1) Apr 12 -- OEWS user survey: The U.S. Bureau of Labor Statistics is conducting a survey of individuals and groups who use data developed as part of the federal-state Occupational Employment and Wage Statistics (OEWS) cooperative program. The survey covers topics including how the data are used, adequacy of the data detail, program documentation, and potential enhancements for the program. As a valued OEWS customer, we appreciate your input. This voluntary survey will take approximately 6 minutes to complete. The survey link (https://www.research.net/r/YNNCV8K
) will be open from April 11, 2023 to May 9, 2023. https://www.bls.gov/oes/update.htm
2) Apr 10 -- The Bureau of Labor Statistics (BLS), Department of Labor (DOL), invites comment to OMB by May 10, 2023 regarding the Occupational Employment and Wages Statistics (OEWS) survey.
The OEWS is a Federal/State establishment survey of wage and salary workers designed to produce data on current detailed occupational employment and wages for each MSA and by detailed industry classification. The OEWS program operates a periodic mail survey of a sample of non-farm establishments conducted by all States, DC, Guam, Puerto Rico, and the U.S. Virgin Islands. Over three-year periods, data on occupational employment and wages are collected by industry at the four- and five-digit North American Industry Classification System (NAICS) levels.
With the release of the May 2021 OEWS estimates in March 2022, the OEWS program implemented a new model-based estimation methodology (MB3). The MB3 methodology uses modeling to predict the staffing pattern and wages for every non-observed establishment on the OEWS population frame using observed OEWS response data along with current data from the Quarterly Census of Employment and Wages program. This differs from the older design-based methodology that used weighting and imputation to make the OEWS response data represent the OEWS population frame. Research and testing indicated the accuracy and reliability of the MB3 estimates improved over the former approach.
The universe for this survey consists of the Quarterly Contribution Reports (QCR) filed by employers subject to State Unemployment Insurance (UI) laws. The U.S. Bureau of Labor Statistics (BLS) receives these QCR for the Quarterly Census of Employment and Wages (QCEW) Program from the 50 States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. The QCEW data, which are compiled for each calendar quarter, provide a comprehensive business name and address file with employment, wage, detailed geography (i.e., county), and industry information at the six-digit NAICS level. This information is provided for nearly ten million business establishments of which about 7.9 million are in the scope of this survey. The final data is stored in a Longitudinal Data Base (LDB), which is then used as a sampling frame for sample selection. Data for Federal Government employees covered by the Unemployment Compensation for Federal Employees program (UCFE), the US Postal Service, and the Tennessee Valley Authority are also included. Other data used for sampling include the universe of railroad establishments obtained from Federal Railroad Administration and a universe of establishments in the U.S. territory of Guam obtained from the Government of Guam, Department of Labor.
The sample size is approximately 1.1 million establishments over a 3-year period. The sample is divided into six panels over three years with two semi-annual samples of about 187,000 establishments selected each year. Units on the sampling frame are stratified by State/MSA and Balance of State, and by three-, four-, five-, or six-digit NAICS industry code. The frame is further stratified into certainty and non-certainty portions for sample selection. Certainty units include Federal and State governments, hospitals, railroads, and large establishments. These are sampled with probability equal to one every 3-year cycle because of their occupational employment coverage and economic significance. All remaining establishments are non-certainty establishments and are selected with probability less than one but greater than zero.
The Model-Based Estimation using 3 years of data (MB3), a product of a long-term research project, was first used in official production for the 2021 OEWS estimates, which were published in March 2022. Testing indicates that the accuracy and reliability of the MB3 estimates improved over the former approach. In September 2019, BLS published 2016 data using the new estimation method as a research series, and a Monthly Labor Review article describing the method with comparisons to the old methods. Additional research series for 2017 through 2020 have since been published.
The MB3 method takes advantage of the fact that BLS observes key determinants of occupational staffing patterns and wages for all units in a target population. In particular, the QCEW provides data on the detailed industry, ownership status, geographic location, and size for every establishment whose workers are covered by state unemployment insurance laws. OEWS sample information is used to model wage distributions and industry/area/size/ownership/time wage adjustments. The estimation system includes redesigned components for model fitting, unit matching, and variance estimation. Further details of the method are presented in the Monthly Labor Review article, Model-based estimates for the Occupational Employment Statistics Program and the Survey Methods and Reliability Statement for MB3 Research Estimates of the Occupational Employment and Wage Statistics Survey (MB3 Survey Methods Statement).
Occupational employment and wage estimates are computed using observed data and predicted data for the population of about 9 million units. Predicted data are created for each unobserved unit of the population, so estimates are computed using full-population expressions.
Estimates of occupational employment totals are computed by summing all employment counts of a given occupation over the modeled population data. Estimates are made over area, industry, and ownership.
BLS would like to ask employers to report data items that many already report without solicitation. Often employers include additional data items such as the number of hours the person is paid for. Many employers already provide many data elements in their electronic OEWS report that we do not ask for. These data elements include information that is requested by customers, but cannot be provided by OEWS or other BLS surveys. For example, establishments report data items along with the occupation and wages such as: part-time or full-time status, hours, whether or not employees are exempt from the Fair Labor Standards Act, gender, age, EEO category, union status, specific job title, department, and others. While some of these occupational characteristics are available from other BLS sources, none are available for states and all areas, and in the case of demographic data, they cannot be associated with a particular employer’s industry or size, and are not available for many occupations. A small-scale test successfully collected extra data elements. These results showed that extra elements can be collected from respondents. While the test was limited in time and scope, the response rates mirrored those of regular OEWS data collection. Also, a Response Analysis Survey (RAS) conducted in 2011-12 showed that most employers are willing to provide additional data like hours worked and part-time/full-time status. BLS would like to continue this research.
Multiunit companies often report occupational wage data for all establishments rather than just providing data for the requested sampled establishments. Since so many employers provide this information without being asked, BLS would like to explore how employers would respond if we specifically asked employers to provide these data. Many employers provide comprehensive electronic data files or data dumps containing payroll data for all of their establishments every year, rather than providing data for just the sampled establishment. The OEWS analyst sorts through the reports, and matches them to the sampled units, saving the respondent the burden of doing so. The OEWS analysts ignore the unsolicited establishments. Some of the volunteered establishments might be included in the OEWS sample a different year, and the newer data will be solicited. For the units that aren’t in the 6-panel sample used for estimates, their inclusion might help local area estimates. Capturing newer data for units that are in older panels might improve the currency of the data. While OEWS is not a time series, there are many customers that would like to use it this way. Capturing data for some employers that report electronically every panel might facilitate the time series qualities of OEWS data. BLS is interested in testing ways to improve time series. Asking more multi-unit reporters to report all their data, rather than selected sample units is one way to do so.
Two categories included in the data dumps have already been proven useful in quality control. For example, hours worked data provided by some airlines helped to improve wage estimates for pilots and flight attendants. Wage rate data has shown the necessity to use wage rate data rather than intervals for the US Post office, where even nationwide, occupational wages are clustered. The job titles provided in the data dumps have helped to find job titles that are coded in the wrong occupations, or paid employees, such as students, who should not be in the scope of the OEWS survey. We would like to explore the possibility of asking selected employers to provide this data in their OEWS report to address any bias that may be the result of self-selection to report this data.
OEWS website: https://www.bls.gov/oes/
BLS submission to OMB: https://www.reginfo.gov/public/do/PRAViewICR?ref_nbr=202303-1220-005
Click IC List for information collection instrument, View Supporting Statement for technical documentation. Submit comments through this webpage.
For AEA members wishing to submit comments, "A Primer on How to Respond to Calls for Comment on Federal Data Collections" is available at https://www.aeaweb.org/content/file?id=5806