Asymmetric effects of the<scp>WTI</scp>crude oil price on unemployment rates: a comparative study of Canadian provinces and the United States
Bibliographic record
Abstract
Abstract Since 2014, Canada has been experiencing a higher unemployment rate due to a slump of the crude oil price. Using a monthly data set of the West Texas Intermediate (WTI) crude oil spot price and unemployment rates of Canadian provinces and theUnited Statesfrom January 1976 to October 2016, we examine the asymmetric effects of oil price on unemployment rates. Specifically, we split the sample into the pre‐technological boom (January 1976 to March 1995) and post‐technological boom periods (April 1995 to October 2016) and analyse whether the asymmetric effects are discernible in these two periods. This is done by applying the ordinary least squares regressions and Granger causality tests. Our findings confirm the asymmetric effects for the full sample in theUnited States. In Canada, the negative and asymmetric relationship is conspicuous particularly in the post‐tech period, and this relationship is more prominent in three oil‐producing Canadian provinces namely Alberta, Saskatchewan and Newfoundland & Labrador. It is noticeable that both in Canada and in the three provinces, the falling oil price affects unemployment adversely only in the post‐tech period. Granger causality tests support the short‐run causal relationships between changes in oil price and unemployment rate in the post‐tech period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".