yyyyy zzzz {{{{{ |||| Report 10

Imputation of Employer Information

for the 1996 Medical Expenditure Panel Survey

Insurance Component

Methodology Methodology 

U.S. Department of Health and Human Services

Public Health Service

Agency for Healthcare Research and Quality

Agency for Healthcare Research and Quality
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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 Healthcare Research and Quality (AHRQ).

MEPS comprises four component surveys. The

Insurance Component (IC) is a survey of employers,

unions, and other providers of health insurance. The IC

has two parts. The household sample is linked to sample

persons in the MEPS Household Component. It consists

of private- and public-sector employers of MEPS

respondents, as well as unions and insurance companies

that provide insurance to the respondents. The list sample

consists of an independent random sample of privatesector business establishments, governments, and the

self-employed with no employees. This report describes

the process used to impute values for missing

establishment and plan characteristics for the IC in four

types of cases: list sample, private sector; list sample,

government; household sample, private sector; and

household sample, government. The description includes

preparation of the data, selection of donors, and the use

of donor and other information to create the item for the

recipient.

Suggested citation

Sommers JP. Imputation of employer information for the 1996

Medical Expenditure Panel Survey Insurance Component. Rockville

(MD):Agency for Healthcare Research and Quality; 2000. MEPS

Methodology Report No. 10.AHRQ Pub. No. 00-0039.

http://www.ahrq.gov/ http://www.ahrq.gov/
Imputation of Employer Information

for the 1996 Medical Expenditure Panel Survey

Insurance Component

U.S. Department of Health and Human Services

Public Health Service

Agency for Healthcare Research and Quality

   yyyyy zzzz {{{{{ |||| Report 10

Methodology Methodology 

AHRQ Pub. No. 00-0039

June 2000
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

Healthcare Research and Quality (AHRQ), formerly the

Agency for Health Care Policy and Research, 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 AHRQ 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 AHRQ Publications Clearinghouse. Write or

call:

AHRQ 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 AHRQ number of the

document or CD-ROM you are requesting. Selected

electronic files are available through the Internet on the

AHRQ Web site: 

http://www.ahrq.gov/

On the AHRQ 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 Healthcare Research and Quality, 2101 East

Jefferson Street, Suite 500, Rockville, MD 20852 

(301-594-1406).
v

.Table of Contents

Introduction and Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

General Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Hot-Deck Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Process for Each Group of Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Government Imputation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Appendixes:

A. Establishment Questionnaire. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

B. Definitions of Selected Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1

Introduction and Background

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 survey

is sponsored by the Agency for Healthcare Research and

Quality and conducted by the U.S. Bureau of the Census.

It is designed to collect employment-related health

insurance information, such as premiums and types of

plans offered. Information on respondent characteristics,

such as size of business, employee characteristics, and

industry, is also collected. The 1996 MEPS IC was first

administered in 1997; data were collected for the entire

1996 calendar year. Hence, 1996 refers to the data year,

not the time of collection. 

The sample has two parts:

• The household sample, which is linked to members

of the household sample from the MEPS Household

Component (HC). It consists of private- and publicsector employers of MEPS respondents, as well as

unions and insurance companies that provide them

insurance. 

• The list sample, which consists of an independent

random sample of private-sector business

establishments, governments, and SENEs. (An

establishment is a single business location, as

opposed to a firm, which is a legal entity that can be

made up of multiple establishments.)

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 HC. The employers and other

providers are proxy respondents for supplemental

information on health insurance offered to the household

respondent through the employer or other insurance

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 HC design. The data collected in the IC

household sample are combined with other information

collected directly from household sample cases, and the

two types of data can be analyzed together. For instance,

with the use data collected in the HC and the description

of coverage in the IC, a person’s use of health care can

be analyzed relative to the types of health insurance

coverage he or she has.

The IC list sample is a random sample of

establishments selected only for the IC. Its selection is

independent of the HC design (Sommers, 1999). The

two IC samples (household and list) are combined for

collection purposes. The data collected for both samples

are very similar for most cases. To understand the

imputation process described in this report, it is essential

to understand the type of information collected for each

type of case, outlined below.

• List sample, private sector–Information on

establishment characteristics, whether insurance is

offered, and characteristics of plans offered.

• List sample, government–Information on government

characteristics, whether insurance is offered, and

characteristics of plans offered.

• Household sample, private sector–Information on

establishment characteristics, whether insurance is

offered, and characteristics of plans offered, plus a

small amount of information concerning the plan

selected by the household respondent.

• Household sample, government–Information on

government characteristics, whether insurance is

offered, and characteristics of plans offered, plus a

small amount of information concerning the plan

selected by the household respondent.

• SENE–Information concerning the self-employed

person’s health plan, if any, and some personal

demographics.

• Household sample, union–Characteristics of the

union, characteristics of plans offered, and which

plan the household respondent selected.

• Household sample, insurance company–Plan

characteristics of insurance selected by household

respondent.

Imputation of Employer Information for the 1996 Medical Expenditure

Panel Survey Insurance Component

by John Paul Sommers, Ph.D., Agency for Healthcare Research and Quality
Very similar information is collected for the

following cases:

• List sample, private sector.

• List sample, government.

• Household sample, private sector.

• Household sample, government.

A core set of employer information, such as total

employment, selected characteristics, and health

coverage offered, is collected for all four of these

samples. For the two household samples there are a few

additional questions about the household respondent.

As with other surveys, individual respondents in the

MEPS IC may not answer all the questions presented.

As is the custom for most surveys, important items that

are missing are completed for all respondents using a

process called imputation. This provides the same full

set of critical items for each respondent to anyone who

wants to perform analyses with the data. 

In the case of the MEPS IC, the core of the

imputation process is a form of “hot-deck” imputation,

by which information for missing items is derived for an

individual respondent using information from a similar

respondent that has provided the necessary information.

The respondent receiving information is called the

recipient and the respondent providing information is

called the donor. (See Cox and Cohen, 1985, and

Kalton and Kasprzyk, 1986.)

This report describes the process used to impute

values for missing establishment and plan characteristics

for the survey in four types of cases (list sample, private

sector; list sample, government; household sample,

private sector; and household sample, government)

when the respondent returned enough information to be

considered usable but did not complete all the questions

on the survey form. The description includes

preparation of the data, selection of donors, and the use

of donor and other information to create the item for the

recipient. 

The process used for all the private-sector cases for

these data items is the same whether the case came from

the household or list sample. Likewise, the process

used for the data for all government cases is the same.

Furthermore, government and private-sector imputation

differ only in the selection of the donors to provide

information for missing data. Government cases that

require imputation receive information only from other

government cases. Private-sector cases receive

information only from other private-sector cases. 

Because of these similarities, the description of the

methodology focuses on the imputation of information

for private-sector cases. The question numbers refer to

questions on Form MEPS-10, which is shown as

Appendix A. After the private-sector process is

described, differences in the process for the publicsector data are noted.

The questions listed in this report were considered

the critical items for the MEPS IC. They were the only

items imputed for survey year 1996.

General Process

The entire process includes more than merely

selecting donor values to replace missing values. Many

steps are required to create data that are both consistent

and logical.

To impute the data for the MEPS IC, the set of

items to be imputed was divided into groups. Items

within each group are usually related. Groups are

composed of either similar types of items or items that

require internal consistency and thus must be imputed

and edited together. For instance, the first group of

items imputed includes numbers of total and part-time

employees eligible for insurance and those who are

enrolled. Because of size relations among the variables,

they were imputed as part of the same process to assure

that the values met consistency criteria (e.g., the total

number of eligible employees cannot be less than the

number enrolled).

The groups of items were also ordered. This is the

order of the actual imputation process, chosen to assure

overall consistency and correlation within the data

records. For instance, if a particular variable was a

primary determinant of the value of another variable, it

had to be imputed first. Size of premium is an example.

The premiums vary by the type of providers that are

available to the insured. Can the insured use only a

selected group of providers, or can the insured use any

provider but some are preferred by the insurance

company, or does the plan pay equally for any and all

2
providers? Thus, before premiums were imputed, type

of providers for the plan was imputed. Then the

imputed value of the type of providers was used to

impute the plan premium, if necessary. This helps

retain the relationship between the two variables.

Three steps were usually used to impute items

within each group:

• Data preparation—In this step, the data were

readied, including such processes as logical edits to

fill in results, determination of the set of cases that

required imputation for the group of items, and

normalization of items to the same basis, as

required. For instance, such items as premiums paid

might be reported for various time periods—weekly,

monthly, or annually. All premium values were

converted to a standard time period before

imputation.

• Selection of donors—The general process to select

donors was the same for all groups. The process is a

type of hot-deck imputation developed by Stiller

and Dalzell (1997), described below.

• Creation of required values—Once a donor was

selected, a value was created for the recipient case.

Sometimes this was as simple as using the donor’s

item response and placing it in the recipient slot. In

other cases, donor information was combined with

information from the recipient in more complex

ways to create the final value. Values also were

edited against other recipient data to create a

consistent set of responses. 

Hot-Deck Process

A hot-deck method was used to select donor cases

to provide information for recipients with items

missing. This is a general class of methods by which

donors and recipients are divided into prediction classes

based on characteristics that predict the value of a given

item. For each recipient with a missing data item, a

donor in the same class for that variable is selected to

provide information for the missing item. For instance,

suppose that one needed to produce values of a plan’s

total premium when the employer did not respond. If

the plan’s premium were predicted by the type of

provider and the State in which the plan was issued,

then each time information on a plan’s premium was

missing, one could randomly select a plan with the

same provider type and State and assign that selected

plan’s premium to the plan with a missing value.

The method is based on the assumption, expressed

in Kalton and Kasprzyk (1986), that the value of the

missing response can be approximated by a regression

model based on n parameters with an error Œ, with

E[Œ] = 0. Thus if y is the missing value, y can be

written as:

According to the model, the item being used from

the donor has the same expected value as the recipient

item being imputed. For the example above, two

parameters—State and type of provider—predict y, the

respondent’s premium. All plans in the same cell

determined by type of provider and State have the same

expected premium.

A problem arises when there are no donors in a cell

with a recipient. In this case, one needs to determine

the best cell from which to select a donor. Many times,

in practice, the person performing the imputation

simply collapses two or more cells that have similar

expected values of the variable to be imputed. The

researcher in this case is using predictive mean

matching (Little, 1988). This is a method for selecting

a value for a recipient from another donor cell with the

same or very similar expected value. Of all cells, the

donor cell selected has an expected value of the item to

be imputed that is the closest to the missing value for

the recipient. In terms of expected values, the chosen

cell would be termed the “nearest neighbor,” among all

cells that contain actual donors, to the cell in which the

recipient lies. For the premium example above, if there

is no plan with the same provider type and State as the

recipient, then the default might be to find a donor in a

neighboring State with the same type of providers.

The general method of selecting donors to provide

information for imputation in the MEPS IC is similar

S

3

y =

i=1

n

aixi + Œ
4

to that described in Little (1988). Developed by Stiller

and Dalzell (1997), it is a simple automated method to

find a very close neighbor to the case with missing data.

The method has the following steps:

• Determine the variables to use to define cells for a

hot-deck process for a particular item.

• Rank the variables from least important to most

important.

• If collapsing must occur, determine the point (going

up the list of variables from least important to most

important) at which collapsing is no longer

acceptable.

• Divide the variables into two groups. Class variables

are those that cannot be collapsed. Sort variables can

be collapsed. 

• Break the file of all respondents that are either donors

or recipients for the item to be imputed into separate

files determined by the class variables. Recipients

within these subsets can receive data only from

donors within the same subset. Thus, donors and

recipients always have the same values for the class

variables.

• Using the sort variables, sort each file defined by the

cross of class variables so that the most important

variables come first. A recipient’s donor generally

will be the first donor on the file above it in the sort

order. Because the file is sorted from most important

to least important, that donor will usually have all sort

variables in common. If not, the values that do not

match will be the least important variables.

Except for restrictions applied to keep from using

the same donor too many times and cases in which there

are multiple recipients in a row on the file, the donor

selected is the nearest donor on the file to the recipient.

(For more detail, see Stiller and Dalzell, 1997.)

Again using the example of missing premium

information, suppose it were decided that type of plan

was the most important variable and that a plan from a

different State would be an acceptable donor if it had the

same provider type. Provider type would be a class

variable and State would be the sort variable. A separate

file of cases with each different provider type would be

produced. One would then process through each file

individually, continuously updating the values of the

current or nearest donor available in that portion of the

file. When a case needing imputation arises, the donor

currently available at that point is selected. If there is a

large ratio of donors to recipients, as is generally the

case, most recipients will be sorted together with donors

that match on all or most variables. Because of the sort

from most to least important, if the donor and recipient

do not match completely, then the matching fails

(collapsing occurs) on the least important variables first.

The donor currently available will be the best or nearbest match in the set.

Process for Each Group of

Variables

This section of the report covers each of the groups

of variables that were imputed for the MEPS IC. For

each group, the variables to be imputed are shown with

their questionnaire number. (The questionnaire is given

in Appendix A.) Any needed preprocessing is described,

sort variables used in the selection of all donors for the

individual variables within the group are given, class

variables used for imputation are described, and the stepby-step process used to create values for the recipient

from the donor information is given. 

All logical edits are assumed to have been performed

before the imputation takes place. Thus, for instance, if a

respondent gave the total number of part-time employees

in question D1b as zero and did not fill in how many

were eligible or enrolled, these values were automatically

set to zero. Because of this assumption, logical edits in

process descriptions are not discussed.

Throughout the process, standard definitions of a

responding establishment and responding plan are

assumed. An establishment was considered a respondent

if it answered either that it did or that it did not provide

insurance for its employees and, if the establishment did

provide insurance for some of its employees, it provided

information on at least one of its plans. Responding

plans are defined as those for which information was

provided on at least one of the following items for the

specific plan:

• Type of providers, question B2.

• Gatekeeper required, question B3.
5

• Purchased or self-insured, question B4.

• Plan enrollment, question B10b.

• Premiums and employer and employee contributions,

question B11.

First Group of Variables

The questions in the first group are shown below,

along with the applicable questionnaire number

(Appendix A).

D1a Total employees, total eligible, total enrolled.

D1b Total part-time employees, total part-time

eligible, total part-time enrolled.

D1d Are retirees offered insurance? 

D1d If retirees are offered insurance, which groups 

are eligible?

Sort Variables

The sort variables are shown in Appendix B. In the

order in which they are used, they are: age of firm,

industry division group, industry division, Firm Size

Class I, and establishment size. 

Class Variables

Since this imputation applies only to establishments

that offer health insurance, the donor set is immediately

limited to the subset of respondents that offer health

insurance. Another variable, Firm Size Class 2, is also

used as a class variable to subset the population for

imputation of this group of variables. This variable

divides the firms that own the establishments into two

groups based on the total employment of the firm

(Appendix B). Using a firm size variable as a sort-order

variable and a second firm size variable with fewer

categories as a class variable allows the selection of a

similar firm size class as a “nearest neighbor.” However,

the selection can only be made in the range of firm size

classes defined by the variable Firm Size Class 2 (see

Appendix B). Using two related variables in this way

permits some use of other values of a given variable for

the donor but sets a limit on those values by using a class

variable with similar characteristics. This is done often

in the MEPS IC imputation process. Industry is also

often used as a sort variable and certain groups of

industries as a class variable. This means that a donor

can have a different industry from the recipient but must

belong to the same larger industry group.

Process

If necessary, the total number of employees was

taken from the Standard Statistical Establishment List

produced by the Census Bureau. This file, which

contains a list of almost all private-sector business

establishments within the United States, is used as the

sampling frame for most Census establishment surveys.

It is compiled each year and gives the total employment

for the establishment as of the second week in March.

Once the total employment is available, the

remaining variables in question D1a are imputed in

order, using a separate imputation run for each. To

impute total eligible employees, a donor for each

recipient is selected from the set of all donors for this

group (which is the set of respondents that completed all

questions in the group). Two ratios are calculated from

the donor: eligible employees to total employees and

eligible employees to enrolled employees. If the

recipient has reported the total enrolled, the eligible-toenrolled ratio from the donor is applied to obtain the

total eligible. In most cases, when the recipient has not

reported the total enrolled, the eligible-to-total ratio is

applied to the recipient value of total employment to

obtain an estimate of the total eligible. The value of the

total enrolled is then imputed by selecting a donor,

calculating the ratio of the donor’s total enrolled to total

eligible, and applying this ratio to the recipient’s total

enrolled eligible employees.

Imputation is continued with the information on

part-time employees, D1b. This is calculated by

selecting another donor, calculating the ratio of the

donor’s part-time to total employees, and applying this

ratio to the recipient’s value of total employees. Total

eligible and enrolled part-time employees are calculated

using a process parallel to that used to calculate total
6

eligible and enrolled employees: that is, ratios from the

donors are applied sequentially, starting with the value of

total part-time employees. 

Because of the need for consistency in the data,

there are minor differences in the process of imputing

eligible and enrolled part-time employees and the

process of imputing the total number of eligible and

enrolled employees. Given that total values have been

produced, these values create limits on the sets of values

that part-time results can be. 

• The two values, eligible and enrolled part-time

employees, are both automatically set to zero if the

number of part-time employees is imputed as zero.

• The donor set for eligible part-time employees is

further limited to only those donors that reported

having part-time employees.

• The donor set for enrolled part-time employees is

limited to those donors that have eligible part-time

employees.

• Each value for part-time employees is limited. These

values cannot be set to a value greater than the

similar value for all employees (question D1a). For

instance, the value of enrolled part-time employees

for a recipient is bounded by the value of the total

number enrolled, which is imputed earlier in the

process. Thus, to impute the number of enrolled

part-time employees, one selects a donor and

multiplies the recipient’s number of part-time eligible

employees times the donor’s ratio of part-time

enrolled employees to eligible part-time employees.

This result is then checked against the recipient’s total

number of enrolled employees. If the imputed

number of part-time employees enrolled is greater

than the total, then the number of part-time enrolled

is decreased to the same value as the total number

enrolled.

The final imputation for this group of questions

relates to the provision of health insurance to retirees,

D1d. This is a two-part question that requires

confirmation that the establishment offers health

insurance to retirees before the second part of the

question is completed. Both parts of the question are

imputed in a similar manner. First, from the set of

donors using the standard sort and class variables, a

donor is selected to provide a yes/no response to all

recipients that require an answer to whether insurance is

offered to retirees. Second, for all recipients that

answered yes to the first part of the question, including

those that had a value of yes imputed for the first part of

the question, an answer is imputed to tell what groups of

retirees are offered health insurance, under 65 years

and/or 65 years and over. Donors for this last step are

limited to donors for the group that offers health

insurance to retirees.

Second Group of Variables

The question in the second group deals with type of

providers (questionnaire number B2 in Appendix A).

Sort Variables

The sort variables, shown in Appendix B, are Census

Division, Firm Size Class 1, industry division group,

industry division, and State.

Class Variables

The class variables are whether there is a deductible

(part of question B14) and Firm Size Class 2.

Process

The process is simple and direct. The donor set is

all plans with a response to question B2 (type of

provider). Recipients are all responding plans with a

missing value of B2. The value for the recipient is

determined by direct substitution of the donor value.

Second Group of Variables for Nonresponding

Plans

Sort Variables. The sort variables are Census

Division, Firm Size Class 2, Firm Size Class 1, industry

division group, industry division, and State.

Class Variable. The class variable is the number of

plans for which information is requested.

Process. For the standard second group imputation,

the value of type of providers, question B2, was imputed
7

for responding plans. However, type of providers, B2,

also can be imputed for nonresponding plans. This

imputation is at the establishment level rather than the

plan level. When establishments reported for some but

not all the plans for which they were required to provide

information, this imputation provides an answer to

question B2 for the missing plans.

The donor set is the set of all establishments that

reported for all the plans required by the survey. An

establishment from this group was selected for each

recipient establishment that was missing at least one

plan. (Note that many establishments reported on some,

but not all, plans. Thus an establishment could have

reported B2 values for some plans and imputed values

for other plans.) A recipient and donor both had to

have reported the same number of plans (edited results

of question A1). 

The plans for both donor and recipient were sorted

in this order: question B2 equal to 2, then B2 equal to 3,

then B2 equal to 1, then any missing plans. This sets a

one-to-one correspondence between the required donor

and recipient plans. The plans from the recipient

establishment without a value of question B2 were

assigned the value of the corresponding donor plan.

Third Group of Variables

The questions in the third group are shown below,

along with the applicable questionnaire number

(Appendix A).

B3 Does plan require referral to a specialist?

B9 Beginning of plan year.

Sort Variables

The sort variables, shown in Appendix B, are

Census Division, State, and Firm Size Class 2.

Class Variable

The class variable is type of providers (Question

B2).

Process

Donors are all plans collected with the two

questions (B3 and B9) completed. Recipients are all

responding plans missing at least one of the two

questions in the group. There is a separate donor

selection for each question. Thus, a recipient missing

both answers would likely have results from two

different donors. 

Note that, for this group, both donors and

recipients are plans rather than establishments. This is a

result of the questionnaire design. The questions in

Section B of the questionnaire must be answered for

every plan offered by an establishment, so answers and

the imputation of these results are normally at the plan

level. Questions in other sections are answered once for

each establishment and thus are worked with at an

establishment level.

Fourth Group of Variables

The questions in the fourth group are shown below,

along with the applicable questionnaire number

(Appendix A).

B11a Total single premium only.

B11b Does employer offer family coverage? 

B11b Total family premium only.

Sort Variables

The sort variables, shown in Appendix B, are

Census Division, State, Firm Size Class 1, and industry

division group. In addition, industry division is used for

imputation of total single premium and whether

employer offers family coverage, and total single

premium is used for total family premium imputation

only.

Class Variables

The class variables are provider type and Firm Size

Class 2.

Process

The imputation is done in three parts: first, total

single premium; second, whether the employer offers

family coverage; and last, total family premium. The

donor set for total single premium is all plans that have

a reported value for both total single and total family

premiums (questions B11a and B11b in Appendix A).

When a donor is selected for a recipient missing the

total single premium, the method of calculating the

recipient’s premium depends on whether the recipient

has a reported value of the total family premium. If
there is a value, then the recipient’s single premium is

the ratio of the total single to total family premium for

the donor multiplied by the recipient’s value of the total

family premium. If the recipient has no family

premium, then the donor single premium value is

imputed for the recipient.

The donor set for whether an establishment offers

family coverage for the plan is the set of all plans that

either stated they do not have family coverage or

reported both a total single and a total family premium.

The recipient set is those plans for which it is unknown

whether family coverage is offered (all plans that did

not enter information for any item in question B11b). If

a plan reported a premium or contribution, it is assumed

that family coverage is offered. If a plan reported that it

does not offer family coverage, then it is known that

family coverage is not offered. The imputation is a

simple yes/no from the donor.

The donor set for the total family premium is the set

of all plans that have a reported value for total single

premium and total family premium. The recipients are

all plans for which family coverage is offered that are

missing the total family premium. For this imputation,

the file is sorted only by the value of total single

premium. The single premium is available for all plans

at this time, since this variable has just been imputed.

Single premium is the sort variable for family premium

because there is a very high negative correlation

between the single premium and the ratio of family

premium to single premium: the higher the single

premium, the lower the ratio of family to single

premium. Because of this correlation, the recipient’s

value of total family premium is calculated as the donor

ratio of total family premium to total single premium

multiplied by the recipient’s value of total single

premium. Using the ratio calculation and this particular

sort variable maintains the relationship among the

values of the single premium, family premium, and their

ratio and produces a pair of imputed family and single

premiums that parallel the reported pairs of values.

Once values are imputed, a last editing step is done

to provide final values for cases that have reported

values for either the employee or employer contribution

in questions B11a or B11b but not both. The process is

the same if either of the contributions is given. If the

employer contribution is reported, the employee

contribution is the difference between the total value

imputed and the employer contribution. If the employer

contribution is greater than the imputed value, the

employee contribution is set to zero and the total

premium is set to the employer contribution.

Fifth Group of Variables

The question in the fifth group is shown below,

along with the applicable questionnaire number

(Appendix A).

B4 Is the plan self-insured?

Sort Variables

The sort variables, shown in Appendix B, are

industry division group, industry division, Firm Size

Class 1, State, same firm.

Class Variables

The class variables are Firm Size Class 2 and “not

all HMOs.”

Process

To impute this variable requires the imputation of

two variables, one at the establishment level and one at

the plan level. A variable is created for each

establishment that indicates if there are any self-insured

plans at the establishment level. The class variable used

for this imputation is whether the establishment offers a

plan that is not an exclusive provider type plan. This is

done because HMOs have been found to be much less

likely to be self-insured. The donor set for this variable

is the set of all establishments that provided an answer

to question B4 (indemnification type) for all their plans.

The recipients are those establishments for which the

self-insured status cannot be determined. These are

respondents that did not report in question B4 that at

least one of their plans is self-insured and did not

answer question B4 for all their plans.

Once it is established whether an establishment

offers at least one self-insured plan, plans for

establishments are logically edited. If establishments

have a self-insured plan and only one plan, the plan is

automatically self-insured. If establishments have no

self-insured plans, all plans are assumed to be purchased

plans.

8
9

For the remaining plans, information on self-insured

status is imputed from establishments that have at least

one self-insured plan. The donor set is the set of plans

from establishments that have at least one self-insured

plan. In order to perform this imputation, another class

variable is added to the process. This is the provider

type for the plan, the answer to question B3. Plans with

different answers to question B3 have significant

differences in the probability of being self-insured.

Sixth Group of Variables

The questions in the sixth group are shown below,

along with the applicable questionnaire number

(Appendix A).

B11a Employer and employee contributions for single

coverage.

B11b Employer and employee contributions for family

coverage.

Sort Variables

The sort variables, shown in Appendix B, are

industry division group, industry division, Firm Size

Class 1, Census Division, and State.

Class Variables

The class variables are Firm Size Class 2 and

provider type.

Process

Employer and employee contributions were imputed

at the firm level and industry division level. This means

that employee contributions in any establishment within

the same firm level within the same type of business

were assumed to be highly correlated. To accomplish

the imputation, first the average employee premium

contributions for family and single coverage across all

reporting establishments within the same industry

division within the same firm level were calculated. For

example, if Apex Corporation had both a manufacturing

and retailing subsidiary, they were treated separately. If

an establishment within the same firm and industry

division failed to report this information and other

establishments within the same firm and industry

division reported, the average employee contribution

percentage for the former was assumed to be the same

as for the latter. Thus, for Apex, if a manufacturing

establishment was missing employee contributions, this

information was imputed, if possible, using the average

of other Apex manufacturing establishments.

For firm/industry division combinations that did

report for some establishments and could not be

completed in this fashion, a donor firm/industry

division combination was selected using the standard

hot-deck procedure, using the sort and class variables

described earlier. In this process, the State for a multiState firm was set equal to missing. 

The donor’s average employee single and family

contribution percentages were applied to the single and

family total premiums for all recipient plans to produce

the employee contributions for the recipient plans.

Once these were calculated, employer contributions

were obtained by subtraction from the appropriate total

premium.

Seventh Group of Variables

The question in the seventh group is shown below,

along with the applicable questionnaire number

(Appendix A).

B10b Total active employees enrolled in an individual

plan.

Sort Variables

The sort variables, shown in Appendix B, are

industry division group, industry division, Firm Size

Class 1, and State.

Class Variables

The class variables are plan count (number of plans

offered to employees) and Firm Size Class 2. 

Process

Imputation for this question was limited to

establishments that had two or more plans and failed to

report for at least two of the plans for which information

was requested. All other plans with missing data on

total active employees enrolled in a plan can be assigned

a value equal to the total enrollment for the

establishment, question D1a, less the values for

question B10b for the remaining reported plans. 
Donors for the process were establishments that

offered employees at least two health plans and

responded to this question for all required plans. To

start the process, donors and recipients were assigned a

value called plan count. This was the number of plans

the respondent indicated it provided to employees. Plan

count was always greater than one for this set.

Selection of donors for recipients was done using

the standard method with the sort and class variables

given. This means that donors and recipients had the

same value of plan count.

Each plan within a selected donor establishment

was assigned the proportion of the establishment’s total

active enrollment that the plan represented (question

D1a). If a private establishment offered more than four

plans, it was asked to report for the three largest plans

plus a fourth selected at random from the remaining

plans. 

The plans within each donor and recipient

establishment were sorted by the values for reported

active enrollees. Proportions were assigned from donor

to recipient by the sort order. 

If the recipient failed to report the active enrollment

for all plans, then enrollment for each of the recipient’s

plans was calculated as the imputed proportion of the

establishment’s active enrollment. If the recipient

reported for some but not all plans, the remaining

enrollment in the recipient establishment was distributed

among the unreported recipient plans using the relative

sizes of the proportions assigned from the donor. For

example, suppose there were three plans in an

establishment with a total active enrollment of 200 and

the respondent gave the active enrollment for the first

plan as 100. Further, suppose the donor’s three plans

represented 50, 30, and 20 percent of the donor’s total

active enrollment. The remaining 100 active enrollment

would be distributed in a ratio of 3 to 2, based on the

relative relationship between the two smallest donor

plans.

This method attempted to create a distribution of

enrollees for the recipient establishment’s plans that was

similar to that of another establishment. This was done

by considering all the plans from both donors and

recipients, each with an equal number of plans, in a

single step.

Government Imputation Process

In the imputation process for sampled governments,

the same data items are imputed as for the private-sector

establishments. The process is similar to that for the

private sector. The only differences are the sort and

class variables used. For government case imputation,

the same sort variables are used for all data groups.

These are, in sort order, Census Division, State, and

government employment size. No class variables are

used to describe the government. The only class

variables used are the specialized variables for plans that

apply to a particular imputation group. For instance, for

Group 4 in the private sector, Firm Size Class 2 and

provider type are class variables. For Group 4

government case imputation, Firm Size Class 2 is

dropped but provider type is kept. This is because size

would refer to government and is not used, but provider

type is a characteristic of the plan and these variables

are used.

Benchmarks

Review of the results of the imputation and its

effects on estimates made with these critical data items

was very thorough. After the data were imputed, results

were edited to determine if the process had produced

inconsistencies in the data. Cell averages were

examined before and after imputation to determine if

key factors had been missed. During the actual process,

problems were found and corrected in this way.

After internal review of the results, estimates were

made and benchmarked to outside sources. One key

part of this process involved checks against results of

the 1994 National Employer Health Insurance Survey

(NEHIS), conducted by the National Center for Health

Statistics (NCHS). NEHIS was a one-time survey

whose purpose, sample design, and questionnaire were

similar to those of the MEPS IC (National Center for

Health Statistics, 1997). There were some differences in

total employment and numbers of establishments, which

were easily explained by the number of years between

the two surveys and slight differences in the survey

frames used. However, the relative numbers

benchmarked quite well and had few significant

statistical differences. For instance, the estimate of the

percent of private-sector establishments that offered

health insurance was 52.8 percent for the MEPS IC and

51.6 percent for the NEHIS. Likewise, the percent of

workers eligible for health insurance was 80.9 percent in

the MEPS IC and 81.7 percent for the NEHIS. A more

thorough review of the comparison of the two surveys

and some individual numbers from other sources can be

10
11

found in the review written by Thorpe and Florence

(1999).

References

Cohen SB. 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.

Cox BG, Cohen SB. Methodological issues for

health care surveys. New York: Marcel Dekker; 1985.

Kalton G, Kasprzyk D. The treatment of missing

survey data. Survey Methodology 1986;12(1):1-16.

Little RJA. Missing data adjustments in large

surveys. Journal of Business and Economic Statistics

1988;6(3):287-96.

National Center for Health Statistics: Employersponsored health insurance: State and national

estimates. Hyattsville (MD); 1997. DHHS Pub. No.

(PHS) 98-1017.

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.

Stiller J, Dalzell D. Hot-deck imputation with SAS

arrays and macros for large surveys. Proceedings of the

10th Annual NESUG Conference; 1997. North East

SAS Users Group. p. 709-14.

Thorpe K, Florence C. An examination of the

Medical Expenditure Panel Survey Insurance

Component [unpublished report]. Rockville (MD):

Agency for Health Care Policy and Research; 1999.
Appendix A. Establishment Questionnaire

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13
14
15
16
17
18

Appendix B. Definitions of

Selected Variables

Firm Size Class 1

1 if firm employment = 0-5

2 if firm employment = 6-24

3 if firm employment = 25-99

4 if firm employment = 100-999

5 if firm employment = 1,000 or more

Firm Size Class 2

1 if firm employment = 0-249

2 if firm employment = 250 or more

Establishment size

1 if establishment employment = 0-10

2 if establishment employment = 11 or more

Industry division (Standard Industry

Code—SIC)

Agriculture if two-digit SIC = 01-09

Construction if two-digit SIC = 15-17 

Retail trade if two-digit SIC = 52-59

Mining if two-digit SIC = 10-14

Finance, insurance, and real estate if two-digit SIC =

60-67

Wholesale trade if two-digit SIC = 50-51

Manufacturing if two-digit SIC = 20-39

Transportation, communication, and utilities if two-digit

SIC = 40-49

Services if two-digit SIC = 70-89

Industry division group

1 if industry division = agriculture, construction, or

retail trade

2 if industry division = manufacturing, transportation,

communication, utilities, or services

3 if industry division = mining, finance, insurance, real

estate, or wholesale trade

Firm age

1 if age = 0-16 years

2 if age = 17 years or more

Census Division

New England if State = ME, NH, VT, MA, CT, RI

Mid-Atlantic if State = NY, NJ, PA

East North Central if State = OH, IN, IL, MI, WI

West North Central if State = MN, IA, MO, ND, SD,

NE, KS

South Atlantic if State = DE, MD, DC, VA, WV, NC,

SC, GA, FL

East South Central if State = KY, TN, AL, MS

West South Central if State = AR, LA, OK, TX

Mountain if State = MT, ID, WY, CO, NM, AZ, UT, NV

Pacific if State = WA, OR, CA, AK, HI

U.S. Department of Health and Human Services

Public Health Service

Agency for Healthcare Research and Quality

2101 East Jefferson Street, Suite 501

Rockville, MD 20852

Official Business

Penalty for Private Use $300

AHRQ Pub. No. 00-0039

June 2000

ISBN 1-58763-004-4

BULK RATE

POSTAGE & FEES PAID

PHS/AHCPR

Permit No. G-282