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High-Tech Occupations by

Metropolitan Statistical Area

Patrick Kilcoyne

The term high-tech has always denoted a very ambiguous and amorphous concept. An industry or an occupational field that is commonly known as high-tech

one year could be using technology or skills that are considered to be standard only a few years later. Low-skilled workers can be found in industries known as high-tech, and most

people would not consider these workers to be employed in

high-tech occupations. Similarly, workers who perform highly

skilled functions using the latest technology could be employed in very low-tech, older industries.

However high-tech occupations are defined, practical

knowledge would lead us to expect to find these occupations grouped together, either by wage rate, industry, or geographical area. This article uses criteria based on the level of

technology utilization to identify high-tech occupations. It

then provides a statistical summary by metropolitan statistical

area (MSA) of the wage and employment estimates for technologically oriented occupations. According to the 2001

Occupational Employment Statistics (OES) survey, there were

dramatic differences in these estimates by MSA.

Defining high-tech

Previous studies have identified high-tech occupations by

the level of education that an individual must have in order

to be employed in a given field. By this standard, many

professional occupations are termed high-tech, even though

the incumbents may be performing their duties using technology that has been available for generations. In other

words, the educational attainment criterion may be useful for

identifying high-wage, or high-potential, jobs but is not directly related to high technology as a concept.

Other researchers have attempted to define high-tech occupations as consisting of workers in industries that utilize

new technologies. The logic of this classification is that,

because the processes being used in these industries are

technically advanced, their employees, to a certain extent,

must be also. However, as with all industries, not all of the

people employed at firms in these industries are producing

technical products or making use of advanced technology

to perform their duties. Therefore, many unskilled workers

and persons not using any cutting-edge technology in their

work would be incorrectly identified as high-tech workers.

Out of consideration for these factors, high-tech occupations are defined for the purposes of this ariticle as consisting of workers who typically or necessarily utilize new technologies—those that are changing the ways in which people

live and work—in order to perform their duties. Using this

criterion and the occupational definitions from the 2000 Standard Occupation Classification (SOC) system, occupations

were selected for inclusion in a high technology subgroup.

A list was compiled of the 36 occupations determined to be

utilizing new technologies to the greatest extent, and they

will be referred to in this article as “high-tech occupations.”

This list of high-tech occupations, shown below, may change

in the future as new technologies become standard, and others are brought into the workplace.

SOC code SOC title

15-1011 Computer and information scientists, research

15-1021 Computer programmers

15-1031 Computer software engineers, applications

15-1032 Computer software engineers, systems software

15-1051 Computer systems analysts

15-1061 Database administrators

15-1081 Network systems and data communications

 analysts

17-2011 Aerospace engineers

17-2031 Biomedical engineers

17-2041 Chemical engineers

17-2061 Computer hardware engineers

17-2071 Electrical engineers

17-2072 Electronics engineers, except computer

17-2151 Mining and geological engineers

17-2161 Nuclear engineers

17-2171 Petroleum engineers

17-3021 Aerospace engineering and operations technicians

17-3023 Electrical and electronic engineering technicians

17-3024 Electro-mechanical technicians

19-1021 Biochemists and biophysicists

19-1022 Microbiologists

19-1041 Epidemiologists

19-1042 Medical scientists, except epidemiologists

19-2011 Astronomers

19-2012 Physicists

19-2021 Atmospheric and space scientists

19-2031 Chemists

Patrick Kilcoyne is an economist in the Division of Occupational

and Administrative Statistics, Bureau of Labor Statistics.
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19-2042 Geoscientists, except hydrologists and geographer

19-4021 Biological technicians

19-4031 Chemical technicians

19-4041 Geological and petroleum technicians

19-4051 Nuclear technicians

27-1014 Multi-media artists and animators

29-2011 Medical and clinical laboratory technologists

29-2033 Nuclear medicine technologists

29-2034 Radiologic technologists and technicians

The criterion used in the selection of these occupations

does not always lead to a clear stratification according to the

SOC. For example, although most of the occupations from

the major group Computer and mathematical science occupations (15-0000), are included as high-tech occupations,

some, such as Computer support specialists (15-1041), were

not included because of the nontechnological aspects of

their job duties. Conversely, only one occupation, Multimedia artists and animators (27-1014), was included from the

major group Arts, design, entertainment, sports, and media

occupations (27-0000). None of the management and supervisory occupations was included in this list because of the

OES coding principle that these individuals must spend at

least 80 percent of their time supervising workers rather than

performing work that might be considered high-tech. Also,

residual occupations (those with titles ending in the phrase,

“all other”) were excluded from this list.

Yuba City, CA MSA ................................................ 0.61 $54,980 $32,800 7.41 $55,750 $31,140

McAllen-Edinburg-Mission, TX MSA ..................... .62 39,870 24,410 5.94 44,830 23,230

Jonesboro, AR MSA ............................................. .63 38,500 26,120 8.68 41,370 24,770

Brownsville-Harlingen-San Benito, TX MSA ......... .67 46,610 24,590 6.05 44,550 23,480

Punta Gorda, FL MSA........................................... .68 43,790 27,430 11.86 43,990 25,350

Vineland-Millville-Bridgeton, NJ PMSA ................. .68 50,220 31,960 6.52 49,430 30,880

Gadsden, AL MSA ................................................ .69 40,310 26,180 8.65 43,870 24,620

Jacksonville, NC MSA .......................................... .72 41,680 24,620 5.83 42,140 23,680

Stockton-Lodi, CA MSA ........................................ .73 51,240 32,400 5.86 54,520 31,190

Yuma, AZ MSA ...................................................... .73 51,790 24,630 6.19 46,520 23,420

San Jose, CA PMSA ............................................. 10.68 $79,800 $45,210 19.92 $73,460 $43,190

Boulder-Longmont, CO PMSA.............................. 9.81 68,630 37,750 20.26 60,260 36,080

Melbourne-Titusville-Palm Bay, FL MSA .............. 7.15 48,120 30,780 17.88 49,830 28,390

Huntsville, AL MSA ................................................ 6.80 62,710 32,800 16.33 56,070 31,310

Lowell, MA-NH PMSA ........................................... 6.43 71,560 39,120 15.53 62,480 37,410

Raleigh-Durham-Chapel Hill, NC MSA ................. 6.20 65,330 34,730 16.48 55,960 32,900

Washington, DC-MD-VA-WV PMSA ..................... 6.16 67,830 41,030 14.72 63,340 39,250

Seattle-Bellevue-Everett, WA PMSA .................... 5.71 69,880 39,830 14.48 63,890 37,910

Boise City, ID MSA ................................................ 5.67 49,940 31,340 13.69 48,730 29,910

Austin-San Marcos, TX MSA ................................ 5.30 66,370 33,650 14.26 55,420 32,140

Text table 1. Employment shares and wage rates for the 10 metropolitan areas with the largest shares

of high-tech workers, 2001

MSA

High tech Tech Non- nology group

high-tech

wage

Nontechnology

group

wage

Employment share

Annual

wage

Employment shareAnnual

wage

Text table 2. Employment shares and wage rates for the 10 metropolitan areas with the smallest shares of hightech workers, 2001

High tech Tech Non- nology group

high-tech

wage

Nontechnology

group

wage

Employment share

Annual

wage

MSA Employment share

Annual

wage

 A different method of classifying workers as high-tech is

to identify major groups in the SOC among whom the use of

technology is prevalent. The four major groups usually considered be the most technologically oriented are: Computer

and mathematical science occupations (15-0000), Architecture and engineering occupations (17-0000), Life, physical,

and social science occupations (19-0000), and Healthcare

practitioner and technical occupations (29-0000). The rationale for including all of the occupations in these groups as

high-tech is that even those workers who are not making use

of cutting-edge technology to perform their tasks are probably associated with other workers who are, thereby achieving higher productivity in their own occupation. When all of

the workers from these major groups are included, the list of

high-tech occupations increases from 36 to more than 100.

The workers in these groups will be referred to in this article

as employed in “technology group occupations.”

Metropolitan Statistical Areas (MSAs)

The Occupational Employment Statistics (OES) program collected occupational employment and wage data in 337 metropolitan statistical areas (MSAs) in 2001. Text table 1 shows

the 10 MSAs with the largest estimates for high-tech workers as a percentage of total MSA employment, and text table

2 shows the MSAs with the lowest estimates. These tables

(Shares in percent)

(Shares in percent)
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list the average annual wages for both the high-tech and

technology group occupations, as well as those of all workers outside of these classifications. The map on the following page displays the percentage of high-tech workers for all

MSAs.

In text table 1, the wages for high-tech workers is higher

than the wages for technology group workers in 9 out of the

10 MSAs with the highest percentages of high-tech workers. Conversely, text table 2 shows that the wage estimate

for technology group workers is higher than the wage estimate for high-tech workers in 7 out of the 10 MSAs with the

lowest percentages of high-tech workers. Not surprisingly,

there is a connection between the percentage of high-tech

workers in an area and the percentage of workers in technology group occupations. From these tables, it is also clear

that there is a correlation between the percentage of hightech workers in an area and the level of wages of both hightech and non-high-tech workers in the same area.

Interpreting the results

A correlation coefficient is a number between -1 and 1 that

measures the degree to which two variables are linearly related. If there is perfect linear relationship with positive slope

between the two variables, we have a correlation coefficient

of 1; if there is positive correlation, whenever one variable

has a high (low) value, so does the other. For all MSAs, the

correlation coefficient between the percentage of high-tech

workers in an MSA and the annual wages paid to those

workers in that area is 0.658, while the correlation coefficient

between the percentage of technology group workers in an

MSA and the annual wages paid to those workers in that

area is only 0.481. This would seem to indicate that technology-oriented workers tend to be clustered in communities

that are, generally speaking, technological centers that are

willing to pay more than other areas for workers who possess advanced technical skills. The relationship between

the percentage of employment and wages is much stronger

for the high-tech workers than for the technology group

workers.

The correlation coefficient between the percentage of

high-tech workers in an MSA and the annual wages paid to

all other workers in that MSA is 0.604, while the correlation

coefficient between the percentage of technology group

workers in an MSA and the annual wages paid to all other

workers in that MSA is only 0.418.

This demonstrates that, once again, the more specialized

class of technology workers appears to have a greater effect

on both their own wages and area wages in general than

does the technology group, and that this effect is not accounted for by the relatively higher incomes of the high-tech

workers.

A number of empirical studies have documented that unskilled labor and capital are substitutes, while skilled labor

and capital are complements. Similarly, high-tech workers,

as defined in this article, are performing work that is both

highly skilled and capital intensive, which not only leads to

increased wages but requires fewer unskilled workers. Because of the relatively lower demand for unskilled workers in

high-tech areas, productivity gains and greater demand for

tech workers tend to result in a wage distribution that is more

positively skewed than those in other areas. This would

appear to support the theory of a skill-biased technological

difference between MSAs.

According to this theory, technological advancements

that are introduced into the workplace decrease the demand

for unskilled workers and increase the demand for skilled

workers. Because a decrease in the demand for certain workers will lower their relative wages if the supply of workers is

held constant, technology improvements result in greater

wage inequality between highly skilled and unskilled workers in the labor market. The data presented in this article

indicate that the wages paid to non-high-tech workers within

high-tech MSAs tend to be higher, possibly caused by rent

sharing,1

 increased productivity of non-high-tech workers

due to their high-tech environment, or a substitution of higher

skilled workers for unskilled labor.

1

The rent-sharing hypothesis of wage behavior applies to situations in which employers, for any of a number of reasons, feel compelled to share some of their profits with their employees.
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