Bibliographic record
Abstract
This thesis investigates the impact of Indian immigration on housing prices in Canadian cities between 2012 and 2022. The study addresses a timely issue, as countries worldwide face the challenge of balancing high immigration targets with rising concerns about housing affordability. Using a balanced panel dataset of 21 major Census Metropolitan Areas (CMAs)—covering approximately 61% of Canada's population—the analysis applies a shift-share instrumental variable (IV) strategy inspired by Card (2001) and Saiz (2003). City fixed effects are included to control for time-invariant local factors, ensuring a credible causal interpretation. The results show that predicted Indian immigration inflows significantly increased the prices of newly built homes and rental prices. Data were drawn primarily from Statistics Canada, ensuring consistency across cities and years. The focus on Indian immigrants reflects both their demographic importance, as India became Canada’s leading source of newcomers during the period, and practical considerations of instrumental strength. These findings highlight that immigration-driven demand pressures are most visible in the housing ownership and rental market. The study underlines the need for better coordination between immigration and housing policies. Future research could extend this analysis to other immigrant groups, differentiate impacts across housing segments, and examine longer-term effects as newcomers transition through the housing market.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".