Is There a Tradeoff between Ethnic Diversity and Redistribution? The Case of Income Assistance in Canada
Bibliographic record
Abstract
Numerous studies conclude that ethnic/cultural/racial diversity has negative impacts on interpersonal trust and support for redistributive social programs. Although some Canadian public opinion data is consistent with this view, whether these impacts on public opinion are important enough to influence policy is unclear. Many scholars argue that Canada is an exception to experience elsewhere. This paper examines this question for the case of Canadian social assistance (welfare) policies - a central component of the social safety net. We exploit two salient features of recent Canadian experience. One is dramatic growth in the ethnic and cultural diversity of Canada's immigrant inflows in recent decades, but the extent of this growth has varied substantially across regions. The second is that welfare policies vary across provinces, and the ability of the provinces to employ different approaches to welfare programs has increased since the mid-1990s. We thus examine whether provinces that became more diverse reduced the generosity of their welfare programs, relative to provinces that experienced little change in the heterogeneity of their populations. We examine impacts of immigration on welfare benefit rates of four family types: single employables, single disabled, lone parents and couples with children. Our main finding is that there is limited evidence of increased immigration on any of these types other than families with children. Even in this case the estimated effects are small. Our study thus supports the view that Canada's experience stands as an example in which greater diversity has not reduced support for redistributive social programs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".