How Did the Canada Child Benefit Affect Household Spending?
Bibliographic record
Abstract
We assess how the July 2016 increase in the Canada Child Benefit (CCB) affected household spending with respect to total current expenditure and its seven constituent categories: clothing, food, health care, household operations, recreation, shelter, and transportation. The increase in the CCB was large: for most recipient households, it increased by more than $2,000 per child per year. We consider households below the median income level and find statistically significant effects of the policy change only for spending on clothing, food, and shelter and only for rental-tenure households. We find that rental-tenure households with children that fell below the median income level increased their annual expenditure by about $3,400 in response to the CCB increase. Spending on food increased by roughly $700; spending on shelter, by nearly $1,400. Spending on clothing increased by roughly $350, but spending mainly increased on children’s clothing, not on adults’ clothing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".