Moving to the hinterlands: agglomeration, search costs and urban to rural business migration
Bibliographic record
Abstract
Business location and relocation decisions tend to favor urban areas over rural areas, mainly due to the benefits derived from agglomeration economies. However, recent data from the USA show that rural counties have attracted some businesses from urban counties. This is the first study to focus on these relocations and to explore what locational factors drive these migration flows. We pay specific attention to measures of agglomeration in the form of urbanization economies, market potential and regional specialization. Using county-to-county relocation data, origin and destination characteristics and differences of those characteristics, we find that while traditional measures of urban agglomeration such as proximity to urban locations and population density as pull factors show statistical significance and the expected positive sign, the role of more specific measures such as regional specialization and market potential has the opposite or no effects on the relocation of businesses from urban to rural areas. A key and strong finding is that relocating establishments seem to prefer destination locations that are similar to their respective origins in most respects, except natural amenities where moving establishments prefer dissimilar locations. In particular, if relocation is to high-amenity rural locations, it takes place even in the absence of significant differences in other location factors.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".