Examining the Link between Crime and Unemployment: A Time Series Analysis for Canada
Bibliographic record
Abstract
We use national and regional Canadian data to analyse the relationship between economic activity (as reflected by the unemployment rate) and crime rates. Given potential aggregation bias, we disaggregate the crime data and look at the relationship between six different types of crimes rates and unemployment rate; we also disaggregate the data by region. We employ an error correction model in our analysis to test for short-run and long-run dynamics. We find no evidence of long-run relationship between crime and unemployment, when we look at both disaggregation by type of crime and disaggregation by region. Lack of evidence of a long-run relationship indicates we have no evidence of the motivation hypothesis. For selected types of property crimes, we find some evidence of a significant negative short-run relationship between crime and unemployment, lending support to the opportunity hypothesis. Inclusion of control variables in the panel analysis does not alter the findings, qualitatively or quantitatively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".