Firm Size Structure in North American Housebuilding: Persistent Deconcentration, 1945–98
Bibliographic record
Abstract
In this paper I document and analyse the evolving firm size structure of the housebuilding industry in North America since World War 2, and place it in a wider context of industrial organisation. This is done first by synthesising the literature on housebuilding, particularly secondary data, to outline the industry's firm size and market share distributions. Second, the literature is extended with new and original data on the housebuilding industry for the province of Ontario, supplied by the Ontario New Home Warranty Program. The data are a complete annual census of builders in the province from 1978 through 1998. Using standard measures of industrial concentration and firm size classifications common to the housebuilding literature, Ontario is placed in the Canadian and North American contexts, to outline how housebuilding has evolved since World War 2. The main findings are that housebuilding shows no long-term trend toward rising market concentration. Rather, the industry's structure appears to change in cycles, while the largest firms have neither the growth rates nor the longevity to produce high levels of concentration common in other industries. On the basis of these findings, I suggest how insights into the firm size structure of housebuilding may benefit from, and contribute to, our understanding of social systems of production and discuss directions for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".