The role of the Occupational Employment Statistics survey in shaping workforce data

Alexia Maggos

The employment and wage data collected via the OES survey are essential labor market indicators with a variety of uses.

Knowing which occupations are growing or declining, where jobs are being created, what skills are in demand, how much someone can expect to earn in a given occupation, and what educational attainment is needed to advance in a career field are all fundamental data points that (should) drive consumer decision making and career counseling, from K-16 through retirement. At the Indiana Department of Workforce Development (IDWD), this is exactly the perspective and labor market information we produce and use to structure effective education and training programs and share publicly through, so Hoosiers can live their best lives.

In providing these economic perspectives, having accurate and timely data is paramount to informing business decisions, career choices and training programs for employers, job seekers and other interest groups. Two notable sources of the data that IDWD collects and analyzes come directly from employers via Quarterly Census of Employment and Wages (QCEW) reports and Occupational Employment Statistics (OES) surveys. Nationally, state workforce agencies (e.g., IDWD) work cooperatively with the U.S. Bureau of Labor Statistics (BLS) to collect these data points, estimating employment and wages for over 800 occupations.

Through the QCEW, employers report industry-level employment and wage data for each of their employees as is required for the administration of the Unemployment Insurance Program. The OES survey samples a portion of these same employers to dive deeper into their quarterly wage reports, gathering more occupational-specific information. All states, including the District of Columbia, Puerto Rico, Guam and the Virgin Islands, distribute the OES survey semi-annually to nonfarm establishments. To reduce respondent burden, establishments are only surveyed at most once every three years.

What data are collected

The OES survey collects wage and employment estimates. Defining these two terms is critical for gathering accurate, relevant data. Wages are defined by the BLS as any straight-time gross pay, including:

  • Base rates – payment for work performed during a specified time
  • Commissions – payment calculated as a percentage of sales
  • Cost-of-living allowances – a change in wages utilizing a cost-of-living formula
  • Deadheading pay – payment for workers’ time spent unloading/loading a return trip
  • Guaranteed pay (also known as “make-up pay”) – this is the minimum payment to an employee on an incentive system (e.g., if a worker is guaranteed payment for two hours of work, but their day only consisted of 1.5 hours of work, they are still paid for the two hours)
  • Hazard pay (also known as “add-on to base rate”) – payment based on working conditions
  • Incentive pay (also known as “pay-for-performance”) – compensation to a worker based on performance
  • Longevity pay – payment that an employee receives for seniority with an employer
  • Over-the-road pay – payment by the mile
  • Piece rates – payment based on a constant pay rate per unit of production
  • Portal-to-portal rates – payment for travel to a job
  • Production bonuses – payment based on excess production of a quota/completion of a job within a standard time
  • Tips – voluntary payment in addition to an employee’s base rate for services provided

Any other wages or benefits are excluded. Employment, on the other hand, includes both part- and full-time workers paid either a wage or a salary.

Confidentiality of records

There is a confidentiality aspect that is central to the BLS’s mission, wherein the data collected from the OES survey are used only for statistical purposes. As an example, consider if a state workforce agency received wage and employment data from the only pharmaceutical sales company in County X, and publishing this data would reveal the wage and employment information of this company. To ensure confidentiality, prior to releasing any statistical data of County X, the data would be thoroughly reviewed by the BLS (looking at geographic location, company size and industry code) to ensure no business or individual can be identified. Furthermore, respondents are protected under federal laws like the Confidential Information Protection and Statistical Efficiency Act, the Privacy Act, the Workforce Innovation and Opportunity Act, and the Trade Secrets Act.

The OES process

Occupation coding

Once the raw employment and wage data are collected from employers, state workforce agencies manually analyze and classify each job reported into a detailed occupation as required by the Standard Occupational Classification (SOC) system. This coding is an important function of the OES survey because employers may have differing job titles for the same occupation. As an example, a labor analyst from a state workforce agency may come across the following job titles in an OES survey panel: concrete foreman, construction area manager, construction superintendent and general contractor. While these job titles differ among employers, the labor analyst may contact the employer to clarify day-to-day job duties of the reported job title. The analyst may find that these different job titles have common tasks that align with the all-encompassing standard occupational code for construction managers (SOC Code 11-9021).

SOC codes (a BLS statistical standard) provide a classification system standardizing all workers across the nation. There are 867 detailed occupations, but workers are classified at four levels of aggregation to determine the detailed occupation. Using the SOC code 11-9021 (construction managers):

  • 11-9021 indicates that the occupation falls within the major group of “management occupations.”
  • 11-9021 indicates the minor group of “other management occupations.”
  • 11-9021 indicates the broad occupation of “construction managers.”
  • 11-9021 indicates the detailed occupation of “construction managers.”

Estimating employment

Once categorized into appropriate standardized occupations, OES then provides these wage and employment estimates by occupation for specific industries, individual states, and metropolitan and nonmetropolitan areas. Industry-specific estimates refer to wage and employment data within one specific industry. Because certain industries employ occupations that may not be found in other industries, the staffing pattern provides an industry-specific resource both at the national and state levels. Table 1 looks at the national-level wage and employment data for the top five occupations that have the highest percentage of jobs within the manufacturing industry.

Table 1: Top five occupations in U.S. manufacturing, 2017-2018

SOC Occupation Employed in industry, 2017 Employed in industry, 2018 Median hourly earnings Percent of total jobs in industry, 2018
51-2098 Assemblers and fabricators, all other,including team assemblers 959,185 956,023 $14.83 7.5%
51-1011 First-line supervisors of production and operating workers 445,352 452,165 $28.02 3.5%
51-9061 Inspectors, testers, sorters, samplers and weighers 336,746 334,460 $18.20 2.6%
51-4041 Machinists 315,800 321,982 $20.45 2.5%
53-7062 Laborers and freight, stock, and material movers, hand 310,372 314,881 $13.10 2.5%

Source: Data was collected from EMSI’s Staffing Pattern function, which primarily utilizes the OES staffing pattern

Further information provided at the state level are cross-industry occupational employment and wage estimates. Cross-industry estimates differ from industry-specific estimates, as these data refer to wage and employment data in all industries in which an occupation is reported. As an example, a machinist (SOC code 51-4041), while primarily found in the manufacturing industry (82.1 percent), also was reported in administrative, support, waste management and remediation services; wholesale trade; other services (including equipment and machinery repairing industries, among others); and professional, scientific and technical services; as well as other industries not shown. While Table 2 looks at the national level, these data are also available at the state and metro levels.

Table 2: Machinist cross-industry U.S. staffing pattern, 2017-2018

NAICS Industry Percent of occupation in industry
31 Manufacturing 82.1%
56 Administrative & support & waste management & remediation services 4.8%
42 Wholesale trade 4.1%
81 Other services (except public administration) 2.0%
54 Professional, scientific and technical services 1.2%

Source: Data was collected from EMSI’s Inverse Staffing Pattern function, which primarily utilizes the OES staffing pattern

Once collected, these data are then aggregated to indicate two- and 10-year wage and employment projections for each occupation.

The Indiana Department of Workforce Development also provides the data for the state’s 11 economic growth regions,1 which are a grouping of counties based on social and economic ties like commuting partners, demographics, similar industries and other quantifiable factors. (For more information, visit

OES survey limitations

There are limitations to the data collected through the OES survey. This survey is not a(n):

  • Time demographics survey
  • Estimation for total establishment employment
  • Tool to assess unemployment for specific occupations
  • Tool to assess job vacancies
  • Collection of self-employed individuals
  • Collection of public/private ownership data (with the exception of industry-specific estimates for a select few states)
  • Collection of benefits associated with occupations

Most notably, however, is that the OES survey is not a time series. A time series functions as a data collection method that observes one (or multiple) variable(s) sequentially. Because the OES survey collects data semi-annually and does not collect from the same establishments for at least three years, the survey is merely a stratified random sample to produce estimations of employment and wages by occupation.

Uses of OES data

Employment and wage data have been essential labor market indicators, and having accurate historical data and statistically sound projections aids in thorough analysis of declining and growing industries and occupations. With data being released in late March or early April each year, employers, job seekers, state and local workforce development boards, educational institutions, and policymakers can use it in a variety of ways to support and sustain a state’s economy.

For employers, these data provide a benchmark comparison among wages paid by occupation, industry and other areas. This free resource provided to employers allows them to identify the competitiveness of the salaries they offer and make informed decisions regarding compensation and other benefits packages for employees.

For job seekers, having access to their region’s in-demand and high-paying occupations helps these individuals focus their training and job applications. These individuals, whether just starting their career or transitioning careers, can then prepare for these occupations by assessing their current skill level in relation to skills or education required for entry.

For state and local workforce development boards, the OES data are a strategic tool used for business and workforce attraction planning. As mandated by the Workforce Innovation and Opportunity Act (WIOA)—a federal act helping job seekers connect to education, training, supportive services and employment opportunities (which in turn provides employers with skilled workers to compete in a global economy)—an imperative function of a state workforce development board is to develop, implement and modify a four-year state plan. To update these four-year plans (which are intended to help align the workforce's education and skills to meet the needs of employers), state boards utilize wage and employment data as provided by the OES survey. Similarly, local boards use these data to develop regional career pathways that guide job seekers toward education and training within high-paying, in-demand occupations.

For educational institutions, employment and wage projections extending two and 10 years into the future shape current and future curriculum and work-based learning opportunities. Work-based learning opportunities present themselves as partnerships among institutions and employers operating within in-demand industries (informed by OES data). These partnerships provide students relevant hands-on work experience so they are prepared to enter into these occupations post-graduation.

For policymakers, using available employment and wage data provides a clear picture of current and future workforce needs, and directs policymakers toward aligning funding streams to meet the needs of employers. As an example, Indiana’s Workforce Ready Grant—offered by the Commission for Higher Education and the Department of Workforce Development—provides tuition assistance to local Indiana community colleges and eligible training providers so students can gain skills and be prepared for in-demand occupations. The qualifying certificate programs were selected using data that OES collects and aggregates like volume of short- and long-term projected demand and growth, opening wages, and real-time employer demand.


The employer-driven data collected through the OES survey directly informs policy decisions that seek to benefit employers and inform job seekers. Furthermore, through employers’ voluntary survey contributions, state workforce agencies work collaboratively in a public-private partnership to gather and report a state’s real and accurate employment and wage data that directly influence education, workforce and economic decision-making, which benefits employers and job seekers alike.



  1. View a map of Indiana’s economic growth regions at