From the AEA Committee on Economic Statistics staff:
BLS invites comments on the Report on Occupational Employment and Wages (OES) survey methodology, data products, and research, by December 23, 2019. https://www.federalregister.gov/documents/2019/10/23/2019-23068/information-collection-activities-comment-request
The draft OES narrative and data collection instruments are available for your review here: https://www.dropbox.com/sh/grtx8v3ty3lbz2a/AAAnqmlccZO_hFe4Ou1zKk-Ia?dl=0
The OES employment data are used as inputs to the Employment Cost Index, the Occupational Requirements Survey, and estimates of Occupational Injury and Illness rates. Data are used by the Centers for Medicare and Medicaid Services in the calculation of reimbursement rates for Medicaid and Medicare providers. Special tabulations of workers in Science, Technology, Engineering, and Math (STEM) are provided annually to the National Science Foundation. Additionally, special tabulations of employment by State, industry, and wage range are supplied to the Bureau of Economic Analysis for estimating Social Security payments from employers.
Scope--The OES measures occupational employment and wage rates of wage and salary workers in nonfarm establishments in the 50 States and the District of Columbia. Guam, Puerto Rico, and the Virgin Islands are also surveyed, but their data are not included in national estimates. The survey covers the following NAICS industry sectors:
11 Logging (1133), support activities for crop production (1151), and support activities for animal production (1152) only
42 Wholesale Trade
44-45 Retail Trade
48-49 Transportation and warehousing
52 Finance and insurance
53 Real estate and rental and leasing
54 Professional, scientific, and technical services
55 Management of companies and enterprises
56 Administrative and support and waste management and remediation services
61 Educational services
62 Health care and social assistance
71 Arts, entertainment, and recreation
72 Accommodation and food services
81 Other services, except public administration [private households (814) are excluded]
99 Federal, State, and local government (OES designation)
Sample Size--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 180,000 establishments selected each year.
Stratification--Units on the sampling frame are stratified by State/Metropolitan Statistical Area (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.
Allocation method--A variation of Neyman allocation procedure called a Power Allocation (Bankier, 1988) is used to allocate the non-certainty sample to each State-/area/3-4-5-6-digit NAICS stratum. The allocation methodology balances employment size of areas and industries with the variability of the occupational employment in each industry. The use of the power allocation shifts sample away from very large areas and industries to medium and smaller ones allowing more comparable estimates for smaller domains.
Frequency of Sampling--Each year, semiannual panels of about 180,000 to 190,000 establishments each are selected for the May and November reference periods.
Estimation Procedures -- Annual estimates of occupational employment and wage rates are produced using data from the current May semiannual panel’s survey and survey data from the five semiannual panels prior to the current panel (a total sample of about 1,080,000 establishments). Data from several panels are combined in order to reduce the sampling error of the estimates at detailed levels (MSA by 3-4-5-6 digit NAICS). Combining samples from six panels increases the sample population counts at the total level by six fold. However, not all cells have sample in them in each panel because many of the detailed cells are sparsely populated. To avoid a multiple count of up to six fold, the sampling weight for each establishment in a “State-MSA/3-4-5-6 digit sampling cell” is reduced by a factor 1/d, where, d is a counter indicating if the sampling cell has establishments from 1, 2, 3, 4, 5, or 6 panels. The six-panel combined sample weight is used to calculate the employment and wage estimates.
Model-Based Estimation Development -- The Model-Based Estimation using 3 years of data (MB3), a product of a long term research project, is under development with potential for use in production. Testing indicates that the accuracy and reliability of the estimates improved over the current approach. This system is one of the components necessary to produce future estimates appropriate for year-to-year comparisons. 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 new methods.
The MB3 system 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. 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 system are presented in the Monthly Labor Review article, Model-based estimates for the Occupational Employment Statistics Program
Response Rate -- A goal of the OES survey is that each State achieves an 80 percent response rate. The overall response rate for the 2018 survey was approximately 71.2 percent based on units.
The supporting statement describes a number of current and future research projects. In particular -- Asking employers for additional data elements:
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 OES report that we do not ask for. These data elements include information that is requested by customers, but cannot be provided by OES 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 OES 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.