Location Theories and Business Location Decision: A Micro-Spatial Investigation in Canada
Bibliographic record
Abstract
This paper draws on location theories to statistically identify the relationship between the location of individual business establishments and the characterization of their local economic environment. Taking a micro-spatial perspective, the paper develops indicators from distance-based measures (DBM) to serve as independent variables in a discrete choice model (DCM). Using a 2006 database of individual business establishments in the Lower-St-Lawrence region—a coherent, nonmetropolitan subsystem of cities in the province of Québec, Canada—we provide an empirical analysis of the determinants of individual establishments’ location decisions in relation to their main economic activity within a random utility model (RUM) framework. The results show that distance to nearby centers, co-location (specialization), and the size of establishments are statistically related to location decisions. However, unlike previous studies, it is also found that discrete location choices of business establishments in service industries are not necessarily influenced by economic diversity or co-location, whereas manufacturing firms’ location decisions are not impacted by distance to markets. All told, we believe the results provide further evidence of the importance of scale in the study of business location decisions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".