yyyyy zzzz {{{{{ |||| Report 8

Construction of Weights for the 1996

Medical Expenditure Panel Survey

Insurance Component List Sample

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. The IC sample comprises a

household sample component (linked to the MEPS

household survey) and a list sample. The list sample is

an independently selected random sample of

governments and private-sector establishments. Its

purpose is to make national and State estimates of

employer insurance characteristics, costs, and the

numbers of employees enrolled. This report describes

the overall response rates for the list sample and the

process used to correct the weights for respondents. The

weights are corrected in order to adequately represent all

nonrespondents and all important subgroups, especially

subgroups that may have had different response rates

from the average for the survey.

Suggested citation

Sommers JP. Construction of weights for the 1996 Medical

Expenditure Panel Survey Insurance Component list sample.

Rockville (MD):Agency for Health Care Policy and Research; 1999.

MEPS Methodology Report No. 8. AHCPR Pub. No. 00-0005.

http://www.ahcpr.gov/ http://www.ahcpr.gov/
Construction of Weights for the 1996

Medical Expenditure Panel Survey

Insurance Component List Sample

U.S. Department of Health and Human Services

Public Health Service

Agency for Health Care Policy and Research

   yyyyy zzzz {{{{{ |||| Report 8

Methodology Methodology 

AHCPR Pub. No. 00-0005

November 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

Response Rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Private-Sector Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Government Weights. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Representative Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Appendix. Definition of Selected Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Tables showing information on the 1996 MEPS Insurance Component list sample:

1. Private-sector response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2. Government response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3. Selected statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1

Introduction

The Medical Expenditure Panel Survey (MEPS)

Insurance Component (IC) is a survey of employers,

unions, and other providers of health insurance. It is

sponsored by the Agency for Health Care Policy and

Research (AHCPR) and is conducted by the U.S. Bureau

of the Census. The IC sample is composed of two sets

of employers and other providers of health insurance.

The first part of the IC sample is the list sample, an

independently selected random sample of governments

and private-sector establishments. The purpose of this

sample of just over 40,000 employers is to make national

and State estimates of employer insurance

characteristics, their costs, and the numbers of

employees enrolled (Sommers, 1999).

The second part of the IC sample, the household

sample component, serves a different purpose: it is

person oriented, not employer oriented. The two samples

are collected together because of the similarity in data

collected from similar types of respondents.

The IC household sample component is the set of

employers and other providers of health insurance for

persons who were members of the sample of the MEPS

Household Component (HC), a household survey.

Respondents in this part of the IC sample serve as proxy

respondents, giving employer and insurance information

for household members who are part of the HC sample.

The data for household members collected during the IC

are attached as data elements to other information

collected directly from the household respondents.

Weights derived for the data from the household sample

component of the IC are thus dependent on the sample

design of the HC (J. Cohen, 1997; S. Cohen, 1997). The

development of weights for the MEPS HC will be

presented in a separate document.

This report briefly describes the overall response

rates for the list sample and the process used to correct

the weights for respondents. The weights are corrected

in order to adequately represent all nonrespondents and

all important subgroups, especially subgroups that may

have had different response rates from the average for

the survey.

Response Rates

In this section, various response rates are given for

the list sample. Response rates for subgroups of the

population that may have characteristics different from

the average are especially important. Their importance

is discussed in later sections describing the construction

of weights for sampling units. Before the rates are

presented, it is necessary to discuss the data collection

methodology used for the IC. This affects the definition

of a response and the method selected to create weights.

The IC sample is composed of governments and

private-sector establishments. Governments were

selected from the Census of Governments, and privatesector establishments from the Census Bureau’s Standard

Statistical Establishment List (Sommers, 1999). For all

samples except State and very large local governments,

each sample unit was prescreened. The purpose of this

step was to determine a point of contact for data

collection and determine whether the employer offered

insurance to its employees. If the employer did not offer

insurance, a small number of questions were

administered and, with this done, the employer was

considered a respondent. This was a quick and

inexpensive method to collect the necessary data from

the large number of employers that do not offer health

insurance to their employees.

Employers that offered insurance were asked several

brief questions and then mailed a questionnaire about

their insurance. If they failed to return the mail

questionnaire, an attempt was made to collect the

information by telephone. For the purpose of this

survey, employers that offered insurance were considered

respondents only if they answered key questions on their

health insurance. Those that did not were considered

nonrespondents.

Construction of Weights for the 1996 Medical Expenditure Panel

Survey Insurance Component List Sample

by John Paul Sommers, Ph.D.,Agency for Health Care Policy and Research
2

If no contact was made by telephone, a

questionnaire was mailed. If the questionnaire was not

returned, another attempt was made to collect

information by telephone. Any employer from this

group that responded by mail or telephone was a

respondent. Note that for this group whether a

nonrespondent had insurance was unknown.

Table 1 shows the private-sector sample by inscope

and respondent status. Out-of-scope private-sector

establishments were those that had gone out of business

or consisted of a self-employed person with no other

employees.

Although an establishment with insurance that does

not answer questions about the plans offered is

considered a nonresponse for the IC, one can argue that

it provided certain valuable information. If one

assumed that all cases were successfully prescreened,

then the entire sample size could be used to estimate the

percentage of establishments that offer insurance. As

will be shown in the section describing the weighting

methodology used, this partial information can be used

as a part of the correction for nonresponse.

Table 2 shows the government sample by inscope

and respondent status. Private-sector and government

employers are shown separately because of the

differences in response rates and other features. To be

out of scope, a government must have ceased to

function. To be a respondent, a government had to

answer questions similar to those for a private-sector

respondent.

Table 1. Private-sector response in the 1996 Medical Expenditure Panel

Survey Insurance Component list sample

Status Number

Total sample 37,710

Out of scope 2,567

In scope 35,143

Respondents (66.1 percent of inscope sample) 23,232

Completed prescreener 20,003

Offered insurance 13,622

Did not offer insurance 6,381

Did not complete prescreener 3,229

Nonrespondents (33.9 percent of inscope sample) 11,911

Completed prescreener, offered insurance 5,061

Incomplete prescreener, unknown insurance status 6,850

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

Survey Insurance Component, 1996.
3

Private-Sector Weights

Motivation for Weighting

Because the sample units were selected with

unequal probabilities and because of nonresponse in the

data collection, sampling weights must be calculated for

each unit in the original sample and the weights of

respondents must be adjusted for nonresponse (Kish,

1965). To motivate this adjustment, it is assumed that pi

is the probability of selection of the ith unit in the

sample, and wti = 1/pi

, the initial weight. Then 

where S and U are the set of sampled units and set of

units in the universe, respectively.

A method of making an unbiased estimate of a total

from the sample is to use the weighted sum from the

sample as the estimate. If there is nonresponse–if, for

example, some of the x values are missing—one would

try to adjust sampling weights for the values of

respondents in cells for which the responding x’s have

the same or very similar expected values to those of the

nonresponding x’s. Thus, the final objective is to have

the sum of the new adjusted weights of respondents over

cells with various characteristics equal to the totals from

the original sample or some other set of known control

totals for the population.

Table 2. Government response in the 1996 Medical Expenditure Panel

Survey Insurance Component list sample

Status Number

Total sample 2,649

Out of scope 20

In scope 2,629

Respondents (84.6 percent of inscope sample) 2,224

Completed prescreener 1,049

Offered insurance 1,049

Did not offer insurance 0

Did not complete prescreener 1,175

Nonrespondents (15.4 percent of inscope sample) 405

Completed prescreener, offered insurance 151

Incomplete prescreener, unknown insurance status 254

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

Survey Insurance Component, 1996.

E[Σ wtixi]= Σ piwtixi = Σ [ ]xi = Σ xi

iεS iεU iεU

pi

pi

iεU
4

Classification of Establishments

Cells defined by size of establishment, size of firm,

and State were used to create cells for weighting. These

same characteristics had been used for sample selection

(Sommers, 1999). It was first thought that the original

cell classification of establishments would be used.

However, the following factors led to a decision to

update the classification:

• The frame used for sampling was preliminary for

1996. The Census Bureau had since produced a final

listing of establishments for March 1996.

• The reported and verified employment sizes of

establishments sometimes differed considerably from

the employment sizes on the frame.

A scheme was developed to update the size

classification. Otherwise, a unit with a frame

employment of 2 but a reported establishment

employment of 2,000 would cause extreme results

during the poststratification process (described later in

this report).

Establishments were divided into the six size classes

used for sample selection. (See Sommers, 1999, and the

appendix.) If the reported employment would have

placed an establishment more than one cell away from

its original frame-defined cell, then the establishment

was reclassified. These establishments were considered

to have been mistakes on the frame. Establishments that

moved only one cell were allowed because the date of

the frame employments was early March and the

reference date in the survey was July of the same year.

Since there is seasonality and growth in employment,

with March being a relatively low month for

employment and July high, it seemed that this change

should be allowed (Bureau of Labor Statistics, 1996).

Approximately 5.6 percent of the private-sector sample

changed its establishment size cell either up or down at

least two classes to correspond to the reported

employment. The remaining establishments kept their

original frame classification.

Prescreener Respondent Weight

Adjustments

As discussed earlier, during data collection a

telephone prescreener was conducted. Employers that

reported during this prescreener that they did not offer

insurance to their employees were considered

respondents. Those that did offer health insurance were

not considered respondents unless they provided further

information on the insurance either by mail or

telephone. As shown in Table 1, 5,061 private-sector

employers (14.4 percent of the sample) were

prescreened, had insurance, and later were classified as

nonrespondents. However, after the prescreener, 25,064,

or 71.3 percent of the inscope sample, had answered

whether they provided insurance. It is of utmost

importance that this information be retained in the

weights after adjustment for nonresponse. Thus, the

first step in weighting was to adjust the weights of

respondents that provided insurance and were

successfully prescreened so that their adjusted total

weight would be equal to the total weight for all

employers who were successfully prescreened and

provided insurance to their employees.

First, weights for the respondents that offered

insurance and had also responded to the prescreener

were adjusted so that their total weight equaled the

weight of all respondents to the prescreener that offered

insurance for a large number of cells. Among the

variables chosen to define these important cells were:

• A variable with 41 levels that was defined by

crossing groups of States with a variable with 8

levels based on employment size of the establishment

and that of the enterprise as listed on the frame

(Sommers, 1999).

• Whether the establishment was from a firm with

multiple establishments.

• The industry of the establishment.

Because crossing all these variables divides the sample

into too many cells, a method called raking was

employed to create an adjustment that retained the sum

of the weights for as many cells as possible.

In an ideal world, if the value of the ith unit’s initial

weight were wti

, the adjusted weight for responding
5

units within each response cell C, where CR is the set of

respondents in C, would be

The adjusted weights for nonrespondents would be set to

zero. This would be done for all cells C and CR defined

by the cross of all the important variables. 

Because the number of cells defined by the cross of

all levels of these variables was very large, the sum of

the respondent weights for each cell created by these

cross-classifications was not made equal to the

population total. Instead, the sums of the respondent

weights for all cells defined by any one of the variables

alone were made to sum to the totals for respondents and

nonrespondents in the cell. Thus, the sum of the

respondents’ weights equaled the total sample weights

for all the marginal cells defined by these variables.

The method can be illustrated with an example.

Suppose one were concerned only with two variables:

(1) type of firm, with 2 levels–single establishment and

multi establishment, and (2) industry, with 7 levels. The

sum of weights of respondents in the 14 cells defined by

a cross of the two variables (for instance, single and

mining) is not made to equal the total of sampling

weights for all establishments in each of the cells.

Instead, the sum of the weights for respondents for each

of the nine marginal cells defined by one of the

individual variables (for instance, single or mining) is

made to equal the total weights of all establishments in

the cell. To accomplish this, one uses an iterative

technique called raking, which is explained below

(Madow, Olkin, and Rubin, 1983). 

Assume wtijk is the sampling weight for the ith

sample unit in the jkth cell. For example, the first

variable could break the population by type of firm and

the second by industry. One would adjust the weights to

create a set of weights wt9ijk such that 

To do this, one first creates values aj such that 

Then wt9ijk = aj

* wtijk for all cases where the ijkth case is

a respondent. Otherwise, the adjusted weight is 0.

One continues this process through all values of j.

This makes the respondents’ weights sum to the proper

values for the first defining variable, but not the second.

One then operates on the values of wt9ijk to adjust them

so that the cells defined by the second variable are equal.

Of course, now this third set of weights may not sum

properly for cells defined by the first variable. The cycle

is repeated, readjusting each new set of weights by

continuing to iterate through the process. Usually, within

several cycles, the weights will converge so that for each

cell defined by either of the two variables, the sum of the

latest set of weights is a value very close to the required

marginal total. This completes the raking process. The

process can be applied to make weights sum to marginal

cell totals for as many variables as one likes. In this

example, two variables were used to create marginal

totals and the weights were forced to add to the sample

total for the type of firm and for each industry, but not

necessarily by type of firm and industry.

In adjustments for the sampling weights for

respondents offering insurance that also responded to the

prescreener, three variables defined the marginal cells

and their totals for this process:

• Eight combinations of establishment size and firm

size within the 41 State sets. (Each of the 40 most

populous States was a set, and the District of

Columbia and the 10 least populous States were

grouped together as a single set.)

• Multi and single establishment firms.

• Industry type.

Exact category definitions can be found in the appendix.

aj = Σ wtijk wti Σ wtj ikεCR

adjwti = jεC

jεCR

Σ wtj

Nj. = Σ wt9ijk = Σ wtijk and N.k = Σ wt9ijk = Σ wtijk

i,kεCR i,kεC i,jεCR

Nj.

i,jεC
6

Prescreener Nonrespondent Weight

Adjustments

Weights for the remaining respondents from which

no prescreener information was collected must be

adjusted to account for nonrespondents that did not

provide prescreener information. Because of the lack of

the prescreener information, there is no partial response

in this group. Establishments in this group could either

provide or not provide insurance to their employees.

This part of the sample adjustment is similar to that done

for most surveys which have only one stage of data

collection. A nonresponse correction adjusted the

current set of weights of all 23,232 (6,381 + 13,622 +

3,229) complete respondents, regardless of their

prescreener status, so that their weights after the

correction also included the total sampling weight of the

6,850 nonrespondents that had an incomplete

prescreener and unknown insurance status. The weights

used for the respondents were those currently available.

For the 6,381 prescreener respondents that did not offer

insurance and the 3,229 respondents that did not answer

the prescreener, these were the original sampling

weights. For the prescreener respondents that offered

insurance, they were the weights after the first

adjustment, described above.

To make this second adjustment, raking was used.

The variables and cells used were the same as those used

for the previous correction for those nonrespondents that

answered the prescreener and offered insurance. 

Poststratification of Private-Sector

Weights

Correction of weights can be taken further using a

technique called poststratification. This method is used

when outside information is available about the

population. The weighted sum of a variable can be

thought of as an estimate of the population total for the

variable. Ideally, the total weighted sum for each

important subset of the population should equal the

population for that subset. Controlling weights to

population totals from another source is called

poststratification (Madow, Olkin, and Rubin, 1983;

Skinner, Holt, and Smith, 1989).

The MEPS IC private-sector sample is selected from

a list of establishments, the Standard Statistical

Establishment List (SSEL) maintained by the Census

Bureau (Kreps, Slater, and Plotkin, 1979; Sommers,

1999). This list contains each establishment that was in

business during the year and its employment size as of

March of the year. It is the source of official Census

Bureau figures of the number and employment size of

establishments in the United States. For this reason, it is

very desirable that the estimates of employment obtained

from the IC match the values from the SSEL for the total

and a variety of subtotals. 

Because establishments go out of business during

the year, the list used for the IC represented a point in

time, establishments in business as of a certain date,

rather than the entire frame for the year. It was felt that

this type of list, showing the entire population of workers

at one instant, would give a better picture of the

percentage of establishments in business at any point that

were offering insurance and the numbers of employees in

the work force offered insurance. It was felt that

including all establishments in business during the year

might skew the results. Small establishments tend not to

offer health insurance and also go in and out of business

more rapidly than larger ones. Thus to include all the

small businesses from the entire year would give a

higher estimate of the numbers of establishments not

offering insurance to their employees and the number of

employees not offered insurance. It also could tend to

double-count employees who moved from closed

establishments where they were likely not to have had

insurance. Thus, one could get a higher proportional

count of people not offered insurance.

The SSEL used to provide control totals for the IC

was the list of establishments in business as of the end of

the third quarter of 1996. Because of the retrospective

time frame of the data collected, this information was

available in late 1997, when weighting took place. This

time frame also fit well with the timing of the data

collected.

To implement this process, the same 41 × 8

categories were used that were assigned during the
7

raking process. These cells were based on the

establishment’s location, firm size, and employment size.

The size used was the one reported for the frame, not the

size reported during the survey, except for establishments

that were reassigned to new establishment size classes

during the classification process described earlier. The

size reported for the SSEL frame was used because the

survey period was later in the year than the SSEL, and it

was felt that the frame categories and employment sizes

were the most comparable variables. The frame

employment for the SSEL is for early March of 1996.

The weighted frame employment sizes from the

responding sample establishments were obtained using

the weights after the two other adjustments and the total

employment on the frame for inscope establishments

were calculated for each of the 328 categories. The ratio

of the frame employment size for March to the estimated

employment size from the sample was calculated for

each category. A new corrected weight for each

respondent in the category was created by multiplying

its category ratio times the current value of its corrected

weight before poststratification. This procedure

corrected the sample weights so that the sample

employment for March equaled the frame employment.

The collected employment estimate was somewhat

higher than the frame employment because employment

tends to have a lower seasonal component in March than

in the later months of the year for which data were

collected (Bureau of Labor Statistics, 1996).

Government Weights

Data for governments were collected in the same

manner as for the private sector: a prescreener phone

contact was used as the first contact, and the process for

adjusting weights parallels that for the private sector.

The differences are in the definition and sizes of the

cells used. Cells were determined by a cross of the 41

State sets (40 most populous States and the residual set

of 10 States and the District of Columbia) and 5 classes

based on size and type of government. The classes are

listed in the appendix. These cells were used for two

nonresponse adjustments and for poststratification of

governments.

As with the private-sector weights, the last step of

the process was a poststratification adjustment. Units

within the 41 × 5 cells were adjusted using the ratio of

the total 1996 frame employment size for the cell

divided by the weighted total 1996 employment size for

the sample within the cell. 

Representative Results

Table 3 contains some typical results for the private

sector along with their relative standard errors.

Table 3. Selected statistics from the 1996 Medical Expenditure Panel Survey

Insurance Component list sample

Item Estimate Relative standard error (percent)

Total establishments 5,998,994 0.6

Total employment 103,846,469 1.5

Percent of establishments offering insurance 53.2 0.5

Average single premium per enrolled employee $1,996.90 0.8

Average single employee contribution $338.33 2.6

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

Survey Insurance Component, 1996.
References

Bureau of Labor Statistics (US). Current

employment statistics. Washington. Series

EEU00500005; 1996.

Cohen J. Design and methods of the Medical

Expenditure Panel Survey Household Component.

Rockville (MD): Agency for Health Care Policy and

Research; 1997. MEPS Methodology Report No. 1.

AHCPR Pub. No. 97-0026.

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.

Kreps J, Slater CM, Plotkin MD. The Standard

Statistical Establishment List Program. Washington: U.S.

Bureau of the Census; 1979. Technical Paper No. 44.

Madow WG, Olkin I, Rubin DR. Incomplete data in

sample surveys, volume 2: theory and bibliographies.

New York: Academic Press; 1983.

Skinner CJ, Holt D, Smith TMF. Analysis of

complex surveys. New York: John Wiley and Sons; 1989.

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.

8
9

Appendix. Definition of Selected

Variables

Industry type (Standard Industry Code)

1 Construction, SIC = 15-17

2 Mining, manufacturing, SIC = 10-14, 20-39

3 Transportation, communications, utilities, 

SIC = 40-49

4 Wholesale trade, SIC = 50-51

5 Agriculture, retail trade, SIC = 01-09, 52-59

6 Finance, insurance, real estate, SIC = 60-67

7 Services, SIC = 70-89

8 Unknown, SIC = missing

Firm size

S Less than 50 employees 

M 50-999 employees

L 1,000 employees or more

Establishment size 

1 1-5 employees

2 6-24 employees 

3 25-49 employees

4 50-249 employees

5 250-999 employees

6 1,000 employees or more

Collapsed firm size × establishment size

1 Firm size = S, establishment size = 1

2 Firm size = S, establishment size = 2

3 Firm size = S, establishment size = 3

4 Firm size = M, establishment size = 1,2

5 Firm size = M, establishment size = 3,4

6 Firm size = L, establishment size = 1,2

7 Firm size = L, establishment size = 3,4

8 Firm size = M, L, establishment size = 5, 6

Multi/single establishment firm

1 if multi, 0 otherwise

State groupings

AK, DE, DC, ID, MT, NH, ND, RI, SD, VT, and WY

are grouped together because of small sample sizes

All 40 other States stand on their own

Government size groups

1 if a State government 

2 if a local certainty

3 if a local noncertainty government and frame

employment < 200 

4 if a local noncertainty government and 199 < frame

employment < 1,000 

5 if a local noncertainty government and frame

employment is 1,000 or more

Note: State governments were classified separately because

there is only one per State (they are unique within each

State).
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. 00-0005

November1999