Effect of Canada Child Benefit on Food Insecurity: A Propensity Score−Matched Analysis
Bibliographic record
Abstract
INTRODUCTION: Food insecurity-the inadequate access to food due to financial constraints-affects 1 in 6 households in Canada, with serious health implications. Family benefit programs supplementing income have shown potential in mitigating the risk of food insecurity, but there is little understanding of their protective effects. The authors aimed to estimate the impacts of the additional Canada Child Benefit provided to families with children aged <6 years on household food insecurity. METHODS: The authors sampled 28,435 households receiving Canada Child Benefit with children aged <8 years from the Canadian Income Survey 2018-2020 fielded in 2019-2021 and analyzed in 2022. Households without children aged <6 years were 1:1 propensity-score matched to those with children aged <6 years, who received up to $1,068 in additional Canada Child Benefit per child annually. The authors compared the 2 groups' 12-month food insecurity likelihood after matching. Subsample analyses were performed by household income, housing tenure, and household type. RESULTS: Receipt of the additional Canada Child Benefit ($724 on average) was associated with 2.89 (95% CI=1.35, 4.42) percentage points lower probability of experiencing food insecurity among recipients with children aged <6 years (from 24.30% to 21.42%). The association was similar across socioeconomic subgroups and significant among all but high-income earners and mortgage-free homeowners. CONCLUSIONS: The authors found plausibly causal evidence that the more generous Canada Child Benefit for Canadian families with young children reduces their probability of food insecurity. Increasing benefits for economically disadvantaged households, characterized by low incomes, single parenthood, and renting (versus owning), may improve the program's efficiency and equity in supporting families' food security.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".