The Effect of Housing Price Changes on Fertility: Evidence from Canada
Bibliographic record
Abstract
To the extent that families’ fertility decisions respond to economic factors, the price of housing is an important and relatively neglected candidate for consideration in fertility decisions. In theory, the effect of changes in housing prices on family size will depend on the quantity of housing that a family already owns, and its elasticity of substitution between children and other “goods”. For renters, rises in rental costs associated with higher housing prices imply only a substitution effect that should reduce their likelihood of having additional children. Home-owners are predicted to have more children in response to higher house prices if they have sufficient housing and low substitution, but fewer children otherwise. In this paper, we combine longitudinal data from the Canadian Survey of Labour Income and Dynamics (SLID) and average housing price data at real estate board (REB) level from the Canadian Real Estate Association to estimate the effect of house prices on fertility. We follow non-moving women aged 18-40 (with their associated families) over time to ask whether changes in lagged housing price affects either total number of children, or the probability of a family having an additional birth. We differ from previous studies in employing person- rather than region-fixed effects, in covering both rural and urban areas, and in exploring the effect of housing price changes on total number of children vs. the probability of having an additional child. For home owners, we find that lagged REB housing prices are positively associated with the probability of a birth in the previous year under pooled cross section or fixed effects. Housing prices are significantly negatively associated with total fertility measures under pooled cross section, but positively associated with number of children in the home under fixed effects. For renters, we find that lagged REB housing prices are not significantly negatively associated with either total or marginal fertility measures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".