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Mapping “The Hunger Games”: Using
location quotients to find the Districts of
Panem
Elizabeth Cross | February 2017
“…Panem, the country that rose up out of the ashes of a place that was once called North America.” –The Hunger
Games (Scholastic Press)
In The Hunger Games, author Suzanne Collins never reveals the exact locations of the Districts of Panem. What if
you could map them by using data from the U.S. Bureau of Labor Statistics (BLS)?
Fans of the popular The Hunger Games trilogy know that the stories are set in Panem, a futuristic area previously
called North America, with a capital located somewhere in what was known as the Rockies. Panem is divided into
districts, each of which has a primary industry. BLS employment data can help you solve the puzzle of where in
North America those districts would be.
Keep reading to learn how to use BLS data to identify 12 districts of Panem. Because BLS data cover the United
States, this article uses clues from U.S. locations rather than from North America as a whole.
Finding data: Total employment and location quotients
Each district’s primary industry offers the best evidence of its geographic location. To find where an industry has a
high concentration of workers, look at employment data by industry and occupation. Industry employment data tell
you how many workers are in a particular type of firm or group of firms; occupation employment data tell you how
many workers do a similar set of tasks.
Two BLS sources of employment data that will help in your analysis are the Quarterly Census of Employment and
Wages (QCEW) and the Occupational Employment Statistics (OES) programs. QCEW data show industry
employment and are provided here at the county level. OES data for occupation employment are shown here for
metropolitan and nonmetropolitan statistical areas.
Industry location quotients. Using QCEW annual averages for 2015, chart 1 ranks the counties with the highest
location quotients for the coal mining industry. Mingo County, West Virginia, had the highest location quotient.
In the United States, there were 64,135 workers in the coal mining industry out of 139,491,699 total workers, or
about .05 percent. In Mingo County, there were 1,216 workers in the coal mining industry out of 5,563 total
workers. Thus, (1,216/5,563) × 100 percent, or about 22 percent, of the workers in Mingo County were employed
in coal mining.
To get the location quotient, divide the percentage of workers in coal mining in Mingo County (22) by the
percentage nationally (.05). The result is a ratio of 440. (The data used to calculate this number were rounded to
simplify the math, so the value shown here differs from the value shown in chart 1.) In other words, Mingo County
has more than 400 times the concentration of coal mining employment nationally.
Mapping the industry location quotients shows a concentration of coal mining in an area that encompasses
Kentucky, West Virginia, and Pennsylvania. (See map.)
Table 1. Occupations in which the coal mining industry is the largest employer, 2015
Occupation Percent of occupation in coal mining Total employment Employment in coal mining
See footnotes at end of table.
Mine shuttle car operators 93.5 2,310 2, Roof bolters, mining 93.1 5,220 4, Loading machine operators, underground mining
Source: U.S. Bureau of Labor Statistics, Occupational Employment Statistics.
Using the 2015 OES estimates, chart 2 ranks the metropolitan and nonmetropolitan areas with the highest location
quotients for mine shuttle car operators. With a location quotient of 163.50, the Southern West Virginia
nonmetropolitan area had the highest location quotient for mine shuttle car operators. This nonmetropolitan area
comprises many counties, including Mingo County.
Conclusion. On the basis of statistical evidence, District 12 is most likely located in an area comprising parts of
Kentucky, West Virginia, and Pennsylvania. This tristate area is where employment in the coal mining industry and
its associated occupations are concentrated.
Finding other districts: Your turn
Every district of Panem has a dominant industry. Profiles for each district use QCEW and OES data; links to these
industry profiles for each district are given below. The data may provide clues, but results are open to
interpretation. Using these data, create your own map of Panem.
“And may the odds be ever in your favor.” –The Hunger Games
Table 2. Occupations in which the other miscellaneous manufacturing industry is a large employer,
Occupation Percent of occupation in other miscellaneous manufacturing
Total employment
Employment in other miscellaneous manufacturing
See footnotes at end of
table.
District 2
Rock quarrying
Note: The North American Industry Classification System groups this industry under “nonmetallic
mineral mining and quarrying.”
Table 3. Occupations in which the nonmetallic mineral mining and quarrying industry is the largest
employer, 2015
Occupation Percent of occupation in nonmetallic mineral mining and quarrying
Total employment
Employment in nonmetallic mineral mining and quarrying
See footnotes at end of
table.
Rock splitters, quarry 80.5 3,790 3,
District 3
Electronic goods manufacturing
Note: The North American Industry Classification System groups this industry under “computer and
electronic product manufacturing.”
Table 4. Occupations in which the computer and electronic product industry is the largest employer,
Occupation
Percent of occupation in computer and electronic products manufacturing
Total employment
Employment in computer and electronic products manufacturing
See footnotes at end of
table.
Back to District Links
District 4
Fishing
Note : The OES survey excludes most of the agricultural sector, with the exception of logging,
support activities for crop production, and support activities for animal production. The OES survey
does not include the fishing industry; therefore, occupation location quotients cannot be calculated.
