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Starting with the 2002 data collection, the sample allocation and design of the MEPS-IC list sample also supports a limited set of reliable metro-level estimates of the following:

• The percent of establishments that offer health insurance
• The percent of employees that enroll in health insurance plans, are eligible to enroll, and enroll when they are eligible
• Average premiums and employee contributions for those enrolled in employer-sponsored health insurance plans

• Counties in metro areas within States (PDF, 37 KB) or HTM format
• Counties that comprise the 20 largest metro areas (PDF, 39 KB) or HTM format.

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For all sample units except State governments and very large local governments, each sample unit is initially prescreened by telephone. The purpose of this screening is to:

• obtain the name and title of an appropriate person in each establishment to whom a MEPS-IC questionnaire will be mailed,
   
• verify the address and identify any businesses that no longer exist, have closed, or have merged with others, and
   
• determine whether or not health insurance was offered to employees at this establishment during the prior calendar year. (The MEPS-IC survey is a retrospective survey, asking about health insurance offerings during the previous calendar year. This is necessary in order to collect data for both the list sample and the household link sample cases at the same time. Beginning in 2008, the MEPS-IC survey will change to a collection of data for the current year.)

If the employer did not offer health insurance in the previous year, a brief set of questions about establishment characteristics are asked and the case is considered a complete respondent. This provides a quick and inexpensive method to collect the necessary data from the large number of employers who did not offer health insurance to their employees.

If the employer did offer health insurance in the previous year, several brief questions are asked and the employer is mailed a MEPS-IC questionnaire. All establishments not reached during the screening process are also mailed questionnaires. If an establishment fails to return the initial mail questionnaire, a follow-up mailing is sent a few weeks later. Establishments that also fail to respond to the second mailing are contacted by telephone and the survey is conducted using computer-assisted telephone interviewing (CAPI) technology.

For the purpose of this survey, establishments indicating that they offered health insurance to their employees must answer key information on their health insurance offerings to be considered full respondents. Callbacks are made to respondents not providing all of the key information in order to complete their questionnaires. Respondents that do not provide this key information, but are known to offer insurance, are considered partial respondents. Establishments that were not prescreened, did not return the mail questionnaires and did not respond to follow-up phone calls are classified as non-respondents. For this group, the availability of health insurance for employees at the establishment is unknown.

Data for large governments and large private-sector firms, reporting for multiple establishments, are collected using specialized staff and forms. This is done to make the collection process simple and flexible and to reduce the burden as much as possible for these important respondents. Sometimes multiple telephone contacts and personal visits are needed to collect these data. For some of these collections, survey staff abstract data directly from company records and plan brochures if the firm insists on such methods.  

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In sample surveys like the MEPS-IC, non-certainty sample establishments represent not only themselves but also other similar establishments in the survey population.  Therefore, in order to produce the survey estimates and standard errors presented in the MEPS-IC tables, weights must be created for all responding establishments.  A brief description of this process is provided here. During the sample design and selection process, each establishment on the frame is given a probability of selection that is dependent on its stratum.  These probabilities vary among establishments and assure that the sample sizes in each stratum are equal to that required by the allocation scheme.  The inverse of this probability of selection is the establishment’s base weight.  The use of the base weight and the formula

• During the sequential sample selection process, each establishment selected is assigned a number corresponding to its place in the order of selection.  These selection numbers are converted to 10 groups numbered 0 to 9 by assigning an establishment to the group determined by the last digit in its selection number.  Thus, if the selection number were 73, the establishment would be assigned to group 3.  Each group can then be thought of as a subsample similar to the full sample with each unit with a chance of selection into the subsample that was one-tenth its chance of selection into the full sample. 
   
• Using subsample weights that are 10 times the nonresponse adjusted weights of the full sample, ten subsample estimates, E_i , i = 1, ...10 are made in addition to the full sample estimate, E.
  
• The standard error is calculated as:

                                                     

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Within each of these categories (excluding Tables IV and IX), tables are subsequently grouped by:

Tables within each of these categories are ordered based on their inter-relationships.

To clarify what each MEPS-IC table is measuring, it will be helpful to use the table (Table 1) below.  For each of the MEPS-IC tables (excluding Tables IV and IX), Table 1 identifies the denominator table of that table.

Examples of how to use this table to calculate approximate counts from the percentage estimates in the MEPS-IC tables are provided in the next section.

Table 1. Listing of MEPS-IC table numbers and denominators for tables

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Many of the MEPS-IC tables contain percent estimates instead of count estimates. For instance, Table I.B.2 gives the percent of employees who work in establishments that offer health insurance. Table I.B.2.a gives the percent of employees who work at establishments that offer health insurance and who are eligible for health insurance. For most tables of percents, a count of the number of employees or establishments in any cell in the table can be calculated using data, for that cell, from the current table and one or more tables containing the denominator(s) for that cell.

To produce count estimates, one simply multiplies the cell values from the selected table and all of the denominators for that cell. For example, if one desired an estimate of total establishments that offer health insurance, one can find the percentage of these establishments in Table I.A.2 and determine from Table 1 on the previous page that Table I.A.1 contains the value in the denominator of this percentage.

Thus, the estimated total number of establishments that offer health insurance in 2002 is:

.572(percents must be converted to decimals) x 6,138,063 = 3,510,972.

An approximate standard error for this count estimate can be computed using this formula:

where Est(1) and Est(2) are the estimates from the two tables and Err(1) and Err(2) are the standard errors for those estimates.

= 22,530

For some tables, a hierarchical structure exists so more than two tables are needed to derive an approximate count. For example, look at Table I.B.2.a, the percent of employees eligible for health insurance. Table I.B.2 is listed as its denominator for Table I.B.2.a and Table I.B.1 is the denominator for Table I.B.2. The values from all three tables, I.B.1, I.B.2, and I.B.2.a must be used to derive an estimate of the count. Thus, the estimated total number of employees eligible for health insurance in 2002 is .771 x .883 x 111,437,203 = 75,865,668, with the three values coming from Tables I.B.2.a, I.B.2, and I.B.1 respectively. Basically, one must multiply by a series of denominators until one reaches a table with numbers instead of percents (the shaded areas of the Table 1 on the previous page). The standard error for this count estimate can be computed by using a logical expansion of the standard error formula provided above.

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IX. References

Madow WG, Olkin I, Rubin DR. Incomplete data in sample surveys, Volume 2: Theory and bibliographies. New York: Academic Press; 1983.

Skinner CT, Holt D, Smith TMF. Analysis of complex surveys. New York: John Wiley and Sons; 1989.

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