Bibliographic record
Abstract
ABSTRACT In conventional modeling of housing demand, consumers choose living arrangement, tenure, and housing on the basis of price, income, wealth, and tastes. However, it is both costly and onerous to alter one's housing conditions. It is argued therefore that consumers employ housing strategies to cope with labor market risks and expectations about their future: strategies that may differ from one demographic group to the next. In conventional modeling of housing demand, it is also well‐known that selection bias can arise: that is, omitted variables that help account for one aspect of housing (say, tenure choice) also subsequently affect the nature of the demand function for other aspects of housing demand (say, the amount spent on housing by a renter household). One such variable is the consumer's wealth, a variable that is typically not available in household survey data. This paper argues that the most important variables that may give rise to selection bias are variables that also reflect the coping strategies employed by consumers. The paper estimates a model of housing choice using Canada‐wide pooled samples from the 1980s and 1990s. In this paper, the prices of housing services and income prospects vary region by region. The paper shows how individuals and families in different housing markets across Canada respond, and how this evidences the use of coping strategies (from doubling up to substandard housing). The paper presents evidence to support the argument that selection bias is important in understanding how consumers cope.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".