The Most Distinctive Economies
Which state economies are built around an occupation the rest of the country barely has, and which look like everywhere else?
What this ranking does not measure
This ranks distinctiveness and nothing else. A location quotient measures only how unusual a state's employment mix is against the national mix — it says nothing about whether the work pays well, whether it is growing, whether it is safe, or whether the state is prosperous. Read the top ten and that is obvious: Montana's signature occupation, gambling and sports book writers and runners, has a median wage of $22,030 against a state median of $48,740, while D.C.'s political scientists read $152,530. Both are 'distinctive'. Across all 51 jurisdictions the signature occupation pays below its own state's median wage in 27 cases — a majority-adjacent share of the list. Nor is this a ranking of how much of a state's economy the occupation represents: these are small occupations. D.C.'s 3,070 political scientists are 0.437% of its 701,920 jobs; the largest signature anywhere is Missouri's 55,780 fast-food cooks, 1.9% of state employment. A high LQ marks a sliver of the workforce that is nationally unusual, not the engine of the state. This ranking also does not track development: the Spearman correlation between signature LQ and the file's composite index score across the 51 jurisdictions is -0.25 (-0.31 with D.C. removed), and the Pearson correlation is -0.035 — no meaningful relationship in either direction, and with n=51 no causal claim is available in any case. Florida ranks 2nd on the composite index and 49th here; West Virginia ranks 51st on the composite index and 2nd here. Finally, the field is bounded by the file's own construction: only the 12 highest-LQ occupations per state are present, and none with fewer than 1,000 jobs, so a state's true most-concentrated occupation could be a smaller one this data cannot see. Do not read a low rank as 'generic in a bad way' — Ohio, Florida and Pennsylvania sit low because their employment mixes resemble the national mix, which is a description, not a defect.
Highest
- 1Washington, D.C.122.81× national — Political Scientists
- 2West Virginia46.62× national — Loading and Moving Machine Operators, Underground Mining
- 3Montana35.57× national — Gambling and Sports Book Writers and Runners
- 4North Dakota30.98× national — Wellhead Pumpers
- 5Kansas30.86× national — Aircraft Structure, Surfaces, Rigging, and Systems Assemblers
- 6Oregon29.99× national — Personal Care and Service Workers, All Other
- 7Louisiana23.94× national — Extraction Workers, All Other
- 8Nevada21.54× national — First-Line Supervisors of Gambling Services Workers
- 9Massachusetts18.81× national — Biochemists and Biophysicists
- 10Mississippi16.01× national — Upholsterers
Lowest
- 46Maine3.81× national — Helpers--Electricians (tied for rank 46)
- 46New Hampshire3.81× national — Cooks, Short Order (tied for rank 46)
- 48Ohio3.51× national — Computer Numerically Controlled Tool Operators
- 49Florida3.24× national — Motorboat Mechanics and Service Technicians
- 50Pennsylvania3.19× national — Shampooers
- 51Rhode Island2.78× national — Fundraisers
All fifty-one jurisdictions carry this measure; none was excluded.
How it was computed
For each of the 51 jurisdictions in states.json, read workforce.signature.lq — the location quotient of that state's single most concentrated occupation — and sort descending. A location quotient is the occupation's share of that state's jobs divided by its share of national jobs: 6.22 means six times the national concentration. No arithmetic is performed on the file's numbers; the ranking is the raw field, sorted. Verification run in-session: (a) all 51 states have a non-null workforce.signature.lq, so nothing is excluded; (b) for all 51, workforce.signature.code equals the code of the maximum-lq row in workforce.specialities, and each specialities list holds exactly 12 rows sorted lq-descending, so the signature is genuinely the state's top-concentration occupation as the file defines it; (c) across all 612 speciality rows the minimum employment is exactly 1,000, so the file applies a 1,000-job floor and any occupation smaller than that could not appear — three signatures (Maine, Pennsylvania, Rhode Island) sit exactly on that floor. Ties keep competition ranking: Maine and New Hampshire both read 3.81 and both hold rank 46, which is why the bottom band lists six jurisdictions rather than five. Splitting a tie the file does not split would be a fabrication. Reproduce with: json.load(open('states.json'))['states'], then sorted((s['name'], s['workforce']['signature']['lq'], s['workforce']['signature']['title']) for s in states.values()), key=lambda r: -r[1]).