Handbook of Methods Occupational Requirements Survey Calculation

See appendix A at the end of this section for a full list of published occupational requirements as well as a list of corresponding estimate types. This section includes the formulas used to calculate the ORS estimates.

Estimation formulas

Percentage of workers. The formula for the percentage of workers with a given job requirement in the domain (occupation or occupational group) is

 where

 I is the total number of establishments,

 Gi is the total number of sampled jobs in establishment i,

i is the establishment, 

g is the occupation within establishment i,

  is the final sampled job weight for occupation g in establishment i,

 X_ig is 1 if sampled job _ig meets the condition set in the domain (denominator) condition and 0 otherwise, and

 Z_ig is 1 if sampled job _ig meets the condition set in the requirement condition and 0 otherwise.

 Average (mean). The formula for the average (mean) estimate of a job requirement is

where

 I is the total number of establishments,

 Gi is the total number of sampled jobs in establishment i,

 i is the establishment,

 g is the occupation within establishment i,

  is the final sampled job weight for occupation g in establishment i,

X_ig is 1 if worker ig meets the condition set in the domain (denominator) condition and 0 otherwise,

Z_ig is 1 if worker ig meets the condition set in the requirement condition and 0 otherwise, and

Q_ig is the value of a quantity for a specific requirement for occupation g in establishment I.

Percentiles. The 10th, 25th, 50th (median), 75th, and 90th, percentiles are calculated. The pth percentile is the value Q_ig, where the value of a quantity is for a specific category for occupation g in establishment i, such that

• the sum of final sampled job weights () across sampled jobs with a value less than Q_ig is less than p percent of all final sampled job weights and
• the sum of final sampled job weights () across sampled jobs with a value more than Q_ig is less than (100 – p) percent of all final sampled job weights.

It is possible that there is no specific sampled job ig for which both of these properties hold. This occurs when there exists a sampled job for which the  of records whose value is less than Qig equals p percent of the total weighted sampled job employment. In that situation, the pth percentile is the average of Q_igand the value of the sampled job with the next lowest value.

Duration

Duration corresponds with the amount of time that workers perform physical demands or the length of exposure to environmental conditions. Exhibit 4 provides the duration levels with the corresponding percent or fraction of the workday that workers perform physical demands or are exposed to environmental conditions. See appendix B at the end of this section for a list of job requirements with associated duration levels.

The ORS calculates a percentage-of-workers estimate for each duration level. Estimates of some physical demands as well as education, training, and experience include averages (means) and percentiles to convey duration. For example, the ORS measures sitting in hours and the average (mean) and percentile estimates (10th, 25th, 50th, 75th, and 90th percentiles) are calculated for both hours and the percentage of the workday spent sitting for a specific occupation or occupational group.

Specific vocational preparation (SVP)

Although the ORS calculates most job requirement estimates from establishment responses about selected jobs' various tasks, some require an additional level of calculation. One of these is the specific vocational preparation (SVP) level, which is the amount of preparation time required for the worker to develop the skills needed to perform the job. The job requirements that contribute to the SVP are the minimum formal education, credentials, prior work experience, and on-the-job training. These requirements' associated time are then aggregated and used to determine the SVP level needed for the job. (See exhibit 5.)

Concurrent time due to credentials necessary for jobs that also require minimum formal education level, experience, or on-the-job training are not included separately in SVP. Concurrent time is reflected in the education, training, and experience requirements where the time overlaps with time necessary to obtain licenses, certifications, or other nondegree credentials.

Strength

Strength levels are derived from several physical requirements. The estimates reflect the amount of weight workers are required to lift or carry, how often, and whether standing or walking is required to perform critical tasks in the workday. The strength levels indicate whether jobs are considered sedentary, light work, medium work, heavy work, and very heavy work. Exhibits 6 and 7 provide the conditions necessary for estimates to reflect the strength levels.

The highest strength level satisfied is the level that represents that sampled job. For example, if a job requires workers to lift or carry 11–25 pounds occasionally, then it is classified as light work. However, if that same job were to require lifting or carrying that same weight frequently, then it is classified as medium work.

As noted, there are special cases for strength. The following table outlines the special cases. In instances where field economists are unable to determine certain job requirements from the respondent, they record these data as "unknown" and strength level is derived through imputation. See the section below, "Benchmarking, weighting, and imputation" for more information.

Low postures

The low postures estimates indicate whether workers are required to perform critical tasks while crawling, crouching, kneeling, or stooping. The amount of the workday workers are required to perform critical tasks in low postures is provided as the percentage of workers by duration level. Individual estimates of low postures are also published as required or worker choice. Job tasks may require workers to perform low postures but workers may choose whether to crawl, crouch, kneel, or stoop to complete the work activities.

Benchmarking, weighting, and imputation

The ORS program addresses establishment refusals, item nonresponse, and out of business and out of scope units. The ORS program adjusts the weights of the responding establishments during the estimation process in order to address nonresponse (specifically unit nonresponse). Imputation is used to address item nonresponse, which is when an establishment responds to the survey but is unable or unwilling to provide all of the occupational requirements data needed for estimation. Benchmarking adjusts final survey weights to reflect the current employment distribution in the economy.

Benchmarking

The ORS uses benchmarking to adjust the weight of each establishment in the survey and match the most current distribution of employment by several establishment and occupational characteristics. The ORS establishment sample is adjusted according to data from the Quarterly Census of Employment and Wages (QCEW) Longitudinal Database, a file of units reporting to the Railroad Retirement Board, the Occupational Employment and Wage Statistics (OEWS) survey, and the Current Employment Statistics (CES) survey. The QCEW, railroad information, and OEWS survey provide historical employment data needed for the benchmarking process, but since these sources do not have current employment data, the ORS also uses CES to make an adjustment to employment. The benchmark process updates the initial establishment weights, assigned during sampling, by current employment. Benchmarking ensures that survey estimates reflect the most current employment distribution by industry, employment size, geographic area, and major occupational group.

As an example of the benchmarking process, 40 private industry, 10 local government, and 5 state government units in the service sector were selected from the ORS sampling frame. These units consist of establishments employing 200,000 private industry workers, 30,000 local government workers, and 10,000 state government workers. If, by the time of survey processing, the private service sector experienced an employment increase of 10,000 workers (5 percent) and there is no increase in employment in the service sectors of state and local government, then the sample would underrepresent current employment in the private industry service sector in the absence of benchmarking. In this example, the ORS program 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.

Weighting

An establishment is considered responding if it provided information for at least one sampled job. Similarly, a nonresponding establishment is one that is unable or unwilling to provide information for at least one sampled job. If the contact person (respondent) for an establishment refuses to participate, then the associated establishment is considered nonresponding. The ORS program adjusts weights for unit (establishment) nonresponse by redistributing the weights of nonresponding establishments to similar establishments. The ORS program groups similar respondents into cells that are defined by characteristics such as the industry, size class, and geographic area of the establishment. For example, if the nonresponding establishment was in the manufacturing industry and had an employment of 350 workers, the ORS program would adjust the weights of responding manufacturing establishments with 100–499 workers during estimation. Applied at the establishment level, this adjustment is a nonresponse adjustment factor (NRAF), and it is calculated using the following formula:

where

∑A = weighted employment of all usable establishments in the nonresponse cell, and

∑B = weighted employment of all viable but not usable establishments in the nonresponse cell.

If there are no responding establishments to reweight within the industry or employment size group, then additional responding units from similar geographic areas are considered. Establishments no longer in operation or out of the scope of the survey and establishments with no workers within the scope of the survey are considered unviable and excluded from survey estimates.

The ORS program may also adjust weights for sampled job nonresponse, which is when an establishment does not provide any occupational requirements data for a given sampled job. The ORS program addresses sampled job nonresponse during the interview with an adjustment that redistributes the weights of nonresponding sampled jobs to responding sampled jobs in the same occupational group, ownership, industry, and size class.

The ORS program applies additional adjustment factors 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 ORS program adjusts the weight of the sampled unit to reflect the employment data for the sampled unit.

Imputation

Item nonresponse occurs when an establishment responds to the survey but is unable or unwilling to provide some of the occupational requirements for a given sampled job. Item nonresponse is addressed through item imputation in certain situations. Item imputation replaces missing values for an item or items with values derived from sampled jobs within similar establishments with similar worker characteristics that have a value for the item. For ORS estimates, items with missing values are imputed within groups of ORS job requirements that are related. For example, one ORS group refers to categorical variables only and includes such requirements as vision and driving. Within the group, the ORS imputes values by a process that matches sampled jobs using occupational information from similar occupations in similar establishments.

For more information, see estimation within the research section of the ORS website.

Reliability of ORS estimates

To assist users in confirming the reliability of ORS estimates, the ORS publishes standard errors. Standard errors provide users with a measure of the precision of an estimate to ensure that it is within an acceptable range for their intended purpose. The standard errors are calculated from collected and imputed data. The ORS program is researching methods for estimating the variance excluding imputed values. Examples on how to build confidence intervals using standard errors are included in the standard error section of the ORS website.

The ORS derives estimates from sampled jobs within responding establishments. Two types of errors are possible in an estimate based on a sample survey: sampling 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 ORS publications are significant at a level of 1.6 standard errors or better. This means that, for differences cited, the estimated difference is more than 1.6 times the standard error of the difference.

The ORS uses balanced repeated replication (BRR) to estimate the standard error. The procedure for BRR starts by first partitioning the sample into 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, the ORS program chooses half-samples 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. By using half-samples, the ORS program can compute a replicate estimate with the same formula for the regular, or full-sample, estimate, except that the final weights are adjusted. 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 ORS publications, k = 0.5, so the multipliers are 1.5 and 0.5.

The BRR estimate of the standard error with R half samples is

where

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

   is the rth replicate estimate, and

    is the full-sample estimate.

Quality assurance

The ORS program uses a variety of quality assurance programs to mitigate collection and processing errors by using data collection reinterviews, observed interviews, computer edits of the data, and systematic professional review of the data. These programs also serve as a training device to provide feedback to field economists, or data collectors, on errors and the sources of errors that can be remedied by improved collection instructions or computer-processing edits. Field economists receive extensive training to maintain high standards in data collection.

Once estimates of occupational requirements are produced, the estimates are validated. The focus of the validation is to compare the estimates with expectations for them. Expectations are based on prior year estimates as well as similar estimates from other sources of data, such as the Occupational Information Network (O*NET). In addition, ORS estimates between similar occupations are compared.

The ORS program investigates estimates that deviate from their expectations to ensure that their underlying data are consistent with ORS collection procedures, and that their calculation is consistent with ORS statistical procedures.

Before publishing any estimate, the ORS program reviews it to make sure that it meets specified statistical reliability and confidentiality requirements. See data review and estimation tabs on the research section of the ORS website. Estimates that are consistent with these procedures are designated as fit for use and released in BLS publications.