Quebec’s Baby Bonus: Can Public Policy Raise Fertility?
Bibliographic record
Abstract
that paid up to $8,000 to a family after the birth of a child. Was the program successful? It achieved its goal of increasing family size, but only at a high cost per additional birth. Each child who would not have been born in the absence of the incentive cost the public purse more than $15,000. The main policy lesson from this episode is that, even if the response to an incentive policy is strong, the effective cost per desired result may be very high. Fertility rates across the countries of the Organisation for Economic Co-operation and Development (OECD) have declined sharply since the 1960s. This decrease raises public policy concerns because the funding of many social programs, such as public pensions and health care, relies on transfers across generations. Elderly citizens receive benefits funded by younger workers, who, in turn, expect to be supported in their retirement by the next generation of taxpayers. As successive generations shrink in size, these fiscal arrangements come under pressure. In addition, some people fear that dwindling populations may threaten the vitality of various cultures whose survival depends on a critical mass of participants. These concerns have led governments to create tax and transfer policies aimed at influencing family decisions as intimate as those surrounding fertility. Of the 29 OECD countries, 26 give families with children special treatment through the tax and transfer system (OECD 2000). In more than half of OECD countries, per child tax benefits increase with the number of children in the family. 1 This policy structure implies that the policymakers ’ goal is to
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.001 |
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".