Business Cycle and Crime: The Case of British Columbia, Canada
Bibliographic record
Abstract
Purpose: Economic expansions and contractions are among the factors that influence crime as a social phenomenon.Yet, the magnitude of the impact of economic growth and recession has remained largely unexplored in British Columbia (BC).In this study, the reference business cycle variable (GDP) is measured in relation to four crime categories: fraud, robbery, violence, and property crimes.Design/methodology/approach: We collected data from Statistics Canada on various socioeconomic indicators for the period of 1986-2019.We employed ARDL method and dependent tests based on Hamza and Lau (2013) and Oyelade (2019) to estimate the causal effects.Findings: Our results do not support any long-run relationships among various crime categories and business cycles.However, we found statistically significant short-term effects of business cycles on crime categories.Economic prosperity has reduced crime in all four categories in the short term, while the recession has caused crime to increase.Furthermore, increasing the number of police officers during our study did not reduce these types of crimes except for property crimes.Practical implications: The results of the paper can help the policy makers and the BC government determine what types of crime will increase or decrease when there is an economic recession or boom, which can then help the government and justice system plan ahead in order to control crime occurrences.Originality value: This study is noteworthy as the research methodology and time series data used in this research are for the first time in British Columbia.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".