April 2011
Occupational Employment Statistics (OES) Highlights:
Using
Location Quotients to Analyze Occupational Data
(PDF
version)
Montana employed bartenders at
3 times the national rate in May 2009, Delaware employed chemists
at nearly 8 times the national rate, fast food cooks were 3 times as
concentrated in Mississippi as in other parts of the country, and computer
software engineers were more than twice as prevalent in Virginia as
elsewhere. These comparisons are easily made through the use of location
quotients.
[Chart
data]
Some familiar and some not-so-familiar patterns emerge when looking at
location quotient data. For example, the areas with the highest location
quotients for several gaming occupations included Atlantic City and
several areas in Nevada. Atlantic City and Las Vegas also had among the
highest concentrations of bartenders, as did areas in the northern states
of Montana, Wisconsin, North Dakota, and Minnesota. Areas that tend to be
tourist destinations had higher location quotients for leisure-related
occupations, such as high concentrations of restaurant
cooks in Nantucket and Martha’s Vineyard and massage
therapists in Napa, CA. Palm Bay-Melbourne-Titusville, FL, the home of
Kennedy Space Center, had one of the highest location quotients for aerospace engineers, while areas in
Michigan, Indiana, and Ohio had high location quotients for several
production occupations.
Location quotients are useful for studying the composition of jobs in
an area relative to the average, or for finding areas that have high
concentrations of jobs in certain occupations. As measured here, a
location quotient shows the occupation’s share of an area’s employment
relative to the national average. 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, and a location quotient of 0.5 indicates
the area’s share of employment in the occupation is half the national
share. For instance, home health
aides accounted for nearly 2 percent of employment in North Carolina
in May 2009, but less than 1 percent of employment in the United States,
giving the occupation a location quotient of more than 2 in North
Carolina.
Location quotients show how occupations are spread out across the
country. The location quotients for some occupations clustered around 1.0,
indicating that they were found in similar proportions in most areas. For
example, the location quotients for janitors ranged from 0.5 to 1.6, and
those for receptionists and
information clerks ranged from 0.5 to 1.7. (Chart 1.) Other
occupations with relatively even geographic distributions included dental assistants, cashiers,
and dishwashers.
Other occupations were more concentrated and had very high location
quotients in some areas. These were often occupations directly related to
industries that are geographically concentrated. For example, the
employment share of textile knitting
and weaving machine setters, operators, and tenders in Dalton, GA, was
nearly 197 times the national average; this area also had high location
quotients for several other textile and apparel production occupations, as
did other southern areas such as Hickory-Lenoir-Morganton, NC; Anderson,
SC; and Greensboro-High Point, NC.
Some of the occupations with the highest location quotients were
associated with geographical features such as waterways or natural
resource deposits. For example, Houma-Bayou Cane-Thibodaux, LA, had very
high location quotients for several water transportation occupations,
including ship engineers, with a
location quotient of 91; sailors and
marine oilers, with a location quotient of 114; and captains,
mates, and pilots of water vessels, with a location quotient of 150.
Similarly, occupations associated with mining or oil and gas extraction
tended to have very high location quotients in some areas. Charleston, WV,
had location quotients of 52 for mine
cutting and channeling machine operators and 66 for mining
roof bolters, while Odessa, TX, had high concentrations of several
oil-related occupations, including location quotients of 29 and 58,
respectively, for roustabouts and
oil, gas, and mining service unit
operators.
In some cases, more complex patterns emerge. Chart 2 shows employment
and location quotients for brokerage
clerks in the largest metropolitan areas in the United States. In
general, areas with higher employment of brokerage clerks also had higher
location quotients for this occupation, suggesting that there is some
advantage to having large numbers of workers in this financial services
occupation clustered together. Because the location quotients control for
area size, we might expect that an occupation’s employment would not be
correlated with the size of the area. However, although the relationship
was not extremely strong, brokerage clerks also were somewhat more likely
to be employed in areas with higher overall employment.
Some occupations had higher location quotients in smaller areas, such
as purchasing agents and buyers of
farm products, which were somewhat more likely to be concentrated in
areas with low total employment. In this case, there was no correlation
between an area’s location quotient and employment of this specific
occupation: because areas with low overall employment also tend to have
low employment of most individual occupations, many of the areas with high
concentrations of purchasing agents and buyers of farm products had
relatively low employment levels for this occupation. For example, Sioux
City, IA-NE-SD, had an employment concentration of nearly 8 times the U.S.
average for purchasing agents and buyers of farm products, but had
employment of only 50 in this occupation. In contrast, the much larger New
York-Northern New Jersey-Long Island, NY-NJ-PA, metropolitan area employed
over 500 purchasing agents and buyers of farm products, but had a location
quotient of 0.7 for this occupation.
[Chart
data]
A closer look at two areas—Durham, NC, and Columbus, IN—is provided in
charts 3 and 4. Durham, in the heart of North Carolina’s Research
Triangle, had high location quotients for several life science
occupations, including soil and plant
scientists, microbiologists,
biochemists and biophysicists, medical scientists, and epidemiologists.
This area also had high concentrations of other occupations associated
with scientific research, including natural sciences managers and statisticians, as well as computer
systems software engineers and several other computer occupations not
shown in the chart.
[Chart
data]
Columbus, IN, had high location quotients for a number of production
occupations, including team
assemblers; tool and die
makers; inspectors, testers,
sorters, samplers, and weighers; and several metal and plastic worker
occupations. In addition, this area had high concentrations of several
occupations associated with the design and engineering stages of the
manufacturing process: the concentration of mechanical
engineers was over 12 times the U.S. average, while both industrial
engineers and mechanical
drafters had concentrations nearly 7 times the U.S. average.
[Chart
data]
Chart 5 shows location quotients for bartenders
and substance abuse and behavioral
disorder counselors in various states. As mentioned above, Montana had
the highest location quotient for bartenders, at 3.3 times the national
average. Montana also had the fourth highest location quotient for
substance abuse and behavioral disorder counselors. Several other states
had location quotients in the top 10 for both bartenders and substance
abuse and behavioral disorder counselors, including South Dakota, Oregon,
and Vermont. However, there are exceptions, such as Nevada, where the
location quotient for substance abuse and behavioral disorder counselors
was smaller than every other state except West Virginia at 0.37.
[Chart
data]
Location quotients can also help explain wage differences among areas.
The composition of employment in an area influences the average wage in
that area. All else equal, areas with higher employment shares of lower
paid occupations such as fast food
cooks and cashiers will tend
to have lower average wages, in part because the concentration of
employment in these occupations helps bring down the average area wage.
The correlation coefficient on the share of fast food cooks in a state and
the state’s cross-occupation wage was -.44, indicating that, generally,
areas with higher concentrations of fast food cooks had lower average
wages. (See chart 6.)
Areas with greater concentrations of higher paying occupations such as
financial managers and biochemists
and biophysicists tended to have higher cross-occupation wages. For
example, states with high shares of business and financial operations
occupations and computer and mathematical science occupations also tended
to have higher wages: average cross-occupation wages and employment shares
in these occupations were correlated with coefficients of 0.85 and 0.76,
respectively. Other occupations that tended to be more concentrated in
higher wage areas were arts, design, entertainment, sports, and media
occupations.
[Chart
data]
The location quotients used in this highlight were calculated from May
2009 Occupational Employment Statistics; location quotients for all
occupations and areas are available at https://www.bls.gov/oes/special.requests/oesm09ma.zip.
Complete May 2009 OES data are available from the OES home page at https://www.bls.gov/oes. This highlight was
prepared by Ben Cover. For more information, please contact the OES
program at https://www.bls.gov/oes/oes_con.htm.
Last Modified Date: March 30, 2018
