The President's Pay Agent

Comparing General Schedule and Non-Federal Pay

How Local Pay Disparities Are Measured

Locality-based comparability payments are a function of local disparities between Federal and non-Federal pay. Pay disparities are measured for each locality pay area by comparing the annual scheduled rates of basic pay ^[1]of workers paid under the General Schedule (GS) pay plan in an area to the annual rates generally paid to non-Federal workers for the same levels of work in the same area. Under OCSP, non-Federal pay is represented by a survey of 115 jobs distributed over 26 occupations (as listed in Table 1). Each of the 115 surveyed jobs has been equated to a GS occupational definition and grade level and classified among five broad "PATCO" categories - professional (P), administrative (A), technical (T), clerical (C), and protective officer (O). Under the NCS program, BLS surveys or models salaries for all non-Federal jobs deemed to match GS positions as shown in the crosswalk in Appendix VII of the 2002 Pay Agent's report.

Non-Federal rates are estimated on a sample basis by BLS area surveys under both survey programs. The rate for each non-Federal job is an estimate of the mean straight-time earnings of full-time non-Federal workers in the job, based on the BLS survey sample. GS rates are determined from Federal personnel records for the relevant populations of GS workers. Each GS rate is the mean scheduled annual rate of all full-time permanent year-round GS workers in the relevant group.

The reference dates of the BLS surveys vary over the cycle of non-Federal salary surveys conducted for the GS locality pay program under both OCSP and NCS. To ensure that local pay disparities are measured as of one common date, it is necessary to "age" the BLS survey data to a common reference date before comparing it to GS pay data of the same date. March 2003 is the common reference and comparison date used in this report. The Employment Cost Index (ECI) based on wages and salaries for white-collar civilian workers, excluding those in sales, was used to age the BLS data. ^[2]

Since 5 U.S.C. 5302(6) requires that each local pay disparity be expressed as a single percentage, the comparison of GS and non-Federal rates of pay in a locality requires that the two sets of rates be reduced to one pair of rates, a GS average and a non-Federal average. An important principle in averaging each set of rates is that the rates of individual survey jobs and job categories are weighted by Federal GS employment in equivalent classifications. Weighting by Federal employment ensures that the influence of each non-Federal survey job on the overall non-Federal average is proportionate to the frequency of that job in the Federal sector.

Table 2.

Number of OCSP Survey Jobs by Grade and PATCO Category

Table 2, above, summarizes the distribution of OCSP survey jobs by PATCO category and grade. The 115 OCSP jobs are distributed among 35 category levels, which are in turn distributed among 14 grade levels (there is no OCSP survey job at grade 10). For example under OCSP, grade GS-1 is represented by only one job in the clerical category (General Clerk I). By contrast, grade GS-5 is represented by 18 jobs distributed among all 5 categories, including 4 in the professional category (Accountant I, Engineer I, Scientist I, and Contracting Specialist I), 4 in the administrative category (Budget Analyst I, Computer Programmer I, Personnel Specialist I, and Tax Collector I), etc. Under the NCS program, all PATCO grade cells with Federal incumbents are represented.

Because of variations in local industry mix, labor force size, and other factors, BLS was not able to publish rates for all 115 OCSP jobs in any area surveyed. On average, an area survey resulted in published pay data for about 59 percent of the 115 jobs, ranging from a low of 39 jobs in the Richmond survey to a high of 94 in the Rest of U.S. survey. Salary data for unpublished jobs was substituted from alternative sources, as explained below in the section on "Publishability and Substitute Data."

Under OCSP, the non-Federal rates from the BLS data are averaged in three stages. In the first stage, job rates are averaged within PATCO category by grade level. The jobs surveyed at each grade represent directly the Federal workers in equivalent job classifications (e.g., engineers) and indirectly other Federal workers in the same PATCO category (e.g., other professionals) at that grade. At grade 5, for example, the four job rates in the professional category are averaged to one rate for the GS-5 professional category. In the same manner, job rates are averaged within the administrative, technical, clerical, and protective officer categories at grade 5. For averaging within category, each job rate is weighted by the CONUS ^[3] full-time permanent year-round employment in GS positions that match the job. ^[4] The reason for CONUS weighting in the first stage is explained below.

Under the NCS program, BLS averages survey estimates (actual or modeled) for each non-Federal job within PATCO categories and grades using national GS employment weights provided by OPM. This weighting is the same as under OCSP, except that the GS employment data are from March 2002 instead of March 2003 to afford time for BLS to do the calculations and deliver the results. The NCS program covers all white-collar jobs, not just the 26 occupations at 115 work levels included in OCSP.

When the first stage averages are complete under OCSP, grade 5 is represented by 5 category rates in lieu of its original 18 job rates. Similarly, grades 1 and 2 are each represented by one category rate, grades 3 and 4 each by two category rates, grade 6 by three category rates, and so on. Under the NCS program, all PATCO/grade categories with Federal incumbents are represented.

In the second stage, the category rates are averaged by grade level to one grade level rate for each grade represented. Thus, at grade 5 the five category rates in OCSP are averaged to one GS-5 rate. For averaging by grade, each category rate is weighted by the local full-time permanent year-round GS employment in the category at the grade. This procedure is exactly the same under the NCS program except that all PATCO categories are represented.

In the third stage under OCSP, the 14 grade rates are weighted by the corresponding local full-time permanent year-round GS grade level employment and averaged to a single overall non-Federal rate for the locality. This overall non-Federal average is the non-Federal rate to which the overall average GS rate is compared. Under the NCS program, all 15 GS grades are represented.

Since GS rates by grade are not based on a sample, but rather on a census of the relevant GS populations, the first two stages of the above process are omitted in deriving the GS average rate. For each grade level represented by a non-Federal average derived in stage two, we average the scheduled rates of all full-time permanent year-round GS employees at the grade in the area. The overall GS average rate is the weighted average of these GS grade level rates, using the same weights as those used to average the non-Federal grade level rates.

The pay disparity, finally, is the percentage by which the overall average non-Federal rate exceeds the overall average GS rate under either survey program. ^[5]

As indicated above, at the first stage of averaging the non-Federal data, the weights represent national or CONUS GS employment, while local GS employment is used to weight the second and third stage averages. GS employment weights are meant to ensure that the effect of each non-Federal pay rate on the overall non-Federal average reflects the relative frequency of Federal employment in matching Federal job classifications.

The methodology employed by the Pay Agent under OCSP to measure local pay disparities does not use local weights in the first (job level) stage of averaging because this would have an undesirable effect. A published survey job whose Federal counterpart has no local GS incumbents will "drop out" in stage one and have no effect on the overall average. This might be appropriate if the survey job represented only those GS workers in the Federal counterpart job; but in the second stage of averaging, each survey job represents part or all of a broader PATCO category level, and in the third stage each PATCO category level represents part or all of a broader grade level. If a job is allowed to drop out due to zero local GS employment, some GS incumbents of other classifications in the same PATCO category level --not represented by a specific survey job--will be unrepresented.

For this reason, national or CONUS weights are used in the first stage of averaging OCSP data. CONUS weights are used only where retention of each published OCSP survey observation is most important--at the job level or stage one. Local weights are used at all other stages.

For the introduction of NCS data in 2002, we left the weighting system essentially unchanged, although the first stage is now done by BLS to permit use of all job data, both published and unpublished. Under the NCS program, PATCO and grade weights would not be necessary, since all white-collar jobs at all grades are represented and weighted by CONUS GS employment separately. However, the Pay Agent concludes that continued use of PATCO and grade weighting is desirable to add the local Federal employment distribution to the calculations and to permit BLS to deliver data by PATCO category/grade so that published and unpublished data can be combined before delivery to the Pay Agent.

Publishability and Substitute Data

Under OCSP, BLS was never able to publish data for all survey jobs. The fact that the set of published jobs varies from area to area was a concern because the disparity between Federal and non-Federal pay varies by job as well as by area. If area pay disparities are not based on the same set of jobs in each area, the differences between those disparities are caused not only by differences in the pay of Federal and non-Federal workers for the same jobs (as intended), but also by differences in the set of jobs for which pay data are published.

For OCSP, the Council and the Pay Agent agreed to use data from an earlier survey to fill in missing values where available, but also developed estimates of non-Federal pay produced by a multiple regression model to estimate salaries for jobs not published by BLS. OPM staff developed the model to estimate local non-Federal pay differentials for the survey jobs. It produced estimates of the pay of unpublished jobs based on multiple regression analysis of the pay of published jobs. The model assumed that pay varies with three factors -- geographic area, occupation, and work level-- and it accounted for about 96 percent^[6] of the variation in the pay rates published by BLS. The use of the model was endorsed by the Federal Salary Council. A technical report on the OPM model was provided in Appendix II of the 1994 Report, and a summary of subsequent years' models appeared in Appendix II or III of later reports.

BLS staff developed and implemented a similar model using NCS data to produce pay estimates for missing non-Federal jobs in NCS. Both the NCS and the OCSP model predict pay as a function of location, occupation, and grade level. ^[7] The NCS model accounts for about 82 percent of the variations in pay, which is very good for models of this type.

Use of modeling is a generally accepted practice, and we have used modeled data in the locality pay program since 1994. The models used in both survey programs are similar in concept and form. They are also similar to the curve fitting process used in the pay comparability process prior to FEPCA. All jobs included on the crosswalk shown in Appendix VII of the 2002 Pay Agent's report were included in developing the model, with the exception of a handful of jobs for which BLS had no data.

However, the Federal Salary Council has expressed concern about the amount of data modeled under the NCS program. Based on GS employment weights used to combine the data at the job level, an average of about 71 percent of the NCS data are modeled in this year's surveys. This varies by area from a high of 81 percent modeled in Dayton to a low of 32 percent modeled in the Rest of U.S. locality pay area. The amount of modeled data also varies considerably by grade level and ranges from an average of 32 percent modeled at GS-4 to an average of 98 percent modeled at GS-15. The Pay Agent shares the Council's concerns about the amount of modeled data.