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The fastest growing large metropolitan areas from 2010–20 were concentrated in the South: Austin, TX, Raleigh, NC, Orlando, FL, Dallas, TX, Phoenix, AZ, San Antonio, TX, and Houston, TX. The populations of these areas grew between 20 and 33 percent over the decade.1 In contrast, the large metropolitan areas with the lowest population growth rates, ranging from a 2-percent decrease to less than 1 percent growth, were concentrated in the Midwest and Northeast: Pittsburgh, PA, Cleveland, OH, Rochester, NY, Hartford, CT, Buffalo, NY, Chicago, IL, and Detroit, MI.
This Beyond the Numbers article will provide an overview of the employment composition and wages of the seven metropolitan areas with the highest population growth rates (high-growth areas) and the seven metropolitan areas with the lowest population growth rates or that have lost population (low-growth or declining population areas) using Occupational Employment and Wage Statistics data from the Bureau of Labor Statistics (BLS).2 This article will report employment shares of all occupational groups in the United States, in high-growth areas and in low-growth or declining population areas. An occupation’s employment share is the percentage of all jobs in a certain occupation. For example, the employment share for construction and extraction is 4.5 percent in high-growth areas. That means that of all the jobs in the seven high-growth areas combined, 4.5 percent of them are in a construction and extraction occupation. Employment share in the nation and for low-growth or declining population areas can be interpreted the same way. This article compares national, high-growth area, and low-growth or declining population area employment shares to help you understand whether jobs are more common in areas with high population growth, areas with low population growth, or neither.
Charts 1 and 2 show the employment shares of all occupational groups for the nation, the low-growth or declining population areas, and the high-growth areas. Food preparation and serving related, construction and extraction, office and administrative support occupations, and sales and related occupations were generally more concentrated in the high-growth areas. (See chart 1.) For each of these groups, the share of total employment in the high-growth areas was about 1 percentage point higher than in the low-growth or declining population areas.3 The high-growth areas also had a higher employment share for installation, maintenance, and repair occupations (4.1 percent), compared with the low-growth or declining population areas (3.6 percent). The employment shares for these five occupational groups within the high-growth areas were also significantly higher than the respective national shares.
Conversely, community and social service occupations, healthcare support occupations, healthcare practitioners and technical occupations, and production occupations were generally more concentrated within the low-growth or declining population areas. Healthcare practitioners and technical occupations had an employment share that was 1.3 percentage points more in low-growth or declining population areas than in high-growth areas. Production occupations had an employment share that was 2.5 percentage points higher in low-growth or declining population areas. Healthcare support occupations made up 4.5 percent of employment in the low-growth or declining population group, which was in line with the national concentration of 4.7 percent but higher than the concentration of 3.8 percent in the high-growth group. Similarly, the employment share of community and social service occupations in low-growth or declining population areas (1.6 percent) was the same as the national share (1.6 percent). However, this occupational group was relatively less prevalent in the high-growth group, making up only 1.1 percent of employment.4
For the remaining occupational groups, there were either no significant differences in employment shares between the high-growth and low-growth or declining population groups, or the differences did not reflect a consistent pattern among individual areas in each group. (See chart 2.) Many occupational groups had similar employment shares across both the high-growth and low-growth or declining population groups and the United States. Other occupational groups had employment shares in the high-growth and low-growth or declining population areas that were similar to one another but differed from the national share. For example, the employment shares of management occupations and business and financial occupations were generally higher in both the high-growth and low-growth or declining population groups relative to the United States. In contrast, the employment share of life, physical, and social science occupations for both high-growth and low-growth or declining population areas was generally smaller than the national share.
Other occupational groups had a higher collective share in one group relative to the other—computer and mathematical occupations made up a larger share for the high-growth group, and educational instruction and library occupations made up a larger share for the low-growth or declining population group. However, these differences were driven by a few metropolitan areas that were outside the norm of their group. As a result, there appears to be a connection between population growth and the concentration of these occupational groups, but a deeper look at the data reveals this is not the case.
Metropolitan areas with lower growth rates had an older population than the areas with higher growth rates.5 Correspondingly, these areas also had higher shares of healthcare practitioners and technical occupations and healthcare support occupations compared with the high-growth areas.
The difference between the high-growth and low-growth or declining population areas was most evident in the concentration of healthcare practitioners and technical occupations as shown in chart 3. Healthcare practitioners and technical occupations made up 6.8 percent of employment within the low-growth or declining population areas and 5.5 percent of employment within the high-growth areas. (See chart 1.) The areas with the highest concentrations of healthcare practitioners and technical occupations were Cleveland, OH (8.0 percent of area employment), Pittsburgh, PA (8.0 percent), and Buffalo, NY (7.4 percent). Conversely, all high-growth areas had employment shares for healthcare practitioners and technical occupations below the national share (6.2 percent), with the lowest share belonging to Austin, TX (4.2 percent).
Registered nurses—the largest occupation within the healthcare practitioners and technical group—followed a similar pattern. Six of the seven low-growth or declining population areas had employment shares of registered nurses higher than the U.S. average of 2.2 percent, and all seven high-growth areas had employment shares lower than the national share. Healthcare support occupations were also generally more concentrated in low-growth or declining population areas relative to high-growth areas. Healthcare support occupations made up 4.5 percent of employment within the low-growth or declining population areas, compared with 3.8 percent of employment within the high-growth areas. (See chart 1.) However, the relationship between population growth and employment share for healthcare support occupations was less clear due to three areas from the low-growth or declining population group that had employment shares below the national average (Cleveland, OH, Chicago, IL, and Detroit, MI) and San Antonio, TX, from the high-growth group, which had a particularly large share of healthcare support occupations. (See chart 4.)
Notably, despite seeing a large gain in population over the previous decade, San Antonio, TX, was among the areas with the highest share of healthcare support occupations at 5.7 percent of employment. San Antonio’s large proportion of jobs responsible for providing healthcare support was driven by higher shares of several occupations within the healthcare support group. Of the 17 occupations within the healthcare support group, 11 occupations—including the largest of the group, home health and personal care aides—made up a higher percentage of employment in San Antonio relative to the nation, with concentrations ranging from 1.2 times to almost double the national concentration.
Within the healthcare support group, home health and personal care aides and nursing assistants—the largest healthcare support occupations in the nation—made up larger shares of employment in the low-growth or declining population group than the corresponding shares in the high-growth group.
Community and social service occupations include careers that provide a variety of services to help individuals and their communities, such as social workers, community health workers, mental health counselors, and religious workers. Although community and social service occupations had a smaller employment share than the healthcare occupations, they followed a similar pattern. (See chart 5.)
The areas with the smallest employment shares of community and social service occupations were within the high-growth group: Dallas, TX (1.0 percent of area employment), Houston, TX (1.0 percent), and Orlando, FL (1.1 percent). Additionally, all seven areas within the high-growth group had concentrations of community and social service occupations below the national average (1.6 percent).
The areas with the largest employment shares of community and social service occupations were within the low-growth or declining population group: Rochester, NY, Hartford, CT, and Buffalo, NY (each above 2 percent). Among the low-growth or declining population areas, only Detroit, MI, and Chicago, IL, had concentrations below the national average.
Metropolitan areas that had low-growth or a declining population had relatively more employment in production occupations compared with the high-growth areas as shown in chart 6. All seven of the high-growth areas had low shares of production occupations, and six of the seven low-growth or declining population areas had high shares of production occupations, relative to the national share. Among the low-growth or declining population areas, metropolitan areas that historically served as industrial centers, such as Detroit, MI (9.7 percent), and Cleveland, OH (8.1 percent), had the largest shares of production occupations. Pittsburgh, PA, was an outlier as the only low-growth or declining population area with a concentration of production occupations (5.1 percent) below the national concentration (6.0 percent).
Houston, TX (5.7 percent), and Dallas, TX (5.1 percent), were notable exceptions among the high-growth areas because they both had shares of production occupations that were 1 percentage point higher or more than that of the next highest employment share in a high-growth area, Phoenix, AZ (4.1 percent). One possible explanation for Houston’s large number of production jobs was its large concentration of specialized plant and system operators. Specifically, operators that were responsible for operating chemical plants, gas pipelines, or petroleum pumps and refineries, were extremely common in the Houston area compared with other parts of the country.
Metropolitan areas with higher growth also had relatively more employment in construction and extraction occupations as shown in chart 7. Five of the seven high-growth areas had employment shares of construction and extraction occupations larger than the national share (4.2 percent of total employment), while six of the seven low-growth or declining population areas had shares below the national average.
Houston, TX (5.3 percent), was the area with the largest share of employment in construction and extraction occupations. Pittsburgh, PA (4.6 percent), was an exception as the only low-growth or declining population area with a concentration of construction and extraction occupations higher than the national concentration. This is because Pittsburgh had a large concentration of extraction workers that operated, assembled, or repaired equipment to extract oil and gas relative to the nation, including oil and gas service unit operators (that operated equipment to increase oil flow from or remove obstructions from oil wells) and roustabouts (that assembled or repaired oil field equipment). Houston also had a large oil and gas industry. Consequently, Houston not only had a large concentration of production jobs, responsible for transporting and refining oil and gas, but also had a high share of extraction workers, which was driven by a large concentration of oil and gas service unit operators and rotary drill operators.
Construction and extraction occupations made up a larger share of employment in the high-growth group (4.5 percent of group employment) versus the low-growth or declining population group (3.3 percent). (See chart 1.) Large occupations within the construction and extraction occupations group followed a similar pattern. For both construction laborers and first-line supervisors of construction and extraction workers, all low-growth or declining population areas except Pittsburgh, PA, had employment shares smaller than the national shares. In contrast, five of the seven high-growth areas had employment shares larger than the national share for construction laborers, and all seven high-growth areas had employment shares above the national share for first-line supervisors of construction and extraction workers.
Food preparation and serving related occupations and sales and related occupations—two of the largest occupational groups in the country with common occupations such as waiters and waitresses, fast food and counter workers, restaurant cooks, cashiers, and retail salespeople—had larger shares in high-growth areas than in low-growth or declining population areas.
Food preparation and serving related occupations constituted 8.6 percent of employment within the high-growth areas and 7.3 percent of employment within the low-growth or declining population areas. (See chart 1.) Although all seven high-growth areas were above the national average in chart 8, five of the seven high-growth areas had an employment share significantly higher than the national share (8.0 percent), which means that the difference between the local and national employment share is unlikely to have happened by random chance in these five areas. Orlando, FL (10.5 percent), and San Antonio, TX (9.4 percent), stood out for their high concentrations of food preparation and serving related occupations. Buffalo, NY, was the only area within the low-growth or declining population group that had an employment share above the national average.
Large occupations within the food preparation and serving related group followed a similar pattern. In the high-growth group, waiters and waitresses and restaurant cooks made up more employment than in the low-growth or declining population group.
Sales and related occupations constituted 10.0 percent of employment within the high-growth areas and 8.9 percent of employment within the low-growth or declining population areas. (See chart 1.)
Like the food preparation and serving related occupations, not all differences between the local employment shares and the national share for sales and related occupations were significant. Five of the seven high-growth areas had shares of sales and related occupations significantly above the national average (9.4 percent). (See chart 9.) Orlando, FL (11.3 percent), again stood out from the rest of the selected areas, followed by Raleigh, NC (10.9 percent). However, six of the seven low-growth or declining population areas had employment shares significantly lower than the national share.
Cashiers and retail salespeople, the largest occupations within the sales and related group, made up larger shares of employment in the high-growth group than their corresponding shares in the low-growth or declining population group.
While employment in high-tech industries showed impressive growth from 2010–20, growing by over half of its initial size by the end of the decade, employment shares for technology-oriented jobs did not show a clear difference between high-growth and low-growth or declining population areas.6 In fact, computer and mathematical occupations highlighted variation across areas within the same population growth category. For example, the high-growth group had both some of the highest concentrations and some of the lowest concentrations of computer and mathematical occupations. (See chart 10.)
Notably, Houston, TX, San Antonio, TX, and Orlando, FL, all had employment shares for computer and mathematical occupations that fell below the national share (3.3 percent) and were among the lowest from high-growth areas. However, the remaining high-growth areas had among the highest concentrations of computer and mathematical occupations, especially Austin, TX (6.6 percent), and Raleigh, NC (5.8 percent).
This divergence within the high-growth group may reflect differences in regional specialization, in which local economies become hubs for providing specific goods or services and attract a larger-than-average share of their associated occupations. For example, some areas focus on providing goods or services that require jobs in science, technology, engineering, and mathematics (STEM) fields, while other areas focus on providing goods or services such as sawing lumber or manufacturing clothing, that do not require these STEM jobs.7
Most high-growth and low-growth or declining population areas had employment shares for education and library occupations that fell within 0.5 percentage point of the national share (5.8 percent) as shown in chart 11.
However, there were some areas that were outside of the norm. The low-growth or declining population areas of Rochester, NY, Hartford, CT, and Buffalo, NY, stood out with the highest employment shares (each with at least 7 percent) of educational instruction and library occupations.
The high-growth areas of Phoenix, AZ (4.5 percent), and Orlando, FL (4.7 percent), along with Detroit, MI (3.7 percent), from the low-growth or declining population group, stood out on the opposite side of the national share. These areas had the lowest concentrations of educational instruction and library occupations, each more than a percentage point less than the national average.
Some jobs have common educational requirements for entry. You may need to be a high school graduate for some or have a bachelor’s degree for others. Not all jobs require a specific level of education for entry. We call these typical entry-level educational requirements.
Metropolitan area | Group | Mean hourly wage | Percentage of jobs by typical entry-level educational requirement | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Doctoral or professional degree | Master's degree | Bachelor's degree | Associate's degree | Postsecondary nondegree award | Some college, no degree | High school diploma or equivalent | No formal educational credential | |||
Hartford-West Hartford-East Hartford, CT |
Low-growth | $32.28 | 3.3 | 2.4 | 27.9 | 2.2 | 6.4 | 3.7 | 37.0 | 17.1 |
Chicago-Naperville-Elgin, IL-IN-WI |
Low-growth | 29.74 | 2.7 | 1.8 | 25.6 | 2.3 | 5.9 | 2.7 | 37.5 | 21.5 |
Austin-Round Rock, TX |
High-growth | 28.97 | 2.3 | 1.9 | 29.7 | 2.3 | 5.0 | 2.9 | 36.0 | 20.0 |
Detroit-Warren-Dearborn, MI |
Low-growth | 28.39 | 2.4 | 1.5 | 25.8 | 2.4 | 6.1 | 2.1 | 40.4 | 19.3 |
Raleigh, NC |
High-growth | 28.15 | 2.6 | 1.6 | 28.4 | 2.5 | 5.8 | 2.9 | 34.9 | 21.2 |
United States |
National | 28.01 | 2.6 | 1.8 | 24.2 | 2.2 | 6.2 | 2.7 | 38.6 | 21.7 |
Dallas-Fort Worth-Arlington, TX |
High-growth | 27.89 | 1.9 | 1.5 | 25.7 | 2.1 | 6.2 | 2.6 | 38.3 | 21.7 |
Houston-The Woodlands-Sugar Land, TX |
High-growth | 27.78 | 2.3 | 1.6 | 23.6 | 2.3 | 6.1 | 2.4 | 39.4 | 22.3 |
Rochester, NY |
Low-growth | 27.32 | 3.3 | 2.7 | 23.3 | 2.3 | 5.7 | 3.1 | 39.5 | 20.1 |
Phoenix-Mesa-Scottsdale, AZ |
High-growth | 27.22 | 2.4 | 1.7 | 23.7 | 2.2 | 6.0 | 2.7 | 39.5 | 21.9 |
Buffalo-Cheektowaga-Niagara Falls, NY |
Low-growth | 26.99 | 3.3 | 2.2 | 22.5 | 2.2 | 5.7 | 2.9 | 40.5 | 20.7 |
Pittsburgh, PA |
Low-growth | 26.95 | 3.2 | 1.7 | 24.3 | 2.6 | 7.0 | 2.6 | 38.6 | 20.0 |
Cleveland-Elyria, OH |
Low-growth | 26.86 | 2.7 | 1.8 | 24.0 | 2.5 | 7.0 | 2.8 | 39.4 | 19.7 |
San Antonio-New Braunfels, TX |
High-growth | 24.87 | 2.1 | [1] | 22.7 | 2.0 | 6.8 | [1] | 39.2 | 22.9 |
Orlando-Kissimmee-Sanford, FL |
High-growth | 24.41 | 1.8 | 1.5 | 22.0 | 2.2 | 5.9 | 2.2 | 36.7 | 27.7 |
[1] Data not available. Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics. |
The concentration of occupations categorized by their typical entry-level educational requirement was very similar between high-growth and low-growth or declining population areas, with both roughly matching the national distribution. Occupations that typically require a high school diploma or equivalent made up the largest share of employment in both groups.
Occupations typically requiring no formal educational credential for entry had the largest difference in their concentrations between the groups, accounting for 22.4 percent of employment in the high-growth areas, 2 percentage points higher than the corresponding employment share in low-growth or declining population areas. Nationally, 58.3 percent of jobs requiring no formal education were food preparation and serving related occupations or sales and related occupations, groups with above average employment shares in most of the high-growth areas. At 27.7 percent, Orlando, FL, had the highest share of employment in occupations requiring no formal credential, with 10.5 percent in food preparation and serving related occupations and 11.3 percent in sales and related occupations. (See charts 8 and 9.) San Antonio had the second-highest share of occupations typically requiring no formal education for entry (22.9 percent) and food preparation and serving related occupations (9.4 percent). (See table 1 and chart 8.)
Hartford, CT, a member of the low-growth or declining population group, had the lowest share of occupations typically requiring no formal educational credential for entry (17.1 percent), as well as the lowest shares of food preparation and serving related occupations (6.4 percent) and sales and related occupations (7.8 percent). (See table 1 and charts 8 and 9.)
The distribution of wages was similar among the high-growth and low-growth or declining population areas, with jobs in the low-growth or declining population group paying an average wage of $28.77 per hour, slightly more than the high-growth group’s mean hourly wage of $27.29. Average wages in areas from both groups were close to the national average ($28.01).
The high-growth and low-growth or declining population groups had a similar share of jobs with the same typical entry-level educational requirements, which could explain why their average hourly wage figures are so similar. Nationally, occupations typically requiring no formal educational credential for entry had an average hourly wage of $15.30 in May 2021, less than the average hourly wage for occupations typically requiring a high school diploma or equivalent ($22.48), an associate degree ($28.94), or a bachelor’s degree ($45.00).
Orlando, FL, San Antonio, TX, and Hartford, CT, had employment shares by typical entry-level educational requirements and hourly mean wages that differed from the rest of their respective groups. In the high-growth group, the average hourly wage across all occupations was below the national average of $28.01 in Orlando, FL ($24.41), and San Antonio, TX ($24.87), where there were larger concentrations of low-paying occupations typically requiring no formal education credential for entry, such as food preparation and serving related occupations. In the low-growth or declining population group, the mean wage was larger than the national mean in Hartford, CT ($32.28), which had a significantly smaller share of the low-paying occupations typically requiring no formal education credential. Higher paying occupations typically requiring a doctoral or professional degree (3.3 percent of area employment), a master’s degree (2.4 percent), or a bachelor’s degree (27.9 percent) also had higher employment shares in the Hartford area than in most of the other selected areas.
These data highlight key similarities and differences between the occupational composition of large metropolitan areas with high and low population growth rates. High-growth areas had higher concentrations of food preparation and serving related occupations; construction and extraction occupations; sales and related occupations; office and administrative support occupations; and installation, maintenance, and repair occupations. Low-growth or declining population areas had higher concentrations of production occupations, community and social service occupations, healthcare practitioners and technical occupations, and healthcare support occupations. However, there was also within-group variation in the concentrations of computer and mathematical occupations and educational instruction and library occupations due to regional specialization of the labor force.
Overall, high-growth and low-growth or declining population areas had similar concentrations of occupations based on typical entry-level educational requirements. The exception was occupations typically requiring no formal educational credential for entry, which made up a higher share of employment in high-growth areas, partly because of the higher concentrations of food preparation and serving related occupations and sales and related occupations. Differences in the concentrations of these low-paying occupational groups also helped explain the relatively low average wages in Orlando, FL, and San Antonio, TX, where there were large numbers of jobs in low-paying occupational groups and the relatively high average wage in Hartford, CT, where low-paying occupational groups like food preparation and serving related jobs were less common.
This Beyond the Numbers article was prepared by Andrew Mattingly, economist in the Office of Employment and Unemployment Statistics: Occupational Employment and Wage Statistics, U.S. Bureau of Labor Statistics. Email: oewsinfo@bls.gov; telephone: (202) 691-6569. Email: mattingly.andrew@bls.gov.
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Andrew Mattingly, “Do you want to work where the people are? These jobs are most common in areas with high (or low) population growth ,” Beyond the Numbers: Employment & Unemployment, vol. 12, no. 8 (U.S. Bureau of Labor Statistics, April 2023), https://www.bls.gov/opub/btn/volume-12/employment-and-population-growth.htm
1 A large metropolitan area is defined as an area with a population of at least 1 million, coinciding with a “Level A” Metropolitan Statistical Area (MSA) per Office of Management and Budget (OMB) Bulletin 99-04 and the population threshold used by the Local Area Unemployment Statistics program at BLS.
2 Note that the metropolitan area county-based delineations used in the Census Bureau’s Population Estimates Program Vintage 2020 estimates (and based on OMB Bulletin 20-01) do not exactly match the metropolitan area definitions used by the Occupational Employment and Wage Statistics program (based on OMB Bulletin 17-01). In the New England states, Occupational Employment and Wage Statistics uses the New England City and Town Area (NECTA) definitions instead of the county-based MSA definitions. The full titles for the metropolitan areas referenced in this article are Austin-Round Rock, TX; Buffalo-Cheektowaga-Niagara Falls, NY; Chicago-Naperville-Elgin, IL-IN-WI; Cleveland-Elyria, OH; Dallas-Fort Worth-Arlington, TX; Detroit-Warren-Dearborn, MI; Hartford-West Hartford-East Hartford, CT; Houston-The Woodlands-Sugar Land, TX; Orlando-Kissimmee-Sanford, FL; Phoenix-Mesa-Scottsdale, AZ; Pittsburgh, PA; Raleigh, NC; Rochester, NY; and San Antonio-New Braunfels, TX.
Employment and wage data are from the Occupational Employment and Wage Statistics program and do not cover the self-employed, owners and partners in unincorporated firms, household workers, or unpaid family workers. Population growth rates are from the Census Bureau's Population Estimates Program.
3 For statistical significance testing, standard errors for the high-growth and low-growth or declining population groups were developed using the published employment estimates and their accompanying relative standard errors from the seven component areas.
4 Generally, the findings of this BTN article are consistent with the results published in an earlier article by Audrey Watson using May 2004 OEWS data to analyze the occupational structure of metropolitan areas based on their 1999–2003 population growth class. In this article and Watson’s analysis, we find that fast-growing areas had lower shares of production occupations, healthcare practitioners and technical occupations, healthcare support occupations, and community and social service occupations, but higher shares of food preparation and serving related occupations, construction and extraction occupations, and sales and related occupations.
5 As a related factor to both the population growth rate and concentration of healthcare occupations, the age distributions for the selected areas differed between the high-growth and low-growth or declining population groups. According to data from the Census Bureau’s American Community Survey, 23.0 percent of the population across the low-growth group was 60 years and older, similar to the national percentage (22.3 percent) but 5.2 percentage points higher than the share across the high-growth group. Moreover, both growth groups fell to opposite sides of the national median age of 38; the median age ranged from 38 to 43 years among the low-growth areas and 35 to 37 years among the high-growth areas.
6 The Bureau of Labor Statistics uses the North American Industrial Classification System to group business establishments into industries based on their primary economic activity. While there may be many possible definitions of “high-tech industries,” a Monthly Labor Review article by Daniel E. Hecker provides one possible definition using industry staffing pattern data based on OEWS data from 2000 to 2002. An industry can be classified as very high-tech (a level-1 high-tech industry) if employment in tech-oriented jobs makes up a proportion of the industry’s total employment that is at least five times the national average. For that article, “tech-oriented” occupations collectively referred to computer and mathematical occupations; engineers, drafters, engineering, and mapping technicians; life and physical scientists; life, physical, and social science technicians; and computer and information systems, natural sciences, and engineering managers. In this article, for simplicity and because we specifically analyze this occupational group, high-tech industries were identified based on their large share of employment in computer and mathematical occupations relative to the national average (3.3 percent of total employment in May 2021). These industries included computer systems design and related services; software publishers; data processing, hosting, and related services; other information services; and computer and peripheral equipment manufacturing.
According to national industry employment data from the Quarterly Census of Employment and Wages, employment for the high-tech industries grew by 59.3 percent from 2.2 million in 2010 to 3.6 million in 2020. In comparison, private sector employment across all industries grew by 11.1 percent over the same period from 106.2 to 117.9 million.
7 Computer and mathematical occupations made up about half of total national STEM employment in May 2021. Including the remaining STEM occupations, Austin-Round Rock, TX (11.1 percent of area employment), and Raleigh, NC (10.8 percent), remain the areas with the highest shares of STEM employment among the selected areas. There was still a divergence within the high-growth group as Orlando-Kissimmee-Sanford, FL (5.5 percent), and San Antonio-New Braunfels, TX (5.3 percent), were also among the areas with the smallest share of employment in STEM occupations. These remaining STEM occupations were identified under the 2018 Standard Occupational Classification based on their job duties. These include architecture and engineering occupations, physical scientists, STEM-related postsecondary teachers, etc. This represents just one possible definition of STEM occupations.
Publish Date: Monday, April 10, 2023