Income and the mental health of Canadian mothers: Evidence from the Universal Child Care Benefit
Bibliographic record
Abstract
The Universal Child Care Benefit, introduced in 2006, was an income transfer for Canadian families with young children. I exploit this exogenous increase in income to answer the following questions: (1) Is there a relationship between income and mental health among Canadian mothers? (2) Is it corroborated by other measures of well-being (i.e. stress, life satisfaction)? (3) Is the effect different for lone mothers compared to those in two-parent families? I answer these questions using a difference-in-differences model and microdata from the Canadian Community Health Survey, 2003 to 2008. The estimating sample includes 26,886 mothers, 6273 of whom are lone parents. I find the income transfer improved mental health and life satisfaction regardless of family structure, albeit not necessarily for a given individual. Rather, average scores were higher for mothers with young children after implementation of the Universal Child Care Benefit. For example, they were more likely to report 'excellent' mental health and less likely to be in each of the other categories. The transfer also reduced stress among lone mothers with young children. Specifically, they were less likely to be 'quite a bit' or 'extremely' stressed on a daily basis, and more likely to be 'not at all' or 'not very' stressed. I argue that assumptions of the model are plausible and show that results are consistent across several robustness checks.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".