How Do Immigration Policies Affect Voter Support for Low-Skilled Immigrants? Evidence from a Survey Experiment
Bibliographic record
Abstract
Countries depend on both high- and low-skilled immigration to meet economic needs. But most voters prefer high-skilled immigrants, despite the fact that multiple economic sectors structurally depend on low-skilled immigrants. In this paper, we examine voter preferences toward low-skilled immigrants as one barrier to effective immigration policy, even in political regimes where immigration is the consequence of highly coordinated or “planned” policies. Specifically, we consider whether government communication around the benefits of low-skilled immigration can increase favorability of such policies. We are particularly interested in the ways in which government communicates immigration messages and whether the scope or concentration of the proposed benefits will move individual preferences. In an online survey experiment, we present Canadians ( N =2,023) with a policy brief that manipulates immigrant skill level (high vs. low), economic outcomes of migration (positive vs. mixed), and the geographic scope of benefits (concentrated vs. sociotropic). Employing two measures of policy support, we find some evidence that positive framing can increase overall support for low-skill migrants. We also find that manipulating framing around high-skilled workers has little effect on support for low-skill workers, even when that framing presents countervailing evidence as to the benefit of high-skilled labor. In sum, our findings suggest that elite level communication around the benefits of low-skill labor may have the ability to disrupt longstanding antipathy for low-skilled labor, even in regimes with longstanding support for high-skilled labor.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".