CONGRESSOFTHEUNITEDSTATES

CONGRESSIONALBUDGET OFFICE

A

How Many People

Lack Health

Insurance and

For How Long?

P A P E R

CBO

MAY 2003

Uninsured

for the

Entire Year

of 1998

Uninsured

at a Specific

Point in Time

During the Year

Uninsured

at Any Time

During the Year

Millions

0

10

20

30

40

50

60

70

80

Survey of Income and Program Participation

National Health Interview Survey

Medical Expenditure Panel Survey

21.1

31.1

40.5

42.6

39.0

56.8

59.0

CBO

A

P A P E R

How Many People

Lack Health Insurance

and For How Long?

May 2003

The Congress of the United States O Congressional Budget Office 
Note

Numbers in the text and tables may not sum to 100 because of rounding.

 
Preface

More than 240 million people in the United States have health insurance today through

a variety of private and public sources. At the same time, however, millions of people lack such

coverage, and policymakers have proposed a range of approaches for expanding health insurance

coverage. Far from being a static group, the uninsured population is constantly changing: while

many people are chronically uninsured, many more are uninsured for shorter periods of time,

such as between jobs. This Congressional Budget Office (CBO) paper, prepared at the request

of the Chairman of the House Committee on Ways and Means, uses data from several federally

sponsored national surveys to examine the size, demographic characteristics, and dynamics of

the uninsured population. In keeping with CBO’s mandate to provide objective, impartial analy

sis, the paper makes no recommendations.

Lyle Nelson of CBO’s Health and Human Resources Division prepared the paper under the

supervision of Steven Lieberman. Carol Frost provided assistance with data and tabulations. The

paper benefited from comments from reviewers outside of CBO, including Katherine Swartz

of Harvard University and Charles Nelson of the U.S. Census Bureau. Within CBO, James

Baumgardner, Seth Giertz, Arlene Holen, and Bruce Vavrichek provided useful comments.

Christine Bogusz edited the manuscript, and John Skeen proofread it. Judith Cromwell produced

drafts of the manuscript, Kathryn Winstead prepared the report for publication, and Annette

Kalicki prepared the electronic versions for CBO’s Web site (www.cbo.gov).

Douglas HoltzEakin

Director

May 2003
 
CONTENTS

Summary vii

Introduction 1

Size of the Uninsured Population 2

The Number of People Who Are Uninsured All Year 2

The Number of People Who Are Uninsured

at Any Time During the Year 3

The Number of People Who Are Uninsured

at a Point in Time 4

The Implications of the Medicaid Undercount 5

The Implications of Less-Than-Full Participation

in Medicaid 5

Demographic Characteristics of the Uninsured Population 6

Dynamics of the Uninsured Population 6

The Duration of Uninsured Spells 6

Characteristics Associated with the Duration

of Uninsured Spells 10

Multiple Spells and Total Uninsured Months 10

Reasons Reported for Lacking Health Insurance 12

Policy Considerations 13

Appendix A

Strengths and Limitations of the Survey Data 15

Appendix B

The Consistency of Different Estimates of the Duration

of Uninsured Spells 19
vi HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

Tables

1. Percentage and Number of Nonelderly People Without

Health Insurance in 1998 and 1999, Estimated from

Four National Surveys 3

2. Nonelderly People Without Health Insurance in 1998,

by Selected Characteristics 7

3. Distribution of Uninsured Spells by Duration and Age 9

4. Distribution of Uninsured Spells by Duration and Selected

Characteristics 11

5. Distribution of Uninsured Spells and Uninsured Months Among

Nonelderly People Who Had a Spell That Began Between

July 1996 and June 1997 12

6. Reasons Reported by Nonelderly Adults for Lacking

Health Insurance 13

B-1. Projected Distribution of Spells in Progress in a Given Month

and a Given Year, by Duration, Based on the Actual Distribution

of New Spells 20

Figures

S-1. Estimated Number of Nonelderly People Without

Health Insurance in 1998 vii

S-2. Distribution of Uninsured Spells Among Nonelderly People

in a Given Year and at a Given Point in Time, by Duration viii

A-1. Distribution of Uninsured Spells That Began Between

July 1996 and June 1997, by Duration 16

Box

1. Two Approaches to Measuring Uninsured Spells 8
0

10

20

30

40

50

60

70

80

Uninsured

for the

Entire Year

Uninsured

at a Specific

Point in Time

During the Year

Uninsured

at Any Time

During the Year

21.1

31.1

40.5 42.6

39.0

56.8 59.0

Survey of Income and Program Participation

Medical Expenditure Panel Survey

National Health Interview Survey

Summary

It is frequently stated that about 40 million Americans

lack health insurance.1

 That estimate, however, overstates

the number of people who are uninsured all year. The

Congressional Budget Office (CBO) estimates that be

tween 21 million and 31 million people were uninsured

for the entire year in 1998—the most recent year for

which reliable comparative data are available. Since then,

the number who are uninsured all year probably has not

changed substantially, given historical trends. The unin

sured population is fluid, with many people gaining and

losing coverage. For example, between half and two

thirds of the people who experienced a period of time

without insurance in 1998 had coverage for other por

tions of the year.

The commonly cited estimate of 40 million uninsured

comes from the Census Bureau’s Current Population Sur

vey (CPS). Based on a large nationally representative

sample, the CPS has been collecting data on health insur

ance status since 1980.

Although the CPS is intended to measure the number of

people who lack health coverage for a whole year, its esti

mate more closely approximates the number of people

who are uninsured at a specific point in time during the

year. Data from three federally sponsored national surveys

—the Survey of Income and Program Participation

(SIPP), the Medical Expenditure Panel Survey (MEPS),

and the National Health Interview Survey (NHIS)—yield

estimates of the number of uninsured at a particular point

in time that are very similar to the CPS estimate of about

40 million (see Summary Figure 1). In contrast, data from

Summary Figure 1.

Estimated Number of Nonelderly

People Without Health Insurance

in 1998

(In millions)

Source: Congressional Budget Office.

Note: The Centers for Disease Control and Prevention, which sponsors

the NHIS, reports only the point-in-time estimate.

1. See, for example, John M. Broder, “Problem of Lost Health

Benefits Is Reaching Into the Middle Class,” New York Times,

November 25, 2002, p. A1; and David Wessel, “The 39 Million

Who Mustn’t Get Sick,” Wall Street Journal, December 27, 2001,

p. A1.
viii HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG? SUMMARY

Four Months or Less

Five to 12 Months

13 to 24 Months

More Than 24 Months Five to 12 Months

More Than 12 Months

Four Months or Less

Spells That Began Between

July 1996 and June 1997 Spells in Progress in March 1998

(14%)

(78%)

(8%)

(45%)

(26%)

(13%)

(16%)

Summary Figure 2.

Distribution of Uninsured Spells Among Nonelderly People in a Given

Year and at a Given Point in Time, by Duration

Source: Congressional Budget Office based on data from the first 11 waves of the 1996 panel of the Survey of Income and Program Participation, which followed

respondents over a period of 41 months (from March 1996 through July 1999).

SIPP and MEPS indicate that 21 million to 31 million

people are uninsured for an entire year.

A third measure of the uninsured is the number of people

who lack insurance at any time during the year. At nearly

60 million, that measure comprises people who are unin

sured for only part of the year and those who are unin

sured throughout the year. Together, those three mea

sures of the uninsured provide a more complete picture

of that population than any single measure could.

Far from being a static group, the uninsured population

is constantly changing. Some people are uninsured for

long periods, but more are without coverage for shorter

times, such as between jobs. For example, about 30 per

cent of nonelderly people who become uninsured in a

given year remain so for more than 12 months, whereas

nearly 50 percent regain health insurance within four

months (see Summary Figure 2). However, about 80 per

cent of the people who lack health insurance at a par

ticular time end up being uninsured for more than 12

months. Although long uninsured spells occur less fre

quently than short spells, they are more likely to be under

way at any given time.

Policies aimed at increasing coverage are most likely to

be effective if they consider the distinction between the

shortterm and longterm uninsured. For people with

short uninsured spells, policies might have the goal of

filling a temporary gap in coverage or of preventing a gap

from occurring. For people with longer periods without

insurance, policies might seek to provide or facilitate an

ongoing source of coverage.

Several sources of uncertainty apply to estimates of the

uninsured. Because the estimates come from population

surveys, they are prone to reporting error and other forms

of statistical error, which could lead to either an under

estimate or an overestimate of the size of the uninsured

population. For example, underrepresentation of certain

segments of the population that are more likely to be

uninsured could lead to an undercount of the number of

uninsured Americans. Survey estimates could also over

state the number of people who are uninsured, however.
SUMMARY HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG? ix

On the basis of comparisons with administrative data,

analysts know that fewer people say they have Medicaid

coverage than actually do. But some evidence, albeit

limited, indicates that many of the Medicaid enrollees

who do not report being covered by that program mis

takenly report another type of insurance coverage, so that

source of bias may be small.

Finally, the concept of insurance and its implications for

access to health care are ambiguous in some respects. For

example, some people who are counted among the unin

sured are eligible for Medicaid. Some policymakers and

analysts believe that such people should be regarded as

insured, because they can apply for Medicaid when they

require care and receive retroactive coverage for their ex

penses. Others believe that such people should be re

garded as uninsured, however, because they do not use

Medicaid for their routine medical care (perhaps because

they are not aware that they are eligible). Moreover, al

though a lack of insurance could lead to insufficient ac

cess to medical care and exposure to significant financial

risk, many people without insurance have access to at

least some sources of health care, either through public

hospitals, community health centers, local health depart

ments, or Department of Veterans Affairs facilities.
 
How Many People Lack Health Insurance

and For How Long?

More than 240 million people in the United

States have health insurance coverage today, through a

variety of sources. The vast majority—about 63 percent

—are covered through their, or a family member’s, em

ployer.1

 Government programs provide coverage to mil

lions more people: about 14 percent have coverage

through Medicare, 11 percent through Medicaid and the

State Children’s Health Insurance Program (SCHIP), and

about 3 percent through military programs. Roughly

8 percent of people purchase coverage from private indi

vidual health insurers.2

Yet millions of people do not have health insurance cov

erage. For those people, extended periods without insur

ance could lead to insufficient access to medical care and

exposure to significant financial risk. From a broader per

spective, a lack of coverage could lead to less efficient use

of health care services and facilities, including emergency

rooms, and to higher public spending for health pro

grams.

Policymakers have proposed alternatives for expanding

health insurance coverage, including providing tax in

ducements to individuals or employers, expanding Med

icaid and SCHIP, reforming rules regulating private

insurance, and requiring employers to offer coverage.3

Designing costeffective policies to expand health cov

erage requires information on the size and characteristics

of the uninsured population. Because many people gain

and lose coverage over time, an important feature of un

insured spells is their duration.

This paper presents estimates of the size, demographic

characteristics, and dynamics of the uninsured popula

tion, using data from four federally sponsored national

surveys: the Current Population Survey (CPS), the Survey

of Income and Program Participation (SIPP), the Medi

cal Expenditure Panel Survey (MEPS), and the National

Health Interview Survey (NHIS). Both the CPS and SIPP

are sponsored by the Census Bureau, MEPS by the

Agency for Healthcare Research and Quality, and NHIS

by the Centers for Disease Control and Prevention. Each

survey’s strengths and limitations are described in Appen

dix A.

The Congressional Budget Office’s (CBO’s) analysis fo

cuses on the nonelderly population because nearly all

Americans age 65 and older are covered by Medicare. It

excludes people in institutions (such as nursing homes

and prisons) because they are not counted in the surveys.

Activeduty military personnel are not included in the

CPS, MEPS, and NHIS and thus are excluded from

CBO’s analysis of the data in those surveys, but the analy

1. The federal government exempts employmentbased health insur

ance, among other noncash benefits, from taxation, providing an

incentive for the provision of employmentbased insurance.

2. See Bureau of the Census, Health Insurance Coverage: 2001, Current

Population Reports, Series P60220 (September 2002). The esti

mates, based on selfreported data from the civilian noninstitu

tionalized population, are not mutually exclusive; people can be

covered by more than one type of insurance in a year.

3. For a discussion of policy options for expanding health insurance

coverage, see Congressional Budget Office, Budget Options (Febru

ary 2001), pp. 4052.
2 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

sis of SIPP includes activeduty military personnel, who

are counted unless they live in military barracks.

Size of the Uninsured Population

In recent years, the number of uninsured people in the

United States has been pegged at approximately 40 mil

lion, or about 16 percent of the nonelderly population.

By CBO’s analysis, that estimate overstates the number

of people who are uninsured all year and more closely

approximates the number who are uninsured at a point

in time during the year. A more accurate estimate of the

number of people who were uninsured for all of 1998—

the most recent year for which reliable comparative data

are available—is 21 million to 31 million, or 9 percent

to 13 percent of nonelderly Americans.

The CPS is the source of that widely cited estimate of

about 40 million uninsured. By interviewing people in

March about their insurance coverage the previous cal

endar year, the CPS is intended to yield an estimate of

the number of people who are uninsured all year. How

ever, comparisons with estimates from other surveys indi

cate that the CPS estimate overstates that number. Some

analysts believe the overstatement stems from an under

reporting of insurance coverage by CPS respondents, who

are asked to recall their coverage over a longer period than

other surveys require.4

 Other analysts have concluded that

the similarity of the CPS estimates to the pointintime

estimates from other surveys suggests that many CPS

respondents report their insurance status as of the time

of the interview rather than for the previous calendar

year, as requested.5

In this paper, CBO uses three measures—the number of

people who are continuously uninsured for an entire year,

the number who are uninsured at any time during the

year, and the number who are uninsured at a point in

time—to gauge the size of the uninsured population.

Because estimates based on the first two measures use

survey data in which people are asked to remember their

insurance coverage over a specified period, those data are

more prone to reporting error. Pointintime estimates

are subject to less error because people are asked to report

their insurance coverage at the time of the interview;

however, those estimates do not distinguish between

people who are uninsured for a long time and other un

insured people, and they do not reveal how fluid the un

insured population is. Together, the three ways of mea

suring the uninsured population give a more complete

picture than any single measure could.

The Number of People Who Are Uninsured All Year

CBO estimated the number of people who are uninsured

all year using data from SIPP and MEPS, two surveys in

which respondents are interviewed multiple times over

the life of the survey. (Such longitudinal surveys allow

researchers to repeatedly observe a set of subjects over

time.) SIPP interviews people every four months about

their insurance coverage during the preceding four

months (called a “wave”), while MEPS interviews people

every four to five months, on average. By asking people

to remember their insurance status over a shorter period

of time than the CPS requires, SIPP and MEPS should

yield more accurate estimates of the number of people

who are uninsured all year.6

According to the most recent SIPP data, 9.1 percent of

the nonelderly population (or 21.1 million people) were

continuously uninsured throughout 1998 (see Table 1).7

According to MEPS, the corresponding figures were 13.3

percent (or 31.1 million people). The discrepancy be

tween those estimates could be due to various factors,

including differences in the wording and sequencing of

4. Robert L. Bennefield, “A Comparative Analysis of Health Insurance

Coverage Estimates: Data from CPS and SIPP” (paper presented

at the Joint Statistical Meetings, American Statistical Association,

Chicago, Ill., August 6, 1996). 

5. Katherine Swartz, “Interpreting the Estimates from Four National

Surveys of the Number of People Without Health Insurance,”

Journal of Economic and Social Measurement, vol. 14 (1986),

pp. 233242.

6. SIPP and MEPS also have certain limitations, which are discussed

in Appendix A.

7. These figures are based on analysis of data from the 1996 panel

of the Survey of Income and Program Participation, which followed

all respondents through July 1999. Because only a limited amount

of data from the 2001 SIPP is now available, CBO’s analysis does

not rely on that version of the survey.
HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG? 3

Table 1.

Percentage and Number of Nonelderly People Without Health Insurance

in 1998 and 1999, Estimated from Four National Surveys

Uninsured Nonelderly People

In percent In millions

1998 1999 1998 1999

Uninsured All Year

SIPP 9.1 n.a. 21.1 n.a.

MEPS 13.3 12.2 31.1 28.9

Uninsured at Any Time During the Year

SIPP 24.5 n.a. 56.8 n.a.

MEPS 25.3 25.1 59.0 59.2

Uninsured at a Point in Time

SIPP 16.6 15.7 40.5 38.5

MEPS 18.3 17.4 42.6 41.0

NHIS 16.5 16.0 39.0 38.3

CPSa 18.4 16.2 43.9 39.0

Source: Congressional Budget Office based on data from the 1996 panel of the Survey of Income and Program Participation (SIPP), the 1998 and 1999 Medical

Expenditure Panel Survey (MEPS), and the March 1999 and March 2000 Current Population Survey (CPS). Estimates from the National Health Interview

Survey (NHIS) are from the Centers for Disease Control and Prevention, "Early Release of Selected Estimates Based on Data from the 2001 NHIS," available

at www.cdc.gov/nchs.

Note: n.a. = not available.

a. The CPS estimate is intended to measure the number of people who are uninsured for the entire year. However, there is considerable evidence that the CPS estimate

overstates the number of people who are uninsured all year and is closer to the number of people who are uninsured at a point in time. About two-thirds of the

reduction in the CPS estimate of the number of uninsured from 1998 to 1999 was due to the inclusion of an additional question in the survey that was designed

to yield more-accurate estimates.

questions on health insurance coverage, data editing pro

cedures, interviewers’ training and knowledge about

health insurance, and the period of time over which

people were asked to recall their coverage.

Data from MEPS also indicate that the number of people

who were uninsured all year fell from 31.1 million in

1998 to 28.9 million in 1999 (estimates from MEPS of

the fullyear uninsured are not available for more recent

years). But recent trends in the CPS estimates—which are

similar to the pointintime estimates from SIPP, MEPS,

and NHIS—suggest that the number of people who were

uninsured all year probably remained relatively stable

from 1999 to 2000 and then increased somewhat in

2001.8

 That conclusion is based on the fact that the full

year and pointintime estimates of the uninsured are

likely to move in a similar manner over time. More

recently, the number who are uninsured all year probably

has not changed substantially, given historical trends.

The Number of People Who Are Uninsured

at Any Time During the Year

CBO’s analysis of data from SIPP and MEPS indicates

that about a quarter of the nonelderly population (or

8. According to the CPS, the number of nonelderly people who lacked

health insurance rose from 39.6 million in 2000 to 40.9 million

in 2001, after falling slightly the previous year.
4 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

about 57 million to 59 million Americans) was uninsured

at any time during 1998 (see Table 1). According to

MEPS, that measure remained essentially unchanged

from 1998 to 1999. If the elderly were included in the

analysis, the percentage of the population that was un

insured at any time during the year would have fallen to

22 percent.9

Analysis of SIPP and MEPS data also shows that the un

insured population is very fluid. According to data from

SIPP, roughly 63 percent of the people who were unin

sured at any time in 1998 lost coverage or gained cover

age (or did both) at some point during the year.10 The

corresponding figure from MEPS was 47 percent, in

creasing to 51 percent in 1999.

The Number of People Who Are Uninsured

at a Point in Time

Data from SIPP, MEPS, and NHIS yield similar esti

mates of the number of people who are uninsured at a

given point in time.11 The pointintime estimates from

those surveys, which are very similar to the CPS esti

mates, ranged from 39.0 million to 42.6 million unin

sured in 1998, or from 16.5 percent to 18.3 percent of

the nonelderly population (see Table 1). That range of

estimates fell slightly in 1999, according to all four sur

veys. Taken altogether, the pointintime estimates from

SIPP, MEPS, and NHIS provide compelling evidence

that the CPS overstates the number of people who are

uninsured all year.

Although analyses of the uninsured typically focus on

individuallevel data, analyses at the family level provide

a measure of the total number of families that are poten

tial targets of policymakers’ efforts to expand coverage.

According to data from SIPP, approximately 26 million

families had at least one person who was uninsured at a

given point in time in 1998.12 In 27 percent of those

families, however, at least one person was insured. Such

families represent a variety of circumstances, including

those in which children are covered under Medicaid or

SCHIP but parents are not or only some members are

covered by an employmentbased (or private nongroup)

policy.

The relationship between the number of people who are

uninsured at a particular point in time and the number

who are uninsured all year appears to have not changed

significantly—at least since 1992—although the evidence

supporting that conclusion is limited. The most direct

comparison of the two measures comes from a study of

SIPP data that found that 14.8 percent of Americans

(including the elderly) were uninsured at a point in time

in 1992, while 7.6 percent were uninsured all year.13 That

nearly twotoone ratio is echoed in the 1998 figures

from SIPP,16.6 percent versus 9.1 percent. Indirect evi

dence that a similar relationship probably held in earlier

years comes from studies (discussed below) showing that

the duration of uninsured spells among the nonelderly

population had a distribution similar to that found in this

analysis.

9. Including military personnel and the institutionalized—all of whom

are either insured or have access to medical care—would also reduce

the percentage of the population that was uninsured at any time

during the year, but by a much smaller amount than would be

obtained by including the elderly. The magnitude of the reduction

cannot be determined from available data; information is not

available on the insurance status of people who spend part of a year

in the military or an institution. However, such an analysis is

possible when measuring insurance coverage at a point in time.

Using data from SIPP, CBO estimates that including the military

and the institutionalized in the analysis would reduce the

percentage of nonelderly who were uninsured at a point in time

in 1999 by about 0.1 percentage point.

10. Some 15.4 percent of the nonelderly population was uninsured

for part, but not all, of 1998. Such people constitute 62.9 percent

of the total nonelderly population that was uninsured at any time

in 1998.

11. NHIS estimates are from Centers for Disease Control and Preven

tion, National Center for Health Statistics, “Early Release of

Selected Estimates Based on Data from the 2001 NHIS” (released

July 15, 2002).

12. Families are defined in this analysis as health insurance eligibility

units, on the basis of eligibility rules of most private insurance

plans. In households with two or more people, those rules were

applied to identify all individuals who would be eligible for cover

age under a family policy. This definition of families also includes

single adults.

13. Bennefield, “A Comparative Analysis of Health Insurance Coverage

Estimates.”
HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG? 5

The Implications of the Medicaid Undercount 

The number of people who report that they have Medic

aid coverage in population surveys is smaller than the

number indicated by the program’s administrative data.

Less clear than the fact of the undercount itself, however,

are its size and its implications for estimates of the unin

sured.

Underreporting of Medicaid coverage could occur for

various reasons. Some people might not report their cov

erage in a survey because of the stigma associated with

participating in a public assistance program. Also, some

people covered by Medicaid may mistakenly believe that

they have another type of coverage, such as private insur

ance. That confusion may be most common among

people enrolled in Medicaid managed care because such

programs often use names designated by private plans or

by a state’s Medicaid agency that do not include the term

“Medicaid.”

According to one study, SIPP undercounts Medicaid en

rollment relative to the administrative data maintained

by the Centers for Medicare and Medicaid Services by

about 12 percent to 15 percent.14 CBO’s analysis of data

from MEPS indicates that that survey undercounts

Medicaid enrollment by a similar amount. Those find

ings may imply that the number of nonelderly people

who are enrolled in Medicaid at any time during the year

could be undercounted in population surveys by about

4 million to 5 million.

Estimates of the size of the Medicaid undercount must

be viewed with caution, however, because of limitations

of the administrative data that are used as the bench

mark.15 Even if those estimates are correct, they do not

necessarily imply a corresponding error in the count of

the uninsured, because some Medicaid enrollees who do

not report having Medicaid coverage may report another

type of coverage. One study that matched Medicaid ad

ministrative records in Minnesota with a population

survey conducted in that state found that the vast ma

jority of Medicaid enrollees who did not report being

covered by Medicaid reported another source of insur

ance.16 As a result, the measured uninsurance rate was

overstated by only about 0.3 percentage points. It is not

known how those findings may be generalized to other

states or other surveys.

Because of uncertainties about the size of the Medicaid

undercount and its implications for estimates of the un

insured, CBO did not adjust its analysis to compensate

for the undercount.

The Implications of Less-Than-Full Participation

in Medicaid

Many people who are eligible for Medicaid do not par

ticipate in the program. Research estimates that about

half of eligible nonparticipants have private coverage and

half are uninsured.17 For uninsured people who are eligi

ble but not enrolled, Medicaid provides a form of condi

tional coverage. Such people can apply for Medicaid at

the time they obtain care and receive retroactive coverage

for their expenses.18 Because of that provision, some

policymakers view those people as insured. Others view

them as uninsured because they may not realize that they

are eligible for Medicaid and therefore may delay or avoid

seeking medical care.

An estimated 2.9 million children were uninsured but

eligible for Medicaid at a given point in time in 1994 (the

most recent year for which estimates are available). That

figure represents about onethird of uninsured children

14. John L. Czajka, Analysis of Children’s Health Insurance Patterns: Findings from the SIPP (report submitted by Mathematica

Policy Research, Inc., to the Department of Health and Human

Services, Assistant Secretary for Planning and Evaluation, May

1999). 

15. The administrative data maintained by the Centers for Medicare

and Medicaid Services are reported separately by each state and

are subject to reporting errors. The “ever enrolled” estimates are

intended to represent an unduplicated count of the number of

people enrolled in Medicaid at any time during the fiscal year.

16. Kathleen Thiede Call and others, “Uncovering the Missing Medic

aid Cases and Assessing Their Bias for Estimates of the Uninsured,”

Inquiry, vol. 38, no. 4 (Winter 2001/2002), pp. 396408.

17. All estimates reported in this section are from Czajka, Analysis of

Children’s Health Insurance Patterns.

18. Jonathan Gruber, Medicaid, Working Paper No. 7829 (Cambridge,

Mass: National Bureau of Economic Research, August 2000).
6 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

and about 17 percent of all children who were eligible for

Medicaid. For many children, being eligible for Medicaid

while uninsured is a shortterm phenomenon. Many such

children are in transition from one source of coverage to

another (for example, from private insurance to Medic

aid), and others are eligible for Medicaid for a short

period because of a temporary decline in family income.

Even so, an estimated 1 million children remained unin

sured all year in 1994 even though they were eligible for

Medicaid.

Demographic Characteristics of the

Uninsured Population

Education and income level are closely tied to the likeli

hood of being uninsured. According to data from SIPP,

25 percent of people in families in which no one had a

high school diploma were uninsured all year in 1998, and

50 percent were uninsured at any time during the year

(see Table 2). Similar percentages of people in families

with income below 200 percent of the poverty level were

uninsured in 1998. Hispanics had a higher rate of being

uninsured all year in 1998 than other racial and ethnic

groups (23 percent), and young adults ages 19 to 24 were

more likely than people in other age groups to be unin

sured all year (14 percent).

The likelihood of being uninsured does not vary greatly

by selfreported health status. According to SIPP data,

about 10 percent of people who said they were in poor

health were uninsured all year in 1998; that figure is simi

lar to the percentages of people in excellent or very good

health who lacked insurance coverage all year.19 Because

individuals in poor health constitute a relatively small

proportion of the total nonelderly population, they ac

counted for only 5 percent of the fullyear uninsured in

1998. As a group, however, they may be of particular

concern to policymakers because they are likely to be the

greatest users of health care services.

Nearly 90 percent of the people who were uninsured all

year in 1998 were in families in which at least one person

worked, either part time or full time (see Table 2, col

umn 3). Research has found that about 75 percent of the

uninsured in working families do not have access to in

surance through their employer, the dominant form of

coverage among the nonelderly, while the other 25 per

cent have access to employmentbased insurance but do

not accept it.20 Lowerwage workers are less likely than

higher earners to have access to employmentbased insur

ance and are less likely to accept it where it is offered.21

Dynamics of the Uninsured Population

CBO’s analysis of SIPP data reveals that although many

uninsured spells are relatively short, some are quite long.

Many people who become uninsured are in transition

from one source of coverage to another (for example, be

cause of a waiting period for coverage at a new job), so

their uninsured spells are relatively brief.

The Duration of Uninsured Spells

CBO measured the duration of uninsured spells in two

ways. First, it estimated the duration of spells that began

during the 12month period from July 1996 through

June 1997.22 Because new spells closely approximate a

representative sample of all uninsured spells, they provide

the most reliable basis for estimating durations.23 Second,

because policy discussions often refer to the uninsured

19. Information on health status was collected in interviews between

August 1997 and November 1997. Survey respondents were at

least 15 years of age.

20. Sherry Glied, “Challenges and Options for Increasing the Number

of Americans with Health Insurance,” Inquiry, vol. 38, no. 2 (Sum

mer 2001), pp. 90105.

21. Philip F. Cooper and Barbara Steinberg Schone, “More Offers,

Fewer Takers for EmploymentBased Health Insurance: 1987 and

1996,” Health Affairs, vol.16, no. 6 (November/December 1997),

pp.142149.

22. CBO also estimated the duration of uninsured spells that began

during other periods—for example, during each month within the

July 1996June 1997 period and during the 24month span from

July 1996 through June 1998. Similar results were obtained for

all of those periods. 

23. Katherine Swartz, John Marcotte, and Timothy D. McBride,

“Spells Without Health Insurance: The Distribution of Durations

When LeftCensored Spells Are Included,” Inquiry, vol. 30 (Spring

1993), pp. 7783. 
HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG? 7

Table 2.

Nonelderly People Without Health Insurance in 1998,

by Selected Characteristics

(In percent)

Nonelderly People

Characteristic

Uninsured

at Any Time

During the Year

Uninsured

All Year 

Distribution

of the Population

Uninsured All Year

Age

Less than 19 26.8 7.3 24.9

19 to 24 41.9 14.4 13.7

25 to 34 31.1 12.3 21.9

35 to 44 20.2 9.3 19.7

45 to 54 15.1 7.6 12.6

55 to 64 14.0 6.7 7.2

Race/Ethnicity

White, Non-Hispanic 18.4 6.3 48.4

Black, Non-Hispanic 33.4 10.7 15.3

Hispanic 47.4 22.5 30.8

Other 31.1 10.9 5.5

Family Income Relative to the Poverty Levela

Less than 200 percent 47.9 19.5 74.9

200 percent to 399 percent 17.4 5.3 19.8

400 percent or more 6.0 1.6 5.3

Educationa,b

No high school diploma 50.4 24.6 28.4

High school graduate 33.1 12.7 36.4

Some college coursework 22.1 7.3 26.6

Bachelor’s degree or higher 9.9 2.6 8.7

Family Employment Statusa

At least one full-time worker all year 15.0 5.9 42.9

Part-time or part-year work only 46.1 16.1 46.6

No work 32.8 13.1 10.6

Health Statusc

Excellent 23.7 8.9 28.8

Very good 25.1 9.3 32.8

Good 24.6 9.1 24.5

Fair 25.1 8.7 8.9

Poor 25.3 10.3 5.1

Memorandum:

Total Nonelderly Population 24.5 9.1 100.0

Source: Congressional Budget Office based on data from the 1996 panel of the Survey of Income and Program Participation.

a. For family-level variables, families are defined as health insurance eligibility units, which are composed of individuals who could be covered as a family under most

private health insurance plans.

b. Education is defined as the highest education level among all adults in the family.

c. Information on health status was collected only for survey respondents who were at least 15 years of age.
8 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

Duration of Uninsured Spells

0 2 4 6 8 10 12

Number of Months

Person A

Person B

Person C

Person D

Person E

Point in Time

Box 1.

Two Approaches to Measuring Uninsured Spells

Consider five people who become uninsured at some

time during a year. Person A becomes uninsured in

January and is uninsured for the entire year. The other

four people are each uninsured for three months, the

first from January through March, the second from

April through June, and so on (see figure, below). If the

duration of uninsured spells is measured by including

all spells that begin during the year, 20 percent (one of

five) last 12 months and 80 percent (four of five) last

three months. If, instead, durations are measured by

including only spells that are in progress at a par

ticular point in time, 50 percent (one of two) last

12 months and 50 percent last three months. The

first approach measures the duration of all uninsured

spells that begin during the year, while the second

approach characterizes spells at a given point in time.

population at a given point in time, CBO estimated the

duration of spells among people who were uninsured in

a given month. The first measure captures the flow of un

insured spells over time, while the second captures the

stock of uninsured spells at a point in time. The two mea

sures yield very different estimates of durations (see

Box 1).

New Spells. Fortyfive percent of the uninsured spells

that began between July 1996 and June 1997 lasted four

months or less, whereas about 16 percent lasted more

than 24 months (see the top panel of Table 3). Those

figures correspond to estimates obtained by other re

searchers using SIPP data from 1983 to 1986 and 1992

to 1994.24 Children under 19 were more likely than

24. Katherine Swartz and Timothy D. McBride, “Spells Without

Health Insurance: Distributions of Durations and Their Link to

PointinTime Estimates of the Uninsured,” Inquiry, vol. 27 (Fall

1990), pp. 281288; and Czajka, Analysis of Children’s Health

Insurance Patterns. The unemployment rate was much higher

during the years covered by those studies, indicating that the

duration of uninsured spells has not varied much with changes in

economic conditions.
HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG? 9

Table 3.

Distribution of Uninsured Spells by Duration and Age

(In percent)

Duration of Uninsured Spell Total Nonelderly Population Children Adults

Spells That Began Between July 1996 and June 1997

Four Months or Less 44.5 49.3 41.0

Five to 12 Months 26.2 25.2 26.9

13 to 24 Months 13.4 11.8 14.5

More Than 24 Months 15.9 13.7 17.6

Spells in Progress in March 1998a

Four Months or Less 7.9 12.9 5.6

Five to 12 Months 14.4 19.3 12.3

More Than 12 Months 77.8 67.8 82.0

Source: Congressional Budget Office based on data from the 1996 panel of the Survey of Income and Program Participation.

Notes: Children are defined as people under 19 years of age, adults as people ages 19 through 64.

Appendix B explains the consistency of the two sets of estimates given in this table.

a. The estimates for spells in progress in March 1998 measure the total duration of such spells, looking backward and forward in time (the observation period extended

through July 1999). Similar estimates were obtained for other months.

adults to have short uninsured spells. Fortynine percent

of the spells experienced by children lasted four months

or less, compared with 41 percent for adults.

A potential limitation of measuring durations from a

sample of new spells is that people who are uninsured for

a long time may be underrepresented. By definition,

analyses of new spells focus on spells for which a starting

point can be observed. Spells that were in progress at the

start of SIPP’s observation period (socalled leftcensored

spells) are excluded, so individuals who were uninsured

throughout the entire period are excluded from the cal

culation of durations.25 Previous research suggests, how

ever, that excluding leftcensored spells does not drama

tically alter the results of the analysis.26

Spells in Progress at a Point in Time. Compared with

the duration of new spells, the duration of those in pro

gress at a given point in time is much more likely to be

relatively long. More than threequarters of the uninsured

spells in progress in March 1998 exceeded 12 months,

whereas only about 8 percent lasted four months or less

(see the bottom panel of Table 3). Those estimates measure

the total length of the spells in progress in March 1998,

looking backward and forward in time. Similar estimates

were obtained for other months.

Although estimates of the duration of new spells and

spells in progress in a particular month differ dramati

cally, they simply represent alternative ways of looking

at the uninsured population. Nearly half of all new spells

end within four months; over time, as those shorter spells

end and longer spells remain in effect, the stock of unin

sured spells at a given point in time has a relatively high

proportion of long spells. Looked at another way, a par

25. Four percent of the people in SIPP’s sample were uninsured

throughout the entire 41month observation period.

26. See Swartz, Marcotte, and McBride, “Spells Without Health

Insurance.” Using sophisticated econometric methods in an analysis

of data from the 1984 SIPP panel, the authors estimated that

including leftcensored spells reduced the share of spells that lasted

five months or less from 50 percent to 48 percent and increased

the share of spells that lasted more than 24 months from 15 percent

to 19 percent. The median duration increased from six months

to seven months. Those findings indicate that longterm uninsured

people are underrepresented among new spells, but not by enough

to invalidate the basic conclusions of analyses that focus solely on

new spells. 
10 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

ticular long spell is more likely to be in progress at a given

point in time than a particular short spell. (Appendix B

demonstrates the consistency of the two sets of estimates

in Table 3.) The analysis in the rest of this paper focuses

on new spells, because they more accurately represent all

uninsured spells.

Characteristics Associated with the Duration

of Uninsured Spells

The duration of spells varies with education level, race/

ethnicity, and income of the uninsured. People with less

education are more likely than highereducated people

to experience long uninsured spells. Some 23 percent of

spells among people in families in which no one gradu

ated from high school last more than two years, com

pared with a figure of only 8 percent among people in

families in which at least one person has a bachelor’s

degree (see Table 4). That relationship probably reflects,

at least in part, the fact that collegeeducated people are

more likely than those with less education to have access

to employmentbased insurance.27 Long uninsured spells

are also more common among Hispanics and people with

low income.28 For example, 23 percent of uninsured

spells among Hispanics last more than two years, com

pared with 14 percent of spells among nonHispanic

whites and 15 percent among nonHispanic blacks.

Eighteen percent of uninsured spells among people with

annual income of less than 200 percent of the federal

poverty level exceed two years, about twothirds higher

than the figure for people whose income is 400 percent

or more of the poverty level.

The duration of uninsured spells does not vary much

with selfreported health status. For instance, 14 percent

of uninsured spells among people in poor health last

more than two years, nearly the same percentage of spells

as among people reporting very good health. By keeping

some people from working full time, however, poor

health may contribute to long uninsured spells. Those

spells may be of particular concern from a policy per

spective because such people are likely to be intensive

users of health care services.

As noted previously, adults are more likely than children

to experience long uninsured spells. The availability of

Medicaid coverage may explain some of that discrepancy:

coverage is available to many children in lowincome

families, but the great majority of lowincome adults are

not eligible for the program. In addition, single adults

without children may be less inclined to seek insurance,

on average, than other adults are, which may lead them

to experience long spells without insurance.29

Multiple Spells and Total Uninsured Months

While the preceding analysis looked only at people who

had one uninsured spell, to obtain a more complete pic

ture of the uninsured this section looks at whether many

uninsured people have multiple spells. The subsequent

experience of people whose initial uninsured spell was

relatively short is of particular interest. Did most of those

people have a single uninsured spell? Or did many of

them have additional spells, perhaps experiencing sub

stantial periods without coverage?

To investigate those issues, CBO analyzed data from the

1996 SIPP panel, following people who had one unin

27. Higherwage workers are more likely to be offered employment

based insurance, and wages are highly correlated with education.

For evidence of the relationship between wages and the likelihood

of being offered employmentbased insurance, see Cooper and

Schone, “More Offers, Fewer Takers for EmploymentBased

Health Insurance.”

28. For this analysis, family income relative to the poverty level was

defined as the mean value during the four months before the un

insured spell began. The intent was to classify families on the basis

of their income before they experienced any reduction in income

that may have accompanied the uninsured spell. Such an income

reduction may have been temporary for many families but longer

lasting for others. The income measure was intended to reflect,

for many families, their longerterm economic circumstances. The

analysis was also conducted using a second income measure, defined

as the mean family income relative to the poverty level during the

first four months of the uninsured spell (or during the entire spell

if it ended within four months). The second measure captures any

changes in families’ economic circumstances that occurred around

the time the uninsured spell began. Estimates using the second

measure (which this paper does not present) are similar to the esti

mates in Table 4.

29. That conclusion is supported by analysis conducted for this study

(but not reported in detail here), which found that after controlling

for differences in age, race/ethnicity, education, and income relative

to the poverty level, single adults without children were much more

likely than other adults to experience long uninsured spells.
HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG? 11

Table 4.

Distribution of Uninsured Spells by Duration and Selected Characteristics

(In percent)

Duration of Uninsured Spell

Characteristic

Four Months

or Less

Five to

12 Months

13 to

24 Months

More Than

24 Months

Agea

Less than 19 49.1 25.4 11.8 13.7

19 to 24 41.1 28.5 16.8 13.6

25 to 34 42.6 26.3 14.2 16.9

35 to 44 40.5 25.6 13.0 20.9

45 to 54 40.4 26.0 13.5 20.1

55 to 64 37.5 28.1 14.4 20.0

Race/Ethnicity

White, Non-Hispanic 46.9 26.6 12.8 13.8

Black, Non-Hispanic 45.1 27.6 12.3 15.1

Hispanic 39.5 21.4 16.0 23.0

Other 38.9 34.7 12.6 13.8

Family Income Relative to the Poverty Levelb,c

Less than 200 percent 41.8 26.1 14.3 17.9

200 percent to 399 percent 47.6 27.7 11.7 13.0

400 percent or more 55.3 22.9 11.1 10.7

Educationa,c

No high school diploma 39.0 22.6 15.2 23.2

High school graduate 40.7 26.7 15.8 16.8

Some college coursework 48.3 27.7 10.1 13.9

Bachelor’s degree or higher 53.0 26.8 12.3 7.9

Health Statusd

Excellent 41.9 26.4 14.1 17.7

Very good 47.5 24.6 13.1 14.8

Good 42.8 29.7 11.6 16.0

Fair 46.1 19.4 12.5 22.0

Poor 46.3 26.8 12.6 14.3

Memorandum:

Total Nonelderly Population 44.5 26.2 13.4 15.9

Source: Congressional Budget Office based on data from the 1996 panel of the Survey of Income and Program Participation.

Notes: Distributions sum to 100 across rows.

 Estimates in this table are based on uninsured spells that began between July 1996 and June 1997.

a. Age and education were measured as of the first month of the uninsured spell. Education is defined as the highest education level among all adults in the family.

b. Family income relative to the poverty level was computed as the mean over the four-month period before the beginning of the uninsured spell. Similar results were

obtained when family income relative to the poverty level was defined as the mean during the first four months of the uninsured spell (or during the entire spell

if it ended within four months).

c. For family-level variables, families are defined as health insurance eligibility units, which are composed of individuals who could be covered as a family under most

private health insurance plans.

d. Information on health status was collected in interviews between August 1997 and November 1997. Survey respondents were at least 15 years of age.
12 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

Table 5.

Distribution of Uninsured Spells and Uninsured Months Among Nonelderly

People Who Had a Spell That Began Between July 1996 and June 1997

(In percent)

All Nonelderly People with an Initial Spell of

People Four Months or Less More Than 12 Months

Adults with Uninsured Spells, by Total Number of Spells

One Spell 57.7 49.7 78.5

Two Spells 32.3 35.9 19.6

Three Spells 9.0 12.7 1.8

More Than Three Spells 1.0 1.7 0.1

Adults with Uninsured Spells, by Total Number of Months

Four Months or Less 21.8 49.0 n.a.

Five to 12 Months 27.4 28.4 n.a.

13 to 24 Months 27.0 17.1 35.2

More Than 24 Months 23.7 5.5 64.8

Source: Congressional Budget Office based on data from the 1996 panel of the Survey of Income and Program Participation.

Notes: n.a. = not applicable.

The total number of uninsured spells and insured months covers the period from July 1996 through June 1999.

sured spell that began between July 1996 and June 1997

over the 36month period ending in June 1999. Just

under 60 percent of such people had no additional unin

sured spells; 32 percent had one additional spell; and 10

percent had more than one additional spell (see the first

column in Table 5, which shows the distribution by total

number of spells). About 22 percent of the people were

uninsured for a total of four months or less during the

36 months examined; nearly onequarter (23.7 percent)

were uninsured for more than 24 months.

About half of the people whose initial uninsured spell

ended within four months had at least one additional

spell during the 36month period (see the second column

in Table 5). More than threequarters of the people with

a short initial spell were uninsured for less than 12

months during the extended 36month period; only

6 percent were uninsured for more than 24 months.

Reasons Reported for Lacking

Health Insurance

The high cost of insurance and lack of access to employ

mentbased coverage are the two most commonly re

ported reasons for being uninsured. More than 60 per

cent of uninsured adults cited one or both of those factors

as contributing to their lack of coverage (see Table 6).30

Only about 4 percent of nonelderly adults cited poor

health as a reason for being uninsured. Although that

figure suggests that a relatively small fraction of the unin

sured had been denied coverage in the nongroup insur

ance market because of poor health, some people who

cited the high cost of insurance as a reason for being un

insured may have declined coverage in the nongroup

market after being “rated up” because of their medical

conditions. One percent of uninsured adults indicated

that they did not have insurance because they could ob

tain medical care from a Department of Veterans Affairs

(VA) hospital.

People who have been uninsured for more than 12

months are more likely than those who have been un

insured for four months or less to lack coverage because

of its high cost (98 percent versus 49 percent), because

they do not have access to employmentbased coverage

30. CBO’s analysis was based on SIPP data on adults with an uninsured

spell that began between July 1996 and June 1997.
HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG? 13

Table 6.

Reasons Reported by Nonelderly Adults for Lacking Health Insurance

(In percent)

All Nonelderly Adults with an Uninsured Spell of

Adults Four Months or Less More Than 12 Months

High Cost 71.0 49.4 97.5

Lack of Access to

Employer-Sponsored Insurance 61.6 43.8 83.3

Attitudes/Preferences

Have not needed insurance 9.9 4.3 17.0

Do not believe in insurance 1.0 1.1 1.6

Other

No longer covered by parents 6.1 4.2 8.8

Poor health 3.5 1.6 6.3

Use VA hospital 1.4 0.4 2.4

Source: Congressional Budget Office based on data from the 1996 panel of the Survey of Income and Program Participation.

Notes: The analysis was based on responses of adults ages 19 through 64 with an uninsured spell that began between July 1996 and June 1997.

Percentages do not sum to 100 because respondents were allowed to give more than one reason.

VA = Department of Veterans Affairs.

(83 percent versus 44 percent), or because they have not

needed insurance in the past (17 percent versus 4 per

cent). Those discrepancies may in part reflect the fact that

people who are uninsured for shorter periods may not

regard their temporary lack of coverage as a problem. For

example, if people’s lack of access to employmentbased

insurance is a temporary phenomenon (for example,

because of a change in jobs), they may not report it in a

survey. Similarly, many people with relatively short unin

sured spells may not cite high cost as a reason for being

uninsured because they did not investigate the cost of in

surance options (such as private nongroup coverage) to

fill the gap in coverage.

Policy Considerations

To expand health insurance coverage, policymakers have

proposed various options. They include offering tax in

ducements for insurance coverage, expanding Medicaid

and the State Children’s Health Insurance Program, re

forming rules regulating private insurance, and requiring

employers to offer coverage.

In considering any approach to expand health coverage,

policymakers should be mindful of the fact that many

people are uninsured for relatively short periods, whereas

others are uninsured for much longer spans. Developing

policies geared toward the longterm uninsured—the

group that some policymakers consider most important

—is not straightforward. For example, one might think

that a program to expand insurance coverage among low

income people would primarily benefit the longterm

uninsured. But CBO’s analysis shows that 42 percent of

uninsured spells among people with income below 200

percent of the poverty level end within four months,

while only about onethird last more than a year.

Another issue that complicates any policy initiative to

expand health insurance is the crowding out of existing

sources of coverage.31 “Crowdout” results when coverage

31. For a review of the literature on crowdout, see Understanding the

Dynamics of “Crowdout”: Defining Public/Private Coverage

Substitution for Policy and Research (report prepared by the Academy

for Health Services Research and Health Policy under The Robert

Wood Johnson Foundation’s Changes in Health Care Financing

and Organization (HCFO) Program, June 2001).
14 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

through a new government policy initiative replaces pri

vate coverage that people would have otherwise had.

Crowdout can occur in various ways. Some employees

may drop their employmentbased coverage if a govern

ment program provides health insurance at a lower pre

mium. Or employers may reduce or drop coverage if

their employees have less demand for such a benefit be

cause a government program provides an alternative

source of coverage. A related issue concerns health insur

ance tax credits. Some proposals for those tax credits

would “buy up the base” by extending credits to people

who would have been insured even without them. Both

phenomena result in federal subsidies being extended to

people who otherwise would have been insured. As a re

sult, the federal cost per newly insured person could be

substantially greater than the cost for each person who

uses the federal program or receives the tax credit.

Several sources of uncertainty apply to estimates of the

uninsured population. Because the estimates come from

population surveys, they are prone to reporting error and

other forms of statistical error, which could lead to either

an underestimate or an overestimate of the size of the

uninsured population. On the one hand, certain segments

of the population that are more likely to be uninsured

(such as Hispanics) are less likely to be fully represented

in those surveys.32 That underrepresentation could lead

to an undercount of the number of uninsured. On the

other hand, to the extent that Medicaid enrollees are in

correctly classified as uninsured, survey estimates over

state the number of people who are uninsured. 

Furthermore, the concept of insurance and its implica

tions for access to health care are ambiguous in some

respects. Some people who report being uninsured may

be eligible for some type of government coverage but are

not enrolled. Eligible lowincome people can apply for

Medicaid when they require care, for example, and re

ceive retroactive coverage for their expenses. For that

reason, some policymakers believe such people should be

viewed as insured. Others view such people as uninsured,

because they do not use Medicaid for their routine medi

cal care (perhaps because they are unaware that they are

eligible). Moreover, although a lack of insurance could

lead to insufficient access to medical care and exposure

to significant financial risk, many people without insur

ance have access to at least some sources of health care.

Safety net providers—such as public hospitals, com

munity health centers, and local health departments—

deliver significant amounts of care to people without in

surance coverage, and military veterans are eligible to re

ceive care at VA facilities.

32. Analysts have studied that phenomenon, which is known as

undercoverage. See U.S. Census Bureau and Bureau of Labor

Statistics, “Technical Paper 63RV: Current Population Survey—

Design and Methodology” (March 2002), available at www.census.

gov/prod/2002pubs/tp63rv.pdf.
A

Strengths and Limitations of the Survey Data

The data underlying the analysis in this report come

from the Current Population Survey (CPS), the Survey

of Income and Program Participation (SIPP), the Med

ical Expenditure Panel Survey (MEPS), and the National

Health Interview Survey (NHIS). All four surveys use

large nationally representative samples of the civilian

noninstitutionalized population to measure the unin

sured. This appendix details each survey’s advantages and

disadvantages.

The Current Population Survey

The Census Bureau’s Current Population Survey is de

signed to measure the number of people in the United

States who are uninsured all year. An important ad

vantage of the CPS is its timely release. Data collected in

the March CPS (for the previous calendar year) are typi

cally released six months later, in September. In addition,

because of the survey’s methodology and sample size, the

data can be broken out by state.

The CPS has some limitations, however. First, it has a

relatively long reference period for measuring insurance

status. That long reference period may partly explain how

the CPS estimate overstates the number of people who

are uninsured all year. Some analysts have speculated that

many CPS respondents report their insurance status as

of the time of the interview; others argue that the

problem with the CPS is a more general underreporting

of insurance coverage. Second, although the CPS estimate

appears to closely approximate the number of people who

are uninsured at a point in time, the survey provides no

information on what fraction of the year people do have

coverage.

The Census Bureau added a question to the CPS in

March 2000 that should yield a more accurate estimate

of the number of uninsured. Previously, people were

defined as uninsured throughout the previous calendar

year if they responded “no” when asked if they had vari

ous types of insurance coverage at any time during that

year. The new question, which is designed to verify those

responses, specifically asks people who reported no cover

age whether in fact they were uninsured. Including that

question reduced the CPS estimate of the number of non

elderly people who were uninsured in 1999 from

42.1 million to 39.0 million (or from 17.4 percent to

16.2 percent of the nonelderly population).1

The Survey of Income and

Program Participation

SIPP is a longitudinal survey in which the same people

are interviewed every four months about their insurance

status during the previous four months. That shorter

reference period should, in principle, yield more accurate

data than the CPS provides. SIPP includes a set of ques

tions to determine whether people had various types of

1. Based on estimates presented in Charles T. Nelson and Robert J.

Mills, “The March CPS Health Insurance Verification Question

and Its Effect on Estimates of the Uninsured,” Bureau of the

Census (August 2001).

APPENDIX
16 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

0

5

10

15

20

25

30

Duration (In months)

 26 or

More

Figure A-1.

Distribution of Uninsured Spells That Began Between July 1996

and June 1997, by Duration

(In percent)

Source: Congressional Budget Office based on data from the 1996 panel of the Survey of Income and Program Participation.

Note: The concentration of spells at 26 months includes all spells that lasted at least 26 months. Some of those spells were still in progress at the end of the observation

period, so their duration could not be determined.

coverage during each month of the fourmonth reference

period. SIPP does not include a verification question like

the one that was added to the March 2000 CPS. Because

SIPP data are monthly, researchers are able to compute

the percentage of people who were uninsured in a given

month, throughout a given year, or at any time during

a given year. The Congressional Budget Office’s (CBO’s)

analysis is based on data from the first 11 waves of the

1996 SIPP panel, which provide information on mem

bers of the sample from March 1996 through July 1999.

One drawback to SIPP is that its data are released on a

less timely basis than are the CPS data. When CBO’s

analysis was conducted, the most current SIPP data were

from the 1996 panel. Second, as with any longitudinal

survey, SIPP suffers from sample attrition. That is, some

members of the sample drop out over time (because they

refuse to continue participating, they move and cannot

be located, and so on). About 25 percent of the original

sample in the 1996 SIPP panel was lost through attrition

by wave 5, and 34 percent was lost by wave 10. If people

who drop out of the sample differ systematically from

those who remain with respect to their likelihood of

being uninsured or experiencing a long spell without in

surance, the SIPP sample may yield biased estimates. To

minimize that possibility, CBO’s analysis used sampling

weights that included an adjustment for the declining

number of responses.2

2. The adjustment is constructed by the Census Bureau using informa

tion on household size, race, education level, assets, and income

sources. However, the adjustment cannot completely account for

the ways in which people who drop out of the sample differ from

those who remain, because all of the relevant differences between

the two groups are not known. Moreover, some attrition may be

associated with important changes in a person’s circumstances

 
APPENDIX A STRENGTHS AND LIMITATIONS OF THE SURVEY DATA 17

A third limitation of SIPP is that the data exhibit a “seam

effect.” Many people appear to report the same insurance

status for the entire fourmonth reference period rather

than accurately reporting their status on a monthby

month basis (see Figure A1). About onequarter of all un

insured spells are reported as lasting four months; other

common durations are eight months, 12 months, and so

forth. Because of the seam effect, some uninsured spells

that are reported as lasting four months, eight months,

and so on were actually somewhat shorter or longer.

Consequently, CBO’s analysis classified spells into cate

gories of reported duration (four months or less, five to

12 months, 13 to 24 months, and more than 24 months).

The Medical Expenditure Panel Survey

As a longitudinal survey, MEPS has the same advantages

and disadvantages relative to the Current Population Sur

vey as SIPP does. Like SIPP, MEPS includes a series of

questions asking people whether they had various types

of insurance in each month of the reference period; the

uninsured are defined as those people who report no cov

erage. Unlike SIPP, in which the reference period is de

fined as four months for everyone, the reference period

in MEPS varies among individuals from three months

to five months. The two surveys also differ in their phras

ing and sequencing of questions on insurance status.3

Those differences may contribute to the different esti

mates the surveys yield.

In this paper, CBO analyzed data from the MEPS 1999

FullYear Population Characteristics File, which includes

information collected in the third, fourth, and fifth

rounds of Panel 3 (which began in 1998) and the first

three rounds of Panel 4 (which began in 1999). CBO also

analyzed data from the 1998 FullYear Population Char

acteristics File to conduct comparisons with 1998 SIPP

data.

The National Health Interview Survey

Unlike SIPP and MEPS, NHIS is a crosssectional sur

vey. Its respondents are not reinterviewed over time to

track changes in their insurance status; instead, they are

asked to report their status as of the time of the interview

and over the previous 12 months. Thus, NHIS yields a

pointintime estimate of the number of people who are

uninsured and can, in principle, yield estimates of the

number who were ever uninsured during the previous 12

months and the number who were continuously unin

sured over that period. However, the Centers for Disease

Control and Prevention (CDC), which sponsors the

NHIS, reports only the pointintime estimate.

CBO did not analyze data from the NHIS for this paper

because the primary objective was to use longitudinal data

from SIPP and MEPS to examine the dynamics of the

uninsured. The NHIS pointintime estimate of the un

insured is included to lend evidence to the argument that

the CPS estimate is closer to a pointintime estimate

than a fullyear estimate.

(such as the loss of employment) that influence the likelihood of

being uninsured in the period after having left the sample.

3. For a detailed discussion of the design of SIPP, see Bureau of the

Census, Survey of Income and Program Participation Users’ Guide,

3rd ed. (prepared by Westat in association with Mathematica Policy

Research, Inc., 2001). The design of MEPS is described in Steven

B. Cohen, MEPS Methodology Report No.11, AHRQ Pub. No.

010001 (Rockville, Md: Agency for Healthcare Research and

Quality, November 2000).
 
B

The Consistency of Different Estimates

of the Duration of Uninsured Spells

New uninsured spells are much more likely to be

relatively short than are spells in progress at a given point

in time. About 45 percent of all new spells last four

months or less, compared with only about 8 percent of

spells in progress at a given point in time (see Table 3 on

page 9). In this appendix, the Congressional Budget Of

fice (CBO) demonstrates how the two sets of estimates

(those for new spells and spells in progress at a given

time) are consistent. CBO also presents estimates of the

duration of uninsured spells that were in progress at any

time during a oneyear period and demonstrates that

those estimates are consistent with the observed duration

of new spells.

Spells in Progress in a Given Month

CBO used the observed distribution of new uninsured

spells to project the distribution of spells in progress at

a given point in time.1

 CBO then compared the projec

tions with the actual distribution to gauge their similarity.

CBO’s method of projecting the duration of spells in

progress in a given month was based on the fact that the

duration of a spell directly relates to the number of

months in which it could have begun. For example, the

only onemonth spells in progress in a particular month

are those that began (and ended) that month, the only

twomonth spells are those that began in that month or

the prior month, and so on.2

 Likewise, the only 25

month spells in progress in a given month are those that

began in that month or in the previous 24 months. 

In statistical terms, letting N represent the number of

new uninsured spells that began each month over the

relevant time period and assuming that N and the dis

tribution of new spells by duration did not change over

the period, the total number of spells in progress in a

given month (NP) can be represented as follows: 

NP = N [.075 + (2)(.062) + (3)(.056) + (4)(.253) + .....]

The proportions in the bracketed term reflect the per

centage distribution of the duration of new spells shown

in Figure A1 (see Appendix A).

The proportion of spells in progress in a given month

that are of a particular duration, say three months, can

be determined by dividing the projected number of three

1. Although CBO measured spells in progress in March 1998, similar

results were obtained for other months.

2. The minimum period of time for which a person’s insurance status

can be determined in the Survey of Income and Program Partici

pation is one month. Therefore, for analytic purposes, CBO as

sumed that uninsured spells begin on the first day of a month and

end on the last day of a month.

APPENDIX
20 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

Table B-1.

Projected Distribution of Spells in Progress in a Given Month and a Given Year,

by Duration, Based on the Actual Distribution of New Spells

(In percent)

Duration of Uninsured Spells Actual Projecteda

Spells That Began Between July 1996 and June 1997

Four Months or Less 44.5 n.a.

Five to 12 Months 26.2 n.a.

More Than 12 Months 29.3 n.a.

Spells in Progress in a Given Monthb

Four Months or Less 7.9 10.1

Five to 12 Months 13.9 16.4

More Than 12 Months 78.2 73.6

Spells in Progress in a Given Yearb

Four Months or Less 22.2 25.4

Five to 12 Months 19.0 20.8

More Than 12 Months 58.8 53.8

Source: Congressional Budget Office based on data from the 1996 panel of the Survey of Income and Program Participation.

Note: n.a. = not applicable.

a. The projected distributions were computed from the actual distribution of spells that began between July 1996 and June 1997. CBO based its projections on the

assumption that the mean duration of new spells that exceed 25 months is 48 months.

b. The distributions are for spells in progress in March 1998 and from July 1997 through June 1998. Similar estimates were obtained for other time periods.

month spells in progress by the projected total number

of spells in progress3

:

(3)(.056) / [.075 + (2)(.062) + (3)(.056)

+ (4)(.253) + .....]

To implement that approach, CBO needed to make an

assumption about the mean duration of new spells that

exceed 25 months (those spells account for 15.6 percent

of all new spells). CBO based its projections on the as

sumption that the mean duration of such spells is 48

months. That assumption yielded a projected distribution

in which 73.6 percent of spells in progress in a given

month exceeded 12 months (see Table B1). That figure

is lower than the corresponding 78.2 percent that was

estimated directly from the data. To make those two

estimates equivalent, CBO would have had to assume

that new spells exceeding 25 months had a mean duration

of 67 months. If, alternatively, CBO assumed that new

spells of more than 25 months lasted, on average, 36

months, the projections would have yielded a distribution

in which 69.1 percent of spells in progress in a given

month exceeded 12 months.4

The projected distribution of spells in progress in a given

month is expected to differ from the directly estimated

3. The mean number of spells per month, N, appears in both the

numerator and denominator of this expression and therefore cancels

out.

4. A lowerbound estimate was obtained under the extreme (and

unrealistic) assumption that all new spells of more than 25 months

lasted 26 months. That assumption yielded a projected distribution

in which 64.4 percent of the spells in progress in a given month

lasted more than 12 months.
APPENDIX B THE CONSISTENCY OF DIFFERENT ESTIMATES OF THE DURATION OF UNINSURED SPELLS 21

distribution for a particular month for two reasons. First,

the projections assume that the distribution of the dura

tion of new spells in previous months and the number of

new spells that began each month during the observation

period remain constant. Departures from that assumption

would yield a projected distribution that differed from

the actual distribution for a particular month. In addi

tion, longterm chronically uninsured people are under

represented in the population of new spells but are fully

represented in the population of spells in progress in a

given month. That discrepancy means that the propor

tion of long spells in the projected distribution of spells

in progress in a given month is lower than in the directly

estimated distribution. 

CBO’s objective in making the projections was not to

duplicate the actual distribution of spells in progress in

a given month. Instead, the objective was to demonstrate

that a flow of new uninsured spells will yield a population

of spells in progress in a given month that has a distribu

tion similar to that of the directly estimated distribution.

Spells in Progress in a Given Year

CBO’s analysis of uninsured spells showed that 25 per

cent of the nonelderly population was uninsured at any

time in 1998 (see Table 1 on page 3). This section of the

appendix looks at the duration of those people’s unin

sured spells and demonstrates that their distribution is

consistent with the distribution of the duration of new

spells. The analysis focused on people who were unin

sured at any time between July 1997 and June 1998

rather than calendar year 1998, in order to provide an

adequate time frame for measuring durations.

Fiftynine percent of the spells experienced by people

who were uninsured at any time during the specified

12month period lasted more than 12 months, whereas

22 percent ended within four months (see the bottom

panel of Table B1). CBO obtained those estimates by

measuring the total length of spells in progress during the

12month period, looking backward and forward in

time.5

People who are uninsured at any time during a particular

year are less likely to be experiencing a long uninsured

spell than people who are uninsured in a particular

month (58.8 percent versus 78.2 percent). As the window

used to identify spells under way is extended, the likeli

hood of capturing short spells increases, because short

spells occur much more frequently than longer spells. An

even larger proportion of short spells would be captured

if the window was extended from, say, 12 months to

24 months.

To illustrate that the estimated distribution of spells in

progress in a given year is generally consistent with the

distribution of new spells, CBO modified the projection

method described earlier. For example, the only one

month spells in progress in a given year are those that

began (and ended) within the same month of the

12month period. Similarly, the only twomonth spells

are those that began in the month just prior to the year

or within the 12month span. Extending that logic to

longer spells, the equation for the number of spells in

progress in a given year (NP) can be expressed as follows:

NP = N [(12)(.075) + (13)(.062) + (14)(.056)

+ (15)(.253) + .....]

As before, N is the number of new spells that began per

month, and the proportions in the bracketed term reflect

the percentage distribution of the duration of new spells.

The proportion of spells in progress in a given year that

are of a particular duration, say three months, can be

determined as follows:

(14)(.056) / [(12)(.075) + (13)(.062) + (14)(.056)

 + (15)(.253) + .....]

The projections presented in Table B1 were based on the

assumption that new spells exceeding 25 months had a

mean duration of 48 months—the same assumption that

was used to project spells in progress in a given month.

CBO’s analysis yielded a projected distribution in which

53.8 percent of spells in progress in a given year were

5. For individuals who had more than one uninsured spell during

the specified 12month period, this analysis focused on the first

spell. Through a sensitivity analysis, CBO determined that the

decision to focus on the first spell did not affect the results.
22 HOW MANY PEOPLE LACK HEALTH INSURANCE AND FOR HOW LONG?

longer than 12 months—five percentage points below the

58.8 percent estimated directly from the data. Making

those estimates equivalent would have required an as

sumption that new spells exceeding 25 months had a

mean duration of 68 months. If, instead, new spells of

more than 25 months were assumed to last 36 months,

on average, the projections would have yielded a distribu

tion in which 49.8 percent of spells in progress in a given

year lasted more than 12 months. For the reasons de

scribed earlier, however, the projected distributions are

not expected to exactly match the actual distributions for

a particular year.

This publication and others by CBO

are available at the agency's Web site:

www.cbo.gov