The Crime Effect of Refugees
Bibliographic record
Abstract
We analyze the impact on crime of 3.7 million refugees who entered and stayed in Turkey as a result of the civil war in Syria.Using a novel administrative data source on the flow of offense records to prosecutors' offices in 81 provinces of the country each year, and utilizing the staggered movement of refugees across provinces over time, we estimate instrumental variables models that address potential endogeneity of the number of refugees and their location, and find that an increase in the number of refugees leads to more crime.We estimate that the influx of refugees between 2012 and 2016 generated additional 75,000 to 150,000 crimes per year, although it is not possible to identify the distribution of these crimes between refugees and natives.Additional analyses reveal that low-educated native population has a separate, but smaller, effect on crime.We also highlight the pitfalls of employing incorrect empirical procedures and using poor proxies of criminal activity which produce the wrong inference about the refugee-crime relationship.Our results underline the need to quickly strengthen the social safety systems, to take actions to dampen the impact on the labor market, and to provide support to the criminal justice system in order to mitigate the repercussions of massive influx of individuals into a country, and to counter the social and political backlash that typically emerges in the wake of such large-scale population movements.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".