Chart book: Occupational Employment and Wages, May 2010

This chart book, Occupational Employment and Wages, 2010, includes graphs, maps, tables, and text describing the U.S. occupational workforce in May 2010. It contains Occupational Employment Statistics (OES) employment and wage data for occupations employed in different industries, States, and metropolitan and nonmetropolitan areas. The material cited below is drawn from this chart book.


Charts, Maps, and Tables

Occupational Employment and Wages, 2010 chart book (complete book as PDF, 7 MB)

Page-by-page breakout:

  • Cover, Preface, Acknowledgments, and Contents (PDF)
  • Organization of charts and applications of OES data (PDF)
  • OES survey coverage, scope, and concept definitions (PDF)


    • Overview (HTML)

  • Figure 1. Employment and percent of total employment for the largest and smallest occupational groups, May 2010 (HTML) (PDF)
  • Figure 2. Annual mean wages for the highest and lowest paying occupational groups, May 2010 (HTML) (PDF)
  • Figure 3. Distribution of private and public sector employment by selected occupational group, May 2010 (HTML) (PDF)
  • Figure 4. Employment and annual mean wages for the largest occupations in the private sector, May 2010 (HTML) (PDF)
  • Figure 5. Employment and annual mean wages for the largest occupations in the public sector, May 2010 (HTML) (PDF)
  • Figure 6. Employment and annual mean wages for the largest occupations in retail trade, May 2010 (HTML) (PDF)


    • STEM (Science, Technology, Engineering, and Mathematics) (HTML)

  • Figure 7. Employment and annual mean wages for the largest STEM occupations, May 2010 (HTML) (PDF)
  • Figure 8. Industry employment for biomedical engineers, May 2010 (HTML) (PDF)
  • Figure 9. Employment and annual mean wages for the largest occupations in scientific research and development services, May 2010 (HTML) (PDF)
  • Figure 10. Employment and annual mean wages for the largest occupations in communications equipment manufacturing, May 2010 (HTML) (PDF)
  • Figure 11. Annual mean wages for the highest and lowest paying life and physical science occupations, May 2010 (HTML) (PDF)
  • Figure 12. Annual mean wages for the highest and lowest paying architecture and engineering occupations, May 2010 (HTML) (PDF)
  • Figure 13. Metropolitan areas with the highest concentrations of biochemists and biophysicists, May 2010 (HTML) (PDF)
  • Figure 14. Metropolitan areas with the highest concentrations of mechanical engineers, May 2010 (HTML) (PDF)
  • Figure 15. STEM occupations with the highest location quotients in Framingham, MA, May 2010 (HTML) (PDF)


    • Healthcare (HTML)

  • Figure 16. Wages for selected health assistants, May 2010 (HTML) (PDF)
  • Figure 17. Wages for selected health aides, May 2010 (HTML) (PDF)
  • Figure 18. Employment and hourly mean wages for the largest occupations in general medical and surgical hospitals, May 2010 (HTML) (PDF)
  • Figure 19. Employment and hourly mean wages for the largest occupations in the medical and diagnostic laboratories industry, May 2010 (HTML) (PDF)
  • Figure 20. Employment by occupational group in outpatient mental health and substance abuse centers, May 2010 (HTML) (PDF)
  • Figure 21. Employment by occupational group in residential mental health and substance abuse facilities, May 2010 (HTML) (PDF)
  • Figure 22. Employment of selected healthcare workers in non-healthcare related industries, May 2010 (HTML) (PDF)
  • Figure 23. Location quotient of medical transcriptionists, by area, May 2010 (HTML) (PDF)

    • Construction (HTML)

  • Figure 24. Employment and hourly mean wages for the largest construction occupations, May 2010 (HTML) (PDF)
  • Figure 25. Construction occupations with the highest mean wages, May 2010 (HTML) (PDF)
  • Figure 26. Employment of the largest occupations in the building construction industry, May 2010 (HTML) (PDF)
  • Figure 27. Mean hourly wages of the largest occupations in the building construction industry, May 2010 (HTML) (PDF)
  • Figure 28. Construction occupations with the largest percent decrease in employment between May 2006 and May 2010 (HTML) (PDF)
  • Figure 29. Construction occupations with an increase in employment between May 2006 and May 2010 (HTML) (PDF)
  • Figure 30. States with the largest percent decrease in employment of construction occupations from May 2006 to May 2010 (HTML) (PDF)
  • Figure 31. States with an increase in employment of construction occupations from May 2006 to May 2010 (HTML) (PDF)
  • Figure 32. Location quotients for construction occupations in Pascagoula, MS, May 2010 (HTML) (PDF)
  • Figure 33. Construction occupations in the San Francisco-San Mateo-Redwood City, CA, metropolitan division with mean wages at least 55 percent higher than average, May 2010 (HTML) (PDF)

    • Manufacturing (HTML)

  • Figure 34. Employment and annual mean wages for the 10 largest occupations in manufacturing, May 2010 (HTML) (PDF)
  • Figure 35. Highest paying production occupations in manufacturing, May 2010 (HTML) (PDF)
  • Figure 36. Location quotient of team assemblers, by state, May 2010 (HTML) (PDF)
  • Figure 37. Annual mean wage of team assemblers, by state, May 2010 (HTML) (PDF)
  • Figure 38. Occupations with the largest location quotients in Elkhart-Goshen, IN, May 2010 (HTML) (PDF)
  • Figure 39. Employment and hourly mean wages for the largest occupations in textile mills, May 2010 (HTML) (PDF)
  • Figure 40. Employment and hourly mean wages for the largest occupations in chemical manufacturing, May 2010 (HTML) (PDF)


Preface

This chartbook, Occupational Employment and Wages, 2010, is a product of the Occupational Employment Statistics (OES) program of the U.S. Bureau of Labor Statistics (BLS). The OES program produces employment and wage estimates for nearly 800 occupations by geographic area and industry.

For every occupation, the OES program has data on the total U.S. employment and the distribution of wages, including the mean wage and the 10th, 25th, 50th (median), 75th, and 90th percentiles. Occupational data for geographic areas include employment and wages for each of the 50 states, the District of Columbia, Puerto Rico, Guam, and the U.S. Virgin Islands. Local area data are available for 380 metropolitan statistical areas (MSAs), 34 metropolitan divisions within 11 of the largest MSAs, and 174 nonmetropolitan areas. National industry-specific estimates are available by industry sector and for 334 industries.

The OES survey is a cooperative effort between BLS and the state workforce agencies. Employment and wage data for nearly 800 occupations were collected from a sample of 1.2 million business establishments, employing more than 80 million workers, in 6 semiannual panels between November 2007 and May 2010. Wage data for all establishments were updated to the May 2010 reference period, and employment data were updated to the average of the November 2009 and the May 2010 reference periods. Information on OES sampling and estimation methodology is provided in the survey methods and reliability statement at www.bls.gov/oes/current/methods_statement.pdf.

Data users can create customized tables using the OES database search tool, or download complete OES data in zipped Microsoft Excel format from www.bls.gov/oes/oes_dl.htm. Material in this publication is in the public domain and, with appropriate citation, may be reproduced without permission. Questions about OES data can be directed to the information phone line at (202) 691-6569 or sent to OESinfo@bls.gov.

Acknowledgments

The information in this chart book is possible through the cooperation of more than a million business establishments that provide information on their workers to their state workforce agency and the U.S. Bureau of Labor Statistics (BLS). State workforce agencies within each state collect and verify almost all data provided. BLS selects the sample, produces the estimates, and provides technical procedures and financial support to the states. BLS also collects a small portion of the data from employers. BLS produced this chartbook with contributions from Claudia Calderón, Benjamin Cover, Swati Patel, Laurie Salmon, George Stamas, and Audrey Watson. Cover art, typesetting, and layout were performed by Bruce Boyd and editorial services were provided by Maureen Soyars.

Organization of charts and applications of OES data

The presentation of figures in this chartbook is intended to demonstrate a variety of applications of OES data. Figures are organized into five sections: the first focuses on a general overview of OES data, the others highlight occupational, geographic, and industry topics in jobs related to construction, healthcare, manufacturing, and STEM (science, technology, engineering, and mathematics). The following are some examples of useful applications of OES data:

Detailed occupational data can be used by jobseekers or employers to study wages for workers in certain occupations and to assess wage variation within and across occupations. Wage variation within an occupation can result from several factors, including industry, geographic location, or a worker’s individual experience or qualifications. Useful data for jobseekers include information on the industries or geographic areas that have the highest employment or the highest average wages for an occupation. Career and guidance counselors can use OES data to examine information on the possible occupational choices of their clients.

Industry-specific occupational data can be used by human resources professionals in salary negotiations or to ensure that their wages are competitive with those of other businesses in their area or industry. Information on the types of jobs within an industry can be used to compare average staffing patterns with the staffing pattern of one’s own company. Occupational employment by industry may be useful in assessing the impact of shifts in technology and other macroeconomic trends on the types of jobs available. BLS and state government employment projections programs use OES data as an input to their employment projections, which can be used to predict training and education demands.

Information about geographic areas can be used to assess labor market features of a particular area. OES state-level data can be used to make assessments about the diversity of a state’s economy or to make comparisons among states. The occupational composition of employment can provide clues to how a state or regional economy can hold up in adverse conditions that affect a certain sector of the economy. Differences in both occupational composition and occupational wage rates also help explain differences in average wages across states. For example, states with high average wages may have larger employment shares of high-paying occupations, higher wages within each occupation, or some combination of both factors.

Like state data, metropolitan and nonmetropolitan area data can be used to study the diversity of local area economies. Businesses can use data to see whether it might be beneficial to relocate to a particular area. OES wage data can be used to compare wages across alternative areas as part of an analysis of labor costs. OES occupational employment data may indicate whether workers are available in occupations that the business will need. For example, businesses that require computer specialists or skilled production workers may want to identify areas that have high employment in these occupations.

OES survey coverage, scope, and concept definitions

The OES survey covers all full- and part-time wage and salary workers in nonfarm industries. The survey does not include the self-employed, owners and partners in unincorporated firms, workers in private households, or unpaid family workers.

An occupation is a set of activities or tasks that employees are paid to perform. Employees who perform essentially the same tasks are in the same occupation, whether or not they are in the same industry. Workers who may be classified in more than one occupation are classified in the occupation that requires the highest level of skill. If there is no measurable difference in skill requirements, workers are included in the occupation in which they spend the most time. Most occupations are classified by the 2010 Standard Occupational Classification (SOC) system.

An industry is a group of establishments that have similar production processes or provide similar services. For example, all establishments that manufacture automobiles are in the same industry. A given industry, or even a particular establishment in that industry, might have employees in many different occupations. The North American Industry Classification System (NAICS) groups similar establishments into industries.

The employment shown is the average employment for the most recent May and November (in this chartbook, May 2010 and November 2009). Employment is defined as the number of workers who can be classified as full- or part-time employees, including workers on paid vacations or other types of paid leave; workers on unpaid short-term absences; salaried officers, executives, and staff members of incorporated firms; employees temporarily assigned to other units; and employees whose reporting unit is their permanent duty station, regardless of whether that unit prepares their paycheck.

Wages for the OES survey are straight-time gross pay, exclusive of premium pay. Included are base rate; cost-of-living allowances; guaranteed pay; hazardous-duty pay; incentive pay, including commissions and production bonuses; and tips. Excluded are back pay, jury duty pay, overtime pay, severance pay, shift differentials, non-production bonuses, employer cost for supplementary benefits, and tuition reimbursements.

Respondents are asked to report the number of employees paid within specific wage intervals, regardless of whether the employees work part time or full time. The responding establishment can reference either the hourly or the annual rate for full-time workers but are instructed to report the hourly rate for part-time workers. Intervals are defined both as hourly rates and the corresponding annual rates, where the annual rate for an occupation is calculated by multiplying the hourly wage rate by a typical work year of 2,080 hours.

Geographic areas are defined by the Office of Management and Budget. Guam, Puerto Rico, and the U.S. Virgin Islands are also surveyed; their data are not included in this publication, but are published on the OES website. The nationwide response rate for the May 2010 survey was 78.2 percent based on establishments and 74.4 percent based on employment. More information on sampling and estimation methodology can be found in the survey methods and reliability statement on website at www.bls.gov/oes/current/methods_statement.pdf.

 

Last Modified Date: November 18, 2011