Handbook of Methods > Occupational Employment and Wage Statistics

Handbook of Methods Occupational Employment and Wage Statistics Presentation

Occupational Employment and Wage Statistics: Presentation

The Occupational Employment and Wage Statistics (OEWS) program publishes cross-industry occupational data for the United States as a whole, for individual states, and for metropolitan and nonmetropolitan areas, along with U.S. industry-specific estimates by 2-, 3-, most 4-, and some 5- and 6-digit NAICS levels. Public–private sector ownership data are available for all industries combined and for schools and hospitals. OEWS publishes employment and wage estimates aggregated by typical entry-level educational requirements and for science, technology, engineering, and mathematics (STEM) occupations. OEWS also publishes a research dataset of estimates by state and industry.

Available data elements include estimates of employment, hourly and annual mean wages, and hourly and annual percentile wages by occupation, as well as relative standard errors (RSEs) for the employment and mean wage estimates.

OEWS data are updated on an annual basis. When updated estimates become available, we announce featured data highlights in a news release.

Accessing OEWS data

OEWS data are available in several formats on the OEWS home page. The OEWS database search tool allows customers to create customized data tables using the most recent OEWS estimates. The OEWS profile application and mapping application allow data users to create occupational profiles and state and area maps using the most recent data. OEWS data can be downloaded as zipped XLSX files from the main OEWS data page. Additional OEWS datasets for STEM occupations and by typical entry-level educational requirements and research datasets by state and industry are also available.

Reliability of the estimates

Estimates developed from a sample will differ from the results of a census. An estimate based on a sample survey is subject to two types of error: sampling and nonsampling error. An estimate based on a census is subject only to nonsampling error.

The OEWS model-based estimation method (MB3) relies on a statistical model that uses information from the population and survey data to predict occupational employment and wage information needed for the OEWS estimates. Under this kind of prediction-based estimation approach, the variance of the estimates is derived from the uncertainty of the model used for prediction, rather than from the sample design. The variability of the estimates is a function of how well the model fits the data. This is measured by the model error term (residuals), which is equal to the difference between the predicted and actual outcomes. This differs from design-based estimation, where variance estimates reflect the variability in the population that arises due to the sample design and how the sample is selected.

Nonsampling error

This type of error is attributable to several causes, such as:

Errors in the sampling frame
Inability to obtain information for all establishments in the sample
Differences in respondents' interpretation of a survey question
Inability or unwillingness of the respondents to provide correct information
Errors made in recording, coding, or processing the data
Errors made in imputing values for missing data

Explicit measures of the effects of nonsampling error are not available.

Sampling error

When a sample, rather than an entire population, is surveyed, estimates differ from the true population values that they represent. This difference, the sampling error, occurs by chance and its variability is measured by the variance of the estimate or the standard error of the estimate (square root of the variance). The relative standard error is the ratio of the standard error to the estimate itself.

Estimates of the sampling error for occupational employment and mean wage rates are provided for all employment and mean wage estimates to allow data users to determine if those statistics are reliable enough for their needs. Only a probability-based sample can be used to calculate estimates of sampling error. The formulas used to estimate OEWS variances are adaptations of formulas appropriate for the survey design used.

The sample used in the OEWS survey is one of many possible samples of the same size that could have been selected using the same sample design. Sample estimates from a given design are said to be unbiased when an average of the estimates from all possible samples yields the true population value. In this case, the sample estimate and its standard error can be used to construct confidence intervals, or ranges of values that include the true population value with known probabilities.

To illustrate, if the process of selecting a sample from the population were repeated many times, if each sample were surveyed under essentially the same unbiased conditions, and if an estimate and a suitable estimate of its standard error were made from each sample, then the following calculations would be accurate:

Approximately 68 percent of the intervals from 1.0 standard error below to 1.0 standard error above the estimate would include the true population value. This interval is called a 68-percent confidence interval.
Approximately 90 percent of the intervals from 1.6 standard errors below to 1.6 standard errors above the estimate would include the true population value. This interval is called a 90-percent confidence interval.
Approximately 95 percent of the intervals from 2.0 standard errors below to 2.0 standard errors above the estimate would include the true population value. This interval is called the 95-percent confidence interval.
Almost all (99.7 percent) of the intervals from 3.0 standard errors below to 3.0 standard errors above the estimate would include the true population value.

For example, suppose that an estimated occupational employment total is 5,000, with an associated estimate of relative standard error of 2.0 percent. Based on these data, the standard error of the estimate is 100 (2 percent of 5,000 occupational employment). To construct a 90-percent confidence interval, add and subtract 160 (1.6 times the standard error multiplied by the standard error of 100) from the estimate to produce a confidence interval of 4,840 to 5,160. Approximately 90 percent of the intervals constructed in this manner will include the true occupational employment if survey methods are nearly unbiased.

Estimated standard errors should be taken to indicate the magnitude of sampling error only. They are not intended to measure nonsampling error, including any biases in the data. Particular care should be exercised in the interpretation of small estimates or of small differences between estimates when the sampling error is relatively large or the magnitude of the bias is unknown.

Data correction

If an error is found in a published OEWS data product (news release, data table, etc.), the product is corrected and republished, or incorrect data products are removed. A record of the error is added to the list of BLS errata, a notice describing the error is posted on the OEWS website, and data users who have signed up to receive notifications from the OEWS program are alerted via email.

Uses

The OEWS survey is a source of detailed occupational employment data for many data users, including individuals and organizations engaged in planning vocational education programs, higher education programs, and employment and training programs. OEWS data also are used to prepare information for career counseling, for job placement activities performed by state workforce agencies, and for personnel planning and market research conducted by private enterprises.

Occupational employment data are used to develop information regarding current and projected employment needs and job opportunities. This information is used in the production of state education and workforce development plans. These data enable users to analyze the occupational composition of different industries and to compare occupational composition across states and local areas, including analysis for economic development purposes. OEWS employment estimates also are used as job placement aids by helping to identify industries that employ the skills gained by enrollees in career-technical training programs. In addition, OEWS survey data serve as primary inputs into occupational information systems designed for those who are exploring career opportunities or assisting others in career decision making.

OEWS data are used by several other BLS and government programs, such as the BLS Occupational Outlook Handbook, Employment Projections program, and the U.S. Department of Labor Employment and Training Administration (ETA). OEWS data are used to establish the fixed employment weights for the Employment Cost Index and in the calculation of occupational rates for the Survey of Occupational Injuries and Illnesses. The Department of Labor Foreign Labor Certification (FLC) program also uses OEWS data in administering visa programs. OEWS data are used as an input into federal locality pay recommendations for the President’s Pay Agent and used for setting prevailing wages for federal contracting. OEWS employment and wage data are used in ETA's CareerOneStop.

Many OEWS data users rely on data provided by the state labor market information programs. OEWS data are used by workforce investment boards and economic development programs to attract businesses. The data provide information on labor availability by occupation as well as wages.

Occupational wage data are also used by jobseekers and employers to gather wage and salary information for different occupations in different locations or different industries.

For additional information, contact the OEWS staff at 202-691-6569 or use the online form to submit an inquiry by email.

Last Modified Date: December 30, 2025