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August 2013

Wage estimates by job characteristic: NCS and OES program data

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Thus, employment by full-time/part-time status for the sampled establishment will be allocated as follows:

    Wage interval                  Description                           Allocation

              B                     ($ 7.50–$9.49/hour)      1 part-time secretary, 1 full-time secretary
              D                     ($12.00–$15.24/hour)   2 part-time secretaries, 4 full-time secretaries
              E                      ($15.25–$19.24/hour)   0 part-time secretaries, 2 full-time secretaries

After the employment counts for all of the OES wage intervals are allocated among the various job characteristics, the allocated counts are used to produce estimates of mean hourly wage rates by area, occupation, and characteristic. These estimates are arrived at by means of the same formula and the same mean wage rates for the intervals as the OES survey uses to aggregate its employment counts to produce mean hourly wage rate estimates by area and occupation. Continuing with the illustration shows that, instead of using the collected value 2 as the employment count for secretaries in wage interval B for the sampled establishment, the estimate for the mean hourly wage among part-time workers would use the allocated value 1. Similarly, the part-time estimate would use the allocated value 2 in wage interval D and the allocated value 0 in wage interval E for the sampled establishment. The full-time estimate would use the allocated value 1 as the employment count for secretaries in wage interval B, the allocated value 4 for secretaries in wage interval D, and the allocated value 2 in wage interval E.

Because the method fully allocates the OES employment counts among the characteristics for each interval, it ensures that the wage estimates by characteristic will be totally consistent with the OES data by area, occupation, and wage interval, thereby taking full advantage of the large OES sample size. The method also takes advantage of the NCS information about the relationship between the characteristic and the wage rate, a relationship that is reflected by the differences in the NCS proportions for the characteristics across the OES wage intervals. For example, if there is a tendency for wage rates to be lower for part-time workers than for full-time workers in the NCS data for the occupation, the proportion of part-time workers will tend to be higher for the lower wage intervals and lower for the higher wage intervals. This difference will translate into a lower estimate for the mean hourly wage rate for part-time workers than for full-time workers.

The NCS–OES estimation method does require assumptions related to the calculation of the proportion for the characteristics, primarily to deal with the much smaller sample size for the NCS relative to the OES survey. An establishment’s employment count for an occupation in a wage interval is allocated on the basis of the proportion for the characteristic among NCS observations from the same area and occupation, and with a wage rate within the interval. Thus, the estimation method assumes that an occupation’s proportion for the characteristics applies uniformly to all establishments within the area. For the matching of the OES establishments to the NCS proportions, occupation is defined by the six-digit Standard Occupational Classification (SOC) code and area is defined as one of the 24 areas listed in table 1.10 These areas comprise 15 large metropolitan areas plus the balance of the nine Census divisions, where the balance of a Census division includes all areas in the division except those in one of the 15 large areas.

Notes

10 See Standard Occupational Classification for a description of the major occupation groups and the six-digit SOC occupation codes.

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About the Author

Michael K. Lettau
lettau.michael@bls.gov

Michael K. Lettau is Branch Chief, Division of Data Validation and Estimation, Office of Compensation and Working Conditions, Bureau of Labor Statistics.

Dee A. Zamora
zamora.dee@bls.gov

Dee A. Zamora is a mathematical statistician in the Statistical Methods Group, Office of Compensation and Working Conditions, Bureau of Labor Statistics.