Does perceived labor market competition increase prejudice between refugees and their local hosts? Evidence from Uganda and Ethiopia
Bibliographic record
Abstract
We study whether perceptions of labor market competition negatively influence out-group attitudes between refugees and their local hosts using a survey vignette experiment conducted in urban and rural Ethiopia and Uganda. Our vignette consists of a short story about a fictional job-seeker in which we randomize the citizenship (refugee/national) and occupation (same as/different from respondent). Our estimates suggest that host attitudes are significantly more negative when the vignette character is a refugee in the same occupation. Such prejudice against the out-group is not confirmed among refugees. Exploring the context-dependency of our results, evidence suggests that negative attitudes towards refugees that are tied to perceived labor market competition largely manifest in contexts of limited refugee worker presence. Hence, perceived labor market competition contributes to prejudicial attitudes, but results suggest that these perceived threats do not necessarily coincide with experienced labor market competition between refugees and their hosts. Additional heterogeneity analysis based on prior contact and ethno-linguistic proximity provides suggestive evidence that cross-group interactions reduce the salience of perceived labor market competition as a driver of out-group prejudice in refugee settings. • We study how perceived job competition influences outgroup attitudes in Ethiopia and Uganda. • Results show that perceived job competition links to negative attitudes toward refugees. • Heterogeneity analysis shows results are driven by hosts competing with few refugees. • Ethnolinguistic proximity and prior contact reduce negative attitudes toward refugees.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".