Handbook of Methods Employer Costs for Employee Compensation Calculation

Measuring costs

The ECEC measures the average costs to employers for wages and salaries and benefits per employee hour worked.

Exhibit 1. Benefit costs measured by the ECEC

Cost data are presented both in dollar amounts and as percentages of total compensation. Cost data are published quarterly.

The ECEC series provides an average cost across all workers. Eligible workers with access to employer-sponsored benefits who do not participate are also included in the calculation. That is, the average cost includes both workers for whom the employer incurred a compensation cost and those for whom no cost was incurred.

To calculate cost levels, the ECEC uses current employment weights to reflect the changing composition of today’s labor force. The employment weights are derived from two BLS programs: the Quarterly Census of Employment and Wages (QCEW) and the Current Employment Statistics (CES). Combined, these programs provide the appropriate industry coverage and the right timeframe of the data needed to benchmark (post-stratify) employment weights for the ECEC series.

In most instances, private industry employment weights used in the ECEC are total employment estimates for two-digit industry groups, such as utilities (NAICS 22) or wholesale trade (NAICS 42). In a few cases, the employment weights associated with more detailed industrial categories are used. This includes the four-digit NAICS categories elementary and secondary schools (6111), junior colleges (6122), colleges and universities (6133); and the six-digit NAICS category aircraft manufacturing (336411). For state and local governments, the ECEC uses a more aggregated level, reflecting the level of detail published by the Current Employment Statistics (CES) program.

Weighting, nonresponse adjustment, imputation, and benchmarking

Participation in the NCS is voluntary; therefore, a company official may refuse to participate in the initial survey or may be unwilling or unable to update previously provided data for one or more occupations during subsequent contact. In addition, some establishments selected from the sample frame may be out of scope for the survey or have gone out of business. To address the problems of nonresponse and missing data, the NCS adjusts the weights of the remaining establishments and imputes missing values (fills in missing values with plausible values). To ensure that published estimates ultimately are representative of compensation in the civilian, private industry, and state and local government sectors, weight adjustments and imputation are made in accordance with the following steps:

Step 1. Unit nonresponse adjustment. An establishment is considered responding if it provided information on at least one usable occupation. A selected occupation is classified as usable if the following data are present: occupational attributes (full-time or part-time schedule, union or nonunion status, and time- or incentive-based of pay), work schedule, and wage data. Wages account for approximately 70 percent of compensation; therefore, if wage data are not available, other data from the establishment cannot be used in calculating estimates. Without the wage data, it is not possible to create benefit-cost estimates because many benefits, such as paid leave, for example, are linked to wages.

An establishment is considered nonresponding if it refused to participate in the survey or did not provide wages and salaries, occupational classification, worker attributes, and work schedule data for any selected occupation. Establishment nonresponse during the initial interview (referred to as initiation) is addressed by introducing nonresponse adjustments that redistribute the weights of nonrespondents to responding sample units in the same ownership, industry, size class, and area. For example, if the nonresponding establishment was in the manufacturing industry and had an employment of 350 workers, the NCS would adjust the weights of responding manufacturing establishments with 100–499 workers by a nonresponse factor calculated by dividing the sum of the product of establishment employment and sample weight for responding and nonresponding establishments by the sum of the product of establishment employment and sample weight for responding establishments.

Step 2. Quote nonresponse adjustment. Quote nonresponse is a situation in which an establishment refuses to provide any wage data for a given sampled occupation (quote). Quote nonresponse during the initial interview is addressed by an adjustment that redistributes the weights of nonresponding quotes to responding sample quotes in the same occupational group, ownership, industry, size class, and area. Quote nonresponse during an update interview is addressed by imputation.

Step 3. Item imputation. Item nonresponse is a situation in which an establishment responds to the survey but is unable or unwilling to provide some or all of the benefits data for a given sampled occupation. Item nonresponse is addressed through item imputation in certain situations. Item imputation replaces missing values for an item with values derived from similar occupations and establishments with similar characteristics.

For benefit estimates, items can be imputed for nonresponse at initial and subsequent data collection. For example, during the initial contact, an establishment reports wage and salary data for a sampled occupation but refuses or is unable to report whether those in the occupation receive paid vacation benefits; the NCS imputes the incidence of vacation benefits for the selected occupation on the basis of the incidence of vacation benefits among similar occupations in similar establishments.

For wages and salaries, cost data are not imputed for item nonresponse during the establishment’s initial data collection but are imputed at subsequent data collections (update). For example, if a manufacturing establishment reported wages and salaries for its full-time nonunion assembly workers during the initial collection, but not in a subsequent collection period (update), the NCS calculates the rate of change in wages and salaries of full-time nonunion workers in similar manufacturing establishments between the two collection periods, where the rate of change in wages and salaries between two collection periods is estimated from a regression model fit to establishments who reported wage data in both periods. This rate is then multiplied by the establishment reported wages and salaries, at initiation, to impute missing wages and salaries. However, if the establishment did not provide wages and salaries for full-time, nonunion assembly workers at the initial collection, the NCS would perform a quote nonresponse adjustment.

Additional adjustment factors are applied to special situations that may have occurred during data collection. For example, when a sample unit is one of two establishments owned by a given company and the respondent provides data for both locations combined instead of data for the sampled unit, the weight of the sampled unit is adjusted to reflect the employment data for the sampled unit.

Step 4. Benchmarking (poststratification). Benchmarking is the process of adjusting the weight of each establishment in the survey to match the most current distribution of employment by industry.

The NCS establishment sample is drawn from the Quarterly Census of Employment and Wages (QCEW). The QCEW and the railroad information from the Railroad Retirement Board (RRB) and Surface Transportation Board (STB) provide employment data, but these sources do not have current employment data, thus, a Current Employment Statistics (CES) factor is calculated to adjust employment. The benchmark process updates the initial establishment weights, assigned during sampling, by current employment. Establishment weights reflect employment at the time of sampling, not collection. Benchmarking ensures that survey estimates reflect the most current industry composition–employment counts in proportions that are consistent with the private industry, state government, and local government sectors. The private industry sample also uses establishment-employment-size class in the benchmarking process.

For example, 40 private industry, 10 local government, and 5 state government units in the service sector were selected from the sampling frame made up of establishments employing 200,000 private workers, 30,000 local government workers, and 10,000 state government workers. By the time of survey processing, the private service sector employment increased by 10,000 workers, or 5 percent, with no increase in employment in the service sectors of state and local government. In the absence of benchmarking, the sample would underrepresent current employment in the private industry service sector. In this example, the NCS would adjust the sample weights of the 40 service sector firms in private industry to ensure that the number of workers in establishments in the sampling frame rises to 210,000. The ownership employment counts for the private industry service sector would then reflect the current proportions of 84 percent for private industry, 12 percent for local government, and 4 percent for state government employment.

Cost estimate calculation

The ECEC estimates of the percentage of total compensation are calculated from unrounded estimates of hourly employer costs, and then, the percentages are rounded to the first decimal place. This method provides the most precise estimates of the percentage of total compensation; estimates calculated from published cost estimates may differ slightly from those calculated from unpublished, unrounded cost estimates.

The unweighted average wage (or benefit cost) is calculated from all workers within a sampled quote (selected job). The wage (or benefit) bill, , is the product of the weighted average wage (or benefit cost) of sampled quotes (selected jobs), , within the cell at the period , in which the wage (or benefit) bill is calculated; and the number of workers is represented by the cell, .

The formula for the mean hourly cost c for domain D is:

where

D is the domain of interest (such as all manufacturing workers),

 is the final quote weight for quote q, calculated as described earlier, with one additional factor included to account for changes in the employment distribution,

and   is the mean hourly cost c for quote q.

The formula for the mean hourly cost c as a percentage of total compensation is:

where

  is the mean hourly cost c for domain D, as before, and

 is the mean hourly cost for total compensation for domain D.

When respondents do not provide all the data needed, a procedure for assigning plausible values for the missing values is used. The process is explained in the Weighting, Nonresponse adjustment, Imputation, and Benchmarking sections.

Compensation percentile estimate calculation

The Compensation Percentile Estimates (CPE) provide the current and constant dollar 10th-, 50th-, and 90th-percentile wage estimates for civilian, private industry, and state and local government workers.^⁠1

The ECEC uses four percentile-wide bands to calculate the Compensation Percentile Estimates (CPE). That is, the 10th-percentile band includes observations with a wage rate within the 8th and 12th percentiles, the 50th-percentile band includes observations with a wage rate within the 48th and 52nd percentiles, and the 90th-percentile band includes observations with a wage rate between the 88th and 92nd wage percentiles.

The image below illustrates a wage band at the 10th wage percentile ($2.00). Since there are only a limited number of observations with a wage of $2.00, to expand the sample size, observations between the 8^⁠th wage percentile ($1.80) and 12th wage percentile ($2.20) are included in the 10th wage percentile band.

Exhibit 2. Example of percentile wage band for Compensation Percentile Estimates

There are instances, particularly at the 10th-wage percentile, where including all observations with wages and salaries within these percentile bands causes the weight of the band to exceed the expected 4-percent of the total weight. To overcome this issue, a pooling process was introduced: weights are adjusted for observations right on the lower and upper cutoffs of the percentile bands.

The pooling formula below is used to determine which observations are included in each percentile band and ensures that only 4 percent of the total weight is allocated to each percentile band.  

Pooling formula: 

Let

i = observation sorted by wages and salaries, and random number value,

p = percentile,

w_i = weight for observation i,

W_i = cumulative weight for observation i (),

N = total weight (),

LC_p = lower cutoff for pth percentile estimate (), and

UC_p = upper cutoff for pth percentile estimate ()

After calculating the lower and upper cutoffs, where p = 10th, 50th, and 90th^⁠ percentiles, the adjusted weight  is determined by evaluating the following five conditions for each value of p. When a condition is met, the value of  will be the value to the left of the comma and condition.  

To determine whether the weight should be adjusted, the cumulative weight for the observations included in the 10th percentile and lower and upper cutoffs are compared against the conditions.

Additionally, to ensure that observations from all weight classes are eligible for inclusion, a random value between 0 and 1, hereafter referred to as “random number,” was assigned to each observation. Introducing a random variable allows us to select small, medium, and large establishments, which provides a better representation of the economy in the sample.

For the Compensation Percentile Estimates, the percentiles are calculated using the wages and salaries of each sampled job first, followed by the calculations for the corresponding benefit costs. CPEs are available starting with March 2009.

Constant dollar (real) estimates

To produce real (inflation-adjusted) estimates, constant dollar estimates are produced by taking the current dollar (nominal) Compensation Percentile Estimates and adjusting them by the Consumer Price Index (CPI).

The formula for calculating constant dollar can be summarized as follows:

 Adjustment Factor

Constant dollar

where

t = the reference period,

i = 0 for the latest March, and

i = 9 for March 2009.

As noted above, the CPEs are available beginning with 2009, and each subsequent year will increase the overall range of i by 1.

Calculating estimate reliability 

Two types of errors are possible in an estimate based on a sample survey: sampling errors and nonsampling errors. Sampling errors occur because the sample makes up only a part of the population it represents. The sample used for the survey is one of a number of possible samples that could have been selected under the sample design, each producing its own estimate. A measure of the variation among sample estimates is the standard error. Nonsampling errors are data errors that stem from any source other than sampling error, such as data collection errors and data processing errors.

Standard errors can be used to measure the precision with which an estimate from a particular sample approximates the expected result of all possible samples. The chances are about 68 out of 100 that an estimate from the survey differs from a complete population figure by less than the standard error. The chances are about 90 out of 100 that this difference is less than 1.6 times the standard error. Statements of comparison appearing in ECEC publications are significant at a level of 1.6 standard errors or better. This means that, for differences cited, the estimated difference is less than 1.6 times the standard error of the difference. To assist users in evaluating the reliability of indexes, relative standard errors for ECEC estimates are available.

The ECEC uses a variation of balanced repeated replication (BRR), a methodology employed to estimate the standard error. The procedure for BRR entails first partitioning the sample into 120 variance strata composed of a single sampling stratum or clusters of sampling strata, and then splitting the sample units in each variance stratum evenly into two variance primary sampling units (PSUs). Next, half-samples are chosen, so that each contains exactly one variance PSU from each variance stratum. Choices are not random, but are designed to yield a “balanced” collection of half-samples. For each half-sample, a “replicate” estimate is computed with the same formula for the regular, or “full-sample,” estimate, except that the final weights are adjusted. A total of 120 replicates are used in this process. If a unit is in the half-sample, its weight is multiplied by (2 – k); if not, its weight is multiplied by k. For all NCS publications, k = 0.5, so the multipliers are 1.5 and 0.5.

The BRR estimate of standard error with R half-sample replicates is

where

the summation is over all half-sample replicates r = 1,...,R,

 is the rth half-sample replicate estimate,

and

 is the full-sample estimate.

Percent relative standard error data display the standard error as a percentage of the full-sample estimate and are provided alongside estimates in ECEC publications.

The percent relative standard error is given by

Data collection and processing errors are mitigated primarily through quality assurance programs that include the use of data collection reinterviews, observed interviews, computer edits of the data, and a systematic professional review of the data. The programs also serve as a training device to provide feedback to field economists on errors and the sources of errors that can be remedied by improved collection instructions or computer-processing edits. Extensive training of field economists is conducted to maintain high standards in data collection.

Before estimates are declared fit for use in BLS publications, estimates are validated. This process compares estimates with expected values derived from historical trends, economic conditions and indicators, changes in legislation (such as minimum wage or leave requirements), labor-management disputes, sample composition, sample rotation, changes in compensation structure, etc. Validation evaluates estimates based on individual establishment and worker domains.