The economic geography of risk: three papers on organization, development and agglomeration
Bibliographic record
Abstract
In this dissertation I examine the role and impact of risk in three areas of Economic Geography-firm organization, development and growth, and agglomeration. I develop the concept of risk as a fundamental part of the foundation of Economic Geography and risk management strategies as explanations for the geographic distribution of economic activity. This dissertation uses the firm as a lens through which to view the role of risk in Economic Geography and looks at the decision processes leading to inter-firm linkages, regional growth in employment, and the agglomeration of firms. This dissertation looks at how decisions based on risk, as opposed to cost, change our understanding of geographic processes. The dissertation includes three papers---each dealing with a specific area in Economic Geography in which risk is relevant. In the first paper I reinterpret portfolio theory in order to explain the spatial implications of control structures and organizational decisions within and between firms. In the second paper I examine the relationship between employment risk and long term growth in employment across census metropolitan cities (CMAs) in Canada. In the third paper I construct a "newsvendor" model of spatial agglomeration that incorporates the risk management strategies of risk averse firms. This dissertation provides a theoretical foundation and methodological approach for Economic Geography that is based on risk management. It emphasizes the behaviour of firms in understanding the spatial distribution of economic activity. It shows that risk operates at multiple spatial scales and that risk management strategies are intrinsically spatial processes. It shows that observed spatial patterns are better explained by behaviour based on risk management than behaviour based on cost minimization or total profit maximization.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".