yyyyy zzzz {{{{{ |||| Report 6

List Sample Design of the 1996 

Medical Expenditure Panel Survey 

Insurance Component 

Methodology Methodology 

U.S. Department of Health and Human Services

Public Health Service

Agency for Health Care Policy and Research

Agency for Health Care Policy and Research
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Abstract

The Medical Expenditure Panel Survey (MEPS) is

the third in a series of nationally representative surveys

of medical care use and expenditures sponsored by the

Agency for Health Care Policy and Research (AHCPR).

MEPS comprises four component surveys. The

Insurance Component (IC) collects employment-related

health insurance information, such as premiums and

types of plans offered. Respondent characteristics–such

as size of business, employee characteristics, and type of

industry–also are collected. This report outlines the

process used to allocate and select the MEPS IC list

sample, including goals, development of allocation

schemes, and selection methods. The list sample is

collected from samples developed from three lists that,

together, cover almost 100 percent of the employers in

the United States.

Suggested citation

Sommers, JP. List sample design of the 1996 Medical

Expenditure Panel Survey Insurance Component.

Rockville (MD): Agency for Health Care Policy and

Research; 1999. MEPS Methodology Report No. 6.

AHCPR Pub. No. 99-0037.

http://www http://www.ahcpr.gov/
List Sample Design of the 1996 

Medical Expenditure Panel Survey

Insurance Component 

U.S. Department of Health and Human Services

Public Health Service

Agency for Health Care Policy and Research

   yyyyy zzzz {{{{{ |||| Report 6

Methodolo Methodology 

AHCPR Pub. No. 99-0037

July 1999
The Medical Expenditure Panel

Survey (MEPS)

Background

The Medical Expenditure Panel Survey (MEPS) is

conducted to provide nationally representative estimates

of health care use, expenditures, sources of payment,

and insurance coverage for the U.S. civilian

noninstitutionalized population. MEPS also includes a

nationally representative survey of nursing homes and

their residents. MEPS is cosponsored by the Agency for

Health Care Policy and Research (AHCPR) and the

National Center for Health Statistics (NCHS).

MEPS comprises four component surveys: the

Household Component (HC), the Medical Provider

Component (MPC), the Insurance Component (IC), and

the Nursing Home Component (NHC). The HC is the

core survey, and it forms the basis for the MPC sample

and part of the IC sample. The separate NHC sample

supplements the other MEPS components. Together

these surveys yield comprehensive data that provide

national estimates of the level and distribution of health

care use and expenditures, support health services

research, and can be used to assess health care policy

implications.

MEPS is the third in a series of national probability

surveys conducted by AHCPR on the financing and use

of medical care in the United States. The National

Medical Care Expenditure Survey (NMCES) was

conducted in 1977, the National Medical Expenditure

Survey (NMES) in 1987. Beginning in 1996, MEPS

continues this series with design enhancements and

efficiencies that provide a more current data resource to

capture the changing dynamics of the health care

delivery and insurance system.

The design efficiencies incorporated into MEPS are

in accordance with the Department of Health and

Human Services (DHHS) Survey Integration Plan of

June 1995, which focused on consolidating DHHS

surveys, achieving cost efficiencies, reducing respondent

burden, and enhancing analytical capacities. To

accommodate these goals, new MEPS design features

include linkage with the National Health Interview

Survey (NHIS), from which the sample for the MEPS

HC is drawn, and enhanced longitudinal data collection

for core survey components. The MEPS HC augments

NHIS by selecting a sample of NHIS respondents,

collecting additional data on their health care

expenditures, and linking these data with additional

information collected from the respondents’ medical

providers, employers, and insurance providers.

Household Component

The MEPS HC, a nationally representative survey

of the U.S. civilian noninstitutionalized population,

collects medical expenditure data at both the person and

household levels. The HC collects detailed data on

demographic characteristics, health conditions, health

status, use of medical care services, charges and

payments, access to care, satisfaction with care, health

insurance coverage, income, and employment.

The HC uses an overlapping panel design in which

data are collected through a preliminary contact

followed by a series of five rounds of interviews over a 

21⁄

2-year period. Using computer-assisted personal

interviewing (CAPI) technology, data on medical

expenditures and use for 2 calendar years are collected

from each household. This series of data collection

rounds is launched each subsequent year on a new

sample of households to provide overlapping panels of

survey data and, when combined with other ongoing

panels, will provide continuous and current estimates of

health care expenditures.

The sampling frame for the MEPS HC is drawn

from respondents to NHIS, conducted by NCHS. NHIS

provides a nationally representative sample of the U.S.

civilian noninstitutionalized population, with

oversampling of Hispanics and blacks.

Medical Provider Component

The MEPS MPC supplements and validates

information on medical care events reported in the

MEPS HC by contacting medical providers and

pharmacies identified by household respondents. The

MPC sample includes all hospitals, hospital physicians,

home health agencies, and pharmacies reported in the

ii
iii

HC. Also included in the MPC are all office-based

physicians: 

• Providing care for HC respondents receiving

Medicaid.

• Associated with a 75-percent sample of households

receiving care through an HMO (health maintenance

organization) or managed care plan.

• Associated with a 25-percent sample of the

remaining households.

Data are collected on medical and financial

characteristics of medical and pharmacy events reported

by HC respondents, including:

• Diagnoses coded according to ICD-9 (9th Revision,

International Classification of Diseases) and DSMIV (Fourth Edition, Diagnostic and Statistical

Manual of Mental Disorders).

• Physician procedure codes classified by CPT-4

(Current Procedural Terminology, Version 4).

• Inpatient stay codes classified by DRG (diagnosisrelated group).

• Prescriptions coded by national drug code (NDC),

medication names, strength, and quantity dispensed.

• Charges, payments, and the reasons for any

difference between charges and payments.

The MPC is conducted through telephone

interviews and mailed survey materials.

Insurance Component

The MEPS IC collects data on health insurance

plans obtained through employers, unions, and other

sources of private health insurance. Data obtained in the

IC include the number and types of private insurance

plans offered, benefits associated with these plans,

premiums, contributions by employers and employees,

and employer characteristics.

Establishments participating in the MEPS IC are

selected through four sampling frames:

• A list of employers or other insurance providers

identified by MEPS HC respondents who report

having private health insurance at the Round 1

interview.

• A Bureau of the Census list frame of private-sector

business establishments.

• The Census of Governments from the Bureau of the

Census.

• An Internal Revenue Service list of the selfemployed.

To provide an integrated picture of health insurance,

data collected from the first sampling frame (employers

and other insurance providers) are linked back to data

provided by the MEPS HC respondents. Data from the

other three sampling frames are collected to provide

annual national and State estimates of the supply of

private health insurance available to American workers

and to evaluate policy issues pertaining to health

insurance.

The MEPS IC is an annual panel survey. Data are

collected from the selected organizations through a

prescreening telephone interview, a mailed

questionnaire, and a telephone followup for

nonrespondents.

Nursing Home Component

The 1996 MEPS NHC was a survey of nursing

homes and persons residing in or admitted to nursing

homes at any time during calendar year 1996. The NHC

gathered information on the demographic

characteristics, residence history, health and functional

status, use of services, use of prescription medications,

and health care expenditures of nursing home residents.

Nursing home administrators and designated staff also

provided information on facility size, ownership,

certification status, services provided, revenues and

expenses, and other facility characteristics. Data on the

income, assets, family relationships, and caregiving

services for sampled nursing home residents were

obtained from next-of-kin or other knowledgeable

persons in the community.

The 1996 MEPS NHC sample was selected using a

two-stage stratified probability design. In the first stage,

facilities were selected; in the second stage, facility

residents were sampled, selecting both persons in

residence on January 1, 1996, and those admitted during

the period January 1 through December 31.

The sampling frame for facilities was derived from

the National Health Provider Inventory, which is

updated periodically by NCHS. The MEPS NHC data

were collected in person in three rounds of data

collection over a 11⁄

2-year period using the CAPI system.

Community data were collected by telephone using

computer-assisted telephone interviewing (CATI)

technology. At the end of three rounds of data collection,
iv













































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|||| the sample consisted of 815 responding facilities, 3,209

residents in the facility on January 1, and 2,690 eligible

residents admitted during 1996.

Survey Management

MEPS data are collected under the authority of the

Public Health Service Act. They are edited and

published in accordance with the confidentiality

provisions of this Act and the Privacy Act. NCHS

provides consultation and technical assistance.

As soon as data collection and editing are

completed, the MEPS survey data are released to the

public in staged releases of summary reports and

microdata files. Summary reports are released as printed

documents and electronic files. Microdata files are

released on CD-ROM and/or as electronic files.

Printed documents and CD-ROMs are available

through the AHCPR Publications Clearinghouse. Write

or call:

AHCPR Publications Clearinghouse

Attn: (publication number)

P.O. Box 8547

Silver Spring, MD 20907

800-358-9295

410-381-3150 (callers outside the United States

only)

888-586-6340 (toll-free TDD service; hearing

impaired only)

Be sure to specify the AHCPR number of the

document or CD-ROM you are requesting. Selected

electronic files are available through the Internet on the

AHCPR Web site: 

http://www.ahcpr.gov/

On the AHCPR Web site, under Data and Surveys, 

click the MEPS icon.

Additional information on MEPS is available from

the MEPS project manager or the MEPS public use data

manager at the Center for Cost and Financing Studies,

Agency for Health Care Policy and Research, 2101 East

Jefferson Street, Suite 500, Rockville, MD 20852 

(301-594-3075).
v

Table of Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

List Sample Design Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Assumptions and Goals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Statistical Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Sample Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Sample Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Tables showing information on the 1996 MEPS Insurance Component:

1. National percent of private-sector establishments per stratum, by firm employment size and

establishment employment size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

2. Projected distribution of private-sector establishments responding for typical State, by firm employment 

size and establishment employment size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

3. Sample allocation of employers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

4. Projected response rates of private-sector establishments for typical State, by firm employment size and 

establishment employment size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

5. Projected losses because of going out of business for the private-sector list sample, by firm employment 

size and establishment employment size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12
1

Introduction

The 1996 Medical Expenditure Panel Survey

(MEPS) Insurance Component (IC) is a survey of

employers, the self-employed with no employees

(SENEs), unions, and insurance companies. The MEPS

IC, which is sponsored by the Agency for Health Care

Policy and Research (AHCPR) and conducted by the

U.S. Bureau of the Census, is designed to collect

employment-related health insurance information, such

as premiums and types of plans offered. Respondent

characteristics—such as size of business, employee

characteristics, and type of industry—also are collected.

Although the 1996 MEPS IC was first administered in

1997, data collected were for the entire calendar year

1996; the survey name refers to the data year, not the

time of collection. 

The MEPS IC sample has two parts:

• The household sample, which consists of the

employers of respondents to the 1996 MEPS

Household Component (HC), as well as unions and

insurance companies that provide insurance to

members of the household sample. Data from this

sample can be attached to information collected from

HC respondents.

• The list sample, which consists of an independent

random sample of employers and SENEs.

Similar data on employer health insurance are

collected from both samples. The combination of

information collected from households and employers

permits the study of household health expenditures and

their relationship to household insurance status.

The IC household sample is defined by the sample

design of the MEPS HC (Cohen, 1997) and has persons

as sample units. Data are collected from the employers

and other insurance providers of the household

respondents from the MEPS HC. The employers and

other providers are proxy respondents for supplemental

information on health insurance offered to the household

respondent through the employer or provider. Hence, the

probabilities of selection and the corresponding weights

for these employers are the same as those of the

household sample members and come from the MEPS

HC design. The data collected are linked to other

information for household sample cases. 

The IC list sample is a random sample of

establishments selected especially for the IC, and its

selection is independent of the HC design. The two IC

samples (household and list) are combined for collection

purposes. The data collected for both samples are

almost identical.

The MEPS IC list sample provides a picture of State

and national employer health insurance. Enrollments,

premiums, employee contributions, types of plans

offered, deductibles, coverages, and other characteristics

are shown for the Nation and 40 individual States.

Estimates for characteristics of employer-related health

insurance for small establishments are of particular

interest. The MEPS IC sample is a primary government

source of information on employer-related health

insurance in the United States. It should prove to be a

key data provider for this crucial portion of health care.

List Sample Design Process

This report outlines the process used to allocate and

select the MEPS IC list sample, including goals,

development of allocation schemes, and selection

methods. The list sample is so named because it is

collected from samples developed from three lists that,

together, cover almost 100 percent of the employers in

the United States. These are as follows:

• The Standard Statistical Establishment List (SSEL)

including agricultural units. The SSEL is a list of

private-sector establishments that is developed and

maintained by the Census Bureau. Although the

Census Bureau obtains information on agricultural

units from the same sources used to produce the

SSEL, such units are normally not in scope for most

List Sample Design of the 1996 Medical Expenditure Panel Survey

Insurance Component

by John Paul Sommers, Ph.D.,Agency for Health Care Policy and Research
surveys. However, agricultural employers are in

scope for the MEPS IC. The SSEL is continually

updated; the list used for sample selection was that

of establishments in business as of the end of 1995.

• The 1992 Census of Governments (COG), a list

containing the Federal, State, and local governments

within the United States that is maintained by the

Census Governments Division.

• A 1994 Internal Revenue Service list of SENEs.

The overall budgeted size of the list sample was

30,000 responding cases with completed data from

among the three frames. The sample design

development process necessitated the following steps:

• Allocate the total sample between the SENEs and

other employers.

• Allocate the sample from the remaining sample of

employers—the private-sector and government lists

combined—among the States.

• Allocate the sample share within each State between

government and private-sector employers.

• Determine a sample allocation scheme within each

State for the SSEL allocation.

• Determine a sample allocation scheme within each

State for the COG allocation.

• Determine sample selection methods within each

State for the SSEL and COG allocations.

• Determine a national sample selection scheme for

the SENEs.

Assumptions and Goals

Data from the 1994 National Employer Health

Insurance Survey (NEHIS) were used as a basis for

developing the design of the MEPS IC list sample

(Marker, Bryant, Wallace, et al., 1996). NEHIS

collected similar data from the same universe, and it

was assumed that the variance structure for the MEPS

IC would follow that of NEHIS. Therefore, the NEHIS

variance results, sample size, and standard sample

variance formulas were used to assist in the

development of the IC list sample design. 

The IC also used the same strata structure as

NEHIS. This structure seemed reasonable, and time

constraints did not allow for the indepth analyses

required to produce another structure. For the private

sector, the structure is determined by the strata of

establishments defined by the State in which the

establishment is located, the size of the establishment,

and the size of the firm that owns the establishment. An

establishment is an economic unit at a single location

where business is conducted or services or industrial

operations are performed. A firm is a business entity

consisting of one or more business establishments under

common ownership or control. Exact categories of firm

and establishment size groups used for private-sector

stratification within each State are displayed in Tables 1

and 2. For governments, the strata are defined by the

State in which the government is located and

employment size (number of employees).

The goals for the establishment sample are as

follows:

• Support estimates for a variety of variables of two

distinct types. The first type are establishment

variables. These are variables that relate to

establishments and tend to have unit variances—that

is, variances of a mean estimate per individual

sample (Kish, 1965)—that do not increase in size

across strata with the size of establishment. An

example is the average number of plans offered per

establishment. The second type are employee

variables. These relate to the number of employees,

and their unit variances increase across strata as the

size of establishment increases. An example is the

average enrollment per establishment.

• Make quality national estimates for the variables

selected. Quality was defined as having a relative

standard error (RSE) of 1 percent or less.

• Make quality estimates in at least 40 States for the

same variables. Quality for a State estimate was

defined as an RSE of 5 percent or less.

• Make a quality national estimate for the SENE

population. Quality was defined as an RSE of 5

percent or less.

For practical purposes, because of the number of

variables involved, two of each type of variable were

chosen for analysis.

2
3

Table 2. Projected distribution of private-sector establishments responding

for typical State, by firm employment size and establishment employment

size: Medical Expenditure Panel Survey Insurance Component, 1996

Firm employment size

Establishment

employment size 50 or less 51-999 1,000 or more

1-5 215 8 11

6-25 171 20 29

26-50 42 13 15

51-249 0 38 23

250-999 0 4 7

1,000 or more 0 0 4

Note: Private-sector list sample = 600. An establishment is an economic unit at a single location where business is conducted

or services or industrial operations are performed. A firm is a business entity consisting of one or more business establishments

under common ownership or control.

Source: Center for Cost and Financing Studies, Agency for Health Care Policy and Research: Medical Expenditure Panel

Survey Insurance Component, 1996.

Table 1. National percent of private-sector establishments per stratum, by

firm employment size and establishment employment size: Medical

Expenditure Panel Survey Insurance Component, 1996

Firm employment size

Establishment

employment size 50 or less 51-999 1,000 or more

1-5 45.13 1.61 2.31

6-25 28.27 3.35 4.81

26-50 5.50 1.54 1.96

51-249 0 2.96 1.81

250-999 0 .23 .37

1,000 or more 0 0 .10

Note: An establishment is an economic unit at a single location where business is conducted or services or industrial

operations are performed. A firm is a business entity consisting of one or more business establishments under common

ownership or control.

Source: Center for Cost and Financing Studies, Agency for Health Care Policy and Research: Medical Expenditure Panel

Survey Insurance Component, 1996.
Statistical Tools

The formulas below were applied relative to the

variance of a population estimate for a variable of

interest based on a stratified sample (Cochran, 1977).

Assuming:

Ni is the number of establishments in the ith 

stratum,

nik is the sample for allocation k in the ith 

stratum,

σi is the standard deviation for the variable 

of interest for the ith stratum,

Vk is the variance estimate of the population 

total for the variable of interest for 

allocation k, and

RSEk is the relative standard error of the 

estimate of the population total for the 

variable of interest for allocation k, then

Because the main concern is with the relative

quality of two types of allocations, a useful ratio is that

of the variances of two different allocations:

If Mj and Mk are the sample sizes for two different

allocations, such that for all cells,

then

and

and

Using these formulas, sample sizes for the 1994 NEHIS,

RSEs from the 1994 NEHIS, and proposed sample sizes

for the 1997 MEPS IC, comparisons of various new

allocation schemes can be produced and estimates made

of their RSEs. 

Sample Allocation

Allocation to SENEs

The first decision was to allocate a national sample

of 1,000 to the population of SENEs and the remaining

sample of 29,000 to the private-sector and government

employers. This was done by determining the total

sample needs for the SENE sample. A brief assessment

showed that this sample size appeared to give an average

RSE of approximately 5 percent for survey estimates for

a set of important variables.

Allocation Among States

The next step required the allocation of the sample

among the States. The equations above were used, and it

was assumed that the variance structure is the same

within each State. In equation (1), the strata are

assumed to be States and the values of unit variances for

each State are assumed to be equal. These assumptions

require the within-stratum variances and the relative

sizes of the strata within States to be equal. The

assumptions seemed reasonable after examining a set of

within-stratum variances for several variables and the

relative sizes of the strata for several States of various

sizes. Thus, to compare allocations, only values of

equation (1) where the values of σ are all equal to 1

must be compared. 

For the 1994 NEHIS, the sample allocation to each

State was done relative to the values of the .3 power of

the number of establishments (Marker, Bryant, Wallace,

et al., 1996.) This allocation method was used to give

adequate sample size to smaller States by increasing

4

nik

Vk = i

N

2

σ

2

Σ i i

nik

Rkj =

Rkj = RkjVj

RSEk = RkjRSEj

= Vk

Vj

i

N

2

σ

2

Σ i i

nij i

N

2

σ

2

Σ i i

Rkj = Mj

MK

= nik

nij

Mk

Mj

.5

(1)

(2)

(3)

(4)

(6)

(5)

RSEk = RkjRSEj

(5)
5

their relative size compared with the larger States. Using

this method, 29,000 remaining units were allocated to the

States, and an RSE for this survey was projected using

equations (3), (4), (5), and (6) along with the RSE for

the 1994 NEHIS. However, the allocation failed to meet

the stated goals for two reasons:

• The national goal of an RSE of 1 percent or less

could not be met. This happened because the sample

was much smaller than the sample of 38,000 for the

1994 survey. The smaller IC sample, combined with

the loss of efficiency for national estimates caused by

the oversampling of all small States, contributed to

failure to meet the goal.

• In spite of the oversampling of the smaller States,

samples for many of them were too small to produce

quality State estimates. 

As a result, neither quality estimates for 40 States

nor a quality national estimate could be made with the

1994 NEHIS sample design and the current smaller

sample size. To remedy this problem, it was necessary to

produce a minimum sample in 40 States and to generate

a more efficient allocation for national estimates. 

Examination of equation (1) in the case of equal

values of σ shows that the best allocation for national

estimates allocates sample proportional to the size of Ni

for each State. Thus, an allocation was needed that was

closer to proportional than the 1994 NEHIS was. At the

same time, the allocation required a minimum sample

within at least 40 States. After a brief analysis, this

number was set at 600. This number would not produce

State estimates that would meet the quality goal of 5

percent or less RSE for estimates of total enrollment.

However, the number was set at 600 for two reasons:

• The goal was close to being achieved, and a further

increase in State allocations might degrade the

national estimates.

• It was possible to gain some improvements in

allocations within States that would produce more

efficient within-State estimates. These

improvements, which are discussed in later sections,

include use of a more accurate frame, a better

allocation of units between government and the

private sector, and a better allocation to strata within

the private sector. These improvements also may

allow a closer approach to the State quality goals

with a sample of 600.

Approximately 19,000 units were allocated

proportional to the number of establishments in each

State. The sample of any of the 40 largest States that did

not receive an allocation of 600 was supplemented. The

value of V for this sample was 1.21 times the value of V

for the optimal proportional sample. The value of V for

the allocation using the 1994 method gave a value

approximately 1.45 times the value for the optimal

sample. Thus, the RSE could be improved by

approximately 10 percent compared with the 1994

design with the current sample size, allowing national

estimation goals to be met. The sample allocation to

States is shown in Table 3.

Allocation Within States

Government and Private Sectors

The next step was to divide each State’s allocation

between the public and private sector. In the 1994

NEHIS, this was done in two steps:

• The State government was selected with certainty.

• The ratio of the employment of local governments in

the State to the sum of private-sector employment

and local government employment was calculated.

Two-thirds of this percentage was allocated to the

local government sample within the State. Thus, if

local government workers constituted 15 percent of

local government and private-sector employment, 2/3

of 15 percent, or 10 percent, of the State’s allocation

was local government.

Because of this allocation, the RSEs for many

government statistics were less than those for the private

sector, although the government sample was smaller than

that of the private sector. This fact and an examination

of equation (1) indicate that the unit variances and

design effects for governments are smaller than those for

the private sector. This was the reason the 1994

government sample was allocated as only 2/3 the

proportion of government employment. However, this

allocation to governments appears to be still too large,

and it would be better to further lessen the government
6

allocation. To do this, assume that in equation (1) there

are two strata—governments and the private sector. Let

the number of governments and private-sector

establishments be Ng and Np. Likewise, let μg and μg be

the means for a variable of interest for governments and

private-sector establishments, and RSEg and RSEp be the

relative standard errors for the estimates of the means of

the variables for the two groups. Then the variance V of

the estimate of the total over the entire population can be

written as

For allocation between the government and private sector

within a State, a fixed sample size is divided between the

two strata of the population. Under these conditions, one

can show that

This relationship can be developed using the

Neyman optimal allocation of the sample when there are

two strata (Cochran, 1977). For the NEHIS government

allocation, all values within this equation are known.

The size of RSEs for governments for many variables,

the relationship of the 1994 NEHIS allocations to

government and the private sector within States, and the

population values all are available.

When these known values were entered into

equation (8), the right side of the equation for the 1994

allocation appeared to be too large. Therefore, the value

of the RSE for governments must be raised, while the

value for the private sector must be lowered. To do this,

the allocation to governments within each State is

decreased. Different reductions are needed to achieve an

optimal design for each of the different variables. Thus, a

conservative method was developed that would reduce

the government allocation but would not be extreme.

The method is as follows:

• If p is the proportion of employment that is

government and n the total sample, then (2/3)np is

the original government allocation. 

• Let E be the total employment for all governments.

Then any government with a total employment that is

a greater proportion of total government employment

than 1/((2/3)np) is a certainty. 

• Remove the certainty governments from the

government list and their number from allocation.

Combine the remaining government and private

sector. This value is the new total employment.

Calculate a value p*, where p* is the ratio of the

remaining government employment to the value of

new total employment. With n* being the remaining

government allocation, check for more certainties

using n* and p* in the formula in the step above

using the remaining government employment.

• Continue to iterate the process until no more certainty

governments are found. At this point, calculate the

employment remaining in each State that is

government and combine it with the private sector in

that State. Let p# be the proportion that the

remaining government constitutes of that total and n#

be the State allocation less any government

certainties. 

• Calculate (2/3)p#n# = nc. The State’s government

allocation is nc plus any certainties found in the

State. The remaining State allocation is for the

private sector.

As an example, suppose there are two States, each

with 50,000 total employees, half of whom are

government employees. Assume the sample allocation is

120, or 60 per State. The government allocation is

.5(2/3)120, or 40. Any government with more than

1,250 employees is selected with certainty. Assume

each State has 6 governments with more than 1,250

employees and the total employment of the 6

governments in each State is 20,000.

Remove the 12 governments and their employees.

This leaves 60,000 total employees, of whom 10,000 are

government employees. The remaining sample is 108, or

54 per State. Government gets

(10,000)/(60,000)(2/3)108 = 12. Check this and find

nothing large enough to be a certainty—that is, no

government in the remainder larger than 10,000/12 =

867.

Now allocate the remaining 54 sample units in each

State. Because the remaining government in each State

is 1/6th of the employment, government gets 2/3 of that

proportion of the remaining sample, or 6. 

The final allocation in each State under this assumed

scenario is 48 private sector, 6 certainty governments,

and 6 noncertainty governments. If the 1994 NEHIS

V = (Ngμg)

2

RSE2

g + (Npμp)

2

RSE2

p (7)

(8)

Ngμg

Npμp

RSEpng

RSEgnp

.5

= .5
7

method had been used, the government allocation would

have been 20 per State.

This method considers the disproportionate amount

represented by large local governments. It essentially

applies optimal allocation of the sample to the remainder

of the universe after these very large governments are

removed. It produced a sample for governments within

States that was approximately 80 percent of the size

produced using the 1994 NEHIS methods. Table 3

shows the breakdown of the samples within States.

Private-Sector Sample

In allocation of private-sector establishments within

States, two types of variables are considered:

• Those with approximately equal unit variances within

strata.

• Those with unit variances that increase as the size of

the establishment increases.

The two variables tend to call for two opposite types of

allocation methods. With the first, allocation is

proportional to the number of establishments in a

stratum. With the second, allocation involves increasing

the sample according to the number of employees in

establishments within a stratum. The first allocates large

percentages of the sample to strata with small

establishments. The second allocates larger percentages

of the sample to large establishments. (Strata definitions

are given in Table 1.)

The probability of allocation for the 1994 NEHIS

sample was proportional to size, with the size measure

for an establishment being the square root of the

establishment’s employment size (Marker, Bryant,

Wallace, et al., 1996). This was a compromise between

the two extremes above. The method worked reasonably

well and gave good error results.

To determine the allocation for the MEPS IC, a

compromise allocation was developed using unit

variances estimated from 1994 NEHIS data. This

analysis was performed to further improve State

estimates to meet the State quality goals that could not

be met using the 1994 NEHIS methodology. 

To perform the analyses, two different sets of unit

variances were used, one to represent each type of

variable. There are 18 possible strata, 6 establishment

sizes crossed with 3 firm sizes. These are listed in Table

1. Note that only 14 strata have establishments in them

because of restrictions placed on establishment sizes by

the size of the total firm. Unit variances for a given

establishment size were assumed to be equal for all firm

sizes. Thus, there are 6 unit variances for each variable

type. For a variable of the first type, which creates an

allocation relative to total numbers of establishments,

these values will be equal; for variables of the second

type, these variances will increase as establishment sizes

increase. The relative values of these variances are as

follows:

Establishment

size (in increasing size) Variable type 1 Variable type 2

Size 1 1 1

Size 2 1 3

Size 3 1 7

Size 4 1 32

Size 5 1 83

Size 6 1 410

After a preliminary analysis, six methods of

allocation were attempted:

1. Optimal for variable type 1, or allocation 

proportional to the number of establishments in a 

stratum.

2. Optimal for variable type 2, or Neyman allocation 

(Cochran, 1977).

3. Allocation proportional to the total employment 

size in each stratum.

4. Neyman allocation using variances for each 

stratum, which are an average of the stratum 

variances for the two types of variables to be 

estimated.

5. Allocation equal to the average of the allocations 

determined by the two optimal allocation schemes 

presented above.

6. Allocation proportional to the sum of the square 

roots of the employment sizes of all establishments 

in the stratum. (This is the method from the 1994 

NEHIS.)

The sizes of the variances obtained from each method

relative to method 1 are shown below. The values were

calculated using equations (1) and (2).
Variable type 1 Variable type 2

Method 1 1.00 1.00

Method 2 1.65 .79

Method 3 4.48 1.74

Method 4 1.10 .79

Method 5 1.08 .86

Method 6 1.39 .81

Method 4 was selected for the MEPS IC because

variable type 2 was considered to be slightly more

important than type 1 in this survey. However, both

methods 4 and 5 were good, and both were robust when

tested under changes in unit variance. Table 2 gives the

percent allocation to each stratum in the private sector

for a typical State using this method.

Final Allocations

At this point in the process, all samples were of the

desired sample size. Samples must be inflated to

account for potential losses due to nonresponse and outof-scope units. To develop these numbers for the private

sector, losses were calculated by strata from previous

Census Bureau SSELs for time periods from the time

the frame was developed to the time of the survey. The

loss rates are shown in Table 5. The general tendency is

for rates to decrease as both the firm size and the

establishment size increase. Losses also vary from year

to year based on economic conditions. The assumption

made was that conditions would be stable with no

recession.

No losses were assumed for governments because

governments that close are small boards and

commissions with few, if any, employees. The Census

Bureau estimates that total losses in employment during

a year would amount to much less than .5 percent of

government employment. To avoid these types of

governments that are of little interest analytically, two

steps were taken:

• All governments with less than one full-time

equivalent employee were removed. This removed a

large number of small boards and the like, but less

than .01 percent of the total employment.

• The sample was selected using employment as a

measure of size. 

These two steps resulted in a sample in which there was

little chance of selecting any governments that would be

lost because of closure. Thus, no inflation was made for

losses because of closure in the government sample. A

90-percent response rate was assumed for governments.

A similar rate had been achieved in the 1994 NEHIS,

and this was believed to be reasonable.

There was no experience as a guide to estimating

losses in the SENE group except a supposition that it

would be a large number. A one-third loss rate because

of closure was chosen for this group.

Sample increases were also made to account for

losses because of nonresponse. Tables 4 and 5 contain

these values for private-sector strata, governments, and

SENEs. The results for the private sector and

government were based on results from the 1994

NEHIS, which were slightly lower than the rates

assumed. The rates from NEHIS were increased

slightly because of the smaller questionnaire being used

for MEPS and because the Census Bureau was the

collection agent. The rate chosen for SENEs was

similar to that assumed for small businesses.

Sample Selection

The sample selection for the three frames was done

independently. All selections employed stratified

sequential sampling. No clustering of units was done

because the data collection was primarily by mail and

telephone.

To allocate the SENEs, only counts by income class

were available. Because only those in higher income

classes were likely to have insurance through their

work, the SENEs were divided into five strata by total

annual income and allocations were made. The

allocations relative to the number of persons in each

stratum increased with income to allow for the fact that

it was very unlikely that any of the SENEs with lower

incomes had insurance through their jobs. Assuming a

binomial distribution of persons with insurance within

each stratum means that the unit variances increase as

the incomes within the strata increase. Under these

assumptions, the resulting optimal allocation (Cochran,

1977) to the strata places a high percentage of the

sample in the strata with higher income persons.

Allocations are:

8
9

Income class Allocation

$1-$4,999 75

$5,000-$9,999 100

$10,000-$14,999 100

$15,000-$19,999 125

$20,000 or more 1,600

Within each stratum, the units were sorted by State,

industry, and income. Each unit within a stratum was

assigned an equal probability, and selection was done

using a systematic selection process.

Governments not selected with certainty were

stratified by State. Each government was given a

measure of size based on the square root of its

employment size. The governments were sorted by type

(city, county, school district, and other) and employment

and were selected systematically within each State.

Each private-sector establishment within a State was

first assigned a probability of selection. This probability

was equal to the sample number allocated to the

establishment’s stratum within its State divided by the

number of establishments in the stratum within the

State. 

For each large firm, the probabilities of selection of

all the firm’s establishments were summed to obtain an

expected number of establishments. To reduce the

burden on central office respondents for firms with

multiple sampled establishments, the probability of

selection of each establishment within a firm was

reduced by a constant. After these reductions, the

expected sample within each stratum was recalculated

to determine the shortfall caused by these reductions in

probabilities of selection. To account for the shortfall,

the probability of selection of each establishment in a

stratum that had not been reduced was increased slightly

so the total probabilities of selection within the stratum

again equaled the allocation for the stratum within the

State.

Using the new probabilities of selection,

establishments were sorted by State, stratum, industry,

and size, and sequential selection was accomplished.

Summary

The MEPS IC sample consists of two parts—the

household and list sample components. Similar data on

employer health insurance are collected for both

samples. The purpose of the household sample is to

provide data that can be attached to information

collected from respondents in the MEPS HC. The

combination of information collected from households

and employers for this household sample allows analysts

to study household health expenditures and how these

expenditures are related to the choices of health

insurance offered to the households.

The MEPS IC list sample is designed to provide

quality estimates for a variety of employer-related health

insurance characteristics. The sample is designed to

provide a picture of State and national employer health

insurance. Among the characteristics shown are

enrollments, premiums, employee contributions, types

of plans offered, deductibles, and coverages. Estimates

made for the Nation should have excellent precision,

with RSEs of about 1 percent for characteristics for the

entire country and for the entire private sector. In

addition, estimates for characteristics of employerrelated health insurance within the 40 largest States can

be made with good precision. 

Estimates for characteristics of employer-related

health insurance for small establishments will be of

particular interest. Oversampling of these

establishments, along with annual surveys, should

permit analysts to track changes and effects of

government policy for this sensitive portion of the

population—for the Nation as well as for the 40 largest

States.

The MEPS IC sample is a primary government

source of information on employer-related health

insurance in the United States. It should prove to be a

key data provider for this crucial portion of health care.

References

Cochran WG. Sampling techniques. New York:

John Wiley and Sons; 1977.

Cohen S. Sample design of the 1996 Medical

Expenditure Panel Survey Household Component.

Rockville (MD): Agency for Health Care Policy and

Research; 1997. MEPS Methodology Report No. 2.

AHCPR Pub. No. 97-0027.

Kish L. Survey sampling. New York: John Wiley

and Sons; 1965.

Marker D, Bryant E, Wallace L, Yansaneh I.

National Employer Health Insurance Survey (NEHIS):

draft final methodology report; volume I: statistical

methodology. Rockville (MD): Westat, Inc.; 1996. 
Table 3. Sample allocation of employers for the Medical Expenditure Panel

Survey Insurance Component, 1996

State State allocation Government Private

Total 29,000 1,757 27,243

AK 100 8 92

AL 600 43 557

AR 600 42 558

AZ 600 34 566

CA 2,014 115 1,899

CO 600 34 566

CT 600 29 571

DC 105 1 104

DE 100 5 95

FL 938 54 884

GA 600 45 555

HI 600 7 593

ID 100 8 92

IL 881 45 836

IN 600 37 563

IA 600 43 557

KS 600 47 553

KY 600 37 563

LA 600 40 560

MA 600 31 569

MD 600 18 582

ME 600 42 558

MI 654 41 613

MN 600 36 564

MO 600 35 565

MS 600 52 548

MT 100 10 90

NC 600 38 562

ND 100 8 92

NE 600 42 558

NH 100 7 93

NJ 600 42 558

NM 600 41 559

NV 600 19 581

NY 1,310 64 1,246

10

Continued
11

Table 3. Sample allocation of employers for the Medical Expenditure Panel

Survey Insurance Component, 1996 (continued)

State State allocation Government Private

OH 808 51 757

OK 600 49 551

OR 600 38 562

PA 848 43 805

RI 100 6 94

SC 600 38 562

SD 100 8 92

TN 600 31 569

TX 1,242 95 1,147

UT 600 30 570

VA 600 34 566

VT 100 8 92

WA 600 36 564

WI 600 35 565

WV 600 47 553

WY 100 11 89

Source: Center for Cost and Financing Studies, Agency for Health Care Policy and Research: Medical Expenditure Panel

Survey Insurance Component, 1996.
12

Table 4. Projected response rates of private-sector establishments for typical

State, by firm employment size and establishment employment size: Medical

Expenditure Panel Survey Insurance Component, 1996

Firm employment size

Establishment

employment size 50 or less 51-999 1,000 or more

1-5 .78 .70 .70

6-25 .75 .70 .70

26-50 .73 .70 .70

51-249 N/A .70 .70

250-999 N/A .70 .70

1,000 or more N/A N/A .70

Note: An establishment is an economic unit at a single location where business is conducted or services or industrial

operations are performed. A firm is a business entity consisting of one or more business establishments under common

ownership or control. N/A is not applicable.

Source: Center for Cost and Financing Studies, Agency for Health Care Policy and Research: Medical Expenditure Panel

Survey Insurance Component, 1996.

Table 5. Projected losses because of going out of business for the privatesector list sample, by firm employment size and establishment employment

size: Medical Expenditure Panel Survey Insurance Component, 1996

Firm employment size

Establishment

employment size 50 or less 51-999 1,000 or more

1-5 .04 .02 .02

6-25 .015 .0075 .0075

26-50 .0125 .0045 .0045

51-249 N/A .01 .005

250-999 N/A .0015 .0075

1,000 or more N/A .0075 .00875

Note: Projected response rates, government: .90. Projected losses from governments going out of business: 0.

Projected response rates, self-employed with no employees (SENEs): .75. Projected losses from SENEs going out of business:

.33.

An establishment is an economic unit at a single location where business is conducted or services or industrial operations are

performed. A firm is a business entity consisting of one or more business establishments under common ownership or control.

N/A is not applicable.

Source: Center for Cost and Financing Studies, Agency for Health Care Policy and Research: Medical Expenditure Panel

Survey Insurance Component, 1996.
U.S. Department of Health and Human Services

Public Health Service

Agency for Health Care Policy and Research

2101 East Jefferson Street, Suite 501

Rockville, MD 20852

AHCPR Pub. No. 99-0037

July 1999