Urban Specialisation; from Sectoral to Functional
Bibliographic record
Abstract
The comparative advantage of many cities is based on their efficiency in the production of 'functions', e.g., business services such as finance, law, engineering, or similar functions that are used by firms in a wide range of sectors.Firms that use these functions may choose to source them locally, or to purchase them from other cities.The former case gives rise to cities developing a pattern of sectoral specialization, and the latter a pattern of functional specialization.A two-city country trades with the larger world, and workers within the country are mobile between the two cities.Productivity in a given function varies across cities, giving rise to urban comparative advantage.This may be due to exogenous technological differences (Ricardian) or to city-and function-specific scale economies.Sectors differ in the intensity with which they use different functions, giving rise to a pattern of sectoral and functional specialisation.We generate a number of economic insights, and examine the model's predictions empirically over a 20-30year period for US states.As geographic fragmentation costs fall, both our theory and empirical analysis show that sector concentration and regional specialization fall for sectors and rise for functions (occupations).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".