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Calculating occupational employment for job requirements

Methods related to multiple job requirements that were previously in this factsheet have been moved to the Identifying jobs with multiple job requirements and Combining multiple job requirement estimates factsheets. All three methods can be located on the Examples applying ORS estimates page.

The Occupational Requirements Survey (ORS) publishes job-related information on physical demands; environmental conditions; education, training, and experience; as well as cognitive and mental requirements. The Occupational Employment and Wage Statistics (OEWS) survey produces employment and wage estimates for approximately 830 occupations. The ORS and OEWS programs survey business establishments (employers). BLS encourages users to consider the employment estimates from OEWS as the most reliable and representative occupational employment data produced by BLS.

ORS can be used to identify the percentage of workers that have specific job requirements. Combining this with OEWS data, users can derive estimates of occupational employment with specific requirements. Understanding the number of jobs with specific requirements can be useful to job seekers, vocational and rehabilitation experts, human resource professionals, hiring managers, safety and medical professionals, and the disability community. The estimates from these two programs can be used to understand the nature of work, benchmark job requirements, develop recruitment plans, and assess job risks.

By combining data from these programs, the BLS can provide detailed occupational employment data such as the estimated number of jobs in an occupation with a specific job requirement. Examples provided are based on the 2023 reference period estimates for ORS and 2022 reference period estimates for OEWS. Users should consider each survey’s design, methodology, and reliability before combining estimates. Note that for all combined ORS-OEWS estimates, the reliability of the combined estimate will necessarily be lower than the reliability of each individual estimate. For more information about each survey’s design, see the ORS Handbook of Methods and the OEWS overview.

Basic approach

To estimate the occupational employment with specific job requirements, multiply the percentage of workers that have the selected job requirements from ORS by the occupational employment from OEWS. Some job requirements may be grouped together and are considered additive, such as levels of strength (e.g., light or medium) or specific vocational preparation (SVP). Additive job requirements can be added together before calculating the occupational employment. For more information, see the category and additive codes factsheet.

For example, to calculate the number of general office clerks required to have specific vocational preparation of a month or less, sum the SVP 1 and 2 estimates.

2.0 percent require a short demonstration only (SVP 1)
33.7 percent require beyond a short demonstration, up to and including one month (SVP 2)

SVP levels are additive, so 35.7 percent of general office clerks are required to have a month or less of specific vocational preparation.

There are 2,517,350 general office clerks in the national economy according to OEWS. Therefore, 2,517,350 multiplied by 35.7 percent means there are around898,694 general office clerks in the national economy where the minimum specific vocational preparation required is a month or less.

Cumulative SVP estimates and their standard errors are available in the Cumulative estimates for selected job requirements dataset. More information on the cumulative estimates is available in the August 2024 special release ORS estimates factsheet.

Range estimates

In instances where percentage of worker estimates cannot be published as point values, the estimate may be published as a range. Ranges incorporate the point estimate’s standard error to form a one-sided 90-percent confidence interval, rounded to the nearest 5 percent. For more information on ranges, see the range estimates factsheet. Calculating occupational employment using range estimates is similar to the basic approach.

Let’s consider the percentage of dental hygienists requiring light or medium strength:

44.1 percent of dental hygienists required light strength
Less than 5 percent of dental hygienists required medium strength

In this example, any percentage of dental hygienists between 0 and 5 percent required medium strength. Users could choose to use the upper bound of the range estimate when working with additive job requirements to get a sense of the number of workers with those requirements.

To calculate the maximum number of dental hygienists that could require light or medium strength:

The upper bound of the range estimate for medium strength, 5 percent, added with the percentage of dental hygienists requiring light strength, 44.1 percent, results in 49.1 percent of dental hygienists that could require light or medium strength.

Multiplying 49.1 percent and the number of dental hygienists in the economy, 214,700 workers, results in up to 105,418 dental hygienists that could be required to use light or medium strength.

Using standard errors to create confidence intervals

When considering the standard errors of combined estimates, one approach is to create a simple confidence interval around each of the estimates. Another more rigorous approach is to use a pooled standard error calculation to produce a confidence interval for the combined estimates. The pooled standard error approach assumes that the ORS and OEWS estimates are independent. More information on standard errors and confidence intervals is available on the ORS standard errors page.[1]

Simple confidence interval (90%) approach

Using the example above, there are 2,517,350 general office clerks and the relative standard error is 0.6 percent. For SVP 2, the estimate is 33.7 percent, and the standard error is 2.3 percent.

For the OEWS estimate, first convert the relative standard error to a standard error.

2,517,350 * 0.6 percent = 15,104

Next, calculate the OEWS confidence interval.

2,517,350 +/- (1.645 * 15,104) = 2,517,350 +/- 24,846 = 2,492,504 to 2,542,196

If an ORS point estimate is available, calculate the confidence interval.

33.7 +/- (1.645 * 2.3) = 33.7 +/- 3.8 = 29.9 to 37.5 percent

Alternatively, if an ORS range estimate is available, use it in place of a calculated confidence interval.

<35% = 0.0 to 35.0 percent

Using the lower bounds will result in a smaller number of workers with selected job requirements and using the upper bounds will result in more workers with those requirements.

Lower bounds: 2,492,504 * 29.9 percent ≈ 745,259 general office clerks with SVP 2

Upper bounds: 2,542,196 * 37.5 percent ≈ 953,324 general office clerks with SVP 2

Summary Calculation: (2,517,350 +/- 24,846) * (33.7 +/- 3.8) ≈ 745,259 to 953,324 general office clerks

Range estimates

This same approach of simple confidence interval (90%) is used to calculate range estimates. When estimates are provided as ranges rather than point values, a confidence interval (90%) is calculated around the estimate. If the estimate is between 0 and 50 percent, the upper bound of the confidence interval is provided and rounded up to the nearest 5 percent (e.g., less than 45 percent). If the estimate is between 50 and 100, the lower bound of the confidence interval is given and rounded down to the nearest 5 percent (e.g., greater than 75 percent).

Pooled variance (90%) approach

Users interested in a more rigorous approach to estimating the variance of a composite ORS-OEWS estimate may consider using the variance of a product of two independent random variables. The variance of a product is found using the following formula:

The variance of X multiplied by Y equals the sum of X squared and variance of X multiplied by the sum of Y squared and variance of Y minus X squared multiplied by Y squared

Continuing with the same example, this formula can be used to estimate the variance of a composite ORS-OEWS estimate as follows. Note that the composite ORS-OEWS estimate is the product of the ORS estimate and the OEWS estimate. The variance of an estimate is equal to the square of its standard error.

Variance of an ORS-OEWS estimate equals the sum of the ORS estimate squared and ORS standard error squared multiplied by the sum of the OEWS estimate squared and OEWS standard error squared minus the ORS estimate squared multiplied by the OEWS estimate squared

= ((0.337)^2 + (0.023)^2 )*((2,517,350)^2 + (15,104)^2 ) - ((0.337)^2*(2,517,350)^2)

= 3,378,329,261

Standard error of an ORS-OEWS estimate equals the square root of the variance of an ORS-OEWS estimate

Therefore, the 90% confidence interval for this estimate is:

The ORS-OEWS estimate plus or minus 1.645 multiplied by the standard error of the ORS-OEWS estimate

≈ (0.337*2,517,350) +/- (1.645*58,123)

≈ 848,347 +/- 95,613

≈ 752,734 to 943,960 general office clerks

Using the lower bound will provide a smaller number of workers with selected job requirements (approximately 752,734 general office clerks with SVP 2) and using the upper bound will provide more workers with those requirements (approximately 943,960 general office clerks with SVP 2).

Geographic areas

OEWS provides employment estimates at multiple levels of geographic detail—national, state, metropolitan, and nonmetropolitan areas—while the ORS provides estimates representative of the national economy and not by geographic area. Users should keep in mind the level of accuracy required for their specific purpose, particularly when using these methods for state, metropolitan, and nonmetropolitan areas.

Users should be aware that although ORS data is not produced by state, metropolitan, or nonmetropolitan areas, certain requirement estimates may differ by region for a variety of reasons. Users should evaluate regional differences before combining estimates from ORS. The following are a few examples of factors that may cause regional differences in requirements.

Credentials for some occupations

The credentials required for some occupations may vary based on state and local laws. Some occupations only require licenses in certain states to operate. Here are a few examples of occupations that have license requirements in some, but not all, states: hairdressers, security guards, electricians, and plumbers. Electricians require a license to perform their work in many states, but in others, like New York, there are no laws requiring credentials, prior work experience, or on-the-job training for electricians. See the National Conference of State Legislatures' National Occupational Licensing Database for more information.

Outdoor exposure

Based on climate and geographical differences as well as differences in industry composition between specific regions, it is reasonable to expect that the frequency of occupational requirements for outdoor exposure would not be consistent for an occupation when comparing different state and metropolitan areas. For example, a cashier in Florida may be required to spend more time outdoors than a cashier in a northern state.

Union membership

Union membership rates vary widely across the United States. This will likely correlate to differences in the requirements placed on workers, especially when looking at localities with large differences in union participation.

Endnotes

[1]A more rigorous approach, beyond the scope of this factsheet, is to use parametric bootstrapping to estimate the variance of a combined ORS-OEWS estimates. Replicate samples can be generated for job requirements estimates by drawing random estimates from the normal distribution defined by the estimate and its standard error. The same can be done for a corresponding OEWS employment estimate. For each replicate, the randomly sampled ORS estimate and the randomly sampled OEWS estimate can be multiplied to produce a combined ORS-OEWS estimate. The combined estimates for each replicate can in turn be used to produce a variance estimate for the combined ORS-OEWS estimate:

The variance of theta bar equals 1 over R minus 1 multiplied by the sum of the square of theta hat sub r minus theta bar as r goes from 1 to R

where:

represents the ORS estimate multiplied by the OEWS estimate,

represents the number of bootstrap replicates, and

represents the product of the randomly sampled ORS estimate and the randomly sampled OEWS estimate for each replicate r.

The standard error for the estimate is the square root of the variance.

Other methods for calculating the reliability of estimates are included for reference, see A measurement error model approach to survey data integration: combining information from two surveys and Data Fusion: Identification Problems, Validity, and Multiple Imputation. The authors and funding for the research are not affiliated with the BLS.

Last Modified Date: August 13, 2026