Moving to the hinterlands: agglomeration, search costs and urban to rural business migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it