2

Updates to the Medical Expenditure Panel Survey Insurance Component List Sample Design, 

2004 

John Sommers 

November 2004 

ABSTRACT

This paper discusses changes in the sample design of the MEPS-IC. Among the changes are an 

improved stratification and new allocation to States. These improvements should improve all 

current estimates and also allow estimates to be made for the private sector for all states. 

John Sommers 

Mathematical Statistician 

Center for Financing, Access, and Cost Trends 

Agency for Healthcare Research and Quality 

540 Gaither Road 

Rockville, MD 20850 

E-mail: JSommers@ahrq.gov 
3

Background

The Medical Expenditure Panel Survey (MEPS), Insurance Component (IC), is an annual, 

National survey of business establishments (locations) and governments sponsored by the 

Agency for Healthcare Research and Quality (AHRQ) and conducted by the United States 

Census Bureau. The survey is designed to collect information on employer-sponsored health 

insurance, such as, whether insurance is offered and if so, enrollments, premiums, employee 

contributions and plan characteristics. Information about the establishment or government, such 

as, size and workforce characteristics are also collected to allow for modeling of results and 

estimation by different business or government characteristics. 

The MEPS-IC has two major purposes. The first is to collect information from employers of 

household respondents to the MEPS Household Component (HC), a household survey collecting 

information on health expenditures, use, insurance and demographics of the non institutionalized 

population of the United States. This sample of employers of the household respondents is 

known as the household sample (Cohen, 1996). This data is primarily used for modeling and is 

not collected annually. Instead it is collected on a periodic basis. The second purpose of the 

survey is to produce national and state level estimates of enrollments, premiums and 

contributions for a variety of categories, such as, industry, firm size and average payroll per 

employee. This requires a random sample of business locations and governments, which for the 

MEPS-IC is referred to as the list sample, because it is selected from lists maintained by the 

Census Bureau. The original list sample, designed for the first MEPS-IC to and used for 

collection of data for the year 1996, (Years in this document will always refer to the year of the 

data, not year of collection. Collection of data crosses years.) supported estimates for the 40 

largest states and the nation as a whole. However, in subsequent years, sample sizes for the 20 

smallest states were changed annually so that although there were published estimates for only 

40 state in a given year, all 50 states and the District of Columbia would have state level 

estimates at least once every four years (Insurance Component Technical Appendix). 

Original List Sample Design

The original list sample design and allocation (Sommers, 1999) considered governments and 

private sectors together in order to yield allocations which produced estimates with a desired 

level of error at the state and national level for the entire set of employers, both public and 

private sectors. This basic design was used with little change through the 2002 survey. This 

design will be called the old, original or current design within this document. The updated 

design which will be completely in place for the 2004 survey will be called the new design. 

After the original allocation took place, the sample was then allocated within each state between 

the public and private sectors based upon each sector’s proportion of total state employment. 

Within each sector these allocations were further allocated to individual strata. This design 

allowed for sufficient sample in the largest states to support national and state estimates, while 

smaller states below a certain size had minimum sample sizes assigned. These minimum sample 

sizes were generally much larger than the sample otherwise required to support reliable national 

estimates. However, the minimum sample sizes were required to support estimates for the 

individual states. 
4

Within each state, strata were formed based upon employment sizes and sequential sample 

selection methods were used to select the final samples. Because the public and private sector 

lists of employers were maintained on two different lists, sampling for public and private sectors 

was done separately once allocations were determined. 

A unique feature of the MEPS-IC list sample is a restriction on the expected numbers of 

establishments in the sample for a private sector firm. (A firm is an entity which controls one or 

more business establishments or locations. For instance, General Motors is a firm and an 

individual General Motors plant location is an establishment.) The reason for this restriction was 

to limit the amount of collection burden on an individual firm, since most firms require 

collection of information for all their establishments at a central location. Because the MEPS-IC 

required collection of both the household and list samples of establishments and the sample of 

establishments in the household sample was predetermined, the restriction on firm size took 

place only on the list sample and was very strict. This restriction significantly raises the design 

effect of the list sample estimates. (Sommers, 1999 and Kish, 1965) 

New Conditions and Information that Allow Updating the Sample Design

Considerable knowledge has been gained since the 1996 MEPS-IC that can be used to improve 

the sample design. During the same time period, operational conditions of the survey have 

changed. These changes in operational climate also allow implementation of methods that can 

improve the sample design. Following are the key new factors which support improvement in 

the sample design of the survey: 

• Estimates of variance components have been made for a variety of important variables. 

These estimates can be used to design new strata and test new sample design proposals. 

• Extensive modeling has been done to gain knowledge of what ancillary information is 

available for sampling units and best predicts survey outcomes, such as premiums and 

enrollment rates. Such information can be used to develop better strata boundaries. 

• The decision was made to suspend annual collection of the household sample. 

• Estimates of the private sector are required for states, not combined estimates of the set of 

both private and public sector employees within the state. 

Combinations of these items have motivated the following changes to the sample design: 

• The first two have allowed for the development of a new stratification and allocation 

sample scheme for the survey. 

• The third and fourth items have allowed budget for extra sample to provide for minimum 

samples for the each state for the private sector alone rather than the combination of 

public and private employers that was originally done, thus allowing estimates for all 

states for the private sector alone. 
5

• The third item allowed changes in the restrictions for the maximum sample allowed per 

firm. Because there is no longer an annual household sample, this allows restrictions on 

the list sample to be loosened without affecting the overall burden on individual firms. 

• The last item allowed development of a new government sample, totally independent of 

any private sector design or allocation. There is no requirement for an over sample of 

governments for smaller states. 

The following sections discuss specific changes in the MEPS-IC sample design that will result in 

an updated design by the year 2004. Included are descriptions of the changes, how they were 

developed and the improvements in sampling errors that are expected as a result of these 

changes. This design will be called the new sample design in this document. 

Allocation to States: Private Sector

Allocation of private sector sample to states for the new design was done in a manner similar to 

the allocation of total sample, government and private sector, for previous surveys. First, the 

proportions of national payroll, employment and number of establishments were calculated for 

each state. For each state these proportions were averaged to give an average proportion across 

the three items. Using these average proportions, 17,000 responding sample units were allocated 

proportionally to the states. Any state with less than 560 units then had its allocation increased 

to 560 units. (Note that in the samples prior to 2003, each state was allocated a responding 

sample of 600, but this included governments. This new allocation is for the private sector only 

and thus has a smaller minimum sample size than the old allocation. (Sommers, 1999) ) 

This initial sample allocation could not be afforded under the IC budget. To reduce costs the 

expected responding sample size for the smallest 11 states was reduced to 520. This new 

allocation should give national results at least comparable to the current sample if no other 

changes were made. For instance, the relative standard error for estimates of the percent of 

establishments that offer health insurance and the average single premium would both be about 

0.5%. The slight reduction in sample for the smallest states would still allow the survey to meet 

this goal since the larger sample in these states has little effect on national estimates due to the 

very small portion of the nation that these states represent. 

Assuming the same variance structure within each state, the overall sampling of states in this 

manner, with the resulting unequal weighting, creates a 20% increase in standard errors for 

national estimates compared to a proportional allocation with equal weights. Note that for the 

current sample, (Sommers, 1999) where only 40 states had a minimum sample size, there was 

only a 10% increase in standard errors in national estimates due to the over sampling. However, 

the overall sample size was smaller. This increase in sample size offsets the extra inefficiency 

caused by the increase in over sampling of small states. With the extra sample, if no other 

changes were made to the sample design except for the change in allocations to the states, the 

errors for national estimates would be very comparable for both allocations. 

The new allocations to states are presented in Appendix A. 
6

Allocation within States: Private Sector

In order to reduce sampling error in IC estimates, new sampling strata were developed. The 

original strata boundaries were developed by Westat as part of the work done on the 1993 

National Employer Health Insurance Survey, a precursor to the IC. These strata were based 

upon firm and establishment employment sizes (Marker, 1996). While employment sizes 

correlate well with such important variables as whether an establishment offers health insurance, 

the percentage of employees enrolled, and the average premiums and contributions towards 

health insurance made by employers and employees, over the years information that has been 

gathered has shown several other independent descriptive variables also correlated with these 

outcome variables. Among these variables are State, age of firm, industry, and average payroll 

of an establishment. AHRQ decided that these variables also should be considered in production 

of IC strata along with the important employment size variables. 

The old stratification of the IC sample was done by simply crossing categories of firm size and 

categories of establishment size to create 14 strata. However, due to the many cells that would be 

created by crossing the categories of 6 different variables, another method was needed to 

consider all the new variables in creating strata. Such a method was required to limit the 

number of strata used. The method chosen was to use the set of variables to create models that 

would predict the probability that an establishment would offer health insurance and the 

expected percentage of employees at the establishment that would enroll in insurance if offered. 

It was assumed that because the models were based upon a large number of variables, each 

which correlated with the key variables, that the predicted values would correlate better with the 

final results than just the employment size classes alone. 

To test this hypotheses, three years of data were used, 1998-2000. Using 1999 data, logistic 

regressions were run, the first to model the probability that an establishment had health insurance 

and the second to model, for those with health insurance, the probability that an employee would 

enroll. The models were used to predict values for the entire frame for the 2000 survey year. 

Using the ‘cum square root f rule’ (Cochran, 1977), the 2000 frame was broken into 6 strata 

based upon the establishment’s probability of offering insurance. 

After this was done, the 3 strata which contained establishments with the highest probabilities of 

offering health insurance were broken into substrata using the ‘cum square root f rule’ applied to 

the expected number of enrollees in health insurance. The stratum which contained the 

establishments with highest probability of offering health insurance was broken into 6 substrata, 

the stratum which contained establishments with the second most likely chances of offering 

health insurance was broken into 3 substrata and the stratum that contained establishments with 

the third highest probability of offering health insurance was broken into 2 substrata. The 

reason for the decreasing number of substrata was that as the probability of offering heath 

insurance decreases, the range of sizes of establishments in the strata based upon this probability 

decreases. Thus, the 3 strata with establishments which have the least likely chance of offering 

health insurance, consist of only small establishments which do not require substratification for 

the size of the potential enrollment. On the other hand, the expected number of enrollees varies 

considerably within the stratum with establishments with a high probability of offering health 
insurance. Breaking this stratum into substrata assures that the variance across the total 

enrollment within each substratum will be smaller which is highly desirable. 

This created 14 strata of establishments for the year 2000 frame based upon 1999 data. Using the 

models based upon 1999 data, predictions were produced for the establishments on the frames 

for the year 1998 and 2000. Using each of these predictions, the establishments on these frames 

were placed into the 14 strata developed using 1999 models. The establishments in the samples 

from the years 1998 and 2000 were used to calculate estimates of variance components for each 

of the strata.

Using these variance components and variance components calculated using the same data for 

the old strata, and counts from the 2000 frame, errors for a variety of allocations for the new and 

old strata could be evaluated using the following formula: 

N

n

n

N

V h

h h

h

h

h

2

2

∑ 1− ⎛

⎝

⎜ ⎞

⎠

⎟

where Nh is the size of the stratum, nh is the sample size for the hth stratum and V is the 

variance within the hth stratum. Assuming that a typical state has a distribution of establishments 

similar to that of the entire country, results could be produced for any allocation using stratum

sizes which are available from the frame and values of the components of variance which were 

estimated using the sample data. 

h

2

Estimates of standard errors were made for two variables, the total number of establishments 

offering health insurance and the total number of employees enrolled. These variables were 

chosen because they represent the two different types of estimates made with IC data. The first 

is driven by numbers of establishments. Such estimates are dominated by the large number of 

small establishments on the frame. The second is dominated by establishments with large 

employments and enrollments. The former requires a large sample of small establishments and 

the latter requires that the sample be dominated by large establishments. These two opposite 

types of variables require a sampling strategy that in some way balances the sample between 

numbers of establishments and numbers of employees. 

Several methods have been recommended to accomplish this type of allocation (Cochran, 1977). 

One is to produce variance components for each stratum which are weighted averages of the 

variance components for each variable for the stratum. A second is to assign the allocation to the 

cell as a weighted average of the optimal allocations for the stratum for the individual variables. 

Another method, used for the 1993 National Employer Health Insurance Survey, a one time 

survey with similar data needs as the IC, is to allocate sample to strata based upon the measure of 

size equal to the square root of the employment at the each establishment. This allocation tends 

to balance enrollment and numbers of establishments. 

The results for several of the allocation tested are shown in Table 1. 

7
8

TABLE 1 

Standard Errors for National Trial Allocations Using Old and New Strata

Current Strata Proposed New Strata 

 Totals 

Estimated 

Allocation 

Establishment

s Offering 

Health 

Insurance 

Number of 

Enrollees 

Establishment

s Offering 

Health 

Insurance 

Number of 

Enrollees 

Optimal for Number of 

Establishments Offering 

Health Insurance 

15,088 4,626,353 14,025 5,054,792 

Optimal for Number of 

Enrollees 

25,052 840,347 27,173 388,512 

Square Root of Employment 19,862 1,047,248 17,885 772,031 

Average of the Optimal 

Allocations 

17,827 997,305 16,673 471,909 

Current 18,802 978,356 Inapplicable Inapplicable 

The table demonstrates the following. 

• For either strata definition, the optimal Neyman allocations for the individual variables 

are very poor for the other type of variable. This demonstrates the need for a balanced 

allocation. 

• The square root of the employment allocation and the average of the optimal allocations 

both tend to balance the results between the two optimal allocations. The current 

allocation also accomplishes that goal. 

• The optimal allocations using the proposed new strata definitions are across the board 

lower than those for the current strata definitions. This means that the potential for a 

meaningful decrease in variances is possible using the new stratification method. Also, 

the average of the optimal allocations using the new stratification methods is better than 

the current stratification and allocation methods. 

Given the possibilities for improvements, further research was undertaken to find an improved 

allocation given the proposed new strata definitions. As part of this process another variable was 

added to the analysis, the total single employee contribution. This variable was added because 

AHRQ decided that many more estimates were being requested by users that related to 
9

employment than to numbers of establishments. Thus, it was decided to weight the new 

allocation more towards that type of variable. 

One of the results of this analysis was the development of a 15th stratum for the proposed new set 

of strata. This is a certainty stratum of approximately 200 of the largest establishments. Adding 

such a stratum to the new strata had a significant effect on results for the two variables correlated 

with employment. When added to the current stratification definitions, a certainty stratum had 

far less effect. 

After much analysis, a final allocation method was accepted that was a weighted average of 50% 

of the optimal allocation for estimates of the number of establishments that offer health insurance 

plus 25% each of the optimal allocations for the estimates of the total enrollment and total single 

contributions. The decision on the final allocation was based primarily upon the improvement in 

variance of the estimates compared with the current sample. However, some allocations which 

were slightly better than the final choice were rejected because of the percentage of the sample 

required from the largest firms. There was concern that extra burden on these respondents and 

the potential loss in response rate was not worth the risk compared with the slightly better 

forecasted errors from the allocations. 
10

Table 2 gives results for the final optimal allocation for the 3 analysis variables along with 

results for the current sample allocation and the chosen new allocation. 

TABLE 2 

Standard Errors for Allocations Using Old and New Strata

 Totals 

Estimated 

 

Strata, Allocation Method 

Establishments 

Offering Health 

Insurance 

Number of 

Enrollees 

Total Single 

Employee 

Contribution 

New Strata without Certainties, 

Minimum Possible Value 

for Each Variable 

14,025 388,512 1.678 x 108

New Strata with Certainties, 

Minimum Possible Value 

for Each Variable 

14,025 354,813 1.606 x 108

Old Strata, Minimum Possible 

Value for Each Variable 

15,088 840,347 2.746 x 108

New Strata, Proposed Weighted 

Allocation 

16,770 408,658 1.729 x 108

Old Strata, Current Allocation 

 18,802 

978,346 3.535 x 108

The first three rows of the table give the standard error that can be obtained with the optimal 

allocation for that variable with that stratification. No one allocation can reach the minimum 

value for all the variables. However, as one can see in the table, the proposed weighted 

allocation using the new strata gives standard errors that are close to the best possible values for 

each of the variables and better than the current allocation for the total number of 

establishments. The projected improvements in standard errors from the new stratification and 

allocation methods, shown in the fourth row, over the current methods shown in the fifth row, 

are 11% for total establishments offering health insurance 58% for total enrollment and 51% for 

total single employee contributions. 

Appendix B gives the overall percentages of the total establishments, total employment, 

enrollment and sample for each of the 15 strata in the new sample design. 
11

Changes in Restrictions on Maximum Sample Size per Firm

The IC list sample design contains a process which limits the total expected sample of 

establishments that can be selected from an individual firm. This was done to limit the burden 

on individual respondents. The total burden for the IC includes sample from the household 

sample and the list sample. The members of the household are predetermined in the sense that 

they are the employers of respondents to the household survey and AHRQ cannot control this 

sample of employers. On the other hand, the list sample is designed by AHRQ and Census and 

selected by the Census. Thus, the expected sample for a firm within this sample can be 

controlled. Given that the total sample size for the private sector from the combined household 

and list samples is about 44,000 private sector establishments, if samples were selected 

proportional to firm’s share of total employment then, given a total employment of 110 million 

employees in the private sector, a company with over 100,000 employees could expect to have 

over 40 (44,000x100,000/110,000,000) establishments in the sample. 

To avoid these large samples, the sum of the probabilities of selection for establishments within 

the same firm, were limited for the list sample. In order to maintain the same overall sample 

size, the probabilities of selection for establishments from smaller firms within the same strata 

must be increased so the total probabilities of selection within a stratum remain equal to the 

allocated sample size. This leads to the situation that within a stratum, establishments with about 

the same expected values for variables, such as, enrollment, can have different selection 

probabilities and thus different weights. This leads to an increase in the sampling error. 

Given that there was an opportunity to change this restriction on the sample design, an effort was 

first made to determine the effect of the current restrictions on sampling errors. To measure this 

effect, several types of design effects were calculated using SUDAAN, a specialized error 

estimation software for complex surveys. These estimates allow one to measure the effects on 

errors of the several different aspects of a sample design, stratification, clustering, over sampling 

and unequal weighting (Research Triangle Park, 2002). Of particular interest to the current 

problem is the ability to measure the design effect of the sample design with and without unequal 

weighting. 

These effects were run for a variety of variables, such as, total enrollment, average premiums and 

contributions, etc.. While the results varied by variable, in general the design effect which took 

into account unequal weighting within each stratum was twice that when this effect was 

removed. Thus to lower this effect could result in significant improvements in the overall errors 

for many variables. 

After considering the distribution of expected sample sizes per firm, before reduction, a new 

reduction method was developed that the Census Bureau felt would result in acceptable sample 

sizes per firm, that would not place unreasonable burden on firms nor the IC budget. The key 

difference in the two methods is that under the old method, the maximum expected sample size 

for all but two large firms was 10 and some type of downward adjustment was used for any firm 

with an original expected sample size of 2 or more. The new method applies no adjustments to 

firms with expected sample size values less than 3 and allows for a maximum expected number 

of units of 50. Even with these lessened restrictions on the list sample, the actual burdens on 
firms when combined with dropping the household sample, are decreased overall. The two 

reduction methods are shown in Appendix C. 

To assess the possible improvement in sampling errors for this method, the following assessment 

was performed. 

Within each stratum with N establishments in the IC, an independent sample of size n is selected. 

Assuming that each establishment selected has a common mean F and the same variance F2

, 

then if units are selected with equal probabilities then the variance of an estimate of a total for 

this variable is N2 F2

/n. In this case the probability of selection of all establishments is n/N. If 

instead, unequal probabilities pf selection are used and the probability of selection for the ith 

establishment is written as n/(ai*N), then the variance of the estimate is: 

N a

n

i

i

2 2 2

2

∑ σ

 

To compare the variance of this estimate to that of the one with equal probability of selection we 

simply calculate the relative value of the variance of the unequal probability estimate to that of 

the equal probability of selection estimate by taking the ratio of the two variances, 

a

n

i

i

2

∑

To obtain a rough estimate of the differences in variances, the value of 3ai

2

 for the sample of all 

firms with more than 1000 employees was calculated for each method. This was done assuming 

that all the establishments were from the same strata and using the actual values of the expected 

sample sizes before adjustment from the 2003 IC sample. The two adjustment methods were 

applied to the expected samples for the largest firms. This decreased the expected sample sizes 

and increased the weights for these establishments. The expected sample sizes for remaining 

firms were adjusted upwards so that the final expected overall sample size was the original value 

of n. This then decreased the weights for these establishments. Although, these are not the 

actual final adjustments because each state has a different stratum for these firms and the strata 

could have different values of F2

, it was felt that this would give an idea of the relative values of

the sums versus n for the various adjustment methods. 

Using the approach the current method yields a value of 1.966n. The new method has a value of 

1.218n. The relative magnitude of the two values is 1.61. Applying the square root to this value 

yields 1.27. Thus, for estimates for the firms with more than 1000 employees, the new method 

of reduction has approximately 27% smaller standard error than similar estimates where the 

sample was selected using the old method of burden reduction. 

12

a

n

i

i

2

∑
13

While the reduction method is only one of the reasons that weights are unequal, AHRQ feels a 

significant reduction in standard error for national estimates and estimates made for the groups of 

firms above 1000 employees of many variables will be obtained using the new method. 

Government Sample Improvements

In surveys prior to the 2003 IC, the sample of governments consisted of two parts, (1) a set of 

certainties which included all governments with more than 5000 full time equivalent employees 

and (2) a sample of smaller governments that was allocated so that governments in smaller states 

were over sampled relative to those in larger states. This happened because using the old 

allocation method the over sampling for smaller states was applied to both governments and 

private sector establishments. This was done because the goal was to produce quality estimates 

for the set of all employers in the state, public and private sector combined. Once each state was 

given an overall allocation of private sector establishments and non certainty governments, the 

state’s total allocation was broken proportional to the relative size of the private sector and non 

certainty governments in each state. Because small states were over sampled relative to large 

states, then non certainty governments were also over sampled relative to the same governments 

in large states. Assuming similar variances among non certainty governments within each of the 

states, this over sampling would yield a less efficient sample for estimates at a larger geographic 

area, such as, Census Regions, than a proportional allocation across all non certainty government 

units within all states in the area. 

This over sampling was necessary if there was a demand for estimates for the combined public 

and private sector universe within each state. However, users have rarely requested an estimate 

of the combination of public and private sector employers. Users request estimates for the 

private sector only and some have requested state estimates for governments only. By imposing 

a slightly lower minimum sample for each state for the private sector as was done in the state 

level allocation discussed earlier in this paper, AHRQ assured that the former would be produced 

for all states with relatively good standard errors. Using the past combined allocation, the state 

estimates for governments could not be produced with a reasonable error rate except for the 

largest states, even with the over sample of non certainty governments. 

Since, with the exception of very large states, good state estimates for the government sector are 

not possible with the over sampling of governments in smaller states, it was decided not to over 

sample any governments at the state level. Instead, the government sample for the non certainty 

governments was redesigned as follows: 

• all state governments were defined as a certainty sample unit, 

• all local governments with more than 5000 full time equivalent employment according to 

the Census of Governments were defined as a certainty sample unit, 

• each of the nine Census Divisions was allocated 200 non certainty government sample 

units. 
14

The first two items were part of the old government sample design for the IC from 1996-2002. 

The final allocation gives the same total national allocation for non certainty governments as in 

past surveys. The totals within Census Divisions are also approximately equal to the past 

allocations at the Census Division level. However, within Census Division the allocation for non 

certainty governments is proportional to the government employment in the each state. The 

effect of this change should be to improve the published estimates for each Census Division, 

since the allocation of the small sample size for each Census Division, is now done optimally for 

each Census Division. This is because there is no over sampling within Census Division by state 

as before. The allocations are proportional to State size within Census Division. This increases 

the sample of the larger states which have the most government employment. Making better 

estimates for each Census Division also will improve national estimates for the government 

sector. This will also allow AHRQ to make better state level estimates for the larger states where 

quality estimates are possible. 

Summary

In summary, several major changes are being implemented for the 2004 MEPS-IC sample. 

• The overall sample for the private sector is being increased to allow for State level 

estimates for all 50 States and the District of Columbia. This is an improvement over the 

current for which estimates were produced for 40 States. Due to the increased sample, 

this over sampling of 11 more States should not effect the quality of the national 

estimates. 

• An improved stratification and allocation process is being implemented for the private 

sector. This should lead to improved estimates at the State level using the same sample 

size. Better State estimates will also mean better national estimates. 

• A change is being made to the process which limits the sample for individual firms. The 

new process will not limit sample for individual firms as severely and should reduce the 

error for private sector estimates made for firms with over a 1000 employees. It should 

also reduce errors for private sector estimates made for the entire population. This should 

be true for State and national level estimates. 

• Finally, the non certainty government sample has been changed to an optimal allocation 

within each Census Division. This should improve both national and Census Division 

government estimates. This allocation should also improve estimates for some of the 

largest States where an estimate can be made. 

Taken together, the IC will soon produce significantly better State and National estimates for the 

private sector and there will be quality estimates for all States, rather than just 40 States. These 

improvements should occur for all variables and for estimates for all sub National and sub State 

cells. There should also be improvements in estimates for nation, Census Divisions and the 

largest States for government results. 
15

References

Cochran WG. Sampling techniques. New York: John Wiley and Sons; 1977. 

Cohen S. Sample design of the 1996 Medical Expenditure Panel Survey Household Component. 

Rockville(MD); Agency for Health Care Policy and Research; 1997. MEPS Methodology 

Report No. 2. AHCPR Pub No. 97-0027. 

Insurance Component Technical Appendix. Agency for Healthcare Research and Quality, 

Rockville, MD. http://www.meps.ahrq.gov/mepsdata/ic/techappendix.htm 

Kish L Survey sampling. New York: John Wiley and Sons; 1965. 

Marker D, Bryant E, Wallace L. Yansaneh L. National Employee Health Insurance Survey 

(NEHIS): draft final methodology report; volume I: statistical methodology. Rockville, (MD): 

Westat, Inc.: 1996. 

Research Triangle Institute (2002). SUDAAN User’s Manual, Release 8.0. Research Triangle 

Park, NC: Research Triangle Institute. 

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 Pub. No. 99-0037. 
16

Appendix A 

Private Sector Allocations and Response Per State 

State Private Sector Sample Expected Private Sector Response 

Alabama 

Alaska 

Arizona 

Arkansas 

California 

Colorado 

Connecticut 

Delaware 

District of Columbia 

 Florida 

 Georgia 

 Hawaii 

 Idaho 

 Illinois 

 Indiana 

 Iowa 

 Kansas 

 Kentucky 

 Louisiana 

 Maine 

 Maryland 

 Massachusetts 

 Michigan 

 Minnesota 

 Mississippi 

 Missouri 

 Montana 

 Nebraska 

 Nevada 

 New Hampshire 

 New Jersey 

 New Mexico 

 New York 

 761 

 704 

 761 

 761 

 2842 

 761 

 761 

 704 

 704 

 1274 

 761 

 704 

 704 

 1139 

 761 

 761 

 761 

 761 

 761 

 761 

 761 

 761 

 846 

 761 

 761 

 761 

 704 

 761 

 761 

 761 

 809 

 761 

 1738 

 560 

 520 

 560 

 560 

 2038 

 560 

 560 

 520 

 520 

 925 

 560 

 520 

 520 

 829 

 560 

 560 

 560 

 560 

 560 

 560 

 560 

 560 

 625 

 560 

 560 

 560 

 520 

 560 

 560 

 560 

 595 

 560 

 1255 
17
18

State Private Sector Sample Expected Private Sector 

Response 

North Carolina 

North Dakota 

Ohio 

Oklahoma 

Oregon 

Pennsylvania 

Rhode Island 

South Carolina 

South Dakota 

Tennessee 

Texas 

Utah 

Vermont 

Virginia 

Washington 

West Virginia 

Wisconsin 

Wyoming 

Certainty Establishments 

(Not Assigned by State) 

 761 

 704 

 967 

 761 

761 

1021 

 704 

 761 

 704 

 761 

1637 

 761 

 704 

 761 

 761 

 761 

 761 

 704 

 100 

 560 

 520 

707 

 560 

 560 

 746 

 520 

560 

520 

 560 

1184 

 560 

 520 

 560 

 560 

 560 

560 

520 

 90 

Total 43708 32070 
19

Appendix B 

Percent of Universe and Sample per Stratum: Private Sector 

Stratum Percent of 

Establishments 

Percent of 

Employment 

Percent of 

Enrollment 

Percent of 

Sample 

Probability of 

Offering Health 

Insurance 

Total 

Enrollment per 

Establishment 

Less than 25% very low 13.4 2.3 1.6 10.8 

25% to 40% very low 14.7 3.4 2.4 11.8 

40% to 58% very low 13.6 4.5 3.5 14.7 

58% to 75% low 11.3 2.4 2.4 10.2 

58% to 75% medium 2.8 3.7 2.6 2.1 

75% to 90% low 10.1 2.0 2.2 10.4 

75% to 90% medium 5.7 4.1 3.9 2.7 

75% to 90% high 1.0 3.6 2.7 1.2 

above 90% very low 13.7 5.1 4.3 8.7 

above 90% low 7.6 9.8 9.4 5.4 

above 90% medium 3.7 11.5 11.4 4.6 

above 90% high 1.9 12.3 12.5 3.8 

above 90% very high 0.9 11.4 12.6 3.1 

above 90% highest 0.5 21.7 25.9 10.5 

Certain highest negligible 2.1 2.5 negligible 
20

Appendix C 

Methods for Reduction of Expected Sample for Private Sector Firms 

Current Method

Let sump equal the original expected sample for a firm and sumf be the final, then the algorithm 

to reduce the value for certain firms is: 

If sump > 50 then sumf = 15 

if 20 < sump=< 50 then sumf = 10 

if 6 < sump <= 20 the sumf = .5*sump 

if 3 < sump <= 6 then sumf = 3 

and if 1 < sump <=3 then sumf = 1. 

New Method

If 0 < sump < 3 the sumf = sump 

if 3<=sump < 6 then sumf = 3 + (2/3)*(sump - 3) 

if 6<=sump < 12 then sumf = 5 + (1⁄2)*(sump - 6) 

if 12<=sump < 36 then sumf = 8 + (1/3)*(sump - 12) 

if 36<=sump then sumf = 16 + (2/3)*(sump - 36)