Entrepreneurs and Cities: Complexity, Thickness, and Balance
Bibliographic record
Abstract
It is well-established that the thickness of large cities' markets can enhance entrepreneurial activity (Vernon (1960)). It has been more recently established that because they carry out so many different tasks, a balance of skills may be beneficial to entrepreneurs (Lazear (2004, 2005)). This paper unifies these approaches to agglomeration and entrepreneurship. It breaks from both by focusing directly on the timeliness of entrepreneurial activity. The paper's model of multidimensional task completion generates several interesting results. First, agglomeration economies arising from market thickness are reflected in shorter completion times. Second, complex projects that are infeasible in small cities may be feasible in large cities, where adaptation costs and completion times are lower. Third, it may be possible for less balanced entrepreneurs to manage successfully in large cities by substituting local market thickness for a balance of skills. Fourth, the Lazear result on the balance of entrepreneurs is shown to be related to Jacobs‟ (1969) classic result on urban diversity (city balance). Both are special cases of a more general sort of balance.
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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 | 0.004 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".