Give and take? Child benefits and prices in Northern Canada
Bibliographic record
Abstract
Abstract Cost of living is comparatively high in Northern Canada, which is a remote and sparsely populated region served by retail oligopolies (about 34% of communities feature a monopoly, while the rest feature a duopoly). Government transfers constitute a large share of household income in Northern communities, and child benefits are particularly important, with these programs having expanded in recent years (Universal Child Care Benefit in 2015 and Canada Child Benefit in 2016). We assess the extent to which increased child benefits are “captured” by higher prices. Using the Longitudinal Administrative Database and community‐level data on prices and food shipments from Nutrition North Canada (2012–2019), we find that expanded child benefits are associated with higher prices (with an elasticity of 0.02), which for a family of four offset about 24% of the increased purchasing power resulting from the expansion. Our results suggest that expanded child benefits increase food demand and that the main transmission mechanism leading to higher prices is markups, as our price effects hold conditional on the quantity of food shipped and are mostly driven by monopoly communities, where about 61% of increased purchasing power is offset by higher food prices. Thus, Northern communities are not pure “price‐takers,” and policies that increase cash assistance should consider the implications for local prices.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".