Regional specialization: From the geography of industries to the geography of jobs
Bibliographic record
Abstract
Abstract Our analysis begins with an empirical investigation of how employment concentration in industries and occupations across regions of the United States has changed over time and how regional specialization has changed. Results show that industry concentration and specialization indices have fallen, while occupation concentration and specialization indices have risen. Using this background as motivation, we develop a model in which the comparative advantage of regions lies in their productivity of supplying functions such as law, finance, advertising and engineering, to multiple sectors. Productivity differences specific to region functions shape the location decisions of industries that use multiple functions and hence determine patterns of regional specialization both in functions and in sectors. A key parameter is the cost of sourcing functions from a different region (fragmentation costs), and we show that a fall in this cost mimics the data: sector concentration and regional specialization fall and function concentration and specialization rise. At high fragmentation costs, regional comparative advantage in sectors determines general equilibrium analogous to a Heckscher–Ohlin model (HO). At low fragmentation costs, comparative advantage in functions drives an equilibrium that has little resemblance to a HO world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".