Tales of the city: what do agglomeration cases tell us about agglomeration in general?
Bibliographic record
Abstract
This article considers the heterogeneous microfoundations of agglomeration economies. It studies the co-location of industries to look for evidence of labour pooling, input sharing and knowledge spillovers. The novel contribution of the article is that it estimates single-industry models using a common empirical framework that exploits the cross-sectional variation in how one industry co-locates with the other industries in the economy. This unified approach yields evidence on the relative importance of the Marshallian microfoundations at the single-industry level, allowing for like-for-like cross-industry comparisons on the determinants of agglomeration. Using UK data, we estimate such microfoundation models for 97 manufacturing sectors, including the classic agglomeration cases of automobiles, computers, cutlery and textiles. These four cases—as with all of the individual industry models we estimate—clearly show the importance of the Marshallian forces. However, they also highlight how the importance of these forces varies across industries—implying that extrapolation from cases should be viewed with caution. The article concludes with an investigation of the pattern of heterogeneity. The degree of an industry’s clustering (localisation), entrepreneurship, incumbent firm size and worker education are shown to contribute to the pattern of heterogeneous microfoundations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".