News Release Information
Thursday, April 25, 2019
Occupational Employment and Wages in Dallas-Fort Worth-Arlington – May 2018
Workers in the Dallas-Fort Worth-Arlington Metropolitan Statistical Area had an average (mean) hourly wage of $25.28 in May 2018, compared to the nationwide average of $24.98, according to the U.S. Bureau of Labor Statistics. Assistant Commissioner for Regional Operations Stanley W. Suchman noted that, after testing for statistical significance, 8 of the 22 major occupational groups had average wages in the local area that were significantly higher than their respective national averages, including transportation and material moving; architecture and engineering; and sales and related. Nine groups had significantly lower wages than their respective national averages, including construction and extraction; arts, design, entertainment, sports, media; and personal care and service.
When compared to the nationwide distribution, Dallas-Fort Worth-Arlington area employment was more highly concentrated in 6 of the 22 occupational groups including office and administrative support; computer and mathematical; and transportation and material moving. Conversely, 13 groups had employment shares significantly below their national representation, including personal care and service; production; and community and social service. (See table A and box note at end of release.)
|Major occupational group||Percent of total employment||Mean hourly wage|
Total, all occupations
Business and financial operations
Computer and mathematical
Architecture and engineering
Life, physical, and social science
Community and social service
Education, training, and library
Arts, design, entertainment, sports, and media
Healthcare practitioners and technical
Food preparation and serving related
Building and grounds cleaning and maintenance
Personal care and service
Sales and related
Office and administrative support
Farming, fishing, and forestry
Construction and extraction
Installation, maintenance, and repair
Transportation and material moving
Note: * The mean hourly wage or percent share of employment is significantly different from the national average of all areas at the 90-percent confidence level.
One occupational group–computer and mathematical–was chosen to illustrate the diversity of data available for any of the 22 major occupational categories. Dallas-Fort Worth-Arlington had 150,970 jobs in computer and mathematical occupations, accounting for 4.2 percent of local area employment, significantly higher than the 3.0-percent national share. The local average hourly wage for this occupational group was $45.11, measurably higher than the national average of $44.01.
Some of the larger detailed occupations within the computer and mathematical group included software developers, applications (34,260), computer user support specialists (22,960), and computer systems analysts (18,980). Among the higher-paying jobs in this group were computer and information research scientists, as well as computer network architects, with mean hourly wages of $62.87 and $60.54, respectively. At the lower end of the wage scale were computer user support specialists ($24.86) and web developers ($36.75). (Detailed data for the computer and mathematical occupations are presented in table 1; for a complete listing of detailed occupations go to www.bls.gov/oes/current/oes_19100.htm.)
Location quotients allow us to explore the occupational make-up of a metropolitan area by comparing the composition of jobs in an area relative to the national average. (See table 1.) For example, a location quotient of 2.0 indicates that an occupation accounts for twice the share of employment in the area than it does nationally. In the Dallas-Fort Worth-Arlington metropolitan area, above-average concentrations of employment were found in many of the detailed occupations within the computer and mathematical group. For instance, operations research analysts were employed at 2.0 times the national rate in the Dallas-Fort Worth area, and database administrators, at 1.7 times the U.S. average. On the other hand, web developers had a location quotient of 0.9 in the greater Dallas-Fort Worth area, indicating that this particular occupation’s local and national employment shares were similar.
These statistics are from the Occupational Employment Statistics (OES) survey, a federal-state cooperative program between BLS and State Workforce Agencies, in this case, the Texas Workforce Commission.
Area Changes to the May 2018 Occupational Employment Statistics (OES)
OES continues to publish data for metropolitan and nonmetropolitan areas that cover the full geography of the United States. However, the level of detail available has decreased.
OES no longer publishes data for metropolitan divisions. Data for the 11 large metropolitan areas that contain divisions are now available at the Metropolitan Statistical Area (MSA) or New England City and Town Area (NECTA) level only.
In addition, some smaller nonmetropolitan areas have been combined to form larger nonmetropolitan areas. The May 2018 OES estimates contain data for 134 nonmetropolitan areas, compared with 167 nonmetropolitan areas in the May 2017 estimates.
More information on these area changes is available at www.bls.gov/oes/areas_2018.htm.
Implementing the 2018 Standard Occupational Classification (SOC) System
The OES program plans to begin implementing the 2018 Standard Occupational Classification (SOC) system with the May 2019 estimates, to be released by early April of 2020. Because each set of OES estimates is produced by combining three years of survey data, estimates for May 2019 and May 2020 will be based on a combination of survey data collected under the 2010 SOC and data collected under the 2018 SOC, and will use a hybrid of the two classification systems. The May 2021 OES estimates, to be released by early April of 2022, will be the first set of estimates based fully on the 2018 SOC. For more information, please see www.bls.gov/oes/soc_2018.htm.
The Occupational Employment Statistics (OES) survey is a semiannual survey measuring occupational employment and wage rates for wage and salary workers in nonfarm establishments in the United States. The OES data available from BLS include cross-industry occupational employment and wage estimates for the nation; over 580 areas, including states and the District of Columbia, metropolitan statistical areas (MSAs), nonmetropolitan areas, and territories; national industry-specific estimates at the NAICS sector, 3-digit, most 4-digit, and selected 5- and 6-digit industry levels, and national estimates by ownership across all industries and for schools and hospitals. OES data are available at www.bls.gov/oes/tables.htm.
The OES survey is a cooperative effort between BLS and the State Workforce Agencies (SWAs). BLS funds the survey and provides the procedures and technical support, while the State Workforce Agencies collect most of the data. OES estimates are constructed from a sample of about 1.2 million establishments. Each year, two semiannual panels of approximately 180,000 to 200,000 sampled establishments are contacted, one panel in May and the other in November. Responses are obtained by mail, Internet or other electronic means, email, telephone, or personal visit. The May 2018 estimates are based on responses from six semiannual panels collected over a 3-year period: May 2018, November 2017, May 2017, November 2016, May 2016, and November 2015. The unweighted sample employment of 83 million across all six semiannual panels represents approximately 58 percent of total national employment. The overall national response rate for the six panels, based on the 50 states and the District of Columbia, is 71 percent based on establishments and 68 percent based on weighted sampled employment. The sample in the Dallas-Fort Worth-Arlington Metropolitan Statistical Area included 12,916 establishments with a response rate of 42 percent. For more information about OES concepts and methodology, go to www.bls.gov/oes/current/oes_tec.htm.
A value that is statistically different from another does not necessarily mean that the difference has economic or practical significance. Statistical significance is concerned with the ability to make confident statements about a universe based on a sample. It is entirely possible that a large difference between two values is not significantly different statistically, while a small difference is, since both the size and heterogeneity of the sample affect the relative error of the data being tested.
The May 2018 OES estimates are based on the 2010 Standard Occupational Classification (SOC) system and the 2017 North American Industry Classification System (NAICS). Information about the 2010 SOC is available on the BLS website at www.bls.gov/soc and information about the 2017 NAICS is available at www.bls.gov/bls/naics.htm.
Metropolitan area definitions
The substate area data published in this release reflect the standards and definitions established by the U.S. Office of Management and Budget.
The Dallas-Fort Worth-Arlington Metropolitan Statistical Area includes Collin, Dallas, Denton, Ellis, Hood, Hunt, Johnson, Kaufman, Parker, Rockwall, Somervell, Tarrant, and Wise Counties in Texas.
OES data are available on our regional web page at www.bls.gov/regions/southwest. Answers to frequently asked questions about the OES data are available at www.bls.gov/oes/oes_ques.htm. Detailed technical information about the OES survey is available in our Survey Methods and Reliability Statement on the BLS website at www.bls.gov/oes/current/methods_statement.pdf.
Information in this release will be made available to sensory impaired individuals upon request. Voice phone: (202) 691-5200; Federal Relay Service: (800) 877-8339.
Computer and mathematical occupations
Computer and information research scientists
Computer systems analysts
Information security analysts
Software developers, applications
Software developers, systems software
Network and computer systems administrators
Computer network architects
Computer user support specialists
Computer network support specialists
Computer occupations, all other
Operations research analysts
Last Modified Date: Thursday, April 25, 2019