of calendar year 2002 income from the 2003 CPS 

ASEC supplement, the 2001 SIPP panel, the 2002 Full-year Consolidated MEPS-HC, and the 2003 

NHIS, and prior 12 months income, inflation-adjusted to calendar year 2002, from the 2002 ACS. 
TABLE 2 

CONTRIBUTION OF EARNED AND UNEARNED INCOME TO TOTAL INCOME: FIVE SURVEYS 

Income Estimate CPS ACS SIPP MEPS NHIS 

Billions of Dollars 

Total Income 6,468.4 6,346.3 5,766.2 6,257.7 6,116.2 

Earned Income 5,354.3 5,207.9 4,760.1 5,263.8 5,261.4 

Wages and salaries 5,026.3 4,817.2 4,142.5 NA NA 

Self-employment income 328.0 390.7 617.6 NA NA 

Unearned Income 1,114.1 1,138.3 1,006.0 994.0 854.8

Percent of Total Income 

Total Income 100.0 100.0 100.0 100.0 100.0 

Earned Income 82.8 82.1 82.6 84.1 86.0 

Wages and salaries 77.7 75.9 71.8 NA NA 

Self-employment income 5.1 6.2 10.7 NA NA 

Unearned Income 17.2 17.9 17.4 15.9 14.0 

Percent of CPS Income by Source 

Total Income 100.0 98.1 89.1 96.7 94.6 

Earned Income 100.0 97.3 88.9 98.3 98.3 

W d l i Wages and salaries 100 0. 95.8 82.4 NA NA 

Self-employment income 100.0 119.1 188.3 NA NA 

Unearned Income 100.0 102.2 90.3 89.2 76.7 

Source: Mathematica Policy Research, from tabulations of calendar year 2002 income from the 2003 CPS 

ASEC supplement, the 2001 SIPP panel, the 2002 Full-year Consolidated MEPS-HC, and the 2003 

NHIS, and prior 12 months income, inflation-adjusted to calendar year 2002, from the 2002 ACS. 
 

TABLE 3 

AGGREGATE EARNED INCOME BY QUINTILE OF FAMILY INCOME: FIVE SURVEYS 

Income Estimate CPS ACS SIPP MEPS NHIS 

Billions of Dollars 

Aggregate Earned Income 5,354.3 5,207.9 4,760.1 5,263.8 5,261.4 

Family Income Quintile 

Lowest 176.1 206.5 200.5 191.5 196.4 

Second 542.9 565.3 528.0 615.5 514.4 

Third 889.2 878.8 795.4 950.8 888.2 

Fourth 1,255.9 1,225.5 1,119.4 1,288.3 1,301.9 

Highest 2,490.2 2,332.0 2,116.7 2,217.7 2,360.5 

Sum through Four Quintiles 2,864.1 2,876.0 2,643.5 3,046.1 2,900.9 

Percent of CPS 

Aggregate Income, All Persons 100.0 97.3 88.9 98.3 98.3

Family Income Quintile 

Lowest 100.0 117.3 113.9 108.8 111.6 

Second 100.0 104.1 97.3 113.4 94.7 

Third 100.0 98.8 89.5 106.9 99.9 

Fourth 100.0 97.6 89.1 102.6 103.7 

Highest 100.0 93.6 85.0 89.1 94.8 

Sum through Four Quintiles Sum through Four Quintiles 100.0 100.4 92.3 106.4 101.3 

Source: Mathematica Policy Research, from tabulations of calendar year 2002 income from the 2003 CPS 

ASEC supplement, the 2001 SIPP panel, the 2002 Full-year Consolidated MEPS-HC, and the 2003 

NHIS, and prior 12 months income, inflation-adjusted to calendar year 2002, from the 2002 ACS. 
 

TABLE 4 

AGGREGATE UNEARNED INCOME BY QUINTILE OF FAMILY INCOME: FIVE SURVEYS 

Income Estimate CPS ACS SIPP MEPS NHIS 

Billions of Dollars 

Aggregate Unearned Income 1,114.1 1,138.3 1,006.0 994.0 854.8 

Family Income Quintile 

Lowest 194.4 162.2 190.8 168.4 117.3 

Second 231.2 213.1 222.7 192.8 203.3 

Third 201.0 208.6 213.4 193.9 170.2 

Fourth 190.9 190.3 187.7 173.5 118.7 

Highest 296.5 364.0 191.3 265.3 245.3 

Sum through Four Quintiles 817.6 774.3 814.7 728.7 609.5 

Percent of CPS 

Aggregate Income, All Persons 100.0 102.2 90.3 89.2 76.7 

Family Income Quintile 

Lowest 100.0 83.4 98.2 86.6 60.3 

Second 100.0 92.2 96.3 83.4 88.0 

Third 100.0 103.8 106.2 96.4 84.6 

Fourth 100.0 99.7 98.3 90.9 62.2 

Highest 100.0 122.8 64.5 89.5 82.7 

Sum through Four Quintiles Sum through Four Quintiles 100.0 94.7 99.6 89.1 74.5 

Source: Mathematica Policy Research, from tabulations of calendar year 2002 income from the 2003 CPS 

ASEC supplement, the 2001 SIPP panel, the 2002 Full-year Consolidated MEPS-HC, and the 2003 

NHIS, and prior 12 months income, inflation-adjusted to calendar year 2002, from the 2002 ACS. 

Note: Unearned income is the difference between total income, reported in Table IV.1, and earned income, 

reported in Table IV.5. 
 ,

TABLE 5 

ESTIMATES OF THE POOR AND NEAR POOR: FIVE SURVEYS 

Estimate CPS ACS SIPP MEPS NHIS 

Millions of Persons 

All Persons 282.55 277.69 281.08 283.30 283.71 

Poverty Status 

Poor 34.38 34.61 33.25 35.35 41.58 

Near Poor 51.81 49.28 56.25 52.14 53.91 

Total Low Income 86.19 83.89 89.50 87.48 95.49 

Percent of the Population 

All Persons 100.0 100.0 100.0 100.0 100.0 

Poverty Status 

Poor 12.2 12.5 11.8 12.5 14.7 

Near Poor 18.3 17.7 20.0 18.4 19.0 

Total Low Income 30.5 30.2 31.8 30.9 33.7 

Source: Mathematica Policy Research, from tabulations of poverty status in calendar year 

2002 from the 2003 CPS ASEC supplement, the 2001 SIPP panel, the 2002 Fullyear Consolidated MEPS-HC, and the 2003 NHIS, and poverty status in the prior 

12 months inflation 12 months, inflation adjusted -adjusted to calendar year 2002, from the 2002 ACS. to calendar year 2002, from the 2002 ACS. 

Note: The poor have a family income below the poverty threshold. The near poor have a 

family income at or above the poverty threshold but below twice the poverty threshold. 
 ,

TABLE 6 

ESTIMATES OF POOR AND NEAR-POOR CHILDREN: FIVE SURVEYS 

Estimate CPS ACS SIPP MEPS NHIS 

Millions of Persons 

All Children under 18 71.67 70.79 71.36 71.80 71.73 

Poverty Status 

Poor 12.03 12.51 12.78 12.47 14.29 

Near Poor 15.38 14.94 17.72 15.47 15.41 

Total Low Income 27.41 27.45 30.50 27.95 29.70 

Percent of the Population 

All Children under 18 100.0 100.0 100.0 100.0 100.0 

Poverty Status 

Poor 16.8 17.7 17.9 17.4 19.9 

Near Poor 21.5 21.1 24.8 21.5 21.5 

Total Low Income 38.2 38.8 42.7 38.9 41.4 

Source: Mathematica Policy Research, from tabulations of poverty status in calendar year 

2002 from the 2003 CPS ASEC supplement, the 2001 SIPP panel, the 2002 Fullyear Consolidated MEPS-HC, and the 2003 NHIS, and poverty status in the prior 

12 months inflation 12 months, inflation adjusted -adjusted to calendar year 2002, from the 2002 ACS. to calendar year 2002, from the 2002 ACS. 

Note: The poor have a family income below the poverty threshold. The near poor have a 

family income at or above the poverty threshold but below twice the poverty threshold. 
 ,

TABLE 7 

ESTIMATES OF POOR AND NEAR-POOR ELDERLY: FIVE SURVEYS 

Population Subgroup CPS ACS SIPP MEPS NHIS 

Millions of Persons 

All Persons 65 and Older 34.22 33.56 33.95 34.15 34.22

Poverty Status 

Poor 3.58 3.20 3.03 3.84 3.76 

Near Poor 9.58 7.98 8.56 9.72 9.10 

Total Low Income 13.16 11.18 11.59 13.56 12.86 

Percent of the Population 

All Persons 65 and Older 100.0 100.0 100.0 100.0 100.0

Poverty Status 

Poor 10.5 9.5 8.9 11.3 11.0 

Near Poor 28.0 23.8 25.2 28.5 26.6 

Total Low Income 38.5 33.3 34.1 39.7 37.6 

Source: Mathematica Policy Research, from tabulations of poverty status in calendar year 

2002 from the 2003 CPS ASEC supplement, the 2001 SIPP panel, the 2002 Fullyear Consolidated MEPS-HC, and the 2003 NHIS, and poverty status in the prior 

12 months inflation 12 months, inflation adjusted -adjusted to calendar year 2002, from the 2002 ACS. to calendar year 2002, from the 2002 ACS. 

Note: The poor have a family income below the poverty threshold. The near poor have a 

family income at or above the poverty threshold but below twice the poverty threshold. 
TABLE 8 

COMPARISON OF THE CPS AND NHIS/MEPS FAMILY CONCEPTS 

WITH RESPECT TO THE ESTIMATED DISTRIBUTION OF 

PERSONS BY INCOME RELATIVE TO POVERTY 

NHIS MEPS 

Family Income as CPS NHIS CPS MEPS 

Percent of Poverrty Family Family Change Family Family Change 

Percent of Persons

 Total Percent 100.0 100.0 100.0 100.0 

Under 100% 14.7 13.7 -0.9 12.5 11.5 -0.9 

100% to under 200% 19.0 19.0 0.0 18.4 18.4 0.0 

200% to under 400% 30.7 30.9 0.2 31.7 32.1 0.4 

400% or more 35.7 36.4 0.8 37.4 37.9 0.5 

Number of Persons (millions)

 Total Persons 283.7 283.9 0.2 283.3 283.3 0.0 

Under 100% 41.6 39.0 -2.6 35.3 32.7 -2.6 

100% to under 200% 53.9 53.8 -0.1 52.1 52.2 0.1 

200% to under 400% 87.1 87.7 0.6 89.8 90.9 1.1 

400% or more 101.2 103.4 2.3 106.0 107.4 1.4 

Source: Mathematica Policy Research, from tabulations of poverty status in calendar year 2002 

from the 2003 NHIS and the 2002 Full-year Consolidated MEPS-HC. 
TABLE 9 

OVERALL RESPONSE RATES AND ALLOCATION OF TOTAL INCOME: FIVE SURVEYS 

Estimate CPS ACS SIPP MEPS NHIS 

Initial Overall Response Rate 92.2% 97.3% 87.7% 70.0% 89.2% 

2002 Response After Attrition NA NA 72.5% 64.7% NA 

Amount of Total Income ($billions) 6,468.4 6,346.3 5,766.2 6,257.7 6,115.2 

Percent of Dollars Allocated: 34.2 17.6 32.4 42.7 32.4

 With Partial Information 0.0 0.0 25.4 35.6 2.2

 Without Partial Information 34.2 17.6 6.9 7.1 30.2

Source: Mathematica Policy Research, from detailed descriptive analysis chapter, and tabulations of 

calendar year 2002 income from the 2003 CPS ASEC supplement, the 2001 SIPP panel, the 

2002 Full-year Consolidated MEPS-HC, and the 2003 NHIS, and prior 12 months income, 

inflation-adjusted to calendar year 2002, from the 2002 ACS. 
 

TABLE 10 

NUMBERS OF PERSONS AND EXCESS OF FAMILY EARNINGS OVER 

FAMILY INCOME IN NHIS FAMILIES IN WHICH 

FAMILY EARNINGS EXCEED FAMILY INCOME 

Excess of Percent 

Family of Persons 

Earnings Percent With 

Number Over of Either of 

of Family Persons by Family 

Povery Relative and Persons Income Poverty Incomes 

Excess Earnings (1,000s) ($Billions) Relative Allocated 

Total Persons 61,673 289.4 21.7 71.5 

Poverty Relative Based 

On Family Income 

Under 100% 9,852 28.1 25.3 82.8 

100% to under 200% 11,364 38.3 21.1 79.9 

200% to under 400% 18,750 83.8 21.4 70.1 

400% and over 21,707 139.1 21.0 63.0 

Excess of Family Earnings 

Over Family Income 

$1,000 or less 7,511 1.4 NA 43.1 

$1,001 to $5,000 14,810 15.3 NA 60.4 

$5,001 to $10,000 11,727 30.4 NA 68.9 

$10,001 to $20,000 12,270 59.3 NA 84.3 

$20 001 to $40 000 $20,001 to $40,000 10 334, 90 6. NA 89 0. 

$40,001 or more 5,021 92.3 NA 85.0 

Source: Mathematica Policy Research, from tabulations of calendar year 2002 

income from the 2003 NHIS. 

Note: Estimates use family composition as reported in the survey (NHIS families). 
 

TABLE 11 

REPORTING OF ROUNDED VALUES BY SOURCE OF INCOME BY SURVEY 

AMONG POSITIVE DOLLAR AMOUNTS BELOW $52,500 

Income Source and 

Level of Rounding CPS ACS SIPP MEPS NHIS 

Earnings 

Percent divisible by $5,000 27.8 29.6 1.3 18.6 39.8 

Percent divisible by $10,000 15.8 17.4 0.8 9.7 22.9 

Percent of income in range 82.1 82.4 88.8 81.8 80.9 

Wages and Salaries 

Percent divisible by $5,000 27.2 29.7 0.9 NA NA 

Percent divisible by $10,000 15.4 17.4 0.6 NA NA 

Percent of income in range 82.2 82.7 89.4 NA NA 

Social Security 

Percent divisible by $5,000 0.6 4.3 0.3 6.9 NA 

Percent divisible by $10,000 0.4 1.9 0.1 3.6 NA 

Percent of income in range 100.0 100.0 100.0 100.0 NA 

Retirement Income 

Percent divisible by $5,000 4.5 8.0 1.0 7.4 NA 

Percent divisible by $10,000 2.7 4.3 0.5 3.7 NA 

Percent of income in range 95.6 95.4 99.0 100.0 NA 

Total Personal Income 

Percent Percent divisible divisible by$5,00013 7. 19 7 . 06. 97 . NA 

Percent divisible by $10,000 7.8 11.5 0.4 5.1 NA 

Percent of income in range 84.6 84.2 90.8 85.5 NA 

Total Family Income 

Percent divisible by $5,000 11.0 16.2 0.6 11.1 35.6 

Percent divisible by $10,000 6.2 9.5 0.4 6.1 20.9 

Percent of income in range 66.9 66.0 77.7 72.0 60.3 

Source: Mathematica Policy Research, from tabulations of calendar year 2002 income 

from the 2003 CPS ASEC supplement, the 2001 SIPP panel, the 2002 Full-year 

Consolidated MEPS-HC, and the 2003 NHIS, and prior 12 months income, 

inflation-adjusted to calendar year 2002, from the 2002 ACS. 

Note: Allocated amounts are excluded from each source. Family income for the NHIS is 

based on the NHIS family, which is the level at which family income was reported.